@online{0_GettingStarted, title = {0\_{{Getting Started}} |}, url = {http://self-star.imag.fr/?page_id=63}, urldate = {2016-12-02} } @inproceedings{0003BCT23, title = {Towards an Extensible Architecture and Tool Support for Model-Based Verification}, booktitle = {{{ACMIEEE Int}}. {{Conf}}. {{Model Driven Eng}}. {{Lang}}. {{Syst}}. {{MODELS}} 2023 {{Companion Västerås Swed}}. {{Oct}}. 1-6 2023}, author = {Delgado, David and Burgueño, Lola and Cámara, Javier and Troya, Javier}, date = {2023}, pages = {806--810}, publisher = {IEEE}, doi = {10.1109/MODELS-C59198.2023.00129}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {TL;DR \par A web-based architecture designed to support the definition of domain models and provide translation capabilities to different verification formalisms and it is shown how this tool has been used to verify properties for the public bus management system in the city of Málaga, Spain.}, timestamp = {Fri, 05 Jan 2024 16:35:45 +0100} } @online{01_YourFirstComponent, title = {01\_{{Your}} First Component with the {{IDE}} |}, url = {http://self-star.imag.fr/?page_id=196}, urldate = {2016-12-02} } @online{02_UsingComponentProperties, title = {02\_{{Using}} Component Properties to Configure Instances |}, url = {http://self-star.imag.fr/?page_id=198}, urldate = {2016-12-02} } @article{0223TOSEM20240012pdf, title = {02-23-{{TOSEM-2024-0012}}.Pdf}, keywords = {⛔ No INSPIRE recid found,LOGSEQ}, note = {\section{Annotazioni\\ (8/4/2024, 16:49:09)} \par - “developers often find it challenging to familiarize themselves with all APIs in the libraries.” (“02-23-TOSEM-2024-0012.pdf”, p. 3) \#5fb236 \par - “limited historical data, consequently diminishes the diversity of recommender systems” (“02-23-TOSEM-2024-0012.pdf”, p. 3) \#5fb236 \par - “In this paper, we propose DDASR, a framework for recommending API sequences containing both popular and tail APIs.” (“02-23-TOSEM-2024-0012.pdf”, p. 3) \#00b036\\ \mkbibemph{this is related to \#bias} \par - “DDASR clusters tail APIs with similar functionality and replaces them with cluster centers to produce a pseudo ground truth. Moreover,” (“02-23-TOSEM-2024-0012.pdf”, p. 3) \#f0ff00\\ \mkbibemph{this is not clear} \par - “Results demonstrate that DDASR significantly achieves the best diversity without sacrificing accurac” (“02-23-TOSEM-2024-0012.pdf”, p. 3) \#a28ae5\\ \mkbibemph{This is very much connected to [[PAPERS/2024-BiasInRSSEwithLLM]]} \par - “It is a challenging problem to find appropriate API sequences from the vast array of APIs according to developer requirements” (“02-23-TOSEM-2024-0012.pdf”, p. 4) \#a28ae5 \par - “rReview53545556575859606162636465666768697071727374757677787980818283848586878889909192939495969798991001011021031042 Nan et al.API sequence:① Robot.init ② Robot.mousePress ③ Robot.mouseRelease ④ Robot.mouseWheelCode snippet: public class MouseActionsExample \{ public static void main(String[] args) \{ Robot robot = new Robot(); robot.mousePress(InputEvent.BUTTON1\_MASK); robot.mouseRelease(InputEvent.BUTTON1\_MASK); robot.mouseWheel(3);\}\}Query: Simulate mouse wheel scrolling to control the scrolling of a webpage.Fig. 1. An example query that needs to be addressed using an API sequence in Java.Moreover, fulfilling user requirements often necessitates more than just a single API. Developers typically need to look up API sequences to solve tasks. For instance, to address a query like ‘simulating mouse wheel scrolling to control the scrolling of a webpage,’ as shown in Fig. 1, a sequence of four APIs, including ‘Robot.init,’ ‘Robot.mousePress,’ ‘Robot.mouseRelease,’ and ‘Robot.mouseWheel,’ needs to be invoked. It is a challenging problem to find appropriate API sequences from the vast array of APIs according to developer requirements [50]. Recently, several approaches have been proposed to recommend APIs for developers. These approaches fall into two major categories: information retrieval-based approaches [24, 48, 62] that search for the most relevant solutions from the historical question repository, and deep learning-based approaches [12, 19, 40] that adopt the sequence-to-sequence (Seq2Seq) model to recommend API sequences generatively. Gu et al. [19] adopt an RNN encoder-decoder and Elnaggar et al. [12] adopt a Transformer encoder-decoder to obtain the results of the API sequence. Martin and Guo [40] apply CodeBERT [14] for the task due to the improved performance of Large Language Models (LLMs). However, existing API recommendation approaches usually recommend popular APIs, neglecting less frequently used ones. For example, deep learning-based approaches [12, 19, 40] remove infrequently appearing words from the vocabulary or treat them as {$<$}UNK{$>$} tags, making it challenging to recommend infrequently occurring APIs to developers.” (“02-23-TOSEM-2024-0012.pdf”, p. 4) \#00b036 \par - “APIs are clustered in a small portion at the head, while tail APIs, despite their low individual occurrence frequency, cover a large scale in quantity” (“02-23-TOSEM-2024-0012.pdf”, p. 4) \#5fb236 \par - “The long-tail effect” (“02-23-TOSEM-2024-0012.pdf”, p. 4) \#a28ae5 \par - “Hence, learning the representation of tail APIs is essential for API recommendation. However, it is a challenge due to the sparse historical usage data of tail API” (“02-23-TOSEM-2024-0012.pdf”, p. 4) \#5fb236 \par - “ignoring tail APIs can harm the diversity of a recommendation system” (“02-23-TOSEM-2024-0012.pdf”, p. 4) \#5fb236 \par - “aggregate diversity” (“02-23-TOSEM-2024-0012.pdf”, p. 4) \#5fb236 \par - “how many different APIs are included in the result se” (“02-23-TOSEM-2024-0012.pdf”, p. 4) \#a28ae5 \par - “boost the visibility of those rarely used APIs and increase the aggregate diversity of recommendation results, thus improving the health of the overall software ecosystem.” (“02-23-TOSEM-2024-0012.pdf”, p. 4) \#a28ae5 \par - “balance between the accuracy and diversity” (“02-23-TOSEM-2024-0012.pdf”, p. 4) \#a28ae5 \par - “To alleviate the sparsity, we cluster tail APIs with a similarity matrix and substitute them with cluster centers” (“02-23-TOSEM-2024-0012.pdf”, p. 5) \#ffd400\\ \mkbibemph{Check if it mentioned as possible threat to validity or bias itself.} \par - “The similarity matrix is built by calculating the similarity through the description and name of tail APIs” (“02-23-TOSEM-2024-0012.pdf”, p. 5) \#ffd400\\ \mkbibemph{Why have you considered this as the most accurate way to do so??? [[question]]} \par - “can recommend both non-tail and tail APIs” (“02-23-TOSEM-2024-0012.pdf”, p. 5) \#ffd400\\ \mkbibemph{Check if they refer our investigation about popularity bias of recsys} \par - “Regarding the diversity metric coverage, DDSAR surpasses the baselines by an increase of 45.83\% on the diverse Java dataset and 8.03\% on the Python dataset.” (“02-23-TOSEM-2024-0012.pdf”, p. 5) \#a28ae5 \par - “We propose DDASR, a novel framework for recommending API sequences, striking a balance between accuracy and diversity.” (“02-23-TOSEM-2024-0012.pdf”, p. 6) \#a28ae5 \par - “long-tail distribution in API sequence recommendation” (“02-23-TOSEM-2024-0012.pdf”, p. 6) \#5fb236 \par - “functional similarity” (“02-23-TOSEM-2024-0012.pdf”, p. 6) \#ffd400 \par - “related work in Section 2” (“02-23-TOSEM-2024-0012.pdf”, p. 6) \#5fb236 \par - “technical details of DDASR in Section” (“02-23-TOSEM-2024-0012.pdf”, p. 6) \#5fb236 \par - “experimental setup and the evaluation results in Section 4.” (“02-23-TOSEM-2024-0012.pdf”, p. 6) \#5fb236 \par - “We discuss threats to validity in Sectio” (“02-23-TOSEM-2024-0012.pdf”, p. 6) \#5fb236 \par - “MAPO” (“02-23-TOSEM-2024-0012.pdf”, p. 6) \#5fb236 \par - “information retrieval-based approaches struggle to capture the relevance of tail APIs” (“02-23-TOSEM-2024-0012.pdf”, p. 6) \#a28ae5 \par - “When applying LLMs, it is important to consider whether to fine-tune them during the training phase. For instance, models like TransRec [16], UNBERT [63], and LMRecSys [65], benefit from fine-tuning during the training phase. In contrast, models such as ChatGPT [11, 23, 39, 51] are designed to operate without the necessity for task-specific fine-tuning during the training phase.” (“02-23-TOSEM-2024-0012.pdf”, p. 7) \#5fb236 \par - “the diverse recommendation as an end-to-end supervised learning task and constructs ground truth labels to explicitly idealize the optimization target.” (“02-23-TOSEM-2024-0012.pdf”, p. 7) \#5fb236 \par - “Simpson’s Diversity Index and considered the evenness of the number of the items’ classes” (“02-23-TOSEM-2024-0012.pdf”, p. 7) \#5fb236 \par - “development requirements often consists of a series of APIs” (“02-23-TOSEM-2024-0012.pdf”, p. 7) \#5fb236 \par - “directly for recommendations.” (“02-23-TOSEM-2024-0012.pdf”, p. 7) \#5fb236 \par - “Ziegler et al. [69] presented topic diversification to balance and diversify recommendation results” (“02-23-TOSEM-2024-0012.pdf”, p. 7) \#5fb236 \par - “Pseudo Ground Truth Building” (“02-23-TOSEM-2024-0012.pdf”, p. 8) \#5fb236 \par - “Recently, researchers have begun exploring the use of LLMs for sequence recommendation tasks [15]. LLMs can be adapted to user data collection, feature engineering, and feature encoder [38]” (“02-23-TOSEM-2024-0012.pdf”, p. 5) \#00b036 \par - “functional similarity among tail APIs with their descriptions and names.” (“02-23-TOSEM-2024-0012.pdf”, p. 8) \#5fb236 \par - “lustering tail APIs and substituting them with cluster centers.” (“02-23-TOSEM-2024-0012.pdf”, p. 8) \#ffd400\\ \mkbibemph{I think an explanatory example would improve the readability of the paper.} \par - “The Seq2Seq model is used to translate a given natural language query to a ranked list of possible API sequences.” (“02-23-TOSEM-2024-0012.pdf”, p. 8) \#5fb236 \par - “ChatGPT [11, 23, 39, 51] are designed to operate without the necessity for task-specific fine-tuning during the training phase.” (“02-23-TOSEM-2024-0012.pdf”, p. 5) \#f0ff00\\ \mkbibemph{Even though training is preferable} \par - “balance accuracy and diversity in the recommended API sequences” (“02-23-TOSEM-2024-0012.pdf”, p. 8) \#a28ae5 \par - “When developers input a query, DDASR can recommend an appropriate API sequence solution” (“02-23-TOSEM-2024-0012.pdf”, p. 8) \#a28ae5 \par - “Each fragment is a symbol or word representing the class or method name.” (“02-23-TOSEM-2024-0012.pdf”, p. 8) \#5fb236 \par - “regularisation to enhance the diversity of recommendations.” (“02-23-TOSEM-2024-0012.pdf”, p. 5) \#00b036 \par - “functionalities” (“02-23-TOSEM-2024-0012.pdf”, p. 9) \#a28ae5 \par - “This pseudo ground truth includes both non-tail and tail APIs, ensuring comprehensive recommendations. In this context, we use 𝑇 𝐴 and 𝑁 𝐴 to represent tail APIs and non-tail APIs, respectively” (“02-23-TOSEM-2024-0012.pdf”, p. 9) \#a28ae5 \par - “Inspired by the word embedding technique [5], we recombine these fragments into complete APIs, allowing the model to better learn the calling relationship between APIs and reducing the computation of decoding APIs” (“02-23-TOSEM-2024-0012.pdf”, p. 9) \#5fb236 \par - “frequency analysis of API occurrences and distinguish between non-tail and tail APIs” (“02-23-TOSEM-2024-0012.pdf”, p. 9) \#a28ae5 \par - “APIs with similar functions typically share analogou” (“02-23-TOSEM-2024-0012.pdf”, p. 9) \#a28ae5 \par - “which contains both non-tail and tail APIs, is established by clustering tail APIs and substituting them with cluster centers.” (“02-23-TOSEM-2024-0012.pdf”, p. 6) \#00b036 \par - “LTR calculates a loss function and ranks these API sequences.” (“02-23-TOSEM-2024-0012.pdf”, p. 6) \#f0ff00\\ \mkbibemph{Loss function of what?} \par - “we append the official descriptions of their parent APIs” (“02-23-TOSEM-2024-0012.pdf”, p. 9) \#a28ae5 \par - “we calculate the functional similarity among tail APIs using both their documentation descriptions and names” (“02-23-TOSEM-2024-0012.pdf”, p. 10) \#5fb236 \par - “For the tail APIs, we mine their descriptions to facilitate functional similarity calculations. To achieve this, we download both official API documentation and third-party documents. Then we parse the HTML file of each API class to extract their descriptions. Generally, APIs with inheritance relationships tend to have similar functions. Therefore, we also mine descriptions of APIs within these inheritance relationships as a supplementary measure.” (“02-23-TOSEM-2024-0012.pdf”, p. 7) \#f0ff00\\ \mkbibemph{Everything occur at runtime?} \par - “Pseudo Ground Truth Building” (“02-23-TOSEM-2024-0012.pdf”, p. 7) \#f0ff00\\ \mkbibemph{Why doing that?} \par - “Similarity Calculation.” (“02-23-TOSEM-2024-0012.pdf”, p. 10) \#ffd400\\ \mkbibemph{This is a crucial step of the approach. The authors decided to introduce their similarity calculation, instead of using existing approaches that can calculate the similarity of third-party libraries.} \par - “The LLM encoder-decoder architecture is used in DDASR, with LLMs serving as the encoder to capture developer requirements, and a six-layer Transformer acting as the decoder.” (“02-23-TOSEM-2024-0012.pdf”, p. 13) \#5fb236 \par - “to model the rankings of all API sequences generated” (“02-23-TOSEM-2024-0012.pdf”, p. 14) \#5fb236 \par - “increase diversity without sacrificing accuracy.” (“02-23-TOSEM-2024-0012.pdf”, p. 14) \#5fb236 \par - “sequence probability distribution” (“02-23-TOSEM-2024-0012.pdf”, p. 14) \#5fb236 \par - “convex function” (“02-23-TOSEM-2024-0012.pdf”, p. 14) \#ffd400\\ \mkbibemph{I think it is necessary to have an explanatory example to show the different phases of the approach in practice on concrete cases.} \par - “DDASR in comparison” (“02-23-TOSEM-2024-0012.pdf”, p. 14) \#5fb236 \par - “improve diversity while mainta” (“02-23-TOSEM-2024-0012.pdf”, p. 14) \#5fb236 \par - “help programmers” (“02-23-TOSEM-2024-0012.pdf”, p. 14) \#5fb236 \par - “seven million natural language annotations, considered as queries, alongside API sequences” (“02-23-TOSEM-2024-0012.pdf”, p. 14) \#ffd400\\ \mkbibemph{What are these annotations?} \par - “more than one star.” (“02-23-TOSEM-2024-0012.pdf”, p. 14) \#ffd400\\ \mkbibemph{It's a not so strict constraint, isn't it?} \par - “To address this imbalance, we primarily rectified the records by adding parentheses where necessary to achieve symmetry.” (“02-23-TOSEM-2024-0012.pdf”, p. 14) \#ffd400\\ \mkbibemph{This can introduce errors, even when the code is syntactically correct. For instance, it is not sure if the missing paranthesis should be added always at a prefixed position (e.g., end of the considered string).} \par - “query-APIseq pairs” (“02-23-TOSEM-2024-0012.pdf”, p. 15) \#ffd400\\ \mkbibemph{It is necessary to better present how query-APIseq pairs are constructed for instance by referring to the example in Fig. 1 or some additional one specifically constructed as running example to be used to show all the building blocks of the proposed approach.} \par - “To adapt BIKER for API sequence recommendation, we make the necessary modifications to its open-source code.” (“02-23-TOSEM-2024-0012.pdf”, p. 16) \#5fb236 \par - “DASR” (“02-23-TOSEM-2024-0012.pdf”, p. 16) \#ffd400\\ \mkbibemph{Why this? This is defined by the authors, right?} \par - “To restore the veritable accuracy, we also utilize the original ground truth as a reference, and in this case, the accuracy metrics are labeled as BLEU𝑂 , MAP𝑂 , and NDCG𝑂 .” (“02-23-TOSEM-2024-0012.pdf”, p. 17) \#ffd400\\ \mkbibemph{This concept of veritable accuracy needs to be better explained and motivated.} \par - “(a) Comparison of top-k results under different accuracy metrics” (“02-23-TOSEM-2024-0012.pdf”, p. 18) \#ffd400\\ \mkbibemph{Which configuration of DDASR has been used to produce the shown accuracy results?} \par - “different architectures” (“02-23-TOSEM-2024-0012.pdf”, p. 18) \#ffd400\\ \mkbibemph{Explain that with architecture you are referring to different LLMs} \par - “We also investigate how DDASR affects accuracy when improving diversity.” (“02-23-TOSEM-2024-0012.pdf”, p. 19) \#5fb236} } @article{0229TOSEM20230413_Proof_hipdf, title = {02-29-{{TOSEM-2023-0413}}\_{{Proof}}\_hi.Pdf}, note = {\section{Annotazioni\\ (16/3/2024, 17:09:22)} \par - “Sarro, Federica” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”) \#5fb236 \par - “Sharma, Tusha” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”) \#5fb236 \par - “Energy-Awareness” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 1) \#5fb236 \par - “FEDERICA SARRO” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 1) \#5fb236 \par - “Software tool vendors have developed relevant tools, such as Code Carbon [10] and Experiment Impact Tracker [34], to estimate power consumption and carbon emissions during the training of dl models.” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 2) \#a28ae5 \par - “Implementing a generic method and framework, FECoM, to accurately measure energy consumption at a fine-grained level. Such a method has been instantiated for TensorFlow, to show its feasibility in practice” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 3) \#a28ae5 \par - “ine-grained Energy Consumption Meter (FECoM)” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 3) \#a28ae5 \par - “static instrumentation we devised for fine-grained energy-consumption measurement.” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 3) \#5fb236 \par - “measure fine-grained energy consumption” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 3) \#5fb236 \par - “FECoM identifies a set of target API calls and instruments the code around the identified calls.” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 3) \#ffd400\\ \mkbibemph{According to what criteria such API calls are identified?} \par - “FECoM’s measurement module” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 3) \#a28ae5 \par - “the temperature and energy consumption remain stabl” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 4) \#5fb236 \par - “Patcher operates at both the method and project levels” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 4) \#5fb236 \par - “This information allows Patcher to locate the API calls corresponding to the specified libraries.” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 4) \#5fb236 \par - “before\_execution\_INSERTED\_INTO\_SCRIPT” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 4) \#5fb236 \par - “records relevant information such as the total execution time and energy consumed” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 4) \#a28ae5 \par - “The Project-level script Patcher follows a similar approach as the method-level Patcher by inserting the same source code statements before and after the entire script, enabling comprehensive energy-consumption measurement throughout the project’s execution.” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 4) \#5fb236 \par - “efore\_execution\_INSERTED\_INTO\_SCRIPT(” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 5) \#ffd400\\ \mkbibemph{Can this placed in the middle of an assignment?} \par - “after\_execution\_INSERTED\_INTO\_SCRIPT” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 5) \#5fb236 \par - “Executability” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 5) \#a28ae5 \par - “n the first step, we ensure the executability of the generated patches, confirming that they execute without any syntactical errors.” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 5) \#5fb236 \par - “Pylance” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 5) \#5fb236 \par - “Automated testing” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 5) \#a28ae5 \par - “Human evaluation” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 5) \#a28ae5 \par - “it accurately extracts all the argument values for a given API call” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 6) \#5fb236 \par - “tool accurately identifies all TensorFlow API calls” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 6) \#ffd400\\ \mkbibemph{As far as I understood, it's the user that annotate the code with those special tags, isn't it?} \par - “API calls made via returned TensorFlow objects from user-defined functions.” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 6) \#5fb236 \par - “We perform two kinds of stability checks as part of the FECoM framework—the temperature check and the energy stability check.” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 6) \#5fb236 \par - “With energy stability check functionality, we ensure that CPU, RAM, and GPU energy observations are not fluctuating” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 6) \#5fb236 \par - “We execute each project (and each API call, in turn) ten times to ensure the reliability of the measurements.” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 7) \#5fb236 \par - “Intel’s Running Average Power Limit (RAPL): is an interface that allows applications to monitor and control the power consumption of various components, such as the CPU and memory, within Intel processors.” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 8) \#5fb236 \par - “The goal of this study is to develop an approach and framework to measure energy consumption at a fine-grained level (e.g., API level) to understand better the energy profile of APIs of dl frameworks so that it can be subsequently used to make their documentation energy-aware.” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 8) \#5fb236 \par - “API level” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 8) \#a28ae5 \par - “input data size” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 8) \#a28ae5 \par - “energy consumption?” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 8) \#a28ae5 \par - “challenges and considerations in developing fine-grained energy measurement tools for dl frameworks” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 9) \#a28ae5 \par - “Verdecchia et al. [77] emphasized the significant scarcity of tools, for example, to measure energy consumption, in the Green Artificial Intelligence domain.” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 9) \#5fb236 \par - “uncover the key challenges, underlying reasons and considerations that arise when developing tools measuring energy consumption for fine-grained profiling of dl frameworks and models” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 9) \#5fb236\\ \mkbibemph{ zotero://note/u/X3FCH6DG/} \par - “Validating the correctness of the measured energy consumption at a fine-grained granularity is a non-trivial challenge due to the lack of existing tools or benchmarks to measure energy consumption at the fine-grained level” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 9) \#5fb236 \par - “API level and at the project level” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 9) \#a28ae5 \par - “method calls belonging to a framework like TensorFlow” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 9) \#ffd400\\ \mkbibemph{This needs to be clarified. They are measuring the consumption related to the usage of frameworks like TensorFlow, right? (not the framework it self....)} \par - “Therefore, the sum of the energy consumed by the measured methods must be less than the total energy consumed by the project” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 9) \#5fb236 \par - “Where methods 𝑚𝑖 for 𝑖 ∈ \{1, 2, · · · , 𝑘 \} are in the scope of energy measurement (e.g., TensorFlow methods and API calls in the considered project code) representing 𝐸 (𝑀𝑠 ) and, hence, measured by FECoM.” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 9) \#ffd400\\ \mkbibemph{AN explanatory example to concretely show the different ingredients of the equation would make the paragraph easier to understand.} \par - “investigating the relationship between energy consumption and execution time at the API level granularity” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 9) \#5fb236 \par - “Previous research suggests a linear relationship between energy consumption and execution time [11]” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 9) \#5fb236\\ \mkbibemph{THis is interesting for the [[PAPERS/MT-ENERGY-CONSUMPTION]] paper.} \par - “his assumption holds when the power 𝑃 remains constant, as energy consumption 𝐸 is given by 𝐸 = 𝑃 × 𝑡, where 𝑡 represents the time duration.” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 9) \#5fb236 \par - “the relationship between energy consumption and parameter size is not known” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 10) \#5fb236 \par - “FECoM to determine the concrete relationship between these two aspects” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 10) \#a28ae5 \par - “we measure energy consumption by an API call multiple times, changing the passed parameters’ data size” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 10) \#5fb236 \par - “RQ3. This RQ explores and discusses the key considerations as well as challenges that one may face while designing and developing frameworks and tools similar to FECoM for fine-grained energy consumption measurement.” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 10) \#5fb236 \par - “meeting minutes” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 10) \#5fb236 \par - “(1) Coding process:” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 10) \#5fb236 \par - “(2) Iterative process:” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 10) \#5fb236 \par - “(3) Emergent coding:” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 10) \#5fb236 \par - “(4) Constant comparison:” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 10) \#5fb236 \par - “The following criteria were used to select a dl project repository for evaluating the RQs.” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 11) \#ffd400\\ \mkbibemph{Only one DL project has been analyzed? Check if this is in the threats to validity.} \par - “Additionally, the tutorials are continually updated to work with the latest TensorFlow versions, maintained by a team of more than 800 contributors.” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 11) \#5fb236 \par - “3.4 Experimental Environment” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 11) \#5fb236\\ \mkbibemph{THis is interesting for the [[PAPERS/MT-ENERGY-CONSUMPTION]] paper.} \par - “The GPU exhibits an idle power of 18 Watts and maximum power consumption of 290 Watts.” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 11) \#5fb236 \par - “The frequency at which energy measurement samples are captured and retrieved is an important factor in measuring energy consumption.” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 11) \#5fb236 \par - “However, using a lower frequency sampling interval can also result in situations where energy consumption readings at API granularity cannot be captured precisely.” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 11) \#5fb236 \par - “We adopt best practices from the literature to achieve and maintain stable conditions during measurements” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 12) \#ffd400\\ \mkbibemph{How? Reducing the power or what?} \par - “setting the cpu power policy to performance mode ensures it operates at maximum frequency” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 12) \#5fb236 \par - “minimize background processes on the machine related to energy measurement.” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 12) \#5fb236\\ \mkbibemph{THis is interesting for the [[PAPERS/MT-ENERGY-CONSUMPTION]] paper.} \par - “Stopping unnecessary background processes” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 12) \#5fb236 \par - “f the sum of energy consumed by the measured APIs is greater than the energy consumed by the entire project, then the proposed approach is falling short of measuring energy consumption at the fine-grained granularity” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 12) \#5fb236 \par - “Such a variance stems from different amounts of energy consumed by methods not in the scope, i.e., 𝐸 (𝑀𝑜 ).” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 12) \#5fb236 \par - “𝐸 (𝑃) {$>>$} 𝐸 (𝑀𝑠 )” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 12) \#5fb236 \par - “We perform statistical tests to determine the significance of the observed differences in energy consumption between method-level and project-level measurements.” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 12) \#5fb236 \par - “Based on the results of the one-tailed Wilcoxon signed-rank test, we can conclude that there is a statistically significant difference between the method-level and project-level energy consumption for all energy types (CPU, GPU, and RAM).” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 13) \#5fb236 \par - “the sum of method-level energy for a project is less than the project-level energy consumption.” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 13) \#a28ae5 \par - “negative average RAM” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 13) \#ffd400\\ \mkbibemph{The negative value for RAM energy consumption for some of the considered project is not clear.} \par - “igure” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 14) \#ff6666 \par - “FECoM enables drilling down into energy consumption patterns within real dl code, empowering developers to write greener, more efficient AI applications” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 14) \#5fb236 \par - “Summary of RQ1: The results provide strong evidence of the effectiveness of FECoM energy consumption measurements at a fine granularity” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 15) \#ffd400\\ \mkbibemph{This is a good remark. However, I'm missing an explicit discussion about using the output. What is the take away message that we can learn and that we can leverage on?} \par - “varying input data sizes.” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 15) \#5fb236 \par - “The figure reveals a linear relationship between input data size and energy consumption for both the CPU and GPU, indicating that 𝐸𝐶𝑃𝑈 (𝑛) and 𝐸𝐺𝑃𝑈 (𝑛) are increasing linear functions for this API call.” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 15) \#5fb236 \par - “𝐸𝑅𝐴𝑀 (𝑛) appears to remain constant.” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 15) \#a28ae5 \par - “The extremely small p-values for CPU, and GPU signify the high statistical significance of these correlations.” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 16) \#a28ae5 \par - “RAM plays a role in the execution at the beginning of the API call execution, where the data is copied to the installed GPU.” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 16) \#a28ae5 \par - “after the data is copied the role of RAM gets over and, hence, we observe low energy consumption from the RAM for the rest of the execution” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 16) \#a28ae5 \par - “The results show that the energy consumption of CPU and GPU exhibits very strong positive correlation with the input data size” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 16) \#a28ae5 \par - “Fine-grained Energy Measurement” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 16) \#5fb236 \par - “ssues that hinder effective energy measurement belong to this categor” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 17) \#5fb236 \par - “calibration processes to calculate stable energy consumption, maximum allowed temperatures, and wait times specific to the used hardware configuration that can be reused.” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 17) \#5fb236 \par - “It is important to consider the granularity of energy attribution, striking a balance between precision and overheads” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 18) \#5fb236 \par - “RELATED WORK” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 19) \#ffd400\\ \mkbibemph{I would move this section earlier in the paper to motivate the work after presenting the state of the art. Before presenting the FECoM approach, it is necessary to explain what are the limitations of existing techniques and what are the challenges that are not addressed yet. Table 2 fairly addresses such points and shows good motivations to support yet another energy measurement technique.~ However, presenting such a table in Section 6 is too late. Thus, I suggest moving such a table before the approach section and adding descriptive texts introducing and motivating the considered comparison features. I would also include technologies like Monsoon in the table to present a complete view of the field. Finally, I would also complete RQ3 with a set of criteria that users can consider when she has to select the energy measurement technique that best fits the particular requirement at hand. For instance, if the expected granularity is at the system level, FECoM might not be necessary, or if the acceptable sampling rate is a matter of seconds, CodeCarbon can be a possible choice.} \par - “Running Average Power Limit (rapl) interface [82],” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 19) \#5fb236 \par - “Power modeling techniques estimate energy consumption by considering factors such as the energy characteristics of the hardware and run-time information.” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 19) \#5fb236 \par - “Roberto Verdecchia, June Sallou, and Luís Cruz. 2023. A systematic review of Green AI. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery (2023), e1507.” (“02-29-TOSEM-2023-0413\_Proof\_hi.pdf”, p. 25) \#5fb236} } @online{03_ProvidingUsingServices, title = {03\_{{Providing}} and Using Services |}, url = {http://self-star.imag.fr/?page_id=204}, urldate = {2016-12-02} } @online{04_BuildingApplicationMultiple, title = {04\_{{Building}} an Application from Multiple Bundles |}, url = {http://self-star.imag.fr/?page_id=206}, urldate = {2016-12-02} } @online{05_iCasaICasaArchitecture, title = {05\_{{iCasa}} {{iCasa Architecture}}}, url = {http://adeleresearchgroup.github.io/iCasa/snapshot/architecture.html}, urldate = {2016-12-02} } @online{06_TutorialFollowMe, title = {06\_{{Tutorial Follow}} Me}, url = {http://self-star.imag.fr/?page_id=61}, urldate = {2016-12-02} } @inproceedings{10.1007/11871637_49, title = {Naive Bayes for Text Classification with Unbalanced Classes}, booktitle = {Knowl. {{Discov}}. {{Databases PKDD}} 2006}, author = {Frank, Eibe and Bouckaert, Remco R.}, editor = {Fürnkranz, Johannes and Scheffer, Tobias and Spiliopoulou, Myra}, date = {2006}, pages = {503--510}, publisher = {Springer Berlin Heidelberg}, location = {Berlin, Heidelberg}, abstract = {Multinomial naive Bayes (MNB) is a popular method for document classification due to its computational efficiency and relatively good predictive performance. It has recently been established that predictive performance can be improved further by appropriate data transformations [1,2]. In this paper we present another transformation that is designed to combat a potential problem with the application of MNB to unbalanced datasets. We propose an appropriate correction by adjusting attribute priors. This correction can be implemented as another data normalization step, and we show that it can significantly improve the area under the ROC curve. We also show that the modified version of MNB is very closely related to the simple centroid-based classifier and compare the two methods empirically.}, isbn = {978-3-540-46048-0} } @inproceedings{10.1007/978-3-030-20948-3_19, ids = {nguyenBuildingInformationSystems2019a,nguyenBuildingInformationSystems2019b,nguyenBuildingInformationSystems2019c}, title = {Building Information Systems Using Collaborative-Filtering Recommendation Techniques}, booktitle = {Adv. {{Inf}}. {{Syst}}. {{Eng}}. {{Workshop}} - {{CAiSE}} 2019 {{Int}}. {{Workshop Rome Italy June}} 3-7 2019 {{Proc}}.}, author = {Nguyen, Phuong T. and Di Rocco, Juri and Di Ruscio, Davide}, editor = {Proper, Henderik A. and Stirna, Janis}, date = {2019}, series = {Lecture {{Notes}} in {{Business Information Processing}}}, volume = {349}, pages = {214--226}, publisher = {Springer International Publishing}, location = {Cham}, doi = {10.1007/978-3-030-20948-3_19}, abstract = {IoT-technologies allow for the connection of miscellaneous devices, thereby creating a platform that sustains rich data sources. Given the circumstances, it is essential to have decent machinery in order to exploit the existing infrastructure and provide users with personalized services. Among others, recommender systems have been widely used to suggest users additional items that best match their needs and expectation. The use of recommender systems has gained considerable momentum in recent years. Nevertheless, the selection of a proper recommendation technique depends much on the input data as well as the domain of applications. In this work, we present an evaluation of two well-known collaborative-filtering (CF) techniques to build an information system for managing and recommending books in the IoT context. To validate the performance, we conduct a series of experiments on two considerably large datasets. The experimental results lead us to some interesting conclusions. In contrast to many existing studies which state that the item-based CF technique outperforms the user-based CF technique, we found out that there is no distinct winner between them. Furthermore, we confirm that the performance of a CF recommender system may be good with regards to some quality metrics, but not to some others.}, isbn = {978-3-030-20948-3}, keywords = {Book recommendation,Collaborative-filtering,IoT}, note = {cited By 2 \par cited By 2 \par TL;DR \par This work presents an evaluation of two well-known collaborative-filtering techniques to build an information system for managing and recommending books in the IoT context and confirms that the performance of a CF recommender system may be good with regards to some quality metrics, but not to some others.} } @inproceedings{10.1007/978-3-030-49461-2_18, title = {{{SAShA}}: {{Semantic-aware}} Shilling Attacks on Recommender Systems Exploiting Knowledge Graphs}, booktitle = {Semantic {{Web}}}, author = {Anelli, Vito Walter and Deldjoo, Yashar and Di Noia, Tommaso and Di Sciascio, Eugenio and Merra, Felice Antonio}, date = {2020}, pages = {307--323}, publisher = {Springer International Publishing}, location = {Cham}, abstract = {Recommender systems (RS) play a focal position in modern user-centric online services. Among them, collaborative filtering (CF) approaches have shown leading accuracy performance compared to content-based filtering (CBF) methods. Their success is due to an effective exploitation of similarities/correlations encoded in user interaction patterns, which is computed by considering common items users rated in the past. However, their strength is also their weakness. Indeed, a malicious agent can alter recommendations by adding fake user profiles into the platform thereby altering the actual similarity values in an engineered way.}, isbn = {978-3-030-49461-2} } @inproceedings{10.1007/978-3-030-64694-3_13, title = {{{AndroLib}}: {{Third-party}} Software Library Recommendation for Android Applications}, booktitle = {Reuse {{Emerg}}. {{Softw}}. {{Eng}}. {{Pract}}. 19th {{Int}}. {{Conf}}. {{Softw}}. {{Syst}}. {{Reuse ICSR}} 2020 {{Hammamet Tunis}}. {{Dec}}. 2–4 2020 {{Proc}}.}, author = {Chouchen, Moataz and Ouni, Ali and Mkaouer, Mohamed Wiem}, date = {2020}, pages = {208--225}, publisher = {Springer-Verlag}, location = {Berlin, Heidelberg}, doi = {10.1007/978-3-030-64694-3_13}, abstract = {Android mobile applications (apps) rely heavily on third-party libraries as a means to save time, reduce implementation costs, and increase software quality while offering rich, robust, and up-to-date features to end users. The selection of third-party libraries is an essential element in any software development project, and particularly, in Android apps given the fast-changing and evolving mobile app ecosystem. Indeed, deciding which libraries to choose is a challenging problem, especially with the exponentially increasing number of available libraries in the Android ecosystem. In this paper, we introduce, AndroLib, a novel approach to recommend third-party libraries for Android apps. In particular, we formulate the problem as a multi-objective combinatorial problem and use the non-dominated sorting genetic algorithm (NSGA-II) as a search method to find and recommend relevant libraries. We aim at guiding the search process towards the best trade-off three objectives to be optimized (i) maximize libraries historical co-usage, (ii) maximize libraries functional diversity, and (iii) maximize libraries reuse from highly rated apps. We conduct an empirical experiment to evaluate our approach on a benchmark of real-world Android apps libraries. Results show the effectiveness of AndroLib compared with three recent state-of-the-art library recommendation approaches.}, isbn = {978-3-030-64693-6}, pagetotal = {18}, keywords = {Android apps,Search based software engineering,Software reuse,Third-party Software Library} } @inproceedings{10.1007/978-3-031-00126-0_26, title = {{{GELibRec}}: {{Third-party}} Libraries Recommendation Using Graph Neural Network}, booktitle = {Database {{Syst}}. {{Adv}}. {{Appl}}.}, author = {Zou, Chengming and Fan, Zhenfeng}, editor = {Bhattacharya, Arnab and Lee Mong Li, Janice and Agrawal, Divyakant and Reddy, P. Krishna and Mohania, Mukesh and Mondal, Anirban and Goyal, Vikram and Uday Kiran, Rage}, date = {2022}, pages = {332--340}, publisher = {Springer International Publishing}, location = {Cham}, abstract = {Third-party libraries have become an indispensable part of the software. The function provided by well-tested third-party libraries can be reused through their programming interfaces, significantly increasing developers' software quality and productivity of developers. However, the vast number of third-party libraries and the complex dependencies are the main obstacles to efficiently exploiting the available resources. So, advanced methods are needed to explore the dependencies between projects and third-party libraries to make meaningful recommendations. This paper proposes GELibRec, which combines graph embedding and collaborative filtering to recommend libraries to developers. We extract a dataset from the open-source dataset libraries.io, and the experimental results show that GELibRec outperforms these methods concerning various quality metrics.}, isbn = {978-3-031-00126-0} } @inproceedings{10.1007/978-3-319-02654-1_11, title = {The State of the Art in Language Workbenches}, booktitle = {Softw. {{Lang}}. {{Eng}}.}, author = {Erdweg, Sebastian and family=Storm, given=Tijs, prefix=van der, useprefix=true and Völter, Markus and Boersma, Meinte and Bosman, Remi and Cook, William R. and Gerritsen, Albert and Hulshout, Angelo and Kelly, Steven and Loh, Alex and Konat, Gabriël D. P. and Molina, Pedro J. and Palatnik, Martin and Pohjonen, Risto and Schindler, Eugen and Schindler, Klemens and Solmi, Riccardo and Vergu, Vlad A. and Visser, Eelco and family=Vlist, given=Kevin, prefix=van der, useprefix=true and Wachsmuth, Guido H. and family=Woning, given=Jimi, prefix=van der, useprefix=true}, editor = {Erwig, Martin and Paige, Richard F. and Van Wyk, Eric}, date = {2013}, pages = {197--217}, publisher = {Springer International Publishing}, location = {Cham}, abstract = {Language workbenches are tools that provide high-level mechanisms for the implementation of (domain-specific) languages. Language workbenches are an active area of research that also receives many contributions from industry. To compare and discuss existing language workbenches, the annual Language Workbench Challenge was launched in 2011. Each year, participants are challenged to realize a given domain-specific language with their workbenches as a basis for discussion and comparison. In this paper, we describe the state of the art of language workbenches as observed in the previous editions of the Language Workbench Challenge. In particular, we capture the design space of language workbenches in a feature model and show where in this design space the participants of the 2013 Language Workbench Challenge reside. We compare these workbenches based on a DSL for questionnaires that was realized in all workbenches.}, isbn = {978-3-319-02654-1} } @inproceedings{10.1007/978-3-319-19069-3_17, title = {Detecting Complex Changes during Metamodel Evolution}, booktitle = {Adv. {{Inf}}. {{Syst}}. {{Eng}}.}, author = {Khelladi, Djamel Eddine and Hebig, Regina and Bendraou, Reda and Robin, Jacques and Gervais, Marie-Pierre}, editor = {Zdravkovic, Jelena and Kirikova, Marite and Johannesson, Paul}, date = {2015}, pages = {263--278}, publisher = {Springer International Publishing}, location = {Cham}, isbn = {978-3-319-19069-3}, note = {TL;DR \par This paper proposes a detection engine of complex changes that simultaneously addresses these two challenges of variability and overlap, and introduces three ranking heuristics to help users to decide which overlapping complex changes are likely to be correct.} } @inproceedings{10.1007/978-3-319-60438-1_47, ids = {nguyen_modification_2017}, title = {Modification to K-Medoids and {{CLARA}} for Effective Document Clustering}, booktitle = {Found. {{Intell}}. {{Syst}}.}, author = {Nguyen, Phuong T. and Eckert, Kai and Ragone, Azzurra and Di Noia, Tommaso}, editor = {Kryszkiewicz, Marzena and Appice, Annalisa and Ślęzak, Dominik and Rybinski, Henryk and Skowron, Andrzej and Raś, Zbigniew W.}, date = {2017}, pages = {481--491}, publisher = {Springer International Publishing}, location = {Cham}, abstract = {Document clustering plays an important role in several applications. K-Medoids and CLARA are among the most notable algorithms for clustering. These algorithms together with their relatives have been employed widely in clustering problems. In this paper we present a solution to improve the original K-Medoids and CLARA by making change in the way they assign objects to clusters. Experimental results on various document datasets using three distance measures have shown that the approach helps enhance the clustering outcomes substantially as demonstrated by three quality metrics, i.e. Entropy, Purity and F-Measure.}, isbn = {978-3-319-60438-1}, note = {TL;DR \par A solution to improve the original K-Medoids and CLARA by making change in the way they assign objects to clusters by demonstrating three quality metrics, i.e. Entropy, Purity and F-Measure.} } @inproceedings{10.1007/978-3-319-74730-9_33, title = {Developer-Centric Knowledge Mining from Large Open-Source Software Repositories ({{CROSSMINER}})}, booktitle = {Softw. {{Technol}}. {{Appl}}. {{Found}}.}, author = {Bagnato et. al., Alessandra}, date = {2018}, pages = {375--384}, publisher = {Springer International Publishing}, abstract = {Deciding if an OSS project meets the required standards for adoption is hard, and keeping up-to-date with a rapidly evolving project is even harder. Making decisions about quality and adoption involves analysing code, documentation, online discussions, and issue trackers. There is too much information to process manually and it is common that uninformed decisions have to be made with detrimental effects. CROSSMINER aims to remedy this by automatically extracting the required knowledge and injecting it into the developers' Integrated Development Environments (IDE), at the time they need it to make design decisions. This allows them to reduce their effort in knowledge acquisition and to increase the quality of their code. CROSSMINER uniquely combines advanced software project analyses with online IDE monitoring. Developers will be monitored to infer which information is timely, based on readily available knowledge stored earlier by a set of advanced offline deep analyses of related OSS projects.}, isbn = {978-3-319-74730-9}, noaddress = {Cham}, note = {TL;DR \par CROSSMINER uniquely combines advanced software project analyses with online IDE monitoring and automatically extracting the required knowledge and injecting it into the developers’ Integrated Development Environments (IDE), at the time they need it to make design decisions.} } @inproceedings{10.1007/978-3-540-30549-1_43, title = {Multinomial Naive Bayes for Text Categorization Revisited}, booktitle = {{{AI}} 2004 {{Adv}}. {{Artif}}. {{Intell}}.}, author = {Kibriya, Ashraf M. and Frank, Eibe and Pfahringer, Bernhard and Holmes, Geoffrey}, editor = {Webb, Geoffrey I. and Yu, Xinghuo}, date = {2005}, pages = {488--499}, publisher = {Springer Berlin Heidelberg}, location = {Berlin, Heidelberg}, abstract = {This paper presents empirical results for several versions of the multinomial naive Bayes classifier on four text categorization problems, and a way of improving it using locally weighted learning. More specifically, it compares standard multinomial naive Bayes to the recently proposed transformed weight-normalized complement naive Bayes classifier (TWCNB) [1], and shows that some of the modifications included in TWCNB may not be necessary to achieve optimum performance on some datasets. However, it does show that TFIDF conversion and document length normalization are important. It also shows that support vector machines can, in fact, sometimes very significantly outperform both methods. Finally, it shows how the performance of multinomial naive Bayes can be improved using locally weighted learning. However, the overall conclusion of our paper is that support vector machines are still the method of choice if the aim is to maximize accuracy.}, isbn = {978-3-540-30549-1}, note = {TL;DR \par It is shown how the performance of multinomial naive Bayes can be improved using locally weighted learning, and that support vector machines are still the method of choice if the aim is to maximize accuracy.} } @inproceedings{10.1007/978-3-540-87875-9_22, title = {{{MOOGLE}}: {{A}} Model Search Engine}, booktitle = {Model {{Driven Eng}}. {{Lang}}. {{Syst}}.}, author = {Lucrédio, Daniel and family=M. Fortes, given=Renata P., prefix=de, useprefix=true and Whittle, Jon}, editor = {Czarnecki, Krzysztof and Ober, Ileana and Bruel, Jean-Michel and Uhl, Axel and Völter, Markus}, date = {2008}, pages = {296--310}, publisher = {Springer Berlin Heidelberg}, location = {Berlin, Heidelberg}, abstract = {Models are becoming increasingly important in the software process. As a consequence, the number of models being used is increasing, and so is the need for efficient mechanisms to search them. Various existing search engines could be used for this purpose, but they lack features to properly search models, mainly because they are strongly focused on text-based search. This paper presents Moogle, a model search engine that uses metamodeling information to create richer search indexes and to allow more complex queries to be performed. The paper also presents the results of an evaluation of Moogle, which showed that the metamodel information improves the accuracy of the search.}, isbn = {978-3-540-87875-9}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {TL;DR \par Moogle is presented, a model search engine that uses metamodeling information to create richer search indexes and to allow more complex queries to be performed and to improve the accuracy of the search.} } @inproceedings{10.1007/978-3-642-03013-0_15, ids = {Zhong2009MAPO}, title = {{{MAPO}}: {{Mining}} and Recommending {{API}} Usage Patterns}, booktitle = {23rd {{Eur}}. {{Conf}}. {{Object-Oriented Program}}.}, author = {Zhong, Hao and Xie, Tao and Zhang, Lu and Pei, Jian and Mei, Hong}, date = {2009}, pages = {318--343}, publisher = {Springer}, location = {Berlin, Heidelberg}, isbn = {978-3-642-03012-3}, note = {TL;DR \par The results show that using MAPO, programmers produce code with fewer bugs when facing relatively complex API usages, comparing with using the two state-of-the-art code search tools.} } @inproceedings{10.1007/978-3-642-37456-2_14, title = {Density-Based Clustering Based on Hierarchical Density Estimates}, booktitle = {Adv. {{Knowl}}. {{Discov}}. {{Data Min}}.}, author = {Campello, Ricardo J. G. B. and Moulavi, Davoud and Sander, Joerg}, editor = {Pei, Jian and Tseng, Vincent S. and Cao, Longbing and Motoda, Hiroshi and Xu, Guandong}, date = {2013}, pages = {160--172}, publisher = {Springer Berlin Heidelberg}, location = {Berlin, Heidelberg}, abstract = {We propose a theoretically and practically improved density-based, hierarchical clustering method, providing a clustering hierarchy from which a simplified tree of significant clusters can be constructed. For obtaining a “flat” partition consisting of only the most significant clusters (possibly corresponding to different density thresholds), we propose a novel cluster stability measure, formalize the problem of maximizing the overall stability of selected clusters, and formulate an algorithm that computes an optimal solution to this problem. We demonstrate that our approach outperforms the current, state-of-the-art, density-based clustering methods on a wide variety of real world data.}, isbn = {978-3-642-37456-2}, note = {TL;DR \par This work proposes a theoretically and practically improved density-based, hierarchical clustering method, providing a clustering hierarchy from which a simplified tree of significant clusters can be constructed, and proposes a novel cluster stability measure. \par TL;DR \par This work proposes a theoretically and practically improved density-based, hierarchical clustering method, providing a clustering hierarchy from which a simplified tree of significant clusters can be constructed, and proposes a novel cluster stability measure.} } @article{10.1007/s10462-012-9364-9, title = {Shilling Attacks against Recommender Systems: {{A}} Comprehensive Survey}, author = {Gunes, Ihsan and Kaleli, Cihan and Bilge, Alper and Polat, Huseyin}, date = {2014-12}, journaltitle = {Artif. Intell. Rev.}, volume = {42}, number = {4}, pages = {767--799}, publisher = {Kluwer Academic Publishers}, location = {USA}, issn = {0269-2821}, doi = {10.1007/s10462-012-9364-9}, abstract = {Online vendors employ collaborative filtering algorithms to provide recommendations to their customers so that they can increase their sales and profits. Although recommendation schemes are successful in e-commerce sites, they are vulnerable to shilling or profile injection attacks. On one hand, online shopping sites utilize collaborative filtering schemes to enhance their competitive edge over other companies. On the other hand, malicious users and/or competing vendors might decide to insert fake profiles into the user-item matrices in such a way so that they can affect the predicted ratings on behalf of their advantages. In the past decade, various studies have been conducted to scrutinize different shilling attacks strategies, profile injection attack types, shilling attack detection schemes, robust algorithms proposed to overcome such attacks, and evaluate them with respect to accuracy, cost/benefit, and overall performance. Due to their popularity and importance, we survey about shilling attacks in collaborative filtering algorithms. Giving an overall picture about various shilling attack types by introducing new classification attributes is imperative for further research. Explaining shilling attack detection schemes in detail and robust algorithms proposed so far might open a lead to develop new detection schemes and enhance such robust algorithms further, even propose new ones. Thus, we describe various attack types and introduce new dimensions for attack classification. Detailed description of the proposed detection and robust recommendation algorithms are given. Moreover, we briefly explain evaluation of the proposed schemes. We conclude the paper by discussing various open questions.}, issue_date = {December 2014}, pagetotal = {33}, keywords = {Attack detection,Collaborative filtering,Profile injection,Push/nuke attacks,Robustness,Shilling}, note = {TL;DR \par Various attack types are described and new dimensions for attack classification are introduced and detailed description of the proposed detection and robust recommendation algorithms are given.} } @article{10.1007/s10664-018-9657-y, title = {What's Spain's Paris? {{Mining}} Analogical Libraries from {{Q}}\&{{A}} Discussions}, author = {Chen, Chunyang and Xing, Zhenchang and Liu, Yang}, date = {2019-06}, journaltitle = {Empir. Softw Engg}, volume = {24}, number = {3}, pages = {1155--1194}, publisher = {Kluwer Academic Publishers}, location = {USA}, issn = {1382-3256}, doi = {10.1007/s10664-018-9657-y}, abstract = {Third-party libraries are an integral part of many software projects. It often happens that developers need to find analogical libraries that can provide comparable features to the libraries they are already familiar with for different programming languages or different mobile platforms. Existing methods to find analogical libraries are limited by the community-curated list of libraries, blogs, or Q\&A posts, which often contain overwhelming or out-of-date information. In this paper, we present a new approach to recommend analogical libraries based on a knowledge base of analogical libraries mined from tags of millions of Stack Overflow questions. The novelty of our approach is to solve analogical-library questions by combining state-of-the-art word embedding technique and domain-specific relational and categorical knowledge mined from Stack Overflow. Given a library and a recommended analogical library, our approach further extracts questions and answer snippets in Stack Overflow about comparison of analogical libraries, which can potentially offer useful information scents for developers to further their investigation of the recommended analogical libraries. We implement our approach in a proof-of-concept web application and more than 34.8 thousands of users visited our website from November 2015 to August 2017. Our evaluation shows that our approach can make accurate recommendation of analogical libraries. We also demonstrate the usefulness of our analogical-library recommendations by using them to answer analogical-library questions in Stack Overflow. Google Analytics of our website traffic and analysis of the visitors' interaction with website contents provide the insights into the usage patterns and the system design of our web application.}, issue_date = {Jun 2019}, pagetotal = {40}, keywords = {Analogical libraries,Categorical knowledge,Knowledge graph,Relational knowledge,Word embedding} } @inproceedings{10.1109/COMPSAC.2015.241, title = {Malware Detection Systems Based on {{API}} Log Data Mining}, booktitle = {Proc. 2015 {{IEEE}} 39th {{Annu}}. {{Comput}}. {{Softw}}. {{Appl}}. {{Conf}}. - {{Vol}}. 03}, author = {Fan, Chun-I and Hsiao, Han-Wei and Chou, Chun-Han and Tseng, Yi-Fan}, date = {2015}, series = {{{COMPSAC}} '15}, pages = {255--260}, publisher = {IEEE Computer Society}, location = {USA}, doi = {10.1109/COMPSAC.2015.241}, abstract = {As information technology improves, the Internet is involved in every area in our daily life. When the mobile devices and cloud computing technology start to play important parts of our life, they have become more susceptible to attacks. In recent years, phishing and malicious websites have increasingly become serious problems in the field of network security. Attackers use many approaches to implant malware into target hosts in order to steal significant data and cause substantial damage. The growth of malware has been very rapid, and the purpose has changed from destruction to penetration. The signatures of malware have become more difficult to detect. In addition to static signatures, malware also tries to conceal dynamic signatures from anti-virus inspection. In this research, we use hooking techniques to trace the dynamic signatures that malware tries to hide. We then compare the behavioural differences between malware and benign programs by using data mining techniques in order to identify the malware. The experimental results show that our detection rate reaches 95\% with only 80 attributes. This means that our method can achieve a high detection rate with low complexity.}, isbn = {978-1-4673-6564-2}, pagetotal = {6}, keywords = {API,Classification,Data Mining,Malware,System Call}, note = {TL;DR \par This research uses hooking techniques to trace the dynamic signatures that malware tries to hide, and compares the behavioural differences between malware and benign programs by using data mining techniques in order to identify the malware.} } @inproceedings{10.1109/ICSM.2011.6080776, title = {Mining Java Class Naming Conventions}, booktitle = {Proc. 2011 27th {{IEEE Int}}. {{Conf}}. {{Softw}}. {{Maint}}.}, author = {Butler, Simon and Wermelinger, Michel and Yu, Yijun and Sharp, Helen}, date = {2011}, series = {{{ICSM}} '11}, pages = {93--102}, publisher = {IEEE Computer Society}, location = {USA}, doi = {10.1109/ICSM.2011.6080776}, abstract = {Class names represent the concepts implemented in object-oriented source code and are key elements in program comprehension and, thus, software maintenance. Programming conventions often state that class names should be noun-phrases, but there is little further guidance for developers on the composition of class names. Other researchers have observed that the majority of Java class identifier names are composed of one or more nouns preceded, optionally, by one or more adjectives. However, no detailed analysis of class identifier name structure has been undertaken that could be leveraged to support program comprehension activities.}, isbn = {978-1-4577-0663-9}, pagetotal = {10} } @article{10.1109/SANER.2017.7884605, title = {Detecting Similar Repositories on {{GitHub}}}, author = {Zhang, Yun and Lo, David and Kochhar, Pavneet Singh and Xia, Xin and Li, Quanlai and Sun, Jianling}, date = {2017}, journaltitle = {2017 IEEE 24th Int. Conf. Softw. Anal. Evol. Reengineering SANER}, volume = {00}, pages = {13--23}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, nodoi = {doi.ieeecomputersociety.org/10.1109/SANER.2017.7884605}, note = {TL;DR \par This paper proposes a novel approach that can effectively detect similar repositories on GitHub called RepoPal based on three heuristics leveraging two data sources (i.e., GitHub stars and readme files) which are not considered in previous works and compares it to a prior state-of-the-art approach CLAN.} } @inproceedings{10.1145/1097047.1097061, title = {Preventing Shilling Attacks in Online Recommender Systems}, booktitle = {Proc. 7th {{Annu}}. {{ACM Int}}. {{Workshop Web Inf}}. {{Data Manag}}.}, author = {Chirita, Paul-Alexandru and Nejdl, Wolfgang and Zamfir, Cristian}, date = {2005}, series = {{{WIDM}} '05}, pages = {67--74}, publisher = {Association for Computing Machinery}, location = {New York, NY, USA}, doi = {10.1145/1097047.1097061}, abstract = {Collaborative filtering techniques have been successfully employed in recommender systems in order to help users deal with information overload by making high quality personalized recommendations. However, such systems have been shown to be vulnerable to attacks in which malicious users with carefully chosen profiles are inserted into the system in order to push the predictions of some targeted items. In this paper we propose several metrics for analyzing rating patterns of malicious users and evaluate their potential for detecting such shilling attacks. Building upon these results, we propose and evaluate an algorithm for protecting recommender systems against shilling attacks. The algorithm can be employed for monitoring user ratings and removing shilling attacker profiles from the process of computing recommendations, thus maintaining the high quality of the recommendations.}, isbn = {1-59593-194-5}, pagetotal = {8}, keywords = {collaborative filtering,recommender systems,shilling attacks,web applications}, note = {TL;DR \par Several metrics for analyzing rating patterns of malicious users are proposed and an algorithm for protecting recommender systems against shilling attacks is evaluated that can be employed for monitoring user ratings and removing shilling attacker profiles from the process of computing recommendations, thus maintaining the high quality of the recommendations.} } @inproceedings{10.1145/1167473.1167508, title = {{{XSnippet}}: {{Mining}} for Sample Code}, booktitle = {Proc. 21st {{Annu}}. {{ACM SIGPLAN Conf}}. {{Object-Oriented Program}}. {{Syst}}. {{Lang}}. {{Appl}}.}, author = {Sahavechaphan, Naiyana and Claypool, Kajal}, date = {2006}, series = {{{OOPSLA}} '06}, pages = {413--430}, publisher = {Association for Computing Machinery}, location = {New York, NY, USA}, doi = {10.1145/1167473.1167508}, abstract = {It is common practice for software developers to use examples to guide development efforts. This largely unwritten, yet standard, practice of "develop by example" is often supported by examples bundled with library or framework packages, provided in textbooks, and made available for download on both official and unofficial web sites. However, the vast number of examples that are embedded in the billions of lines of already developed library and framework code are largely untapped. We have developed XSnippet, a context-sensitive code assistant framework that allows developers to query a sample repository for code snippets that are relevant to the programming task at hand. In particular, our work makes three primary contributions. First, a range of queries is provided to allow developers to switch between a context-independent retrieval of code snippets to various degrees of context-sensitive retrieval for object instantiation queries. Second, a novel graph-based code mining algorithm is provided to support the range of queries and enable mining within and across method boundaries. Third, an innovative context-sensitive ranking heuristic is provided that has been experimentally proven to provide better ranking for best-fit code snippets than context-independent heuristics such as shortest path and frequency. Our experimental evaluation has shown that XSnippet has significant potential to assist developers, and provides better coverage of tasks and better rankings for best-fit snippets than other code assistant systems.}, isbn = {1-59593-348-4}, pagetotal = {18}, keywords = {code assistants,code mining,code reuse,ranking code samples}, note = {TL;DR \par XSnippet is developed, a context-sensitive code assistant framework that allows developers to query a sample repository for code snippets that are relevant to the programming task at hand and provides better coverage of tasks and better rankings for best-fit snippets than other code assistant systems.} } @inproceedings{10.1145/1454008.1454012, title = {The Long Tail of Recommender Systems and How to Leverage It}, booktitle = {Proc. 2008 {{ACM Conf}}. {{Recomm}}. {{Syst}}.}, author = {Park, Yoon-Joo and Tuzhilin, Alexander}, date = {2008}, series = {{{RecSys}} '08}, pages = {11--18}, publisher = {Association for Computing Machinery}, location = {New York, NY, USA}, doi = {10.1145/1454008.1454012}, abstract = {The paper studies the Long Tail problem of recommender systems when many items in the Long Tail have only few ratings, thus making it hard to use them in recommender systems. The approach presented in the paper splits the whole itemset into the head and the tail parts and clusters only the tail items. Then recommendations for the tail items are based on the ratings in these clusters and for the head items on the ratings of individual items. If such partition and clustering are done properly, we show that this reduces the recommendation error rates for the tail items, while maintaining reasonable computational performance.}, isbn = {978-1-60558-093-7}, pagetotal = {8}, keywords = {clustering,data mining,long tail,recommendation}, note = {TL;DR \par This paper splits the whole itemset into the head and the tail parts and clusters only the tail items, and shows that this reduces the recommendation error rates for the Tail items, while maintaining reasonable computational performance.} } @inproceedings{10.1145/170035.170072, title = {Mining Association Rules between Sets of Items in Large Databases}, booktitle = {Proc. 1993 {{ACM SIGMOD Int}}. {{Conf}}. {{Manag}}. {{Data}}}, author = {Agrawal, Rakesh and Imieliński, Tomasz and Swami, Arun}, date = {1993}, series = {{{SIGMOD}} '93}, pages = {207--216}, publisher = {Association for Computing Machinery}, location = {New York, NY, USA}, doi = {10.1145/170035.170072}, abstract = {We are given a large database of customer transactions. Each transaction consists of items purchased by a customer in a visit. We present an efficient algorithm that generates all significant association rules between items in the database. The algorithm incorporates buffer management and novel estimation and pruning techniques. We also present results of applying this algorithm to sales data obtained from a large retailing company, which shows the effectiveness of the algorithm.}, isbn = {0-89791-592-5}, pagetotal = {10}, note = {TL;DR \par An efficient algorithm is presented that generates all significant association rules between items in the database of customer transactions and incorporates buffer management and novel estimation and pruning techniques.} } @inproceedings{10.1145/1772690.1772780, title = {Exploiting Query Reformulations for Web Search Result Diversification}, booktitle = {Proc. 19th {{Int}}. {{Conf}}. {{World Wide Web}}}, author = {Santos, Rodrygo L.T. and Macdonald, Craig and Ounis, Iadh}, date = {2010}, series = {{{WWW}} '10}, pages = {881--890}, publisher = {Association for Computing Machinery}, location = {New York, NY, USA}, doi = {10.1145/1772690.1772780}, abstract = {When a Web user's underlying information need is not clearly specified from the initial query, an effective approach is to diversify the results retrieved for this query. In this paper, we introduce a novel probabilistic framework for Web search result diversification, which explicitly accounts for the various aspects associated to an underspecified query. In particular, we diversify a document ranking by estimating how well a given document satisfies each uncovered aspect and the extent to which different aspects are satisfied by the ranking as a whole. We thoroughly evaluate our framework in the context of the diversity task of the TREC 2009 Web track. Moreover, we exploit query reformulations provided by three major Web search engines (WSEs) as a means to uncover different query aspects. The results attest the effectiveness of our framework when compared to state-of-the-art diversification approaches in the literature. Additionally, by simulating an upper-bound query reformulation mechanism from official TREC data, we draw useful insights regarding the effectiveness of the query reformulations generated by the different WSEs in promoting diversity.}, isbn = {978-1-60558-799-8}, pagetotal = {10}, keywords = {diversity,relevance,web search}, note = {TL;DR \par A novel probabilistic framework for Web search result diversification, which explicitly accounts for the various aspects associated to an underspecified query, is introduced and diversify a document ranking by estimating how well a given document satisfies each uncovered aspect and the extent to which different aspects are satisfied by the ranking as a whole.} } @inproceedings{10.1145/1835449.1835482, title = {On the Existence of Obstinate Results in Vector Space Models}, booktitle = {Proc. 33rd {{Int}}. {{ACM SIGIR Conf}}. {{Res}}. {{Dev}}. {{Inf}}. {{Retr}}.}, author = {Radovanović, Milos and Nanopoulos, Alexandros and Ivanović, Mirjana}, date = {2010}, series = {{{SIGIR}} '10}, pages = {186--193}, publisher = {Association for Computing Machinery}, location = {New York, NY, USA}, doi = {10.1145/1835449.1835482}, abstract = {The vector space model (VSM) is a popular and widely applied model in information retrieval (IR). VSM creates vector spaces whose dimensionality is usually high (e.g., tens of thousands of terms). This may cause various problems, such as susceptibility to noise and difficulty in capturing the underlying semantic structure, which are commonly recognized as different aspects of the "curse of dimensionality." In this paper, we investigate a novel aspect of the dimensionality curse, which is referred to as hubness and manifested by the tendency of some documents (called hubs) to be included in unexpectedly many search result lists. Hubness may impact VSM considerably since hubs can become obstinate results, irrelevant to a large number of queries, thus harming the performance of an IR system and the experience of its users. We analyze the origins of hubness, showing it is primarily a consequence of high (intrinsic) dimensionality of data, and not a result of other factors such as sparsity and skewness of the distribution of term frequencies. We describe the mechanisms through which hubness emerges by exploring the behavior of similarity measures in high-dimensional vector spaces. Our consideration begins with the classical VSM (tf-idf term weighting and cosine similarity), but the conclusions generalize to more advanced variations, such as Okapi BM25. Moreover, we explain why hubness may not be easily mitigated by dimensionality reduction, and propose a similarity adjustment scheme that takes into account the existence of hubs. Experimental results over real data indicate that significant improvement can be obtained through consideration of hubness.}, isbn = {978-1-4503-0153-4}, pagetotal = {8}, keywords = {cosine similarity,curse of dimensionality,hubs,nearest neighbors,similarity concentration,text retrieval,vector space model}, note = {TL;DR \par The origins of hubness are analyzed, showing it is primarily a consequence of high (intrinsic) dimensionality of data, and not a result of other factors such as sparsity and skewness of the distribution of term frequencies.} } @article{10.1145/2579991, title = {Textual and {{Content-Based Search}} in {{Repositories}} of {{Web Application Models}}}, author = {Bislimovska, Bojana and Bozzon, Alessandro and Brambilla, Marco and Fraternali, Piero}, date = {2014}, journaltitle = {ACM Trans, Web}, volume = {8}, number = {2}, pages = {1--47}, publisher = {Association for Computing Machinery}, location = {New York, NY, USA}, issn = {1559-1131}, doi = {10.1145/2579991}, articleno = {11}, issue_date = {March 2014}, langid = {english}, pagetotal = {47}, keywords = {⛔ No INSPIRE recid found,domain-specific language,Information retrieval,search,Web application}, note = {TL;DR \par This article examines two different techniques for indexing and searching model repositories, with a focus on Web development projects encoded in a domain-specific language.} } @inproceedings{10.1145/2766462.2767823, title = {Reducing Hubness: {{A}} Cause of Vulnerability in Recommender Systems}, booktitle = {Proc. 38th {{Int}}. {{ACM SIGIR Conf}}. {{Res}}. {{Dev}}. {{Inf}}. {{Retr}}.}, author = {Hara, Kazuo and Suzuki, Ikumi and Kobayashi, Kei and Fukumizu, Kenji}, date = {2015}, series = {{{SIGIR}} '15}, pages = {815--818}, publisher = {Association for Computing Machinery}, location = {New York, NY, USA}, doi = {10.1145/2766462.2767823}, abstract = {It is known that memory-based collaborative filtering systems are vulnerable to shilling attacks. In this paper, we demonstrate that hubness, which occurs in high dimensional data, is exploited by the attacks. Hence we explore methods for reducing hubness in user-response data to make these systems robust against attacks. Using the MovieLens dataset, we empirically show that the two methods for reducing hubness by transforming a similarity matrix(i) centering and (ii) conversion to a commute time kernel-can thwart attacks without degrading the recommendation performance.}, isbn = {978-1-4503-3621-5}, pagetotal = {4}, keywords = {collaborative filtering,hubness,shilling attack} } @article{10.1145/2827872, title = {The {{MovieLens}} Datasets: {{History}} and Context}, author = {Harper, F. Maxwell and Konstan, Joseph A.}, date = {2015-12}, journaltitle = {ACM Trans. Interact. Intell. Syst.}, volume = {5}, number = {4}, publisher = {Association for Computing Machinery}, location = {New York, NY, USA}, issn = {2160-6455}, doi = {10.1145/2827872}, abstract = {The MovieLens datasets are widely used in education, research, and industry. They are downloaded hundreds of thousands of times each year, reflecting their use in popular press programming books, traditional and online courses, and software. These datasets are a product of member activity in the MovieLens movie recommendation system, an active research platform that has hosted many experiments since its launch in 1997. This article documents the history of MovieLens and the MovieLens datasets. We include a discussion of lessons learned from running a long-standing, live research platform from the perspective of a research organization. We document best practices and limitations of using the MovieLens datasets in new research.}, articleno = {19}, issue_date = {January 2016}, pagetotal = {19}, keywords = {Datasets,MovieLens,ratings,recommendations} } @inproceedings{10.1145/2976749.2978333, title = {Reliable Third-Party Library Detection in Android and Its Security Applications}, booktitle = {Proc. 2016 {{ACM SIGSAC Conf}}. {{Comput}}. {{Commun}}. {{Secur}}.}, author = {Backes, Michael and Bugiel, Sven and Derr, Erik}, date = {2016}, series = {{{CCS}} '16}, pages = {356--367}, publisher = {Association for Computing Machinery}, location = {New York, NY, USA}, doi = {10.1145/2976749.2978333}, abstract = {Third-party libraries on Android have been shown to be security and privacy hazards by adding security vulnerabilities to their host apps or by misusing inherited access rights. Correctly attributing improper app behavior either to app or library developer code or isolating library code from their host apps would be highly desirable to mitigate these problems, but is impeded by the absence of a third-party library detection that is effective and reliable in spite of obfuscated code. This paper proposes a library detection technique that is resilient against common code obfuscations and that is capable of pinpointing the exact library version used in apps. Libraries are detected with profiles from a comprehensive library database that we generated from the original library SDKs. We apply our technique to the top apps on Google Play and their complete histories to conduct a longitudinal study of library usage and evolution in apps. Our results particularly show that app developers only slowly adapt new library versions, exposing their end-users to large windows of vulnerability. For instance, we discovered that two long-known security vulnerabilities in popular libs are still present in the current top apps. Moreover, we find that misuse of cryptographic APIs in advertising libs, which increases the host apps' attack surface, affects 296 top apps with a cumulative install base of 3.7bn devices according to Play. To the best of our knowledge, our work is first to quantify the security impact of third-party libs on the Android ecosystem.}, isbn = {978-1-4503-4139-4}, pagetotal = {12}, keywords = {android,third-party library detection}, note = {TL;DR \par This paper proposes a library detection technique that is resilient against common code obfuscations and that is capable of pinpointing the exact library version used in apps, and is first to quantify the security impact of third-party libs on the Android ecosystem.} } @inproceedings{10.1145/3172871.3172891, title = {Intelligent Software Engineering: {{Synergy}} between {{AI}} and Software Engineering}, booktitle = {Proc. 11th {{Innov}}. {{Softw}}. {{Eng}}. {{Conf}}.}, author = {Xie, Tao}, date = {2018}, series = {{{ISEC}} '18}, publisher = {Association for Computing Machinery}, location = {New York, NY, USA}, doi = {10.1145/3172871.3172891}, abstract = {There has been a long history of applying AI technologies to address software engineering problems especially on tool automation. On the other hand, given the increasing importance and popularity of AI software, recent research efforts have been on exploring software engineering solutions to improve the productivity of developing AI software and the dependability of AI software. The emerging field of intelligent software engineering is to focus on two aspects: (1) instilling intelligence in solutions for software engineering problems; (2) providing software engineering solutions for intelligent software. This extended abstract shares perspectives on these two aspects of intelligent software engineering.}, articleno = {1}, isbn = {978-1-4503-6398-3}, pagetotal = {1}, keywords = {artificial intelligence,Intelligent software engineering,software dependability} } @inproceedings{10.1145/3180155.3180220, title = {{{DeepTest}}: {{Automated}} Testing of Deep-Neural-Network-Driven Autonomous Cars}, booktitle = {Proc. 40th {{Int}}. {{Conf}}. {{Softw}}. {{Eng}}.}, author = {Tian, Yuchi and Pei, Kexin and Jana, Suman and Ray, Baishakhi}, date = {2018}, series = {{{ICSE}} '18}, pages = {303--314}, publisher = {Association for Computing Machinery}, location = {New York, NY, USA}, doi = {10.1145/3180155.3180220}, abstract = {Recent advances in Deep Neural Networks (DNNs) have led to the development of DNN-driven autonomous cars that, using sensors like camera, LiDAR, etc., can drive without any human intervention. Most major manufacturers including Tesla, GM, Ford, BMW, and Waymo/Google are working on building and testing different types of autonomous vehicles. The lawmakers of several US states including California, Texas, and New York have passed new legislation to fast-track the process of testing and deployment of autonomous vehicles on their roads.However, despite their spectacular progress, DNNs, just like traditional software, often demonstrate incorrect or unexpected corner-case behaviors that can lead to potentially fatal collisions. Several such real-world accidents involving autonomous cars have already happened including one which resulted in a fatality. Most existing testing techniques for DNN-driven vehicles are heavily dependent on the manual collection of test data under different driving conditions which become prohibitively expensive as the number of test conditions increases.In this paper, we design, implement, and evaluate DeepTest, a systematic testing tool for automatically detecting erroneous behaviors of DNN-driven vehicles that can potentially lead to fatal crashes. First, our tool is designed to automatically generated test cases leveraging real-world changes in driving conditions like rain, fog, lighting conditions, etc. DeepTest systematically explore different parts of the DNN logic by generating test inputs that maximize the numbers of activated neurons. DeepTest found thousands of erroneous behaviors under different realistic driving conditions (e.g., blurring, rain, fog, etc.) many of which lead to potentially fatal crashes in three top performing DNNs in the Udacity self-driving car challenge.}, isbn = {978-1-4503-5638-1}, pagetotal = {12}, keywords = {autonomous vehicle,deep learning,deep neural networks,neuron coverage,self-driving cars,testing}, note = {TL;DR \par DeepTest is a systematic testing tool for automatically detecting erroneous behaviors of DNN-driven vehicles that can potentially lead to fatal crashes and systematically explore different parts of the DNN logic by generating test inputs that maximize the numbers of activated neurons.} } @inproceedings{10.1145/3183440.3195011, title = {{{LibraryGuru}}: {{API}} Recommendation for Android Developers}, booktitle = {Proc. 40th {{Int}}. {{Conf}}. {{Softw}}. {{Eng}}. {{Companion Proceeedings}}}, author = {Yuan, Weizhao and Nguyen, Hoang H. and Jiang, Lingxiao and Chen, Yuting}, date = {2018}, series = {{{ICSE}} '18}, pages = {364--365}, publisher = {Association for Computing Machinery}, location = {New York, NY, USA}, doi = {10.1145/3183440.3195011}, abstract = {Developing modern mobile applications often require the uses of many libraries specific for the mobile platform, which can be overwhelmingly too many for application developers to find what are needed for a functionality and where and how to use them properly. This paper presents a tool, named LibraryGuru, to recommend suitable Android APIs for given functionality descriptions. It not only recommends functional APIs that can be invoked for implementing the functionality, but also recommends event callback APIs that are inherent in the Android framework and need to be overridden in the application. LibraryGuru internally builds correlation databases among various functionality descriptions and Android APIs. These correlations are extracted from Android development tutorials and SDK documents with domain-specific code parsing and natural language processing techniques adapted for functional APIs and event callback APIs separately, and are matched against functionality queries to recommend relevant APIs for developers. LibraryGuru is publicly accessible at http://libraryguru.info, and a demo video is available at https://youtu.be/f7MtjliUM-4.}, isbn = {978-1-4503-5663-3}, pagetotal = {2}, note = {TL;DR \par A tool, named LibraryGuru, to recommend suitable Android APIs for given functionality descriptions, which not only recommends functional APIs that can be invoked for implementing the functionality, but also recommends event callback APIs that are inherent in the Android framework and need to be overridden in the application.} } @inproceedings{10.1145/3196398.3196401, title = {On the Impact of Security Vulnerabilities in the Npm Package Dependency Network}, booktitle = {Proc. 15th {{Int}}. {{Conf}}. {{Min}}. {{Softw}}. {{Repos}}.}, author = {Decan, Alexandre and Mens, Tom and Constantinou, Eleni}, date = {2018}, series = {{{MSR}} '18}, pages = {181--191}, publisher = {Association for Computing Machinery}, location = {New York, NY, USA}, doi = {10.1145/3196398.3196401}, abstract = {Security vulnerabilities are among the most pressing problems in open source software package libraries. It may take a long time to discover and fix vulnerabilities in packages. In addition, vulnerabilities may propagate to dependent packages, making them vulnerable too. This paper presents an empirical study of nearly 400 security reports over a 6-year period in the npm dependency network containing over 610k JavaScript packages. Taking into account the severity of vulnerabilities, we analyse how and when these vulnerabilities are discovered and fixed, and to which extent they affect other packages in the packaging ecosystem in presence of dependency constraints. We report our findings and provide guidelines for package maintainers and tool developers to improve the process of dealing with security issues.}, isbn = {978-1-4503-5716-6}, pagetotal = {11}, keywords = {dependency network,security vulnerability,semantic versioning,software ecosystem,software repository mining} } @inproceedings{10.1145/3236024.3264838, title = {Software Fairness}, booktitle = {Proc. 2018 26th {{ACM Jt}}. {{Meet}}. {{Eur}}. {{Softw}}. {{Eng}}. {{Conf}}. {{Symp}}. {{Found}}. {{Softw}}. {{Eng}}.}, author = {Brun, Yuriy and Meliou, Alexandra}, date = {2018}, series = {{{ESEC}}/{{FSE}} 2018}, pages = {754--759}, publisher = {Association for Computing Machinery}, location = {New York, NY, USA}, doi = {10.1145/3236024.3264838}, abstract = {A goal of software engineering research is advancing software quality and the success of the software engineering process. However, while recent studies have demonstrated a new kind of defect in software related to its ability to operate in fair and unbiased manner, software engineering has not yet wholeheartedly tackled these new kinds of defects, thus leaving software vulnerable. This paper outlines a vision for how software engineering research can help reduce fairness defects and represents a call to action by the software engineering research community to reify that vision. Modern software is riddled with examples of biased behavior, from automated translation injecting gender stereotypes, to vision systems failing to see faces of certain races, to the US criminal justice sytem relying on biased computational assessments of crime recidivism. While systems may learn bias from biased data, bias can also emerge from ambiguous or incomplete requirement specification, poor design, implementation bugs, and unintended component interactions. We argue that software fairness is analogous to software quality, and that numerous software engineering challenges in the areas of requirements, specification, design, testing, and verification need to be tackled to solve this problem.}, isbn = {978-1-4503-5573-5}, pagetotal = {6}, keywords = {software bias,Software fairness,software process}, note = {TL;DR \par It is argued that software fairness is analogous to software quality, and that numerous software engineering challenges in the areas of requirements, specification, design, testing, and verification need to be tackled to solve this problem.} } @article{10.1145/3301285, title = {A Survey on Various Threats and Current State of Security in Android Platform}, author = {Bhat, Parnika and Dutta, Kamlesh}, date = {2019-02}, journaltitle = {ACM Comput. Surv.}, volume = {52}, number = {1}, publisher = {Association for Computing Machinery}, location = {New York, NY, USA}, issn = {0360-0300}, doi = {10.1145/3301285}, abstract = {The advent of the Android system has brought smartphone technology to the doorsteps of the masses. The latest technologies have made it affordable for every section of the society. However, the emergence of the Android platform has also escalated the growth of cybercrime through the mobile platform. Its open source operating system has made it a center of attraction for the attackers. This article provides a comprehensive study of the state of the Android Security domain. This article classifies the attacks on the Android system in four categories (i) hardware-based attacks, (ii) kernel-based attacks, (iii) hardware abstraction layer-based attacks, and (iv) application-based attacks. The study deals with various threats and security measures relating to these categories and presents an in-depth analysis of the underlying problems in the Android security domain. The article also stresses the role of Android application developers in realizing a more secure Android environment. This article attempts to provide a comparative analysis of various malware detection techniques concerning their methods and limitations. The study can help researchers gain knowledge of the Android security domain from various aspects and build a more comprehensive, robust, and efficient solution to the threats that Android is facing.}, articleno = {21}, issue_date = {February 2019}, pagetotal = {35}, keywords = {Android,intra library collusion,malware,malware detection,privilege escalation}, note = {TL;DR \par This article classifies the attacks on the Android system in four categories and presents an in-depth analysis of the underlying problems in the Android security domain and stresses the role of Android application developers in realizing a more secure Android environment.} } @inproceedings{10.1145/3357384.3357971, title = {Detecting Malicious Accounts in Online Developer Communities Using Deep Learning}, booktitle = {Proc. 28th {{ACM Int}}. {{Conf}}. {{Inf}}. {{Knowl}}. {{Manag}}.}, author = {Gong, Qingyuan and Zhang, Jiayun and Chen, Yang and Li, Qi and Xiao, Yu and Wang, Xin and Hui, Pan}, date = {2019}, series = {{{CIKM}} '19}, pages = {1251--1260}, publisher = {Association for Computing Machinery}, location = {New York, NY, USA}, doi = {10.1145/3357384.3357971}, abstract = {Online developer communities like GitHub provide services such as distributed version control and task management, which allow a massive number of developers to collaborate online. However, the openness of the communities makes themselves vulnerable to different types of malicious attacks, since the attackers can easily join and interact with legitimate users. In this work, we formulate the malicious account detection problem in online developer communities, and propose GitSec, a deep learning-based solution to detect malicious accounts. GitSec distinguishes malicious accounts from legitimate ones based on the account profiles as well as dynamic activity characteristics. On one hand, GitSec makes use of users' descriptive features from the profiles. On the other hand, GitSec processes users' dynamic behavioral data by constructing two user activity sequences and applying a parallel neural network design to deal with each of them, respectively. An attention mechanism is used to integrate the information generated by the parallel neural networks. The final judgement is made by a decision maker implemented by a supervised machine learning-based classifier. Based on the real-world data of GitHub users, our extensive evaluations show that GitSec is an accurate detection system, with an F1-score of 0.922 and an AUC value of 0.940.}, isbn = {978-1-4503-6976-3}, pagetotal = {10}, keywords = {deep learning,malicious account detection,online developer community,social networks} } @inproceedings{10.1145/3359591.3359732, title = {Active Learning for Software Engineering}, booktitle = {Proc. 2019 {{ACM SIGPLAN Int}}. {{Symp}}. {{New Ideas New Paradig}}. {{Reflect}}. {{Program}}. {{Softw}}.}, author = {Cambronero, José P. and Dang, Thurston H. Y. and Vasilakis, Nikos and Shen, Jiasi and Wu, Jerry and Rinard, Martin C.}, date = {2019}, series = {Onward! 2019}, pages = {62--78}, publisher = {Association for Computing Machinery}, location = {New York, NY, USA}, doi = {10.1145/3359591.3359732}, abstract = {Software applications have grown increasingly complex to deliver the features desired by users. Software modularity has been used as a way to mitigate the costs of developing such complex software. Active learning-based program inference provides an elegant framework that exploits this modularity to tackle development correctness, performance and cost in large applications. Inferred programs can be used for many purposes, including generation of secure code, code re-use through automatic encapsulation, adaptation to new platforms or languages, and optimization. We show through detailed examples how our approach can infer three modules in a representative application. Finally, we outline the broader paradigm and open research questions.}, isbn = {978-1-4503-6995-4}, pagetotal = {17}, keywords = {active learning,program inference,program modeling} } @article{10.1145/3360578, title = {Aroma: {{Code Recommendation}} via {{Structural Code Search}}}, shorttitle = {Aroma}, author = {Luan, Sifei and Yang, Di and Barnaby, Celeste and Sen, Koushik and Chandra, Satish}, date = {2018-12}, journaltitle = {Proc. ACM Program. Lang.}, volume = {3}, publisher = {Association for Computing Machinery}, location = {New York, NY, USA}, doi = {10.1145/3360578}, abstract = {Programmers often write code that has similarity to existing code written somewhere. A tool that could help programmers to search such similar code would be immensely useful. Such a tool could help programmers to extend partially written code snippets to completely implement necessary functionality, help to discover extensions to the partial code which are commonly included by other programmers, help to cross-check against similar code written by other programmers, or help to add extra code which would fix common mistakes and errors. We propose Aroma, a tool and technique for code recommendation via structural code search. Aroma indexes a huge code corpus including thousands of open-source projects, takes a partial code snippet as input, searches the corpus for method bodies containing the partial code snippet, and clusters and intersects the results of the search to recommend a small set of succinct code snippets which both contain the query snippet and appear as part of several methods in the corpus. We evaluated Aroma on 2000 randomly selected queries created from the corpus, as well as 64 queries derived from code snippets obtained from Stack Overflow, a popular website for discussing code. We implemented Aroma for 4 different languages, and developed an IDE plugin for Aroma. Furthermore, we conducted a study where we asked 12 programmers to complete programming tasks using Aroma, and collected their feedback. Our results indicate that Aroma is capable of retrieving and recommending relevant code snippets efficiently.}, articleno = {152}, issue = {OOPSLA}, issue_date = {October 2019}, langid = {english}, pagetotal = {28}, keywords = {clone detection,clustering,code recommendation,Computer Science - Software Engineering,feature-based code representation,structural code search}, note = {arXiv: 1812.01158 \par arXiv: 1812.01158} } @inproceedings{10.1145/3368089.3409745, title = {Borrowing Your Enemy’s Arrows: {{The}} Case of Code Reuse in Android via Direct Inter-App Code Invocation}, booktitle = {Proc. 28th {{ACM Jt}}. {{Meet}}. {{Eur}}. {{Softw}}. {{Eng}}. {{Conf}}. {{Symp}}. {{Found}}. {{Softw}}. {{Eng}}.}, author = {Gao, Jun and Li, Li and Kong, Pingfan and Bissyandé, Tegawendé F. and Klein, Jacques}, date = {2020}, series = {{{ESEC}}/{{FSE}} 2020}, pages = {939--951}, publisher = {Association for Computing Machinery}, location = {New York, NY, USA}, doi = {10.1145/3368089.3409745}, abstract = {The Android ecosystem offers different facilities to enable communication among app components and across apps to ensure that rich services can be composed through functionality reuse. At the heart of this system is the Inter-component communication (ICC) scheme, which has been largely studied in the literature. Less known in the community is another powerful mechanism that allows for direct inter-app code invocation which opens up for different reuse scenarios, both legitimate or malicious. This paper exposes the general workflow for this mechanism, which beyond ICCs, enables app developers to access and invoke functionalities (either entire Java classes, methods or object fields) implemented in other apps using official Android APIs. We experimentally showcase how this reuse mechanism can be leveraged to “plagiarize" supposedly-protected functionalities. Typically, we were able to leverage this mechanism to bypass security guards that a popular video broadcaster has placed for preventing access to its video database from outside its provided app. We further contribute with a static analysis toolkit, named DICIDer, for detecting direct inter-app code invocations in apps. An empirical analysis of the usage prevalence of this reuse mechanism is then conducted. Finally, we discuss the usage contexts as well as the implications of this studied reuse mechanism.}, isbn = {978-1-4503-7043-1}, pagetotal = {13}, keywords = {Android,DICI,Java Reflection} } @inproceedings{10.1145/3379597.3387478, title = {Detecting and Characterizing Bots That Commit Code}, booktitle = {Proc. 17th {{Int}}. {{Conf}}. {{Min}}. {{Softw}}. {{Repos}}.}, author = {Dey, Tapajit and Mousavi, Sara and Ponce, Eduardo and Fry, Tanner and Vasilescu, Bogdan and Filippova, Anna and Mockus, Audris}, date = {2020}, series = {{{MSR}} '20}, pages = {209--219}, publisher = {Association for Computing Machinery}, location = {New York, NY, USA}, doi = {10.1145/3379597.3387478}, abstract = {Background: Some developer activity traditionally performed manually, such as making code commits, opening, managing, or closing issues is increasingly subject to automation in many OSS projects. Specifically, such activity is often performed by tools that react to events or run at specific times. We refer to such automation tools as bots and, in many software mining scenarios related to developer productivity or code quality, it is desirable to identify bots in order to separate their actions from actions of individuals. Aim: Find an automated way of identifying bots and code committed by these bots, and to characterize the types of bots based on their activity patterns. Method and Result: We propose BIMAN, a systematic approach to detect bots using author names, commit messages, files modified by the commit, and projects associated with the commits. For our test data, the value for AUC-ROC was 0.9. We also characterized these bots based on the time patterns of their code commits and the types of files modified, and found that they primarily work with documentation files and web pages, and these files are most prevalent in HTML and JavaScript ecosystems. We have compiled a shareable dataset containing detailed information about 461 bots we found (all of which have more than 1000 commits) and 13,762,430 commits they created.}, isbn = {978-1-4503-7517-7}, pagetotal = {11}, keywords = {automated commits,bots,ensemble model,random forest,social coding platforms,software engineering}, note = {TL;DR \par BIMAN is proposed, a systematic approach to detect bots using author names, commit messages, files modified by the commit, and projects associated with the commits to find an automated way of identifying bots and code committed by these bots.} } @inproceedings{10.1145/3468264.3468552, title = {Embedding App-Library Graph for Neural Third Party Library Recommendation}, booktitle = {Proc. 29th {{ACM Jt}}. {{Meet}}. {{Eur}}. {{Softw}}. {{Eng}}. {{Conf}}. {{Symp}}. {{Found}}. {{Softw}}. {{Eng}}.}, author = {Li, Bo and He, Qiang and Chen, Feifei and Xia, Xin and Li, Li and Grundy, John and Yang, Yun}, date = {2021}, series = {{{ESEC}}/{{FSE}} 2021}, pages = {466--477}, publisher = {Association for Computing Machinery}, location = {New York, NY, USA}, doi = {10.1145/3468264.3468552}, abstract = {The mobile app marketplace has fierce competition for mobile app developers, who need to develop and update their apps as soon as possible to gain first mover advantage. Third-party libraries (TPLs) offer developers an easier way to enhance their apps with new features. However, how to find suitable candidates among the high number and fast-changing TPLs is a challenging problem. TPL recommendation is a promising solution, but unfortunately existing approaches suffer from low accuracy in recommendation results. To tackle this challenge, we propose GRec, a graph neural network (GNN) based approach, for recommending potentially useful TPLs for app development. GRec models mobile apps, TPLs, and their interactions into an app-library graph. It then distills app-library interaction information from the app-library graph to make more accurate TPL recommendations. To evaluate GRec’s performance, we conduct comprehensive experiments based on a large-scale real-world Android app dataset containing 31,432 Android apps, 752 distinct TPLs, and 537,011 app-library usage records. Our experimental results illustrate that GRec can significantly increase the prediction accuracy and diversify the prediction results compared with state-of-the-art methods. A user study performed with app developers also confirms GRec's usefulness for real-world mobile app development.}, isbn = {978-1-4503-8562-6}, pagetotal = {12}, keywords = {app-library graph,graph neural network,mobile app development,recommendation,third-party library}, note = {TL;DR \par GRec, a graph neural network (GNN) based approach, is proposed for recommending potentially useful TPLs for app development and results illustrate that GRec can significantly increase the prediction accuracy and diversify the prediction results compared with state-of-the-art methods.} } @inproceedings{10.1145/3468264.3468571, title = {A Large-Scale Empirical Study on Java Library Migrations: {{Prevalence}}, Trends, and Rationales}, booktitle = {Proc. 29th {{ACM Jt}}. {{Meet}}. {{Eur}}. {{Softw}}. {{Eng}}. {{Conf}}. {{Symp}}. {{Found}}. {{Softw}}. {{Eng}}.}, author = {He, Hao and He, Runzhi and Gu, Haiqiao and Zhou, Minghui}, date = {2021}, series = {{{ESEC}}/{{FSE}} 2021}, pages = {478--490}, publisher = {Association for Computing Machinery}, location = {New York, NY, USA}, doi = {10.1145/3468264.3468571}, abstract = {With the rise of open-source software and package hosting platforms, reusing 3rd-party libraries has become a common practice. Due to various failures during software evolution, a project may remove a used library and replace it with another library, which we call library migration. Despite substantial research on dependency management, the understanding of how and why library migrations occur is still lacking. Achieving this understanding may help practitioners optimize their library selection criteria, develop automated approaches to monitor dependencies, and provide migration suggestions for their libraries or software projects. In this paper, through a fine-grained commit-level analysis of 19,652 Java GitHub projects, we extract the largest migration dataset to-date (1,194 migration rules, 3,163 migration commits). We show that 8,065 (41.04\%) projects having at least one library removal, 1,564 (7.96\%, lower-bound) to 5,004 (25.46\%, upper-bound) projects have at least one migration, and a median project with migrations has 2 to 4 migrations in total. We discover that library migrations are dominated by several domains (logging, JSON, testing and web service) presenting a long tail distribution. Also, migrations are highly unidirectional in that libraries are either mostly abandoned or mostly chosen in our project corpus. A thematic analysis on related commit messages, issues, and pull requests identifies 14 frequently mentioned migration reasons (e.g., lack of maintenance, usability, integration, etc), 7 of which are not discussed in previous work. Our findings can be operationalized into actionable insights for package hosting platforms, project maintainers, and library developers. We provide a replication package at ¡a¿https://doi.org/10.5281/zenodo.4816752¡/a¿.}, isbn = {978-1-4503-8562-6}, pagetotal = {13}, keywords = {empirical software engineering,evolution and maintenance,library migration,mining software repositories} } @article{10.1145/3485275, title = {A Systematic Literature Review on the Use of Deep Learning in Software Engineering Research}, author = {Watson, Cody and Cooper, Nathan and Palacio, David Nader and Moran, Kevin and Poshyvanyk, Denys}, date = {2022-03}, journaltitle = {ACM Trans. Softw. Eng. Methodol.}, volume = {31}, number = {2}, publisher = {Association for Computing Machinery}, location = {New York, NY, USA}, issn = {1049-331X}, doi = {10.1145/3485275}, articleno = {32}, issue_date = {April 2022}, pagetotal = {58}, keywords = {Deep learning,literature review,machine learning,neural networks,software engineering}, note = {TL;DR \par A systematic literature review of research at the intersection of SE \& DL, from its modern inception to the present, that delineates the foundations of DL techniques applied to SE research and highlights likely areas of fertile exploration for the future.} } @article{10.1145/3487571, title = {Context- and Fairness-Aware in-Process Crowdworker Recommendation}, author = {Wang, Junjie and Yang, Ye and Wang, Song and Hu, Jun and Wang, Qing}, date = {2022-03}, journaltitle = {ACM Trans. Softw. Eng. Methodol.}, volume = {31}, number = {3}, publisher = {Association for Computing Machinery}, location = {New York, NY, USA}, issn = {1049-331X}, doi = {10.1145/3487571}, abstract = {Identifying and optimizing open participation is essential to the success of open software development. Existing studies highlighted the importance of worker recommendation for crowdtesting tasks in order to improve bug detection efficiency, i.e., detect more bugs with fewer workers. However, there are a couple of limitations in existing work. First, these studies mainly focus on one-time recommendations based on expertise matching at the beginning of a new task. Second, the recommendation results suffer from severe popularity bias, i.e., highly experienced workers are recommended in almost all the tasks, while less experienced workers rarely get recommended. This article argues the need for context- and fairness-aware in-process crowdworker recommendation in order to address these limitations. We motivate this study through a pilot study, revealing the prevalence of long-sized non-yielding windows, i.e., no new bugs are revealed in consecutive test reports during the process of a crowdtesting task. This indicates the potential opportunity for accelerating crowdtesting by recommending appropriate workers in a dynamic manner, so that the non-yielding windows could be shortened. Besides, motivated by the popularity bias in existing crowdworker recommendation approach, this study also aims at alleviating the unfairness in recommendations.Driven by these observations, this article proposes a context- and fairness-aware in-process crowdworker recommendation approach, iRec2.0, to detect more bugs earlier, shorten the non-yielding windows, and alleviate the unfairness in recommendations. It consists of three main components: (1) the modeling of dynamic testing context, (2) the learning-based ranking component, and (3) the multi-objective optimization-based re-ranking component. The evaluation is conducted on 636 crowdtesting tasks from one of the largest crowdtesting platforms, and results show the potential of iRec2.0 in improving the cost-effectiveness of crowdtesting by saving the cost, shortening the testing process, and alleviating the unfairness among workers. In detail, iRec2.0 could shorten the non-yielding window by a median of 50\%–66\% in different application scenarios, and consequently have potential of saving testing cost by a median of 8\%–12\%. Meanwhile, the recommendation frequency of the crowdworker drop from 34\%–60\% to 5\%–26\% under different scenarios, indicating its potential in alleviating the unfairness among crowdworkers.}, articleno = {35}, issue_date = {July 2022}, pagetotal = {31}, keywords = {Crowdsourced testing,fair recommendation,multi-objective optimization,worker recommendation} } @article{10.1145/3564284, title = {Bias and Debias in Recommender System: {{A}} Survey and Future Directions}, author = {Chen, Jiawei and Dong, Hande and Wang, Xiang and Feng, Fuli and Wang, Meng and He†, Xiangnan}, date = {2022-10}, journaltitle = {ACM Trans. Inf. Syst.}, publisher = {Association for Computing Machinery}, location = {New York, NY, USA}, issn = {1046-8188}, doi = {10.1145/3564284}, abstract = {While recent years have witnessed a rapid growth of research papers on recommender system (RS), most of the papers focus on inventing machine learning models to better fit user behavior data. However, user behavior data is observational rather than experimental. This makes various biases widely exist in the data, including but not limited to selection bias, position bias, exposure bias, and popularity bias. Blindly fitting the data without considering the inherent biases will result in many serious issues, e.g., the discrepancy between offline evaluation and online metrics, hurting user satisfaction and trust on the recommendation service, etc. To transform the large volume of research models into practical improvements, it is highly urgent to explore the impacts of the biases and perform debiasing when necessary. When reviewing the papers that consider biases in RS, we find that, to our surprise, the studies are rather fragmented and lack a systematic organization. The terminology “bias” is widely used in the literature, but its definition is usually vague and even inconsistent across papers. This motivates us to provide a systematic survey of existing work on RS biases. In this paper, we first summarize seven types of biases in recommendation, along with their definitions and characteristics. We then provide a taxonomy to position and organize the existing work on recommendation debiasing. Finally, we identify some open challenges and envision some future directions, with the hope of inspiring more research work on this important yet less investigated topic. The summary of debiasing methods reviewed in this survey can be found at https://github.com/jiawei-chen/RecDebiasing.}, keywords = {Adaption,Efficiency,Recommendation,Sampling}, note = {Just Accepted \par TL;DR \par This paper summarizes seven types of biases in recommendation, along with their definitions and characteristics, and provides a taxonomy to position and organize the existing work on recommendation debiasing.} } @article{10.1145/963770.963772, title = {Evaluating Collaborative Filtering Recommender Systems}, author = {Herlocker, Jonathan L. and Konstan, Joseph A. and Terveen, Loren G. and Riedl, John T.}, date = {2004-01}, journaltitle = {ACM Trans. Inf. Syst.}, volume = {22}, number = {1}, pages = {5--53}, publisher = {Association for Computing Machinery}, location = {New York, NY, USA}, issn = {1046-8188}, doi = {10.1145/963770.963772}, abstract = {Recommender systems have been evaluated in many, often incomparable, ways. In this article, we review the key decisions in evaluating collaborative filtering recommender systems: the user tasks being evaluated, the types of analysis and datasets being used, the ways in which prediction quality is measured, the evaluation of prediction attributes other than quality, and the user-based evaluation of the system as a whole. In addition to reviewing the evaluation strategies used by prior researchers, we present empirical results from the analysis of various accuracy metrics on one content domain where all the tested metrics collapsed roughly into three equivalence classes. Metrics within each equivalency class were strongly correlated, while metrics from different equivalency classes were uncorrelated.}, issue_date = {January 2004}, pagetotal = {49}, keywords = {Collaborative filtering,evaluation,metrics,recommender systems}, note = {TL;DR \par The key decisions in evaluating collaborative filtering recommender systems are reviewed: the user tasks being evaluated, the types of analysis and datasets being used, the ways in which prediction quality is measured, the evaluation of prediction attributes other than quality, and the user-based evaluation of the system as a whole.} } @article{10.5555/1577069.1755883, title = {A Survey of Accuracy Evaluation Metrics of Recommendation Tasks}, author = {Gunawardana, Asela and Shani, Guy}, date = {2009-12}, journaltitle = {J. Mach. Learn. Res.}, volume = {10}, pages = {2935--2962}, publisher = {JMLR.org}, issn = {1532-4435}, abstract = {Recommender systems are now popular both commercially and in the research community, where many algorithms have been suggested for providing recommendations. These algorithms typically perform differently in various domains and tasks. Therefore, it is important from the research perspective, as well as from a practical view, to be able to decide on an algorithm that matches the domain and the task of interest. The standard way to make such decisions is by comparing a number of algorithms offline using some evaluation metric. Indeed, many evaluation metrics have been suggested for comparing recommendation algorithms. The decision on the proper evaluation metric is often critical, as each metric may favor a different algorithm. In this paper we review the proper construction of offline experiments for deciding on the most appropriate algorithm. We discuss three important tasks of recommender systems, and classify a set of appropriate well known evaluation metrics for each task. We demonstrate how using an improper evaluation metric can lead to the selection of an improper algorithm for the task of interest. We also discuss other important considerations when designing offline experiments.}, issue_date = {12/1/2009}, pagetotal = {28}, note = {TL;DR \par This paper reviews the proper construction of offline experiments for deciding on the most appropriate algorithm, and discusses three important tasks of recommender systems, and classify a set of appropriate well known evaluation metrics for each task.} } @inproceedings{10.5555/3001460.3001507, title = {A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise}, booktitle = {Proc. {{Second Int}}. {{Conf}}. {{Knowl}}. {{Discov}}. {{Data Min}}.}, author = {Ester, Martin and Kriegel, Hans-Peter and Sander, Jörg and Xu, Xiaowei}, date = {1996}, series = {{{KDD}}'96}, pages = {226--231}, publisher = {AAAI Press}, location = {Portland, Oregon}, abstract = {Clustering algorithms are attractive for the task of class identification in spatial databases. However, the application to large spatial databases rises the following requirements for clustering algorithms: minimal requirements of domain knowledge to determine the input parameters, discovery of clusters with arbitrary shape and good efficiency on large databases. The well-known clustering algorithms offer no solution to the combination of these requirements. In this paper, we present the new clustering algorithm DBSCAN relying on a density-based notion of clusters which is designed to discover clusters of arbitrary shape. DBSCAN requires only one input parameter and supports the user in determining an appropriate value for it. We performed an experimental evaluation of the effectiveness and efficiency of DBSCAN using synthetic data and real data of the SEQUOIA 2000 benchmark. The results of our experiments demonstrate that (1) DBSCAN is significantly more effective in discovering clusters of arbitrary shape than the well-known algorithm CLAR-ANS, and that (2) DBSCAN outperforms CLARANS by a factor of more than 100 in terms of efficiency.}, pagetotal = {6}, keywords = {arbitrary shape of clusters,clustering algorithms,efficiency on large spatial databases,handling nlj4-275oise} } @online{10MCPAI, title = {10 {{MCP}}, {{AI Agents}}, and {{RAG}} Projects for {{AI Engineers}}:}, url = {https://www.linkedin.com/posts/avi-chawla_10-mcp-ai-agents-and-rag-projects-for-ai-activity-7317122234542616576-QKi4/?utm_source=share&utm_medium=member_desktop&rcm=ACoAAACaoXoBMvdj060syLix4vC3rwOgjAE9MpU}, urldate = {2025-04-14}, langid = {english}, keywords = {/unread} } @inproceedings{11697_100182, ids = {diruscioSupportingVariabilityExploration2016,diruscioSupportingVariabilityExploration2016a,ruscioSupportingVariabilityExploration2016,ruscioSupportingVariabilityExploration2016a}, title = {Supporting Variability Exploration and Resolution during Model Migration}, booktitle = {Model. {{Found}}. {{Appl}}. 12th {{Eur}}. {{Conf}}. {{ECMFA}} 2016 {{Held Part STAF}} 2016 {{Vienna Austria July}} 6-7 2016 {{Proc}}.}, author = {Di Ruscio, Davide and Etzlstorfer, Juergen and Iovino, Ludovico and Pierantonio, Alfonso and Schwinger, Wieland}, date = {2016}, series = {{{LECTURE NOTES IN COMPUTER SCIENCE}}}, volume = {9764}, pages = {231--246}, publisher = {Springer Verlag}, doi = {10.1007/978-3-319-42061-5_15}, isbn = {978-3-319-42060-8}, keywords = {Computer Science (all),Theoretical Computer Science}, note = {cited By 9 \par cited By 9} } @inproceedings{11697_107894, ids = {bascianiAutomatedChainingModel2014,bascianiAutomatedChainingModel2014a,bascianiAutomatedChainingModel2014b,bascianiAutomatedChainingModel2014c,inproceedings}, title = {Automated Chaining of Model Transformations with Incompatible Metamodels}, booktitle = {Model-{{Driven Eng}}. {{Lang}}. {{Syst}}. - 17th {{Int}}. {{Conf}}. {{MODELS}} 2014 {{Valencia Spain Sept}}. 28 - {{Oct}}. 3 2014 {{Proc}}.}, author = {Basciani, Francesco and Di Ruscio, Davide and Iovino, Ludovico and Pierantonio, Alfonso}, date = {2014}, series = {{{LECTURE NOTES IN COMPUTER SCIENCE}}}, volume = {8767}, pages = {602--618}, publisher = {Springer Verlag}, doi = {10.1007/978-3-319-11653-2_37}, keywords = {Computer Science (all),Theoretical Computer Science}, note = {cited By 26 \par cited By 26 \par TL;DR \par In Model-Driven Engineering models are first-class entities that are manipulated by means of model transformations and the development of complex and large transformations can benefit from the reuse of smaller ones that can be composed according to user requirements.} } @inproceedings{11697_111414, ids = {atleeMessageWorkshopChairs2016,atleeMessageWorkshopChairs2016a}, title = {Message from the Workshop Chairs}, booktitle = {Proc. - 8th {{Int}}. {{Workshop Model}}. {{Softw}}. {{Eng}}. {{MiSE}} 2016}, author = {Atlee, Joanne and Baillargeon, Robert and Di Ruscio, Davide and Rumpe, Bernhard}, date = {2016}, pages = {vii}, publisher = {Association for Computing Machinery, Inc}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84973488172&partnerID=40&md5=4c53454d1905560dc7e0f2a62805c560}, isbn = {978-1-4503-4164-6}, keywords = {Modeling and Simulation,Software}, note = {cited By 0 \par cited By 0 \par TL;DR \par The workshop serves a central purpose in helping to define a research community that is really just beginning to emerge in the field of “hardware/software co-design”.} } @inproceedings{11697_111418, ids = {diruscioCEURWorkshopProceedings2016,diruscioCEURWorkshopProceedings2016a}, title = {{{CEUR}} Workshop Proceedings: {{Preface}}}, booktitle = {{{CEUR Workshop Proc}}.}, author = {Di Ruscio, Davide and De Lara, Juan and Pierantonio, Alfonso}, date = {2016}, series = {{{CEUR WORKSHOP PROCEEDINGS}}}, volume = {1694}, publisher = {CEUR-WS}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84992533486&partnerID=40&md5=bd42da7c52ba8ecd02ca81bcc01132c9}, keywords = {Computer Science (all)}, note = {cited By 0 \par cited By 0} } @inproceedings{11697_111420, ids = {osmanSATToSE2016Postproceedings2016,osmanSATToSE2016Postproceedings2016a}, title = {{{SATToSE}} 2016: {{The}} Post-Proceedings Editorial}, booktitle = {{{CEUR Workshop Proc}}.}, author = {Osman, Haidar and Di Ruscio, Davide and Zaytsev, Vadim and Lungu, Mircea and Bagge, Anya Helene}, date = {2016}, series = {{{CEUR WORKSHOP PROCEEDINGS}}}, volume = {1791}, publisher = {CEUR-WS}, url = {http://ceur-ws.org/}, keywords = {Computer Science (all)}, note = {cited By 0 \par cited By 0} } @inproceedings{11697_111428, ids = {bascianiModelRepositoriesWill2015,bascianiModelRepositoriesWill2015a,bascianiModelRepositoriesWill2015b}, title = {Model Repositories: {{Will}} They Become Reality? {{A}} Position Statement}, booktitle = {{{CEUR Workshop Proc}}.}, author = {Basciani, Francesco and DI ROCCO, Juri and DI RUSCIO, Davide and Pierantonio, Alfonso and Iovino, Ludovico}, date = {2015}, series = {{{CEUR WORKSHOP PROCEEDINGS}}}, volume = {1563}, pages = {37--42}, publisher = {CEUR-WS}, url = {http://ceur-ws.org/}, keywords = {Computer Science (all)}, note = {cited By 10 \par cited By 10} } @inproceedings{11697_111431, ids = {osmanSATToSE2015Postproceedings2015,osmanSATToSE2015Postproceedings2015a}, title = {{{SATToSE}} 2015: {{The}} Post-Proceedings Editorial}, booktitle = {{{CEUR Workshop Proc}}.}, author = {Osman, Haidar and Di Ruscio, Davide and Zaytsev, Vadim and Lungu, Mircea and Bagge, Anya Helene}, date = {2015}, series = {{{CEUR WORKSHOP PROCEEDINGS}}}, volume = {1820}, publisher = {CEUR-WS}, url = {http://ceur-ws.org/}, keywords = {Computer Science (all)}, note = {cited By 0 \par cited By 0} } @inproceedings{11697_111437, ids = {diruscioRolePartsSystem2014,diruscioRolePartsSystem2014a,ruscioRolePartsSystem2014,ruscioRolePartsSystem2014a}, title = {The Role of Parts in the System Behaviour}, booktitle = {Softw. {{Eng}}. {{Resilient Syst}}. - 6th {{Int}}. {{Workshop SERENE}} 2014 {{Bp}}. {{Hung}}. {{Oct}}. 15-16 2014 {{Proc}}.}, author = {DI RUSCIO, Davide and Malavolta, Ivano and Pelliccione, Patrizio}, date = {2014}, series = {{{LECTURE NOTES IN COMPUTER SCIENCE}}}, volume = {8785}, pages = {24--39}, publisher = {Springer Verlag}, doi = {10.1007/978-3-319-12241-0_3}, isbn = {978-3-319-12240-3}, keywords = {Computer Science (all),Theoretical Computer Science}, note = {cited By 3 \par cited By 3 \par TL;DR \par In today’s world, the authors are surrounded by software-based systems that control so many critical activities and there is an unavoidable shift from stand-alone systems to systems of systems, to ecosystems, to cyber-physical systems and in general to systems that are composed of various independent parts that collaborate and cooperate to realise the desired goal.} } @inproceedings{11697_111464, ids = {delaraReusingModelTransformations2017,delaraReusingModelTransformations2017a,delaraReusingModelTransformations2017b,laraReusingModelTransformations2017}, title = {Reusing Model Transformations through Typing Requirements Models}, booktitle = {Fundam. {{Approaches Softw}}. {{Eng}}. - 20th {{Int}}. {{Conf}}. {{FASE}} 2017 {{Held Part Eur}}. {{Jt}}. {{Conf}}. {{Theory Pract}}. {{Softw}}. {{ETAPS}} 2017 {{Upps}}. {{Swed}}. {{April}} 22-29 2017 {{Proc}}.}, author = {family=Lara, given=Juan, prefix=de, useprefix=true and DI ROCCO, Juri and DI RUSCIO, Davide and Guerra, Esther and Iovino, Ludovico and Pierantonio, Alfonso and Cuadrado, Jesús Sánchez}, date = {2017}, series = {{{LECTURE NOTES IN COMPUTER SCIENCE}}}, volume = {10202}, pages = {264--282}, publisher = {Springer Verlag}, doi = {10.1007/978-3-662-54494-5_15}, isbn = {978-3-662-54494-5}, keywords = {Computer Science (all),Theoretical Computer Science}, note = {cited By 12 \par cited By 12} } @incollection{11697_121392, ids = {chechikMessageWorkshopChairs2017,chechikMessageWorkshopChairs2017a}, title = {Message from the Workshop Chairs {{MiSE}} 2017}, booktitle = {Proceedings - 2017 {{IEEE}}/{{ACM}} 9th International Workshop on Modelling in Software Engineering, {{MiSE}} 2017}, author = {Chechik, Marsha and Di Ruscio, Davide and Rumpe, Bernhard}, date = {2017}, pages = {vii-vii}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/MiSE.2017.11}, isbn = {978-1-5386-0426-7}, keywords = {Modeling and Simulation,Software}, note = {cited By 0 \par cited By 0} } @inproceedings{11697_121394, ids = {diruscioFeaturebasedApproachVariability2017,diruscioFeaturebasedApproachVariability2017a,ruscioFeatureBasedApproachVariability2017}, title = {A Feature-Based Approach for Variability Exploration and Resolution in Model Transformation Migration}, booktitle = {Model. {{Found}}. {{Appl}}. - 13th {{Eur}}. {{Conf}}. {{ECMFA}} 2017 {{Held Part STAF}} 2017 {{Marburg Ger}}. {{July}} 19-20 2017 {{Proc}}.}, author = {Di Ruscio, Davide and Etzlstorfer, Juergen and Iovino, Ludovico and Pierantonio, Alfonso and Schwinger, Wieland}, date = {2017}, series = {{{LECTURE NOTES IN COMPUTER SCIENCE}}}, volume = {10376 LNCS}, pages = {71--89}, publisher = {Springer Verlag}, doi = {10.1007/978-3-319-61482-3_5}, isbn = {978-3-319-61481-6}, keywords = {Computer Science (all),Theoretical Computer Science}, note = {cited By 7 \par cited By 7} } @incollection{11697_121395, title = {Proceedings of the 3rd {{Workshop}} on {{Scalable Model Driven Engineering}} Part of the {{Software Technologies}}: {{Applications}} and {{Foundations}} ({{STAF}} 2015) Federation of Conferences}, booktitle = {{{CEUR}} Workshop Proceedings}, author = {Kolovos, Dimitris and Di Ruscio, Davide and Matragkas, Nicholas and Cuadrado, Jesus Sanchez and Rath, Istvan and Tisi, Massimo}, date = {2015}, volume = {1406}, publisher = {CEUR-WS}, url = {http://ceur-ws.org/}, keywords = {Computer Science (all)} } @inproceedings{11697_121397, ids = {diruscioProceedings2ndWorkshop2014}, title = {Proceedings of the 2nd Workshop on Scalability in Model Driven Engineering Co-Located with the Software Technologies: {{Applications}} and Foundations Conference, {{BigMDE}}@{{STAF2014}}}, booktitle = {{{CEUR Workshop Proc}}.}, author = {Di Ruscio, Davide and De Lara, Juan and Kolovos, Dimitris and Matragkas, Nicholas and Rath, Istvan and Tisi, Massimo}, date = {2014}, series = {{{CEUR WORKSHOP PROCEEDINGS}}}, volume = {1206}, publisher = {CEUR-WS}, url = {http://ceur-ws.org/}, keywords = {Computer Science (all)} } @inproceedings{11697_121399, ids = {diruscioSATToSE2014Postproceedings2014,diruscioSATToSE2014Postproceedings2014a}, title = {{{SATToSE}} 2014: {{The}} Post-Proceedings Editorial}, booktitle = {{{CEUR Workshop Proc}}.}, author = {Di Ruscio, Davide and Zaytsev, Vadim}, date = {2014}, series = {{{CEUR WORKSHOP PROCEEDINGS}}}, volume = {1354}, pages = {1--5}, publisher = {CEUR-WS}, url = {http://ceur-ws.org/}, keywords = {Computer Science (all)}, note = {cited By 0 \par cited By 0 \par TL;DR \par An approach to understand structural characteristics of metamodels by looking at how model transformations depend on metamodels of their source and target models is presented.} } @inproceedings{11697_121403, ids = {diruscioACMStudentResearch2013,diruscioACMStudentResearch2013a,diruscioACMStudentResearch2013b}, title = {{{ACM}} Student Research Competition at {{MoDELS}} 2013}, booktitle = {Jt. {{Proc}}. {{Model}}. {{Invit}}. {{Talks Demonstr}}. {{Sess}}. {{Poster Sess}}. {{ACM Stud}}. {{Res}}. {{Compet}}. {{Co-Located}} 16th {{Int}}. {{Conf}}. {{Model Driven Eng}}. {{Lang}}. {{Syst}}. {{MODELS}} 2013}, author = {Di Ruscio, Davide and Jackson, Ethan}, date = {2013}, series = {{{CEUR WORKSHOP PROCEEDINGS}}}, volume = {1115}, publisher = {CEUR-WS}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84921919454&partnerID=40&md5=03f4a0df3c070c47a6f17cca8571698b}, keywords = {Computer Science (all)}, note = {cited By 0 \par cited By 0} } @incollection{11697_121405, title = {Proceedings of the 2nd International Workshop on Model Comparison in Practice}, booktitle = {{{ACM}} International Conference Proceeding Series}, author = {Di Ruscio, Davide and Kolovos, Dimitris}, date = {2011}, isbn = {978-1-4503-0668-3}, keywords = {1707,Computer Networks and Communications,Human-Computer Interaction,Software} } @incollection{11697_121406, title = {Proceedings of the 1st International Workshop on Model Comparison in Practice}, booktitle = {{{ACM}} International Conference Proceeding Series}, author = {Di Ruscio, Davide and Kolovos, Dimitris}, date = {2010}, isbn = {978-1-60558-960-2}, keywords = {1707,Computer Networks and Communications,Human-Computer Interaction,Software}, note = {TL;DR \par The number of submitted and accepted papers indicates that the field of model comparison is an important and actively-researched field within the community and it is hoped that this workshop will trigger useful discussions and help with establishing a deeper understanding - and potentially new collaborations - between researchers.} } @inproceedings{11697_121413, title = {Developer-Centric Knowledge Mining from Large Open-Source Software Repositories ({{CROSSMINER}})}, booktitle = {Softw. {{Technol}}. {{Appl}}. {{Found}}. - {{STAF}} 2017 {{Collocated Workshop Marburg Ger}}. {{July}} 17-21 2017 {{Revis}}. {{Sel}}. {{Pap}}.}, author = {Bagnato, Alessandra and Barmpis, Konstantinos and Bessis, Nik and Adrian Cabrera-Diego, Luis and DI ROCCO, Juri and DI RUSCIO, Davide and Gergely, Tamas and Hansen, Scott and Kolovos, Dimitris S. and Krief, Philippe and Korkontzelos, Ioannis and Lauriere, Stephane and Manrique Lopez de la Fuente, Jose and Malo, Pedro and Paige, Richard F. and Spinellis, Diomidis and Thomas, Cedric and Vinju, Jurgen J.}, date = {2017}, volume = {10748}, publisher = {Springer}, doi = {10.1007/978-3-319-74730-9}, isbn = {978-3-319-74729-3}, note = {TL;DR \par CROSSMINER uniquely combines advanced software project analyses with online IDE monitoring and automatically extracting the required knowledge and injecting it into the developers’ Integrated Development Environments (IDE), at the time they need it to make design decisions.} } @article{11697_126147, entrysubtype = {magazine}, title = {Automated {{Selection}} of {{Optimal Model Transformation Chains}} via {{Shortest-Path Algorithms}}}, author = {Basciani, Francesco and Demidio, Mattia and Di Ruscio, Davide and Frigioni, Daniele and Iovino, Ludovico and Pierantonio, Alfonso}, date = {2018}, journaltitle = {IEEE TRANSACTIONS ON SOFTWARE ENGINEERING}, volume = {46}, pages = {251--279}, url = {http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=32}, keywords = {Adaptation models,Analytical models,Bridges,Ecosystems,Graph Algorithms,Model driven engineering,Model Transformation Composition,model-driven engineering,Model-driven engineering,Shortest Paths,Software,Unified modeling language}, note = {TL;DR \par This paper proposes an approach, based on well-established algorithms, to support modellers when multiple transformation chains are available to bridge a source metamodel with a target one.} } @inproceedings{11697_128310, ids = {diroccoResilienceSiriusEditors2018,diroccoResilienceSiriusEditors2018a,diroccoResilienceSiriusEditors2018b,roccoResilienceSiriusEditors2018a,roccoResilienceSiriusEditors2018b}, title = {Resilience in Sirius Editors: {{Understanding}} the Impact of Meta-Model Changes}, booktitle = {Proc. {{MODELS}} 2018 {{Workshop ModComp MRT OCL FlexMDE EXE COMMitMDE MDETools GEMOC MORSE MDE4IoT MDEbug MoDeVVa ME MULTI HuFaMo AMMoRe PAINS Co-Located ACMIEEE}} 21st {{Int}}. {{Conf}}. {{Model Driven Eng}}. {{Lang}}. {{Syst}}. {{MODELS}} 2018 {{Cph}}. {{Den}}. {{Oct}}. 14 2018}, author = {Di Rocco, Juri and Di Ruscio, Davide and Narayanankutty, Hrishikesh and Pierantonio, Alfonso}, date = {2018}, series = {{{CEUR WORKSHOP PROCEEDINGS}}}, volume = {2192}, pages = {620--630}, publisher = {CEUR-WS}, url = {http://ceur-ws.org/}, keywords = {Co-evolution,Computer Science (all),model-driven engineering,Model-Driven Engineering,Sirius Editors}, note = {cited By 0 \par cited By 0 \par cited By 3 \par cited By 3 \par TL;DR \par A study is presented that analyzes the impact of meta-model changes over visual editors based on the Sir-ius framework to provide designers with the possibility to perform an early assessment of the early assessment of the editor consistency needed to restore the editor consistency.} } @inproceedings{11697_128311, ids = {diroccoSystematicRecoveryMDE2018,diroccoSystematicRecoveryMDE2018a,diroccoSystematicRecoveryMDE2018b,roccoSystematicRecoveryMDE2018}, title = {Systematic Recovery of {{MDE}} Technology Usage}, booktitle = {Lect. {{Notes Comput}}. {{Sci}}. {{Subser}}. {{Lect}}. {{Notes Artif}}. {{Intell}}. {{Lect}}. {{Notes Bioinforma}}.}, author = {Di Rocco, Juri and Di Ruscio, Davide and Härtel, Johannes and Iovino, Ludovico and Lämmel, Ralf and Pierantonio, Alfonso}, date = {2018}, series = {{{LECTURE NOTES IN ARTIFICIAL INTELLIGENCE}}}, volume = {10888}, pages = {110--126}, publisher = {Springer Verlag}, doi = {10.1007/978-3-319-93317-7_5}, isbn = {978-3-319-93316-0}, keywords = {Computer Science (all),Theoretical Computer Science}, note = {cited By 4 \par cited By 4} } @article{11697_132481, entrysubtype = {magazine}, ids = {bozhinoskiSafetyMobileRobotic2019a}, title = {Requirements, {{Human Values}}, and the {{Development Technology Landscape}}}, author = {Carver, Jeffrey C. and Minku, Leandro L. and Penzenstadler, Birgit and {undefined} and {undefined} and {undefined} and {undefined}}, date = {2017-01}, journaltitle = {IEEE Software}, volume = {151}, number = {1}, pages = {150--179}, issn = {0740-7459}, url = {http://ieeexplore.ieee.org/document/7819412/}, langid = {english}, keywords = {duplicate-citation-key}, note = {TL;DR \par This issue's column reports on papers from the 24th International Requirements Engineering Conference, 38th International Conference on Software Engineering, and the 10th International Symposium on Empirical Software Engineering and Measurement, which discuss performance and security requirements, injecting human values into software engineering, and mapping the software development technology landscape.} } @article{11697_135647, title = {Automated Reuse of Model Transformations through Typing Requirements Models}, author = {DE LARA, Juan and Guerra, Esther and DI RUSCIO, Davide and DI ROCCO, Juri and SANCHEZ CUADRADO, Jesus and Iovino, Ludovico and Pierantonio, Alfonso}, date = {9999}, journaltitle = {ACM Trans. Softw. Eng. Methodol.} } @incollection{11697_26169, ids = {bucchiaroneRequirementsJavaCode2008,bucchiaroneRequirementsJavaCode2008a}, title = {From Requirements to {{Java}} Code: {{An}} Architecture-Centric Approach for Producing Quality Systems}, booktitle = {From Requirements to {{Java}} Code: {{An}} Architecture-Centric Approach for Producing Quality Systems}, author = {{ANTONIO} and {BUCCHIARONE} and RUSCIO, DAVIDE DI and family=HENRY, given=MUCCINI, given-i=MUCCINI and family=PELLICCIONE, given=PATRIZIO, given-i=PATRIZIO}, date = {2008}, volume = {abs/0910.0493}, doi = {10.4018/978-1-60566-006-6.ch011}, isbn = {978-1-60566-006-6}, note = {cited By 4 \par cited By 4} } @article{11697_287, ids = {balzeraniSupportingWebApplications2006b}, title = {Supporting {{Web}} Applications Development with a Product Line Architecture}, author = {Balzerani, L and Di Ruscio, D and Pierantonio, A and De Angelis, G}, date = {2006}, journaltitle = {J. WEB Eng.}, volume = {5}, pages = {25--42} } @inproceedings{11697_30601, ids = {cicchettiManagingModelConflicts2008,cicchettiManagingModelConflicts2008a,cicchettiManagingModelConflicts2008b}, title = {Managing Model Conflicts in Distributed Development}, booktitle = {Model {{Driven Eng}}. {{Lang}}. {{Syst}}. 11th {{Int}}. {{Conf}}. {{MoDELS}} 2008 {{Toulouse Fr}}. {{Sept}}. 28 - {{Oct}}. 3 2008 {{Proc}}.}, author = {{ANTONIO} and {CICCHETTI} and RUSCIO, DAVIDE DI and PIERANTONIO, A}, date = {2008}, series = {{{LECTURE NOTES IN COMPUTER SCIENCE}}}, volume = {5301 LNCS}, pages = {311--325}, doi = {10.1007/978-3-540-87875-9_23}, note = {cited By 70 \par cited By 70 \par TL;DR \par A domain specific language able to define and manage conflicts caused by cooperative updates over the same model elements is proposed, which relies on a model-based representation of model differences and enables the specification and the detection of both syntactical and semantic conflicts.} } @inproceedings{11697_30657, title = {Composition of Model Differences}, booktitle = {1st {{Eur}}. {{Workshop Compos}}. {{Model Transform}}. - {{CMT}} 2006}, author = {CICCHETTI, A. and DI RUSCIO, D. and PIERANTONIO, A}, date = {2006}, note = {TL;DR \par This paper illustrates how to deal with conflicting modifications of the same model elements by conceiving the composition of difference models as a particular kind of model transformation composition.} } @inproceedings{11697_32865, ids = {diruscioModeldrivenTechniquesEnhance2012,diruscioModeldrivenTechniquesEnhance2012a,ruscioModelDrivenTechniquesEnhance2012}, title = {Model-Driven Techniques to Enhance Architectural Languages Interoperability}, booktitle = {15th {{Int}}. {{Conf}}. {{Fundam}}. {{Approaches Softw}}. {{Eng}}. {{FASE}}}, author = {Ruscio, Davide Di and {Ivano} and {Malavolta} and Muccini, H and Patrizio, Pelliccione and Alfonso, Pierantonio}, date = {2012}, series = {{{LECTURE NOTES IN COMPUTER SCIENCE}}}, volume = {7212}, pages = {26--42}, publisher = {Springer-Verlag}, location = {BERLIN HEIDELBERG}, doi = {10.1007/978-3-642-28872-2_2}, abstract = {The current practice of software architecture modeling and analysis would benefit of using different architectural languages, each specialized on a particular view and each enabling specific analysis. Thus, it is fundamental to pursue architectural language interoperability. An approach for enabling interoperability consists in defining a transformation from each single notation to a pivot language, and vice versa. When the pivot assumes the form of a small and abstract kernel, extension mechanisms are required to compensate the loss of information. The aim of this paper is to enhance architectural languages interoperability by means of hierarchies of pivot languages obtained by systematically extending a root pivot language. Model-driven techniques are employed to support the creation and the management of such hierarchies and to realize the interoperability by means of model transformations. Even though the approach is applied to the software architecture domain, it is completely general.}, isbn = {978-3-642-28871-5}, note = {cited By 14 \par cited By 14} } @incollection{11697_34038, ids = {cicchettiTestdrivenApproachMetamodel2011,cicchettiTestdrivenApproachMetamodel2011a}, title = {A Test-Driven Approach for Metamodel Development}, booktitle = {Emerging Technologies for the Evolution and Maintenance of Software Models}, author = {Cicchetti, A and Di Ruscio, D and Kolovos, D and Pierantonio, A}, date = {2012}, pages = {319--342}, publisher = {IGI Global}, location = {NEY YORK}, doi = {10.4018/978-1-61350-438-3.ch012}, note = {cited By 13 \par cited By 13} } @inproceedings{11697_35267, ids = {caporuscioEngineeringMDACompositional2005,caporuscioEngineeringMDACompositional2005a,caporuscioEngineeringMDACompositional2005b}, title = {Engineering {{MDA}} into Compositional Reasoning for Analyzing Middleware-Based Applications}, booktitle = {Softw. {{Archit}}. 2nd {{Eur}}. {{Workshop EWSA}} 2005 {{Pisa Italy June}} 13-14 2005 {{Proc}}.}, author = {Caporuscio, M and DI RUSCIO, D and Inverardi, P and Pelliccione, P and Pierantonio, A}, date = {2005}, series = {{{LECTURE NOTES IN COMPUTER SCIENCE}}}, volume = {3527}, pages = {130--145}, publisher = {Springer - LNCS series}, doi = {10.1007/11494713_9}, isbn = {3-540-26275}, note = {cited By 2 \par cited By 2 \par TL;DR \par This paper engineer the architectural decomposability theorem to the analysis of middleware-based applications by automatically generating the proxies needed by the components in order to properly interact with each other via the middleware.} } @inproceedings{11697_37088, ids = {cicchettiModelPatchesModelDriven2009,cicchettiModelPatchesModeldriven2010,cicchettiModelPatchesModeldriven2010a}, title = {Model Patches in Model-Driven Engineering}, booktitle = {Models {{Softw}}. {{Eng}}. {{Workshop Symp}}. {{MODELS}} 2009 {{Denver CO USA Oct}}. 4-9 2009 {{Rep}}. {{Revis}}. {{Sel}}. {{Pap}}.}, author = {Cicchetti, A and DI RUSCIO, Davide and Pierantonio, Alfonso}, date = {2010}, series = {{{LECTURE NOTES IN COMPUTER SCIENCE}}}, volume = {6002 LNCS}, pages = {190--204}, doi = {10.1007/978-3-642-12261-3_19}, abstract = {Increasingly, recording the various kinds of design-level structural evolution that a system undergoes throughout its entire life-cycle is gaining relevance in software modeling and development. In this respect, an interesting and useful operation between subsequent system versions is model difference consisting in calculation, representation, and visualization. This work shows how to generalize the application of differences, represented as first-class artefacts, in order to abstract from persistent identifiers and enable more flexibility. Then, modifications can be applied as model patches to arbitrary models according to weaving specifications.}, isbn = {978-3-642-12260-6}, note = {cited By 14 \par cited By 14 \par TL;DR \par This work shows how to generalize the application of differences, represented as first-class artefacts, in order to abstract from persistent identifiers and enable more flexibility.} } @inproceedings{11697_37101, ids = {cicchettiManagingDependentChanges2009,cicchettiManagingDependentChanges2009a,cicchettiManagingDependentChanges2009b,cicchettiManagingDependentChanges2009c}, title = {Managing Dependent Changes in Coupled Evolution}, booktitle = {Theory {{Pract}}. {{Model Transform}}. - 2nd {{Int}}. {{Conf}}. {{ICMTTOOLS}} 2009 {{Zurich Switz}}. {{June}} 29-30 2009 {{Proc}}.}, author = {Cicchetti, A and Di Ruscio, D and Pierantonio, A}, date = {2009}, series = {{{LECTURE NOTES IN COMPUTER SCIENCE}}}, volume = {5563 LNCS}, pages = {35--51}, doi = {10.1007/978-3-642-02408-5_4}, isbn = {978-3-642-02407-8}, note = {cited By 60 \par cited By 60 \par TL;DR \par The paper illustrates a dependency analysis, classifies such dependencies, and proposes a metamodeling language driven resolution which enables a decomposition and consequent scheduling of the adaptation steps allowing the full automation of the process.} } @inproceedings{11697_37103, ids = {cicchettiBeContentModelDrivenPlatform2009,cicchettiBeContentModeldrivenPlatform2009,cicchettiBeContentModeldrivenPlatform2009a}, title = {{{BeContent}}: {{A}} Model-Driven Platform for Designing and Maintaining Web Applications}, booktitle = {Web {{Eng}}. 9th {{Int}}. {{Conf}}. {{ICWE}} 2009}, author = {Cicchetti, A and Di Ruscio, D and Eramo, R and Maccarrone, F and Pierantonio, A}, date = {2009}, series = {{{LECTURE NOTES IN COMPUTER SCIENCE}}}, volume = {5648}, pages = {518--522}, doi = {10.1007/978-3-642-02818-2_52}, note = {cited By 5 \par cited By 5} } @inproceedings{11697_37139, ids = {cicchettiJTLBidirectionalChange2010,cicchettiJTLBidirectionalChange2011,cicchettiJTLBidirectionalChange2011a}, title = {{{JTL}}: {{A}} Bidirectional and Change Propagating Transformation Language}, booktitle = {Softw. {{Lang}}. {{Eng}}. - {{Third Int}}. {{Conf}}. {{SLE}} 2010 {{Eindh}}. {{Neth}}. {{Oct}}. 12-13 2010 {{Revis}}. {{Sel}}. {{Pap}}.}, author = {Cicchetti, A and Di Ruscio, D and Eramo, R and Pierantonio, A}, date = {2010}, series = {{{LECTURE NOTES IN COMPUTER SCIENCE}}}, volume = {6563 LNCS}, pages = {183--202}, doi = {10.1007/978-3-642-19440-5_11}, isbn = {978-3-642-19439-9}, note = {cited By 119 \par cited By 119 \par TL;DR \par The Janus Transformation Language (JTL) is presented, a bidirectional model transformation language specifically designed to support nonbijective transformations and change propagation and its expressivity and applicability are validated against a reference benchmark.} } @inproceedings{11697_37543, ids = {diruscioModelTransformationsDevelopment2005,diruscioModelTransformationsDevelopment2005a,ruscioModelTransformationsDevelopment2005}, title = {Model Transformations in the Development of Data-Intensive Web Applications}, booktitle = {Adv. {{Inf}}. {{Syst}}. {{Eng}}. 17th {{Int}}. {{Conf}}. {{CAiSE}} 2005 {{Porto Port}}. {{June}} 13-17 2005 {{Proc}}.}, author = {Di Ruscio, D and Pierantonio, A}, date = {2005}, series = {Lecture {{Notes}} in {{Computer Science}}}, volume = {3520}, pages = {475--490}, doi = {10.1007/11431855_33}, note = {cited By 6 \par cited By 6 \par TL;DR \par This paper presents model-driven transformations between platform-independent (conceptual descriptions of Web applications) and platform-specific (Model-View-Controller conformant) models.} } @inproceedings{11697_89156, ids = {diruscioSupportingEvolutionFree2013,diruscioSupportingEvolutionFree2013a,ruscioSupportingEvolutionFree2013,ruscioSupportingEvolutionFree2013a}, title = {Supporting the Evolution of Free and Open Source Software Distributions}, booktitle = {Softw. {{Eng}}. {{Resilient Syst}}. - 5th {{Int}}. {{Workshop Proc}}.}, author = {Di Ruscio, D and Pelliccione, P}, date = {2013}, series = {{{LECTURE NOTES IN COMPUTER SCIENCE}}}, volume = {8166 LNCS}, pages = {56--63}, publisher = {Springer-Verlag}, location = {BERLIN HEIDELBERG}, doi = {10.1007/978-3-642-40894-6_5}, abstract = {This paper overviews Mancoosi, an European project in the 7th Research Framework Programme (FP7) of the European Commission, on managing software complexity. The focus of the project has been on managing the evolution of Free and Open Source Software distributions. Evolution of these distributions is realized through the upgrade, the addition, and the removal of software packages. The project has two main objectives: (i) develop a model-based approach to safely support the upgrade of FOSS systems, (ii) develop better algorithms and tools to plan upgrade paths based on various information sources about software packages and on optimization criteria. The paper focuses on the first objective of the project. The main result of this objective is an approach that promotes the simulation of upgrades to predict failures before affecting the real system. Both fine-grained static aspects (e.g., configuration incoherences) and dynamic aspects (e.g., the execution of configuration scripts) are taken into account, improving over the state of the art of package managers.}, isbn = {978-3-642-40893-9}, note = {cited By 0 \par cited By 0} } @inproceedings{11697_89171, ids = {arcelliApplyingModelDifferences2013,arcelliApplyingModelDifferences2013a,arcelliApplyingModelDifferences2013b,arcelliApplyingModelDifferences2013c}, title = {Applying Model Differences to Automate Performance-Driven Refactoring of Software Models}, booktitle = {Comput. {{Perform}}. {{Eng}}. - 10th {{Eur}}. {{Workshop EPEW}} 2013 {{Venice Italy Sept}}. 16-17 2013 {{Proc}}. {{Lect}}. {{Notes Comput}}. {{Sci}}.}, author = {Arcelli, D and Cortellessa, Vittorio and DI RUSCIO, Davide}, date = {2013}, series = {{{LECTURE NOTES IN COMPUTER SCIENCE}}}, volume = {8168 LNCS}, pages = {312--324}, publisher = {Springer}, doi = {10.1007/978-3-642-40725-3_24}, abstract = {Identifying and removing the causes of poor performance in software systems are complex problems, and these issues are usually tackled after software deployment only with human-based means. Performance antipatterns can be used to harness these problems since they capture design patterns that are known leading to performance problems, and they suggest refactoring actions that can solve the problems. This paper introduces an approach to automate software model refactoring based on performance antipatterns. A Role-Based Modeling Language is used to model antipattern problems as Source Role Models (SRMs), and antipattern solutions as Target Role Models (TRMs). Each (SRM, TRM) pair is represented by a difference model that encodes refactoring actions to be operated on a software model to remove the corresponding antipattern. Differences are applied to software models through a model transformation automatically generated by a higher-order transformation. The approach is shown at work on an example in the e-commerce domain.}, isbn = {978-3-642-40724-6}, keywords = {Computer Science (all),Theoretical Computer Science}, note = {cited By 6 \par cited By 6 \par TL;DR \par This paper introduces an approach to automate software model refactoring based on performance antipatterns, and shows the approach at work on an example in the e-commerce domain.} } @inproceedings{11697_89209, ids = {diruscioEngineeringPlatformMission2013,diruscioEngineeringPlatformMission2013a,ruscioEngineeringPlatformMission2013}, title = {Engineering a Platform for Mission Planning of Autonomous and Resilient Quadrotors}, booktitle = {Softw. {{Eng}}. {{Resilient Syst}}. 5th {{Int}}. {{Workshop SERENE}} 2013 {{Kiev Ukr}}. {{Oct}}. 3-4 2013 {{Proc}}. {{Lect}}. {{Notes Comput}}. {{Sci}}.}, author = {DI RUSCIO, Davide and Malavolta, Ivano and Pelliccione, Patrizio}, date = {2013}, series = {{{LECTURE NOTES IN COMPUTER SCIENCE}}}, volume = {8166}, pages = {33--47}, publisher = {Springer-Verlag}, location = {BERLIN HEIDELBERG}, doi = {10.1007/978-3-642-40894-6_3}, isbn = {978-3-642-40893-9}, keywords = {open source software}, note = {cited By 10 \par cited By 10 \par TL;DR \par Quadrotors and UAVs in general are becoming as attractive instruments to safely and efficiently perform environmental monitoring missions and in professional use, quadrotors are manually controlled by expert operators via a remote controller.} } @inproceedings{11697_89217, ids = {diruscioMethodologicalApproachCoupled2013,diruscioMethodologicalApproachCoupled2013a,diruscioMethodologicalApproachCoupled2013b,ruscioMethodologicalApproachCoupled2013}, title = {A Methodological Approach for the Coupled Evolution of Metamodels and {{ATL}} Transformations}, booktitle = {Theory {{Pract}}. {{Model Transform}}. - 6th {{Int}}. {{Conf}}. {{ICMTSTAF}} 2013 {{Bp}}. {{Hung}}. {{June}} 18-19 2013 {{Proc}}.}, author = {Di Ruscio, D and Iovino, L and Pierantonio, A}, date = {2013}, series = {{{LECTURE NOTES IN COMPUTER SCIENCE}}}, volume = {7909 LNCS}, pages = {60--75}, publisher = {Springer-Verlag}, location = {BERLIN HEIDELBERG}, doi = {10.1007/978-3-642-38883-5_9}, abstract = {Model-Driven Engineering is a software discipline that relies on (meta) models as first class entities and that aims to develop, maintain and evolve software by exploiting model transformations. Analogously to software, metamodels are subject to evolutionary pressures which might compromise a wide range of artefacts including transformations. In contrast with the problem of metamodel/model co-evolution, the problem of adapting model transformations according to the changes operated on the corresponding metamodels is to a great extent unexplored. This is largely due to its intricacy but also to the difficulty in having a mature process which on one hand is able to evaluate the cost and benefits of adaptations, and on the other hand ensures that consistent methods are used to maintain quality and design integrity during the adaptation. This paper proposes a methodological approach to the coupled evolution of ATL transformations aiming at evaluating its sustainability prior to any adaptation step based on the assessment of change impact significance.}, isbn = {978-3-642-38882-8}, note = {cited By 25 \par cited By 25} } @inproceedings{11697_89303, ids = {wagelaarTranslationalSemanticsCoevolution2012,wagelaarTranslationalSemanticsCoevolution2012a,wagelaarTranslationalSemanticsCoevolution2012b}, title = {Translational Semantics of a Co-Evolution Specific Language with the {{EMF}} Transformation Virtual Machine}, booktitle = {Theory {{Pract}}. {{Model Transform}}.}, author = {Wagelaar, D and Iovino, L and Di Ruscio, D and Pierantonio, A}, date = {2012}, series = {{{LECTURE NOTES IN COMPUTER SCIENCE}}}, volume = {7307}, pages = {192--207}, doi = {10.1007/978-3-642-30476-7_13}, abstract = {Model-to-model transformations are often employed to establish translational semantics of Domain-Specific Languages (DSLs) by mapping high-level models into more concrete ones. Such semantics are also executable when there exists a target platform able to execute the target models. Conceiving a transformation that targets a low-level language still remains arduous due to the large semantic gap between the DSL and the corresponding target language. In this respect, depending on the domain of the DSL, this task can be made easier by reusing an existing platform and bytecode language for that domain, as for instance the EMF Transformation Virtual Machine (EMFTVM) for the domain of model transformation. This paper defines executable semantics for EMFMigrate, a model transformation language specifically designed for managing the coupled evolution in model-driven development. To this end, the approach considers EMFTVM as the runtime engine targeted by the proposed semantic mappings.}, isbn = {978-3-642-30475-0}, note = {cited By 41 \par cited By 41 \par TL;DR \par This paper defines executable semantics for EMFMigrate, a model transformation language specifically designed for managing the coupled evolution in model-driven development, and considers EMFTVM as the runtime engine targeted by the proposed semantic mappings.} } @inproceedings{11697_89304, ids = {diruscioModelTransformations2012,diruscioModelTransformations2012a,ruscioModelTransformations2012}, title = {Model Transformations}, booktitle = {Form. {{Methods Model-Driven Eng}}. - 12th {{Int}}. {{Sch}}. {{Form}}. {{Methods Des}}. {{Comput}}. {{Commun}}. {{Softw}}. {{Syst}}. {{SFM}} 2012 {{Bertinoro Italy June}} 18-23 2012 {{Adv}}. {{Lect}}.}, author = {Di Ruscio, D and Eramo, R and Pierantonio, A}, date = {2012}, series = {{{LECTURE NOTES IN COMPUTER SCIENCE}}}, volume = {7320 LNCS}, pages = {91--136}, publisher = {Springer-Verlag}, location = {BERLIN HEIDELBERG}, doi = {10.1007/978-3-642-30982-3_4}, isbn = {978-3-642-30981-6}, note = {cited By 12 \par cited By 12} } @inproceedings{11697_89346, ids = {diruscioEvolutionaryTogethernessHow2012,diruscioEvolutionaryTogethernessHow2012a,ruscioEvolutionaryTogethernessHow2012}, title = {Evolutionary Togetherness: How to Manage Coupled Evolution in Metamodeling Ecosystems}, booktitle = {Lect. {{Notes Comput}}. {{Sci}}. {{Subser}}. {{Lect}}. {{Notes Artif}}. {{Intell}}. {{Lect}}. {{Notes Bioinforma}}.}, author = {Di Ruscio, D and Iovino, L and Pierantonio, A}, date = {2012}, series = {{{LECTURE NOTES IN COMPUTER SCIENCE}}}, volume = {7562 LNCS}, pages = {20--37}, publisher = {Springer-Verlag}, location = {BERLIN HEIDELBERG}, doi = {10.1007/978-3-642-33654-6_2}, abstract = {In Model-Driven Engineering (MDE) metamodels are cornerstones for defining a wide range of related artifacts interlaced with explicit or implicit correspondences. According to this view, models, transformations, editors, and supporting tools can be regarded as a whole pursuing a common scope and therefore constituting an ecosystem. Analogously to software, metamodels are subject to evolutionary pressures too. However, changing a metamodel might compromise the validity of the artifacts in the ecosystem which therefore require to co-evolve as well in order to restore their validity. Different approaches have been proposed to support at different extent the adaptation of artifacts according to the changes operated on the corresponding metamodels. Each technique is specialized in the adaptation of specific kind of artifact (e.g., models, or transformations) by forcing modelers to learn different technologies and languages. This paper discusses the different relations occurring in a typical metamodeling ecosystem among the metamodel and the related artifacts, and identifies the commonalities which can be leveraged to define a unifying and comprehensive adaptation process. A language and corresponding supporting tools are also proposed for the management of metamodel evolution and the corresponding togetherness with the related artifacts.}, isbn = {978-3-642-33653-9}, note = {cited By 35 \par cited By 35 \par TL;DR \par The different relations occurring in a typical meetamodeling ecosystem among the metamodel and the related artifacts are discussed, and the commonalities which can be leveraged to define a unifying and comprehensive adaptation process are identified.} } @online{12ResearchPapers, title = {12 Research Papers Can Help You Understand {{AI Agents}} Better}, url = {https://www.linkedin.com/posts/rakeshgohel01_these-12-research-papers-can-help-you-understand-activity-7278779223413039104-MrR0?utm_source=share&utm_medium=member_android}, keywords = {source\_mobile} } @online{13ParallelProgramming, title = {1.3 {{A Parallel Programming Model}}}, url = {http://www.mcs.anl.gov/~itf/dbpp/text/node9.html}, urldate = {2017-02-23} } @article{1514443, title = {A Survey of Controlled Experiments in Software Engineering}, author = {Sjoeberg, D.I.K. and Hannay, J.E. and Hansen, O. and Kampenes, V.B. and Karahasanovic, A. and Liborg, N.-K. and Rekdal, A.C.}, date = {2005}, journaltitle = {IEEE Trans. Softw. Eng.}, volume = {31}, number = {9}, pages = {733--753}, doi = {10.1109/TSE.2005.97} } @article{159059, title = {Fuzzy {{ARTMAP}}: {{A}} Neural Network Architecture for Incremental Supervised Learning of Analog Multidimensional Maps}, author = {Carpenter, G. A. and Grossberg, S. and Markuzon, N. and Reynolds, J. H. and Rosen, D. B.}, date = {1992-09}, journaltitle = {IEEE Trans. Neural Netw.}, volume = {3}, number = {5}, pages = {698--713}, issn = {1045-9227}, doi = {10.1109/72.159059}, keywords = {adaptive resonance theory,analog multidimensional maps,Computational modeling,fuzzy ARTMAP,fuzzy logic,Fuzzy logic,Fuzzy neural networks,fuzzy set theory,Fuzzy sets,Fuzzy systems,incremental supervised learning,learning systems,Multidimensional systems,neural nets,neural network architecture,Neural networks,pattern recognition,Resonance,Salzberg's NGE systems,Simpson's FMMC system,Subspace constraints,Supervised learning}, note = {TL;DR \par The fuzzy ARTMAP system is compared with Salzberg's NGE systems and with Simpson's FMMC system, and its performance in relation to benchmark backpropagation and generic algorithm systems.} } @online{1744613722936, title = {1744613722936}, url = {https://media.licdn.com/dms/document/media/v2/D4D1FAQFr-hrDj8LGdQ/feedshare-document-pdf-analyzed/B4DZYzE_o_HAAY-/0/1744613722936?e=1745452800&v=beta&t=cXj7lVjYkHLqCdpJREdNhEQQ59OAiULUKMqvaNmy7P8}, urldate = {2025-04-14}, langid = {english} } @online{19PostLinkedIn, title = {(19) Post | {{LinkedIn}}}, url = {https://www.linkedin.com/posts/avi-chawla_what-is-function-calling-mcp-for-llms-activity-7319639412240261120-Tisw/?utm_source=share&utm_medium=member_desktop&rcm=ACoAAACaoXoBMvdj060syLix4vC3rwOgjAE9MpU}, urldate = {2025-04-23}, langid = {english} } @online{1IntroductionBuilding, title = {1. {{Introduction}} - {{Building Machine Learning Pipelines}} [{{Book}}]}, url = {https://www.oreilly.com/library/view/building-machine-learning/9781492053187/ch01.html}, urldate = {2021-03-18}, abstract = {Chapter 1. Introduction In this first chapter, we will introduce machine learning pipelines and outline all the steps that go into building them. We’ll explain what needs to happen to … - Selection from Building Machine Learning Pipelines [Book]}, langid = {english} } @online{2025WillSee, title = {2025 Will See Huge Advances in Quantum Computing. {{So}} What Is a Quantum Chip and How Does It Work?}, url = {https://theconversation.com/2025-will-see-huge-advances-in-quantum-computing-so-what-is-a-quantum-chip-and-how-does-it-work-246336}, keywords = {source\_mobile} } @online{21PostFeed, ids = {21PostFeeda}, title = {(21) {{Post}} | {{Feed}} | {{LinkedIn}}}, url = {https://www.linkedin.com/feed/update/urn:li:activity:7279088776570654720/?origin=NETWORK_CONVERSATIONS&midToken=AQEQBL0DzqZRxg&midSig=1Yqakj3S4xubA1&trk=eml-email_network_conversations_01-network~post-0-wrapper~link&trkEmail=eml-email_network_conversation}, urldate = {2025-01-06} } @online{21PostLinkedIn, title = {(21) Post | {{LinkedIn}}}, url = {https://www.linkedin.com/posts/dr-faheem-ullah_phd-research-supervision-activity-7279088776570654720-nW7a/?utm_source=share&utm_medium=member_android}, urldate = {2025-01-06}, langid = {english}, keywords = {/unread} } @online{24PostLinkedIn, title = {(24) {{Post}} | {{LinkedIn}}}, url = {https://www.linkedin.com/posts/kalyanksnlp_llms-scratch-nlproc-activity-7330429534166568961-VoWl/?utm_source=share&utm_medium=member_desktop&rcm=ACoAAACaoXoBMvdj060syLix4vC3rwOgjAE9MpU}, urldate = {2025-05-20} } @book{3rdworkshopFlexibleModel2017, ids = {3rdworkshopFlexibleModel2017a}, title = {3rdworkshop on Flexible Model Driven Engineering ({{FlexMDE}} 2017)}, date = {2017}, journaltitle = {CEUR Workshop Proceedings}, volume = {2019}, pages = {385--386}, publisher = {CEUR-WS}, pagetotal = {385–386} } @inproceedings{4688070, title = {Evaluating Recommender Systems}, booktitle = {2008 {{Int}}. {{Conf}}. {{Autom}}. {{Solut}}. {{Cross Media Content Multi-Channel Distrib}}.}, author = {Zaier, Zied and Godin, Robert and Faucher, Luc}, date = {2008}, pages = {211--217}, doi = {10.1109/AXMEDIS.2008.21}, note = {TL;DR \par This paper discusses how to compare recommenders based on a set of properties that are relevant for the application, and focuses on comparative studies, where a few algorithms are compared using some evaluation metric, rather than absolute benchmarking of algorithms.} } @inproceedings{5279910, title = {{{RASCAL}}: {{A}} Domain Specific Language for Source Code Analysis and Manipulation}, booktitle = {2009 {{Ninth IEEE Int}}. {{Work}}. {{Conf}}. {{Source Code Anal}}. {{Manip}}.}, author = {Klint, P. and family=Storm, given=T., prefix=v. d., useprefix=false and Vinju, J.}, date = {2009-09}, pages = {168--177}, doi = {10.1109/SCAM.2009.28}, keywords = {ad hoc integration,automated software engineering tool,complex software refactoring,conceptual-syntactic-semantic-technical level,domain specific language,Domain specific languages,Impedance,impedance mismatch,Informatics,Java,Libraries,Logic programming,meta-programming,object-oriented languages,Pattern matching,program diagnostics,RASCAL,Scalability,software engineering,software maintenance,source code analysis,source code manipulation,Storms,transformation}, note = {TL;DR \par Rascal is a domain-specific language that takes away most of this boilerplate by integrating source code analysis and manipulation at the conceptual, syntactic, semantic and technical level.} } @article{5287006, ids = {robillard2009makes}, title = {What Makes {{APIs}} Hard to Learn? {{Answers}} from Developers}, author = {Robillard, M. P.}, date = {2009-11}, journaltitle = {IEEE Softw.}, volume = {26}, number = {6}, pages = {27--34}, issn = {0740-7459}, doi = {10.1109/MS.2009.193}, keywords = {API design,API learnability,API usability,application program interface,application program interfaces,Application software,code examples,code reuse,empirical study,Microsoft developers,Programming profession,software development management,software development technologies,software documentation,software reusability,Usability}, note = {TL;DR \par The article focuses on the obstacles to learning an API and concludes that as APIs keep growing larger, developers will need to learn a proportionally smaller fraction of the whole.} } @inproceedings{5428690, title = {Improving Prediction Accuracy in Trust-Aware Recommender Systems}, booktitle = {2010 43rd {{Hawaii Int}}. {{Conf}}. {{Syst}}. {{Sci}}.}, author = {Ray, S. and Mahanti, A.}, date = {2010}, pages = {1--9}, doi = {10.1109/HICSS.2010.225} } @online{5RulesKeep, title = {5 {{Rules}} to {{Keep People Engaged During Your Talk}}}, url = {https://www.linkedin.com/posts/vincent-angelo-coach_charismatic-people-are-often-boring-speakers-activity-7281695781793767424-TCH_?utm_source=share&utm_medium=member_android}, keywords = {source\_mobile} } @inproceedings{6224306, title = {App Store Mining and Analysis: {{MSR}} for App Stores}, booktitle = {2012 9th {{IEEE Work}}. {{Conf}}. {{Min}}. {{Softw}}. {{Repos}}. {{MSR}}}, author = {Harman, M. and Jia, Y. and Zhang, Y.}, date = {2012-06}, pages = {108--111}, issn = {2160-1860}, doi = {10.1109/MSR.2012.6224306}, keywords = {app download,app store analysis,app store mining,app store MSR,Blackberry app store,Business,business aspect,Clustering algorithms,Correlation,customer aspect,customer rating,customer review,data mining,Data mining,Feature extraction,feature information extraction,Measurement,pricing,Software,software development management,software packages,software repository mining,technical aspect} } @inproceedings{6233407, title = {Example {{Overflow}}: {{Using}} Social Media for Code Recommendation}, booktitle = {2012 {{Third Int}}. {{Workshop Recomm}}. {{Syst}}. {{Softw}}. {{Eng}}. {{RSSE}}}, author = {Zagalsky, A. and Barzilay, O. and Yehudai, A.}, date = {2012-06}, pages = {38--42}, issn = {2327-0934}, note = {TL;DR \par Examples Overflow is presented, a code search and recommendation tool which brings together social media and code recommendation systems and enables crowd-sourced software development by utilizing both textual and social information, which accompany source code on the Web.} } @inproceedings{6233418, title = {Context-Based Recommendation to Support Problem Solving in Software Development}, booktitle = {2012 {{Third Int}}. {{Workshop Recomm}}. {{Syst}}. {{Softw}}. {{Eng}}. {{RSSE}}}, author = {Cordeiro, J. and Antunes, B. and Gomes, P.}, date = {2012-06}, pages = {85--89}, issn = {2327-0934}, doi = {10.1109/RSSE.2012.6233418}, keywords = {Console View,Context,Context Modelling,context-based recommendation,Data mining,Eclipse,exception occurrence,exception stack traces,IDE,information searching,keyword-based approach,online front-ends,problem solving,Problem Solving,Problem-solving,Programming,question answering (information retrieval),question-answering Web resources retrieval,recommendation systems,recommender systems,Search engines,Servers,Software,Software Development,software development management,software development process,Web browser,Web pages,Web sites} } @inproceedings{6671293, ids = {LibRec}, title = {Automated Library Recommendation}, booktitle = {2013 20th {{Work}}. {{Conf Reverse Eng}}. {{WCRE}}}, author = {Thung, F. and Lo, D. and Lawall, J.}, date = {2013-10}, pages = {182--191}, issn = {1095-1350}, keywords = {association rule mining,Association rules,automated library recommendation,Collaboration,collaborative filtering,data mining,Feature extraction,Generators,Itemsets,Libraries,recall rate,software projects,software reliability,third-party libraries}, note = {TL;DR \par A new technique is proposed that automatically recommends libraries to developers based on a set of library usage patterns that an application currently uses, and recommends other libraries that are likely to be relevant.} } @inproceedings{6671295, title = {Find Your Library Experts}, booktitle = {2013 20th {{Work}}. {{Conf}}. {{Reverse Eng}}. {{WCRE}}}, author = {Teyton, Cédric and Falleri, Jean-Rémy and Morandat, Floréal and Blanc, Xavier}, date = {2013-10}, pages = {202--211}, issn = {1095-1350}, doi = {10.1109/WCRE.2013.6671295}, keywords = {Apache HBase project,Communities,data mining,GitHub developers,Indexes,Java,Java libraries,Libraries,Libtic search engine,Prototypes,public domain software,search engines,Software,software development,software maintenance,software repositories mining,third-party libraries} } @inproceedings{6671315, title = {Documenting {{APIs}} with Examples: {{Lessons}} Learned with the {{APIMiner}} Platform}, booktitle = {2013 20th {{Work}}. {{Conf}}. {{Reverse Eng}}. {{WCRE}}}, author = {Montandon, J. E. and Borges, H. and Felix, D. and Valente, M. T.}, date = {2013-10}, pages = {401--408}, issn = {1095-1350}, doi = {10.1109/WCRE.2013.6671315}, keywords = {Android API,Androids,API documentation,API learning process,APIMiner platform,application program interfaces,application programming interfaces,Computer architecture,data mining,Documentation,field study,Google,Humanoid robots,Instruments,Java,JavaDoc,Measurement,private source code repository,software development,software engineering,source code examples,standard Java-based API documentation format,static slicing algorithm,system documentation} } @online{67OpenSource, title = {67 Open Source Tools and Resources for the {{Internet}} of {{Things}} ({{IoT}})}, url = {http://techbeacon.com/67-open-source-tools-resources-iot}, urldate = {2016-09-27} } @inproceedings{7070485, ids = {Basten2015M3}, title = {M3: {{A}} General Model for Code Analytics in Rascal}, booktitle = {2015 {{IEEE}} 1st {{Int}}. {{Workshop Softw}}. {{Anal}}. {{SWAN}}}, author = {Basten, B. and Hills, M. and Klint, P. and Landman, D. and Shahi, A. and Steindorfer, M. J. and Vinju, J. J.}, date = {2015-03}, pages = {25--28}, doi = {10.1109/SWAN.2015.7070485}, keywords = {Abstracts,Analytical models,code analytics,Computational modeling,general model,Java,M3 framework,Measurement,Object oriented modeling,Rascal meta programming language,software libraries,source code,source code (software),specification languages,standard library}, note = {TL;DR \par This short paper introduces M3, a simple and extensible model for capturing facts about source code for future analysis that is a core part of the standard library of the Rascal meta programming language.} } @inproceedings{7332619, title = {Detecting and Quantifying Different Types of Self-Admitted Technical {{Debt}}}, booktitle = {2015 {{IEEE}} 7th {{Int}}. {{Workshop Manag}}. {{Tech}}. {{Debt MTD}}}, author = {Maldonado, Everton da S. and Shihab, Emad}, date = {2015}, pages = {9--15}, doi = {10.1109/MTD.2015.7332619}, note = {TL;DR \par This paper examines code comments to determine the different types of technical debt, and finds that self-admitted technical debt can be classified into five main types - design debt, defect debt, documentation debt, requirement debt and test debt.} } @inproceedings{7372018, title = {Automated Tagging of Software Projects Using Bytecode and Dependencies (n)}, booktitle = {2015 30th {{IEEEACM Int}}. {{Conf}}. {{Autom}}. {{Softw}}. {{Eng}}. {{ASE}}}, author = {Vargas-Baldrich, S. and Linares-Vásquez, M. and Poshyvanyk, D.}, date = {2015-11}, pages = {289--294}, doi = {10.1109/ASE.2015.38}, keywords = {automatic software tagging approach,bytecode,categorization approach,closed source repository group software systems,Data mining,dependency relations,Feature extraction,information retrieval,Internet,Maven-based software projects,online repositories,open source community,open source repository group software systems,project management,public domain software,recommendation systems,Sally,Software algorithms,software assets,software management,Software systems,software tagging,Support vector machines,Tagging,term assignment} } @inproceedings{7403546, title = {A Longitudinal Study of the {{Google}} App Market}, booktitle = {2015 {{IEEEACM Int}}. {{Conf}}. {{Adv}}. {{Soc}}. {{Netw}}. {{Anal}}. {{Min}}. {{ASONAM}}}, author = {Carbunar, B. and Potharaju, R.}, date = {2015}, pages = {242--249}, doi = {10.1145/2808797.2808823} } @article{7569018, ids = {hebigApproachesCoEvolutionMetamodels2017}, title = {Approaches to Co-Evolution of Metamodels and Models: {{A}} Survey}, shorttitle = {Approaches to {{Co-Evolution}} of {{Metamodels}} and {{Models}}}, author = {Hebig, R. and Khelladi, D. E. and Bendraou, R.}, date = {2017-05}, journaltitle = {IIEEE Trans. Software Eng.}, volume = {43}, number = {5}, pages = {396--414}, issn = {2326-3881}, doi = {10.1109/TSE.2016.2610424}, abstract = {Modeling languages, just as all software artifacts, evolve. This imposes the risk that legacy models of a company get lost, when they become incompatible with the new language version. To address this risk a multitude of approaches for metamodel-model co-evolution was proposed in the last 10 years. However, the high number of solutions makes it difficult for practitioners to choose an appropriate approach.}, langid = {english}, keywords = {Atmospheric modeling,Biological system modeling,coevolution approaches,Companies,decision support,design notations and documentation,Libraries,metamodel-model coevolution,metamodels,models,Productivity,software engineering,solution technique taxonomy,Survey,Taxonomy,Unified modeling language}, note = {TL;DR \par A survey on 31 approaches to support metamodel-model co-evolution is presented, a taxonomy of solution techniques is introduced and the existing approaches are classified to support researchers and practitioners.} } @inproceedings{7816479, title = {Understanding the Factors That Impact the Popularity of {{GitHub}} Repositories}, booktitle = {2016 {{IEEE Int}}. {{Conf}}. {{Softw}}. {{Maint}}. {{Evol}}. {{ICSME}}}, author = {Borges, H. and Hora, A. and Valente, M. T.}, date = {2016-10}, pages = {334--344}, doi = {10.1109/ICSME.2016.31}, keywords = {Documentation,GitHub,GitHub projects,GitHub Repositories,HTML,Java,Libraries,open source developers,open source software,Open Source software,Organizations,programming language,project popularity,public domain software,Social coding,Software,software acceptance,software market,Software Popularity,software reviews,software system popularity,source code (software),stargazers button,time series} } @inproceedings{7884616, title = {Historical and Impact Analysis of {{API}} Breaking Changes: {{A}} Large-Scale Study}, booktitle = {2017 {{IEEE}} 24th {{Int Conf Softw}}. {{Anal}}. {{Evol}}. {{Reengineering SANER}}}, author = {Xavier, L. and Brito, A. and Hora, A. and Valente, M. T.}, date = {2017}, pages = {138--147}, note = {TL;DR \par The authors' large-scale analysis on 317 real-world Java libraries, 9K releases, and 260K client applications shows that systems with higher frequency of breaking changes are larger, more popular, and more active.} } @inproceedings{7884645, title = {Analyzing the Evolution of Testing Library Usage in Open Source {{Java}} Projects}, booktitle = {2017 {{IEEE}} 24th {{Int}}. {{Conf}}. {{Softw}}. {{Anal}}. {{Evol}}. {{Reengineering SANER}}}, author = {Zerouali, Ahmed and Mens, Tom}, date = {2017}, pages = {417--421}, doi = {10.1109/SANER.2017.7884645}, note = {TL;DR \par It is found that some libraries are considerably more popular than their competitors, while some libraries become more popular over time, and many projects tend to use multiple libraries together.} } @inproceedings{7961519, title = {Bug Localization with Combination of Deep Learning and Information Retrieval}, booktitle = {2017 {{IEEEACM}} 25th {{Int}}. {{Conf}}. {{Program Comprehension ICPC}}}, author = {Lam, A. N. and Nguyen, A. T. and Nguyen, H. A. and Nguyen, T. N.}, date = {2017}, pages = {218--229}, note = {TL;DR \par The new model, DNNLOC, with a combination of the features built from DNN, rVSM, and project'sbug-fixing history, achieves higher accuracy than the state-of-the-artIR and machine learning techniques.} } @inproceedings{7961718, title = {An All-in-One Convolutional Neural Network for Face Analysis}, booktitle = {2017 12th {{IEEE Int Conf Autom}}. {{Face Gesture Recognit}}. {{FG}} 2017}, author = {Ranjan, R. and Sankaranarayanan, S. and Castillo, C. D. and Chellappa, R.}, date = {2017-05}, pages = {17--24}, doi = {10.1109/FG.2017.137}, keywords = {age estimation,all-in-one convolutional neural network,CNN shared parameters,Face,face alignment,face analysis,Face detection,face recognition,Face recognition,gender recognition,learning (artificial intelligence),multitask learning framework,neural nets,pose estimation,Pose estimation,Robustness,simultaneous face detection,smile detection,Training} } @inproceedings{7985645, title = {Semantically Enhanced Software Traceability Using Deep Learning Techniques}, booktitle = {2017 {{IEEEACM}} 39th {{Int}}. {{Conf}}. {{Softw}}. {{Eng}}. {{ICSE}}}, author = {Guo, J. and Cheng, J. and Cleland-Huang, J.}, date = {2017}, pages = {3--14} } @inproceedings{7985674, title = {{{LibD}}: {{Scalable}} and Precise Third-Party Library Detection in Android Markets}, booktitle = {2017 {{IEEEACM}} 39th {{Int}}. {{Conf}}. {{Softw}}. {{Eng}}. {{ICSE}}}, author = {Li, M. and Wang, W. and Wang, P. and Wang, S. and Wu, D. and Liu, J. and Xue, R. and Huo, W.}, date = {2017}, pages = {335--346}, note = {TL;DR \par Results show that compared to existing tools, LibD can better handle multi-package third-party libraries in the presence of name-based obfuscation, leading to significantly improved precision without the loss of scalability.} } @online{7AbsicherungGenAIapplikationen, title = {(7) Absicherung von {{GenAI-applikationen}} in Der Versicherungswirtschaft | {{LinkedIn}}}, url = {https://www.linkedin.com/pulse/absicherung-von-genai-applikationen-der-harald-st%C3%B6rrle-roigf/?trackingId=h3fbEit3Q2ynDmSnD1h7nw%3D%3D}, urldate = {2025-03-27}, langid = {english}, keywords = {/unread} } @inproceedings{8009930, title = {Towards Better Understanding of Software Quality Evolution through Commit-Impact Analysis}, booktitle = {2017 {{IEEE Int}}. {{Conf}}. {{Softw}}. {{Qual}}. {{Reliab}}. {{Secur}}. {{QRS}}}, author = {Behnamghader, P. and Alfayez, R. and Srisopha, K. and Boehm, B.}, date = {2017-07}, pages = {251--262}, doi = {10.1109/QRS.2017.36}, keywords = {Apache Java software systems,commit-impact analysis,Computer bugs,Measurement,mining software repositories,program compilers,Security,software metrics,software quality,Software quality,software quality evolution,software quality indicator,software quality metrics,Software systems,source code,source code (software),Tools} } @inproceedings{8025917, title = {Source Code Classification Using Neural Networks}, booktitle = {2017 14th {{Int}}. {{Jt}}. {{Conf}}. {{Comput}}. {{Sci}}. {{Softw}}. {{Eng}}. {{JCSSE}}}, author = {Gilda, S.}, date = {2017-07}, pages = {1--6}, doi = {10.1109/JCSSE.2017.8025917}, keywords = {artificial neural network,Artificial neural network,convolutional neural network,feature extraction,Feature extraction,file extension,HTML,intelligent feature extraction,learning (artificial intelligence),Multi-layer neural network,multilayer neural network,neural nets,neural networks,pattern classification,programming languages,software development industry,software engineering,source code (software),source code classification,supervised learning,Supervised learning,Syntactics,Training,word embedding layers} } @inproceedings{8327318, title = {A Picture Is Worth a Thousand Words: {{Code}} Clone Detection Based on Image Similarity}, booktitle = {2018 {{IEEE}} 12th {{Int}}. {{Workshop Softw}}. {{Clones IWSC}}}, author = {Ragkhitwetsagul, C. and Krinke, J. and Marnette, B.}, date = {2018-03}, pages = {44--50}, issn = {2572-6587}, doi = {10.1109/IWSC.2018.8327318}, keywords = {clone pairs,Cloning,code clone detection technique,code clone detectors,code image blurring,Detectors,Earth,Image color analysis,image conversion,image processing,image similarity,Jaccard similarity,Java,Java clone detection,normalisation technique,pervasive code modifications,public domain software,raw source code text,similarity computation,similarity measures,software maintenance,software systems,source code (software),syntax highlighting,type-3 clones,Visualization} } @inproceedings{8595172, title = {A Graph-Based Dataset of Commit History of Real-World Android Apps}, booktitle = {2018 {{IEEEACM}} 15th {{Int}}. {{Conf}}. {{Min}}. {{Softw}}. {{Repos}}. {{MSR}}}, author = {Geiger, F. and Malavolta, I. and Pascarella, L. and Palomba, F. and Di Nucci, D. and Bacchelli, A.}, date = {2018}, pages = {30--33} } @inproceedings{8595234, ids = {lamothe_exploring_2018}, title = {Exploring the Use of Automated {{API}} Migrating Techniques in Practice: {{An}} Experience Report on Android}, booktitle = {2018 {{IEEEACM}} 15th {{Int}}. {{Conf}}. {{Min}}. {{Softw}}. {{Repos}}. {{MSR}}}, author = {Lamothe, M. and Shang, W.}, date = {2018-05}, pages = {503--514}, issn = {2574-3864}, nopublisher = {ACM Press}, nourl = {http://dl.acm.org/citation.cfm?doid=3196398.3196420}, keywords = {Android API,Android APIs,API consumers,API documentation,API migration,application program interfaces,automated API migrating techniques,Data mining,Documentation,freely available application program interfaces,historical code changes,historical code-change information,History,Keyword search,migration guidance,migration suggestions,Mining Software Repositories,mobile computing,open source software libraries,public domain software,robust applications,Software,software documentation,Software evolution,software libraries,software maintenance,Task analysis,Tools}, note = {TL;DR \par The experiences suggest that migration through historical code-changes presents various challenges and that API documentation is undervalued, and that the challenges of API migration lie beyond migration suggestions, in aspects such as coping with parameter type changes in new API.} } @article{8630054, title = {Mining Likely Analogical {{APIs}} across Third-Party Libraries via Large-Scale Unsupervised {{API}} Semantics Embedding}, author = {Chen, Chunyang and Xing, Zhenchang and Liu, Yang and Xiong, Kent Ong Long}, date = {2021}, journaltitle = {IEEE Trans. Softw. Eng.}, volume = {47}, number = {3}, pages = {432--447}, doi = {10.1109/TSE.2019.2896123} } @online{8FutureAI, title = {(8) the Future of {{AI}}: Thinking Slow to Solve Big | {{LinkedIn}}}, url = {https://www.linkedin.com/pulse/future-ai-thinking-slow-solve-big-bhavana-narayanan-rpmvc/?trackingId=tdWGxfNPQVCnEmHhg3NB1A%3D%3D}, urldate = {2024-12-30}, langid = {english}, keywords = {LLMs} } @online{8MachineLearning, title = {8 {{Machine Learning Algorithms}} ( {{A}} Quick Revision)}, url = {https://www.linkedin.com/posts/beginners-blog_machine-learning-algorithms-activity-7305777128866754560-YW8b/?utm_source=share&utm_medium=member_desktop&rcm=ACoAAACaoXoBMvdj060syLix4vC3rwOgjAE9MpU}, urldate = {2025-03-19}, langid = {english} } @article{9043686, title = {Diversified Third-Party Library Prediction for Mobile App Development}, author = {He, Q. and Li, B. and Chen, F. and Grundy, J. and Xia, X. and Yang, Y.}, date = {2020}, journaltitle = {IEEE Trans. Softw. Eng.}, pages = {1--1} } @inproceedings{9054865, title = {{{Req2Lib}}: {{A}} Semantic Neural Model for Software Library Recommendation}, booktitle = {2020 {{IEEE}} 27th {{Int}}. {{Conf}}. {{Softw}}. {{Anal}}. {{Evol}}. {{Reengineering SANER}}}, author = {Sun, Z. and Liu, Y. and Cheng, Z. and Yang, C. and Che, P.}, date = {2020-02}, pages = {542--546}, publisher = {IEEE Computer Society}, location = {Los Alamitos, CA, USA}, issn = {1534-5351}, doi = {10.1109/SANER48275.2020.9054865}, abstract = {Third-party libraries are crucial to the development of software projects. To get suitable libraries, developers need to search through millions of libraries by filtering, evaluating, and comparing. The vast number of libraries places a barrier for programmers to locate appropriate ones. To help developers, researchers have proposed automated approaches to recommend libraries based on library usage pattern. However, these prior studies can not sufficiently match user requirements and suffer from cold-start problem. In this work, we would like to make recommendations based on requirement descriptions to avoid these problems. To this end, we propose a novel neural approach called Req2Lib which recommends libraries given descriptions of the project requirement. We use a Sequence-to-Sequence model to learn the library linked-usage information and semantic information of requirement descriptions in natural language. Besides, we apply a domain-specific pre-trained word2vec model for word embedding, which is trained over textual corpus from Stack Overflow posts. In the experiment, we train and evaluate the model with data from 5,625 java projects. Our preliminary evaluation demonstrates that Req2Lib can recommend libraries accurately.}, keywords = {conferences,deep learning,filtering,java,natural languages,semantics,software libraries}, note = {TL;DR \par This work proposes a novel neural approach called Req2Lib which recommends libraries given descriptions of the project requirement, which uses a Sequence-to-Sequence model to learn the library linked-usage information and semantic information of requirement descriptions in natural language.} } @inproceedings{9463106, title = {Applying {{CodeBERT}} for Automated Program Repair of Java Simple Bugs}, booktitle = {2021 {{IEEEACM}} 18th {{Int}}. {{Conf}}. {{Min}}. {{Softw}}. {{Repos}}. {{MSR}}}, author = {Mashhadi, Ehsan and Hemmati, Hadi}, date = {2021}, pages = {505--509}, doi = {10.1109/MSR52588.2021.00063}, note = {TL;DR \par A novel automated program repair approach based on CodeBERT, which is a transformer-based neural architecture pre-trained on large corpus of source code, which can generate varied-length fixes and can fix different types of bugs, even if only a few instances of those types of Bugs exists in the training dataset.} } @inproceedings{9474384, title = {Identifying Bot Activity in {{GitHub}} Pull Request and Issue Comments}, booktitle = {2021 {{IEEEACM Third Int}}. {{Workshop Bots Softw}}. {{Eng}}. {{BotSE}}}, author = {Golzadeh, Mehdi and Decan, Alexandre and Constantinou, Eleni and Mens, Tom}, date = {2021}, pages = {21--25}, doi = {10.1109/BotSE52550.2021.00012}, note = {TL;DR \par A classification model based on natural language processing is proposed for identification of development bots using a combination of the bag of words and TF-IDF techniques to predict the type of comment (human or bot) based on this vector representation.} } @inproceedings{9609135, title = {Human, Bot or Both? {{A}} Study on the Capabilities of Classification Models on Mixed Accounts}, booktitle = {2021 {{IEEE Int}}. {{Conf}}. {{Softw}}. {{Maint}}. {{Evol}}. {{ICSME}}}, author = {Cassee, Nathan and Kitsanelis, Christos and Constantinou, Eleni and Serebrenik, Alexander}, date = {2021}, pages = {654--658}, doi = {10.1109/ICSME52107.2021.00075}, note = {TL;DR \par This study investigates three comment-level classification models and finds that the best classifiers based on these classification models achieve a precision and recall between 88\% and 96\% and even the most accurate comment- level classifier cannot accurately detect mixed accounts.} } @inproceedings{9678712, title = {Thinking like a Developer? {{Comparing}} the Attention of Humans with Neural Models of Code}, booktitle = {2021 36th {{IEEEACM Int}}. {{Conf}}. {{Autom}}. {{Softw}}. {{Eng}}. {{ASE}}}, author = {Paltenghi, Matteo and Pradel, Michael}, date = {2021}, pages = {867--879}, doi = {10.1109/ASE51524.2021.9678712}, note = {TL;DR \par This paper investigates to what extent the attention weights of effective neural models match the reasoning of skilled humans, and presents a methodology for recording human attention and uses it to gather 1,508 human attention maps, which is the largest such dataset the authors are aware of.} } @inproceedings{9825781, title = {Mining Software Information Sites to Recommend Cross-Language Analogical Libraries}, booktitle = {2022 {{IEEE Int}}. {{Conf}}. {{Softw}}. {{Anal}}. {{Evol}}. {{Reengineering SANER}}}, author = {Nafi, Kawser Wazed and Asaduzzaman, Muhammad and Roy, Banani and Roy, Chanchal K. and Schneider, Kevin A.}, date = {2022}, pages = {913--924}, doi = {10.1109/SANER53432.2022.00109}, note = {TL;DR \par An extensive evaluation show that the proposed XLibRec technique can recommend cross-language analogical libraries with great accuracy and has achieved 8-45\% higher precision than the state-of-the-art technique.} } @article{9866550, title = {Flakify: {{A}} Black-Box, Language Model-Based Predictor for Flaky Tests}, author = {Fatima, Sakina and Ghaleb, Taher A. and Briand, Lionel}, date = {2023}, journaltitle = {IEEE Trans. Softw. Eng.}, volume = {49}, number = {4}, pages = {1912--1927}, doi = {10.1109/TSE.2022.3201209} } @book{9ae2f829db7d4233bdf77953d5055637, title = {Code Generation with Templates}, author = {Arnoldus, B.J. and Brand, van den, M.G.J. and Serebrenik, A. and Brunekreef, J.J.}, date = {2012}, series = {Atlantis Studies in Computing}, publisher = {Atlantis Press}, location = {Netherlands}, doi = {10.2991/978-94-91216-56-5}, abstract = {Templates are used to generate all kinds of text, including computer code. The last decade, the use of templates gained a lot of popularity due to the increase of dynamic web applications. Templates are a tool for programmers, and implementations of template engines are most times based on practical experience rather than based on a theoretical background. This book reveals the mathematical background of templates and shows interesting findings for improving the practical use of templates. First, a framework to determine the necessary computational power for the template metalanguage is presented. The template metalanguage does not need to be Turing-complete to be useful. A non-Turing-complete metalanguage enforces separation of concerns between the view and model. Second, syntactical correctness of all languages of the templates and generated code is ensured. This includes the syntactical correctness of the template metalanguage and the output language. Third, case studies show that the achieved goals are applicable in practice. It is even shown that syntactical correctness helps to prevent cross-site scripting attacks in web applications. The target audience of this book is twofold. The first group exists of researcher interested in the mathematical background of templates. The second group exists of users of templates. This includes designers of template engines on one side and programmers and web designers using templates on the other side}, isbn = {978-94-91216-55-8}, langid = {english}, note = {TL;DR \par The mathematical background of templates is revealed, a framework to determine the necessary computational power for the template metalanguage is presented and it is shown that syntactical correctness helps to prevent cross-site scripting attacks in web applications.} } @article{ab.rahimSurveyApproachesVerifying2013, title = {A Survey of Approaches for Verifying Model Transformations}, author = {Ab.~Rahim, Lukman and Whittle, Jon}, date = {2013-06-13}, journaltitle = {Softw Syst Model}, pages = {1--26}, issn = {1619-1366, 1619-1374}, doi = {10.1007/s10270-013-0358-0}, abstract = {As with other software development artifacts, model transformations are not bug-free and so must be systematically verified. Their nature, however, means that transformations require specialist verification techniques. This paper brings together current research on model transformation verification by classifying existing approaches along two dimensions. Firstly, we present a coarse-grained classification based on the technical details of the approach (e.g., testing, theorem proving, model checking). Secondly, we present a finer-grained classification which categorizes approaches according to criteria such as level of formality, transformation language, properties verified. The purpose of the survey is to bring together research in model transformation verification to act as a resource for the community. Furthermore, based on the survey, we identify a number of trends in current and past research on model transformation verification.}, langid = {english}, keywords = {software engineering}, note = {TL;DR \par A finer-grained classification is presented which categorizes approaches according to criteria such as level of formality, transformation language, properties verified and a number of trends in current and past research on model transformation verification are identified.} } @inproceedings{Abbar09context-awarerecommender, title = {Context-Aware Recommender Systems: {{A}} Service Oriented Approach}, booktitle = {{{VLDB PersDB}} Workshop}, author = {Abbar, Sofiane and Bouzeghoub, Mokrane and Lopez, Stéphane}, date = {2009} } @article{Abdalhadi20221356, title = {An Optimal Proportional Integral Derivative Tuning for a Magnetic Levitation System Using Metamodeling Approach}, author = {Abdalhadi, A. and Wahid, H. and Burhanuddin, D.H.}, date = {2022}, journaltitle = {Indones. J. Electr. Eng. Comput. Sci.}, volume = {25}, number = {3}, pages = {1356--1366}, publisher = {{Institute of Advanced Engineering and Science}}, issn = {25024752}, doi = {10.11591/ijeecs.v25.i3.pp1356-1366}, abbrev_source_title = {Indones. J. Electrical Eng. Comput. Sci.}, affiliation = {Control and Mechatronics Engineering Division, School of Electrical Engineering, Universiti Teknologi Malaysia, Johor, Malaysia; Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, Parit Raja, Malaysia}, correspondence_address1 = {Wahid, H.; School of Electrical Engineering, Johor, Malaysia; email: herman@utm.my}, document_type = {Article}, langid = {english}, source = {Scopus}, note = {cited By 0 \par TL;DR \par A comparative assessment of controllers for the magnetic levitation system using proportional integral derivative (PID) controller based optimal tuning using a Simscape model has been developed and it was found that the model has produced about similar performance as what has been obtained from the MATLAB simulation.} } @article{abdalkareemCodeReuseStackOverflow2017, title = {On Code Reuse from {{StackOverflow}}: {{An}} Exploratory Study on {{Android}} Apps}, author = {Abdalkareem, Rabe and Shihab, Emad and Rilling, Juergen}, date = {2017}, journaltitle = {Inf. Softw. Technol.}, volume = {88}, pages = {148--158}, issn = {0950-5849}, url = {http://www.sciencedirect.com/science/article/pii/S0950584917303610}, abstract = {Context: Source code reuse has been widely accepted as a fundamental activity in software development. Recent studies showed that StackOverflow has emerged as one of the most popular resources for code reuse. Therefore, a plethora of work proposed ways to optimally ask questions, search for answers and find relevant code on StackOverflow. However, little work studies the impact of code reuse from StackOverflow. Objective: To better understand the impact of code reuse from StackOverflow, we perform an exploratory study focusing on code reuse from StackOverflow in the context of mobile apps. Specifically, we investigate how much, why, when, and who reuses code. Moreover, to understand the potential implications of code reuse, we examine the percentage of bugs in files that reuse StackOverflow code. Method: We perform our study on 22 open source Android apps. For each project, we mine their source code and use clone detection techniques to identify code that is reused from StackOverflow. We then apply different quantitative and qualitative methods to answer our research questions. Results: Our findings indicate that 1) the amount of reused StackOverflow code varies for different mobile apps, 2) feature additions and enhancements in apps are the main reasons for code reuse from StackOverflow, 3) mid-age and older apps reuse StackOverflow code mostly later on in their project lifetime and 4) that in smaller teams/apps, more experienced developers reuse code, whereas in larger teams/apps, the less experienced developers reuse code the most. Additionally, we found that the percentage of bugs is higher in files after reusing code from StackOverflow. Conclusion: Our results provide insights on the potential impact of code reuse from StackOverflow on mobile apps. Furthermore, these results can benefit the research community in developing new techniques and tools to facilitate and improve code reuse from StackOverflow.}, nodoi = {https://doi.org/10.1016/j.infsof.2017.04.005}, keywords = {Code reuse,Mobile app,StackOverflow} } @inproceedings{abdeen2014multi, title = {Multi-Objective Optimization in Rule-Based Design Space Exploration}, booktitle = {Proc. 29th {{ACMIEEE Int}}. {{Conf}}. {{Autom}}. {{Softw}}. {{Eng}}.}, author = {Abdeen, Hani and Varró, Dániel and Sahraoui, Houari and Nagy, András Szabolcs and Debreceni, Csaba and Hegedüs, Ábel and Horváth, Ákos}, date = {2014}, pages = {289--300}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {TL;DR \par This paper proposes to integrate multi-objective optimization techniques by using Non-dominated Sorting Genetic Algorithms (NSGA) to drive rule-based design space exploration using Eclipse framework, preserving both domain independence and a high-level of abstraction.} } @inproceedings{abdelbakyComputingContinuumCombining2017, title = {Computing in the {{Continuum}}: {{Combining Pervasive Devices}} and {{Services}} to {{Support Data-Driven Applications}}}, shorttitle = {Computing in the {{Continuum}}}, booktitle = {2017 {{IEEE}} 37th {{Int}}. {{Conf}}. {{Distrib}}. {{Comput}}. {{Syst}}. {{ICDCS}}}, author = {AbdelBaky, Moustafa and Zou, Mengsong and Zamani, Ali Reza and Renart, Eduard and Diaz-Montes, Javier and Parashar, Manish}, date = {2017-06}, pages = {1815--1824}, publisher = {IEEE}, location = {Atlanta, GA, USA}, doi = {10.1109/ICDCS.2017.323}, eventtitle = {2017 {{IEEE}} 37th {{International Conference}} on {{Distributed Computing Systems}} ({{ICDCS}})}, isbn = {978-1-5386-1792-2}, keywords = {LOGSEQ}, note = {TL;DR \par This paper presents the vision for enabling an approach for computing in the continuum, i.e., realizing a fluid ecosystem where distributed resources and services are programmatically aggregated on-demand to support emerging data-driven application workflows.} } @inproceedings{abdelhediLogicalUnifiedModeling2017, title = {Logical {{Unified Modeling}} for {{NoSQL Databases}}:}, shorttitle = {Logical {{Unified Modeling}} for {{NoSQL Databases}}}, author = {Abdelhedi, Fatma and Brahim, Amal Ait and Atigui, Faten and Zurfluh, Gilles}, date = {2017}, pages = {249--256}, publisher = {{SCITEPRESS - Science and Technology Publications}}, doi = {10.5220/0006311702490256}, abstract = {Big Data, NoSQL, UML Conceptual Model, MDA, QVT.}, isbn = {978-989-758-247-9 978-989-758-248-6 978-989-758-249-3}, langid = {english}, note = {TL;DR \par This paper proposes a generic logical model that is compatible with the three types of NoSQL systems (column, document and graph) and proposes transformation rules formalized with QVT to generate NoSQL physical models.} } @inproceedings{abeywickramaSimSOTAEngineeringSimulating2013, title = {{{SimSOTA}}: Engineering and Simulating Feedback Loops for Self-Adaptive Systems}, shorttitle = {{{SimSOTA}}}, booktitle = {Proc. {{Int}}. {{C Conf}}. {{Comput}}. {{Sci}}. {{Softw}}. {{Eng}}.}, author = {Abeywickrama, Dhaminda B. and Hoch, Nicklas and Zambonelli, Franco}, date = {2013}, pages = {67--76}, publisher = {ACM}, url = {http://dl.acm.org/citation.cfm?id=2494446}, urldate = {2016-08-21} } @article{abid_facer_2021, title = {{{FACER}}: {{An API}} Usage-Based Code-Example Recommender for Opportunistic Reuse}, shorttitle = {{{FACER}}}, author = {Abid, Shamsa and Shamail, Shafay and Basit, Hamid Abdul and Nadi, Sarah}, date = {2021-11}, journaltitle = {Empir. Softw. Eng.}, volume = {26}, number = {6}, pages = {110}, issn = {1382-3256, 1573-7616}, doi = {10.1007/s10664-021-10000-w}, abstract = {To save time, developers often search for code examples that implement their desired software features. Existing code search techniques typically focus on finding code snippets for a single given query, which means that developers need to perform a separate search for each desired functionality. In this paper, we propose FACER (Feature-driven API usagebased Code Examples Recommender), a technique that avoids repeated searches through opportunistic reuse. Specifically, given the selected code snippet that matches the initial search query, FACER finds and suggests related code snippets that represent features that the developer may want to implement next. FACER first constructs a code fact repository by parsing the source code of open-source Java projects to obtain methods’ textual information, call graphs, and Application Programming Interface (API) usages. It then detects unique features by clustering methods based on similar API usages, where each cluster represents a feature or functionality. Finally, it detects frequently co-occurring features across projects using frequent pattern mining and recommends related methods from the mined patterns. To evaluate FACER, we run it on 120 Java Android apps from GitHub. We first manually validate that the detected method clusters represent methods with similar functionality. We then perform an automated evaluation to determine the best parameters (e.g., similarity threshold) for FACER. We recruit 10 professional developers along with 39 experienced students to judge FACER’s recommendation of related methods. Our results show that, on average, FACER’s recommendations are 80\% precise. We also survey a total of 20 professional Android and Java developers to understand their code search and reuse experiences, and also to obtain their feedback on the usability and usefulness of FACER. The survey results show that 95\% of our surveyed professional developers find the idea of related method recommendations useful during code reuse.}, langid = {english}, note = {TL;DR \par FACER finds and suggests related code snippets that represent features that the developer may want to implement next and recommends related methods from the mined patterns, a technique that avoids repeated searches through opportunistic reuse.} } @inproceedings{Abid2019355, title = {Towards Machine Learning for Learnability of {{MDD}} Tools}, author = {Abid, S.B. and Mahajan, V. and Lucio, L.}, date = {2019}, series = {Proceedings of the {{International Conference}} on {{Software Engineering}} and {{Knowledge Engineering}}, {{SEKE}}}, volume = {2019-July}, pages = {355--360}, publisher = {Knowledge Systems Institute Graduate School}, issn = {23259000}, doi = {10.18293/SEKE2019-050}, abbrev_source_title = {Proc. Int. Conf. Softw. Eng. Knowl. Eng., SEKE}, affiliation = {Model-based Systems Engineering (MbSE) fortiss GmbH, Munich, Germany; Technical University of Munich, Munich, Germany}, document_type = {Conference Paper}, isbn = {1-891706-48-9}, langid = {english}, source = {Scopus}, keywords = {GOAL\_Model-Repair,notion,TECHNIQUE\_DNN,TECHNIQUE\_GENETIC\_ALGORITHMS,TECHNIQUE\_ILP,TECHNIQUE\_MARKOV\_DECISION\_PROCESS,TECHNIQUE\_NN}, note = {cited By 2 \par TL;DR \par A machine learning-based recommendation system named M AGNET is described for aiding beginner and intermediate users of AF3 in learning the tool.} } @article{abrahaoFocusingDevelopersEra2023, ids = {abrahaoFocusingDevelopersEra2023a}, title = {Focusing on {{Developers}} in the {{Era}} of {{AI}} and {{ML}}}, author = {Abrahão, Silvia and Staron, Miroslaw and Serebrenik, Alexander and Penzenstadler, Birgit and Prikladnicki, Rafael and Muccini, Henry}, date = {2023-11}, journaltitle = {IEEE Softw.}, volume = {40}, number = {6}, pages = {126--129}, issn = {0740-7459, 1937-4194}, doi = {10.1109/MS.2023.3302884}, langid = {english} } @book{abrahaoModelDrivenEngineeringLanguages2014, title = {Model-{{Driven Engineering Languages}} and {{Systems}}: 17th {{International Conference}}, {{MODELS}} 2014, {{Valencia}}, {{Spain}}, {{September}} 283- {{October}} 4, 2014. {{Proceedings}}}, shorttitle = {Model-{{Driven Engineering Languages}} and {{Systems}}}, editor = {Abrahao, Silvia and Dingel, Juergen and Insfran, Emilio and Ramos, Isidro and Schulte, Wolfram}, date = {2014}, series = {Programming and {{Software Engineering}}}, edition = {1st ed. 2014}, number = {8767}, publisher = {Springer International Publishing : Imprint: Springer}, location = {Cham}, doi = {10.1007/978-3-319-11653-2}, abstract = {This book constitutes the refereed proceedings of the 17th International Conference on Model Driven Engineering Languages and Systems, MODELS 2014, held in Valencia, Spain, in September/October 2014. The 41 full papers presented in this volume were carefully reviewed and selected from a total of 126 submissions. The scope of the conference series is broad, encompassing modeling languages, methods, tools, and applications considered from theoretical and practical angles and in academic and industrial settings. The papers report on the use of modeling in a wide range of cloud, mobile, and web computing, model transformation behavioral modeling, MDE: past, present, future, formal semantics, specification, and verification, models at runtime, feature and variability modeling, composition and adaptation, practices and experience, modeling for analysis, pragmatics, model extraction, manipulation and persistence, querying, and reasoning}, isbn = {978-3-319-11653-2}, pagetotal = {1}, keywords = {{Programming Languages, Compilers, Interpreters},Computer logic,Computer science,Computer simulation,Computer system failures,Logics and Meanings of Programs,Management information systems,Management of Computing and Information Systems,Programming languages (Electronic computers),Simulation and Modeling,Software engineering,Software Engineering,System Performance and Evaluation}, note = {Cloud, Mobile and Web Computing -- Model-Driven Development of Mobile Applications Allowing Role-Driven Variants -- A Model-Based System to Automate Cloud Resource Allocation and Optimization -- An Evaluation of the Effectiveness of the Atomic Section Model -- Model Transformation 1 -- Parsing in a Broad Sense -- Streaming Model Transformations by Complex Event Processing -- On the Use of Signatures for Source Incremental Model-to-text Transformation -- Behavioral Modeling -- Modeling Systemic Behavior by State-Based Holonic Modular Units -- Semantic Model Differencing Utilizing Behavioral Semantics Specifications -- Formalizing Execution Semantics of UML Profiles with fUML Models -- MDE: Past, Present and Future.-Who Knows/Uses What of the UML: A Personal Opinion Survey -- Assessing the State-of-Practice of Model-Based Engineering in the Embedded Systems Domain -- The Relevance of Model-Driven Engineering Thirty Years from Now -- Formal Semantics, Specification and Verification -- Verifying Compilation of Synchronous Distributed Applications -- Environment-Centric Contracts for Design of Cyber-Physical Systems -- Removing Redundancies and Deducing Equivalences in UML Class Diagrams -- Models at Runtime -- A Native Versioning Concept to Support Historized Models at Runtime -- Modelling Adaptation Policies as Domain-Specific Constraints -- Scalable Armies of Model Clones through Data Sharing -- Feature and Variability Modeling -- Three Cases of Feature-Based Variability Modeling in Industry -- Supporting Multiplicity and Hierarchy in Model-Based Configuration: Experiences and Lessons Learned -- Propagating Decisions to Detect and Explain Conflicts in a Multi-step Configuration Process -- Composition and Adaptation -- An MDA Approach for the Generation of Communication Adapters Integrating SW and FW Components from Simulink -- A UML Model-Driven Approach to Efficiently Allocate Complex Communication Schemes -- Model-Integrating Software Components -- Practices and Experience -- Experiences in Applying Model Driven Engineering to the Telescope and Instrument Control System Domain -- Model Driven Grant Proposal Engineering -- Agile Model-Driven Engineering in Mechatronic Systems - An Industrial Case Study -- Modeling for Analysis -- Using UML for Modeling Procedural Legal Rules: Approach and a Study of Luxembourg's Tax Law -- Resolution of Interfering Product Fragments in Software Product Line Engineering -- Ontology-Based Modeling of Context-Aware Systems -- Pragmatics -- Comprehending Feature Models Expressed in CVL -- On the Impact of Layout Quality to Understanding UML Diagrams: Size Matters -- Enabling the Development of Cognitive Effective Visual DSLs -- Model Extraction, Manipulation and Persistence -- JUMP-From Java Annotations to UML Profiles -- SIGMA: Scala Internal Domain-Specific Languages for Model Manipulations -- A Framework to Benchmark NoSQL Data Stores for Large-Scale Model Persistence -- Model Transformation 2 -- Automated Chaining of Model Transformations with Incompatible Metamodels -- Classification of Model Transformation Tools: Pattern Matching Techniques -- Learning Implicit and Explicit Control in Model Transformations by Example -- Querying and Reasoning IncQuery-D: A Distributed Incremental Model Query Framework in the Cloud -- Translating OCL to Graph Patterns} } @article{abu-elkheirDataManagementInternet2013, title = {Data {{Management}} for the {{Internet}} of {{Things}}: {{Design Primitives}} and {{Solution}}}, shorttitle = {Data {{Management}} for the {{Internet}} of {{Things}}}, author = {Abu-Elkheir, Mervat and Hayajneh, Mohammad and Ali, Najah}, date = {2013-11-14}, journaltitle = {Sensors}, volume = {13}, number = {11}, pages = {15582--15612}, issn = {1424-8220}, doi = {10.3390/s131115582}, langid = {english}, keywords = {Data analysis,internet of things}, note = {TL;DR \par This paper surveys the data management solutions proposed for IoT, and proposes a data management framework for IoT that takes into consideration the discussed design elements and acts as a seed to a comprehensive IoT data management solution.} } @book{ACMInternationalConference2010, title = {{{ACM International Conference Proceeding Series}}: {{Foreword}}}, date = {2010}, journaltitle = {ACM International Conference Proceeding Series}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84921919454&partnerID=40&md5=03f4a0df3c070c47a6f17cca8571698b} } @book{acmsigchisymposiumonengineeringinteractivecomputingsystemsEICS13Proceedings2013, title = {{{EICS}} '13: Proceedings of the {{ACM SIGCHI Symposium}} on {{Engineering Interactive Computing Systems}} : {{June}} 24-27, 2013, {{London}}, {{United Kingdom}}}, shorttitle = {{{EICS}} '13}, author = {{ACM SIGCHI Symposium on Engineering Interactive Computing Systems} and Forbrig, Peter and Dewan, Prasun and {SIGCHI (Group : U.S.)} and City University (London, England) and {Springer (Firm)} and {IFIP Working Group 2.7/13.4} and {Association for Computing Machinery} and {ACM Digital Library}}, date = {2013}, url = {http://dl.acm.org/citation.cfm?id=2494603}, urldate = {2016-09-24}, langid = {english} } @book{ACMStudentResearch2017, ids = {ACMStudentResearch2017a}, title = {{{ACM}} Student Research Competition at {{MoDELS}} 2017}, date = {2017}, journaltitle = {CEUR Workshop Proceedings}, volume = {2019}, pages = {547--548}, publisher = {CEUR-WS}, pagetotal = {547–548} } @article{acretoaieHypersonicModelAnalysis2014, title = {Hypersonic: {{Model Analysis}} and {{Checking}} in the {{Cloud}}}, author = {Acretoaie, Vlad and Störrle, Harald}, date = {2014}, note = {TL;DR \par This paper investigates the conceptual and technical feasibility of a new software architecture for modeling tools, where certain advanced features are factored out of the client and moved towards the Cloud, by applying standards such as REST and JSON in combination with Prolog as an implementation language.} } @article{Adamopoulos_TIST, title = {On Unexpectedness in Recommender Systems: {{Or}} How to Better Expect the Unexpected}, author = {Adamopoulos, Panagiotis and Tuzhilin, Alexander}, date = {2014-12}, journaltitle = {ACM Trans. Intell. Syst. Technol.}, volume = {5}, number = {4}, pages = {54:1-54:32}, publisher = {ACM}, address = {New York, NY, USA}, issn = {2157-6904}, url = {http://doi.acm.org/10.1145/2559952}, acmid = {2559952}, articleno = {54}, issue_date = {January 2015}, nodoi = {10.1145/2559952}, numpages = {32}, keywords = {Diversity,evaluation,novelty,recommendation systems,recommender systems,serendipity,unexpectedness,utility theory} } @software{AddonItem, title = {Addon {{Item}}}, note = {2H3TDIAF\{"readingTime":\{"page":25,"data":\{"7":10,"8":60,"9":40,"10":10,"11":30,"12":10,"13":50,"14":10,"15":20\}\}\} \par 2WFLI77E\{"readingTime":\{"page":11,"data":\{"0":1030,"1":770,"2":500,"3":100,"4":10,"5":10,"6":260,"7":520,"8":200,"9":160\}\}\} \par 6WDBYKIZ\{"readingTime":\{"page":832,"data":\{"0":10,"1":10,"4":10,"8":10\}\}\} \par 8E5M8QKA\{"readingTime":\{"page":12,"data":\{"0":530,"1":1430,"2":1400,"3":1140,"4":450,"5":240,"6":330,"7":210,"8":250,"9":50\}\}\} \par 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Construction Industry”, p. 11) \#5fb236\\ \mkbibemph{ } \par - “optimization of processes” (“Addressing Organizational and Technical Barriers in Low-Code Adoption for Advancing Digitalization in the Construction Industry”, p. 11) \#5fb236\\ \mkbibemph{ } \par - “digitalization in a variety of industrial scenarios” (“Addressing Organizational and Technical Barriers in Low-Code Adoption for Advancing Digitalization in the Construction Industry”, p. 11) \#5fb236\\ \mkbibemph{ } \par - “there is a lack of scholarly research analysing how the low-code technology paradigm can support digitalization in the construction sector” (“Addressing Organizational and Technical Barriers in Low-Code Adoption for Advancing Digitalization in the Construction Industry”, p. 11) \#5fb236\\ \mkbibemph{ } \par - “The objective of this research project is therefore to address the critical organizational and technical barriers to fill current research gaps and derive practical insights to advance low-code technology in construction organizations.” (“Addressing Organizational and Technical Barriers in Low-Code Adoption for Advancing Digitalization in the Construction Industry”, p. 11) \#a28ae5\\ \mkbibemph{ } \par - “RQ1) What are the organizational pre-requisites to ensure low-code scalability in the context of a construction enterprise?” (“Addressing Organizational and Technical Barriers in Low-Code Adoption for Advancing Digitalization in the Construction Industry”, p. 11) \#a28ae5\\ \mkbibemph{ } \par - “RQ2) How low-code technology can be integrated with Building Information Modelling (BIM)?” (“Addressing Organizational and Technical Barriers in Low-Code Adoption for Advancing Digitalization in the Construction Industry”, p. 11) \#ffd400\\ \mkbibemph{Why is BIM of interest here? } \par - “this research will contribute to the body of knowledge in a relatively new field of research by structurally addressing prevailing knowledge gaps and delineating areas for further research” (“Addressing Organizational and Technical Barriers in Low-Code Adoption for Advancing Digitalization in the Construction Industry”, p. 11) \#5fb236\\ \mkbibemph{ } \par - “By optimizing resource utilization and streamlining processes, digitalization in the construction industry becomes fundamental to improve overall productivity and support the delivery of a sustainable built environment” (“Addressing Organizational and Technical Barriers in Low-Code Adoption for Advancing Digitalization in the Construction Industry”, p. 11) \#5fb236\\ \mkbibemph{ } \par - “inadequate understanding of operations” (“Addressing Organizational and Technical Barriers in Low-Code Adoption for Advancing Digitalization in the Construction Industry”, p. 11) \#5fb236\\ \mkbibemph{ } \par - “ow-code technology emerges as a new Information Technology (IT) paradigm having the potential to bridge the gap between technology development and operations.” (“Addressing Organizational and Technical Barriers in Low-Code Adoption for Advancing Digitalization in the Construction Industry”, p. 12) \#5fb236\\ \mkbibemph{ } \par - “supporting rapid development of customer-facing applications, requiring minimal hand-coding and enabling productive new development practices” (“Addressing Organizational and Technical Barriers in Low-Code Adoption for Advancing Digitalization in the Construction Industry”, p. 12) \#5fb236\\ \mkbibemph{ } \par - “This “democratization” in technology development allows increased participation of subject/domain experts in technology development, facilitating the development process, and ensuring that the final solution meets the requirements of people and their processes” (“Addressing Organizational and Technical Barriers in Low-Code Adoption for Advancing Digitalization in the Construction Industry”, p. 12) \#5fb236\\ \mkbibemph{ } \par - “5 to 10 times faster than traditional programming” (“Addressing Organizational and Technical Barriers in Low-Code Adoption for Advancing Digitalization in the Construction Industry”, p. 12) \#5fb236\\ \mkbibemph{ } \par - “technical limitations such restricted customization options” (“Addressing Organizational and Technical Barriers in Low-Code Adoption for Advancing Digitalization in the Construction Industry”, p. 12) \#5fb236\\ \mkbibemph{ } \par - “Low-code research in industrial setups highlight the need for further research in low-code integration with Industry 4.0, Internet of Things, and Digital Twin” (“Addressing Organizational and Technical Barriers in Low-Code Adoption for Advancing Digitalization in the Construction Industry”, p. 12) \#5fb236\\ \mkbibemph{ } \par - “organizational and strategical aspects surrounding the introduction of low-code in corporate environments” (“Addressing Organizational and Technical Barriers in Low-Code Adoption for Advancing Digitalization in the Construction Industry”, p. 12) \#5fb236\\ \mkbibemph{ } \par - “organizations need standard processes to channel best practices and guidelines to improve consistency, compliance, and governance throughout the enterprise” (“Addressing Organizational and Technical Barriers in Low-Code Adoption for Advancing Digitalization in the Construction Industry”, p. 12) \#5fb236\\ \mkbibemph{ } \par - “70\% of business applications will be developed using some sort of low-code technology [27] with the number of “citizen developers” largely surpassing the number of professional developers” (“Addressing Organizational and Technical Barriers in Low-Code Adoption for Advancing Digitalization in the Construction Industry”, p. 12) \#a28ae5\\ \mkbibemph{ } \par - “by enabling people closer to construction operations to independently develop digita l solutions, low-code could facilitate the digitalization of a variety of construction processes which are currently run using pen and paper” (“Addressing Organizational and Technical Barriers in Low-Code Adoption for Advancing Digitalization in the Construction Industry”, p. 13) \#5fb236\\ \mkbibemph{ } \par - “here is a critical contextual gap and a lack of scholarly research analysing how the low-code technology paradigm can support digitalization in the construction sector” (“Addressing Organizational and Technical Barriers in Low-Code Adoption for Advancing Digitalization in the Construction Industry”, p. 13) \#ffd400\\ \mkbibemph{What are the challenges in the construction sector? What are the issues that are critical and that require the introduction of low-code technologies? } \par - “through 3 scholarly articles” (“Addressing Organizational and Technical Barriers in Low-Code Adoption for Advancing Digitalization in the Construction Industry”, p. 13) \#a28ae5\\ \mkbibemph{ } \par - “This includes the results of a survey to the construction professionals involved in the case studies to understand to what extent low-code benefits and limitations identified in previous literature resonate to them.” (“Addressing Organizational and Technical Barriers in Low-Code Adoption for Advancing Digitalization in the Construction Industry”, p. 13) \#5fb236\\ \mkbibemph{ } \par - “This potential derives from the pivotal role of low-code in bridging the gap between development and operations, empowering individuals with expertise in construction processes and operations to actively engage in the development process” (“Addressing Organizational and Technical Barriers in Low-Code Adoption for Advancing Digitalization in the Construction Industry”, p. 13) \#5fb236\\ \mkbibemph{ } \par - “applicant's research results allowed the identification of critical limitations and research gaps essential for a broader understanding of low-code in the construction industry” (“Addressing Organizational and Technical Barriers in Low-Code Adoption for Advancing Digitalization in the Construction Industry”, p. 14) \#ffd400\\ \mkbibemph{What are such critical limitations? The construction domain is a critical one requiring deep expertise. How can you put "citizen developer" in a such a critical domain? This is not clear! } \par - “Area 1: Organizational/strategical - RQ 1: What are the organizational pre-requisites to ensure low-code scalability in the context of a construction enterprise?” (“Addressing Organizational and Technical Barriers in Low-Code Adoption for Advancing Digitalization in the Construction Industry”, p. 14) \#5fb236\\ \mkbibemph{ } \par - “Consequently, understanding the essential competencies of a "citizen developer" within the construction industry context becomes critical to expedite low-code” (“Addressing Organizational and Technical Barriers in Low-Code Adoption for Advancing Digitalization in the Construction Industry”, p. 14) \#ffd400\\ \mkbibemph{What does it mean? } \par - “low-code, particularly in project-based industries like construction” (“Addressing Organizational and Technical Barriers in Low-Code Adoption for Advancing Digitalization in the Construction Industry”, p. 14) \#ffd400\\ \mkbibemph{What's the practical implication for this? } \par - “there is no studies addressing these research gaps in the construction industry” (“Addressing Organizational and Technical Barriers in Low-Code Adoption for Advancing Digitalization in the Construction Industry”, p. 14) \#ffd400\\ \mkbibemph{ } \par - “RQ 2: How low -code technology can be integrated with Building Information Modelling (BIM)” (“Addressing Organizational and Technical Barriers in Low-Code Adoption for Advancing Digitalization in the Construction Industry”, p. 14) \#ffd400\\ \mkbibemph{Before the HOW I would have preferred to read WHY. } \par - “BIM stands out as the most relevant technology supporting technical activities and collaboration during design, construction, and maintenance of the built assets, and it is cornerstone for Construction industry 4.0” (“Addressing Organizational and Technical Barriers in Low-Code Adoption for Advancing Digitalization in the Construction Industry”, p. 14) \#ffd400\\ \mkbibemph{ } \par - “low-code interoperability with BIM is the critical first step to streamline BIM processes and leverage the integrated utility of these technologies on the field.” (“Addressing Organizational and Technical Barriers in Low-Code Adoption for Advancing Digitalization in the Construction Industry”, p. 14) \#ffd400\\ \mkbibemph{What does it mean? I think it is necessary an example!!!! The discussion so far has been vague. } \par - “intersection” (“Addressing Organizational and Technical Barriers in Low-Code Adoption for Advancing Digitalization in the Construction Industry”, p. 15) \#5fb236\\ \mkbibemph{ } \par - “construction digitalization” (“Addressing Organizational and Technical Barriers in Low-Code Adoption for Advancing Digitalization in the Construction Industry”, p. 15) \#5fb236\\ \mkbibemph{ } \par - “low-code” (“Addressing Organizational and Technical Barriers in Low-Code Adoption for Advancing Digitalization in the Construction Industry”, p. 15) \#5fb236\\ \mkbibemph{ } \par - “Nevertheless, the results of this inquiry were not conclusive, as we discovered the emerging nature of low-code technology and that the current development of research still does not provide answer to the several gaps we realized” (“Addressing Organizational and Technical Barriers in Low-Code Adoption for Advancing Digitalization in the Construction Industry”, p. 15) \#ffd400\\ \mkbibemph{What are the gaps you are referring to? } \par - “a) Introducing and pioneering the use of low-code to the construction industry” (“Addressing Organizational and Technical Barriers in Low-Code Adoption for Advancing Digitalization in the Construction Industry”, p. 15) \#ffd400\\ \mkbibemph{Already said! } \par - “Nevertheless, further research in the areas proposed in this project is critical to find alternatives to address prevailing gaps to expand scientific knowledge about how this technology can foster a holistic digital transformation in the construction sector” (“Addressing Organizational and Technical Barriers in Low-Code Adoption for Advancing Digitalization in the Construction Industry”, p. 15) \#ffd400\\ \mkbibemph{The proposal contains many sentences like this that are vague and they are not precise in presenting the problems and the issues that the applicant aims at solving with this project proposal. In other words so far the project has not presented the problem clearly. } \par - “in the context of a construction enterprise” (“Addressing Organizational and Technical Barriers in Low-Code Adoption for Advancing Digitalization in the Construction Industry”, p. 15) \#ffd400\\ \mkbibemph{Concerning this considered application domain I have the following question: - why such a domain requires the usage of low-code platforms?\\ - what are the criticalities of currently available systems that instead should be replaced by low-code platforms?\\ - why it makes sense and it is really doable considering "citizen-developers" in the building construction domain? } \par - “to understand the available knowledge about organizational and strategical considerations for low-code implementation” (“Addressing Organizational and Technical Barriers in Low-Code Adoption for Advancing Digitalization in the Construction Industry”, p. 16) \#ffd400\\ \mkbibemph{Some preliminary result of this study are required to justify and support the project proposal. } \par - “Develop a technical solution to facilitate data interoperability between low -code databases and Industry Foundation Classes (IFC) schema in BIM models (e.g., via low-code development platform connectors or Application Programming Interfaces) [45].” (“Addressing Organizational and Technical Barriers in Low-Code Adoption for Advancing Digitalization in the Construction Industry”, p. 17) \#ffd400\\ \mkbibemph{It's not clear what the project aims at producing. Is it a study? Is it a software platform / framework? Is it a set of reusable components? What is the goal of the proposed technical solution? } \par - “use cases where low-code can be integrated with BIM” (“Addressing Organizational and Technical Barriers in Low-Code Adoption for Advancing Digitalization in the Construction Industry”, p. 17) \#ffd400\\ \mkbibemph{It seems that is something completely new and the applicant has to evaluate if and how the integration of low-code platforms with BIM make sense. The kind of integration that is expected is not clear at all. } \par - “A representative use case will be selected for the development serving as a baseline to derive the requirements of the technical system.” (“Addressing Organizational and Technical Barriers in Low-Code Adoption for Advancing Digitalization in the Construction Industry”, p. 17) \#5fb236\\ \mkbibemph{ } \par - “Validated technical solution” (“Addressing Organizational and Technical Barriers in Low-Code Adoption for Advancing Digitalization in the Construction Industry”, p. 18) \#ffd400\\ \mkbibemph{Which technical solution? } \par - “construction sites in Chile” (“Addressing Organizational and Technical Barriers in Low-Code Adoption for Advancing Digitalization in the Construction Industry”, p. 26) \#5fb236\\ \mkbibemph{ }} } @inproceedings{Adomavicius:2008:CRS:1454008.1454068, title = {Context-Aware Recommender Systems}, booktitle = {Proc. 2008 {{ACM Conf}}. {{Recomm}}. {{Syst}}.}, author = {Adomavicius, Gediminas and Tuzhilin, Alexander}, date = {2008}, series = {{{RecSys}} '08}, pages = {335--336}, publisher = {ACM}, location = {New York, NY, USA}, url = {http://doi.acm.org/10.1145/1454008.1454068}, acmid = {1454068}, isbn = {978-1-60558-093-7}, nodoi = {10.1145/1454008.1454068}, numpages = {2}, keywords = {collaborative filtering,contextual information,personalization,recommender systems,tutorial} } @article{Adomavicius:2012:aggrDiv, title = {Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques}, author = {Adomavicius, Gediminas and Kwon, YoungOk}, date = {2012-05}, journaltitle = {IEEE Trans Knowl Data Eng}, volume = {24}, number = {5}, pages = {896--911}, publisher = {IEEE Educational Activities Department}, address = {Piscataway, NJ, USA}, issn = {1041-4347}, url = {http://dx.doi.org/10.1109/TKDE.2011.15}, acmid = {2197127}, issue_date = {May 2012}, nodoi = {10.1109/TKDE.2011.15}, numpages = {16}, keywords = {collaborative filtering.,performance evaluation metrics,ranking functions,recommendation diversity} } @article{adomavicius2012impact, title = {Impact of Data Characteristics on Recommender Systems Performance}, author = {Adomavicius, Gediminas and Zhang, Jingjing}, date = {2012}, journaltitle = {ACM Trans. Manag. Inf. Syst. TMIS}, volume = {3}, number = {1}, pages = {1--17}, publisher = {ACM New York, NY, USA} } @article{AdversarialMachineLearning, title = {Adversarial {{Machine Learning}} —{{An Introduction}}}, journaltitle = {Mach. Learn.}, pages = {26}, langid = {english}, keywords = {adversarial machine learning} } @online{Agentarium, title = {Agentarium}, url = {https://www.linkedin.com/posts/shubhamsaboo_just-discovered-a-python-framework-that-makes-activity-7282236256929878016-293r?utm_source=share&utm_medium=member_android}, keywords = {source\_mobile} } @article{AgentDrivenAutomaticSoftware2024, title = {Agent-{{Driven Automatic Software Improvement}}}, date = {2024}, langid = {english} } @article{AgenticAINew, title = {Agentic {{AI}} – the New Frontier in {{GenAI}}}, langid = {english} } @online{AgenticragGenerativeaiMachinelearning, title = {\#agenticrag \#generativeai \#machinelearning \#artificialintelligence | {{Generative AI}} | 60 Commenti}, url = {https://www.linkedin.com/posts/genai-works_agenticrag-generativeai-machinelearning-activity-7316008934748700674-xRfI}, urldate = {2025-04-11}, abstract = {🚨Move Over RAG And Say Hello to Agentic RAG! Why Agentic RAG changes everything: 🔹Autonomy: Document-specific agents analyze, summarize, and cross-reference data independently. 🔹Multi-step reasoning: Breaks complex queries into sub-tasks (e.g., “Compare X vs. Y” → retrieves, synthesizes, validates). 🔹Proactive retrieval: Anticipates gaps in data and fetches missing context before finalizing responses. 🔹Adaptive learning: Meta-agents refine strategies based on real-time feedback and evolving datasets. Native RAG’s limitations laid bare: ➡️Linear pipelines struggle with interconnected data. ➡️Static retrieval misses nuanced, multi-document insights. ➡️Zero capacity for iterative refinement. The verdict? Agentic RAG isn’t an upgrade—it’s a paradigm shift. Enterprises leveraging this architecture are already seeing 40\% higher accuracy in legal analysis and medical research. Which industries are about to get redefined... and which ones won’t survive? Credits: Armand Ruiz ☄️Boost your business with us—advertise where 10M+ AI leaders engage https://lnkd.in/dNHGZSq7 \#agenticrag \#generativeai \#machinelearning \#artificialintelligence | 60 commenti su LinkedIn} } @misc{AgentsCompanion, title = {Agents {{Companion}}} } @incollection{aggarwalNeighborhoodbasedCollaborativeFiltering2016, title = {Neighborhood-Based Collaborative Filtering}, booktitle = {Recommender Systems: {{The}} Textbook}, author = {Aggarwal, Charu}, date = {2016}, pages = {29--70}, publisher = {Springer International Publishing}, location = {Cham}, doi = {10.1007/978-3-319-29659-3₂}, abstract = {Neighborhood-based collaborative filtering algorithms, also referred to as memory-based algorithms, were among the earliest algorithms developed for collaborative filtering. These algorithms are based on the fact that similar users display similar patterns of rating behavior and similar items receive similar ratings. There are two primary types of neighborhood-based algorithms:}, isbn = {978-3-319-29659-3} } @book{aggarwalRecommenderSystems2016, ids = {DBLP:books/sp/Aggarwal16}, title = {Recommender Systems}, author = {Aggarwal, Charu C.}, date = {2016}, publisher = {Springer International Publishing}, location = {Cham}, doi = {10.1007/978-3-319-29659-3}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/books/sp/Aggarwal16.bib}, isbn = {978-3-319-29657-9 978-3-319-29659-3}, langid = {english}, timestamp = {Thu, 28 Nov 2019 10:42:05 +0100} } @inproceedings{AGIRRE10.534, title = {Exploring Knowledge Bases for Similarity}, booktitle = {Proc. {{Seventh Int}}. {{Conf}}. {{Lang}}. {{Resour}}. {{Eval}}. {{LREC10}}}, author = {Agirre, Eneko and Cuadros, Montse and Rigau, German and Soroa, Aitor}, editor = {Chair), Nicoletta Calzolari (Conference and Choukri, Khalid and Maegaard, Bente and Mariani, Joseph and Odijk, Jan and Piperidis, Stelios and Rosner, Mike and Tapias, Daniel}, year = {19-21, 2010-05}, publisher = {European Language Resources Association (ELRA)}, location = {Valletta, Malta}, isbn = {2-9517408-6-7}, langid = {english} } @inproceedings{agirrePersonalizingPageRankWord2009, title = {Personalizing {{PageRank}} for Word Sense Disambiguation}, booktitle = {Proc. 12th {{Conf}}. {{Eur}}. {{Chapter Assoc}}. {{Comput}}. {{Linguist}}.}, author = {Agirre, Eneko and Soroa, Aitor}, date = {2009}, series = {{{EACL}} '09}, pages = {33--41}, publisher = {Association for Computational Linguistics}, location = {Stroudsburg, PA, USA}, url = {http://dl.acm.org/citation.cfm?id=1609067.1609070}, acmid = {1609070}, numpages = {9}, note = {TL;DR \par This paper proposes a new graph-based method that uses the knowledge in a LKB (based on WordNet) in order to perform unsupervised Word Sense Disambiguation, performing better than previous approaches in English all-words datasets.} } @article{agt-rickauerSupportingDomainModeling, title = {Supporting Domain Modeling with Automated Knowledge Acquisition and Modeling Recommendations}, author = {Agt-Rickauer, Henning}, pages = {196}, langid = {ngerman} } @article{Aha:1991:ILA:104713.104717, title = {Instance-Based Learning Algorithms}, author = {Aha, David W. and Kibler, Dennis and Albert, Marc K.}, date = {1991-01}, journaltitle = {Mach. Learn.}, volume = {6}, number = {1}, pages = {37--66}, publisher = {Kluwer Academic Publishers}, address = {Hingham, MA, USA}, issn = {0885-6125}, url = {https://doi.org/10.1023/A:1022689900470}, acmid = {104717}, issue_date = {Jan. 1991}, nodoi = {10.1023/A:1022689900470}, numpages = {30}, keywords = {incremental learning,instance-based concept descriptions,learning theory,noise,similarity,Supervised concept learning}, note = {TL;DR \par This paper describes how storage requirements can be significantly reduced with, at most, minor sacrifices in learning rate and classification accuracy and extends the nearest neighbor algorithm, which has large storage requirements.} } @misc{ahmad2023humanbot, title = {Towards Human-Bot Collaborative Software Architecting with {{ChatGPT}}}, author = {Ahmad, Aakash and Waseem, Muhammad and Liang, Peng and Fehmideh, Mahdi and Aktar, Mst Shamima and Mikkonen, Tommi}, date = {2023}, journaltitle = {Proceedings of the 27th international conference on evaluation and assessment in software engineering (EASE)}, eprint = {2302.14600}, eprinttype = {arXiv}, eprintclass = {cs.SE}, note = {TL;DR \par A case study that involves collaboration between a novice software architect and ChatGPT to architect a service-based software and explores socio-technical aspects of architecting with ChatG PT to tackle challenges of ACSE is detail.} } @online{ahmadUnifiedPretrainingProgram2021, ids = {Ahmad_Chakraborty_Ray_Chang_2021}, title = {Unified {{Pre-training}} for {{Program Understanding}} and {{Generation}}}, author = {Ahmad, Wasi Uddin and Chakraborty, Saikat and Ray, Baishakhi and Chang, Kai-Wei}, date = {2021-04-10}, eprint = {2103.06333}, eprinttype = {arXiv}, eprintclass = {cs}, url = {http://arxiv.org/abs/2103.06333}, urldate = {2023-05-04}, abstract = {Code summarization and generation empower conversion between programming language (PL) and natural language (NL), while code translation avails the migration of legacy code from one PL to another. This paper introduces PLBART, a sequence-to-sequence model capable of performing a broad spectrum of program and language understanding and generation tasks. PLBART is pre-trained on an extensive collection of Java and Python functions and associated NL text via denoising autoencoding. Experiments on code summarization in the English language, code generation, and code translation in seven programming languages show that PLBART outperforms or rivals state-of-the-art models. Moreover, experiments on discriminative tasks, e.g., program repair, clone detection, and vulnerable code detection, demonstrate PLBART's effectiveness in program understanding. Furthermore, analysis reveals that PLBART learns program syntax, style (e.g., identifier naming convention), logical flow (e.g., if block inside an else block is equivalent to else if block) that are crucial to program semantics and thus excels even with limited annotations.}, issue = {arXiv:2103.06333}, pubstate = {prepublished}, keywords = {Computer Science - Computation and Language,Computer Science - Programming Languages}, note = {arXiv:2103.06333 [cs] \par Comment: NAACL 2021 (camera ready) \par TL;DR \par Analysis reveals that PLBART learns program syntax, style, logical flow, and style that are crucial to program semantics and thus excels even with limited annotations, and outperforms or rivals state-of-the-art models.} } @online{AiAgenticAgents, title = {\#ai \#agentic \#agents \#opensource \#rag \#mcp | {{Eduardo Ordax}} | 60 Commenti}, url = {https://www.linkedin.com/posts/eordax_ai-agentic-agents-activity-7328742367086227456-6T2P}, urldate = {2025-05-15}, abstract = {Why everyone is not talking about this today? Yesterday LangChain just released Open Agent Platform. This is an open-source, no-code agent building platform. OAP enables non-developers to build highly customizable agents, which connect to: 🛠️ MCP Tools 📄 LangConnect for RAG 🤖 Other LangGraph Agents They have released also two pre built agents: - MCP/RAG Tools Agent - Multi-Agent Supervisor And the MCP server is powered by Arcade.dev that provides acces to 100’s of tools out of the box. Watching the demo you can see how easily you can create agents! Link to the public demo and GitHub repo in the comments. \#ai \#agentic \#agents \#opensource \#rag \#mcp | 60 commenti su LinkedIn} } @online{AIAgentStarter, title = {{{AI}} Agent Starter Using Llama 3.3. | Dr. {{Walid}} Soula | Kinomoto.Mag {{AI}}}, url = {https://medium.com/kinomoto-mag/ai-agent-starter-using-llama-3-3-603ad4b9e6e2}, urldate = {2024-12-30}, langid = {english}, keywords = {source\_mobile} } @misc{AIIndexReport, title = {{{AI Index Report}} 2025} } @online{AiMcpTechexplained, title = {\#ai \#mcp \#techexplained \#futureofai \#aiintegration | {{Lorenzo De Tomasi}}}, url = {https://www.linkedin.com/posts/lorenzo-de-tomasi-ai-data-platforms_ai-mcp-techexplained-activity-7317444292552065024-_QhH}, urldate = {2025-04-14}, abstract = {Let Me Explain MCP (Model Context Protocol): The AI Connection Tool Everyone\&\#39;s Talking About 🔥 MCP is revolutionizing how AI connects to our tools and data—it\&\#39;s like installing a universal translator between your AI and everything else. Ever wonder how AI assistants like Claude or ChatGPT could access your company files, databases, or tools without massive custom development? That\&\#39;s exactly what MCP solves! 💡 Before MCP, connecting AI to each tool (Slack, Gmail, your CRM) required custom code for EACH connection. MCP creates ONE standard way for ALL connections. Like how USB replaced all those different cables! Introduced by Anthropic in November 2024, adoption exploded in early 2025 with over 1,000 community-built servers already available. 🔌 MCP creates a client-server architecture where your AI is the client. Each tool or data source runs an \"MCP server\" that exposes its capabilities. The AI discovers available tools through a standardized process and can use them when needed – everything communicates through consistent messages and protocols. 🔄 The technical magic happens through three core components: Tools (functions the AI can call like searchFiles or queryDatabase), Resources (data objects the AI can access), and Prompts (templates for common interactions). All are described in a way that helps the AI understand when and how to use them. 🧠 Real-world applications are already transforming businesses. Connect AI to your Google Drive to find and analyze documents. Let AI check your calendar and schedule meetings automatically. Have AI query your customer database and generate reports. Enable AI to browse the web for you and summarize findings. 🌐 What makes MCP superior to RAG and other approaches is that it gets real-time data instead of outdated embeddings, has much lower computational cost (no vector searches), enhances security because data doesn\&\#39;t need to be copied everywhere, and enables complex multi-step workflows across different systems. 🔒 The beauty of MCP is its simplicity and standardization. Instead of the N×M problem (connecting N AI models to M tools requires N×M custom integrations), MCP reduces this to just N+M connections total. Companies like Block (Square), Replit, and Sourcegraph have already embraced this approach for its efficiency. What tool or data source would you most want your AI assistant to access directly? Has your company started exploring MCP yet? 💭 \#AI \#MCP \#TechExplained \#FutureOfAI \#AIIntegration} } @unknown{AkbarPrePrint, title = {Ethical Aspects of {{ChatGPT}} in Software Engineering Research}, author = {Azeem Akbar, Muhammad and Khan, Arif}, date = {2023-03} } @inproceedings{Al-Azzoni202087, title = {Model Driven Approach for Neural Networks}, author = {Al-Azzoni, I.}, editor = {Alsmirat M., Jararweh Y., Aloqaily M., Lloret Mauri J.}, date = {2020}, series = {2020 {{International Conference}} on {{Intelligent Data Science Technologies}} and {{Applications}}, {{IDSTA}} 2020}, pages = {87--94}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/IDSTA50958.2020.9264067}, abbrev_source_title = {Int. Conf. Intell. Data Sci. Technol. Appl., IDSTA}, affiliation = {College of Engineering, Al Ain University, Al Ain, United Arab Emirates}, art_number = {9264067}, correspondence_address1 = {Al-Azzoni, I.; College of Engineering, United Arab Emirates; email: issam.alazzoni@aau.ac.ae}, document_type = {Conference Paper}, isbn = {978-1-7281-8376-3}, langid = {english}, source = {Scopus}, note = {cited By 0 \par TL;DR \par New meta-models for addressing machine learning problems using artificial neural networks can capture the main elements of learning problems and neural networks, and help users to develop solutions with less dependence on a particular set of tools and technologies.} } @article{al-azzoniSTAF_2025_paper_42, title = {{{STAF}}\_2025\_paper\_42}, author = {Al-Azzoni, Issam and Heckel, Reiko and Erum, Zobia}, abstract = {Domain-specific languages (DSLs) express requirements or designs, often through visual abstractions. To be effective and reliable for complex development tasks such as code generation, testing and analysis, DSLs need semantic foundations. This paper introduces a model-based framework for testing and analysis based on operational semantics for DSLs expressed through graph rewriting.}, langid = {english}, keywords = {LOGSEQ}, note = {\section{Annotazioni\\ (24/3/2025, 11:45:46)} \par - “DSLs need semantic foundations.” (Al-Azzoni et al., p. 1) \#5fb236\\ \mkbibemph{ } \par - “model-based framework for testing and analysis” (Al-Azzoni et al., p. 1) \#a28ae5\\ \mkbibemph{ } \par - “operational semantics for DSLs expressed through graph rewriting” (Al-Azzoni et al., p. 1) \#a28ae5\\ \mkbibemph{ } \par - “role-based access control models (RBACMs)” (Al-Azzoni et al., p. 1) \#e56eee\\ \mkbibemph{ } \par - “constraints on who can access which resources under a dynamic notion of role membership.” (Al-Azzoni et al., p. 1) \#5fb236\\ \mkbibemph{ } \par - “access control policies for smart contracts involve multiple parties that are members of different groups or organisations” (Al-Azzoni et al., p. 1) \#a28ae5\\ \mkbibemph{ } \par - “DSL for multi-party role-based access control policies which is defined as an extension of the iContractML 2.0 metamodel.” (Al-Azzoni et al., p. 1) \#a28ae5\\ \mkbibemph{What is iContractML? } \par - “The diagrammatic notation supports complex authorisation patterns, including alternatives and multiplicities, to address the nuanced access control requirements of smart contract applications.” (Al-Azzoni et al., p. 1) \#5fb236\\ \mkbibemph{ } \par - “Groove model checker” (Al-Azzoni et al., p. 1) \#a28ae5\\ \mkbibemph{ } \par - “Digital Asset Modelling Language (DAML).” (Al-Azzoni et al., p. 1) \#a28ae5\\ \mkbibemph{ } \par - “We use an extended model to validate dynamic access control scenarios generated by ChatGPT.” (Al-Azzoni et al., p. 1) \#a28ae5\\ \mkbibemph{ } \par - “Smart contracts support workflows of multiple parties without the need for a central authority” (Al-Azzoni et al., p. 1) \#a28ae5\\ \mkbibemph{ } \par - “smart contracts implement platformspecific access control mechanisms,” (Al-Azzoni et al., p. 1) \#a28ae5\\ \mkbibemph{ } \par - “role-based access control (” (Al-Azzoni et al., p. 1) \#5fb236\\ \mkbibemph{ } \par - “multiparty authorisation,” (Al-Azzoni et al., p. 1) \#5fb236\\ \mkbibemph{ } \par - “hard to understand and verify against security requirements at code level, leaving applications open to data leaks and unauthorised access” (Al-Azzoni et al., p. 1) \#a28ae5\\ \mkbibemph{ } \par - “approach to testing and validating access control policies (ACPs).” (Al-Azzoni et al., p. 1) \#e56eee\\ \mkbibemph{ } \par - “The metamodel and the diagrammatic language it defines help developers to understand and review multi-party RBAC models.” (Al-Azzoni et al., p. 2) \#5fb236\\ \mkbibemph{ } \par - “dynamic RBAC, where dedicated role owners are authorised to add and remove members at runtime.” (Al-Azzoni et al., p. 2) \#5fb236\\ \mkbibemph{SO the dynamism is related to the possibility of adding or removing members at runtime ? } \par - “homework grading authorised by an individually assigned grader together with either a second named grader or two members of a role of evaluators.” (Al-Azzoni et al., p. 2) \#5fb236\\ \mkbibemph{ } \par - “modelling concepts for roles, role membership, alternatives and cardinalities for authorisation relations and map such models to the smart contract language DAML1 which supports multi-party authorisation but no roles nor alternatives.” (Al-Azzoni et al., p. 2) \#2ea8e5\\ \mkbibemph{ } \par - “Solidity use assertions for access control but do not support multi-party authorisations.” (Al-Azzoni et al., p. 2) \#5fb236\\ \mkbibemph{ } \par - “access control but do not support multi-party authorisations. Our modelbased approach expresses these differences at a higher level and supports code generation to DAML and Solidity.” (Al-Azzoni et al., p. 2) \#ffd400\\ \mkbibemph{ } \par - “critical security properties must be tested.” (Al-Azzoni et al., p. 2) \#ffd400\\ \mkbibemph{Which one? At what level? WHat are the sucurty properties are you referring to? } \par - “we generate test scripts including assertions as oracles to run automatically and fail when encountering access control errors.” (Al-Azzoni et al., p. 2) \#a28ae5\\ \mkbibemph{ } \par - “ACPs” (Al-Azzoni et al., p. 2) \#ffd400\\ \mkbibemph{What's the execution environmnet you are supporting? What kinds of application can you support? } \par - “participant P must authorise action A” (Al-Azzoni et al., p. 2) \#a28ae5\\ \mkbibemph{ } \par - “n members of role R must authorise action A” (Al-Azzoni et al., p. 2) \#a28ae5\\ \mkbibemph{ } \par - “Accepting or rejecting traces are produced by the Groove model checker (Ghamarian et al. 2012) using a start graph and temporal logic formulas derived from the RBACM. Test scripts in DAML are produced based on a test model using a Java EMF application and Acceleo templates” (Al-Azzoni et al., p. 2) \#ffd400\\ \mkbibemph{Without exampes it's difficult to grasp the details. Readers is supposed to figure out how this works on his/her own. } \par - “Our tests discover design-level faults in access control code. Design-level faults are deviations from the access control requirements expressed in an RBACM, that can arise from implementing (correctly) a faulty model, not from arbitrary implementation errors. This limitation is in line with related work on testing access control policies (Daoudagh et al. 2015; Martin \& Xie 2007) which must be complemented by code-based techniques for comprehensive testing.” (Al-Azzoni et al., p. 2) \#ffd400\\ \mkbibemph{See my previous comment about the need of examples. } \par - “Who can authorise a transaction if a participant is away? or If a given sequence of actions cannot be authorised, can I enable the process by adding a new member to a role? where adding a role member itself requires authorisation.” (Al-Azzoni et al., p. 2) \#ffd400\\ \mkbibemph{In addition to these cases, what's the support that authors want to introduce in addition to what existing approaches already permit to do? For instance, appraches for specifying documental workflows, already permit to do so, isn't it? } \par - “As a use case, we generate scenarios responding to such questions using OpenAI’s o1-preview and o1-mini to validate them with the Groove model checker, combining the domain knowledge of LLMs with the safety of a formal model.” (Al-Azzoni et al., p. 2) \#ffd400\\ \mkbibemph{Here, the use of LLMs appear very suddenly. What's the problem you want to solve that requires automation capabilities based on LLMs? } \par - “model-based testing and validation based on domain-specific languages with complex graphlike structure and operational semantics.” (Al-Azzoni et al., p. 2) \#a28ae5\\ \mkbibemph{ } \par - “DLS” (Al-Azzoni et al., p. 2) \#ff6666\\ \mkbibemph{-{$>$} DSL } \par - “validate scenarios suggested by LLMs for testing or helping users at runtime” (Al-Azzoni et al., p. 2) \#ffd400\\ \mkbibemph{Maybe this is the answer to my previous question. In any case, it is necessary to introduce the challenges that authors propose to solve by means of LLMs,. The usage of such technologies needs to be justifyed too! } \par - “DLS” (Al-Azzoni et al., p. 2) \#ff6666\\ \mkbibemph{-{$>$} DSL } \par - “The innovation in this paper is to use the graphical operational semantics to generated tests from counterexamples to LTL formulas that describe the scenarios to be validated through testing.” (Al-Azzoni et al., p. 3) \#a28ae5\\ \mkbibemph{ } \par - “membership” (Al-Azzoni et al., p. 3) \#5fb236\\ \mkbibemph{ } \par - “authorisation” (Al-Azzoni et al., p. 3) \#5fb236\\ \mkbibemph{ } \par - “The language also supports alternative authorisations indicated by a ∨ (or) symbol within a white square” (Al-Azzoni et al., p. 3) \#ffd400\\ \mkbibemph{This not very well explained in the figure. Moreover, Alice is both a submitted and an evaluator? So it means that she is part of the evaluation process for her homework???? } \par - “outlined in Fig. 1.” (Al-Azzoni et al., p. 3) \#ffd400\\ \mkbibemph{It's not the right reference I guess, which should be instead the listing at pag. 4. } \par - “Figure 1 Outline of our approach” (Al-Azzoni et al., p. 3) \#ffd400\\ \mkbibemph{The label of the arrow between EMF Test Generator and RBACM Test Model should be generates, isn't it? } \par - “GXL” (Al-Azzoni et al., p. 3) \#ff6666\\ \mkbibemph{This is not accessible, the reader gets redirected to~https://unblock.uni-koblenz.de/redirect/ } \par - “template” (Al-Azzoni et al., p. 4) \#ffd400\\ \mkbibemph{The template requires explanation for readers that are not expert of DAML } \par - “alice : Party bob : Party chris : Party doug : Party emma : Party comments : Text” (Al-Azzoni et al., p. 4) \#ffd400\\ \mkbibemph{so this is not done at the metalevel, instead it is required to know the instances of each concept, right? } \par - “The effect is that this choice requires the authorisation of Alice in addition to that of Bob or at least two members in the role HWEvaluator\_Role as specified by the RBACM.” (Al-Azzoni et al., p. 5) \#ffd400\\ \mkbibemph{I would expected to see that HWEaluator should not HWSubmitter. } \par - “support the representation of test cases as a basis for code generation.” (Al-Azzoni et al., p. 7) \#5fb236\\ \mkbibemph{ } \par - “This is useful because the system under test may incorrectly require authorisation by a different participant than specified in the model, which corresponds to an incorrect accept trace with that participant added.” (Al-Azzoni et al., p. 7) \#5fb236\\ \mkbibemph{ } \par - “In this section, we discuss the results of a case study involving the use of ChatGPT in generating RBACMs to test our approach and for creating scenarios representing successful executions of workflows with dynamic control access.” (Al-Azzoni et al., p. 7) \#ffd400\\ \mkbibemph{IT's not clear what are the motivations supporting the need of such technology. I'm not against it, I'm just wondering what's the issues in using e.g., existing mutation technniques that are able to generate mutations starting from a seed model (see e.g., Wodel). In other words, what's authors wanted to check/analyze with the generated models? } \par - “generate test cases and validate scenarios based on a DSL for multi-party rolebased access control in smart contracts.” (Al-Azzoni et al., p. 12) \#ffd400\\ \mkbibemph{ } \par - “generate and validate access control traces.” (Al-Azzoni et al., p. 12) \#ffd400\\ \mkbibemph{ } \par - “We experimented with chatGPT to generate “realworld” RBACMs in different domains. This approach could be used to generate a set of benchmark models.” (Al-Azzoni et al., p. 12) \#ffd400\\ \mkbibemph{ }} } @article{al-garadiSurveyMachineDeep2018, title = {A {{Survey}} of {{Machine}} and {{Deep Learning Methods}} for {{Internet}} of {{Things}} ({{IoT}}) {{Security}}}, author = {Al-Garadi, Mohammed Ali and Mohamed, Amr and Al-Ali, Abdulla and Du, Xiaojiang and Guizani, Mohsen}, date = {2018-07-29}, journaltitle = {ArXiv180711023 Cs}, eprint = {1807.11023}, eprinttype = {arXiv}, eprintclass = {cs}, url = {http://arxiv.org/abs/1807.11023}, urldate = {2021-01-10}, abstract = {The Internet of Things (IoT) integrates billions of smart devices that can communicate with one another with minimal human intervention. It is one of the fastest developing fields in the history of computing, with an estimated 50 billion devices by the end of 2020. On the one hand, IoT play a crucial role in enhancing several real-life smart applications that can improve life quality. On the other hand, the crosscutting nature of IoT systems and the multidisciplinary components involved in the deployment of such systems introduced new security challenges. Implementing security measures, such as encryption, authentication, access control, network security and application security, for IoT devices and their inherent vulnerabilities is ineffective. Therefore, existing security methods should be enhanced to secure the IoT system effectively. Machine learning and deep learning (ML/DL) have advanced considerably over the last few years, and machine intelligence has transitioned from laboratory curiosity to practical machinery in several important applications. Consequently, ML/DL methods are important in transforming the security of IoT systems from merely facilitating secure communication between devices to security-based intelligence systems. The goal of this work is to provide a comprehensive survey of ML /DL methods that can be used to develop enhanced security methods for IoT systems. IoT security threats that are related to inherent or newly introduced threats are presented, and various potential IoT system attack surfaces and the possible threats related to each surface are discussed. We then thoroughly review ML/DL methods for IoT security and present the opportunities, advantages and shortcomings of each method. We discuss the opportunities and challenges involved in applying ML/DL to IoT security. These opportunities and challenges can serve as potential future research directions.}, note = {TL;DR \par A comprehensive survey of ML methods and recent advances in DL methods that can be used to develop enhanced security methods for IoT systems and presents the opportunities, advantages and shortcomings of each method.} } @article{Al-Janan2017686, title = {Optimizing the Double Inverted Pendulum’s Performance via the Uniform Neuro Multiobjective Genetic Algorithm}, author = {Al-Janan, D.H. and Chang, H.-C. and Chen, Y.-P. and Liu, T.-K.}, date = {2017}, journaltitle = {Int. J. Autom. Comput.}, volume = {14}, number = {6}, pages = {686--695}, publisher = {Chinese Academy of Sciences}, issn = {14768186}, doi = {10.1007/s11633-017-1069-8}, abbrev_source_title = {Int. J. Autom. Comput.}, affiliation = {Institute of Engineering Science and Technology, National Kaohsiung First University of Science and Technology, Taiwan; Mechanical Engineering Departement, Engineeering Faculty, Semarang State University, Semarang, Center of Java, Indonesia}, correspondence_address1 = {Liu, T.-K.; Institute of Engineering Science and Technology, Taiwan; email: tkliu@nkfust.edu.tw}, document_type = {Article}, langid = {english}, source = {Scopus}, note = {cited By 10} } @inproceedings{Al-Subaihin:2016:CMA:2961111.2962600, title = {Clustering Mobile Apps Based on Mined Textual Features}, booktitle = {Proc. 10th {{ACMIEEE Int}}. {{Symp}}. {{Empir}}. {{Softw}}. {{Eng}}. {{Meas}}.}, author = {Al-Subaihin, A. A. and Sarro, F. and Black, S. and Capra, L. and Harman, M. and Jia, Y. and Zhang, Y.}, date = {2016}, series = {{{ESEM}} '16}, pages = {38:1-38:10}, publisher = {ACM}, location = {New York, NY, USA}, url = {http://doi.acm.org/10.1145/2961111.2962600}, acmid = {2962600}, articleno = {38}, isbn = {978-1-4503-4427-2}, nodoi = {10.1145/2961111.2962600}, numpages = {10} } @inproceedings{Alaa2019, title = {Demystifying Black-Box Models with Symbolic Metamodels}, author = {Alaa, A.M. and family=Schaar, given=M., prefix=van der, useprefix=true}, date = {2019}, series = {Advances in {{Neural Information Processing Systems}}}, volume = {32}, publisher = {Neural information processing systems foundation}, issn = {10495258}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078483653&partnerID=40&md5=e624a02d5e67fdb84876eed77ec60513}, abbrev_source_title = {Adv. neural inf. proces. syst.}, affiliation = {ECE Department UCLA; UCLA, University of Cambridge, Alan Turing Institute, United Kingdom}, document_type = {Conference Paper}, langid = {english}, source = {Scopus}, note = {cited By 19 \par TL;DR \par It is shown that symbolic metamodeling provides an all-encompassing framework for model interpretation — all common forms of global and local explanations of a model can be analytically derived from its symbolic meetamodel.} } @article{alamin2021empirical, title = {An {{Empirical Study}} of {{Developer Discussions}} on {{Low-Code Software Development Challenges}}}, author = {Alamin, Md Abdullah Al and Malakar, Sanjay and Uddin, Gias and Afroz, Sadia and Haider, Tameem Bin and Iqbal, Anindya}, date = {2021-05}, journaltitle = {2021 IEEEACM 18th Int. Conf. Min. Softw. Repos. MSR}, eprint = {2103.11429}, eprinttype = {arXiv}, eprintclass = {cs.SE}, pages = {46--57}, doi = {10.1109/MSR52588.2021.00018}, abstract = {Low-code software development (LCSD) is an emerging paradigm that combines minimal source code with interactive graphical interfaces to promote rapid application development. LCSD aims to democratize application development to software practitioners with diverse backgrounds. Given that LCSD is relatively a new paradigm, it is vital to learn about the challenges developers face during their adoption of LCSD platforms. The online developer forum, Stack Overflow (SO), is popular among software developers to ask for solutions to their technical problems. We observe a growing body of posts in SO with discussions of LCSD platforms. In this paper, we present an empirical study of around 5K SO posts (questions + accepted answers) that contain discussions of nine popular LCSD platforms. We apply topic modeling on the posts to determine the types of topics discussed. We find 13 topics related to LCSD in SO. The 13 topics are grouped into four categories: Customization, Platform Adoption, Database Management, and Third-Party Integration. More than 40\% of the questions are about customization, i.e., developers frequently face challenges with customizing user interfaces or services offered by LCSD platforms. The topic "Dynamic Event Handling" under the "Customization" category is the most popular (in terms of average view counts per question of the topic) as well as the most difficult. It means that developers frequently search for customization solutions such as how to attach dynamic events to a form in low-code UI, yet most (75.9\%) of their questions remain without an accepted answer. We manually label 900 questions from the posts to determine the prevalence of the topics' challenges across LCSD phases. We find that most of the questions are related to the development phase, and low-code developers also face challenges with automated testing.}, keywords = {Computer Science - Software Engineering}, note = {TL;DR \par An empirical study of around 5K SO posts that contain discussions of nine popular LCSD platforms finds that most of the questions are related to the development phase, and low-code developers also face challenges with automated testing.} } @article{aldallalEmpiricalEvaluationImpact2018, title = {Empirical {{Evaluation}} of the {{Impact}} of {{Object-Oriented Code Refactoring}} on {{Quality Attributes}}: {{A Systematic Literature Review}}}, shorttitle = {Empirical {{Evaluation}} of the {{Impact}} of {{Object-Oriented Code Refactoring}} on {{Quality Attributes}}}, author = {Al Dallal, Jehad and Abdin, Anas}, date = {2018-01-01}, journaltitle = {IEEE Trans. Softw. Eng.}, volume = {44}, number = {1}, pages = {44--69}, issn = {0098-5589, 1939-3520}, doi = {10.1109/TSE.2017.2658573}, note = {TL;DR \par It is indicated that different refactoring scenarios sometimes have opposite impacts on different quality attributes, and it is false thatRefactoring always improves all software quality aspects.} } @online{AlessioTonioniAutonomousFlightROSSimple, title = {{{AlessioTonioni}}/{{Autonomous-Flight-ROS}}: {{A}} Simple Autopilot for a Quadrotor Realized Using {{MoveIt}}!. {{The}} System Use a Simulated {{RGBD}} Sensor to Reconstruct the Map, Then Ompl for Path Generation.}, url = {https://github.com/AlessioTonioni/Autonomous-Flight-ROS}, urldate = {2016-09-11} } @inproceedings{alexanderCertificationAutonomousSystems2007, title = {Certification of Autonomous Systems}, booktitle = {Proc. 2nd {{Syst}}. {{Eng}}. {{Auton}}. {{Syst}}. {{SEAS Def}}. {{Technol}}. {{Cent}}. {{DTC Annu}}. {{Tech}}. {{Conf}}.}, author = {Alexander, Robert and Hall-May, Martin and Kelly, Tim}, date = {2007}, publisher = {Citeseer}, url = {http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.126.7288&rep=rep1&type=pdf}, urldate = {2016-08-21}, note = {TL;DR \par The aim of the project is to show that it is possible to automate the system design validation as well as the verification of the implementation in software so that there is considerably reduced time and cost while still being able to meet certification requirements.} } @book{alf, title = {Action Language for Foundational {{UML}} (Alf)}, date = {2017}, publisher = {Object Management Group (OMG)}, url = {https://www.omg.org/spec/ALF/1.1/PDF}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found} } @article{alfonsoSelfadaptiveArchitecturesIoT2021, title = {Self-Adaptive {{Architectures}} in {{IoT Systems}}: {{A Systematic Literature Review}}}, shorttitle = {Self-Adaptive {{Architectures}} in {{IoT Systems}}}, author = {Alfonso, Iván and Garcés, Kelly and Castro, Harold and Cabot, Jordi}, date = {2021-09-07}, journaltitle = {ArXiv210903312 Cs}, eprint = {2109.03312}, eprinttype = {arXiv}, eprintclass = {cs}, url = {http://arxiv.org/abs/2109.03312}, urldate = {2021-10-04}, abstract = {Over the past few years, the relevance of the Internet of Things (IoT) has grown significantly and is now a key component of many industrial processes and even a transparent participant in various activities performed in our daily life. IoT systems are subjected to changes in the dynamic environments they operate in. These changes (e.g. variations in the bandith consumption or new devices joining/leaving) may impact the Quality of Service (QoS) of the IoT system. A number of self-adaptation strategies for IoT architectures to better deal with these changes have been proposed in the literature. Nevertheless, they focus on isolated types of changes. We lack a comprehensive view of the trade-offs of each proposal and how they could be combined to cope with dynamic situations involving simultaneous types of events. In this paper, we identify, analyze, and interpret relevant studies related to IoT adaptation and develop a comprehensive and holistic view of the interplay of different dynamic events, their consequences on the architecture QoS, and the alternatives for the adaptation. To do so, we have conducted a systematic literature review of existing scientific proposals and defined a research agenda for the near future based on the findings and weaknesses identified in the literature.}, keywords = {Computer Science - Networking and Internet Architecture}, note = {Comment: 18 pages, 3 figures, 8 tables, submitted to Journal of Internet Services and Applications \par TL;DR \par A comprehensive and holistic view of the interplay of different dynamic events, their consequences on QoS, and the alternatives for the adaptation is developed based on the findings and weaknesses identified in the literature.} } @article{Ali20192979, title = {Artificial Neural Network Based Screening of Cervical Cancer Using a Hierarchical Modular Neural Network Architecture ({{HMNNA}}) and Novel Benchmark Uterine Cervix Cancer Database}, author = {Ali, M. and Sarwar, A. and Sharma, V. and Suri, J.}, date = {2019}, journaltitle = {Neural Comput. Appl.}, volume = {31}, number = {7}, pages = {2979--2993}, publisher = {Springer London}, issn = {09410643}, doi = {10.1007/s00521-017-3246-7}, abstract = {The work reported in this paper presents a novel hierarchical modular neural network architecture (HMNNA) for automated screening of cervical cancer. HMNNA consists of three neural networks trained specifically on different areas of problem space under consideration, and the trained networks are then arranged in a tree structure forming hierarchical modular neural network architecture. The three specialized neural networks are trained by Levenberg–Maarquardt neural network algorithm. As compared to the standard back propagation algorithm, Levenberg–Maarquardt is fast and stable for convergence with only one drawback, i.e., storage requirement for estimated Hessian Matrix. For training and testing of HMNNA, a huge primary database is created which contains 8091 cervical cell images pertaining to 200 clinical cases collected from two health care institutions of northern India. The raw cases of cervical cancer in the form of Pap smear slides were photographed under a multi-headed digital microscope. Individual cells were manually cropped off from these slide images which were then passed through a feature extraction module for morphological profiling. Each cell was calibrated on the basis of 40 features from both cytoplasm and nucleus. After profiling, these cells were vigilantly assigned cell classes as per the latest 2001-Bethesda system of cervical cancer cell classification, by trained cytotechnicians and histopathologists. HMNNA is also trained and tested on the Herlev Benchmark dataset created by the Denmark University, which consists of 1417 cervical cancer cells. Both the datasets have seven classes of diagnosis, i.e., superficial squamous, intermediate squamous, columnar, mild dysplasia, moderate dysplasia, severe dysplasia, and carcinoma in situ, corresponding to the level of abnormality in cervical cells. These datasets are available in public domain at http://digitalpapsmeardb.in/ and http://mde-lab.aegean.gr/index.php/downloads. The screening potential of the HMNNA is compared with 25 well-known machine learning algorithms available in MatlabR2016 (Machine learning and statistics toolbox 10.2) and monolithic neural network algorithms available in Matlab neural network pattern recognition toolbox. The HMNNA outperformed in all the 25 algorithms for both the datasets. For the Novel Benchmark database, it produced a classification accuracy of 95.32\% with an F-value of 0.949310 and classification accuracy of 88.41\% with an F-value of 0.89145 for the Herlev dataset. The screening potential of HMNNA was also evaluated and compared with the other diagnostic systems available in the recently published literature and was found to be performing much better than the counterparts on multiple parameters of performance evaluation. © 2017, The Natural Computing Applications Forum.}, document_type = {Article}, source = {Scopus}, note = {cited By 9 \par TL;DR \par The screening potential of HMNNA was evaluated and compared with the other diagnostic systems available in the recently published literature and was found to be performing much better than the counterparts on multiple parameters of performance evaluation.} } @online{AllenaMente_Memoria, title = {{{AllenaMente}}\_ {{La}} Memoria}, url = {https://www.cimec.unitn.it/alfresco/download/workspace/SpacesStore/bfc4856f-331d-4c4c-aa28-4462f9bb2939/AllenaMente_%20La%20Memoria.pdf}, urldate = {2022-07-15}, langid = {english} } @incollection{allenEngineeringAcademicSoftware2017, title = {Engineering {{Academic Software}} ({{Dagstuhl Perspectives Workshop}} 16252)}, booktitle = {Dagstuhl {{Manifestos}}}, author = {Allen, Alice and Aragon, Cecilia and Becker, Christoph and Carver, Jeffrey and Chis, Andrei and Combemale, Benoit and Croucher, Mike and Crowston, Kevin and Garijo, Daniel and Gehani, Ashish and others}, date = {2017}, volume = {6}, number = {1}, publisher = {Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik}, url = {http://drops.dagstuhl.de/opus/volltexte/2017/7146/}, urldate = {2017-05-30} } @article{allhoffInternetThingsFoundational2018, title = {The {{Internet}} of {{Things}}: {{Foundational}} Ethical Issues}, shorttitle = {The {{Internet}} of {{Things}}}, author = {Allhoff, Fritz and Henschke, Adam}, date = {2018-09}, journaltitle = {Internet Things}, volume = {1--2}, pages = {55--66}, issn = {25426605}, doi = {10.1016/j.iot.2018.08.005}, abstract = {This paper surveys foundational ethical issues that attach to the Internet of Things (IoT). In Section 1, we provide an overview of the technology, indicating both current and future applications. Subsequent sections consider particular ethical issues, including: informed consent (Section 2), privacy (Section 3), information security (Section 4), physical safety (Section 5), and trust (Section 6). Section 7 emphasizes that these ethical issues do not exist in isolation, but converge and intersect in myriad ways. And that these issues are not comprehensive, but rather are foundational starting points that stand to be expanded and further elucidated through future research.}, langid = {english} } @article{almonte_automating_2021, title = {Automating the {{Synthesis}} of {{Recommender Systems}} for {{Modelling Languages}}}, author = {Almonte, Lissette and Pérez-Soler, Sara and Guerra, Esther and Cantador, Iván and family=Lara, given=Juan, prefix=de, useprefix=true}, date = {2021}, pages = {14}, abstract = {We are witnessing an increasing interest in building recommender systems (RSs) for all sorts of Software Engineering activities. Modelling is no exception to this trend, as modelling environments are being enriched with RSs that help building models by providing recommendations based on previous solutions to similar problems in the same domain. However, building a RS from scratch requires considerable effort and specialized knowledge. To alleviate this problem, we propose an automated approach to the generation of RSs for modelling languages. Our approach is model-based, and we provide a domain-specific language called Droid to configure every aspect of the RS (like the type and features of the recommended items, the recommendation method, and the evaluation metrics). The RS so configured can be deployed as a service, and we offer out-of-the-box integration of this service with the EMF tree editor. To assess the usefulness of our proposal, we present a case study on the integration of a generated RS with a modelling chatbot, and report on an offline experiment measuring the precision and completeness of the recommendations.}, langid = {english} } @article{almonteAutomatingConstructionRecommender2020, title = {Towards Automating the Construction of Recommender Systems for Low-Code Development Platforms}, author = {Almonte, Lissette and Cantador, Iván and Guerra, Esther}, date = {2020}, pages = {10}, abstract = {Low-code development platforms allow users with a low technical background to build complete software solutions, typically by means of graphical user interfaces, diagrams or declarative languages. In these platforms, recommender systems play an important role as they can provide users with relevant, personalised suggestions generated according to previously developed software solutions. However, developing recommender systems requires a high investment of time as it implies the selection and implementation of a suitable recommendation method, its configuration for the problem and domain at hand, and its evaluation to assess the accuracy of its recommendations.}, langid = {english}, note = {TL;DR \par This paper presents the first steps towards a generic model-driven framework capable of generating ad-hoc, task-oriented recommender systems for their integration on low-code platforms and presents some preliminary results obtained from an offline evaluation of the framework.} } @article{almonteEngineeringRecommenderSystems2024, title = {Engineering Recommender Systems for Modelling Languages: Concept, Tool and Evaluation}, shorttitle = {Engineering Recommender Systems for Modelling Languages}, author = {Almonte, Lissette and Guerra, Esther and Cantador, Iván and {de~Lara}, Juan}, date = {2024-07}, journaltitle = {Empir Software Eng}, volume = {29}, number = {4}, pages = {102}, issn = {1382-3256, 1573-7616}, doi = {10.1007/s10664-024-10483-3}, abstract = {Recommender systems (RSs) are ubiquitous in all sorts of online applications, in areas like shopping, media broadcasting, travel and tourism, among many others. They are also common to help in software engineering tasks, including software modelling, where we are recently witnessing proposals to enrich modelling languages and environments with RSs. Modelling recommenders assist users in building models by suggesting items based on previous solutions to similar problems in the same domain. However, building a RS for a modelling language requires considerable effort and specialised knowledge. To alleviate this problem, we propose an automated, model-driven approach to create RSs for modelling languages. The approach provides a domain-specific language called Droid to configure every aspect of the RS: the type of the recommended modelling elements, the gathering and preprocessing of training data, the recommendation method, and the metrics used to evaluate the created RS. The RS so configured can be deployed as a service, and we offer out-of-the-box integration with Eclipse modelling editors. Moreover, the language is extensible with new data sources and recommendation methods. To assess the usefulness of our proposal, we report on two evaluations. The first one is an offline experiment measuring the precision, completeness and diversity of recommendations generated by several methods. The second is a user study –with 40 participants – to assess the perceived quality of the recommendations. The study also contributes with a novel evaluation methodology and metrics for RSs in model-driven engineering.}, langid = {english} } @article{almonteRecommenderSystemsModeldriven2021, ids = {almonte_recommender_2021}, title = {Recommender Systems in Model-Driven Engineering: {{A}} Systematic Mapping Review}, shorttitle = {Recommender Systems in Model-Driven Engineering}, author = {Almonte, Lissette and Guerra, Esther and Cantador, Iván and family=Lara, given=Juan, prefix=de, useprefix=true}, date = {2021-07-26}, journaltitle = {Softw Syst Model}, issn = {1619-1366, 1619-1374}, doi = {10.1007/s10270-021-00905-x}, abstract = {Recommender systems are information filtering systems used in many online applications like music and video broadcasting and e-commerce platforms. They are also increasingly being applied to facilitate software engineering activities. Following this trend, we are witnessing a growing research interest on recommendation approaches that assist with modelling tasks and modelbased development processes. In this paper, we report on a systematic mapping review (based on the analysis of 66 papers) that classifies the existing research work on recommender systems for model-driven engineering (MDE). This study aims to serve as a guide for tool builders and researchers in understanding the MDE tasks that might be subject to recommendations, the applicable recommendation techniques and evaluation methods, and the open challenges and opportunities in this field of research.}, langid = {english}, note = {TL;DR \par A systematic mapping review that classifies the existing research work on recommender systems for model-driven engineering (MDE) and serves as a guide for tool builders and researchers in understanding the MDE tasks that might be subject to recommendations, the applicable recommendation techniques and evaluation methods, and the open challenges and opportunities in this field of research.} } @inproceedings{almorsySuiteDomainspecificVisual2013, title = {A Suite of Domain-Specific Visual Languages for Scientific Software Application Modelling}, booktitle = {Vis. {{Lang}}. {{Hum}}.-{{Centric Comput}}. {{VLHCC}} 2013 {{IEEE Symp}}. {{On}}}, author = {Almorsy, Mohamed and Grundy, John and Sadus, Richard and family=Straten, given=Willem, prefix=van, useprefix=true and Barnes, David G. and Kaluza, Owen}, date = {2013}, pages = {91--94}, publisher = {IEEE}, url = {http://ieeexplore.ieee.org/abstract/document/6645249/}, urldate = {2017-02-23} } @inproceedings{alomranChoosingNLPLibrary2017, title = {Choosing an {{NLP Library}} for {{Analyzing Software Documentation}}: {{A Systematic Literature Review}} and a {{Series}} of {{Experiments}}}, shorttitle = {Choosing an {{NLP Library}} for {{Analyzing Software Documentation}}}, author = {Al Omran, Fouad Nasser A and Treude, Christoph}, date = {2017-05}, pages = {187--197}, publisher = {IEEE}, doi = {10.1109/MSR.2017.42}, isbn = {978-1-5386-1544-7} } @article{Alreshedy2018SCCAC, title = {{{SCC}}: {{Automatic}} Classification of Code Snippets}, author = {Alreshedy, Kamel and Dharmaretnam, Dhanush and German, Daniel M. and Srinivasan, Venkatesh and Gulliver, T. Aaron}, date = {2018}, journaltitle = {CoRR}, volume = {abs/1809.07945} } @article{alrubaye_how_nodate, title = {How {{Does API Migration Impact Software Quality}} and {{Comprehension}}? {{An Empirical Study}}}, author = {Alrubaye, Hussein and AlShoaibi, Deema and Mkaouer, Mohamed Wiem and Ouni, Ali}, pages = {12}, abstract = {The migration process between different thirdparty software libraries is hard, complex and error-prone. Typically, during a library migration process, developers opt to replace methods from the retired library with other methods from a new library without altering the software behavior. However, the extent to which such a migration process to new libraries will be rewarded with an improved software quality is still unknown. In this paper, we aim at studying and analyzing the impact of library API migration on software quality. We conduct a large-scale empirical study on 9 popular API migrations, collected from a corpus of 57,447 open-source Java projects. We compute the values of commonly-used software quality metrics before and after a migration occurs. The statistical analysis of the obtained results provides evidence that library migrations are likely to improve different software quality attributes including a significantly reduced coupling, increased cohesion, and improved code readability. Furthermore, we release an online portal that helps software developers to understand the pre-impact of a library migration on software quality and recommend migration examples that adopt best design and implementation practices to improve software quality. Finally, we provide the software engineering community with a large scale dataset to foster research in software library migration.}, langid = {english}, note = {TL;DR \par An online portal is released that helps software developers to understand the pre-impact of a library migration on software quality and recommend migration examples that adopt the best design and implementation practices to improve software quality.} } @article{alrubaye2019learning, title = {Learning to Recommend Third-Party Library Migration Opportunities at the {{API}} Level}, author = {Alrubaye, Hussein and Mkaouer, Mohamed Wiem and Khokhlov, Igor and Reznik, Leon and Ouni, Ali and Mcgoff, Jason}, date = {2020}, journaltitle = {Appl. Soft Comput.}, volume = {90}, pages = {106--140}, issn = {1568-4946} } @inproceedings{alrubayeUseInformationRetrieval2019, ids = {alrubaye_use_2019}, title = {On the {{Use}} of {{Information Retrieval}} to {{Automate}} the {{Detection}} of {{Third-Party Java Library Migration}} at the {{Method Level}}}, booktitle = {2019 {{IEEEACM}} 27th {{Int}}. {{Conf}}. {{Program Comprehension ICPC}}}, author = {Alrubaye, Hussein and Mkaouer, Mohamed Wiem and Ouni, Ali}, date = {2019-05}, pages = {347--357}, issn = {2643-7147}, doi = {10.1109/ICPC.2019.00053}, abstract = {The migration process between different third-party libraries is hard, complex and error-prone. Typically, during a library migration, developers need to find methods in the new library that are most adequate in replacing the old methods of the retired library. This process is subjective and time-consuming as developers need to fully understand the documentation of both libraries' Application Programming Interfaces, and find the right matching between their methods, if it exists. In this context, several studies rely on mining existing library migrations to provide developers with by-example approaches for similar scenarios. In this paper, we introduce a novel mining approach that extracts existing instances of library method replacements that are manually performed by developers for a given library migration to automatically generate migration patterns in the method level. Thereafter, our approach combines the mined method-change patterns with method-related lexical similarity to accurately detect mappings between replacing/replaced methods. We conduct a large scale empirical study to evaluate our approach on a benchmark of 57,447 open-source Java projects leading to 9 popular library migrations. Our qualitative results indicate that our approach significantly increases the accuracy of mining method-level mappings by an average accuracy of 12\%, as well as increasing the number of discovered method mappings, in comparison with existing state-of-the-art studies. Finally, we provide the community with an open source mining tool along with a dataset of all mined migrations at the method level.}, eventtitle = {2019 {{IEEE}}/{{ACM}} 27th {{International Conference}} on {{Program Comprehension}} ({{ICPC}})}, keywords = {API,API migration,application program interfaces,data mining,information retrieval,Information Retrieval,Java,library method replacements,library migration,method-related lexical similarity,migration patterns,migration process,mined method-change patterns,open source mining tool,open-source Java,public domain software,Software Evolution,software libraries,Third Party Library,third-party Java library migration,third-party libraries}, note = {ISSN: 2643-7147} } @article{alsrehinIntelligentTransportationControl2019, title = {Intelligent {{Transportation}} and {{Control Systems Using Data Mining}} and {{Machine Learning Techniques}}: {{A Comprehensive Study}}}, shorttitle = {Intelligent {{Transportation}} and {{Control Systems Using Data Mining}} and {{Machine Learning Techniques}}}, author = {Alsrehin, Nawaf O. and Klaib, Ahmad F. and Magableh, Aws}, date = {2019}, journaltitle = {IEEE Access}, volume = {7}, pages = {49830--49857}, issn = {2169-3536}, doi = {10.1109/ACCESS.2019.2909114}, abstract = {Traffic congestion is becoming the issues of the entire globe. This study aims to explore and review the data mining and machine learning technologies adopted in research and industry to attempt to overcome the direct and indirect traffic issues on humanity and societies. The study’s methodology is to comprehensively review around 165 studies, criticize, and categorize all these studies into a chronological and understandable category. The study is focusing on the traffic management approaches that were depended on data mining and machine learning technologies to detect and predict the traffic only. This study has found that there is no standard traffic management approach that the community of traffic management has agreed on. This study is important to the traffic research communities, traffic software companies, and traffic government officials. It has a direct impact on drawing a clear path for new traffic management propositions. This study is one of the largest studies with respect to the size of its reviewed articles that were focused on data mining and machine learning. Additionally, this study will draw general attention to a new traffic management proposition approach.}, langid = {english}, note = {TL;DR \par It is found that there is no standard traffic management approach that the community of traffic management has agreed on and this study will draw general attention to a new traffic management proposition approach.} } @article{altulyanRecommenderSystemsInternet2020, title = {Recommender {{Systems}} for the {{Internet}} of {{Things}}: {{A Survey}}}, shorttitle = {Recommender {{Systems}} for the {{Internet}} of {{Things}}}, author = {Altulyan, May and Yao, Lina and Wang, Xianzhi and Huang, Chaoran and Kanhere, Salil S. and Sheng, Quan Z.}, date = {2020-07-13}, journaltitle = {ArXiv200706758 Cs Stat}, eprint = {2007.06758}, eprinttype = {arXiv}, eprintclass = {cs, stat}, url = {http://arxiv.org/abs/2007.06758}, urldate = {2020-12-14}, abstract = {Recommendation represents a vital stage in developing and promoting the benefits of the Internet of Things (IoT). Traditional recommender systems fail to exploit ever-growing, dynamic, and heterogeneous IoT data. This paper presents a comprehensive review of the state-of-the-art recommender systems, as well as related techniques and application in the vibrant field of IoT. We discuss several limitations of applying recommendation systems to IoT and propose a reference framework for comparing existing studies to guide future research and practices.}, langid = {english}, keywords = {internet of things,machine learning,recommendation systems}, note = {TL;DR \par A comprehensive review of the state-of-the-art recommender systems, as well as related techniques and application in the vibrant field of IoT, and proposes a reference framework for comparing existing studies to guide future research and practices.} } @article{alurSystemsComputingChallenges2016, title = {Systems {{Computing Challenges}} in the {{Internet}} of {{Things}}}, author = {Alur, Rajeev and Berger, Emery and Drobnis, Ann W. and Fix, Limor and Fu, Kevin and Hager, Gregory D. and Lopresti, Daniel and Nahrstedt, Klara and Mynatt, Elizabeth and Patel, Shwetak and others}, date = {2016}, journaltitle = {ArXiv Prepr. ArXiv160402980}, eprint = {1604.02980}, eprinttype = {arXiv}, url = {http://arxiv.org/abs/1604.02980}, urldate = {2016-08-21}, note = {TL;DR \par Success in developing value-added capabilities around IoT requires a broad approach that includes expertise in sensing and hardware, machine learning, networked systems, human-computer interaction, security, and privacy.} } @article{alvarezMTCFlowTool2013, title = {{{MTC Flow}}: A Tool to Design, Develop and Deploy Model Transformation Chains}, author = {Alvarez, Camilo and Casallas, Rubby}, date = {2013}, journaltitle = {Proc. Workshop Acad. Tool. Eclipse - ACME 13}, pages = {1--9}, doi = {10.1145/2491279.2491286}, note = {TL;DR \par This paper presents a tool called MTC Flow, which allows model-driven developers to design, develop, test and deploy Model Transformation Chains (MTCs), and offers a graphical DSL for defining MTC workflow models independently of the technologies that support the transformations.} } @article{alvinoLessonsLearnedLarge, title = {Lessons {{Learned}} from {{Large Scale Real World Recommender Systems}}}, author = {Alvino, Chris}, pages = {22}, langid = {english} } @inproceedings{amershiSoftwareEngineeringMachine2019, ids = {DBLP:conf/icse/AmershiBBDGKNN019,amershiSoftwareEngineeringMachine2019a}, title = {Software {{Engineering}} for {{Machine Learning}}: {{A Case Study}}}, shorttitle = {Software {{Engineering}} for {{Machine Learning}}}, booktitle = {2019 {{IEEEACM}} 41st {{Int}}. {{Conf}}. {{Softw}}. {{Eng}}. {{Softw}}. {{Eng}}. {{Pract}}. {{ICSE-SEIP}}}, author = {Amershi, Saleema and Begel, Andrew and Bird, Christian and DeLine, Robert and Gall, Harald and Kamar, Ece and Nagappan, Nachiappan and Nushi, Besmira and Zimmermann, Thomas}, date = {2019-05}, pages = {291--300}, publisher = {IEEE}, location = {Montreal, QC, Canada}, doi = {10.1109/ICSE-SEIP.2019.00042}, abstract = {Recent advances in machine learning have stimulated widespread interest within the Information Technology sector on integrating AI capabilities into software and services. This goal has forced organizations to evolve their development processes. We report on a study that we conducted on observing software teams at Microsoft as they develop AI-based applications. We consider a nine-stage workflow process informed by prior experiences developing AI applications (e.g., search and NLP) and data science tools (e.g. application diagnostics and bug reporting). We found that various Microsoft teams have united this workflow into preexisting, well-evolved, Agile-like software engineering processes, providing insights about several essential engineering challenges that organizations may face in creating large-scale AI solutions for the marketplace. We collected some best practices from Microsoft teams to address these challenges. In addition, we have identified three aspects of the AI domain that make it fundamentally different from prior software application domains: 1) discovering, managing, and versioning the data needed for machine learning applications is much more complex and difficult than other types of software engineering, 2) model customization and model reuse require very different skills than are typically found in software teams, and 3) AI components are more difficult to handle as distinct modules than traditional software components — models may be “entangled” in complex ways and experience non-monotonic error behavior. We believe that the lessons learned by Microsoft teams will be valuable to other organizations.}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/icse/AmershiBBDGKNN019.bib}, eventtitle = {2019 {{IEEE}}/{{ACM}} 41st {{International Conference}} on {{Software Engineering}}: {{Software Engineering}} in {{Practice}} ({{ICSE-SEIP}})}, isbn = {978-1-7281-1760-7}, langid = {english}, note = {TL;DR \par A study conducted on observing software teams at Microsoft as they develop AI-based applications finds that various Microsoft teams have united this workflow into preexisting, well-evolved, Agile-like software engineering processes, providing insights about several essential engineering challenges that organizations may face in creating large-scale AI solutions for the marketplace.}, timestamp = {Wed, 16 Oct 2019 14:14:49 +0200} } @article{Ameur2022, title = {Merits of {{Bayesian}} Networks in Overcoming Small Data Challenges: A Meta-Model for Handling Missing Data}, author = {Ameur, H. and Njah, H. and Jamoussi, S.}, date = {2022}, journaltitle = {Int. J. Mach. Learn. Cybern.}, publisher = {{Springer Science and Business Media Deutschland GmbH}}, issn = {18688071}, doi = {10.1007/s13042-022-01577-9}, abbrev_source_title = {Intl. J. Mach. Learn. Cybern.}, affiliation = {Multimedia, InfoRmation Systems and Advanced Computing Laboratory, Sfax, Tunisia; Higher Institute of Computer Sciences and Multimedia, University of Sfax, Sfax, Tunisia; Higher Institute of Computer Sciences and Multimedia, University of Gabes, Gabes, Tunisia}, correspondence_address1 = {Ameur, H.; Multimedia, Tunisia; email: ameurhanen@gmail.com}, document_type = {Article}, langid = {english}, source = {Scopus}, note = {cited By 0} } @article{amine_benelallam_2018_1489120, title = {Maven Central Dependency Graph}, author = {Benelallam, Amine and Harrand, Nicolas and Valero, César Soto and Baudry, Benoit and Barais, Olivier}, date = {2018-11}, doi = {10.5281/zenodo.1489120}, note = {The Maven dependency graph is the fruit of a collaboration between the DiverSE team (Inria Rennes, France) and CASTOR project (KTH, Sweden). Instructions on how to use and reproduce the dataset can be found in the dataset's repository on [Github](https://github.com/diverse-project /maven-miner). A complete description of the dataset and usages can be found in the accompanying [paper] (https://arxiv.org/abs/1901.05392). \par The Maven dependency graph is the fruit of a collaboration between the DiverSE team (Inria Rennes, France) and CASTOR project (KTH, Sweden). Instructions on how to use and reproduce the dataset can be found in the dataset's repository on [Github](https://github.com/diverse-project /maven-miner). A complete description of the dataset and usages can be found in the accompanying [paper] (https://arxiv.org/abs/1901.05392). \par The Maven dependency graph is the fruit of a collaboration between the DiverSE team (Inria Rennes, France) and CASTOR project (KTH, Sweden). Instructions on how to use and reproduce the dataset can be found in the dataset's repository on [Github](https://github.com/diverse-project /maven-miner). A complete description of the dataset and usages can be found in the accompanying [paper] (https://arxiv.org/abs/1901.05392). \par The Maven dependency graph is the fruit of a collaboration between the DiverSE team (Inria Rennes, France) and CASTOR project (KTH, Sweden). Instructions on how to use and reproduce the dataset can be found in the dataset's repository on [Github](https://github.com/diverse-project /maven-miner). A complete description of the dataset and usages can be found in the accompanying [paper] (https://arxiv.org/abs/1901.05392). \par The Maven dependency graph is the fruit of a collaboration between the DiverSE team (Inria Rennes, France) and CASTOR project (KTH, Sweden). Instructions on how to use and reproduce the dataset can be found in the dataset's repository on [Github](https://github.com/diverse-project /maven-miner). A complete description of the dataset and usages can be found in the accompanying [paper] (https://arxiv.org/abs/1901.05392).} } @article{Ammar2018247, title = {Automatic Planning: {{From}} Event-{{B}} to {{PDDL}}}, author = {Ammar, S. and Bhiri, M.T.}, editor = {Golfarelli M., Bellatreche L., Nakamatsu K., Ordonez C., Mery D., Benslimane D., Abdelwahed E.H., Jean S.}, date = {2018}, journaltitle = {Commun. Comput. Inf. Sci.}, volume = {929}, pages = {247--254}, publisher = {Springer Verlag}, issn = {18650929}, doi = {10.1007/978-3-030-02852-7_21}, abstract = {Automatic planning is a separate discipline of Artificial Intelligence (AI). It aims to formalize the planning problems described by the concept of state space. The Planning Domain Definition Language (PDDL) is a de facto standard language in the field of automatic planning. PDDL-related dynamic analysis tools, namely planners and validators, are insufficient for verifying and validating PDDL descriptions. Such tools make it possible to detect errors a posteriori by means of a test activity. In this article, we recommend a rigorous approach coupling Event-B and PDDL for automatic planning. Event-B is used for formal modeling by stepwise refinement with mathematical proofs of planning problems. A refinement strategy appropriate to planning problems is, then, proposed. The ultimate Event-B model, correct by construction, supposed to be translatable into PDDL, is automatically translated into PDDL using our MDE Event-B2PDDL tool. The obtained PDDL description is submitted to efficient planners for generation of solution plans. © Springer Nature Switzerland AG 2018.}, document_type = {Conference Paper}, isbn = {9783030028510}, source = {Scopus}, note = {cited By 0 \par TL;DR \par This article recommends a rigorous approach coupling Event-B and PDDL, and proposes a refinement strategy appropriate to planning problems, which is automatically translated into PDDL using the MDE Event- B2PDDL tool.} } @inproceedings{Ammar2021261, title = {A Formal Approach Combining Event-b and Pddl for Planning Problems}, author = {Ammar, S. and Bhiri, M.T.}, editor = {Fill H.-G., van Sinderen M., Maciaszek L., Maciaszek L.}, date = {2021}, series = {Proceedings of the 16th {{International Conference}} on {{Software Technologies}}, {{ICSOFT}} 2021}, pages = {261--268}, publisher = {SciTePress}, doi = {10.5220/0010577102610268}, abstract = {In artificial intelligence, the goal of automatic planning is to structure actions in the form of a plan to achieve an expressed goal. The PDDL (Planning Domain Definition Language) was designed to allow the common representation of planning problems during ICAPS (International Conference on Automated Planning and Scheduling) competitions. PDDL has many verification and validation tools allowing the description, resolution and validation of planning problems. But they only allow the reliability of PDDL descriptions a posteriori. In this article, we recommend a rigorous approach coupling Event-B and PDDL favoring obtaining PDDL descriptions deemed correct, a priori, from an ultimate Event-B model. The formal Event-B method allows us to obtain, by successive refinements with mathematical proofs, correct by construction formal models of planning problems. A refinement strategy appropriate to planning problems is, then, proposed. The ultimate Event-B model, correct by construction, is automatically translated into PDDL using our MDE Event-B2PDDL tool. The obtained PDDL description is submitted to efficient planners for generation of correct and efficient plan-solutions. Copyright © 2021 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved}, document_type = {Conference Paper}, isbn = {978-989-758-523-4}, source = {Scopus}, note = {cited By 0 \par TL;DR \par A rigorous approach is recommended coupling Event-B and PDDL favoring obtaining PDDL descriptions deemed correct, a priori, from an ultimate Event-Bs model, correct by construction formal models of planning problems.} } @article{Amouzgar201828, title = {Radial Basis Functions with a Priori Bias as Surrogate Models: {{A}} Comparative Study}, author = {Amouzgar, K. and Bandaru, S. and Ng, A.H.C.}, date = {2018}, journaltitle = {Eng. Appl. Artif. Intell.}, volume = {71}, pages = {28--44}, publisher = {Elsevier Ltd}, issn = {09521976}, doi = {10.1016/j.engappai.2018.02.006}, abbrev_source_title = {Eng Appl Artif Intell}, affiliation = {School of Engineering Science, University of Skövde, Skövde, 541 28, Sweden}, coden = {EAAIE}, correspondence_address1 = {Amouzgar, K.; School of Engineering Science, Sweden; email: kaveh.amouzgar@his.se}, document_type = {Article}, langid = {english}, source = {Scopus}, note = {cited By 16} } @inproceedings{amraniTridimensionalApproachStudying2012, title = {A {{Tridimensional Approach}} for {{Studying}} the {{Formal Verification}} of {{Model Transformations}}}, booktitle = {2012 {{IEEE Fifth Int}}. {{Conf}}. {{Softw}}. {{Test}}. {{Verification Valid}}. {{ICST}}}, author = {Amrani, M. and Lucio, L. and Selim, G. and Combemale, B. and Dingel, J. and Vangheluwe, H. and Le Traon, Y. and Cordy, J.R.}, date = {2012-04}, pages = {921--928}, doi = {10.1109/ICST.2012.197}, abstract = {In Model Driven Engineering (MDE), models are first-class citizens, and model transformation is MDE's "heart and soul". Since model transformations are executed for a family of conforming models, their validity becomes a crucial issue. This paper proposes to explore the question of the formal verification of model transformation properties through a tri-dimensional approach: the transformation involved, the properties of interest addressed, and the formal verification techniques used to establish the properties. This work allows a better understanding of the expected properties for a particular transformation, and facilitates the identification of the suitable tools and techniques for enabling their verification.}, eventtitle = {2012 {{IEEE Fifth International Conference}} on {{Software Testing}}, {{Verification}} and {{Validation}} ({{ICST}})}, note = {\textbf{SPUNTI INTERESSANTI} \par \begin{itemize} \item \textbf{Idea per il forge}: "we would like to propose the community to contribute for a comprehensive \textbf{benchmark} \textbf{for FV (Formal Verification) of transformations}: it consists of storing pairs constituted by a transformation together with its properties of interest. This benchmark can help researchers as well as practitioners, and could provide a common reference for playing with verification of transformations by easily targeting a technique, a property kind amongthose identified in this paper, and comparing efficiency and scalability of approaches." \item Tra gli approcci di \textbf{formal verification} che ci interessano ci sono quelli che ricascano in "\mkbibemph{Type II: Transformation-Dependent and Input-Independent}". Questi sono: \begin{itemize} \item \textbf{Model checking} \item \textbf{Static Analysis}: Becker et al. [43 - \mkbibemph{Symbolic Invariant}\\ \mkbibemph{Verification For Systems With Dynamic Structural Adaptation}] proposed a static\\ analysis technique to check whether a model transformation\\ (formalized as graph rewriting) preserved constraints\\ expressed as (conditional) forbidden patterns in the output\\ model. The study proved that the structural adaptation does\\ not transform a safe system state to an unsafe one by verifying\\ that the backward application of each rule to each forbidden\\ pattern cannot result in a safe state. \item \textbf{Theorem Proving~} \end{itemize} \item Le proprietà che possono essere provate sono: \begin{itemize} \item Language related \begin{itemize} \item at execution-time (termination and determinism) \item at design-tyme (static semantics) \end{itemize} \item Transformation related \begin{itemize} \item ~properties involving transformations’s source and/or target models \begin{itemize} \item per esempio in caso di \textbf{merging}: they enunciate several properties merge\\ operators should possess: completeness means no information is lost during the merge; non-redundancy ensures that the\\ merge does not duplicate redundant information spread over source models; minimality ensures that the merge produces\\ models with information solely originating from sources; totality ensures that the merge can actually be computed\\ on any pair of models; idempotency, which ensures that a model merged with itself produces an identical model. \end{itemize} \item syntax-guided properties \item properties involving the underlying semantics of models \end{itemize} \end{itemize} \end{itemize} \par TL;DR \par This paper proposes to explore the question of the formal verification of model transformation properties through a tri-dimensional approach: the transformation involved, the properties of interest addressed, and the formal verify techniques used to establish the properties.} } @thesis{AnalisiSperimentazioneDi, title = {Analisi e Sperimentazione Di {{Algoritmi}} Di {{Outlier Detection}} in {{Sistemi GDO}}} } @article{AnalysisLicenseInconsistency, entrysubtype = {newspaper}, title = {Analysis of License Inconsistency in Large Collections of Open Source Projects}, url = {http://rdcu.be/tez8} } @online{AnalysisMetamodelingPractices, title = {An Analysis of Metamodeling Practices for {{MOF}} and {{OCL}}}, url = {http://www.sciencedirect.com/science/article/pii/S1477842415000068}, urldate = {2015-06-12} } @online{AnalyzeUnderstandText, title = {Analyze and {{Understand Text}}: {{Guide}} to {{Natural Language Processing}} - {{Strumenta}}}, url = {https://tomassetti.me/guide-natural-language-processing/?utm_source=newsletter&utm_medium=email&utm_campaign=onboardingsequence}, urldate = {2021-02-01} } @article{anangMathLogicChatGPT, title = {The Math and Logic behind {{ChatGPT}}. {{This}} Paper Is All You Need.}, author = {Anang, Joshua}, langid = {english} } @article{AnastasakisBGR10, title = {On Challenges of Model Transformation from {{UML}} to Alloy}, author = {Anastasakis, Kyriakos and Bordbar, Behzad and Georg, Geri and Ray, Indrakshi}, date = {2010}, journaltitle = {Software Syst. Model.}, volume = {9}, number = {1}, pages = {69--86}, doi = {10.1007/S10270-008-0110-3}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Fri, 18 Sep 2020 11:19:22 +0200} } @article{Anavangot20216314, title = {Signal Source Distribution Approximation to Speedup Scalar Quantizer Design}, author = {Anavangot, V. and Kumar, A.}, date = {2021}, journaltitle = {IEEE Trans. Signal Process.}, volume = {69}, pages = {6314--6328}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {1053587X}, doi = {10.1109/TSP.2021.3125602}, abstract = {Classical quantizer design approaches using the Lloyd-Max algorithm (or k-means) have served signal processing applications for more than three decades. With the advent of distributed signal processing and machine learning at edge devices, novel alternatives for quantizers design will be desired to address the energy, communication and hardware constraints. To address these resource challenges, we propose a model-driven approach, termed Approximate Lloyd-Max (ALM) design, based on piecewise linear approximation of the signal-source probability density. From the principles of the ALM design, we develop a data-driven quantizer, or Learning ALM (LALM), using statistical learning methods. By mathematical analysis, we show convergence of the ALM quantizer near the limit of the Lloyd-Max quantizer. Both ALM and LALM quantizers satisfy asymptotic optimality and exponential convergence rate. Simulation performed over smooth signal source distributions validate our mathematical analysis. Experiments for LALM quantizer are implemented on an Android-based edge device, and the proposed quantizer demonstrate improved performance over k-means, in terms of algorithm speedup, energy usage and memory utilization. © 1991-2012 IEEE.}, coden = {ITPRE}, document_type = {Article}, source = {Scopus}, note = {cited By 0 \par TL;DR \par A model-driven approach, termed Approximate Lloyd-Max (ALM) design, based on piecewise linear approximation of the signal-source probability density is proposed, and a data-driven quantizer is developed, or Learning ALM (LALM), using statistical learning methods.} } @book{Anderson:2006:LTW:1197299, title = {The Long Tail: {{Why}} the Future of Business Is Selling Less of More}, author = {Anderson, Chris}, date = {2006}, publisher = {Hyperion}, isbn = {1-4013-0237-8} } @video{andrejkarpathyDeepDiveLLMs2025, entrysubtype = {video}, title = {Deep Dive into {{LLMs}} like {{ChatGPT}}}, editor = {{Andrej Karpathy}}, editortype = {director}, date = {2025-02-05}, url = {https://www.youtube.com/watch?v=7xTGNNLPyMI}, urldate = {2025-05-02}, abstract = {This is a general audience deep dive into the Large Language Model (LLM) AI technology that powers ChatGPT and related products. It is covers the full training stack of how the models are developed, along with mental models of how to think about their "psychology", and how to get the best use them in practical applications. I have one "Intro to LLMs" video already from \textasciitilde year ago, but that is just a re-recording of a random talk, so I wanted to loop around and do a lot more comprehensive version. Instructor Andrej was a founding member at OpenAI (2015) and then Sr. Director of AI at Tesla (2017-2022), and is now a founder at Eureka Labs, which is building an AI-native school. His goal in this video is to raise knowledge and understanding of the state of the art in AI, and empower people to effectively use the latest and greatest in their work. Find more at https://karpathy.ai/ and https://x.com/karpathy Chapters 00:00:00 introduction 00:01:00 pretraining data (internet) 00:07:47 tokenization 00:14:27 neural network I/O 00:20:11 neural network internals 00:26:01 inference 00:31:09 GPT-2: training and inference 00:42:52 Llama 3.1 base model inference 00:59:23 pretraining to post-training 01:01:06 post-training data (conversations) 01:20:32 hallucinations, tool use, knowledge/working memory 01:41:46 knowledge of self 01:46:56 models need tokens to think 02:01:11 tokenization revisited: models struggle with spelling 02:04:53 jagged intelligence 02:07:28 supervised finetuning to reinforcement learning 02:14:42 reinforcement learning 02:27:47 DeepSeek-R1 02:42:07 AlphaGo 02:48:26 reinforcement learning from human feedback (RLHF) 03:09:39 preview of things to come 03:15:15 keeping track of LLMs 03:18:34 where to find LLMs 03:21:46 grand summary Links ChatGPT https://chatgpt.com/ FineWeb (pretraining dataset): https://huggingface.co/spaces/Hugging... Tiktokenizer: https://tiktokenizer.vercel.app/ Transformer Neural Net 3D visualizer: https://bbycroft.net/llm llm.c Let's Reproduce GPT-2 https://github.com/karpathy/llm.c/dis... Llama 3 paper from Meta: https://arxiv.org/abs/2407.21783 Hyperbolic, for inference of base model: https://app.hyperbolic.xyz/ InstructGPT paper on SFT: https://arxiv.org/abs/2203.02155 HuggingFace inference playground: https://huggingface.co/spaces/hugging... DeepSeek-R1 paper: https://arxiv.org/abs/2501.12948 TogetherAI Playground for open model inference: https://api.together.xyz/playground AlphaGo paper (PDF): https://discovery.ucl.ac.uk/id/eprint... AlphaGo Move 37 video: ~~~•~Lee~Sedol~vs~AlphaGo~~Move~37~reactio...~~ LM Arena for model rankings: https://lmarena.ai/ AI News Newsletter: https://buttondown.com/ainews LMStudio for local inference https://lmstudio.ai/ The visualization UI I was using in the video: https://excalidraw.com/ The specific file of Excalidraw we built up: https://drive.google.com/file/d/1EZh5... Discord channel for Eureka Labs and this video: ~~/~discord}, langid = {english}, keywords = {/unread} } @inproceedings{andSwingSWTBack2010, ids = {thiago_tonelli_swing_2010}, title = {Swing to {{SWT}} and Back: {{Patterns}} for {{API}} Migration by Wrapping}, booktitle = {2010 {{IEEE Int}}. {{Conf}}. {{Softw}}. {{Maint}}.}, author = {{and}}, date = {2010-09}, pages = {1--10}, issn = {1063-6773}, doi = {10.1109/ICSM.2010.5610429}, keywords = {Adaptive arrays,API migration re-engineering,application code,application program interfaces,Containers,graphical user interfaces,Graphical user interfaces,Java,object-oriented API,object-oriented methods,Open source software,open-source GUI wrapper,wrapper-based migration,wrapper-based re-implementation,wrapping,Wrapping,XML}, note = {ISSN: 1063-6773} } @article{anelliElliotComprehensiveRigorous2021, title = {Elliot: A {{Comprehensive}} and {{Rigorous Framework}} for {{Reproducible Recommender Systems Evaluation}}}, shorttitle = {Elliot}, author = {Anelli, Vito Walter and Bellogín, Alejandro and Ferrara, Antonio and Malitesta, Daniele and Merra, Felice Antonio and Pomo, Claudio and Donini, Francesco Maria and Di Noia, Tommaso}, date = {2021-03-03}, journaltitle = {ArXiv210302590 Cs}, eprint = {2103.02590}, eprinttype = {arXiv}, eprintclass = {cs}, url = {http://arxiv.org/abs/2103.02590}, urldate = {2021-03-09}, abstract = {Recommender Systems have shown to be an effective way to alleviate the over-choice problem and provide accurate and tailored recommendations. However, the impressive number of proposed recommendation algorithms, splitting strategies, evaluation protocols, metrics, and tasks, has made rigorous experimental evaluation particularly challenging. Puzzled and frustrated by the continuous recreation of appropriate evaluation benchmarks, experimental pipelines, hyperparameter optimization, and evaluation procedures, we have developed an exhaustive framework to address such needs. Elliot is a comprehensive recommendation framework that aims to run and reproduce an entire experimental pipeline by processing a simple configuration file. The framework loads, filters, and splits the data considering a vast set of strategies (13 splitting methods and 8 filtering approaches, from temporal training-test splitting to nested K-folds Cross-Validation). Elliot optimizes hyperparameters (51 strategies) for several recommendation algorithms (50), selects the best models, compares them with the baselines providing intra-model statistics, computes metrics (36) spanning from accuracy to beyond-accuracy, bias, and fairness, and conducts statistical analysis (Wilcoxon and Paired t-test). The aim is to provide the researchers with a tool to ease (and make them reproducible) all the experimental evaluation phases, from data reading to results collection. Elliot is available on GitHub (https://github.com/sisinflab/elliot).}, keywords = {Computer Science - Information Retrieval}, note = {Comment: 10 pages, 1 figure \par TL;DR \par Elliot is a comprehensive recommendation framework that aims to run and reproduce an entire experimental pipeline by processing a simple configuration file and optimizes hyperparameters for several recommendation algorithms.} } @article{anelliSemanticInterpretationTopN2020, title = {Semantic {{Interpretation}} of {{Top-N Recommendations}}}, author = {Anelli, Vito Walter and Di Noia, Tommaso and Di Sciascio, Eugenio and Ragone, Azzurra and Trotta, Joseph}, date = {2020}, journaltitle = {IEEE Trans. Knowl. Data Eng.}, pages = {1--1}, issn = {1041-4347, 1558-2191, 2326-3865}, doi = {10.1109/TKDE.2020.3010215}, abstract = {Over the years, model-based approaches have shown their effectiveness in computing recommendation lists in different domains and settings. By relying on the computation of latent factors, they can recommend items with a very high level of accuracy. Unfortunately, when moving to the latent space, even if the model embeds content-based information, we miss references to the actual semantics of the recommended item. It makes the interpretation of the recommendation process non-trivial. In this paper, we show how to initialize latent factors in Factorization Machines by using semantic features coming from knowledge graphs to train an interpretable model, which is, in turn, able to provide recommendations with a high level of accuracy. In the presented approach, semantic features are injected into the learning process to retain the original informativeness of the items available in the dataset. By relying on the information encoded in the original knowledge graph, we also propose two metrics to evaluate the semantic accuracy and robustness of knowledge-aware interpretability. An extensive experimental evaluation on six different datasets shows the effectiveness of the interpretable model in terms of both accuracy and diversity of recommendation results and interpretability robustness.}, langid = {english}, note = {TL;DR \par This paper shows how to initialize latent factors in Factorization Machines by using semantic features coming from knowledge graphs to train an interpretable model, which is, in turn, able to provide recommendations with a high level of accuracy.} } @online{angeliniCrackingNutsSledgehammer2022, title = {Cracking Nuts with a Sledgehammer: When Modern Graph Neural Networks Do Worse than Classical Greedy Algorithms}, shorttitle = {Cracking Nuts with a Sledgehammer}, author = {Angelini, Maria Chiara and Ricci-Tersenghi, Federico}, date = {2022-06-27}, eprint = {2206.13211}, eprinttype = {arXiv}, eprintclass = {cond-mat}, url = {http://arxiv.org/abs/2206.13211}, urldate = {2022-08-03}, abstract = {The recent work ``Combinatorial Optimization with Physics-Inspired Graph Neural Networks'' [Nat Mach Intell 4 (2022) 367] introduces a physics-inspired unsupervised Graph Neural Network (GNN) to solve combinatorial optimization problems on sparse graphs. To test the performances of these GNNs, the authors of the work show numerical results for two fundamental problems: maximum cut and maximum independent set (MIS). They conclude that "the graph neural network optimizer performs on par or outperforms existing solvers, with the ability to scale beyond the state of the art to problems with millions of variables." In this comment, we show that a simple greedy algorithm, running in almost linear time, can find solutions for the MIS problem of much better quality than the GNN. The greedy algorithm is faster by a factor of \$10\textasciicircum 4\$ with respect to the GNN for problems with a million variables. We do not see any good reason for solving the MIS with these GNN, as well as for using a sledgehammer to crack nuts. In general, many claims of superiority of neural networks in solving combinatorial problems are at risk of being not solid enough, since we lack standard benchmarks based on really hard problems. We propose one of such hard benchmarks, and we hope to see future neural network optimizers tested on these problems before any claim of superiority is made.}, pubstate = {prepublished}, keywords = {Computer Science - Artificial Intelligence,Computer Science - Machine Learning,Condensed Matter - Disordered Systems and Neural Networks,Mathematics - Optimization and Control}, note = {Comment: Comment to "Combinatorial Optimization with Physics-Inspired Graph Neural Networks'' [Nat Mach Intell 4 (2022) 367] https://www.nature.com/articles/s42256-022-00468-6} } @inproceedings{aniculaeseiHolisticSoftwareSystems2018, title = {Toward a {{Holistic Software Systems Engineering Approach}} for {{Dependable Autonomous Systems}}}, booktitle = {2018 {{IEEEACM}} 1st {{Int}}. {{Workshop Softw}}. {{Eng}}. {{AI Auton}}. {{Syst}}. {{SEFAIAS}}}, author = {Aniculaesei, Adina and Grieser, Jörg and Rausch, Andreas and Rehfeldt, Karina and Warnecke, Tim}, date = {2018-05}, pages = {23--30}, url = {https://ieeexplore.ieee.org/document/8452726}, urldate = {2023-09-28}, abstract = {Autonomous systems are gaining momentum in various application domains, such as autonomous vehicles, autonomous transport robotics and self-adaptation in smart homes. Product liability regulations impose high standards on manufacturers of such systems with respect to dependability (safety, security and privacy). Today's conventional engineering methods are not adequate for providing guarantees with respect to dependability requirements in a cost-efficient manner, e.g. road tests in the automotive industry sum up millions of miles before a system can be considered sufficiently safe. System engineers will no longer be able to test and respectively formally verify autonomous systems during development time in order to guarantee the dependability requirements in advance. In this vision paper, we introduce a new holistic software systems engineering approach for autonomous systems, which integrates development time methods as well as operation time techniques. With this approach, we aim to give the users a transparent view of the confidence level of the autonomous system under use with respect to the dependability requirements. We present already obtained results and point out research goals to be addressed in the future.}, eventtitle = {2018 {{IEEE}}/{{ACM}} 1st {{International Workshop}} on {{Software Engineering}} for {{AI}} in {{Autonomous Systems}} ({{SEFAIAS}})} } @article{aNoSQLImplementationConceptual2018, title = {{{NoSQL Implementation}} of a {{Conceptual Data Model}} : {{UML Class Diagram}} to a {{Document Oriented Model}}}, shorttitle = {{{NoSQL Implementation}} of a {{Conceptual Data Model}}}, author = {A, Benmakhlouf}, date = {2018-04-30}, journaltitle = {IJDMS}, volume = {10}, number = {2}, pages = {01--10}, issn = {09755985, 09755705}, doi = {10.5121/ijdms.2018.10201} } @article{Ansari2022190, title = {{{ESAR}}, an Expert Shoplifting Activity Recognition System}, author = {Ansari, M.A. and Singh, D.K.}, date = {2022}, journaltitle = {Cybern. Inf. Technol.}, volume = {22}, number = {1}, pages = {190--200}, publisher = {Sciendo}, issn = {13119702}, doi = {10.2478/cait-2022-0012}, abbrev_source_title = {Cybern. Inf. Technol.}, affiliation = {CSED, MNNIT, Prayagraj, Allahabad, India}, document_type = {Article}, langid = {english}, source = {Scopus}, note = {cited By 0} } @misc{AntoniomastropaolophdthesisPdf, title = {Antonio-Mastropaolo-Phd-Thesis.Pdf}, keywords = {LOGSEQ}, note = {\section{Annotazioni\\ (8/5/2024, 16:51:39)} \par - “Prof. Davide Di Ruscio” (“antonio-mastropaolo-phd-thesis.pdf”, p. i) \#f0ff00\\ \mkbibemph{ } \par - “log statements” (“antonio-mastropaolo-phd-thesis.pdf”, p. iii) \#f0ff00\\ \mkbibemph{What are the goals? } \par - “William \& Mary, Virginia (USA).” (“antonio-mastropaolo-phd-thesis.pdf”, p. vi) \#f0ff00\\ \mkbibemph{ } \par - “Pre-trained deep learning models can effectively support the automation of tasks characterized by both code and natural language. Novel metrics are needed to properly assess their performance in the context of generative tasks.” (“antonio-mastropaolo-phd-thesis.pdf”, p. 4) \#a28ae5\\ \mkbibemph{NOVEL METRICS } \par - “investigate the effectiveness of pre-trained models for several code related tasks, including those characterized by bi-modal data” (“antonio-mastropaolo-phd-thesis.pdf”, p. 5) \#5fb236\\ \mkbibemph{ } \par - “snippet-level code summarization” (“antonio-mastropaolo-phd-thesis.pdf”, p. 5) \#5fb236\\ \mkbibemph{ } \par - “log injection” (“antonio-mastropaolo-phd-thesis.pdf”, p. 5) \#5fb236\\ \mkbibemph{ } \par - “novel metric” (“antonio-mastropaolo-phd-thesis.pdf”, p. 5) \#5fb236\\ \mkbibemph{ } \par - “natural language summary is appropriate for a given code” (“antonio-mastropaolo-phd-thesis.pdf”, p. 5) \#5fb236\\ \mkbibemph{ } \par - “its sensitivity to the prompt provided as input” (“antonio-mastropaolo-phd-thesis.pdf”, p. 5) \#5fb236\\ \mkbibemph{ } \par - “three high-level categories” (“antonio-mastropaolo-phd-thesis.pdf”, p. 5) \#a28ae5\\ \mkbibemph{ } \par - “definition and experimentation of DL-based techniques exploiting pre-trained models to automate code-related tasks” (“antonio-mastropaolo-phd-thesis.pdf”, p. 5) \#a28ae5\\ \mkbibemph{ } \par - “novel metric aimed at assessing the effectiveness of code summarization methods” (“antonio-mastropaolo-phd-thesis.pdf”, p. 5) \#a28ae5\\ \mkbibemph{ } \par - “empirical assessment of the robustness of GitHub Copilot [cop] for the task of code generation, covered in Chapter 4.” (“antonio-mastropaolo-phd-thesis.pdf”, p. 5) \#a28ae5\\ \mkbibemph{ } \par - “The higher the textual similarity between the generated summary and a reference summary written by developers, the higher its quality.” (“antonio-mastropaolo-phd-thesis.pdf”, p. 6) \#a28ae5\\ \mkbibemph{ } \par - “capturing the extent to which the generated summary aligns with the semantics of the documented code snippet, independently from the reference summary.” (“antonio-mastropaolo-phd-thesis.pdf”, p. 6) \#a28ae5\\ \mkbibemph{ } \par - “ontrastive learning” (“antonio-mastropaolo-phd-thesis.pdf”, p. 7) \#a28ae5\\ \mkbibemph{ } \par - “it is still unclear the extent to which semantic-preserving changes in the natural language description provided to the model have an effect on the generated code” (“antonio-mastropaolo-phd-thesis.pdf”, p. 7) \#a28ae5\\ \mkbibemph{ } \par - “different but semantically equivalent natural language descriptions result in the same recommended function” (“antonio-mastropaolo-phd-thesis.pdf”, p. 7) \#a28ae5\\ \mkbibemph{ } \par - “DL-based techniques for code-related tasks and software engineering automation” (“antonio-mastropaolo-phd-thesis.pdf”, p. 7) \#a28ae5\\ \mkbibemph{ } \par - “advantages and limitations of AI-driven solutions for software development” (“antonio-mastropaolo-phd-thesis.pdf”, p. 7) \#5fb236\\ \mkbibemph{ } \par - “We compared our technique against the state-of-the-art techniques proposed at that time for solving the same tasks.” (“antonio-mastropaolo-phd-thesis.pdf”, p. 7) \#5fb236\\ \mkbibemph{ } \par - “logging” (“antonio-mastropaolo-phd-thesis.pdf”, p. 8) \#a28ae5\\ \mkbibemph{ } \par - “code summarization” (“antonio-mastropaolo-phd-thesis.pdf”, p. 8) \#a28ae5\\ \mkbibemph{ } \par - “Chapter 3” (“antonio-mastropaolo-phd-thesis.pdf”, p. 11) \#a28ae5\\ \mkbibemph{ } \par - “499,618 English sentences and 1,569,889 source code components (i.e., Java methods).” (“antonio-mastropaolo-phd-thesis.pdf”, p. 11) \#a28ae5\\ \mkbibemph{ } \par - “develop four different models” (“antonio-mastropaolo-phd-thesis.pdf”, p. 11) \#ffd400\\ \mkbibemph{You developed four models? [[question]] } \par - “One open question related to the adoption of this tool is what is the impact of the wording used when defining the prompt on the generated source code” (“antonio-mastropaolo-phd-thesis.pdf”, p. 11) \#a28ae5\\ \mkbibemph{ } \par - “The basic idea is to provide the model with the buggy version of a code (e.g., a buggy Java method) asking it to generate its fixed version.” (“antonio-mastropaolo-phd-thesis.pdf”, p. 13) \#a28ae5\\ \mkbibemph{ } \par - “s[” (“antonio-mastropaolo-phd-thesis.pdf”, p. 14) \#ff6666\\ \mkbibemph{ } \par - “Transformer architecture” (“antonio-mastropaolo-phd-thesis.pdf”, p. 14) \#5fb236\\ \mkbibemph{ } \par - “The T5 model we adopted, has then been also exploited in different fash- ions in several of the subsequent works” (“antonio-mastropaolo-phd-thesis.pdf”, p. 14) \#5fb236\\ \mkbibemph{ } \par - “These models undergo extensive pre-training across diverse datasets, encompassing billions of instances that include not just code files, but also a vast array of textual content sourced from the internet.” (“antonio-mastropaolo-phd-thesis.pdf”, p. 14) \#5fb236\\ \mkbibemph{ } \par - “Figure 2.1. Example of bug fix using OpenAI Codex LLM [CTJ+21]” (“antonio-mastropaolo-phd-thesis.pdf”, p. 15) \#ff6666\\ \mkbibemph{The code in (3) has nothing to do with the code in the given example (I meas related to the fault used for the training by means of the shot). } \par - “Contribution in the area” (“antonio-mastropaolo-phd-thesis.pdf”, p. 15) \#ffd400\\ \mkbibemph{I understand this was produced before ChatGPT. What can you say about the novelty of your contribution despite the ChatGPT/GenAI era. (Automated Bug Fixing) } \par - “predefined catalogues of mutation operators” (“antonio-mastropaolo-phd-thesis.pdf”, p. 16) \#5fb236\\ \mkbibemph{ } \par - “we were the first employing pre-trained transformers to mutate source code” (“antonio-mastropaolo-phd-thesis.pdf”, p. 16) \#5fb236\\ \mkbibemph{ } \par - “generation of assert statements.” (“antonio-mastropaolo-phd-thesis.pdf”, p. 17) \#5fb236\\ \mkbibemph{ } \par - “being trained to predict how to generate an appropriate assert statement for the input test method” (“antonio-mastropaolo-phd-thesis.pdf”, p. 17) \#5fb236\\ \mkbibemph{ } \par - ““pretrain-then-finetune” training strategy helps in substantially boost performance when generating assert statements as compared to the state-of-the-art” (“antonio-mastropaolo-phd-thesis.pdf”, p. 17) \#a28ae5\\ \mkbibemph{ } \par - “Method-level Code Summarization” (“antonio-mastropaolo-phd-thesis.pdf”, p. 17) \#a28ae5\\ \mkbibemph{ } \par - “extractive” (“antonio-mastropaolo-phd-thesis.pdf”, p. 17) \#a28ae5\\ \mkbibemph{ } \par - “abstractive” (“antonio-mastropaolo-phd-thesis.pdf”, p. 17) \#a28ae5\\ \mkbibemph{ } \par - “In our exploration of the role pre-trained models play in facilitating code-related tasks (Chapter 3), we demonstrated that our bi-modal pre-trained technique significantly surpassed the then-current state of the art [HLWM20].” (“antonio-mastropaolo-phd-thesis.pdf”, p. 19) \#ffd400\\ \mkbibemph{The same question that I have dove for the other three chapters presenting the contribution in the area. } \par - “y computing their Cyclomatic Complexity and Cognitive Complexity” (“antonio-mastropaolo-phd-thesis.pdf”, p. 19) \#5fb236\\ \mkbibemph{ } \par - “this advancement calls for a critical evaluation of these AI-driven recommenders for software engineering tasks. In this context, we explored the aspect of Robustness, particularly how different but semantically equivalent prompts impact the performance of Copilot in code generation (Chapter 4).” (“antonio-mastropaolo-phd-thesis.pdf”, p. 20) \#ffd400\\ \mkbibemph{What else do you envision? } \par - “Automatic bug-fixing” (“antonio-mastropaolo-phd-thesis.pdf”, p. 21) \#a28ae5\\ \mkbibemph{ } \par - “Injection of code mutants” (“antonio-mastropaolo-phd-thesis.pdf”, p. 21) \#a28ae5\\ \mkbibemph{ } \par - “Generation of assert statements in test methods” (“antonio-mastropaolo-phd-thesis.pdf”, p. 21) \#a28ae5\\ \mkbibemph{ } \par - “Code Summarization” (“antonio-mastropaolo-phd-thesis.pdf”, p. 21) \#a28ae5\\ \mkbibemph{ } \par - “pre-training phase,” (“antonio-mastropaolo-phd-thesis.pdf”, p. 22) \#5fb236\\ \mkbibemph{ } \par - “multi-task fine-tuning” (“antonio-mastropaolo-phd-thesis.pdf”, p. 22) \#5fb236\\ \mkbibemph{ } \par - “Considering the available computational resources, we decided to use the simplest T5small model.” (“antonio-mastropaolo-phd-thesis.pdf”, p. 23) \#5fb236\\ \mkbibemph{ } \par - “masking tokens in natural language sentences and asking the model to guess the masked tokens.” (“antonio-mastropaolo-phd-thesis.pdf”, p. 23) \#5fb236\\ \mkbibemph{ } \par - “can handle any sort of training instance as long as it can be formulated as a text-to-text transformation” (“antonio-mastropaolo-phd-thesis.pdf”, p. 25) \#5fb236\\ \mkbibemph{ } \par - “automate code-related tasks” (“antonio-mastropaolo-phd-thesis.pdf”, p. 29) \#5fb236\\ \mkbibemph{ } \par - “role of pre-training” (“antonio-mastropaolo-phd-thesis.pdf”, p. 29) \#5fb236\\ \mkbibemph{ } \par - “role of multi-task learning” (“antonio-mastropaolo-phd-thesis.pdf”, p. 29) \#5fb236\\ \mkbibemph{ } \par - “able 3.11. Training time (hours) for the trained T5 models” (“antonio-mastropaolo-phd-thesis.pdf”, p. 41) \#a28ae5\\ \mkbibemph{ } \par - “that” (“antonio-mastropaolo-phd-thesis.pdf”, p. 45) \#ff6666\\ \mkbibemph{than } \par - “(ii) did not need the additional ∼22 hours of computation required by the pre-training.” (“antonio-mastropaolo-phd-thesis.pdf”, p. 45) \#ffd400\\ \mkbibemph{How would you organize a tradeoff analysis? } \par - “Copilot is able to automatically synthesize entire functions just starting from their signature and natural language descriptions.” (“antonio-mastropaolo-phd-thesis.pdf”, p. 47) \#5fb236\\ \mkbibemph{ } \par - “This means that two developers providing different natural language descriptions for the same function they would like to automatically generate could receive two different recommendations.” (“antonio-mastropaolo-phd-thesis.pdf”, p. 47) \#5fb236\\ \mkbibemph{ } \par - “semantically equivalent natural language descriptions would pose questions on the robustness and usability of DL-based code recommenders.” (“antonio-mastropaolo-phd-thesis.pdf”, p. 47) \#a28ae5\\ \mkbibemph{ } \par - “we generated different paraphrases” (“antonio-mastropaolo-phd-thesis.pdf”, p. 48) \#ffd400\\ \mkbibemph{What are the criteria that have been used to this end? } \par - “four of the authors,” (“antonio-mastropaolo-phd-thesis.pdf”, p. 48) \#ff6666\\ \mkbibemph{ } \par - “automated paraphrasing techniques” (“antonio-mastropaolo-phd-thesis.pdf”, p. 48) \#5fb236\\ \mkbibemph{ } \par - “1,401 repositories” (“antonio-mastropaolo-phd-thesis.pdf”, p. 49) \#5fb236\\ \mkbibemph{ } \par - “projects to use a testing framework and to be compilable” (“antonio-mastropaolo-phd-thesis.pdf”, p. 49) \#5fb236\\ \mkbibemph{ } \par - “This gives us confidence that the related test cases exercise an acceptable number of behaviors and, therefore, could allow to spot cases in which different generated functions for semantically-equivalent descriptions actually behave differently.” (“antonio-mastropaolo-phd-thesis.pdf”, p. 49) \#a28ae5\\ \mkbibemph{ } \par - “PEGASUS” (“antonio-mastropaolo-phd-thesis.pdf”, p. 51) \#5fb236\\ \mkbibemph{ } \par - “Trans- lation Pivoting (TP)” (“antonio-mastropaolo-phd-thesis.pdf”, p. 51) \#5fb236\\ \mkbibemph{ } \par - “TP failed to generate a valid paraphrase (i.e., a sentence different from the original one) in 100 cases (out of 892), while this only happened once with PEGASUS.” (“antonio-mastropaolo-phd-thesis.pdf”, p. 51) \#5fb236\\ \mkbibemph{ } \par - “For this reason, we developed a toolchain able to automatically invoke Copilot on the subject instances: We exploit the AppleScript language to automate this task on a MacBook Pro, simulating the developer’s interaction with Visual Studio Code (vscode).” (“antonio-mastropaolo-phd-thesis.pdf”, p. 52) \#a28ae5\\ \mkbibemph{ } \par - “preceding and following” (“antonio-mastropaolo-phd-thesis.pdf”, p. 52) \#ffd400\\ \mkbibemph{Have you considered to variate also the size of the code preceding and following the emptied method? } \par - “TLev representing the token-level Levenshtein distance between the two descriptions” (“antonio-mastropaolo-phd-thesis.pdf”, p. 53) \#ffd400\\ \mkbibemph{You wanted to do an analysis based on semantically different or similar sentences.... Why have you selected Levenshtein, which is a "syntactical" metric } \par - “CodeBLEU measures how similar two methods are” (“antonio-mastropaolo-phd-thesis.pdf”, p. 53) \#5fb236\\ \mkbibemph{ } \par - “State-of-the-art paraphrasing techniques” (“antonio-mastropaolo-phd-thesis.pdf”, p. 55) \#5fb236\\ \mkbibemph{ } \par - “high percentage of Java methods (∼73\% for the original and ∼72\% for the paraphrased description) for which Copilot was not able to synthesize a method passing the related unit tests.” (“antonio-mastropaolo-phd-thesis.pdf”, p. 55) \#a28ae5\\ \mkbibemph{ } \par - “higher similarity between the compared methods” (“antonio-mastropaolo-phd-thesis.pdf”, p. 55) \#a28ae5\\ \mkbibemph{ } \par - “Different (but semantically equivalent) natural language descriptions of the same method are likely to result in different code recommendations generated by DL-based code generation models.” (“antonio-mastropaolo-phd-thesis.pdf”, p. 61) \#a28ae5\\ \mkbibemph{ } \par - “semantically equivalent natural language descriptions” (“antonio-mastropaolo-phd-thesis.pdf”, p. 62) \#ffd400\\ \mkbibemph{ } \par - “∼46\% of cases semantically equivalent but different method descrip- tions result in different code recommendations” (“antonio-mastropaolo-phd-thesis.pdf”, p. 62) \#a28ae5\\ \mkbibemph{ } \par - “Our results highlight the importance of providing a proper code description when asking DL-based recommenders to synthesize code.” (“antonio-mastropaolo-phd-thesis.pdf”, p. 62) \#ffd400\\ \mkbibemph{A dsl for Prompt Engineering??? } \par - “LANCE (Log stAtemeNt reCommEnder)” (“antonio-mastropaolo-phd-thesis.pdf”, p. 65) \#a28ae5\\ \mkbibemph{ } \par - “LANCE must generate a complete log statement and inject it in the proper location.” (“antonio-mastropaolo-phd-thesis.pdf”, p. 65) \#5fb236\\ \mkbibemph{ } \par - “assumes that only one log statement is needed in a Java method provided as input.” (“antonio-mastropaolo-phd-thesis.pdf”, p. 65) \#5fb236\\ \mkbibemph{ } \par - “LANCE cannot assess whether log statements are needed at all.” (“antonio-mastropaolo-phd-thesis.pdf”, p. 65) \#5fb236\\ \mkbibemph{ } \par - “1 to n log statements in a given method” (“antonio-mastropaolo-phd-thesis.pdf”, p. 70) \#a28ae5\\ \mkbibemph{ } \par - “We used srcML [src] to extracted all Java methods in the selected projects.” (“antonio-mastropaolo-phd-thesis.pdf”, p. 71) \#5fb236\\ \mkbibemph{ } \par - “randomly masking 15\% of the tokens composing a training instance (i.e., a Java method) asking the model to predict them” (“antonio-mastropaolo-phd-thesis.pdf”, p. 72) \#ffd400\\ \mkbibemph{This partially answer the comment below. However, the percentage of the masking can be a dimension to investigate, isn't it? } \par - “Figure 6.1. Example of Pre-training instance” (“antonio-mastropaolo-phd-thesis.pdf”, p. 72) \#ffd400\\ \mkbibemph{This comment applies to different works, where you have used such a pre-training technique. How have you selected the number of MASKS, and where to locate them in the training data? } \par - “Jaccard similarity” (“antonio-mastropaolo-phd-thesis.pdf”, p. 74) \#ffd400\\ \mkbibemph{This is a syntactic similarity. How to cover semantically similar log messages that are syntactically different? } \par - “For this reason, we also compute the following four metrics used in Natural Language Processing (NLP) for the assessment of automatically generated t” (“antonio-mastropaolo-phd-thesis.pdf”, p. 80) \#ffd400\\ \mkbibemph{ } \par - “A New Metric and an Empirical Characterization” (“antonio-mastropaolo-phd-thesis.pdf”, p. 94) \#5fb236\\ \mkbibemph{ } \par - “ct as a proxy for the quality of au- tomatically generated text (e.g., a translation) by comparing it with a reference (expected) text: The higher the words’ overlap between the generated and the reference text, the higher the assessed quality.” (“antonio-mastropaolo-phd-thesis.pdf”, p. 135) \#5fb236\\ \mkbibemph{ } \par - “no guarantee that the reference text is of high quality” (“antonio-mastropaolo-phd-thesis.pdf”, p. 135) \#a28ae5\\ \mkbibemph{ } \par - “Metrics based on word overlap penalize generated summaries for being different but semantically equivalent to the reference one, thus again not being good proxies for the summary quality.” (“antonio-mastropaolo-phd-thesis.pdf”, p. 135) \#a28ae5\\ \mkbibemph{ } \par - “a high similarity to a low-quality reference summary may provide a misleading good assessment of a generated summary” (“antonio-mastropaolo-phd-thesis.pdf”, p. 136) \#5fb236\\ \mkbibemph{ } \par - “SIDE” (“antonio-mastropaolo-phd-thesis.pdf”, p. 136) \#a28ae5\\ \mkbibemph{ } \par - “viability of utilizing pre-trained deep learning (DL) models for code-related tasks, with a specific focus on tasks requiring the manipulation of bi-modal data, such as code summarization and the generation of log statements” (“antonio-mastropaolo-phd-thesis.pdf”, p. 157) \#a28ae5\\ \mkbibemph{ } \par - “Java as the primary programming language” (“antonio-mastropaolo-phd-thesis.pdf”, p. 157) \#a28ae5\\ \mkbibemph{ } \par - “driving 112 gasoline-powered vehicles a year” (“antonio-mastropaolo-phd-thesis.pdf”, p. 159) \#a28ae5\\ \mkbibemph{ } \par - “Despite the substantial environmental impact associated with training these LLMs, the inference process (i.e., prompting) has been projected to become the primary contributors to carbon emissions, as indicated by recent research” (“antonio-mastropaolo-phd-thesis.pdf”, p. 159) \#a28ae5\\ \mkbibemph{ } \par - “This leaves us with the question of how software engineering will change in the near future, and how developers will need to adapt in this context. These are questions that will inform our future research.” (“antonio-mastropaolo-phd-thesis.pdf”, p. 159) \#e56eee\\ \mkbibemph{ }} } @article{AnvikM11, title = {Reducing the Effort of Bug Report Triage: {{Recommenders}} for Development-Oriented Decisions}, author = {Anvik, John and Murphy, Gail C.}, date = {2011}, journaltitle = {ACM Trans. Softw. Eng. Methodol.}, volume = {20}, number = {3}, pages = {10:1--10:35}, note = {TL;DR \par A machine learning approach to create recommenders that assist with a variety of decisions aimed at streamlining the development process and it is shown that recommenders for which developer should fix a bug can be quickly configured with this approach and that the configured recommenders are within 15\% precision of hand-tuned developer recommenders.} } @article{anwarMachineLearningData, title = {Machine {{Learning}}/{{Data Science Interview Cheat}} Sheets}, author = {Anwar, Aqeel}, langid = {english} } @misc{apache_log4j_2020, title = {Log4j – {{Apache Log4j}} 2}, author = {{Apache}}, date = {2020-06}, nourl = {https://logging.apache.org/log4j/2.x/} } @article{app10010012, title = {Low-Code as Enabler of Digital Transformation in Manufacturing Industry}, author = {Sanchis, Raquel and García-Perales, Óscar and Fraile, Francisco and Poler, Raul}, date = {2020}, journaltitle = {Appl. Sci.}, volume = {10}, number = {12}, issn = {2076-3417}, doi = {10.3390/app10010012}, abstract = {Currently, enterprises have to make quick and resilient responses to changing market requirements. In light of this, low-code development platforms provide the technology mechanisms to facilitate and automate the development of software applications to support current enterprise needs and promote digital transformation. Based on a theory-building research methodology through the literature and other information sources review, the main contribution of this paper is the current characterisation of the emerging low-code domain following the foundations of the computer-aided software engineering field. A context analysis, focused on the current status of research related to the low-code development platforms, is performed. Moreover, benchmarking among the existing low-code development platforms addressed to manufacturing industry is analysed to identify the current lacking features. As an illustrative example of the emerging low-code paradigm and respond to the identified uncovered features, the virtual factory open operating system (vf-OS) platform is described as an open multi-sided low-code framework able to manage the overall network of a collaborative manufacturing and logistics environment that enables humans, applications, and Internet of Things (IoT) devices to seamlessly communicate and interoperate in the interconnected environment, promoting resilient digital transformation.}, issue = {1}, note = {TL;DR \par The main contribution of this paper is the current characterisation of the emerging low-code domain following the foundations of the computer-aided software engineering field.} } @article{ApplicationofAIandMLinIoTpdf, entrysubtype = {newspaper}, title = {{{ApplicationofAIandMLinIoT}}.Pdf}, keywords = {internet of things}, note = {\textbf{Green Annotations (17/12/2020, 14:33:13)} \par "IoT with AI and ML in industries has the potential to transform their outputs and aid them to yield better results" (\href{zotero://open-pdf/library/items/RXAEHVFX?page=13}{ :13}) \par "he industries may also use connected tools and machinery that eliminate the chances of errors made while setting the parameters manually." (\href{zotero://open-pdf/library/items/RXAEHVFX?page=13}{ :13}) \par "In the future, it will be nearly impossible to find IoT systems that don't utilize AI services" (\href{zotero://open-pdf/library/items/RXAEHVFX?page=18}{ :18}) \par "the future of IoT is AI" (\href{zotero://open-pdf/library/items/RXAEHVFX?page=18}{ :18}) \par "he main purpose is to make life easier by working smarter and not harder." (\href{zotero://open-pdf/library/items/RXAEHVFX?page=18}{ :18}) \par "t is expected that in the future, we will have devices that will make our lives more convenient than it is today by performing our tasks timely, even before we think of doing it, and give us real-time data and insights about every aspect of our livesbe it personal, professional or social." (\href{zotero://open-pdf/library/items/RXAEHVFX?page=18}{ :18}) \par "automation which will bring out the true essence of IoT" (\href{zotero://open-pdf/library/items/RXAEHVFX?page=18}{ :18})} } @article{aranegaUsingFeatureModel2012, title = {Using {{Feature Model}} to {{Build Model Transformation Chains}}}, author = {Aranega, Vincent and Etien, Anne and Mosser, Sebastien}, date = {2012}, journaltitle = {Model Driven Eng. Lang. Syst.}, volume = {7590}, pages = {562--578}, doi = {10.1007/978-3-642-33666-9_36} } @inproceedings{arcainiModelingAnalyzingMAPEK2015, title = {Modeling and {{Analyzing MAPE-K Feedback Loops}} for {{Self-Adaptation}}}, booktitle = {2015 {{IEEEACM}} 10th {{Int}}. {{Symp}}. {{Softw}}. {{Eng}}. {{Adapt}}. {{Self-Manag}}. {{Syst}}.}, author = {Arcaini, Paolo and Riccobene, Elvinia and Scandurra, Patrizia}, date = {2015-05}, pages = {13--23}, publisher = {IEEE}, location = {Florence, Italy}, doi = {10.1109/SEAMS.2015.10}, abstract = {The MAPE-K (Monitor-Analyze-Plan-Execute over a shared Knowledge) feedback loop is the most influential reference control model for autonomic and self-adaptive systems. This paper presents a conceptual and methodological framework for formal modeling, validating, and verifying distributed self-adaptive systems. We show how MAPE-K loops for selfadaptation can be naturally specified in an abstract stateful language like Abstract State Machines. In particular, we exploit the concept of multi-agent Abstract State Machines to specify decentralized adaptation control by using MAPE computations. We support techniques for validating and verifying adaptation scenarios, and getting feedback of the correctness of the adaptation logic as implemented by the MAPE-K loops. In particular, a verification technique based on meta-properties is proposed to allow discovering unwanted interferences between MAPE-K loops at the early stages of the system design. As a proof-ofconcepts, we model and analyze a traffic monitoring system.}, eventtitle = {2015 {{IEEE}}/{{ACM}} 10th {{International Symposium}} on {{Software Engineering}} for {{Adaptive}} and {{Self-Managing Systems}} ({{SEAMS}})}, isbn = {978-0-7695-5567-6}, langid = {english} } @online{ArchivaDocumentationInstalling, title = {Archiva {{Documentation}} - {{Installing Apache Archiva}}}, url = {http://archiva.apache.org/docs/2.2.0/adminguide/installing.html}, urldate = {2015-04-16} } @article{Areferencearchitecturefortheinternetofthings, title = {A-Reference-Architecture-for-the-Internet-of-Things}, keywords = {iot,reference architecture,se4as} } @article{arendtEMFMetricsSpecification, title = {{{EMF Metrics}}: {{Specification}} and {{Calculation}} of {{Model Metrics}} within the {{Eclipse Modeling Framework}}}, author = {Arendt, Thorsten and Stepien, Pawel and Taentzer, Gabriele}, note = {TL;DR \par EMF Metrics is presented, a prototype Eclipse plug-in providing specification and calculation of metrics wrt.} } @article{arendtIntegrationSmellsRefactorings2012, title = {Integration of Smells and Refactorings within the {{Eclipse}} Modeling Framework}, author = {Arendt, Thorsten and Taentzer, Gabriele}, date = {2012}, journaltitle = {Proc. Fifth Workshop Refactoring Tools - WRT 12}, pages = {8--15}, doi = {10.1145/2328876.2328878} } @article{arendtToolEnvironmentQuality2012, title = {A Tool Environment for Quality Assurance Based on the {{Eclipse Modeling Framework}}}, author = {Arendt, Thorsten and Taentzer, Gabriele}, date = {2012}, journaltitle = {Autom. Softw. Eng.}, volume = {20}, number = {2}, pages = {141--184}, doi = {10.1007/s10515-012-0114-7}, note = {TL;DR \par This paper presents a tool environment conveniently supporting the proposed model quality assurance process, and presents tools support metrics reporting, smell detection, and refactoring for models being based on the Eclipse Modeling Framework.} } @misc{AreWeThere, title = {Are We There yet? {{Filling}} the Gap between Binary Similar Analysis and Binary Software Composition Analysis}, langid = {english} } @book{arhippainenUseIntegrationThridparty2003, title = {Use and Integration of Thrid-Party Components in Software Development}, author = {Arhippainen, Leena}, date = {2003}, series = {{{VTT}} Publications}, number = {489}, publisher = {VTT}, location = {Espoo}, isbn = {978-951-38-6032-5 978-951-38-6033-2}, langid = {english}, pagetotal = {68}, keywords = {software engineering}, note = {Internet-Adresse: URL: http://www.inf.vtt.fi/pdf} } @inproceedings{ariasOrccadRobotController2010, title = {Orccad, Robot Controller Model and Its Support Using Eclipse Modeling Tools}, booktitle = {5th {{Natl}}. {{Conf}}. {{Control Archit}}. {{Robots}}}, author = {Arias, Soraya and Boudin, Florine and Pissard-Gibollet, Roger and Simon, Daniel}, date = {2010}, url = {https://hal.archives-ouvertes.fr/inria-00482559/}, urldate = {2016-08-21} } @article{arkinModelDrivenTransformationsMapping2013, title = {Model-{{Driven Transformations}} for {{Mapping Parallel Algorithms}} on {{Parallel Computing Platforms}}.}, author = {Arkin, Ethem and Tekinerdogan, Bedir}, date = {2013}, journaltitle = {MDHPCL MoDELS}, volume = {2013}, pages = {63--72}, url = {http://ceur-ws.org/Vol-1118/08-paper.pdf}, urldate = {2017-02-23} } @article{arlot2010, ids = {arlot2010survey}, title = {A Survey of Cross-Validation Procedures for Model Selection}, author = {Arlot, Sylvain and Celisse, Alain}, date = {2010}, journaltitle = {Stat. Surv}, volume = {4}, pages = {40--79}, publisher = {{The American Statistical Association, the Bernoulli Society, the Institute of Mathematical Statistics, and the Statistical Society of Canada}}, added-at = {2017-04-15T09:32:51.000+0200}, biburl = {https://www.bibsonomy.org/bibtex/295dfb73fc5adfb8b1c5dba6435132f15/becker}, fjournal = {Statistics Surveys}, interhash = {ce81fa08863c054ec939cc798387b0b8}, intrahash = {95dfb73fc5adfb8b1c5dba6435132f15}, nodoi = {10.1214/09-SS054}, keywords = {cross validation inthesis diss citedby:scholar:count:1216 citedby:scholar:timestamp:2017-4-15}, note = {TL;DR \par This survey intends to relate the model selection performances of cross-validation procedures to the most recent advances of model selection theory, with a particular emphasis on distinguishing empirical statements from rigorous theoretical results.}, timestamp = {2017-04-15T09:32:51.000+0200} } @article{article, title = {Model Differences in the Eclipse Modelling Framework}, author = {Brun, Cédric and Pierantonio, Alfonso}, date = {2008-01}, journaltitle = {UPGRADE Eur. J. Inform. Prof.}, volume = {9}, pages = {29--34}, keywords = {/unread,⛔ No INSPIRE recid found} } @online{ArtificialintelligenceMachinelearningLlm, title = {\#artificialintelligence \#machinelearning \#llm \#deeplearning | {{Generative AI}}}, url = {https://www.linkedin.com/posts/genai-works_artificialintelligence-machinelearning-activity-7320752193735684098-o2GY}, urldate = {2025-04-23}, abstract = {🔥Ever Wondered What’s Really Going On Inside LLMs? Let’s decode it: 1. Tokenization \& vector embeddings Every input is split into tokens and mapped to high-dimensional vectors. Words with similar context cluster—but the real nuance? Subwords let the model learn morphology, not just semantics. That’s why it handles “unbelievability” as gracefully as “cat.” 2. Self-attention (the real star) Each token attends to every other token—assigning weight dynamically across layers. This is where \"bank\" in river bank isn’t mistaken for Goldman Sachs. It’s why context matters more than keyword matching. 3. Feed-forward layers After attention -\> Dense layers apply learned weights, refining representations. No recurrence. Just deep stacks of computation—pattern extraction at scale. And it’s identical across tokens, which means massive parallelization. 4. Layer stacking (aka depth) LLMs don\&\#39;t get smart in one pass. They iterate - 96+ transformer blocks deep - each one reprocessing the token stream to extract more abstract representations. Think of it as meaning refinement, not memory recall. 5. Prediction through sampling Final vectors hit a softmax: a probability distribution over the vocabulary. But it doesn’t always pick the top word. Temperature, top-k, and nucleus sampling shape how creative or deterministic the output is. This isn’t theory - It’s the architecture behind ChatGPT, Claude, Gemini, Mistral, and more. What part of the transformer architecture do you think still gets underestimated in real-world AI builds? 👇 Contact us if you’ve created an AI tool to be featured on our community of 10M+ -\> https://lnkd.in/dj8iSyJ8 \#artificialintelligence \#machinelearning \#llm \#deeplearning} } @article{Aspenberg2013245, title = {Robust Optimization of Front Members in a Full Frontal Car Impact}, author = {Aspenberg, D. and Jergeus, J. and Nilsson, L.}, date = {2013}, journaltitle = {Eng. Optim.}, volume = {45}, number = {3}, pages = {245--264}, issn = {0305215X}, doi = {10.1080/0305215X.2012.669380}, abbrev_source_title = {Eng Optim}, affiliation = {Division of Solid Mechanics, Linköping University, SE-581 83, Linköping, Sweden; Volvo Cars Safety Centre, SE-405 31, Göteborg, Sweden}, coden = {EGOPA}, correspondence_address1 = {Aspenberg, D.; Division of Solid Mechanics, , SE-581 83, Linköping, Sweden; email: david.aspenberg@liu.se}, document_type = {Article}, langid = {english}, source = {Scopus}, note = {cited By 14} } @incollection{assmannReferenceArchitectureRoadmap2014, title = {A Reference Architecture and Roadmap for {{Models}}@ Run. Time Systems}, booktitle = {Models@ Run. Time}, author = {A\textbackslash s smann, Uwe and Götz, Sebastian and Jézéquel, Jean-Marc and Morin, Brice and Trapp, Mario}, date = {2014}, pages = {1--18}, publisher = {Springer}, url = {http://link.springer.com/chapter/10.1007/978-3-319-08915-7_1}, urldate = {2016-08-21} } @inproceedings{atkinson2016demystifying, title = {Demystifying Ontological Classification in Language Engineering}, booktitle = {Model. {{Found}}. {{Appl}}. 12th {{Eur}}. {{Conf}}. {{ECMFA}} 2016 {{Held Part STAF}} 2016 {{Vienna Austria July}} 6-7 2016 {{Proc}}. 12}, author = {Atkinson, Colin and Kühne, Thomas}, date = {2016}, pages = {83--100}, publisher = {Springer}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found} } @article{AtkinsonK03a, title = {Model-Driven Development: A Metamodeling Foundation}, author = {Atkinson, Colin and Kühne, Thomas}, date = {2003}, journaltitle = {IEEE Softw,}, volume = {20}, number = {5}, pages = {36--41}, doi = {10.1109/MS.2003.1231149}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Mon, 15 Feb 2021 14:16:47 +0100} } @article{atkinsonUnifyingApproachConnections, title = {A {{Unifying Approach}} to {{Connections}} for {{Multi-Level Modeling}}}, author = {Atkinson, Colin and Gerbig, Ralph and Kühne, Thomas}, url = {http://homepages.ecs.vuw.ac.nz/~tk/publications/papers/deep-connections.pdf}, urldate = {2015-09-24} } @article{ATL, title = {{{ATL}}: A Model Transformation Tool}, author = {Jouault, Frédéric and Allilaire, Freddy and Bézivin, Jean and Kurtev, Ivan}, date = {2008}, journaltitle = {Sci. Comput. Program.}, volume = {72}, number = {1--2}, pages = {31--39}, issn = {0167-6423}, langid = {english}, x-doi = {DOI: 10.1016/j.scico.2007.08.002}, keywords = {/unread,⛔ No INSPIRE recid found} } @article{Atouani202155, title = {Artifact and {{Reference Models}} for {{Generative Machine Learning Frameworks}} and {{Build Systems}}}, author = {Atouani, Abdallah and Kirchhof, Jörg Christian and Kusmenko, Evgeny and Rumpe, Bernhard}, date = {2021}, series = {{{GPCE}} 2021 - {{Proceedings}} of the 20th {{ACM SIGPLAN International Conference}} on {{Generative Programming}}: {{Concepts}} and {{Experiences}}, Co-Located with {{SPLASH}} 2021}, pages = {55--68}, doi = {10.1145/3486609.3487199}, abbrev_source_title = {GPCE - Proc. ACM SIGPLAN Int. Conf. Gener. Program.: Concepts Exp., co-located SPLASH}, abstract = {Machine learning is a discipline which has become ubiquitous in the last few years. While the research of machine learning algorithms is very active and continues to reveal astonishing possibilities on a regular basis, the wide usage of these algorithms is shifting the research focus to the integration, maintenance, and evolution of AI-driven systems. Although there is a variety of machine learning frameworks on the market, there is little support for process automation and DevOps in machine learning-driven projects. In this paper, we discuss how metamodels can support the development of deep learning frameworks and help deal with the steadily increasing variety of learning algorithms. In particular, we present a deep learning-oriented artifact model which serves as a foundation for build automation and data management in iterative, machine learning-driven development processes. Furthermore, we show how schema and reference models can be used to structure and maintain a versatile deep learning framework. Feasibility is demonstrated on several state-of-the-art examples from the domains of image and natural language processing as well as decision making and autonomous driving.}, affiliation = {Rwth Aachen University, Germany}, document_type = {Conference Paper}, langid = {english}, source = {Scopus}, note = {cited By 0 \par TL;DR \par A deep learning-oriented artifact model is presented which serves as a foundation for build automation and data management in iterative, machine learning-driven development processes and it is shown how schema and reference models can be used to structure and maintain a versatile deep learning framework.} } @article{atzeniDataModelDescriptions2011, title = {Data Model Descriptions and Translation Signatures in a Multi-Model Framework}, author = {Atzeni, Paolo and Gianforme, Giorgio and Cappellari, Paolo}, date = {2011-12}, journaltitle = {Ann. Math. Artif. Intell.}, volume = {63}, number = {3--4}, pages = {287--315}, issn = {1012-2443, 1573-7470}, doi = {10.1007/s10472-012-9277-y}, langid = {english}, note = {TL;DR \par An approach where translations are specified as Datalog-like programs is considered, which shows that the target model of a translation can be completely characterized given the description of the source model and the signatures of the rules.} } @article{atzeniModelindependentSchemaTranslation2008, title = {Model-Independent Schema Translation}, author = {Atzeni, Paolo and Cappellari, Paolo and Torlone, Riccardo and Bernstein, Philip A. and Gianforme, Giorgio}, date = {2008-11}, journaltitle = {VLDB J.}, volume = {17}, number = {6}, pages = {1347--1370}, issn = {1066-8888, 0949-877X}, doi = {10.1007/s00778-008-0105-2}, langid = {english}, note = {TL;DR \par A proposal for the implementation of the model management operator ModelGen, which translates schemas from one model to another, for example from object-oriented to SQL or from SQL to XML schema descriptions, is discussed.} } @incollection{atzeniModelsNoSQLDatabases2015, title = {Models for {{NoSQL Databases}}: {{A Contradiction}}?}, shorttitle = {Models for {{NoSQL Databases}}}, booktitle = {Advances in {{Conceptual Modeling}}}, author = {Atzeni, Paolo}, editor = {Jeusfeld, Manfred A. and Karlapalem, Kamalakar}, date = {2015}, volume = {9382}, pages = {133--133}, publisher = {Springer International Publishing}, location = {Cham}, doi = {10.1007/978-3-319-25747-1_13}, isbn = {978-3-319-25746-4 978-3-319-25747-1}, langid = {english}, note = {TL;DR \par This talk will discuss how traditional notions related to modeling can be useful in this context as well, both in the search for standardization and uniform access and in the support to generic approaches to logical and physical design.} } @article{atzeniRuntimeApproachModelgeneric2012, title = {A Runtime Approach to Model-Generic Translation of Schema and Data}, author = {Atzeni, Paolo and Bellomarini, Luigi and Bugiotti, Francesca and Celli, Fabrizio and Gianforme, Giorgio}, date = {2012-05}, journaltitle = {Inf. Syst.}, volume = {37}, number = {3}, pages = {269--287}, issn = {03064379}, doi = {10.1016/j.is.2011.11.003}, langid = {english} } @article{atzeniUniformAccessNoSQL2014, title = {Uniform Access to {{NoSQL}} Systems}, author = {Atzeni, Paolo and Bugiotti, Francesca and Rossi, Luca}, date = {2014-07}, journaltitle = {Inf. Syst.}, volume = {43}, pages = {117--133}, issn = {03064379}, doi = {10.1016/j.is.2013.05.002}, langid = {english} } @incollection{atzeniUniversalMetamodelIts2009, title = {A {{Universal Metamodel}} and {{Its Dictionary}}}, booktitle = {Transactions on {{Large-Scale Data-}} and {{Knowledge-Centered Systems I}}}, author = {Atzeni, Paolo and Gianforme, Giorgio and Cappellari, Paolo}, editor = {Hameurlain, Abdelkader and Küng, Josef and Wagner, Roland}, date = {2009}, volume = {5740}, pages = {38--62}, publisher = {Springer Berlin Heidelberg}, location = {Berlin, Heidelberg}, doi = {10.1007/978-3-642-03722-1_2}, abstract = {We discuss a universal metamodel aimed at the representation of schemas in a way that is at the same time model-independent (in the sense that it allows for a uniform representation of different data models) and model-aware (in the sense that it is possible to say to whether a schema is allowed for a data model). This metamodel can be the basis for the definition of a complete model-management system. Here we illustrate the details of the metamodel and the structure of a dictionary for its representation. Exemplifications of a concrete use of the dictionary are provided, by means of the representations of the main data models, such as relational, object-relational or XSD-based. Moreover, we demonstrate how set operators can be redefined with respect to our dictionary and easily applied on it. Finally, we show how such a dictionary can be exploited to automatically produce detailed descriptions of schema and data models, in a textual (i.e. XML) or visual (i.e. UML class diagram) way.}, isbn = {978-3-642-03721-4 978-3-642-03722-1}, langid = {english}, note = {TL;DR \par A universal metamodel aimed at the representation of schemas in a way that is at the same time model-independent and model-aware is discussed, which can be the basis for the definition of a complete model-management system.} } @article{atzoriInternetThingsSurvey2010, title = {The {{Internet}} of {{Things}}: {{A}} Survey}, shorttitle = {The {{Internet}} of {{Things}}}, author = {Atzori, Luigi and Iera, Antonio and Morabito, Giacomo}, date = {2010-10}, journaltitle = {Computer Networks}, volume = {54}, number = {15}, pages = {2787--2805}, issn = {13891286}, doi = {10.1016/j.comnet.2010.05.010}, abstract = {This paper addresses the Internet of Things. Main enabling factor of this promising paradigm is the integration of several technologies and communications solutions. Identification and tracking technologies, wired and wireless sensor and actuator networks, enhanced communication protocols (shared with the Next Generation Internet), and distributed intelligence for smart objects are just the most relevant. As one can easily imagine, any serious contribution to the advance of the Internet of Things must necessarily be the result of synergetic activities conducted in different fields of knowledge, such as telecommunications, informatics, electronics and social science. In such a complex scenario, this survey is directed to those who want to approach this complex discipline and contribute to its development. Different visions of this Internet of Things paradigm are reported and enabling technologies reviewed. What emerges is that still major issues shall be faced by the research community. The most relevant among them are addressed in details.}, langid = {english}, keywords = {relevant}, note = {TL;DR \par The definitions, architecture, fundamental technologies, and applications of IoT are systematically reviewed and the major challenges which need addressing by the research community and corresponding potential solutions are investigated.} } @article{augusteijnNeuralNetworkClassification2002, title = {Neural Network Classification and Novelty Detection}, author = {Augusteijn, M. F. and {B. A. Folkert}}, date = {2002}, journaltitle = {Int. J. Remote Sens.}, volume = {23}, number = {14}, pages = {2891--2902}, publisher = {Taylor \& Francis}, nodoi = {10.1080/01431160110055804} } @inproceedings{authorExtensionsScalabilityExperiments2024, title = {Extensions and {{Scalability Experiments}} of a {{Generic Model-Driven Architecture}} for {{Variability Model Reasoning}}}, author = {Author, Anonymous}, date = {2024}, abstract = {Until recently, the state-of-the-art of Software Product Line (SPL) configuration and verification automation consisted of a collection of ad-hoc approaches tightly coupling a single input Variability Modeling Language (VML) with a single constraint solver. To remedy this situation, a novel generic Model-Driven Architecture (MDA) was then proposed that enables using a variety of VMLs and solvers. The key ideas of this proposal were (a) the use of a standard logical language (CLIF) as a pivot between VMLs and solvers, and (b) the use of a standard data exchange format (JSON) to explicilty and declaratively specify the abstract syntax and semantics of the VMLs to be used in an SPL engineering project and the automated reasoning task to be performed by the solvers.}, langid = {english}, keywords = {LOGSEQ}, note = {\section{Annotazioni\\ (13/5/2024, 11:06:30)} \par - “xtensions and Scalability Experiments of a Generic Model-Driven Architecture for Variability Model Reasoning” (Author, 2024, p. 1) \#ffd400\\ \mkbibemph{What are the scientific challenges you addressed with this paper? It seems you performed engineering work to add the support for textual specification of variability models and for additional solvers. I appreciated the development effort even thought the research challenges are not clearly discussed.} \par - “ABSTRACT” (Author, 2024, p. 1) \#f0ff00\\ \mkbibemph{The abstract needs to be revised. Its not effecting in describing what's the goal of the paper.} \par - “situation,” (Author, 2024, p. 1) \#f0ff00\\ \mkbibemph{What situation? What's the problem?} \par - “this initial proposal in three key ways: (1) we add the ability to reason on textual” (Author, 2024, p. 1) \#f0ff00\\ \mkbibemph{What's the problem and what are the limitations of exiting technologies? That's not clear.} \par - “there is no standard language to express variability models.” (Author, 2024, p. 1) \#f0ff00\\ \mkbibemph{What about feature models and corresponding tools like featureide} \par - “These approaches do not fulfill the need for Domain-Specific VMLs (VML) beyond EFMs which is well attested in the literature. In particular, the use of DSVMLs in SPLE projects has both been argued for [17] and their use in combination with or as outright replacements for traditional FMs was explored in [67]. Unfortunately, tooling for a DSVML implies constructing, as is the case with the approaches cited above, an ad-hoc model transformation and analysis pipeline that will tightly couple the DSVML to the solver.” (Author, 2024, p. 2) \#f0ff00\\ \mkbibemph{What's the problem?} \par - “implements the MDA” (Author, 2024, p. 2) \#f0ff00\\ \mkbibemph{What does it mean?} \par - “make the MDA demonstrably more versatile both in terms of the input VMLs and the output solver input languages.” (Author, 2024, p. 2) \#f0ff00\\ \mkbibemph{More versatile with respect to what?} \par - “A Proposal for a Generic MDA for Variability Model Reasoning” (Author, 2024, p. 2) \#ffd400\\ \mkbibemph{It's hard to follow without a concrete and explanatory example. The paper is not self-contained. There are many references to underpinning technologies and background concepts that make the paper difficult to follow.} \par - “The PLEIADES MDA uses CLIF as a pivot language between various VMLs and various solver input languages handling 𝑁 VMLs and 𝑀 solver input languages with only 𝑁 + 𝑀 transformations rather than 𝑁 × 𝑀 if variability models were directly transformed into formal knowledge bases as done in almost all previous tools reasoning on variability models.” (Author, 2024, p. 2) \#f0ff00\\ \mkbibemph{Too long} \par - “hus superior or equal to that of the languages accepted as input by the four main solver paradigms used to reason on variability models in previous work” (Author, 2024, p. 2) \#ffd400\\ \mkbibemph{How can you claim this in general?} \par - “The gray elements with dotted outlines correspond to the extensions necessary to cover the MDA’s limitations,” (Author, 2024, p. 3) \#ffd400\\ \mkbibemph{We are at sec 2.1 and I'm still missing a clear discussion of the "MDA limitations" mentioned by the authors.} \par - “MDA” (Author, 2024, p. 3) \#ffd400\\ \mkbibemph{MDA is precise technology consisting of different and precise OMG standard. In this paper, MDA is instead used to refer to some previous work presented by the authors elsewhere and that is extended by this work. The use of the term MDA is thus confusing in the paper.} \par - “The MDA is designed as a REST Web Service [56] where the model in a given VML is accompanied by two declarative specifications. The first encodes the semantics of the VML in the form of a mapping from the VML abstract syntactic elements to CLIF sentence elements.” (Author, 2024, p. 3) \#ffd400\\ \mkbibemph{The paper is full of sentences like this that refers terms and concepts that are not properly introduced in the paper. The reader is supposed to know them.} \par - “Textual VMLs” (Author, 2024, p. 4) \#5fb236 \par - “larger set of solver families” (Author, 2024, p. 4) \#ffd400\\ \mkbibemph{larger with respect to what?} \par - “calability benchmarks” (Author, 2024, p. 4) \#5fb236 \par - “measure the “cost”” (Author, 2024, p. 5) \#ffd400\\ \mkbibemph{What is the used metric?} \par - “transforms the textual model directly” (Author, 2024, p. 5) \#5fb236 \par - “The Universal Variability Language.” (Author, 2024, p. 5) \#a28ae5 \par - “Common Variability Language (CVL)” (Author, 2024, p. 5) \#5fb236 \par - “Variability Exchange Language (VEL)” (Author, 2024, p. 5) \#5fb236 \par - “Implementing the Extension.” (Author, 2024, p. 5) \#a28ae5 \par - “yellow” (Author, 2024, p. 6) \#ffd400\\ \mkbibemph{Something is wrong. I don't see elements in yellow.} \par - “Adding solvers from the SMT family” (Author, 2024, p. 6) \#ffd400\\ \mkbibemph{What are left then?} \par - “the other two solver families?” (Author, 2024, p. 6) \#ffd400 \par - “the of the” (Author, 2024, p. 6) \#ff6666 \par - “blue” (Author, 2024, p. 7) \#ffd400\\ \mkbibemph{I don't see components in blue.} \par - “Solver Bridge, the only change to the MDA is that an additional subclass must be added corresponding the new solver, though this is mainly tied to the implementation.” (Author, 2024, p. 7) \#ffd400 \par - “scaling as the sizes of the variability models grow.” (Author, 2024, p. 7) \#5fb236 \par - “hundreds of features and constraints” (Author, 2024, p. 7) \#5fb236 \par - “Following these works, we therefore informally define “a reasonable amount of time” as reporting results in less than 2 seconds, using the lower bound given by said research.” (Author, 2024, p. 7) \#ffd400\\ \mkbibemph{Given an input specification in my opinion this very much depends on the solver and not on the components you devised, isn't?} \par - “Nevertheless, for the cases examined, this overhead is minimal and is unlikely to be the cause of future scalability issues.” (Author, 2024, p. 8) \#ffd400\\ \mkbibemph{I don't see motivations for potential overheads.} \par - “Both the curves and the data suggest, nevertheless, a clear exponential trend as the model size grows, which corresponds to the expectations one would have of combinatorial problems as is the case of determining satisfiability of variability models” (Author, 2024, p. 8) \#ffd400\\ \mkbibemph{This means that eventually you can go beyond the 2 seconds if the model size increases.} \par - “This is quite unexpected, as we originally expected Z3 to outperform all others, as it is a mature and well-supported tool” (Author, 2024, p. 9) \#ffd400\\ \mkbibemph{BY the way, this is out of your control, right?} \par - “variability model verification and configuration automation for models written in a variety of languages and leveraging solvers from different paradigms.” (Author, 2024, p. 9) \#a28ae5 \par - “Concerning the PLEIADES extensions that we present in this paper, the closest related approach is FlamaPy’s [30]. It supports multiple VMLs, including UVL. However, it supports a single solving paradigm, SAT, whose KRL is known to be far less expressive than those of the CSP, CLP and SMT paradigms incorporated in our PLEIADES extension. These three paradigms can all be viewed as extensions of SAT to concisely represent non-Boolean, first-order and soft constraints. Adding new solver paradigms would require rethinking FlamaPy’s MDA with its pivot language at the abstract syntax level, in the direction of PLEIADES with a semantic level pivot language.” (Author, 2024, p. 10) \#ffd400\\ \mkbibemph{I suggest to move this part earler in the paper to motivate and support the paper!} \par - “The first extension, detailed in Section 3.1, concerns the capacity of the MDA to handle purely textual VMLs, whose absence was its main limitation” (Author, 2024, p. 10) \#a28ae5 \par - “extended MDA to make it capable of reasoning on models written in UVL” (Author, 2024, p. 10) \#a28ae5 \par - “SMT solvers[23], complementing the CSP and CLP paradigms already incorporated in the original MDA.” (Author, 2024, p. 10) \#a28ae5 \par - “The second major contribution” (Author, 2024, p. 10) \#ffd400\\ \mkbibemph{WHat is the first major contribution? It is not listed, maybe it should be the extensions of PLEIADES previously mentioned?} \par - “The extended MDA has one major limitation that is worth discussing: while integrating new textual languages or solvers will not, in principle, require any architectural changes, they do imply a non-trivial implementation effort. This is due to the simple fact that a custom CLIF code generator must be fit to a (new or existing) parser for said language, and, for the solver, a code generator from a generic CSP representation has to be designed.” (Author, 2024, p. 10) \#ffd400\\ \mkbibemph{This goes in contraddition with the "lowcost" for extending the architecture mentioned in Sec. 3}} } @article{authorMetricRecommendationService, title = {A {{Metric Recommendation Service}} for {{Online Systems}} Using {{Graph Learning}}}, author = {Author, Anonymous}, pages = {12}, abstract = {Today’s monitoring and failure management mechanisms for online systems heavily rely on metrics, which are time series data that can describe the real-time state of a system from various perspectives. Though several attempts have been devoted to automatic failure management based on metrics, the primary step, metric selection, remains manual to a large extent. To better understand the prior practice, we conduct an empirical study on the selected metrics in prior work and obtain some findings. Based on the findings, we develop a metric recommendation service for online systems, which can automate the metrics selection practice and greatly ease the burden in managing an online system. Specifically, we analyze the needs of two key failure management tasks, i.e., anomaly detection and fault diagnosis, and design metric recommendation mechanisms for them respectively. Graph learning techniques are employed in the automation of metric recommendation. Our experiments demonstrate that the proposed approach can achieve an F1score of 0.912 in selecting metrics for anomaly detection, and an accuracy of 0.859 in retrieving metrics for faults diagnosis, which significantly outperforms the compared baselines.}, langid = {english} } @inproceedings{authorModelNotBuilt2024, title = {A {{Model Is Not Built By A Single Prompt}}: {{LLM-Based Conceptual Modeling With Question Decomposition}}}, author = {Author, Anonymous}, date = {2024}, abstract = {Conceptual modeling plays a crucial role in model-driven engineering. When the system description is highly complicated or the modelers lack sufficient domain knowledge, the task can become particularly challenging. Large language models (LLMs) can facilitate the task by automatically generating an initial conceptual model from the system description. Nevertheless, model generation is more complex than code generation, and a single prompt to LLMs cannot solve the problem well. This paper proposes an LLMbased conceptual modeling approach via question decomposition. Following conventional modeling guidelines, we divide the model generation task into several sub-problems, i.e., class generation, association and aggregation generation, and inheritance generation. For each sub-problem, we carefully design the prompt by choosing more efficient query words and providing essential modeling knowledge to unlock the modeling potential of LLMs. Finally, we sum up the answers of all the sub-problems and programmatically create a conceptual model to avoid trivial syntactic model errors. We evaluate our approach with 10 systems from different application domains. The preliminary results show that our approach outperforms the singe-prompt-based baseline by improving recall values and F1 scores in most systems.}, langid = {english}, keywords = {LOGSEQ}, note = {\section{Annotazioni\\ (19/4/2024, 22:17:28)} \par - “Large language models (LLMs) can facilitate the task by automatically generating an initial conceptual model from the system description.” (Author, 2024, p. 1) \#00b036 \par - “singe-prompt-” (Author, 2024, p. 1) \#ff4400 \par - “Step3. Semantically check and remove the associations relationships···” (Author, 2024, p. 4) \#f0ff00\\ \mkbibemph{This is not clear.} \par - “choose a default type.” (Author, 2024, p. 5) \#f0ff00\\ \mkbibemph{Can this be a bias or source of error?} \par - “default multiplicity,” (Author, 2024, p. 5) \#f0ff00\\ \mkbibemph{See my previous comment.} \par - “4” (Author, 2024, p. 6) \#f0ff00\\ \mkbibemph{Maybe it is too short to justify a separate section. You can merge it with previous section.} \par - “with a 1-gram similarity higher than 0.9)” (Author, 2024, p. 6) \#f0ff00\\ \mkbibemph{What's the encoding that you have used?} \par - “their types are equal,” (Author, 2024, p. 6) \#f0ff00\\ \mkbibemph{Can you also identify those that have been killed due to the default type?} \par - “-shot version” (Author, 2024, p. 7) \#f0ff00\\ \mkbibemph{What's the difference with the baseline?} \par - “For our approach, we choose temperature 0.6 for class/attribute generation and 0.3 for relationship generation (see Section 5.6 for the details about temperature selection).” (Author, 2024, p. 7) \#f0ff00\\ \mkbibemph{It is necessary to discuss the temperature definition} \par - “manually examined the relationships generated by the baseline and strictly followed R3 to fill the” (Author, 2024, p. 7) \#f0ff00\\ \mkbibemph{This is a threat to validity. Check if it has been discussed.} \par - “Table” (Author, 2024, p. 8) \#f0ff00\\ \mkbibemph{Please put in bold the highest values for each metric/approach pair.} \par - “Answer.” (Author, 2024, p. 8) \#f0ff00 \par - “two parallel tasks enhance” (Author, 2024, p. 8) \#f0ff00\\ \mkbibemph{Why parallel?} \par - “baseline” (Author, 2024, p. 9) \#f0ff00\\ \mkbibemph{The baseline is your approach here, isn't it?} \par - “temperature 0.3” (Author, 2024, p. 9) \#f0ff00\\ \mkbibemph{How is it defined?} \par - “send the classes in Oracle models to our prompts. We” (Author, 2024, p. 9) \#f0ff00\\ \mkbibemph{What does it mean?} \par - “11a” (Author, 2024, p. 9) \#f0ff00 \par - “we ignore the internal validity.” (Author, 2024, p. 10) \#f0ff00\\ \mkbibemph{I'm not sure this makes sense.} \par - “refers to the degree to which the metrics used in a study measures the performance of our approach.” (Author, 2024, p. 10) \#f0ff00\\ \mkbibemph{Not sure} \par - “Conclusion validity refers to the reliability and accuracy of the conclusions drawn from a study. To avoid a threat to conclusion validity, all results and answers to the research questions in our evaluation were thoroughly discussed until the authors reached an agreement.” (Author, 2024, p. 10) \#f0ff00\\ \mkbibemph{Not sure}} } @article{authorTopFilterApproachRecommend2020, title = {{{TopFilter}}: {{An Approach}} to {{Recommend Relevant GitHub Topics}}}, author = {Author, Anonymous}, date = {2020}, pages = {11}, langid = {english}, note = {TL;DR \par The results confirm that collaborative filtering techniques can successfully be used to provide relevant topics for GitHub repositories, and show that TopFilter can gain a significant boost in prediction performances by employing the outcomes obtained by the MNB network as its initial set of topics.} } @inproceedings{autiliIntegrationArchitectureSynthesis2012, ids = {autiliIntegrationArchitectureSynthesis2012a,autiliIntegrationArchitectureSynthesis2012b,autiliIntegrationArchitectureSynthesis2012c,autiliIntegrationArchitectureSynthesis2012d}, title = {Integration {{Architecture Synthesis}} for {{Taming Uncertainty}} in the {{Digital Space}}}, booktitle = {Large-{{Scale Complex IT Syst}}. {{Dev}}. {{Oper}}. {{Manag}}. - 17th {{Monterey Workshop}} 2012 {{Oxf}}. {{UK March}} 19-21 2012 {{Revis}}. {{Sel}}. {{Pap}}. {{Lect}}. {{Notes Comput}}. {{Sci}}.}, author = {Autili, Marco and Cortellessa, Vittorio and DI RUSCIO, Davide and Inverardi, Paola and Pelliccione, Patrizio and Tivoli, Massimo}, date = {2012}, series = {Lecture {{Notes}} in {{Computer Science}}}, volume = {7539 LNCS}, pages = {118--131}, publisher = {Springer-Verlag}, location = {BERLIN HEIDELBERG}, doi = {10.1007/978-3-642-34059-8_6}, abstract = {"\textbackslash "\textbackslash "The abundance of software that will be more and more available will promote the production of appropriate integration means (architectures, connectors, integration patterns). The produced software will need to be able to evolve, react and adapt quickly to a continuously changing environment, while guaranteeing dependability through (on-the-fly) validation. The strongest adversary to this view is the lack of information about the software, notably about its structure, behavior, and execution context. Despite the possibility to extract observational models from existing software, a producer will always operate with software artifacts that exhibit a degree of uncertainty in terms of their functional and non functional characteristics. Uncertainty can only be controlled by making it explicit and by using it to drive the production process itself. This calls for a production process that explores available software and assesses its degree of uncertainty in relation to the opportunistic goal G, assists the producer in creating the appropriate integration means towards G, and validates the quality of the integrated system with respect to the goal G and the current context. In this paper we discuss how goal-oriented software systems can be opportunistically created by integrating under uncertainty existing pieces of software. © 2012 Springer-Verlag.\textbackslash "\textbackslash ""}, isbn = {978-3-642-34058-1}, note = {cited By 10 \par cited By 10 \par TL;DR \par How goal-oriented software systems can be opportunistically created by integrating under uncertainty existing pieces of software is discussed.} } @inproceedings{autiliModelbasedSynthesisProcess2013, ids = {autiliModelBasedSynthesisProcess2013,autiliModelbasedSynthesisProcess2013a,autiliModelbasedSynthesisProcess2013b}, title = {A Model-Based Synthesis Process for Choreography Realizability Enforcement}, booktitle = {Fundam. {{Approaches Softw}}. {{Eng}}. - 16th {{Int}}. {{Conf}}. {{FASE}} 2013 {{Held Part Eur}}. {{Jt}}. {{Conf}}. {{Theory Pract}}. {{Softw}}. {{ETAPS}} 2013 {{Rome Italy March}} 16-24 2013 {{Proc}}.}, author = {Autili, Marco and DI RUSCIO, Davide and DI SALLE, Amleto and Inverardi, Paola and Tivoli, Massimo}, date = {2013}, series = {Lecture {{Notes}} in {{Computer Science}}}, volume = {7793 LNCS}, pages = {37--52}, publisher = {Springer Berlin Heidelberg}, doi = {10.1007/978-3-642-37057-1_4}, abstract = {""The near future in service-oriented system development envisions a ubiquitous world of available services that collaborate to fit users’ needs. Modern service-oriented applications are often built by reusing and assembling distributed services. This can be done by considering a global specification of the interactions between the participant services, namely the choreography. In this paper, we propose a synthesis approach to automatically synthesize a choreography out of a specification of it and a set of services discovered as suitable participants. The synthesis is model-based in the sense that it works by assuming a finite state model of the services’s protocol and a BPMN model for the choreography specification. The result of the synthesis is a set of distributed components, called coordination delegates, that coordinate the services’ interaction in order to realize the specified choreography. The work advances the state-of-the-art in two directions: (i) we provide a solution to the problem of choreography realizability enforcement, and (ii) we provide a model-based tool chain to support the development of choreography-based systems.""}, isbn = {978-3-642-37056-4}, keywords = {Choreography Realizability Enforcement,Service Choreographies,Service Oriented Architectures}, note = {cited By 38 \par cited By 38 \par TL;DR \par A synthesis approach to automatically synthesize a choreography out of a specification of it and a set of services discovered as suitable participants and provides a model-based tool chain to support the development of choreography-based systems.} } @incollection{autiliModelLANDWhereModels2014, ids = {autiliModelLANDWhereModels2011,autiliModelLANDWhereModels2011a,autiliModelLANDWhereModels2014a,autiliModelLANDWhereModels2014b,autiliModelLANDWhereModels2014c}, title = {{{ModelLAND}}: {{Where}} Do Models Come From?}, booktitle = {Lecture {{Notes}} in {{Computer Science}} (Including Subseries {{Lecture Notes}} in {{Artificial Intelligence}} and {{Lecture Notes}} in {{Bioinformatics}})}, author = {Autili, Marco and DI RUSCIO, Davide and Inverardi, Paola and Pelliccione, Patrizio and Tivoli, Massimo}, date = {2014}, series = {Lecture {{Notes}} in {{Computer Science}}}, volume = {LNCS 8378}, pages = {162--187}, publisher = {Springer Verlag}, doi = {10.1007/978-3-319-08915-7_6}, isbn = {978-3-319-08914-0}, keywords = {Computer Science (all),Theoretical Computer Science}, note = {cited By 5 \par cited By 5 \par TL;DR \par This work states that a flourishing of model-based engineering techniques has been defined for building, managing, verifying, validating and controlling software systems.} } @article{AutoEmbedAutomatedSoftware, title = {{{AutoEmbed}}: Towards Automated Software Development for Generic Embedded {{IoT}} Systems via {{LLMs}}}, abstract = {Embedded Internet of Things (IoT) system development is crucial for enabling seamless connectivity and functionality across a wide range of applications. However, such a complex process requires cross-domain knowledge of hardware and software and hence often necessitates direct developer involvement, making it labor-intensive, time-consuming, and error-prone. To address this challenge, this paper introduces AutoEmbed, the first fully automated software development platform for general-purpose embedded IoT systems. The key idea is to leverage the reasoning ability of Large Language Models (LLMs) and embedded system expertise to automate the hardware-in-the-loop development process. The main methods include a component-aware library resolution method for addressing hardware dependencies, a library knowledge generation method that injects utility domain knowledge into LLMs, and an auto-programming method that ensures successful deployment. We evaluate AutoEmbed’s performance across 71 modules and four mainstream embedded development platforms with over 350 IoT tasks. Experimental results show that AutoEmbed can generate codes with an accuracy of 95.7\% and complete tasks with a success rate of 86.5\%, surpassing human-in-the-loop baselines by 15.6\%–37.7\% and 25.5\%–53.4\%, respectively. We also show AutoEmbed ’s potential through case studies in environmental monitoring and remote control systems development.}, langid = {english}, keywords = {LOGSEQ}, note = {I'm reviewing a research paper and I took the following notes: \par \section{Annotazioni\\ (6/5/2025, 15:15:36)} \par - “AutoEmbed: Towards Automated Software Development for Generic Embedded IoT Systems via LLMs” (“AutoEmbed: towards automated software development for generic embedded IoT systems via LLMs”, p. 1) \#ffd400\\ \mkbibemph{Overall it is an interesting paper about a relevant topic. In my opinion there are some presentation issues, which affect the quality of the paper, which requires improvements to better present the approach and to be more convincing from the evaluation point of view.} \par - “this paper introduces AutoEmbed, the first fully automated software development platform for general-purpose embedded IoT systems.” (“AutoEmbed: towards automated software development for generic embedded IoT systems via LLMs”, p. 1) \#a28ae5 \par - “automate the hardware-in-the-loop development process” (“AutoEmbed: towards automated software development for generic embedded IoT systems via LLMs”, p. 1) \#a28ae5 \par - “We evaluate AutoEmbed’s performance across 71 modules and four mainstream embedded development platforms with over 350 IoT tasks.” (“AutoEmbed: towards automated software development for generic embedded IoT systems via LLMs”, p. 1) \#a28ae5 \par - “accuracy of 95.7\%” (“AutoEmbed: towards automated software development for generic embedded IoT systems via LLMs”, p. 1) \#e56eee \par - “We also show AutoEmbed ’s potential through case studies in environmental monitoring and remote control systems development.” (“AutoEmbed: towards automated software development for generic embedded IoT systems via LLMs”, p. 1) \#a28ae5 \par - “For example, in a smart city [34, 42], embedded systems control street lighting and traffic signals based on sensor data to optimize energy use and traffic flow.” (“AutoEmbed: towards automated software development for generic embedded IoT systems via LLMs”, p. 1) \#5fb236 \par - “understanding” (“AutoEmbed: towards automated software development for generic embedded IoT systems via LLMs”, p. 1) \#5fb236 \par - “how devices interact with the physical world” (“AutoEmbed: towards automated software development for generic embedded IoT systems via LLMs”, p. 1) \#5fb236 \par - “Initially, developers must manually address dependencies by installing and configuring the essential libraries for hardware modules.” (“AutoEmbed: towards automated software development for generic embedded IoT systems via LLMs”, p. 1) \#5fb236 \par - “Keil μVision, and IAR” (“AutoEmbed: towards automated software development for generic embedded IoT systems via LLMs”, p. 1) \#ffd400\\ \mkbibemph{what are these? To check} \par - “A fully automated platform for developing embedded IoT systems” (“AutoEmbed: towards automated software development for generic embedded IoT systems via LLMs”, p. 1) \#ffd400\\ \mkbibemph{Let's see. but I expect a strong evaluation consisting of IoT systems developed with AutoEmbed as promised/described in the paper.} \par - “diversity” (“AutoEmbed: towards automated software development for generic embedded IoT systems via LLMs”, p. 1) \#5fb236 \par - “hardware modules” (“AutoEmbed: towards automated software development for generic embedded IoT systems via LLMs”, p. 1) \#5fb236 \par - “ncreases the complexity” (“AutoEmbed: towards automated software development for generic embedded IoT systems via LLMs”, p. 1) \#5fb236 \par - “the traditional embedded IoT system development process is labor-intensive, time-consuming, and error-prone” (“AutoEmbed: towards automated software development for generic embedded IoT systems via LLMs”, p. 1) \#5fb236 \par - “Large Language Models (LLMs) to streamline embedded IoT system development.” (“AutoEmbed: towards automated software development for generic embedded IoT systems via LLMs”, p. 1) \#a28ae5 \par - “harness their capabilities to simplify the development process by automating processes such as dependency-solving, coding, and deployment.” (“AutoEmbed: towards automated software development for generic embedded IoT systems via LLMs”, p. 1) \#e56eee \par - “they fall short in driving specific hardware devices (e.g., microcontrollers and sensors) due to a lack of hardware-specific knowledge in embedded systems, such as peripheral interface configurations and library dependencies.” (“AutoEmbed: towards automated software development for generic embedded IoT systems via LLMs”, p. 1) \#e56eee \par - “LLM-powered automation system that streamlines the dependency-solving, programming, and deployment processes in embedded system development, resulting in fully-developed embedded systems for various IoT applications as illustrated in Fig. 1.” (“AutoEmbed: towards automated software development for generic embedded IoT systems via LLMs”, p. 1) \#e56eee \par - “Challenge 1: Diversity in Hardware Dependency” (“AutoEmbed: towards automated software development for generic embedded IoT systems via LLMs”, p. 1) \#2ea8e5 \par - “Each component relies on specific library dependencies to function effectively, leading to unique challenges in dependency resolution.” (“AutoEmbed: towards automated software development for generic embedded IoT systems via LLMs”, p. 1) \#a28ae5 \par - “accurately identifying and selecting the essential libraries for different hardware components is a significant challenge.” (“AutoEmbed: towards automated software development for generic embedded IoT systems via LLMs”, p. 2) \#e56eee \par - “ibrary selection, revealing that different libraries exhibit distinct compatibility with specific models and varying support for development board architectures” (“AutoEmbed: towards automated software development for generic embedded IoT systems via LLMs”, p. 2) \#a28ae5 \par - “we propose an automated dependency solving method that can efficiently identify the most suitable libraries for specific hardware components.” (“AutoEmbed: towards automated software development for generic embedded IoT systems via LLMs”, p. 2) \#e56eee \par - “Challenge 2: Lack of Library Knowledge” (“AutoEmbed: towards automated software development for generic embedded IoT systems via LLMs”, p. 2) \#2ea8e5 \par - “knowledge generation method that extracts and injects library API and utility knowledge into the LLM’s memory, enabling syntactically and contextually appropriate solutions.” (“AutoEmbed: towards automated software development for generic embedded IoT systems via LLMs”, p. 2) \#a28ae5 \par - “Challenge 3: Complexity of Embedded System Programming.” (“AutoEmbed: towards automated software development for generic embedded IoT systems via LLMs”, p. 2) \#2ea8e5 \par - “In general-purpose programming, the workflow typically involves coding, debugging, and deployment. In contrast, embedded system programming introduces additional steps such as compiling and flashing, which require specialized configurations and are particularly error-prone” (“AutoEmbed: towards automated software development for generic embedded IoT systems via LLMs”, p. 2) \#ffd400\\ \mkbibemph{Compilation can occur also in general-purpose programming and not only in embedded system programming!} \par - “compile loop” (“AutoEmbed: towards automated software development for generic embedded IoT systems via LLMs”, p. 2) \#2ea8e5 \par - “Figure 3: Programming pipeline comparison.” (“AutoEmbed: towards automated software development for generic embedded IoT systems via LLMs”, p. 2) \#ffd400\\ \mkbibemph{I would improve Fig. 3.b with a loop involving Compiling-{$>$}Flashing-{$>$}Debugging-{$>$}Compiling .... because otherwise it seems that the typical pipeline involve only two flashing stepts, whcih of course it is not what the authors meant.} \par - “flash loop” (“AutoEmbed: towards automated software development for generic embedded IoT systems via LLMs”, p. 2) \#2ea8e5 \par - “we design and implement AutoEmbed, a comprehensive framework that fully automates the dependency-solving, programming, and deployment processes for embedded system development.” (“AutoEmbed: towards automated software development for generic embedded IoT systems via LLMs”, p. 2) \#e56eee \par - “Our extensive evaluation, involving over 70 hardware modules, four development platforms, and over 350 IoT tasks, demonstrates that AutoEmbed achieves an average coding accuracy of 95.7\% and an average completion rate of 86.5\%, highlighting the effectiveness of AutoEmbed.” (“AutoEmbed: towards automated software development for generic embedded IoT systems via LLMs”, p. 2) \#e56eee\\ \mkbibemph{Interesting to see how these configurations have been actually checked (see the evaluation section later in the paper).} \par - “library resolution method for hardware dependency solving” (“AutoEmbed: towards automated software development for generic embedded IoT systems via LLMs”, p. 2) \#2ea8e5 \par - “knowledge generation method that enhances LLMs with specialized library knowledge” (“AutoEmbed: towards automated software development for generic embedded IoT systems via LLMs”, p. 2) \#2ea8e5 \par - “auto-programming method to ensure successful deployment” (“AutoEmbed: towards automated software development for generic embedded IoT systems via LLMs”, p. 2) \#2ea8e5 \par - “EmbedTask dataset” (“AutoEmbed: towards automated software development for generic embedded IoT systems via LLMs”, p. 2) \#2ea8e5 \par - “which will be made publicly available upon paper acceptance.” (“AutoEmbed: towards automated software development for generic embedded IoT systems via LLMs”, p. 2) \#ffd400\\ \mkbibemph{This poses some issues concerning the reproducibility of the approach during the review phase.} \par - “95.7\% across various IoT tasks” (“AutoEmbed: towards automated software development for generic embedded IoT systems via LLMs”, p. 2) \#5fb236 \par - “Es = g(D, M)” (“AutoEmbed: towards automated software development for generic embedded IoT systems via LLMs”, p. 2) \#5fb236 \par - “development platform” (“AutoEmbed: towards automated software development for generic embedded IoT systems via LLMs”, p. 2) \#2ea8e5 \par - “connected modules” (“AutoEmbed: towards automated software development for generic embedded IoT systems via LLMs”, p. 2) \#2ea8e5 \par - “Figure 4:” (“AutoEmbed: towards automated software development for generic embedded IoT systems via LLMs”, p. 3) \#ffd400\\ \mkbibemph{This looks like Fig. 3.b. I suggest to remove one of them.} \par - “Solving Dependencies.” (“AutoEmbed: towards automated software development for generic embedded IoT systems via LLMs”, p. 3) \#ffd400\\ \mkbibemph{Why not putting in the formalization, Ls directly in Es? Ls should be found depending on the modules building up the system being developed.} \par - “probability is computed using a softmax function” (“AutoEmbed: towards automated software development for generic embedded IoT systems via LLMs”, p. 3) \#5fb236 \par - “memory-augmented LLMs to generate task prompts and automate system programming” (“AutoEmbed: towards automated software development for generic embedded IoT systems via LLMs”, p. 3) \#5fb236 \par - “3.1.1 Hardware Configuration” (“AutoEmbed: towards automated software development for generic embedded IoT systems via LLMs”, p. 3) \#2ea8e5 \par - “3.1.2 Solving Library Dependencies” (“AutoEmbed: towards automated software development for generic embedded IoT systems via LLMs”, p. 3) \#2ea8e5 \par - “The score is based on the number of available library versions, normalized between 0 and 1” (“AutoEmbed: towards automated software development for generic embedded IoT systems via LLMs”, p. 4) \#ffd400\\ \mkbibemph{Why between 0 and 1? This is not clear. Many versions of the same library might be available, isn't it?} \par - “3.2.1 Library Knowledge Extraction” (“AutoEmbed: towards automated software development for generic embedded IoT systems via LLMs”, p. 4) \#2ea8e5 \par - “This involves two main steps: API extraction and obtaining API usage information” (“AutoEmbed: towards automated software development for generic embedded IoT systems via LLMs”, p. 4) \#5fb236 \par - “searches for example (.ino) files within the same library and extracts knowledge on how to use the extracted APIs (e.g., orders, parameters, and return values).” (“AutoEmbed: towards automated software development for generic embedded IoT systems via LLMs”, p. 4) \#5fb236 \par - “3.2.2 Functionality Knowledge Understanding” (“AutoEmbed: towards automated software development for generic embedded IoT systems via LLMs”, p. 4) \#2ea8e5 \par - “functionality” (“AutoEmbed: towards automated software development for generic embedded IoT systems via LLMs”, p. 4) \#ffd400\\ \mkbibemph{What do you mean with functionality here? The given formalizations (equations) do not add more. I would replace them with some concrete examples, to complement the graphical representation given in Fig. 7. Without concrete examples, terms like function or functionality might be understood differently by reviewers.} \par - “Hence, we opt to incorporate only the most crucial information necessary for accomplishing the user’s task.” (“AutoEmbed: towards automated software development for generic embedded IoT systems via LLMs”, p. 4) \#5fb236 \par - “Knowledge Extraction” (“AutoEmbed: towards automated software development for generic embedded IoT systems via LLMs”, p. 5) \#ffd400\\ \mkbibemph{This does not mach with Fig. 8. There is not steps in the figure that refers knowledge extraction phases.} \par - “2) API Table Lookup: Next, we look up the API table to retrieve usage information about the matched APIs. This information AT is then incorporated into the prompt, allowing for more accurate and relevant code generation” (“AutoEmbed: towards automated software development for generic embedded IoT systems via LLMs”, p. 5) \#ffd400\\ \mkbibemph{Again, there is some mismatch with Fig. 8. In paragraph 3.3.1 we are missing an explicit reference to the "Insert Memory into Prompt" phase shown in Fig. 8.} \par - “3.4.1 Coder” (“AutoEmbed: towards automated software development for generic embedded IoT systems via LLMs”, p. 5) \#2ea8e5 \par - “P (G |xT ; θ ) = T Ö t =1 P (Gt |G{$<$}t , x; θ ),” (“AutoEmbed: towards automated software development for generic embedded IoT systems via LLMs”, p. 5) \#ffd400\\ \mkbibemph{See my previous comment related to the opportunity of putting of not equations that do not add clarifications. They stay at a too high level of details and as a such they do not contribute too much to the presentation. I would replace them with some concrete and explanatory example.} \par - “3.4.2 Compile Loop” (“AutoEmbed: towards automated software development for generic embedded IoT systems via LLMs”, p. 5) \#2ea8e5 \par - “3.4.3 Flash Loop.” (“AutoEmbed: towards automated software development for generic embedded IoT systems via LLMs”, p. 5) \#2ea8e5 \par - “The Coder generates the initial code G with embedded DEBUG INFO statements D (G) (step 1 ).” (“AutoEmbed: towards automated software development for generic embedded IoT systems via LLMs”, p. 6) \#ffd400\\ \mkbibemph{What's the input of the process? How are the requirements of the wanted system given?} \par - “If the code is not ready, the Compile Validator identifies the issues and prompts corrections.” (“AutoEmbed: towards automated software development for generic embedded IoT systems via LLMs”, p. 6) \#ffd400\\ \mkbibemph{What if the code compiles correctly but the system does not implement all the wanted functionalities or none of them at all?} \par - “Flash Validator identifies a logical error where the LED is incorrectly turned off when the temperature exceeds 30°C.” (“AutoEmbed: towards automated software development for generic embedded IoT systems via LLMs”, p. 6) \#ffd400\\ \mkbibemph{Related to the previous comment, it is not clear how the set of validations that get executed are obtained from the initial input. In other words, it is not clear how the set of validations are derived from the input.} \par - “modifications. (step 4 )” (“AutoEmbed: towards automated software development for generic embedded IoT systems via LLMs”, p. 6) \#ff6666\\ \mkbibemph{Move the point after ")".} \par - “Prompt design:” (“AutoEmbed: towards automated software development for generic embedded IoT systems via LLMs”, p. 6) \#ff6666\\ \mkbibemph{I think this can be dropped from the text.} \par - “EmbedTask includes 355 tasks, covering different modules and varying complexity levels.” (“AutoEmbed: towards automated software development for generic embedded IoT systems via LLMs”, p. 6) \#ffd400\\ \mkbibemph{Are these tasks kinds of requirements of different applications? What's the granularity of the task description?} \par - “EmbedTask classifies tasks into three difficulty levels:” (“AutoEmbed: towards automated software development for generic embedded IoT systems via LLMs”, p. 6) \#ffd400\\ \mkbibemph{It's not clear how this can work in general. How these three level have been decided? It's alway three levels? It's difficult to distinguish the steps that are example specific from those that are supposed to be generic.} \par - “Figure 10: Experimental devices.” (“AutoEmbed: towards automated software development for generic embedded IoT systems via LLMs”, p. 7) \#ffd400\\ \mkbibemph{The experimental settings involving all these different platforms and devises should be better described especially concerning the level of human involvement in the evaluation process.} \par - “Library Solving” (“AutoEmbed: towards automated software development for generic embedded IoT systems via LLMs”, p. 7) \#ffd400\\ \mkbibemph{It's not clear how the library solving problem has been investigated by using the "coding accuracy" and "completion rate" metrics that seem to work at a different level of abstraction (more at code level). Isn't it?} \par COnsider that those that are tagget with \#5fb236 are just highlights, those that are tagged with \#e56eee and \#a28ae5 are imporant sentences. Please pay attention instead to the notes that are tagged with \#ffd400. Those that are tagged with \#ff6666 are typos or errors. Could you please draft a review by organizing it as follows: SUMMARY: Just a few sentence to summarize the work STRENGHTS: \par WEAKNESSES: \par COMMENTS: Organize the notes with respect to the following criteria: \par \begin{itemize} \item \texttt{Novelty} \item \texttt{Rigor} \item \texttt{Relevance (of the contribution)} \item \texttt{Verifiability and Transparency} \item \texttt{Presentation} \end{itemize} \par And then add a Detailed Comments section to report the notets that contain issues or typos.} } @misc{AutomatedCoevolutionMetamodels, title = {Automated Co-Evolution of Metamodels and Code}, langid = {english} } @online{AutomatedCommitMessage, title = {Automated {{Commit Message Generation With Large Language Models}}: {{An Empirical Study}} and {{Beyond}}}, url = {https://www.computer.org/csdl/journal/ts/2024/12/10713474/20Xx2rTmjGE}, keywords = {source\_mobile} } @article{AutomatingConfigurationDependency2026, title = {On Automating Configuration Dependency Validation via Retrieval-Augmented Generation}, date = {2026}, abstract = {Configuration dependencies arise when multiple technologies within a software system require coordinated settings for correct interplay. Existing approaches for detecting such dependencies often suffer from high false-positive rates, require additional validation mechanisms, and are typically limited to specific projects or technologies. Recent work that incorporates large language models (LLMs) for dependency validation still suffers from inaccuracies due to projectand technology-specific variations, as well as from missing contextual information. A promising solution to missing contextual information represents retrieval-augmented generation (RAG) systems, which can dynamically retrieve project- and technology-specific knowledge for validating configuration dependencies. However, it is unclear which architectural decisions, which information resources, and what kind of information help best to automate the validation of configuration dependencies. In this work, we set out to evaluate whether RAG can improve LLM-based validation of configuration dependencies and which architectural decisions, as well as contextual information are needed to overcome the static knowledge base of LLMs. To this end, we conducted a large empirical study on validating configuration dependencies. Our evaluation of six state-of-the-art LLMs and eight RAG variants shows that vanilla LLMs already demonstrate solid validation abilities, while RAG has only marginal or even negative effects on the validation performance of the models. By incorporating tailored contextual information into the RAG system–derived from a qualitative analysis of validation failures–we achieve significantly more accurate validation results across all models, with an average precision of 0.84 and recall of 0.70, representing improvements of 35 \% and 133 \% over vanilla LLMs, respectively. In addition, these results offer two important insights: Simplistic RAG systems may not benefit from additional information if it is not tailored to the task at hand, and it is often unclear upfront what kind of information yields improved performance.}, langid = {english}, keywords = {LOGSEQ}, note = {I'm reviewing a research paper and I took the following notes: \par \section{Annotazioni\\ (16/5/2025, 17:16:43)} \par - “Configuration dependencies arise when multiple technologies within a software system require coordinated settings for correct interplay.” (“On automating configuration dependency validation via retrieval-augmented generation”, 2026, p. 1) \#5fb236 \par - “high false-positive rates,” (“On automating configuration dependency validation via retrieval-augmented generation”, 2026, p. 1) \#a28ae5 \par - “large language models (LLMs) for dependency validation still suffers from inaccuracies due to projectand technology-specific variations, as well as from missing contextual information.” (“On automating configuration dependency validation via retrieval-augmented generation”, 2026, p. 1) \#a28ae5 \par - “it is unclear which architectural decisions, which information resources, and what kind of information help best to automate the validation of configuration dependencies” (“On automating configuration dependency validation via retrieval-augmented generation”, 2026, p. 1) \#a28ae5 \par - “we conducted a large empirical study on validating configuration dependencies.” (“On automating configuration dependency validation via retrieval-augmented generation”, 2026, p. 1) \#5fb236 \par - “LLMs already demonstrate solid validation abilities,” (“On automating configuration dependency validation via retrieval-augmented generation”, 2026, p. 1) \#5fb236 \par - “while RAG has only marginal or even negative effects on the validation performance of the models.” (“On automating configuration dependency validation via retrieval-augmented generation”, 2026, p. 1) \#5fb236 \par - “Simplistic RAG systems may not benefit from additional information if it is not tailored to the task at hand” (“On automating configuration dependency validation via retrieval-augmented generation”, 2026, p. 1) \#a28ae5 \par - “and it is often unclear upfront what kind of information yields improved performance.” (“On automating configuration dependency validation via retrieval-augmented generation”, 2026, p. 1) \#a28ae5\\ \mkbibemph{This is interesting!!!} \par - “Consider a typical Spring Boot application: the database credentials specified in the application.yml in Listing 1 must match those defined in the docker-compose.yml in Listing 2 for the application to successfully connect to the database. This constraint constitutes a configuration dependency. Such dependencies can occur within a single technology (intra-technology) and across multiple technologies (cross-technology).” (“On automating configuration dependency validation via retrieval-augmented generation”, 2026, p. 1) \#e56eee \par - “configuration dependencies are rarely fully documented [6], leading to incomplete and often outdated documentation [14, 16, 26].” (“On automating configuration dependency validation via retrieval-augmented generation”, 2026, p. 1) \#5fb236 \par - “For example, a recent configuration update at CrowdStrike caused a massive outage, crashing millions Windows systems [17]. In 2021, a minor configuration change at Facebook resulted in a six-hour global service outage [8]. Similarly, a misconfiguration at Microsoft left the Azure Compute service unavailable for more than two hours [24].” (“On automating configuration dependency validation via retrieval-augmented generation”, 2026, p. 2) \#5fb236 \par - “Several dependency detection approaches have been proposed, using heuristics [6, 7, 35, 42], domain-specific languages [2, 12], and machine learning techniques [29, 30, 45]. Unfortunately, these approaches often suffer from a high rate of false positives, where, for example, independent configuration options are incorrectly linked due to coincidental similarities.” (“On automating configuration dependency validation via retrieval-augmented generation”, 2026, p. 2) \#5fb236 \par - “outdated training data of LLMs (e.g., due to changes in configuration formats and frameworks), the unavailability of project-specific information (e.g., module organization or available resources), and ambiguous naming schemes for configuration options.” (“On automating configuration dependency validation via retrieval-augmented generation”, 2026, p. 2) \#a28ae5 \par - “In this work, we set out to evaluate the usefulness of RAG for configuration dependency validation. To this end, we analyze the validation effectiveness of vanilla LLMs compared to RAG, the effect of different RAG architectures on the validation performance, and what types of information LLMs actually require for effective dependency validation.” (“On automating configuration dependency validation via retrieval-augmented generation”, 2026, p. 2) \#e56eee \par - “we develop a novel RAG system for dependency validation, which integrates four widely-used context sources (i.e., technology docs, Stack Overflow posts, GitHub repositories, and Web search results) to provide additional context information to LLMs.” (“On automating configuration dependency validation via retrieval-augmented generation”, 2026, p. 2) \#e56eee \par - “(1) reducing false positives by providing accurate classification capabilities in a automated and technology-agnostic way” (“On automating configuration dependency validation via retrieval-augmented generation”, 2026, p. 2) \#2ea8e5 \par - “(2) assessing the usefulness of RAG for dependency validation” (“On automating configuration dependency validation via retrieval-augmented generation”, 2026, p. 2) \#2ea8e5 \par - “(3) identifying what information LLMs actually require for effective dependency validation” (“On automating configuration dependency validation via retrieval-augmented generation”, 2026, p. 2) \#2ea8e5 \par - “350 configuration dependencies” (“On automating configuration dependency validation via retrieval-augmented generation”, 2026, p. 2) \#5fb236 \par - “six state-of-the-art LLMs” (“On automating configuration dependency validation via retrieval-augmented generation”, 2026, p. 2) \#5fb236 \par - “eight RAG variants” (“On automating configuration dependency validation via retrieval-augmented generation”, 2026, p. 2) \#5fb236 \par - “Our results show that vanilla LLMs already exhibit solid validation abilities, while RAG, contrary to its promise, only has marginal or even negative effects on their validation effectiveness.” (“On automating configuration dependency validation via retrieval-augmented generation”, 2026, p. 2) \#5fb236 \par - “it often lacks dependency-related information and thus increases noise for LLMs rather than providing meaningful information, negatively affecting their validation performance.” (“On automating configuration dependency validation via retrieval-augmented generation”, 2026, p. 2) \#e56eee \par - “the key takeaway is that merely adding contextual information does not guarantee better validation effectiveness of LLMs. Instead, a systematic refinement of RAG components and particularly context sources is essential to unleash the full potential of RAG systems for dependency validation, and thus needs to be done in any RAG development activity.” (“On automating configuration dependency validation via retrieval-augmented generation”, 2026, p. 2) \#a28ae5 \par - “We provide a supplementary website with all code scripts, dependency datasets, prompt templates, and validation results at https://anonymous.4open.science/r/rag-config-val-D115.” (“On automating configuration dependency validation via retrieval-augmented generation”, 2026, p. 3) \#5fb236 \par - “Configuration Dependency Detection” (“On automating configuration dependency validation via retrieval-augmented generation”, 2026, p. 3) \#2ea8e5 \par - “These approaches offer valuable solutions for detecting configuration dependencies in software systems, but often require extensive additional validation efforts. In contrast to the aforementioned approaches, our goal is to enable an automated and technologyagnostic validation of dependencies using RAG.” (“On automating configuration dependency validation via retrieval-augmented generation”, 2026, p. 3) \#a28ae5 \par - “Configuration Validation” (“On automating configuration dependency validation via retrieval-augmented generation”, 2026, p. 3) \#2ea8e5 \par - “Both approaches require manual engineering to define specification rules.” (“On automating configuration dependency validation via retrieval-augmented generation”, 2026, p. 3) \#5fb236 \par - “The work most closely related to ours is that of Lian et. al [20], which investigates the effectiveness of using LLMs for configuration validation” (“On automating configuration dependency validation via retrieval-augmented generation”, 2026, p. 3) \#5fb236 \par - “They introduce Ciri, an LLM-based framework, to detect constraint and dependency violations in single configuration files.” (“On automating configuration dependency validation via retrieval-augmented generation”, 2026, p. 3) \#5fb236 \par - “While constraint violations (e.g., syntax and value range) are more common, dependency violations are often project- and technologyspecific and thus harder for LLMs to capture without fine-tuning on dependency-related data or additional relevant contextual information.” (“On automating configuration dependency validation via retrieval-augmented generation”, 2026, p. 3) \#5fb236 \par - “The key difference to our work is that Lian et al. [20] focus on single-file constraint and dependency violations, while our approach explicitly focuses on validating cross-technology configuration dependencies using RAG.” (“On automating configuration dependency validation via retrieval-augmented generation”, 2026, p. 3) \#a28ae5 \par - “3 CONFIGURATION DEPENDENCY VALIDATION WITH RAG” (“On automating configuration dependency validation via retrieval-augmented generation”, 2026, p. 3) \#ffd400\\ \mkbibemph{Overall the section is a bit high level. It is supposed to describe and explain the main technical aspects of the system shown in Fig. 1. Unfortunately, the subsections stay too high level without conveying the technical details that reader is supposed to see to grasp how the system works.} \par - “To index content derived from the context sources, we first generate documents with metadata, such as the origin of a context source.” (“On automating configuration dependency validation via retrieval-augmented generation”, 2026, p. 3) \#5fb236 \par - “we generate a retrieval query for each validation request by adding all relevant information specific to the configuration dependency of interest.” (“On automating configuration dependency validation via retrieval-augmented generation”, 2026, p. 4) \#ffd400\\ \mkbibemph{How? See my previous comment.} \par - “retrieved context information” (“On automating configuration dependency validation via retrieval-augmented generation”, 2026, p. 4) \#ffd400\\ \mkbibemph{Can you give some example?} \par - “configuration options” (“On automating configuration dependency validation via retrieval-augmented generation”, 2026, p. 4) \#ffd400\\ \mkbibemph{How are they distilled?} \par - “Inconsistencies in these configuration values can lead to configuration errors. Importantly, configuration options may have equal values by accident, meaning that there is no actual dependency, but it just happens that they have equal values.” (“On automating configuration dependency validation via retrieval-augmented generation”, 2026, p. 4) \#ffd400\\ \mkbibemph{It would be interesting to read why you had to add such a clarification. What happened during the iterative defintion of the prompt? without such a clarification, what did the author experience?} \par - “\{context\}” (“On automating configuration dependency validation via retrieval-augmented generation”, 2026, p. 4) \#ffd400\\ \mkbibemph{We need a concrete example of context here?} \par - “(1) plan: a string that describes the plan how to validate the dependency based on the contextual information step by step; (2) rationale: a string that explains whether and why the configuration options depend on each other or not; and (3) isDependency: a boolean indicating the final decision of the LLM whether the given dependency is a true dependency or a false positive.” (“On automating configuration dependency validation via retrieval-augmented generation”, 2026, p. 4) \#ffd400\\ \mkbibemph{Also in this case it is necessary to give some examples. Authors correctly added in the introduction section an explanatory example to present the problem of dependency violation. Why not relying on the same case to give explanatory examples of the different components of the process and related artifacts?} \par - “RAG for Dependency Validation” (“On automating configuration dependency validation via retrieval-augmented generation”, 2026, p. 4) \#2ea8e5 \par - “We evaluate whether and to what extent RAG systems improve over vanilla LLMs for dependency validation” (“On automating configuration dependency validation via retrieval-augmented generation”, 2026, p. 4) \#5fb236 \par - “How effective are vanilla LLMs compared to RAG in validating configuration dependencies?” (“On automating configuration dependency validation via retrieval-augmented generation”, 2026, p. 5) \#5fb236 \par - “To what extent do RAG architecture variants affect the validation performance?” (“On automating configuration dependency validation via retrieval-augmented generation”, 2026, p. 5) \#5fb236 \par - “What information does RAG retrieve for dependency validation?” (“On automating configuration dependency validation via retrieval-augmented generation”, 2026, p. 5) \#5fb236 \par - “Types of Information Needed for Dependency Validation” (“On automating configuration dependency validation via retrieval-augmented generation”, 2026, p. 5) \#2ea8e5 \par - “With a RAG system at our hand, we can qualitatively analyze the causes of failures as we have explicit contextual information available.” (“On automating configuration dependency validation via retrieval-augmented generation”, 2026, p. 5) \#5fb236 \par - “What kind of failures occur in LLM-based dependency validation?” (“On automating configuration dependency validation via retrieval-augmented generation”, 2026, p. 5) \#5fb236 \par - “What types of information do LLMs require for accurate dependency validation?” (“On automating configuration dependency validation via retrieval-augmented generation”, 2026, p. 5) \#5fb236 \par - “RAG variants.” (“On automating configuration dependency validation via retrieval-augmented generation”, 2026, p. 5) \#ffd400\\ \mkbibemph{Authors mention may times "RAG variants" without clarifying what are the peculiar characteristics of each of them. What are the variability components that have been analyzed to produce the mentioned variants?} \par - “This procedure ensures that all LLMs receive exactly the same context for a given dependency, such that we do not have effects of order or time and, thus, enabling a sound comparison of the validation performance across different LLMs and RAG variants.” (“On automating configuration dependency validation via retrieval-augmented generation”, 2026, p. 5) \#ffd400\\ \mkbibemph{At this stage reviewers have not received enough details to appreciate sentences like this one. No concrete elements have been previously given. Reviewers is supposed to guess what are the different ingredients.} \par - “We examine names and values of configuration options, as well as the technologies involved and assess whether the LLM’s reasoning aligns with its incorrect final assessmen” (“On automating configuration dependency validation via retrieval-augmented generation”, 2026, p. 5) \#ffd400\\ \mkbibemph{In sentences like this one you can refer to the example given at the beginning of the paper and that unfortunately you have no longer considered in the paper to explain the details of the proposed approach. The readability of the paper is really affected by the lack of details.} \par - “From these failure categories, we derive what type of information an LLM needs to improve dependency validation.” (“On automating configuration dependency validation via retrieval-augmented generation”, 2026, p. 5) \#ffd400\\ \mkbibemph{Again, see my previous comment. Can you give an intuition about a possible improvement?} \par - “Datasets” (“On automating configuration dependency validation via retrieval-augmented generation”, 2026, p. 5) \#2ea8e5 \par - “pring Boot microservice architecture as it represents a technology stack of significant practical relevance” (“On automating configuration dependency validation via retrieval-augmented generation”, 2026, p. 5) \#5fb236 \par - “To obtain a dataset of configuration dependencies, we ran CfgNet on the subject systems. For each software project, we sampled 50 potential dependencies, leading to a final set of 500 dependency candidates — the largest cross-technology dependency dataset we are aware of. We manually check each candidate in detail and classify it into one of three categories: (1) true if it represents a valid dependency, (2) false if it is a false positive, and (3) borderline if no decision can be made based on domain knowledge.” (“On automating configuration dependency validation via retrieval-augmented generation”, 2026, p. 5) \#5fb236 \par - “350 configuration dependencies, is used to investigate whether RAG enables effective dependency validation.” (“On automating configuration dependency validation via retrieval-augmented generation”, 2026, p. 6) \#a28ae5 \par - “150 configuration dependencies, serves as an unseen test set to evaluate the refined RAG variant’s ability to reliably validate configuration dependencies.” (“On automating configuration dependency validation via retrieval-augmented generation”, 2026, p. 6) \#a28ae5 \par - “RAG Variants and studied LLMs.” (“On automating configuration dependency validation via retrieval-augmented generation”, 2026, p. 6) \#2ea8e5 \par - “Hence, we adopt eight different RAG variants that differ in several core components, such as the embedding model (ada2 vs. Qwen2), the re-ranking algorithm (Colbert vs. Sentence Transformer), and the final number of context chunks provided to the LLM (3 vs. 5)” (“On automating configuration dependency validation via retrieval-augmented generation”, 2026, p. 6) \#ffd400\\ \mkbibemph{That's good, it answer one of my previous concern. I suggest anticipating this paragraph before the first reference of "RAG Variants" or adding references to this paragraph when "RAG Variants" are mentioned in the previous sections of the paper.} \par - “a 4” (“On automating configuration dependency validation via retrieval-augmented generation”, 2026, p. 6) \#ff6666 \par - “Context Sources” (“On automating configuration dependency validation via retrieval-augmented generation”, 2026, p. 6) \#2ea8e5 \par - “All these sources were scraped prior to generation queries (i.e., representing static context), whereas the Web (i.e., dynamic context) was scraped dynamically for each generation query.” (“On automating configuration dependency validation via retrieval-augmented generation”, 2026, p. 6) \#5fb236 \par - “Evaluation Metrics.” (“On automating configuration dependency validation via retrieval-augmented generation”, 2026, p. 6) \#2ea8e5 \par - “hese metrics highlight the trade-off between identifying as many true dependencies as possible (recall) and ensuring the correctness of positively labeled dependencies (precision).” (“On automating configuration dependency validation via retrieval-augmented generation”, 2026, p. 6) \#5fb236 \par - “The F1-score, the harmonic mean of precision and recall, provides a single scalar for better comparability.” (“On automating configuration dependency validation via retrieval-augmented generation”, 2026, p. 6) \#5fb236 \par - “By contrast, the additional context of a RAG system leads to a higher recall, which comes with a reduced precision” (“On automating configuration dependency validation via retrieval-augmented generation”, 2026, p. 7) \#5fb236 \par - “Good news is that a single architecture is constantly better than others, indicating that not model performance is affecting the outcome, but actual architectural decisions.” (“On automating configuration dependency validation via retrieval-augmented generation”, 2026, p. 7) \#5fb236 \par - “We refined RAG variant R1: (1) We incorporated project-specific details from each subject project by extracting its project structure, module organization, implementation details, and available resources. (2) We refined the validation prompt to make the definition of configuration dependencies more precise and avoiding any ambiguity. (3) We collected exemplary dependencies from each failure category along with their correct classification and added the two most similar examples based on cosine similarity. We apply refinement (2) also to the vanilla LLMs as this is clearly a measure, which can improve the baseline and is not RAG specific. To not jeopardize the validity of the results, we evaluate the final validation effectiveness of both the refined vanilla LLMs and the refined RAG variant R1 on an unseen dataset of 150 real-world configuration dependencies.” (“On automating configuration dependency validation via retrieval-augmented generation”, 2026, p. 9) \#ffd400\\ \mkbibemph{It would be interesting to see how this is actually done in practice and to what extent this process can be automated.} \par - “vanilla LLMs miss many dependencies, but are less prone to misclassifications than RAG systems” (“On automating configuration dependency validation via retrieval-augmented generation”, 2026, p. 10) \#5fb236 \par - “when enriched with special information derived from a qualitative analysis of validation failures, such as projectspecific details, precise and unambiguous prompts, as well as examples of similar dependencies, our proposed RAG system achieves substantially more accurate validation results for all models, with improvements of precision by 35 \% and recall by 133 \% over vanilla LLMs.” (“On automating configuration dependency validation via retrieval-augmented generation”, 2026, p. 10) \#ffd400\\ \mkbibemph{The qualitative analysis need to be added by humans.~ What's the price/effort of adding such information? \#question} \par - “The key takeaway is that merely adding context does not automatically enhance the validation effectiveness of LLMs. Instead, a systemic refinement of RAG components and particularly context sources is necessary to unleash the full potential of RAG for configuration dependency validation” (“On automating configuration dependency validation via retrieval-augmented generation”, 2026, p. 10) \#ffd400\\ \mkbibemph{Related to the previous comment. Can you elaborate more on the efforts required to do so? \#question} \par COnsider that those that are tagget with \#5fb236 are just highlights, those that are tagged with \#e56eee and \#a28ae5 are imporant sentences. Please pay attention instead to the notes that are tagged with \#ffd400. Those that are tagged with \#ff6666 are typos or errors. Could you please draft a review by organizing it as follows: SUMMARY: Just a few sentence to summarize the work STRENGHTS: \par WEAKNESSES: \par COMMENTS: Organize the notes with respect to the following criteria: \par \begin{itemize} \item \texttt{Novelty} \item \texttt{Rigor} \item \texttt{Relevance (of the contribution)} \item \texttt{Verifiability and Transparency} \item \texttt{Presentation} \end{itemize} \par And then add a Detailed Comments section to report the notets that contain issues or typos. Can you also formulate explicit questions by considering the comments that are tagged with \#question? Please merge those that are about the same topics and lead to 4 questions. \par Concerning the detailed comments, can you mention the page number instead of the tag? Example: Page 1 - “Cost-effective”: The term is central to the paper but remains vague at this point. I suggest clarifying even at this stage what kind of cost is being considered (e.g., token usage, financial cost, latency).} } @online{AutonomousSemiAutonomousSoftware, title = {Autonomous and {{Semi-Autonomous Software Systems}}}, url = {http://aosgrp.com/featured-research/autonomy_and_agents/autonomous_systems/autonomous_and_semi-autonom.html}, urldate = {2016-08-24} } @online{AutonomousSystems, title = {Autonomous {{Systems}}}, url = {https://www.cranfield.ac.uk/Academic%20disciplines/Autonomous-Systems}, urldate = {2016-08-21} } @online{AutonomousSystemsFormerly, title = {Autonomous {{Systems}} (Formerly {{Unmanned Systems}})}, url = {http://www.northropgrumman.com/Capabilities/AutonomousSystems/Pages/default.aspx}, urldate = {2016-08-26} } @online{Autonomy, title = {Autonomy}, url = {http://aosgrp.com/featured-research/autonomy_and_agents/autonomous_systems/autonomy.html}, urldate = {2016-08-24} } @article{AutoTaskLearningGenerate2021, title = {{{AutoTask}}: {{Learning}} to {{Generate Machine Learning Pipelines}}}, date = {2021}, pages = {11}, langid = {english} } @article{avgeriou_et_al:DR:2016:6693, title = {Managing Technical Debt in Software Engineering (Dagstuhl Seminar 16162)}, author = {Avgeriou, Paris and Kruchten, Philippe and Ozkaya, Ipek and Seaman, Carolyn}, editor = {Avgeriou, Paris and Kruchten, Philippe and Ozkaya, Ipek and Seaman, Carolyn}, date = {2016}, journaltitle = {Dagstuhl Rep.}, volume = {6}, number = {4}, pages = {110--138}, location = {Dagstuhl, Germany}, issn = {2192-5283}, doi = {10.4230/DagRep.6.4.110}, nopublisher = {Schloss Dagstuhl–Leibniz-Zentrum fuer Informatik}, nourl = {http://drops.dagstuhl.de/opus/volltexte/2016/6693}, urn = {urn:nbn:de:0030-drops-66938}, note = {Keywords: coding tools and techniques, design tools and techniques, management, metrics, software engineering \par TL;DR \par This report documents the program and outcomes of Dagstuhl Seminar 16162, “Managing Technical Debt in Software Engineering,” and summarizes the goals and format, results from the breakout groups, and a definition for technical debt.} } @article{avgeriouOverviewComparisonTechnical2021, title = {An {{Overview}} and {{Comparison}} of {{Technical Debt Measurement Tools}}}, author = {Avgeriou, Paris C. and Taibi, Davide and Ampatzoglou, Apostolos and Fontana, Francesca Arcelli and Besker, Terese and Chatzigeorgiou, Alexander and Lenarduzzi, Valentina and Martini, Antonio and Moschou, Athanasia and Pigazzini, Ilaria and Saarimaki, Nyyti and Sas, Darius Daniel and family=Toledo, given=Saulo Soares, prefix=de, useprefix=false and Tsintzira, Angeliki Agathi}, date = {2021-05-01}, journaltitle = {IEEE Softw.}, volume = {38}, number = {03}, pages = {61--71}, publisher = {IEEE Computer Society}, issn = {0740-7459}, doi = {10.1109/MS.2020.3024958}, abstract = {Different tools adopt different terms, metrics, and ways to identify and measure technical debt. We attempt to clarify the situation by comparing the features and popularity of technical debt measurement tools and analyzing the existing empirical evidence on their validity.}, langid = {english}, note = {TL;DR \par This work attempts to clarify the situation by comparing the features and popularity of technical debt measurement tools and analyzing the existing empirical evidence on their validity.} } @article{avila2023chatgpt, title = {Chatgpt as a Support Tool for Online Behavioral Task Programming}, author = {Avila-Chauvet, Laurent and Mejía, Diana and Acosta Quiroz, Christian Oswaldo}, date = {2023}, journaltitle = {Available SSRN 4329020} } @article{avitabileDefeatingMassSurveillance, title = {Towards {{Defeating Mass Surveillance}} and {{SARS-CoV-2}}: {{The Pronto-C2 Fully Decentralized Automatic Contact Tracing System}}}, author = {Avitabile, Gennaro and Botta, Vincenzo and Iovino, Vincenzo and Visconti, Ivan}, pages = {25}, abstract = {Mass surveillance can be more easily achieved leveraging fear and desire of the population to feel protected while affected by devastating events. In such cases governments are more legitimate to adopt exceptional measures that limit civil rights, usually receiving large support from their citizens.}, langid = {english}, note = {TL;DR \par Taking into account the privacy and integrity vulnerabilities of DP-3T systems, the design of a decentralized contact tracing system named Pronto-C2 is shown that has better resilience against various attacks.} } @inproceedings{azzaraPyoTMacroprogrammingFramework2014, title = {{{PyoT}}, a Macroprogramming Framework for the {{Internet}} of {{Things}}}, booktitle = {Ind. {{Embed}}. {{Syst}}. {{SIES}} 2014 9th {{IEEE Int}}. {{Symp}}. {{On}}}, author = {Azzara, Andrea and Alessandrelli, Daniele and Bocchino, Stefano and Petracca, Matteo and Pagano, Paolo}, date = {2014}, pages = {96--103}, publisher = {IEEE}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6871193}, urldate = {2016-05-30}, note = {TL;DR \par PyoT is presented, a framework designed to simplify the development of complex applications for the Internet of Things, coordinating the activities of group of nodes, adopting the macroprogramming paradigm.} } @article{baba-cheikhPreliminaryStudyOpensource2020, title = {A Preliminary Study of Open-Source {{IoT}} Development Frameworks}, author = {Baba-Cheikh, Zeineb and El-Boussaidi, Ghizlane and Gascon-Samson, Julien and Mili, Hafedh and Guéhéneuc, Yann-Gael}, date = {2020}, pages = {8}, abstract = {The Internet of Things (IoT) market is growing fast with an increasing number of connected devices. This led many software companies to shift their focus to develop and provide IoT solutions. IoT development has its own challenges as typical IoT solutions are composed of heterogeneous devices, protocols and software. To cope with these challenges, many frameworks are available to help developers to build IoT applications. Some of these frameworks are open source and might be of great interest for small and mediumsized companies wishing to build IoT solutions at a lower cost. In this paper, we present the results of a preliminary study of four open source IoT development frameworks. In particular, we used these frameworks to implement a sample of three IoT applications and we analyze them against a minimal set of IoT requirements. We focus in our study on the IoT development for Raspberry PI as it is a very low-cost and popular platform.}, langid = {english}, note = {TL;DR \par This paper presents the results of a preliminary study of four open source IoT development frameworks used to implement a sample of three IoT applications and analyzes them against a minimal set of IoT requirements.} } @inproceedings{babaeigiglouLLMs4OLLargeLanguage2023, title = {{{LLMs4OL}}: {{Large Language Models}} for~{{Ontology Learning}}}, shorttitle = {{{LLMs4OL}}}, booktitle = {Semantic {{Web}} – {{ISWC}} 2023}, author = {Babaei Giglou, Hamed and D’Souza, Jennifer and Auer, Sören}, editor = {Payne, Terry R. and Presutti, Valentina and Qi, Guilin and Poveda-Villalón, María and Stoilos, Giorgos and Hollink, Laura and Kaoudi, Zoi and Cheng, Gong and Li, Juanzi}, date = {2023}, series = {Lecture {{Notes}} in {{Computer Science}}}, pages = {408--427}, publisher = {Springer Nature Switzerland}, location = {Cham}, doi = {10.1007/978-3-031-47240-4_22}, abstract = {We propose the LLMs4OL approach, which utilizes Large Language Models (LLMs) for Ontology Learning (OL). LLMs have shown significant advancements in natural language processing, demonstrating their ability to capture complex language patterns in different knowledge domains. Our LLMs4OL paradigm investigates the following hypothesis: Can LLMs effectively apply their language pattern capturing capability to OL, which involves automatically extracting and structuring knowledge from natural language text? To test this hypothesis, we conduct a comprehensive evaluation using the zero-shot prompting method. We evaluate nine different LLM model families for three main OL tasks: term typing, taxonomy discovery, and extraction of non-taxonomic relations. Additionally, the evaluations encompass diverse genres of ontological knowledge, including lexicosemantic knowledge in WordNet, geographical knowledge in GeoNames, and medical knowledge in UMLS.}, isbn = {978-3-031-47240-4}, langid = {english}, keywords = {Large Language Models,LLMs,Ontologies,Ontology Learning,Prompt-based Learning,Prompting} } @incollection{babikianAutomatedGenerationConsistent2020, title = {Automated Generation of Consistent Graph Models with First-Order Logic Theorem Provers}, booktitle = {Fundamental {{Approaches}} to {{Software Engineering}}}, author = {Babikian, Aren A. and Semeráth, Oszkár and Varró, Dániel}, editor = {Wehrheim, Heike and Cabot, Jordi}, date = {2020}, volume = {12076}, pages = {441--461}, publisher = {Springer International Publishing}, location = {Cham}, doi = {10.1007/978-3-030-45234-6_22}, isbn = {978-3-030-45233-9 978-3-030-45234-6}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {TL;DR \par This paper proposes a transformation technique to map such graph generation tasks to a problem consisting of first-order logic formulae, which can be solved by state-of-the-art TPTP-compliant theorem provers, producing valid graph models as outputs.} } @article{BabikianSLMV22, title = {Automated Generation of Consistent Models Using Qualitative Abstractions and Exploration Strategies}, author = {Babikian, Aren A. and Semeráth, Oszkár and Li, Anqi and Marussy, Kristóf and Varró, Dániel}, date = {2022}, journaltitle = {Software Syst. Model.}, volume = {21}, number = {5}, pages = {1763--1787}, doi = {10.1007/S10270-021-00918-6}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Tue, 06 Dec 2022 13:15:29 +0100} } @inproceedings{Babur2016888, title = {Statistical Analysis of Large Sets of Models}, author = {Babur, Ö.}, editor = {Khurshid S., Lo D., Apel S.}, date = {2016}, series = {{{ASE}} 2016 - {{Proceedings}} of the 31st {{IEEE}}/{{ACM International Conference}} on {{Automated Software Engineering}}}, pages = {888--891}, publisher = {Association for Computing Machinery, Inc}, doi = {10.1145/2970276.2975938}, abbrev_source_title = {ASE - Proc. IEEE/ACM Int. Conf. Autom. Softw. Eng.}, affiliation = {Eindhoven University of Technology, Eindhoven, 5600 MB, Netherlands}, correspondence_address1 = {Babur, Ö.; Eindhoven University of TechnologyNetherlands; email: O.Babur@tue.nl}, document_type = {Conference Paper}, isbn = {978-1-4503-3845-5}, langid = {english}, source = {Scopus}, keywords = {GOAL\_Model-Comparison,notion,TECHNIQUE\_K-NEAREST-NEIGHBORS}, note = {cited By 7 \par TL;DR \par This work would like to develop a generic approach for model comparison and analysis for large datasets; using techniques from information retrieval, natural language processing and machine learning.} } @article{Babur2018129, title = {Models, More Models, and Then a Lot More}, author = {Babur, Ö. and Cleophas, L. and family=Brand, given=M., prefix=van den, useprefix=true and Tekinerdogan, B. and Aksit, M.}, editor = {Zschaler S., Seidl M.}, date = {2018}, journaltitle = {Lect. Notes Comput. Sci. Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinforma.}, volume = {10748 LNCS}, pages = {129--135}, publisher = {Springer Verlag}, issn = {03029743}, doi = {10.1007/978-3-319-74730-9_10}, abbrev_source_title = {Lect. Notes Comput. Sci.}, affiliation = {Eindhoven University of Technology, Eindhoven, Netherlands; Wageningen University \& Research, Wageningen, Netherlands; University of Twente, Enschede, Netherlands}, correspondence_address1 = {Babur, Ö.; Eindhoven University of TechnologyNetherlands; email: O.Babur@tue.nl}, document_type = {Conference Paper}, isbn = {9783319747293}, langid = {english}, source = {Scopus}, note = {cited By 8} } @inproceedings{Babur2018778, title = {{{BIDE}}: {{Efficient}} Mining of Frequent Closed Sequences}, booktitle = {Proc. 4th {{ACM Workshop Secur}}. {{Artif}}. {{Intell}}.}, author = {Wang, Jianyong and Han, Jiawei}, date = {2004}, series = {{{AISec}} '11}, volume = {15}, pages = {43--58}, publisher = {IEEE Computer Society}, location = {Washington, DC, USA}, issn = {16130073}, doi = {10.1145/2046684.2046692}, abbrev_source_title = {CEUR Workshop Proc.}, abstract = {In this paper (expanded from an invited talk at AISEC 2010), we discuss an emerging field of study: adversarial machine learning—the study of effective machine learning techniques against an adversarial opponent. In this paper, we: give a taxonomy for classifying attacks against online machine learning algorithms; discuss application-specific factors that limit an adversary's capabilities; introduce two models for modeling an adversary's capabilities; explore the limits of an adversary's knowledge about the algorithm, feature space, training, and input data; explore vulnerabilities in machine learning algorithms; discuss countermeasures against attacks; introduce the evasion challenge; and discuss privacy-preserving learning techniques.}, affiliation = {Eindhoven University of Technology, Eindhoven, Netherlands; Chalmers | University of Gothenburg, Gothenburg, Sweden; Eindhoven University of Technology Eindhoven, Netherlands Stellenbosch University, Stellenbosch, South Africa; University of L'Aquila, L'Aquila, Italy; University of York York, United Kingdom}, document_type = {Conference Paper}, isbn = {0-7695-2065-0}, langid = {english}, pagetotal = {16}, source = {Scopus}, keywords = {adversarial learning,computer security,duplicate-citation-key,game theory,intrusion detection,machine learning,security metrics,spam filters,statistical learning}, note = {TL;DR \par BIDE is an efficient algorithm for mining frequent closed sequences without candidate maintenance, which adopts a novel sequence closure checking scheme called bidirectional extension, and prunes the search space more deeply compared to the previous algorithms by using the BackScan pruning method and the Scan-Skip optimization technique.} } @incollection{Babur2018778, ids = {baburAMMoRe2018First2018,baburAMMoRe2018First2018a,baburAMMoRe2018First2018b,baburAMMoRe2018First2018c,baburAMMoRe2018First2018d}, title = {{{AMMoRe}} 2018: {{First}} International Workshop on Analytics and Mining of Model Repositories}, booktitle = {{{CEUR Workshop Proceedings}}}, author = {Babur, O. and Chaudron, M. R. V. and Cleophas, L. and Di Ruscio, D. and Kolovos, D.}, date = {2018}, series = {{{CEUR Workshop Proceedings}}}, volume = {2245}, pages = {778--779}, publisher = {CEUR-WS}, issn = {16130073}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85063109105&partnerID=40&md5=9b6af321cedc951abe47939224d38e2b}, abbrev_source_title = {CEUR Workshop Proc.}, abstract = {Model-based approaches promote the use of models and related artifacts (such as metamodels and model transformations) as central elements to tackle the complexity of building systems. Both in academia and in industry there is a growing need to efficiently i) store; ii) analyze; and ii) search \& navigate, and iii) curate large collections of models. Such collections include for example large sets of software models such as the Lindholmen UML dataset [1], or of heterogeneous models in large MDE ecosystems and systems-of-systems, including e.g. software, hardware, and business models. The workshop Analytics and Mining of Model Repositories (AMMoRe) aims to gather modelling researchers and practitioners to discuss the emerging problems and propose solutions. The scope ranges from industrial reports and empirical analyses in the problem domain to novel cross-disciplinary approaches for large-scale analytics and management, e.g. exploiting techniques from data analytics, repository mining and machine learning. © 2018 CEUR-WS. All rights reserved.}, affiliation = {Eindhoven University of Technology, Eindhoven, Netherlands; Chalmers | University of Gothenburg, Gothenburg, Sweden; Eindhoven University of Technology Eindhoven, Netherlands Stellenbosch University, Stellenbosch, South Africa; University of L'Aquila, L'Aquila, Italy; University of York York, United Kingdom}, document_type = {Conference Paper}, source = {Scopus}, keywords = {duplicate-citation-key}, note = {cited By 2 \par cited By 2 \par cited By 2 \par cited By 3 \par cited By 3 \par TL;DR \par The workshop Analytics and Mining of Model Repositories (AMMoRe) aims to gather modelling researchers and practitioners to discuss the emerging problems and propose solutions to solve the growing need to efficiently store, analyze, and curate large collections of models.} } @dataset{baburLabeledEcoreMetamodel2019, ids = {onder_babur_2019_2585456}, title = {A Labeled {{Ecore}} Metamodel Dataset for Domain Clustering}, author = {Babur, Önder}, date = {2019-03-06}, publisher = {Zenodo}, doi = {10.5281/ZENODO.2585456}, abstract = {Manually labeled 555 metamodels mined from GitHub in April 2017. Domains: (1) bibliography, (2) conference management, (3) bug/issue tracker, (4) build systems, (5) document/office products, (6) requirement/use case, (7) database/sql, (8) state machines, (9) petri nets Procedure for constructing the dataset: fully manual, by searching for certain keywords and regexes (e.g. "state" and "transition" for state machines) in the metamodels and inspecting the results for inclusion. Format for the file names: ABSINDEX\_CLUSTER\_ITEMINDEX\_name\_hash.ecore}, version = {0.1.1}, keywords = {clustering,metamodel,model analytics,model-driven engineering} } @article{Bachinger2020263, title = {Concept for a Technical Infrastructure for Management of Predictive Models in Industrial Applications}, author = {Bachinger, F. and Kronberger, G.}, editor = {Moreno-Diaz R., Quesada-Arencibia A., Pichler F.}, date = {2020}, journaltitle = {Lect. Notes Comput. Sci. Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinforma.}, volume = {12013 LNCS}, pages = {263--270}, publisher = {Springer}, issn = {03029743}, doi = {10.1007/978-3-030-45093-9_32}, abstract = {With the increasing number of created and deployed prediction models and the complexity of machine learning workflows we require so called model management systems to support data scientists in their tasks. In this work we describe our technological concept for such a model management system. This concept includes versioned storage of data, support for different machine learning algorithms, fine tuning of models, subsequent deployment of models and monitoring of model performance after deployment. We describe this concept with a close focus on model lifecycle requirements stemming from our industry application cases, but generalize key features that are relevant for all applications of machine learning. © 2020, Springer Nature Switzerland AG.}, document_type = {Conference Paper}, isbn = {9783030450922}, source = {Scopus}, note = {cited By 0 \par TL;DR \par This work describes the technological concept for a model management system that includes versioned storage of data, support for different machine learning algorithms, fine tuning of models, subsequent deployment of models and monitoring of model performance after deployment.} } @article{Bae2015, title = {A Personal Credit Rating Prediction Model Using Data Mining in Smart Ubiquitous Environments}, author = {Bae, J.K. and Kim, J.}, date = {2015}, journaltitle = {Int. J. Distrib. Sens. Netw.}, volume = {2015}, publisher = {Hindawi Publishing Corporation}, issn = {15501329}, doi = {10.1155/2015/179060}, abbrev_source_title = {Int. J. Distrib. Sens. Netw.}, affiliation = {Department of Management Information Systems, Keimyung University, 1095 Dalgubeoldaero, Daegu, 704-701, South Korea; School of Business, Sogang University, 1 Sinsu-dong, Seoul, 121-742, South Korea}, art_number = {179060}, correspondence_address1 = {Bae, J.K.; Department of Management Information Systems, Keimyung University, 1095 Dalgubeoldaero, South Korea}, document_type = {Article}, langid = {english}, source = {Scopus}, note = {cited By 5} } @article{Bae2022, title = {Limit Surface/States Searching Algorithm with a Deep Neural Network and {{Monte Carlo}} Dropout for Nuclear Power Plant Safety Assessment}, author = {Bae, J. and Park, J.W. and Lee, S.J.}, date = {2022}, journaltitle = {Appl. Soft Comput.}, volume = {124}, publisher = {Elsevier Ltd}, issn = {15684946}, doi = {10.1016/j.asoc.2022.109007}, abbrev_source_title = {Appl. Soft Comput.}, affiliation = {Department of Nuclear Engineering, Ulsan National Institute of Science and Technology, 50 UNIST-gil, Ulsan, 44919, South Korea}, art_number = {109007}, correspondence_address1 = {Lee, S.J.; Department of Nuclear Engineering, 50 UNIST-gil, South Korea; email: sjlee420@unist.ac.kr}, document_type = {Article}, langid = {english}, source = {Scopus}, note = {cited By 0} } @inproceedings{bagnatoDeveloperCentricKnowledgeMining2017, ids = {bagnatoDeveloperCentricKnowledgeMining2017a,bagnatoDeveloperCentricKnowledgeMining2018,bagnatoDeveloperCentricKnowledgeMining2018a}, title = {Developer-{{Centric Knowledge Mining}} from {{Large Open-Source Software Repositories}} ({{CROSSMINER}})}, booktitle = {Softw. {{Technol}}. {{Appl}}. {{Found}}. - {{STAF}} 2017 {{Collocated Workshop Marburg Ger}}. {{July}} 17-21 2017 {{Revis}}. {{Sel}}. {{Pap}}.}, author = {Bagnato, Alessandra and Barmpis, Konstantinos and Bessis, Nik and Cabrera-Diego, Luis Adrián and Rocco, Juri Di and Ruscio, Davide Di and Gergely, Tamás and Hansen, Scott and Kolovos, Dimitris S. and Krief, Philippe and Korkontzelos, Ioannis and Laurière, Stéphane and family=Fuente, given=Jose Manrique Lopez, prefix=de la, useprefix=false and Maló, Pedro and Paige, Richard F. and Spinellis, Diomidis and Thomas, Cedric and Vinju, Jurgen J.}, editor = {Seidl, Martina and Zschaler, Steffen}, date = {2017}, series = {Lecture {{Notes}} in {{Computer Science}}}, volume = {10748}, pages = {375--384}, publisher = {Springer}, doi = {10.1007/978-3-319-74730-9_33}, note = {cited By 9 \par cited By 9} } @article{Bai2022112, title = {Prior Information Aided Deep Learning Method for Grant-Free {{NOMA}} in {{mMTC}}}, author = {Bai, Y. and Chen, W. and Ai, B. and Zhong, Z. and Wassell, I.J.}, date = {2022}, journaltitle = {IEEE J. Sel. Areas Commun.}, volume = {40}, number = {1}, pages = {112--126}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {07338716}, doi = {10.1109/JSAC.2021.3126071}, abstract = {In massive machine-type communications (mMTC), the conflict between millions of potential access devices and limited channel freedom leads to a sharp decrease in spectrum efficiency. The nature of sporadic activity in mMTC provides a solution to enhance spectrum efficiency by employing compressive sensing (CS) to perform multiuser detection (MUD). However, CS-MUD suffers from high computation complexity and fails to meet the strict latency requirement in some critical applications. To address this problem, in this paper, we propose a novel deep learning (DL) based framework for grant-free non-orthogonal multiple access (GF-NOMA), where we utilize the information distilled from the initial data recovery phase to further enhance channel estimation, which in turn improves data recovery performance. Besides, we design an interpretable and structured Model-driven Prior Information Aided Network (M-PIAN) and provide theoretical analysis that demonstrates the proposed M-PIAN can converge faster and support more users. Experiments show that the proposed method outperforms existing CS algorithms and DL methods in both computation complexity and reconstruction accuracy. © 1983-2012 IEEE.}, coden = {ISACE}, document_type = {Article}, source = {Scopus}, note = {cited By 2 \par TL;DR \par A novel deep learning (DL) based framework for grant-free non-orthogonal multiple access (GF-NOMA) is proposed, where the information distilled from the initial data recovery phase is utilized to further enhance channel estimation, which in turn improves data recovery performance.} } @unpublished{bakerSingularValueDecomposition2005, title = {Singular Value Decomposition Tutorial}, author = {Baker, Kirk and Baker, Kirk}, date = {2005}, added-at = {2009-04-17T10:27:21.000+0200}, biburl = {https://www.bibsonomy.org/bibtex/2abdcfb3746e47898ac93929ab88298af/voigtmannc}, interhash = {52abe308f6c46087df51a5876c1ab6e9}, intrahash = {abdcfb3746e47898ac93929ab88298af}, keywords = {algorithm datamining decomposition singular svd tutorial}, timestamp = {2009-04-17T10:27:21.000+0200} } @article{baki2016multi, title = {Multi-Step Learning and Adaptive Search for Learning Complex Model Transformations from Examples}, author = {Baki, Islem and Sahraoui, Houari}, date = {2016}, journaltitle = {ACM Trans. Softw. Eng. Methodol. (TOSEM)}, volume = {25}, number = {3}, pages = {1--37}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {TL;DR \par This article proposes an MTBE process to learn complex model transformations by considering three common requirements: element context and state dependencies and complex value derivation, and relies on two strategies to reduce the size of the search space and to better explore it, namely, multi-step learning and adaptive search.} } @article{balabanPatternbasedApproachImproving2015, title = {A Pattern-Based Approach for Improving Model Quality}, author = {Balaban, Mira and Maraee, Azzam and Sturm, Arnon and Jelnov, Pavel}, date = {2015-10}, journaltitle = {Softw. Syst. Model.}, volume = {14}, number = {4}, pages = {1527--1555}, issn = {1619-1366, 1619-1374}, doi = {10.1007/s10270-013-0390-0}, langid = {english} } @article{Balasubramanian20153, title = {{{DREMS ML}}: {{A}} Wide Spectrum Architecture Design Language for Distributed Computing Platforms}, author = {Balasubramanian, D. and Dubey, A. and Otte, W. and Levendovszky, T. and Gokhale, A. and Kumar, P. and Emfinger, W. and Karsai, G.}, date = {2015}, journaltitle = {Sci. Comput. Program.}, volume = {106}, pages = {3--29}, publisher = {Elsevier B.V.}, issn = {01676423}, doi = {10.1016/j.scico.2015.04.002}, abstract = {Complex sensing, processing and control applications running on distributed platforms are difficult to design, develop, analyze, integrate, deploy and operate, especially if resource constraints, fault tolerance and security issues are to be addressed. While technology exists today for engineering distributed, real-time component-based applications, many problems remain unsolved by existing tools. Model-driven development techniques are powerful, but there are very few existing and complete tool chains that offer an end-to-end solution to developers, from design to deployment. There is a need for an integrated model-driven development environment that addresses all phases of application lifecycle including design, development, verification, analysis, integration, deployment, operation and maintenance, with supporting automation in every phase. Arguably, a centerpiece of such a model-driven environment is the modeling language. To that end, this paper presents a wide-spectrum architecture design language called DREMS ML that itself is an integrated collection of individual domain-specific sub-languages. We claim that the language promotes "correct-by-construction" software development and integration by supporting each individual phase of the application lifecycle. Using a case study, we demonstrate how the design of DREMS ML impacts the development of embedded systems. © 2015 Elsevier B.V. All rights reserved.}, coden = {SCPGD}, document_type = {Article}, source = {Scopus}, note = {cited By 9} } @article{balogDeepCoderLearningWrite2016, title = {{{DeepCoder}}: {{Learning}} to {{Write Programs}}}, shorttitle = {{{DeepCoder}}}, author = {Balog, Matej and Gaunt, Alexander L. and Brockschmidt, Marc and Nowozin, Sebastian and Tarlow, Daniel}, date = {2016}, journaltitle = {ArXiv Prepr. ArXiv161101989}, eprint = {1611.01989}, eprinttype = {arXiv}, url = {https://arxiv.org/abs/1611.01989}, urldate = {2017-02-25} } @article{baloghModelTransformationExample2009, title = {Model Transformation by Example Using Inductive Logic Programming}, author = {Balogh, Zoltán and Varró, Dániel}, date = {2009-07}, journaltitle = {Softw Syst Model}, volume = {8}, number = {3}, pages = {347--364}, issn = {1619-1366, 1619-1374}, doi = {10.1007/s10270-008-0092-1}, abstract = {Model transformation by example is a novel approach in model-driven software engineering to derive model transformation rules from an initial prototypical set of interrelated source and target models, which describe critical cases of the model transformation problem in a purely declarative way. In the current paper, we automate this approach using inductive logic programming (Muggleton and Raedt in J Logic Program 19-20:629–679, 1994) which aims at the inductive construction of first-order clausal theories from examples and background knowledge.}, langid = {english} } @article{balouek-thomertComputingContinuumEnabling2019, ids = {balouek-thomertComputingContinuumEnabling2019a}, title = {Towards a Computing Continuum: {{Enabling}} Edge-to-Cloud Integration for Data-Driven Workflows}, shorttitle = {Towards a Computing Continuum}, author = {Balouek-Thomert, Daniel and Renart, Eduard Gibert and Zamani, Ali Reza and Simonet, Anthony and Parashar, Manish}, date = {2019-11}, journaltitle = {The International Journal of High Performance Computing Applications}, volume = {33}, number = {6}, pages = {1159--1174}, issn = {1094-3420, 1741-2846}, doi = {10.1177/1094342019877383}, abstract = {Dramatic changes in the technology landscape marked by increasing scales and pervasiveness of compute and data have resulted in the proliferation of edge applications aimed at effectively processing data in a timely manner. As the levels and fidelity of instrumentation increases and the types and volumes of available data grow, new classes of applications are being explored that seamlessly combine real-time data with complex models and data analytics to monitor and manage systems of interest. However, these applications require a fluid integration of resources at the edge, the core, and along the data path to support dynamic and data-driven application workflows, that is, they need to leverage a computing continuum. In this article, we present our vision for enabling such a computing continuum and specifically focus on enabling edge-to-cloud integration to support data-driven workflows. The research is driven by an online data-driven tsunami warning use case that is supported by the deployment of large-scale national environment observation systems. This article presents our overall approach as well as current status and next steps.}, langid = {english}, note = {TL;DR \par This article presents the overall approach as well as current status and next steps for enabling edge-to-cloud integration to support data-driven workflows and is driven by an online data- driven tsunami warning use case that is supported by the deployment of large-scale national environment observation systems.} } @inproceedings{baltesSOTorrentReconstructingAnalyzing2018, title = {{{SOTorrent}}: {{Reconstructing}} and Analyzing the Evolution of Stack Overflow Posts}, booktitle = {Proc. 15th {{Int}}. {{Conf}}. {{Min}}. {{Softw}}. {{Repos}}.}, author = {Baltes, Sebastian and Dumani, Lorik and Treude, Christoph and Diehl, Stephan}, date = {2018}, series = {{{MSR}} '18}, pages = {319--330}, publisher = {ACM}, location = {New York, NY, USA}, url = {http://doi.acm.org/10.1145/3196398.3196430}, acmid = {3196430}, isbn = {978-1-4503-5716-6}, nodoi = {10.1145/3196398.3196430}, numpages = {12}, keywords = {code snippets,open dataset,software evolution,stack overflow} } @online{bamaniaBuildingYourFirst2025, title = {Building {{Your First AI Agent}} ({{That Will Actually Improve You As An AI Engineer}})}, author = {Bamania, Dr Ashish}, date = {2025-03-19T11:16:47}, url = {https://levelup.gitconnected.com/building-your-first-ai-agent-that-will-actually-improve-you-as-an-ai-engineer-4cb99e590d30}, urldate = {2025-03-23}, abstract = {A guide to building a helpful Multi-agent AI system that helps you find the top 10 AI research papers published in ArXiv every day}, keywords = {MOSAICO,source\_mobile} } @book{banchsInformationRetrievalTechnology2013, title = {Information {{Retrieval Technology}}}, editor = {Banchs, Rafael E. and Silvestri, Fabrizio and Liu, Tie-Yan and Zhang, Min and Gao, Sheng and Lang, Jun}, editora = {Hutchison, David and Kanade, Takeo and Kittler, Josef and Kleinberg, Jon M. and Mattern, Friedemann and Mitchell, John C. and Naor, Moni and Nierstrasz, Oscar and Pandu Rangan, C. and Steffen, Bernhard and Sudan, Madhu and Terzopoulos, Demetri and Tygar, Doug and Vardi, Moshe Y. and Weikum, Gerhard}, editoratype = {redactor}, date = {2013}, series = {Lecture {{Notes}} in {{Computer Science}}}, volume = {8281}, publisher = {Springer Berlin Heidelberg}, location = {Berlin, Heidelberg}, doi = {10.1007/978-3-642-45068-6}, isbn = {978-3-642-45067-9 978-3-642-45068-6}, note = {TL;DR \par This work investigates the relationship between relevance likelihood and retrieval rank, seeking to identify plausible methods for estimating document relevance and hence computing an inferred gain.} } @article{bansiyaHierarchicalModelObjectoriented2002, title = {A Hierarchical Model for Object-Oriented Design Quality Assessment}, author = {Bansiya, J. and Davis, C.G.}, date = {2002}, journaltitle = {IEEE Trans. Softw. Eng.}, volume = {28}, number = {1}, pages = {4--17}, doi = {10.1109/32.979986}, keywords = {⛔ No INSPIRE recid found,OO metrics,quality assessment} } @inproceedings{bao_mining_2018, title = {Mining Sandboxes: {{Are}} We There Yet?}, shorttitle = {Mining Sandboxes}, booktitle = {2018 {{IEEE}} 25th {{Int}}. {{Conf}}. {{Softw}}. {{Anal}}. {{Evol}}. {{Reengineering SANER}}}, author = {Bao, L. and Le, T. B. and Lo, D.}, date = {2018-03}, pages = {445--455}, doi = {10.1109/SANER.2018.8330231}, abstract = {The popularity of Android platform on mobile devices has attracted much attention from many developers and researchers, as well as malware writers. Recently, Jamrozik et al. proposed a technique to secure Android applications referred to as mining sandboxes. They used an automated test case generation technique to explore the behavior of the app under test and then extracted a set of sensitive APIs that were called. Based on the extracted sensitive APIs, they built a sandbox that can block access to APIs not used during testing. However, they only evaluated the proposed technique with benign apps but not investigated whether it was effective in detecting malicious behavior of malware that infects benign apps. Furthermore, they only investigated one test case generation tool (i.e., Droidmate) to build the sandbox, while many others have been proposed in the literature. In this work, we complement Jamrozik et al.'s work in two ways: (1) we evaluate the effectiveness of mining sandboxes on detecting malicious behaviors; (2) we investigate the effectiveness of multiple automated test case generation tools to mine sandboxes. To investigate effectiveness of mining sandboxes in detecting malicious behaviors, we make use of pairs of malware and benign app it infects. We build a sandbox based on sensitive APIs called by the benign app and check if it can identify malicious behaviors in the corresponding malware. To generate inputs to apps, we investigate five popular test case generation tools: Monkey, Droidmate, Droidbot, GUIRipper, and PUMA. We conduct two experiments to evaluate the effectiveness and efficiency of these test case generation tools on detecting malicious behavior. In the first experiment, we select 10 apps and allow test case generation tools to run for one hour; while in the second experiment, we select 102 pairs of apps and allow the test case generation tools to run for one minute. Our experiments highlight that 75.5\%-77.2\% of malware in our dataset can be uncovered by mining sandboxes - showing its power to protect Android apps. We also find that Droidbot performs best in generating test cases for mining sandboxes, and its effectiveness can be further boosted when coupled with other test case generation tools.}, keywords = {Android (operating system),Android apps,Android Malware,Androids,application program interfaces,Automated Test Case Generation,automated test case generation technique,benign apps,data mining,Droidbot,Droidmate,GUIRipper,Humanoid robots,invasive software,Java,malicious behavior,malware,Malware,Mining Sandboxing,mobile computing,Mobile handsets,Monkey,multiple automated test case generation tools,program testing,PUMA,sandboxes mining,sensitive APIs,test case generation tool,Testing,Tools} } @article{Bao2021706, title = {An Automated Approach to Generate SysML Models from Restricted Natural Language Requirements in Chinese [基于限定中文自然语言需求的SysML模型自动生成方法]}, author = {Bao, Y. and Yang, Z. and Yang, Y. and Xie, J. and Zhou, Y. and Yue, T. and Huang, Z. and Guo, P.}, date = {2021}, journaltitle = {Jisuanji Yanjiu Yu FazhanComputer Res. Dev.}, volume = {58}, number = {4}, pages = {706--730}, publisher = {Science Press}, issn = {10001239}, doi = {10.7544/issn1000-1239.2021.20200757}, abbrev_source_title = {Jisuanji Yanjiu yu Fazhan}, affiliation = {School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China; Key Laboratory of Safety-critical Software, Ministry of Industry and Information Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China; Aviation Computing Technology Research Institute, Xi'an, 710065, China}, coden = {JYYFE}, correspondence_address1 = {Yang, Z.; School of Computer Science and Technology, China; email: yangzhibin168@163.com}, document_type = {Article}, langid = {chinese}, source = {Scopus}, keywords = {GOAL\_Model-synthesis,notion,TECHNIQUE\_DNN}, note = {cited By 0 \par ONLY THE ABSTRACT IS WRITTEN IN ENGLISH!} } @incollection{barberoPracticalApproachModel2007, title = {A {{Practical Approach}} to {{Model Extension}}}, booktitle = {Model {{Driven Architecture- Foundations}} and {{Applications}}}, author = {Barbero, Mikaël and Jouault, Frédéric and Gray, Jeff and Bézivin, Jean}, editor = {Akehurst, David H. and Vogel, Régis and Paige, Richard F.}, date = {2007}, volume = {4530}, pages = {32--42}, publisher = {Springer Berlin Heidelberg}, location = {Berlin, Heidelberg}, doi = {10.1007/978-3-540-72901-3_3}, abstract = {In object technology, reusability is achieved primarily through class inheritance. In model engineering, where reusability is also important, it should be possible to extend a modeling artifact in a similar manner to add new capabilities. This paper presents a conceptual and practical approach to model extensibility, in which new models are created as derivations from base models. There are several situations where such an extensibility mechanism is useful and essential (e.g., in the case of hierarchies of metamodels). In order to achieve the goal of model extension, a precise definition of the extension mechanism is needed, based on a strict model definition. After describing the context of model extension through a motivating example, the paper outlines a practical implementation with characterization of its main conceptual properties. The solution is being implemented as part of the AMMA model engineering platform under Eclipse.}, isbn = {978-3-540-72900-6}, langid = {english} } @article{baresiBuildingSoftwareInternet2015, title = {Building {{Software}} for the {{Internet}} of {{Things}}}, author = {Baresi, Luciano and Mottola, Luca and Dustdar, Schahram}, date = {2015-03}, journaltitle = {IEEE Internet Comput.}, volume = {19}, number = {2}, pages = {6--8}, issn = {1089-7801}, doi = {10.1109/MIC.2015.31}, langid = {english}, note = {TL;DR \par The guest editors present a special issue on building software for the Internet of Things (IoT) with a focus on artificial intelligence (AI) and robotics.} } @article{Barmpis20141, title = {Evaluation of Contemporary Graph Databases for Ecient Persistence of Large-Scale Models}, author = {Barmpis, K. and Kolovos, D.S.}, date = {2014}, journaltitle = {J. Object Technol.}, volume = {13}, number = {3}, pages = {1--26}, publisher = {Association Internationale pour les Technologies Objets}, issn = {16601769}, doi = {10.5381/jot.2014.13.3.a3}, abbrev_source_title = {J. Object Technol.}, affiliation = {Department of Computer Science, University of York, Heslington, York, YO10 5DD, United Kingdom}, document_type = {Article}, langid = {english}, source = {Scopus}, note = {cited By 25} } @article{Barriga20221135, title = {{{AI-powered}} Model Repair: An Experience Report—Lessons Learned, Challenges, and Opportunities}, author = {Barriga, A. and Rutle, A. and Heldal, R.}, date = {2022}, journaltitle = {Softw. Syst. Model.}, volume = {21}, number = {3}, pages = {1135--1157}, publisher = {{Springer Science and Business Media Deutschland GmbH}}, issn = {16191366}, doi = {10.1007/s10270-022-00983-5}, abbrev_source_title = {Softw. Syst. Model.}, affiliation = {Western Norway University of Applied Sciences, Bergen, Norway}, correspondence_address1 = {Barriga, A.; Western Norway University of Applied SciencesNorway; email: abar@hvl.no}, document_type = {Article}, langid = {english}, source = {Scopus}, keywords = {GOAL\_Model-Repair,notion,TECHNIQUE\_DNN,TECHNIQUE\_GENETIC\_ALGORITHMS,TECHNIQUE\_ILP,TECHNIQUE\_MARKOV\_DECISION\_PROCESS,TECHNIQUE\_NN}, note = {cited By 0 \par TL;DR \par A number of research opportunities are presented by taking inspiration from other fields which have successfully used artificial intelligence, such as code repair, in the field of AI-powered model repair.} } @article{barriga2022parmorel, title = {{{PARMOREL}}: A Framework for Customizable Model Repair}, author = {Barriga, Angela and Heldal, Rogardt and Rutle, Adrian and Iovino, Ludovico}, date = {2022}, journaltitle = {Software Syst. Model.}, volume = {21}, number = {5}, pages = {1739--1762}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {TL;DR \par A customizable and extensible model repair framework, PARMOREL, that enables users to deal with different issues in different types of models and uses reinforcement learning to automatically find the best sequence of actions for repairing a broken model according to user preferences.} } @inproceedings{barrigaDesigningSimulatingIoT2022, title = {Designing and Simulating {{IoT}} Environments by Using a Model-Driven Approach {\textsuperscript{*}}}, booktitle = {2022 17th {{Iber}}. {{Conf}}. {{Inf}}. {{Syst}}. {{Technol}}. {{CISTI}}}, author = {Barriga, Jose A. and Clemente, Pedro J.}, date = {2022-06-22}, pages = {1--6}, publisher = {IEEE}, location = {Madrid, Spain}, doi = {10.23919/CISTI54924.2022.9820477}, eventtitle = {2022 17th {{Iberian Conference}} on {{Information Systems}} and {{Technologies}} ({{CISTI}})}, isbn = {978-989-33-3436-2} } @article{barrigaIntegratingHardwareLoop, title = {Integrating {{Hardware}} in the {{Loop}} into {{IoT Systems Simulations}}: {{A Model-Driven Development Approach}}}, author = {Barriga, Jose A and Alonso, Pablo and Sosa-Sanchez, Encarna and Perez-Toledano, Miguel A and Clemente, Pedro J}, abstract = {Given the high heterogeneity, complexity and expensive nature of Internet of Things (IoT) environments, their scenarios are usually simulated before they are developed and deployed. However, current simulation tools and the underlying models on which these simulators are based often present significant variations between simulated results and real-life assessments. To address this challenge, the Hardware in the Loop (HiL) technique has been employed over time. HiL involves integrating actual hardware into a simulated environment, thereby creating a hybrid system that combines both simulated and physical components. As a layer composed of real hardware, sensitive devices that need to be accurately assessed can be deployed in the same way as they would be in the final system, leading to more reliable results than those purely based on mathematical simulation models. In this study, a model-driven IoT simulator, SimulateIoT, has been extended to incorporate HiL for realistic test scenarios. During this work, a comprehensive analysis of the novel combination of Model-Driven Development (MDD) and HiL techniques for simulating IoT systems has also been undertaken. Finally, to validate the applicability of the proposal, an excerpt of an IoT system based on a generic smart building is presented.}, langid = {english}, keywords = {LOGSEQ}, note = {\section{Annotazioni\\ (21/6/2024, 19:27:10)} \par - “heir scenarios are usually simulated before they are developed and deployed” (Barriga et al., p. 1) \#5fb236\\ \mkbibemph{ } \par - “variations between simulated results and real-life assessments” (Barriga et al., p. 1) \#a28ae5\\ \mkbibemph{ } \par - “a model-driven IoT simulator, SimulateIoT, has been extended to incorporate HiL for realistic test scenarios” (Barriga et al., p. 1) \#5fb236\\ \mkbibemph{ } \par - “continuous development and enhancement of IoT systems through new proposals, tools and methodologies” (Barriga et al., p. 1) \#5fb236\\ \mkbibemph{ } \par - “users can design, deploy, test and, later on, analyse the system without an initial investment in hardware and software.” (Barriga et al., p. 1) \#5fb236\\ \mkbibemph{ } \par - “due to the vast diversity within IoT along with the variability in simulation tools and their underlying models, there might be notable differences between simulated results and real-world assessments” (Barriga et al., p. 1) \#a28ae5\\ \mkbibemph{ } \par - “simulation of certain nodes or devices may lack accuracy or, in some cases, may not be available.” (Barriga et al., p. 1) \#a28ae5\\ \mkbibemph{ } \par - “HiL introduces a layer of real devices within the simulation to deploy sensitive components in the same way as they would in the final system.” (Barriga et al., p. 1) \#5fb236\\ \mkbibemph{ } \par - “This means that developers only need to focus on modeling their systems and later, from these models, generate all necessary code and resources for them automatically” (Barriga et al., p. 2) \#2ea8e5\\ \mkbibemph{ } \par - “seamless orchestration and integration of these nodes with the rest of the simulation, regardless of their quantity.” (Barriga et al., p. 2) \#5fb236\\ \mkbibemph{ } \par - “extension of SimulateIoT towards HiL that allows the integration of real devices into its simulations” (Barriga et al., p. 2) \#ffd400\\ \mkbibemph{This means that SimulateIoT was already presented somewhere else? } \par - “Thus, attaching real devices to a simulated mathematical model.” (Barriga et al., p. 2) \#ff6666\\ \mkbibemph{DOes not parse. } \par - “In brief, these articles present different HiL approaches to integrate physical and simulated devices in specific working environments, so they all focus on low-level abstractions to manage the simulations. However, this communication addresses the HiL integration from a higher level of abstraction by using MDD.” (Barriga et al., p. 2) \#ffd400\\ \mkbibemph{THe goal of the approach is not clear at this stage. } \par - “HiL in an IoT simulation scenario, it would be possible to test such environments by implementing genuine component architectures alongside fully simulated ones that seek to replicate the functionalities of the original systems” (Barriga et al., p. 2) \#5fb236\\ \mkbibemph{ } \par - “SimulateIoT” (Barriga et al., p. 3) \#ffd400\\ \mkbibemph{What are the main objects of the simulation? What's the focus? } \par - “assess” (Barriga et al., p. 3) \#ffd400\\ \mkbibemph{What can be simulated? An example is needed here. } \par - “validate” (Barriga et al., p. 3) \#ffd400\\ \mkbibemph{Validate with respect what? WHat are the properties that can be modeled and then checked? } \par - “Attributes: the IP attribute allows all other nodes, either simulated or through HiL, to know the IP address of the HiL node to communicate with it.” (Barriga et al., p. 3) \#ffd400\\ \mkbibemph{what about the communication protocol? }} } @inproceedings{Barzdins202076, title = {Metamodel Specialization Based {{DSL}} for {{DL}} Lifecycle Data Management}, author = {Barzdins, P. and Celms, E. and Barzdins, J. and Kalnins, A. and Sprogis, A. and Grasmanis, M. and Rikacovs, S.}, date = {2020}, series = {Proceedings - 23rd {{ACM}}/{{IEEE International Conference}} on {{Model Driven Engineering Languages}} and {{Systems}}, {{MODELS-C}} 2020 - {{Companion Proceedings}}}, pages = {76--77}, publisher = {Association for Computing Machinery, Inc}, doi = {10.1145/3417990.3420050}, abbrev_source_title = {Proc. - ACM/IEEE Int. Conf. Model Driven Eng. Lang. Syst., MODELS-C - Companion Proc.}, affiliation = {Institute of Mathematics and Computer Science, University of Latvia, Riga, Latvia; Innovation Labs LETA, Riga, Latvia}, document_type = {Conference Paper}, isbn = {978-1-4503-8135-2}, langid = {english}, source = {Scopus}, note = {cited By 1 \par TL;DR \par A new Domain Specific Language (DSL) based approach to Deep Learning (DL) lifecycle data management (LDM) is presented and an advanced extension mechanism is presented that converts the Core tool into a DSL tool building framework for DL LDM tasks.} } @article{Barzdins202217, title = {Metamodel Specialisation Based Tool Extension}, author = {Barzdins, P. and Kalnins, A. and Celms, E. and Barzdins, J. and Sprogis, A. and Grasmanis, M. and Rikacovs, S. and Barzdins, G.}, date = {2022}, journaltitle = {Balt. J. Mod. Comput.}, volume = {10}, number = {1}, pages = {17--35}, publisher = {University of Latvia}, issn = {22558942}, doi = {10.22364/BJMC.2022.10.1.02}, abbrev_source_title = {Baltic J. Mod. Comp.}, affiliation = {Institute of Mathematics and Computer Science, University of Latvia, Raiņa bulvaris 29, Riga, LV-1459, Latvia; Innovation Labs LETA, Latvia, Riga, Satekles iela 2b, Riga, LV 1050, Latvia}, document_type = {Article}, langid = {english}, source = {Scopus}, keywords = {notion}, note = {cited By 0} } @inproceedings{bascianiAutomatedClusteringMetamodel2016, ids = {10.1007/978-3-319-39696-5_21,bascianiAutomatedClusteringMetamodel2016a,bascianiAutomatedClusteringMetamodel2016c,bascianiAutomatedClusteringMetamodel2016d,bascianiAutomatedClusteringMetamodel2016e,bascianiAutomatedClusteringMetamodel2016f}, title = {Automated Clustering of Metamodel Repositories}, booktitle = {Adv. {{Inf}}. {{Syst}}. {{Eng}}. - 28th {{Int}}. {{Conf}}. {{CAiSE}} 2016 {{Ljubl}}. {{Slov}}. {{June}} 13-17 2016 {{Proc}}.}, author = {Basciani, Francesco and Di Rocco, Juri and Di Ruscio, Davide and Iovino, Ludovico and Pierantonio, Alfonso}, date = {2016}, series = {{{LECTURE NOTES IN COMPUTER SCIENCE}}}, volume = {abs/1006.5761}, pages = {342--358}, publisher = {Springer International Publishing}, location = {Cham}, doi = {10.1007/978-3-319-39696-5_21}, abstract = {Over the last years, several model repositories have been proposed in response to the need of the MDE community for advanced systems supporting the reuse of modeling artifacts. Modelers can interact with MDE repositories with different intents ranging from merely repository browsing, to searching specific artifacts satisfying precise requirements. The organization and browsing facilities provided by current repositories is limited since they do not produce structured overviews of the contained artifacts, and the ategorization mechanisms (if any) are based on manual activities. When dealing with large numbers of modeling artifacts, such limitations increase the effort for managing and reusing artifacts stored in model repositories. By focusing on metamodel repositories, in this paper we propose the application of clustering techniques to automatically organize stored metamodels and to provide users with overviews of the application domains covered by the available metamodels. The approach has been implemented in the MDEForge repository.}, isbn = {978-3-319-39695-8 978-3-319-39696-5}, langid = {english}, keywords = {⛔ No INSPIRE recid found,Computer Science (all),GOAL\_Model-Classification,MDEForge,Metamodel clustering,Model Driven Engineering,Model repositories,notion,TECHNIQUE\_K-NEAREST-NEIGHBORS,Theoretical Computer Science}, note = {cited By 31 \par cited By 31 \par TL;DR \par This paper proposes the application of clustering techniques to automatically organize stored metamodels and to provide users with overviews of the application domains covered by the available metAModels. \par TL;DR \par This paper proposes the application of clustering techniques to automatically organize stored metamodels and to provide users with overviews of the application domains covered by the available metAModels.} } @article{basiliSoftwareEngineeringResearch2018, title = {Software {{Engineering Research}} and {{Industry}}: {{A Symbiotic Relationship}} to {{Foster Impact}}}, shorttitle = {Software {{Engineering Research}} and {{Industry}}}, author = {Basili, V. and Briand, L. and Bianculli, D. and Nejati, S. and Pastore, F. and Sabetzadeh, M.}, date = {2018-09}, journaltitle = {IEEE Softw.}, volume = {35}, number = {5}, pages = {44--49}, issn = {0740-7459}, doi = {10.1109/MS.2018.290110216}, abstract = {Software engineering is not only an increasingly challenging endeavor that goes beyond the intellectual capabilities of any single individual engineer but also an intensely human one. Tools and methods to develop software are employed by engineers of varied backgrounds within a large variety of organizations and application domains. As a result, the variation in challenges and practices in system requirements, architecture, and quality assurance is staggering. Human, domain, and organizational factors define the context within which software engineering methodologies and technologies are to be applied and therefore the context that research needs to account for, if it is to be impactful. This article provides an assessment of the current challenges faced by software engineering research in achieving its potential, a description of the root causes of such challenges, and a proposal for the field to move forward and become more impactful through collaborative research and innovation between public research and industry. This article is part of a theme issue on software engineering’s 50th anniversary.}, keywords = {software engineering} } @inproceedings{Basmer201986, title = {Encoding Adaptability of Software Engineering Tools as Algorithm Configuration Problem: {{A}} Case Study}, author = {Basmer, M. and Kehrer, T.}, date = {2019}, series = {Proceedings - 2019 34th {{IEEE}}/{{ACM International Conference}} on {{Automated Software Engineering Workshops}}, {{ASEW}} 2019}, pages = {86--89}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/ASEW.2019.00035}, abbrev_source_title = {Proc. - IEEE/ACM Int. Conf. Autom. Softw. Eng. Workshops, ASEW}, affiliation = {Department of Computer Science, Humboldt-Universität zu Berlin, Germany}, art_number = {8967417}, document_type = {Conference Paper}, isbn = {978-1-7281-4136-7}, langid = {english}, source = {Scopus}, note = {cited By 0 \par TL;DR \par This paper reports on ongoing work encoding the adaptability of SiDiff as an algorithm configuration problem which is amenable to a sequential model-based optimization tool known as SMAC.} } @inproceedings{Bataleblu20153418, title = {Robust Trajectory Optimization of Space Launch Vehicle Using Computational Intelligence}, author = {Bataleblu, A.A. and Roshanian, J.}, date = {2015}, series = {2015 {{IEEE Congress}} on {{Evolutionary Computation}}, {{CEC}} 2015 - {{Proceedings}}}, pages = {3418--3425}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/CEC.2015.7257318}, abbrev_source_title = {IEEE Congr. Evol. Comput., CEC - Proc.}, affiliation = {Department of Aerospace Engineering, K. N. Toosi University of Technology, Tehran, Iran}, art_number = {7257318}, document_type = {Conference Paper}, isbn = {978-1-4799-7492-4}, langid = {english}, source = {Scopus}, note = {cited By 6} } @inproceedings{batot2016generic, title = {A Generic Framework for Model-Set Selection for the Unification of Testing and Learning {{MDE}} Tasks}, booktitle = {Proc. {{ACMIEEE}} 19th {{Int}}. {{Conf}}. {{Model Driven Eng}}. {{Lang}}. {{Syst}}.}, author = {Batot, Edouard and Sahraoui, Houari}, date = {2016}, pages = {374--384}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {TL;DR \par A generic framework for model-set selection for learning or testing Model-Driven Engineering tasks that apply to or manipulate models, such as model definition, model well-formedness checking, and model transformation is proposed.} } @inproceedings{batot2017heuristic, title = {Heuristic-Based Recommendation for Metamodel—Ocl Coevolution}, booktitle = {2017 {{ACMIEEE}} 20th {{Int}}. {{Conf}}. {{Model Driven Eng}}. {{Lang}}. {{Syst}}. {{MODELS}}}, author = {Batot, Edouard and Kessentini, Wael and Sahraoui, Houari and Famelis, Michalis}, date = {2017}, pages = {210--220}, publisher = {IEEE}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found} } @article{batot2022promoting, title = {Promoting Social Diversity for the Automated Learning of Complex {{MDE}} Artifacts}, author = {Batot, Edouard R. and Sahraoui, Houari}, date = {2022-06}, journaltitle = {Softw Syst Model}, volume = {21}, number = {3}, pages = {1159--1178}, issn = {1619-1366, 1619-1374}, doi = {10.1007/s10270-021-00969-9}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found,GOAL\_Model-Assistance,notion,TECHNIQUE\_GENETIC\_ALGORITHMS} } @inproceedings{Bauer:2012:SAA:2473496.2473600, title = {A Structured Approach to Assess Third-Party Library Usage}, booktitle = {Proc. 2012 {{IEEE Int}}. {{Conf}}. {{Softw}}. {{Maint}}.}, author = {Bauer, Veronika and Heinemann, Lars and Deissenboeck, Florian}, date = {2012}, series = {{{ICSM}} '12}, pages = {483--492}, publisher = {IEEE Computer Society}, location = {Washington, DC, USA}, url = {http://dx.doi.org/10.1109/ICSM.2012.6405311}, acmid = {2473600}, isbn = {978-1-4673-2313-0}, nodoi = {10.1109/ICSM.2012.6405311}, numpages = {10}, keywords = {API,Libraries,library,Maintenance engineering,Measurement,Modeling,software maintenance,Software maintenance,software reuse,Software systems} } @incollection{bauerIoTReferenceModel2013, title = {{{IoT Reference Model}}}, booktitle = {Enabling {{Things}} to {{Talk}}}, author = {Bauer, Martin and Bui, Nicola and De Loof, Jourik and Magerkurth, Carsten and Nettsträter, Andreas and Stefa, Julinda and Walewski, Joachim W.}, editor = {Bassi, Alessandro and Bauer, Martin and Fiedler, Martin and Kramp, Thorsten and family=Kranenburg, given=Rob, prefix=van, useprefix=true and Lange, Sebastian and Meissner, Stefan}, date = {2013}, pages = {113--162}, publisher = {Springer Berlin Heidelberg}, location = {Berlin, Heidelberg}, url = {http://link.springer.com/10.1007/978-3-642-40403-0_7}, urldate = {2016-05-30}, isbn = {978-3-642-40402-3 978-3-642-40403-0}, langid = {english}, note = {TL;DR \par This Chapter introduces the IoT Reference Model as a precondition for working with the Reference Architecture that is introduced in Chap.} } @article{bauerTestSuiteQuality2011, title = {Test {{Suite Quality}} for {{Model Transformation Chains}}}, author = {Bauer, Eduard and Küster, Jochen M. and Engels, Gregor}, date = {2011}, journaltitle = {Objects Models Compon. Patterns}, volume = {6705}, pages = {3--19}, doi = {10.1007/978-3-642-21952-8_3}, note = {TL;DR \par This paper presents a coverage analysis approach for measuring test suite quality for model transformation chains that combines different coverage criteria and yields detailed coverage information that can be used to identify missing and redundant test cases.} } @article{Baxter2008QualitativeCS, title = {Qualitative Case Study Methodology: {{Study}} Design and Implementation for Novice Researchers}, author = {Baxter, Pamela and Jack, Susan M.}, date = {2008}, journaltitle = {Qual. Rep.}, volume = {13}, pages = {544--559}, note = {TL;DR \par An overview of the types of case study designs is provided along with general recommendations for writing the research questions, developing propositions, determining the “case” under study, binding the case and a discussion of data sources and triangulation.} } @article{Bayraktar2021, title = {Prediction against a Limited Adversary}, author = {Bayraktar, E. and Ekren, I. and Zhang, X.}, date = {2021}, journaltitle = {J. Mach. Learn. Res.}, volume = {22}, publisher = {Microtome Publishing}, issn = {15324435}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105844594&partnerID=40&md5=24bb99dc7bd439e1c91273ea25c881d6}, abstract = {We study the problem of prediction with expert advice with adversarial corruption where the adversary can at most corrupt one expert. Using tools from viscosity theory, we characterize the long-time behavior of the value function of the game between the forecaster and the adversary. We provide lower and upper bounds for the growth rate of regret without relying on a comparison result. We show that depending on the description of regret, the limiting behavior of the game can significantly differ. © 2021 Erhan Bayraktar, Ibrahim Ekren and Xin Zhang.}, author_keywords = {Asymptotic expansion; Discontinuous viscosity solutions; Expert advice framework; Machine learning}, document_type = {Article}, source = {Scopus}, keywords = {Artificial intelligence,Comparison result,Limiting behavior,Long time behavior,Lower and upper bounds,Prediction with expert advice,Software engineering,Value functions}, note = {cited By 0} } @online{BeautifulMonitoringGrafana, title = {Beautiful {{Monitoring With Grafana}} and {{InfluxDB}}}, url = {https://www2.slideshare.net/leesjensen/beautiful-monitoring-with-grafana-and-influxdb?qid=2eb80839-115d-421d-afaa-e6dcbd79c280&v=&b=&from_search=4}, urldate = {2021-01-05}, keywords = {grafana,influxdb} } @article{beckerProcessModelClassification, title = {A Process Model and Classification Scheme for Semi-Automatic Meta-Model Evolution}, author = {Becker, Steffen and Goldschmidt, Thomas and Gruschko, Boris and Koziolek, Heiko}, abstract = {Model Driven Software Development (MDSD) has matured over the last few years and is now becoming an established technology. As a consequence, dealing with evolving meta-models and the necessary migration activities of instances of this meta-model is becoming increasingly important. Approaches from database schema migration tackle a similar problem, but cannot be adapted easily to MDSD. This paper presents work towards a solution in the model-driven domain. Firstly, we introduce a process model, which defines the necessary steps to migrate model instances upon an evolving meta-model. Secondly, we have created an initial classification of metamodel changes in EMF/Ecore utilised by our process model.}, langid = {english} } @inproceedings{beckerSymbolicInvariantVerification2006, title = {Symbolic Invariant Verification for Systems with Dynamic Structural Adaptation}, booktitle = {Proc. 28th {{Int}}. {{Conf}}. {{Softw}}. {{Eng}}.}, author = {Becker, Basil and Beyer, Dirk and Giese, Holger and Klein, Florian and Schilling, Daniela}, date = {2006}, pages = {72--81}, publisher = {ACM}, url = {http://dl.acm.org/citation.cfm?id=1134297}, urldate = {2015-04-07} } @article{beechamPreparingTomorrowsSoftware2017, entrysubtype = {magazine}, title = {Preparing {{Tomorrow}}'s {{Software Engineers}} for {{Work}} in a {{Global Environment}}}, author = {Beecham, Sarah and Clear, Tony and Barr, John and Daniels, Mats and Oudshoorn, Michael and Noll, John and {undefined} and {undefined} and {undefined} and {undefined}}, date = {2017-01}, journaltitle = {IEEE Software}, volume = {34}, number = {1}, pages = {9--12}, issn = {0740-7459}, url = {http://ieeexplore.ieee.org/document/7819397/}, abstract = {Global software engineering (GSE) is becoming common. It's thus important to educate university software engineering students in GSE. The authors discuss challenges to and recommendations for implementing such instruction.}, langid = {english}, keywords = {software engineering}, note = {TL;DR \par The authors discuss challenges to and recommendations for implementing such instruction in GSE and suggest ways to improve the quality of instruction.} } @inproceedings{begelAnalyzeThis1452014, title = {Analyze This! 145 Questions for Data Scientists in Software Engineering}, booktitle = {Proc. 36th {{Int}}. {{Conf}}. {{Softw}}. {{Eng}}.}, author = {Begel, Andrew and Zimmermann, Thomas}, date = {2014}, pages = {12--23}, publisher = {ACM}, url = {http://dl.acm.org/citation.cfm?id=2568233}, urldate = {2016-01-22} } @inproceedings{begoliHeterogeneousPolystorelikeData2016, title = {Towards a Heterogeneous, Polystore-like Data Architecture for the {{US Department}} of {{Veteran Affairs}} ({{VA}}) Enterprise Analytics}, author = {Begoli, Edmon and Kistler, Derek and Bates, Jack}, date = {2016-12}, pages = {2550--2554}, publisher = {IEEE}, doi = {10.1109/BigData.2016.7840896}, isbn = {978-1-4673-9005-7}, langid = {english} } @inproceedings{Behjat2020, title = {Metamodel Based Forward and Inverse Design for Passive Vibration Suppression}, author = {Behjat, A. and Oddiraju, M. and Attarzadeh, M.A. and Nouh, M. and Chowdhury, S.}, date = {2020}, series = {Proceedings of the {{ASME Design Engineering Technical Conference}}}, volume = {11B-2020}, publisher = {American Society of Mechanical Engineers (ASME)}, doi = {10.1115/DETC2020-22747}, abbrev_source_title = {Proc. ASME Des. Eng. Tech. Conf.}, affiliation = {Department of Mechanical and Aerospace Engineering, University at Buffalo, Buffalo, NY 14260, United States}, art_number = {V11BT11A024}, correspondence_address1 = {Chowdhury, S.; Department of Mechanical and Aerospace Engineering, United States; email: soumacho@buffalo.edu}, document_type = {Conference Paper}, isbn = {978-0-7918-8401-0}, langid = {english}, source = {Scopus}, note = {cited By 2} } @article{belaireRegretbasedDefenseAdversarial2024, title = {Regret-Based {{Defense}} in {{Adversarial Reinforcement Learning}}}, author = {Belaire, Roman}, date = {2024}, journaltitle = {N. Z.}, abstract = {Deep Reinforcement Learning (DRL) policies are vulnerable to adversarial noise in observations, which can have disastrous consequences in safety-critical environments. For instance, a self-driving car receiving adversarially perturbed sensory observations about traffic signs (e.g., a stop sign physically altered to be perceived as a speed limit sign) can be fatal. Leading existing approaches for making RL algorithms robust to an observation-perturbing adversary have focused on (a) regularization approaches that make expected value objectives robust by adding adversarial loss terms; or (b) employing “maximin” (i.e., maximizing the minimum value) notions of robustness. While regularization approaches are adept at reducing the probability of successful attacks, their performance drops significantly when an attack is successful. On the other hand, maximin objectives, while robust, can be extremely conservative. To this end, we focus on optimizing a well-studied robustness objective, namely regret. To ensure the solutions provided are not too conservative, we optimize an approximation of regret using three different methods. We demonstrate that our methods outperform existing best approaches for adversarial RL problems across a variety of standard benchmarks from literature.}, langid = {english}, keywords = {⛔ No INSPIRE recid found} } @article{belgacemAutomatedAnomalyDetection2024, title = {Automated Anomaly Detection for Categorical Data by Repurposing a Form Filling Recommender System}, author = {Belgacem, Hichem and Li, Xiaochen and Bianculli, Domenico and Briand, Lionel}, date = {2024-09-30}, journaltitle = {J. Data and Information Quality}, volume = {16}, number = {3}, pages = {1--28}, issn = {1936-1955, 1936-1963}, doi = {10.1145/3696110}, abstract = {Data quality is crucial in modern software systems, like data-driven decision support systems. However, data quality is affected by data anomalies, which represent instances that deviate from most of the data. These anomalies affect the reliability and trustworthiness of software systems, and may propagate and cause more issues. Although many anomaly detection approaches have been proposed, they mainly focus on numerical data. Moreover, the few approaches targeting anomaly detection for categorical data do not yield consistent results across datasets. In this article, we propose a novel anomaly detection approach for categorical data named LAFF-AD (LAFF-based Anomaly Detection), which takes advantage of the learning ability of a state-of-the-art form filling tool (LAFF) to perform value inference on suspicious data. LAFF-AD runs a variant of LAFF that predicts the possible values of a suspicious categorical field in the suspicious instance. LAFF-AD then compares the output of LAFF to the recorded values in the suspicious instance, and uses a heuristic-based strategy to detect categorical data anomalies. We evaluated LAFF-AD by assessing its effectiveness and efficiency on six datasets. Our experimental results show that LAFF-AD can accurately determine a high range of data anomalies, with recall values between 0.6 and 1 and a precision value of at least 0.808. Furthermore, LAFF-AD is efficient, taking at most 7000s and 735ms to perform training and prediction, respectively.}, langid = {english} } @article{BellogiN:2013:CSH:2397740.2398191, title = {A Comparative Study of Heterogeneous Item Recommendations in Social Systems}, author = {Bellogín, Alejandro and Cantador, IváN and Castells, Pablo}, date = {2013-02}, journaltitle = {Inf. Sci.}, volume = {221}, pages = {142--169}, publisher = {Elsevier Science Inc.}, issn = {0020-0255}, url = {http://dx.doi.org/10.1016/j.ins.2012.09.039}, acmid = {2398191}, issue_date = {February, 2013}, noaddress = {New York, NY, USA}, nodoi = {10.1016/j.ins.2012.09.039}, numpages = {28}, keywords = {Collaborative tagging,Evaluation,Implicit feedback,Recommender system,Social network,Social Web} } @inproceedings{Bellogin2011, title = {Precision-Oriented Evaluation of Recommender Systems: {{An}} Algorithmic Comparison}, booktitle = {{{ACM RecSys}} 11}, author = {Bellogin, A. and Castells, P. and Cantador, I.}, date = {2011}, pages = {333--336} } @inproceedings{Benaben20191549, title = {Integrating Model-Driven Engineering as the next Challenge for Artificial Intelligence - Application to Risk and Crisis Management}, author = {Benaben, F. and Lauras, M. and Fertier, A. and Salatge, N.}, date = {2019}, series = {Proceedings - {{Winter Simulation Conference}}}, volume = {2019-December}, pages = {1549--1563}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {08917736}, doi = {10.1109/WSC40007.2019.9004828}, abbrev_source_title = {Proc. Winter Simul. Conf.}, affiliation = {IMT Mines Albi, Centre Génie Industriel, Albi, 81000, France}, art_number = {9004828}, coden = {WSCPD}, document_type = {Conference Paper}, isbn = {978-1-7281-3283-9}, langid = {english}, source = {Scopus}, note = {cited By 2 \par TL;DR \par The use of Model-Driven Engineering tools (such as metamodel and model transformation) could benefit to the domain of AI by introducing a way to extend the apprehension of unknown situations.} } @inproceedings{Bencomo20121, title = {Summary of the 7th {{International Workshop}} on {{Models}}@run.Time}, author = {Bencomo, N. and Blair, G. and Götz, S. and Morin, B. and Rumpe, B.}, date = {2012}, series = {Proceedings of the 7th {{Workshop}} on {{Models}}@run.Time, {{MRT}} 2012 - {{Being Part}} of the {{ACM}}/{{IEEE}} 15th {{International Conference}} on {{Model Driven Engineering Languages}} and {{Systems}}, {{MODELS}} 2012}, pages = {1--2}, doi = {10.1145/2422518.2422519}, abbrev_source_title = {Proc. Workshop Models@run.time, MRT - Being Part ACM/IEEE Int. Conf. Model Driven Eng. Lang. Syst., MODELS}, affiliation = {INRIA Paris-Rocquencourt, France; Lancester University, United Kingdom; Technische Universität, Dresden, Germany; Stiftelsen SINTEF, Norway; RWTH Aachen, Germany}, correspondence_address1 = {Bencomo, N.; INRIA Paris-RocquencourtFrance; email: nelly@acm.org}, document_type = {Conference Paper}, isbn = {978-1-4503-1799-3}, langid = {english}, source = {Scopus}, note = {cited By 0} } @online{bencomoAbstractionEngineering2024, title = {Abstraction Engineering}, author = {Bencomo, Nelly and Cabot, Jordi and Chechik, Marsha and Cheng, Betty H. C. and Combemale, Benoit and Wąsowski, Andrzej and Zschaler, Steffen}, date = {2024-08-26}, eprint = {2408.14074}, eprinttype = {arXiv}, eprintclass = {cs}, doi = {10.48550/arXiv.2408.14074}, abstract = {Modern software-based systems operate under rapidly changing conditions and face ever-increasing uncertainty. In response, systems are increasingly adaptive and reliant on artificial-intelligence methods. In addition to the ubiquity of software with respect to users and application areas (e.g., transportation, smart grids, medicine, etc.), these high-impact software systems necessarily draw from many disciplines for foundational principles, domain expertise, and workflows. Recent progress with lowering the barrier to entry for coding has led to a broader community of developers, who are not necessarily software engineers. As such, the field of software engineering needs to adapt accordingly and offer new methods to systematically develop high-quality software systems by a broad range of experts and non-experts. This paper looks at these new challenges and proposes to address them through the lens of Abstraction. Abstraction is already used across many disciplines involved in software development -- from the time-honored classical deductive reasoning and formal modeling to the inductive reasoning employed by modern data science. The software engineering of the future requires Abstraction Engineering -- a systematic approach to abstraction across the inductive and deductive spaces. We discuss the foundations of Abstraction Engineering, identify key challenges, highlight the research questions that help address these challenges, and create a roadmap for future research.}, langid = {english}, pubstate = {prepublished}, keywords = {/unread,Computer Science - Software Engineering} } @article{BencomoGS19, title = {Models@run.Time: A Guided Tour of the State of the Art and Research Challenges}, author = {Bencomo, Nelly and Götz, Sebastian and Song, Hui}, date = {2019}, journaltitle = {Software Syst. Model.}, volume = {18}, number = {5}, pages = {3049--3082}, doi = {10.1007/S10270-018-00712-X}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Fri, 18 Sep 2020 11:19:25 +0200} } @book{bencomoModelsruntime2014, title = {Models@run.Time}, editor = {Bencomo, Nelly and France, Robert and Cheng, Betty H. C. and Aßmann, Uwe}, editora = {Hutchison, David and Kanade, Takeo and Kittler, Josef and Kleinberg, Jon M. and Kobsa, Alfred and Mattern, Friedemann and Mitchell, John C. and Naor, Moni and Nierstrasz, Oscar and Pandu Rangan, C. and Steffen, Bernhard and Terzopoulos, Demetri and Tygar, Doug and Weikum, Gerhard}, editoratype = {redactor}, date = {2014}, series = {Lecture {{Notes}} in {{Computer Science}}}, volume = {8378}, publisher = {Springer International Publishing}, location = {Cham}, url = {http://link.springer.com/10.1007/978-3-319-08915-7}, urldate = {2016-08-21}, isbn = {978-3-319-08914-0 978-3-319-08915-7}, note = {TL;DR \par This book comprises four research roadmaps, written by the original participants of the Dagstuhl Seminar over the course of two years following the seminar, and seven research papers from experts in the area that provide insights to key features of the use of runtime models.} } @online{BendingBreakingOptimal, title = {Bending without Breaking: Optimal Design Patterns for Effective Agents — {{LlamaIndex}} - {{Build Knowledge Assistants}} over Your {{Enterprise Data}}}, shorttitle = {Bending without Breaking}, url = {https://www.llamaindex.ai/blog/bending-without-breaking-optimal-design-patterns-for-effective-agents}, urldate = {2025-04-27}, abstract = {LlamaIndex is a simple, flexible framework for building knowledge assistants using LLMs connected to your enterprise data.} } @article{bendraouComparisonSixUMLBased2010, title = {A {{Comparison}} of {{Six UML-Based Languages}} for {{Software Process Modeling}}}, author = {Bendraou, Reda and Jezequel, Jean-Marc and Gervais, Marie-Pierre and Blanc, Xavier}, date = {2010-09}, journaltitle = {IIEEE Trans. Software Eng.}, volume = {36}, number = {5}, pages = {662--675}, issn = {0098-5589}, doi = {10.1109/TSE.2009.85}, abstract = {Describing and managing activities, resources, and constraints of software development processes is a challenging goal for many organizations. A first generation of Software Process Modeling Languages (SPMLs) appeared in the 1990s but failed to gain broad industrial support. Recently, however, a second generation of SPMLs has appeared, leveraging the strong industrial interest for modeling languages such as UML. In this paper, we propose a comparison of these UML-based SPMLs. While not exhaustive, this comparison concentrates on SPMLs most representative of the various alternative approaches, ranging from UML-based framework specializations to full-blown executable metamodeling approaches. To support the comparison of these various approaches, we propose a frame gathering a set of requirements for process modeling, such as semantic richness, modularity, executability, conformity to the UML standard, and formality. Beyond discussing the relative merits of these approaches, we also evaluate the overall suitability of these UML-based SPMLs for software process modeling. Finally, we discuss the impact of these approaches on the current state of the practice, and conclude with lessons we have learned in doing this comparison.}, langid = {english}, keywords = {comparison analysis,uml}, note = {TL;DR \par This article proposes a comparison of these UML-based SPMLs most representative of the various alternative approaches, ranging from UML-based framework specializations to full-blown executable meta-modeling approaches, and proposes a frame gathering a set of requirements for process modeling.} } @article{benelallamMavenDependencyGraph2019, title = {The Maven Dependency Graph: A Temporal Graph-Based Representation of Maven Central}, author = {Benelallam, Amine and Harrand, Nicolas and Soto-Valero, César and Baudry, Benoit and Barais, Olivier}, date = {2019}, journaltitle = {CoRR}, volume = {abs/1901.05392}, eprint = {1901.05392}, eprinttype = {arXiv}, url = {http://arxiv.org/abs/1901.05392}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/bib/journals/corr/abs-1901-05392}, timestamp = {Fri, 01 Feb 2019 13:39:59 +0100} } @incollection{bengioPracticalRecommendationsGradientbased2012, title = {Practical Recommendations for Gradient-Based Training of Deep Architectures}, booktitle = {Neural Networks: {{Tricks}} of the Trade: {{Second}} Edition}, author = {Bengio, Yoshua}, editor = {Montavon, Grégoire and Orr, Geneviève B. and Müller, Klaus-Robert}, date = {2012}, pages = {437--478}, publisher = {Springer Berlin Heidelberg}, location = {Berlin, Heidelberg}, doi = {10.1007/978-3-642-35289-8₂6}, abstract = {Learning algorithms related to artificial neural networks and in particular for Deep Learning may seem to involve many bells and whistles, called hyper-parameters. This chapter is meant as a practical guide with recommendations for some of the most commonly used hyperparameters, in particular in the context of learning algorithms based on back-propagated gradient and gradient-based optimization. It also discusses how to deal with the fact that more interesting results can be obtained when allowing one to adjust many hyper-parameters. Overall, it describes elements of the practice used to successfully and efficiently train and debug large-scale and often deep multi-layer neural networks. It closes with open questions about the training difficulties observed with deeper architectures.}, isbn = {978-3-642-35289-8}, note = {TL;DR \par Overall, this chapter describes elements of the practice used to successfully and efficiently train and debug large-scale and often deep multi-layer neural networks and closes with open questions about the training difficulties observed with deeper architectures.} } @article{benoitGlobalizingModelingLanguages, title = {Globalizing {{Modeling Languages}}}, author = {Benoit, Comemale and Julien, DeAntoni and Benoit, Baudry and Robert B., France and Jean-Marc, Jezequel and Jeff, Gray}, doi = {10.1109/MC.2014.147}, note = {TL;DR \par A research initiative is described that broadens the DSML research focus beyond independent DSML development to one that supports globalized DSMLs-that is, DS MLs that facilitate coordination of work across different domains of expertise.} } @article{BenSalem2018719, title = {Automatic Selection for General Surrogate Models}, author = {Ben Salem, M. and Tomaso, L.}, date = {2018}, journaltitle = {Struct. Multidiscip. Optim.}, volume = {58}, number = {2}, pages = {719--734}, publisher = {Springer Verlag}, issn = {1615147X}, doi = {10.1007/s00158-018-1925-3}, abbrev_source_title = {Struct. Mutltidiscip. Opt.}, affiliation = {Ecole des mines de St-Etienne, Saint Étienne, France; ANSYS, Inc., Villeurbanne, France}, coden = {SMOTB}, correspondence_address1 = {Ben Salem, M.; ANSYS, France; email: Malek.ben-salem@emse.fr}, document_type = {Article}, langid = {english}, source = {Scopus}, keywords = {GOAL\_Model-Search,notion}, note = {cited By 21 \par TL;DR \par A universal criterion that can be applied to any type of surrogate models is introduced, composed of three complementary components measuring the quality of general surrogate models: internal accuracy, predictive performance and a roughness penalty. \par TO BE EXCLUDED} } @inproceedings{bergmayrOutplaceTransformationEvolution2014, title = {From Out-Place Transformation Evolution to in-Place Model Patching}, author = {Bergmayr, Alexander and Troya, Javier and Wimmer, Manuel}, date = {2014}, pages = {647--652}, publisher = {ACM Press}, doi = {10.1145/2642937.2642946}, isbn = {978-1-4503-3013-8}, langid = {english}, note = {TL;DR \par The idea is to infer from evolved out-place transformations patch transformations that propagate changes to existing models by re-executing solely the affected parts based on an in-place execution strategy, so that existing models are only updated by a patch instead of newly produced.} } @article{Berkhin2006, ids = {B06,berkhin2006survey}, title = {A Survey of Clustering Data Mining Techniques.}, author = {Berkhin, Pavel and others}, date = {2006}, journaltitle = {Group. Multidimens. Data Recent Adv. Clust.}, volume = {25}, pages = {25--71}, doi = {10.1007/3-540-28349-8_2} } @article{bermejo-alonsoOntologicalFrameworkAutonomous2010, title = {An Ontological Framework for Autonomous Systems Modelling}, author = {Bermejo-Alonso, Julita and Sanz, Ricardo and Rodríguez, Manuel and Hernández, Carlos}, date = {2010}, journaltitle = {Int. J. Adv. Intell. Syst.}, volume = {3}, number = {3}, url = {http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.641.7853&rep=rep1&type=pdf#page=57}, urldate = {2016-08-21}, note = {TL;DR \par This work develops a domain ontology and an ontologybased methodology to support the conceptual modelling of autonomous systems, and exploits the ontology to generate the conceptual models for a generic engineering process.} } @article{bermejoalonsoEngineeringOntologyAutonomous2011, title = {Engineering an {{Ontology}} for {{Autonomous Systems-The OASys Ontology}}}, author = {Bermejo Alonso, Julita and Sanz Bravo, Ricardo and Rodríguez, Manuel and Hernández Corbato, Carlos}, date = {2011}, url = {http://oa.upm.es/11957/}, urldate = {2016-08-21} } @online{bernardinisDifferenzeIASimbolica2024, title = {Differenze {{IA}} Simbolica e Connessionista: Approcci {{AI}} a Confronto}, shorttitle = {Differenze {{IA}} Simbolica e Connessionista}, author = {Bernardinis, Riccardo De}, date = {2024-04-25}, url = {https://www.riccardodebernardinis.com/blog/differenze-ia-simbolica-e-connessionista-approcci-ai-a-confronto/}, urldate = {2025-01-10}, abstract = {Esplora le diverse prospettive dell'Intelligenza Artificiale: regole logiche vs neuroni artificiali. Come queste forme moldano l'IA.}, langid = {english}, organization = {Riccardo de Bernardinis}, keywords = {/unread} } @incollection{bernardo_object_2012, title = {Object {{Constraint Language}} ({{OCL}}): {{A Definitive Guide}}}, shorttitle = {Object {{Constraint Language}} ({{OCL}})}, booktitle = {Formal {{Methods}} for {{Model-Driven Engineering}}}, author = {Cabot, Jordi and Gogolla, Martin}, editor = {Bernardo, Marco and Cortellessa, Vittorio and Pierantonio, Alfonso}, date = {2012}, volume = {7320}, pages = {58--90}, publisher = {Springer Berlin Heidelberg}, location = {Berlin, Heidelberg}, doi = {10.1007/978-3-642-30982-3_3}, abstract = {The Object Constraint Language (OCL) started as a complement of the UML notation with the goal to overcome the limitations of UML (and in general, any graphical notation) in terms of precisely specifying detailed aspects of a system design. Since then, OCL has become a key component of any model-driven engineering (MDE) technique as the default language for expressing all kinds of (meta)model query, manipulation and specification requirements. Among many other applications, OCL is frequently used to express model transformations (as part of the source and target patterns of transformation rules), well-formedness rules (as part of the definition of new domain-specific languages), or code-generation templates (as a way to express the generation patterns and rules).}, isbn = {978-3-642-30981-6 978-3-642-30982-3}, langid = {english}, note = {Series Title: Lecture Notes in Computer Science \par TL;DR \par This chapter pretends to provide a comprehensive view of this language, its many applications and available tool support as well as the latest research developments and open challenges around it.} } @article{berrouyneMoDIoT1stInternational, title = {{{MoDIoT}} - 1st International Workshop on Model-Based Development for the {{IoT}}}, author = {Berrouyne, Imad and Gueheneuc, Yann-Gael and Moustafa, Hassnaa}, abstract = {The IoT is a paradigm that promotes the principle that physical objects should be connected to the internet—Anything, Anywhere, Anytime (AAA). This vision presents several software engineering challenges due to the inherent heterogeneity of software and hardware platforms. Several perspectives have been explored to address this problem systematically. However, most approaches focus on low-level solutions, such as introducing IoT platforms or fixed architectures. Model-based development o!ers an opportunity to operate at a higher level of abstraction, allowing engineers to bypass such complexities. This enables them to concentrate on business logic at the model level and use model transformation to generate the corresponding code for low-level platforms. The goal of this workshop is to provide a platform for PhD students, postdocs, researchers, and practitioners in model-based software development to discuss the potential of model-based development in addressing the challenges of IoT.}, langid = {english}, keywords = {LOGSEQ} } @book{Berry:1997:DMT:560675, title = {Data Mining Techniques: {{For}} Marketing, Sales, and Customer Support}, author = {Berry, Michael J. and Linoff, Gordon}, date = {1997}, publisher = {John Wiley \& Sons, Inc.}, location = {New York, NY, USA}, isbn = {0-471-17980-9}, note = {TL;DR \par One of the first practical guides to mining business data, Data Mining Techniques describes techniques for detecting customer behavior patterns useful in formulating marketing, sales, and customer support strategies.} } @misc{bert2018, ids = {BERT18}, title = {{{BERT}}: {{Pre-training}} of Deep Bidirectional Transformers for Language Understanding}, author = {Devlin, Jacob and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina}, date = {2018}, eprint = {1810.04805}, eprinttype = {arXiv}, url = {https://arxiv.org/abs/1810.04805}, bibsource = {dblp computer science bibliography, https://dblp.org}, organization = {arXiv}, keywords = {Computation and Language (cs.CL),FOS: Computer and information sciences}, timestamp = {Tue, 30 Oct 2018 20:39:56 +0100} } @article{bertoaQualityAttributesSoftware2010, ids = {bertoaQualityAttributesSoftware}, title = {Quality Attributes for Software Metamodels}, author = {Bertoa, Manuel and Vallecillo, Antonio}, date = {2010}, journaltitle = {Málaga Spain}, url = {http://www.lcc.uma.es/~av/Publicaciones/10/qaoose10.pdf}, urldate = {2015-09-15}, note = {TL;DR \par A quality model for Metamodels is proposed, that defines a set of quality attributes for evaluating MetAModels.} } @inproceedings{bertramNeuralLanguageModels2022, title = {Neural {{Language Models}} and {{Few Shot Learning}} for {{Systematic Requirements Processing}} in {{MDSE}}}, booktitle = {Proc. 15th {{ACM SIGPLAN Int}}. {{Conf}}. {{Softw}}. {{Lang}}. {{Eng}}.}, author = {Bertram, Vincent and Boß, Miriam and Kusmenko, Evgeny and Nachmann, Imke Helene and Rumpe, Bernhard and Trotta, Danilo and Wachtmeister, Louis}, date = {2022-12-01}, series = {{{SLE}} 2022}, pages = {260--265}, publisher = {Association for Computing Machinery}, location = {New York, NY, USA}, doi = {10.1145/3567512.3567534}, abstract = {Systems engineering, in particular in the automotive domain, needs to cope with the massively increasing numbers of requirements that arise during the development process. The language in which requirements are written is mostly informal and highly individual. This hinders automated processing of requirements as well as the linking of requirements to models. Introducing formal requirement notations in existing projects leads to the challenge of translating masses of requirements and the necessity of training for requirements engineers. In this paper, we derive domain-specific language constructs helping us to avoid ambiguities in requirements and increase the level of formality. The main contribution is the adoption and evaluation of few-shot learning with large pretrained language models for the automated translation of informal requirements to structured languages such as a requirement DSL.}, isbn = {978-1-4503-9919-7} } @online{BestDataPipeline, title = {The {{Best Data Pipeline Tools List}} for 2021 | {{Hevo Blog}}}, url = {https://hevodata.com/blog/data-pipeline-tools-list/}, urldate = {2021-03-18} } @inproceedings{Beyer:2018:ACP:3196321.3196333, title = {Automatically Classifying Posts into Question Categories on Stack Overflow}, booktitle = {Proc. 26th {{Conf}}. {{Program Comprehension}}}, author = {Beyer, Stefanie and Macho, Christian and Pinzger, Martin and Di Penta, Massimiliano}, date = {2018}, series = {{{ICPC}} '18}, pages = {211--221}, publisher = {ACM}, location = {New York, NY, USA}, url = {http://doi.acm.org/10.1145/3196321.3196333}, acmid = {3196333}, isbn = {978-1-4503-5714-2}, nodoi = {10.1145/3196321.3196333}, numpages = {11} } @article{beyer2020kind, title = {What Kind of Questions Do Developers Ask on {{Stack Overflow}}? {{A}} Comparison of Automated Approaches to Classify Posts into Question Categories}, author = {Beyer, Stefanie and Macho, Christian and Di Penta, Massimiliano and Pinzger, Martin}, date = {2020}, journaltitle = {Empir. Softw. Eng.}, volume = {25}, number = {3}, pages = {2258--2301}, publisher = {Springer} } @article{beyhlSmartModelSearch, title = {Smart {{Model Search}} in {{Model Repositories}} by {{Modular Search Index Generation}} and {{Querying}} ({{Submitted}} to {{SLE2014}} - Confidential)}, author = {Beyhl, Thomas and Giese, Holger} } @article{beyhlSmartModelSearcha, title = {Smart {{Model Search}} in {{Model Repositories}} by {{Modular Search Index Generation}} and {{Querying}}}, author = {Beyhl, Thomas and Giese, Holger}, pages = {20}, abstract = {Model search engines (MSEs) retrieve knowledge embodied by model repositories. Existing MSEs perform text-based search and exploit meta-models to enable queries that require meta-information. However, model repositories embody relevant hidden knowledge as well. Existing MSEs do not retrieve such hidden knowledge, because their general-purpose search index does not support to derive hidden knowledge effectively. In this paper, we present a smart model search approach, which exploits low-level knowledge to derive high-level knowledge by supporting modules that allow the integration of existing querying and mining techniques. Our approach permits the pre-computation of results for time-consuming modules in terms of a search index to guarantee reasonable response times, while less time-consuming modules are computed on demand. Our approach guides the systematic integration of modules by means of well-formedness checks to guarantee reasonable search results. We evaluate our approach by a case study using multiple data sets derived from an open source project.}, langid = {english} } @inproceedings{BezivinJRV05, title = {Modeling in the {{Large}} and {{Modeling}} in the {{Small}}}, booktitle = {Eur. {{MDA Workshop MDAFA}} 2003 {{MDAFA}} 2004 {{Revis}}. {{Sel}}. {{Pap}}.}, author = {Bézivin, Jean and Jouault, Frédéric and Rosenthal, Peter and Valduriez, Patrick}, date = {2005}, series = {{{LNCS}}}, volume = {3599}, pages = {33--46}, publisher = {Springer} } @inproceedings{BezivinJV04, ids = {bezivin2004need}, title = {On the Need for {{Megamodels}}}, booktitle = {Proc {{OOPSLAGPCE Best Pract}}. {{Model-Driven Softw}}. {{Dev}}. {{Workshop}}}, author = {Bézivin, J. and Jouault, F. and Valduriez, P.}, date = {2004}, note = {TL;DR \par Besides argumenting for the need to use mega-models in a variety of situations, the paper argues on the importance of this concept as an essential part of any model-driven software development platform.} } @article{bezivinUnificationPowerModels2005, ids = {Bezivin05}, title = {On the Unification Power of Models}, author = {Bézivin, Jean}, date = {2005-05}, journaltitle = {Softw Syst Model}, volume = {4}, number = {2}, pages = {171--188}, issn = {1619-1366, 1619-1374}, doi = {10.1007/s10270-005-0079-0}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Fri, 18 Sep 2020 11:19:18 +0200} } @inproceedings{bhandariSerendipitousRecommendationMobile2013, title = {Serendipitous Recommendation for Mobile Apps Using Item-Item Similarity Graph.}, booktitle = {{{AIRS}}}, author = {Bhandari, Upasna and Sugiyama, Kazunari and Datta, Anindya and Jindal, Rajni}, editor = {Banchs, Rafael E. and Silvestri, Fabrizio and Liu, Tie-Yan and Zhang, Min and Gao, Sheng and Lang, Jun}, date = {2013}, series = {Lecture Notes in Computer Science}, volume = {8281}, pages = {440--451}, publisher = {Springer}, url = {http://dblp.uni-trier.de/db/conf/airs/airs2013.html#BhandariSDJ13}, added-at = {2013-12-14T00:00:00.000+0100}, biburl = {https://www.bibsonomy.org/bibtex/202e78647742172b1076224a8af342fea/dblp}, ee = {http://dx.doi.org/10.1007/978-3-642-45068-6₃8}, interhash = {9b6654f3f311708df5e2faf14c15d05a}, intrahash = {02e78647742172b1076224a8af342fea}, isbn = {978-3-642-45067-9}, keywords = {dblp}, timestamp = {2015-04-28T17:38:09.000+0200} } @inproceedings{Bhattacharjee20191607, title = {{{STRATUM}}: {{A BigData-as-a-Service}} for Lifecycle Management of {{IoT}} Analytics Applications}, author = {Bhattacharjee, A. and Barve, Y. and Khare, S. and Bao, S. and Kang, Z. and Gokhale, A. and Damiano, T.}, editor = {Baru C., Huan J., Hu X.T., Ak R., Tian Y., Barga R., Zaniolo C., Lee K., Ye Y.F., Khan L.}, date = {2019}, series = {Proceedings - 2019 {{IEEE International Conference}} on {{Big Data}}, {{Big Data}} 2019}, pages = {1607--1612}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/BigData47090.2019.9006518}, abbrev_source_title = {Proc. - IEEE Int. Conf. Big Data, Big Data}, affiliation = {Vanderbilt University, EECS Dept, Nashville, TN, United States; Lockheed Martin Advanced Technology Labs, Cherry Hill, NJ, United States}, art_number = {9006518}, document_type = {Conference Paper}, isbn = {978-1-7281-0858-2}, langid = {english}, source = {Scopus}, note = {cited By 6 \par TL;DR \par This paper presents Stratum, which is an event-driven Big Data-as-a-Service offering for IoT analytics lifecycle management, which provides users with an intuitive, declarative mechanism based on the principles of model-driven engineering to specify the application and infrastructure requirements.} } @incollection{biancofioreConversationalUserInterfaces2025, title = {Conversational User Interfaces and Agents}, booktitle = {Human-Centered {{AI}}: An {{Illustrated Scientific Quest}}}, author = {Biancofiore, Giovanni Maria and Di Palma, Dario and Pomo, Claudio and Narducci, Fedelucio and Di Noia, Tommaso}, date = {2025}, pages = {399--438}, publisher = {Springer, Cham}, issn = {2524-4477}, doi = {10.1007/978-3-031-61375-3_4}, abstract = {Conversational agents (CAs), such as chatbots or virtual assistants, represent Artificial Intelligence (AI) systems designed to facilitate human\&\#8211;machine interaction through natural language. These systems are revolutionizing communication by improving...}, isbn = {978-3-031-61375-3}, langid = {english}, keywords = {/unread} } @software{BiasInRSSErepo, title = {Artifacts: {{Popularity}} Bias in {{RSSE}}}, author = {{Anonymous}}, date = {2023-01}, url = {https://github.com/MSR23-BiasInRSSE/BiasInRSSE}, organization = {Zenodo} } @article{BickfordBBP20, title = {Operationalizing Digital Twins through Model-Based Systems Engineering Methods}, author = {Bickford, Jason and Bossuyt, Douglas L. Van and Beery, Paul T. and Pollman, Anthony G.}, date = {2020}, journaltitle = {Syst. Eng.}, volume = {23}, number = {6}, pages = {724--750}, doi = {10.1002/SYS.21559}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {TL;DR \par How objectives in DT development align with those of model-based systems engineering (MBSE), and how the MBSE process can answer questions necessary to define the digital twin (DT) is explored.}, timestamp = {Wed, 13 Jul 2022 12:40:35 +0200} } @inproceedings{bielik_adversarial_2020, ids = {pmlr-v119-bielik20a}, title = {Adversarial {{Robustness}} for {{Code}}}, booktitle = {Int. {{Conf}}. {{Mach}}. {{Learn}}.}, author = {Bielik, Pavol and Vechev, Martin}, date = {2020-11}, pages = {896--907}, publisher = {PMLR}, url = {http://proceedings.mlr.press/v119/bielik20a.html}, urldate = {2021-04-15}, abstract = {Machine learning and deep learning in particular has been recently used to successfully address many tasks in the domain of code such as finding and fixing bugs, code completion, decompilation, typ...}, langid = {english}, note = {ISSN: 2640-3498 \par TL;DR \par This work instantiating adversarial attacks for code, and showing that, similar to other domains, neural models for code are vulnerable to adversarial attack, and combining existing and novel techniques to improve robustness while preserving high accuracy are explored.} } @article{BiermannEKKTW06, title = {{{EMF}} Model Refactoring Based on Graph Transformation Concepts}, author = {Biermann, Enrico and Ehrig, Karsten and Köhler, Christian and Kuhns, Günter and Taentzer, Gabriele and Weiss, Eduard}, date = {2006}, journaltitle = {Electron. Commun. Eur. Assoc. Softw. Sci. Technol.}, volume = {3}, doi = {10.14279/TUJ.ECEASST.3.34}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Tue, 25 Aug 2020 16:49:29 +0200} } @inproceedings{BiermannEKKTW06, title = {Graphical Definition of In-Place Transformations in the Eclipse Modeling Framework}, booktitle = {Model {{Driven Eng}}. {{Lang}}. {{Syst}}. 9th {{Int}}. {{Conf}}. {{MoDELS}} 2006 {{Genova Italy Oct}}. 1-6 2006 {{Proc}}.}, author = {Biermann, Enrico and Ehrig, Karsten and Köhler, Christian and Kuhns, Günter and Taentzer, Gabriele and Weiss, Eduard}, editor = {Nierstrasz, Oscar and Whittle, Jon and Harel, David and Reggio, Gianna}, date = {2006}, series = {Lecture Notes in Computer Science}, volume = {4199}, pages = {425--439}, publisher = {Springer}, doi = {10.1007/11880240\_30}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {TL;DR \par A framework for in-place EMF model transformation based on graph transformation is presented, which can be compiled to Java code building up on generated EMF classes.}, timestamp = {Tue, 24 May 2022 15:28:49 +0200} } @online{BigDAWGPolystoreSystem, title = {The {{BigDAWG}} Polystore System and Architecture — {{Northwestern Scholars}}}, url = {https://www.scholars.northwestern.edu/en/publications/the-bigdawg-polystore-system-and-architecture}, urldate = {2018-04-16} } @article{BIGGIO2018317, title = {Wild Patterns: {{Ten}} Years after the Rise of Adversarial Machine Learning}, author = {Biggio, Battista and Roli, Fabio}, date = {2018}, journaltitle = {Pattern Recognit.}, volume = {84}, pages = {317--331}, issn = {0031-3203}, doi = {10.1016/j.patcog.2018.07.023}, abstract = {Learning-based pattern classifiers, including deep networks, have shown impressive performance in several application domains, ranging from computer vision to cybersecurity. However, it has also been shown that adversarial input perturbations carefully crafted either at training or at test time can easily subvert their predictions. The vulnerability of machine learning to such wild patterns (also referred to as adversarial examples), along with the design of suitable countermeasures, have been investigated in the research field of adversarial machine learning. In this work, we provide a thorough overview of the evolution of this research area over the last ten years and beyond, starting from pioneering, earlier work on the security of non-deep learning algorithms up to more recent work aimed to understand the security properties of deep learning algorithms, in the context of computer vision and cybersecurity tasks. We report interesting connections between these apparently-different lines of work, highlighting common misconceptions related to the security evaluation of machine-learning algorithms. We review the main threat models and attacks defined to this end, and discuss the main limitations of current work, along with the corresponding future challenges towards the design of more secure learning algorithms.}, keywords = {Adversarial examples,Adversarial machine learning,Deep learning,Evasion attacks,Poisoning attacks,Secure learning} } @article{BillFTMW19, title = {A Local and Global Tour on {{MOMoT}}}, author = {Bill, Robert and Fleck, Martin and Troya, Javier and Mayerhofer, Tanja and Wimmer, Manuel}, date = {2019}, journaltitle = {Software Syst. Model.}, volume = {18}, number = {2}, pages = {1017--1046}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found} } @inproceedings{BinTang2007, title = {Document Representation and Dimension Reduction for Text Clustering}, booktitle = {2013 {{IEEE}} 29th {{Int Conf Data Eng}}. {{Workshop ICDEW}}}, author = {Tang, B. and Spiteri, R. and Milios, E. and Zhang, R. and Wang, S. and Tougas, J. and Shafiei, M.}, date = {2007-04}, pages = {770--779}, publisher = {IEEE Computer Society}, location = {Los Alamitos, CA, USA}, nodoi = {10.1109/ICDEW.2007.4401066}, note = {TL;DR \par A systematic study in which three different document representation methods for text are used, together with three Dimension Reduction Techniques (DRT), in the context of the text clustering problem, and it is demonstrated that a profile length of 2000 is sufficient to capture the information.} } @book{Bishop:1995:NNP:525960, title = {Neural Networks for Pattern Recognition}, author = {Bishop, Christopher M.}, date = {1995}, publisher = {Oxford University Press, Inc.}, location = {New York, NY, USA}, isbn = {0-19-853864-2} } @article{bizer_linked_2009, title = {Linked Data - the Story so Far}, author = {Bizer, C. and Heath, T. and Berners-Lee, T.}, date = {2009}, journaltitle = {Int. J. Semantic Web Inf. Syst.}, volume = {5}, number = {3}, pages = {1--22}, added-at = {2011-11-02T15:40:20.000+0100}, biburl = {https://www.bibsonomy.org/bibtex/25e13b99f0fe4d28c1261158410041c70/mgraube}, interhash = {599c4dfb0c1625c0c4368a1ab8346646}, intrahash = {5e13b99f0fe4d28c1261158410041c70}, timestamp = {2011-11-02T15:40:20.000+0100} } @article{bjarnasonAligningRequirementsTesting2017, entrysubtype = {magazine}, title = {Aligning {{Requirements}} and {{Testing}}: {{Working Together}} toward the {{Same Goal}}}, shorttitle = {Aligning {{Requirements}} and {{Testing}}}, author = {Bjarnason, Elizabeth and Borg, Markus and {undefined} and {undefined} and {undefined} and {undefined}}, date = {2017}, journaltitle = {IEEE Software}, volume = {34}, number = {1}, pages = {20--23}, issn = {0740-7459}, abstract = {The proper alignment of requirements engineering and testing (RET) can be key to software's success. Three practices can provide effective RET alignment: using test cases as requirements, harvesting trace links, and reducing distances between requirements engineers and testers. The Web extra https://youtu.be/M65ZKxfxqME is an audio podcast of author Elizabeth Bjarnason reading the the Requirements column she cowrote with Markus Borg.}, keywords = {software engineering} } @article{bjarnasonChallengesPracticesAligning2014, title = {Challenges and Practices in Aligning Requirements with Verification and Validation: A Case Study of Six Companies}, shorttitle = {Challenges and Practices in Aligning Requirements with Verification and Validation}, author = {Bjarnason, Elizabeth and Runeson, Per and Borg, Markus and Unterkalmsteiner, Michael and Engström, Emelie and Regnell, Björn and Sabaliauskaite, Giedre and Loconsole, Annabella and Gorschek, Tony and Feldt, Robert}, date = {2014-12}, journaltitle = {Empir Software Eng}, volume = {19}, number = {6}, pages = {1809--1855}, issn = {1382-3256, 1573-7616}, doi = {10.1007/s10664-013-9263-y}, langid = {english}, note = {TL;DR \par A multi-unit case study is performed to gain insight into issues around aligning RE and VV by interviewing 30 practitioners from 6 software developing companies, involving 10 researchers in a flexible research process for case studies and provides a strategic roadmap for practitioners improvement work to address alignment challenges.} } @article{Blondel:2004:MSG:1035533.1035557, title = {A Measure of Similarity between Graph Vertices: {{Applications}} to Synonym Extraction and Web Searching}, author = {Blondel, Vincent D. and Gajardo, Anahí and Heymans, Maureen and Senellart, Pierre and Dooren, Paul Van}, date = {2004-04}, journaltitle = {SIAM Rev.}, volume = {46}, number = {4}, pages = {647--666}, publisher = {{Society for Industrial and Applied Mathematics}}, address = {Philadelphia, PA, USA}, issn = {0036-1445}, url = {http://dx.doi.org/10.1137/S0036144502415960}, acmid = {1035557}, issue_date = {2004}, nodoi = {10.1137/S0036144502415960}, numpages = {20}, keywords = {algorithms,eigenvalues of graphs,graph algorithms,graph theory} } @article{blouinFirstInternationalWorkshop, title = {First {{International Workshop}} on {{Model Management}} ({{MoM}})}, author = {Blouin, Dominique and Guerin, Sylvain and Amaral, Vasco and Bhobe, Anish and Almeida, Joao}, abstract = {Model-Based Systems Engineering (MBSE) is a popular way to specify, design, implement, deploy and maintain complex systems with high quality and lower costs. These systems combine multiple areas of engineering, including mechanical, electrical, hydraulic, biochemical, control, signal processing, and more. To represent all these aspects, a large number of heterogeneous models are required. However, managing these models correctly can be challenging, especially when different teams work on them simultaneously, which is common in collaborative and concurrent engineering. This activity is called Model Management (MoM) and includes activities beyond maintaining model consistency, such as managing model views, model validity, model versions, and development workflows.}, langid = {english} } @article{blouinKomprenModelingGenerating2012, title = {Kompren: Modeling and Generating Model Slicers}, author = {Blouin, Arnaud and Combemale, Benoît and Baudry, Benoit and Beaudoux, Olivier}, date = {2012}, journaltitle = {Softw. Syst. Model.}, doi = {10.1007/s10270-012-0300-x}, note = {TL;DR \par This paper proposes the Kompren language to model and generate model slicers for any DSL (e.g. modeling for software development or for civil engineering) and for different purposes and provides a set of expected properties about the slices that are extracted by the different forms of the slicer.} } @article{blouinSlicingbasedTechniquesVisualizing, title = {Slicing-Based {{Techniques}} for {{Visualizing Large Metamodels}}}, author = {Blouin, Arnaud and Moha, Naouel and Baudry, Benoit and Saharaoui, Houaru}, abstract = {In model-driven engineering, a model describes an aspect of a system. A model conforms to a metamodel that defines the concepts and relationships of a given domain. Metamodels are thus corner-stones of various meta-modeling activities that require a good understanding of the metamodels or parts of them. Current metamodel editing tools are based on standard visualization and navigation features, such as physical zooms. However, as soon as metamodels become larger, navigating through large metamodels becomes a tedious task that hinders their understanding. In this work, we promote the use of model slicing techniques to build visualization techniques dedicated to metamodels. We propose an approach based on model slicing, inspired from program slicing, to build interactive visualization techniques dedicated to metamodels. These techniques permit users to focus on metamodel elements of interest, which aims at improving the understandability. This approach is implemented in a metamodel visualizer, called Explen.}, note = {TL;DR \par This work proposes an approach based on model slicing, inspired from program slicing, to build interactive visualization techniques dedicated to metamodels, which permits users to focus on meetamodel elements of interest, which aims at improving the understand ability.} } @book{Blum:1992:NNC:129269, title = {Neural Networks in {{C}}++: {{An}} Object-Oriented Framework for Building Connectionist Systems}, author = {Blum, Adam}, date = {1992}, publisher = {John Wiley \& Sons, Inc.}, location = {New York, NY, USA}, isbn = {0-471-53847-7}, note = {TL;DR \par Besides providing a wealth of examples in C++, full coverage of three major application areas of neural network programming is included along with a complete C++ class library especially designed for neural network usage.} } @inproceedings{boardmanSystemSystemstheMeaning2006, title = {System of {{Systems-the}} Meaning of Of}, booktitle = {2006 {{IEEESMC Int}}. {{Conf}}. {{Syst}}. {{Syst}}. {{Eng}}.}, author = {Boardman, John and Sauser, Brian}, date = {2006}, pages = {6--pp}, publisher = {IEEE}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1652284}, urldate = {2016-08-21}, note = {TL;DR \par The difference in these terms in a fundamental sense is one that impacts their structure, behavior and realization, and the distinction comes from the manner in which parts and relationships are gathered together and therefore in the nature of the emergent whole.} } @article{bobadillaRecommenderSystemsSurvey2013a, title = {Recommender Systems Survey}, author = {Bobadilla, J. and Ortega, F. and Hernando, A. and Gutiérrez, A.}, date = {2013-07}, journaltitle = {Knowl.-Based Syst.}, volume = {46}, pages = {109--132}, issn = {09507051}, doi = {10.1016/j.knosys.2013.03.012} } @article{bockLowCodePlatform2021, title = {Low-{{Code Platform}}}, author = {Bock, Alexander C. and Frank, Ulrich}, date = {2021-12}, journaltitle = {Bus Inf Syst Eng}, volume = {63}, number = {6}, pages = {733--740}, issn = {2363-7005, 1867-0202}, doi = {10.1007/s12599-021-00726-8}, langid = {english}, keywords = {LOGSEQ}, note = {TL;DR \par Under the heading of ‘low-code’, a new class of software development environments has emerged in recent years which is not only said to afford the prospect of a substantial increase in software development productivity, but also to yield new ways of promoting business IT alignment and user empowerment.} } @inproceedings{bockSearchEssenceLowCode2021, title = {In {{Search}} of the {{Essence}} of {{Low-Code}}: {{An Exploratory Study}} of {{Seven Development Platforms}}}, shorttitle = {In {{Search}} of the {{Essence}} of {{Low-Code}}}, booktitle = {2021 {{ACMIEEE Int}}. {{Conf}}. {{Model Driven Eng}}. {{Lang}}. {{Syst}}. {{Companion MODELS-C}}}, author = {Bock, Alexander C. and Frank, Ulrich}, date = {2021-10}, pages = {57--66}, publisher = {IEEE}, location = {Fukuoka, Japan}, doi = {10.1109/MODELS-C53483.2021.00016}, abstract = {Rapidly growing attention has been directed in recent years toward a type of software development and execution environment now passing under the name of ‘low-code development platforms.’ The fundamental claim is that limiting traditional coding mechanisms in favor of a variety of alternative means of design and specification yields substantial efficiency gains in professional and private software development. But although much stir at present surrounds low-code development platforms, it is by no means clear what, if any, features are distinctive of these systems, and whether any of these features mark out a technology which can be considered original. This paper presents an exploratory study of seven low-code development platforms, with the aim of discovering their essence and assessing them critically in the light of research in information systems development. An analysis framework covering a number of criteria regarding professional information systems development is used to characterize the selected platforms, and to point out features commonly, occasionally, and rarely possessed by them. The study reveals that hardly any features of low-code development are innovative in and of themselves, with novelty primarily consisting in their combination and integration. Still, we argue in conclusion, a number of research opportunities can be made out with an eye on the leitmotif of low-code development.}, eventtitle = {2021 {{ACM}}/{{IEEE International Conference}} on {{Model Driven Engineering Languages}} and {{Systems Companion}} ({{MODELS-C}})}, isbn = {978-1-6654-2484-4}, langid = {english}, keywords = {LOGSEQ}, note = {TL;DR \par An exploratory study of seven low-code development platforms is presented, with the aim of discovering their essence and assessing them critically in the light of research in information systems development, revealing that hardly any features of low- code development are innovative in and of themselves.} } @inproceedings{Bogoclu20163344, title = {A Benchmark of Contemporary Metamodeling Algorithms}, author = {Bogoclu, C. and Roos, D.}, editor = {Stefanou G., Papadrakakis M., Plevris V., Papadopoulos V.}, date = {2016}, series = {{{ECCOMAS Congress}} 2016 - {{Proceedings}} of the 7th {{European Congress}} on {{Computational Methods}} in {{Applied Sciences}} and {{Engineering}}}, volume = {2}, pages = {3344--3360}, publisher = {National Technical University of Athens}, doi = {10.7712/100016.2039.7645}, abbrev_source_title = {ECCOMAS Congress - Proc. Euro. Congr. Comput. Methods Appl. Sci. Eng.}, affiliation = {Institute of Modeling and High-Performance Computing, Niederrhein University of Applied Sciences, Germany}, document_type = {Conference Paper}, isbn = {978-618-82844-0-1}, langid = {english}, source = {Scopus}, keywords = {notion}, note = {cited By 4} } @article{bononiIoTSensorData, title = {{{IoT Sensor Data Management}}}, author = {Bononi, L and Felice, M Di}, pages = {28}, langid = {english}, keywords = {data processing,DONE,GOOD\_SOURCE,internet of things,slides} } @article{bononiIoTSensorDataa, title = {{{IoT Sensor Data Processing}}}, author = {Bononi, L and Felice, M Di}, pages = {43}, langid = {english}, keywords = {data processing,DONE,internet of things,slides} } @article{boochHistorySoftwareEngineering2018, title = {The {{History}} of {{Software Engineering}}}, author = {Booch, G.}, date = {2018-09}, journaltitle = {IEEE Softw.}, volume = {35}, number = {5}, pages = {108--114}, issn = {0740-7459}, doi = {10.1109/MS.2018.3571234}, abstract = {Grady Booch, one of UML’s original authors, offers his perspective on the history of software engineering. This article is part of a theme issue on software engineering’s 50th anniversary. The Web Extra, a version of the article with an expanded bibliography, is at https://extras.computer.org/extra/mso2018050108s1.pdf.}, keywords = {software engineering} } @incollection{books/sp/mining2012/AggarwalZ12a, title = {A Survey of Text Clustering Algorithms.}, booktitle = {Mining Text Data}, author = {Aggarwal, Charu C. and Zhai, ChengXiang}, editor = {Aggarwal, Charu C. and Zhai, ChengXiang}, date = {2012}, pages = {77--128}, publisher = {Springer}, url = {http://dblp.uni-trier.de/db/books/collections/Mining2012.html#AggarwalZ12a}, added-at = {2012-02-18T00:00:00.000+0100}, biburl = {http://www.bibsonomy.org/bibtex/28cfea77bb8aecd46ab2ba9db26c4338b/dblp}, ee = {http://dx.doi.org/10.1007/978-1-4614-3223-4₄}, interhash = {a8614bc450f82d917149afd58fabf02a}, intrahash = {8cfea77bb8aecd46ab2ba9db26c4338b}, isbn = {978-1-4419-8462-3}, keywords = {dblp}, timestamp = {2012-02-21T11:35:00.000+0100} } @inproceedings{Borg:2014:RSD:2652524.2652556, title = {A Replicated Study on Duplicate Detection: {{Using}} Apache Lucene to Search among Android Defects}, booktitle = {Proc. 8th {{ACMIEEE Int}}. {{Symp}}. {{Empir}}. {{Softw}}. {{Eng}}. {{Meas}}.}, author = {Borg, Markus and Runeson, Per and Johansson, Jens and Mäntylä, Mika V.}, date = {2014}, series = {{{ESEM}} '14}, pages = {8:1-8:4}, publisher = {ACM}, location = {New York, NY, USA}, url = {http://doi.acm.org/10.1145/2652524.2652556}, acmid = {2652556}, articleno = {8}, isbn = {978-1-4503-2774-9}, nodoi = {10.1145/2652524.2652556}, numpages = {4}, keywords = {information retrieval,issue management,replication,software evolution} } @article{borgPipelineInfrastructureRequired2023, title = {Pipeline {{Infrastructure Required}} to {{Meet}} the {{Requirements}} on {{AI}}}, author = {Borg, Markus}, date = {2023-01}, journaltitle = {IEEE Softw.}, volume = {40}, number = {1}, pages = {18--22}, issn = {0740-7459, 1937-4194}, doi = {10.1109/MS.2022.3211687}, langid = {english} } @article{borgSupportingChangeImpact2016, title = {Supporting {{Change Impact Analysis Using}} a {{Recommendation System}}: {{An Industrial Case Study}} in a {{Safety-Critical Context}}}, shorttitle = {Supporting {{Change Impact Analysis Using}} a {{Recommendation System}}}, author = {Borg, Markus and Wnuk, Krzysztof and Regnell, Bjorn and Runeson, Per}, date = {2016}, journaltitle = {IEEE Trans. Softw. Eng.}, pages = {1--1}, issn = {0098-5589, 1939-3520}, doi = {10.1109/TSE.2016.2620458}, note = {TL;DR \par A case study on ImpRec, a recommendation System for Software Engineering (RSSE), tailored for CIA at a process automation company, shows the potential of reusing traceability associated with developers’ past activities in an RSSE.} } @article{Borji201941, title = {Pros and Cons of {{GAN}} Evaluation Measures}, author = {Borji, A.}, date = {2019}, journaltitle = {Comput. Vis. Image Underst.}, volume = {179}, pages = {41--65}, publisher = {Academic Press Inc.}, issn = {10773142}, doi = {10.1016/j.cviu.2018.10.009}, abstract = {Generative models, in particular generative adversarial networks (GANs), have gained significant attention in recent years. A number of GAN variants have been proposed and have been utilized in many applications. Despite large strides in terms of theoretical progress, evaluating and comparing GANs remains a daunting task. While several measures have been introduced, as of yet, there is no consensus as to which measure best captures strengths and limitations of models and should be used for fair model comparison. As in other areas of computer vision and machine learning, it is critical to settle on one or few good measures to steer the progress in this field. In this paper, I review and critically discuss more than 24 quantitative and 5 qualitative measures for evaluating generative models with a particular emphasis on GAN-derived models. I also provide a set of 7 desiderata followed by an evaluation of whether a given measure or a family of measures is compatible with them. © 2018 Elsevier Inc.}, author_keywords = {Deep learning; Evaluation; Generative adversarial nets; Generative models; Neural networks}, coden = {CVIUF}, document_type = {Article}, source = {Scopus}, keywords = {Adversarial networks,Deep learning,Evaluation,Evaluation measures,Generative adversarial nets,Generative model,Image understanding,Model comparison,Neural networks,Software engineering}, note = {cited By 340} } @article{borjiBattleWordsmithsComparing2023, title = {Battle of the {{Wordsmiths}}: {{Comparing ChatGPT}}, {{GPT-4}}, {{Claude}}, and {{Bard}}}, shorttitle = {Battle of the {{Wordsmiths}}}, author = {Borji, Ali and Mohammadian, Mehrdad}, date = {2023}, journaltitle = {SSRN Journal}, issn = {1556-5068}, doi = {10.2139/ssrn.4476855}, abstract = {Although informal evaluations of modern LLMs can be found on social media, blogs, and news outlets, a formal and comprehensive comparison among them has yet to be conducted. In response to this gap, we have undertaken an extensive benchmark evaluation of LLMs and conversational bots. Our evaluation involved the collection of 1002 questions encompassing 27 categories, which we refer to as the “Wordsmiths dataset.” These categories include reasoning, logic, facts, coding, bias, language, humor, and more. Each question in the dataset is accompanied by an accurate and verified answer. We meticulously assessed four leading chatbots: ChatGPT, GPT-4, Bard, and Claude, using this dataset. The results of our evaluation revealed the following key findings: a) GPT-4 emerged as the top-performing chatbot across almost all categories, achieving a success rate of 84.1\%. On the other hand, Bard faced challenges and achieved a success rate of 62.4\%. b) Among the four models evaluated, one of them responded correctly approximately 93\% of the time. However, all models were correct only about 44\%. c) Bard is less correlated with other models while ChatGPT and GPT-4 are highly correlated in terms of their responses. d) Chatbots demonstrated proficiency in language understanding, facts, and self-awareness. However, they encountered difficulties in areas such as math, coding, IQ, and reasoning. e) In terms of bias, discrimination, and ethics categories, models generally performed well, suggesting they are relatively safe to utilize. To make future model evaluations on our dataset easier, we also provide a multiple-choice version of it (called Wordsmiths-MCQ). Dataset link: [MASKED].}, langid = {english}, keywords = {LOGSEQ} } @inproceedings{bosu2015characteristics, title = {Characteristics of Useful Code Reviews: {{An}} Empirical Study at Microsoft}, booktitle = {Proc. {{Int}}. {{Conf}}. {{Min}}. {{Softw}}. {{Repos}}.}, author = {Bosu, Amiangshu and Greiler, Michaela and Bird, Christian}, date = {2015-05}, edition = {Proceedings of the International Conference on Mining Software Repositories}, publisher = {{IEEE - Institute of Electrical and Electronics Engineers}}, url = {https://www.microsoft.com/en-us/research/publication/characteristics-of-useful-code-reviews-an-empirical-study-at-microsoft/}, abstract = {Over the past decade, both open source and commercial software projects have adopted contemporary peer code review practices as a quality control mechanism. Prior research has shown that developers spend a large amount of time and effort performing code reviews. Therefore, identifying factors that lead to useful code reviews can benefit projects by increasing code review effectiveness and quality. In a three-stage mixed research study, we qualitatively investigated what aspects of code reviews make them useful to developers, used our findings to build and verify a classification model that can distinguish between useful and not useful code review feedback, and finally we used this classifier to classify review comments enabling us to empirically investigate factors that lead to more effective code review feedback. In total, we analyzed 1.5 millions review comments from five Microsoft projects and uncovered many factors that affect the usefulness of review feedback. For example, we found that the proportion of useful comments made by a reviewer increases dramatically in the first year that he or she is at Microsoft but tends to plateau afterwards. In contrast, we found that the more files that are in a change, the lower the proportion of comments in the code review that will be of value to the author of the change. Based on our findings, we provide recommendations for practitioners to improve effectiveness of code reviews.}, note = {TL;DR \par The proportion of useful comments made by a reviewer increases dramatically in the first year that he or she is at Microsoft but tends to plateau afterwards, and it is found that the more files that are in a change, the lower the proportion of comments in the code review that will be of value to the author of the change.} } @article{Böttcher2021, title = {{{ELSA}}: {{An}} Efficient, Adaptive Ensemble Learning-Based Sampling Approach}, author = {Böttcher, M. and Fuchs, A. and Leichsenring, F. and Graf, W. and Kaliske, M.}, date = {2021}, journaltitle = {Adv. Eng. Softw.}, volume = {154}, publisher = {Elsevier Ltd}, issn = {09659978}, doi = {10.1016/j.advengsoft.2021.102974}, abbrev_source_title = {Adv Eng Software}, affiliation = {Institute for Structural Analysis, Technische Universität Dresden, Dresden, 01062, Germany}, art_number = {102974}, coden = {AESOD}, correspondence_address1 = {Kaliske, M.; Institute for Structural Analysis, Germany; email: michael.kaliske@tu-dresden.de}, document_type = {Article}, langid = {english}, source = {Scopus}, note = {cited By 1} } @inproceedings{Bottou91stochasticgradient, title = {Stochastic Gradient Learning in Neural Networks}, booktitle = {Proc. {{Neuro-Nîmes EC2}}}, author = {Bottou, Léon}, date = {1991}, note = {TL;DR \par This chapter discusses connectionist learning algorithms, which consists of minimizing a cost of the form C(w) = E(J(z,w)dP(z) where dP is an unknown probability distribution that characterizes the problem to learn, and J, the loss function, defines the learning system itself.} } @inproceedings{Boubekeur202084, title = {Automatic Assessment of Students' Software Models Using a Simple Heuristic and Machine Learning}, author = {Boubekeur, Y. and Mussbacher, G. and McIntosh, S.}, date = {2020}, series = {Proceedings - 23rd {{ACM}}/{{IEEE International Conference}} on {{Model Driven Engineering Languages}} and {{Systems}}, {{MODELS-C}} 2020 - {{Companion Proceedings}}}, pages = {84--93}, publisher = {Association for Computing Machinery, Inc}, doi = {10.1145/3417990.3418741}, abbrev_source_title = {Proc. - ACM/IEEE Int. Conf. Model Driven Eng. Lang. Syst., MODELS-C - Companion Proc.}, affiliation = {McGill University, Montreál, Canada; University of Waterloo, Waterloo, Canada}, document_type = {Conference Paper}, isbn = {978-1-4503-8135-2}, langid = {english}, source = {Scopus}, keywords = {GOAL\_Automatic\_Model\_Assessment,notion,TECHNIQUE\_DECISION-TREE,TECHNIQUE\_K-NEAREST-NEIGHBORS,TECHNIQUE\_LOGIC-REGRESSION,TECHNIQUE\_NAIVE-BAYES,TECHNIQUE\_RandomForests}, note = {cited By 5 \par “Logistic regression (LR), Naïve Bayes (NB), Decision Tree (DT), Random Forest (RF), and k-Nearest Neighbors (KNN)” (Boubekeur et al., 2020, p. 5) \par TL;DR \par This paper investigates how a tool that combines a simple heuristic with machine learning techniques can be used to help assess student submissions in model-driven engineering courses and results are comparable to human grading and a complex rule-based technique.} } @inproceedings{Boubekeur202094, title = {Towards a Better Understanding of Interactions with a Domain Modeling Assistant}, author = {Boubekeur, Y. and Mussbacher, G.}, date = {2020}, series = {Proceedings - 23rd {{ACM}}/{{IEEE International Conference}} on {{Model Driven Engineering Languages}} and {{Systems}}, {{MODELS-C}} 2020 - {{Companion Proceedings}}}, pages = {94--103}, publisher = {Association for Computing Machinery, Inc}, doi = {10.1145/3417990.3418742}, abbrev_source_title = {Proc. - ACM/IEEE Int. Conf. Model Driven Eng. Lang. Syst., MODELS-C - Companion Proc.}, affiliation = {McGill University, Montreál, Canada}, document_type = {Conference Paper}, isbn = {978-1-4503-8135-2}, langid = {english}, source = {Scopus}, keywords = {GOAL\_Model-Assistance,notion}, note = {cited By 1 \par HIGH LEVEL. NO SPECIFIC TECHNIQUES ARE MENTIONED. \par TL;DR \par The interactions between a student modeler and an interactive domain modeling assistant are explored with the aim of better understanding the required interaction and forming a corpus of learning material that supports the assistant interactions.} } @book{bourqueGuideSoftwareEngineering2014, title = {Guide to the Software Engineering Body of Knowledge}, author = {Bourque, Pierre and Fairley, R. E and {IEEE Computer Society}}, date = {2014}, isbn = {978-0-7695-5166-1}, langid = {english}, note = {TL;DR \par Data types Sorting and searching parallel and distributed algorithms 3.0 and 4.0 are presented, covering sorting, searching, and distributing in the context of distributed systems.} } @article{boussaid2017survey, title = {A Survey on Search-Based Model-Driven Engineering}, author = {Boussaïd, Ilhem and Siarry, Patrick and Ahmed-Nacer, Mohamed}, date = {2017}, journaltitle = {Autom. Software Eng.}, volume = {24}, pages = {233--294}, publisher = {Springer}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {TL;DR \par The purpose of this paper is to survey the relatively recent research activity lying at the interface between these two fields, an area that has come to be known as search-based model-driven engineering.} } @inproceedings{bousseGenerativeApproachDefine2015, title = {A {{Generative Approach}} to {{Define Rich Domain-Specific Trace Metamodels}}}, booktitle = {11th {{Eur}}. {{Conf}}. {{Model}}. {{Found}}. {{Appl}}. {{ECMFA}}}, author = {Bousse, Erwan and Mayerhofer, Tanja and Combemale, Benoit and Baudry, Benoit}, date = {2015}, url = {https://hal.inria.fr/hal-01154225/document}, urldate = {2015-06-24}, keywords = {⛔ No INSPIRE recid found}, note = {TL;DR \par This work evaluates a generative approach that defines a rich and domain-specific trace metamodel enabling the construction of execution traces for models conforming to a given xDSML and shows a significant performance improvement and simplification of the semantic differencing rules as compared to the usage of a generic trace metAModel.} } @article{boutotIoTMoFRequirementsDrivenModelling, title = {{{IoTMoF}}: {{A Requirements-Driven Modelling Framework}} for {{Adaptive IoT Systems}}}, author = {Boutot, Paul and Mustafiz, Sadaf}, langid = {english}, keywords = {LOGSEQ,MOSAICO}, note = {\section{Annotazioni\\ (14/1/2024, 22:55:13)} \par “he engineering of IoT systems brings about various challenges due to the inherent complexities associated with such adaptive systems.” (Boutot e Mustafiz, p. 1) This is a test to check how notes are managed by LogSeq} } @inproceedings{Bouzeraib2020, title = {A Blockchain Data Balance Using a Generative Adversarial Network Approach: {{Application}} to Smart House {{IDS}}}, author = {Bouzeraib, W. and Ghenai, A. and Zeghib, N.}, date = {2020}, series = {{{ICAASE}} 2020 - {{Proceedings}}, 4th {{International Conference}} on {{Advanced Aspects}} of {{Software Engineering}}}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/ICAASE51408.2020.9380110}, abstract = {The rapid development of information and communication technologies makes the Internet of Things (IoT) devices much more complex and heterogeneous. In this context, the massive end devices (IoTs) and the large volume of data raise security and privacy challenges. To tackle these issues, the joint use of the Bockchain (BC) and Machine Learning (ML) seems attractive to achieve decentralized, secure, intelligent and efficient management of networks. On the one hand, the BC can greatly facilitate the sharing of training data and ML models, the decentralization of intelligence, security, privacy and reliable ML decision-making. On the other hand, ML may have significant impacts on the development of BC in communications and networking systems, including energy and resource efficiency, scalability, security, privacy and smart contracting. An important aspect of security intends to detect unusual and potentially inappropriate activities according to traffic patterns. This paper focuses on the problem of imbalance data where the number of abnormal samples is significantly lower than that of the normal (secure) ones. In particular, this paper presents a new equilibrium model based on an exciting recent innovation in ML namely Generator Adverse Networks (GANs) to address the problem of class imbalance and data noise to Intrusion Detection System (IDS) performance. The proposed approach use is illustrated by a case study: a smart house system-based scenario. © 2020 IEEE.}, art_number = {9380110}, author_keywords = {Blockchain; Generative Adversarial Network (GAN); Internet of Things (IoT); Intrusion Detection System IDS; Machine Learning; Security}, document_type = {Conference Paper}, isbn = {978-1-6654-2231-4}, source = {Scopus}, keywords = {Blockchain,Communications and networkings,Data Sharing,Decision making,Efficient managements,Equilibrium modeling,Information and Communication Technologies,Internet of thing (IOT),Internet of things,Intrusion detection,Intrusion Detection Systems,Network security,Privacy by design,Resource efficiencies,Security and privacy,Software engineering}, note = {cited By 0} } @article{brandLaunchNewJournal2023, title = {Launch of {{New Journal JSEAS}}}, author = {family=Brand, given=Mark, prefix=van den, useprefix=false and Dajsuren, Yanja and Saberi, Arash}, date = {2023-01-23}, journaltitle = {J. Softw. Eng. Auton. Syst.}, publisher = {Athena Publishing}, issn = {2949-9372}, doi = {10.55060/j.jseas.230123.001}, langid = {english}, keywords = {LOGSEQ} } @book{bredaNemicoDiMussolini2024, title = {Il nemico di Mussolini: Giacomo Matteotti, storia di un eroe dimenticato}, shorttitle = {Il nemico di Mussolini}, author = {Breda, Marzio and Caretti, Stefano}, date = {2024}, series = {Saggi}, publisher = {Solferino}, location = {Milano}, isbn = {978-88-282-1347-5}, langid = {italian} } @inproceedings{breuTenPrinciplesLiving2010, title = {Ten {{Principles}} for {{Living Models}} - {{A Manifesto}} of {{Change-Driven Software Engineering}}}, booktitle = {2010 {{Int}}. {{Conf}}. {{Complex Intell}}. {{Softw}}. {{Intensive Syst}}.}, author = {Breu, Ruth}, date = {2010-02}, pages = {1--8}, publisher = {IEEE}, location = {Krakow, TBD, Poland}, doi = {10.1109/CISIS.2010.73}, abstract = {The new generation of open networked IT systems poses particular challenges to software engineering due to their evolving nature and their high quality requirements. In particular, the management of service oriented systems requires the integration of perspectives from IT management, software engineering and systems operation and a systematic way to handle changes. In this paper we will present the core ideas of Living Models – a novel paradigm of model–based development, management and operation of evolving service oriented systems. A core concern of Living Models is to support the cooperation of stakeholders from IT management, software engineering and systems operation by providing appropriate model-based abstractions and the fostering of interdependencies. Based on this idea the running services together with their modelling environments constitute the basic unit of quality management and evolution. Living Models provides a coherent view of the quality status of the system (integrating the perspectives of all stakeholders) which evolves together with the running systems. This comes along with a software engineering process in which change is a first–class citizen.}, eventtitle = {2010 {{International Conference}} on {{Complex}}, {{Intelligent}} and {{Software Intensive Systems}} ({{CISIS}})}, isbn = {978-1-4244-5917-9}, langid = {english}, note = {TL;DR \par The core ideas of Living Models are presented - a novel paradigm of model-based development, management and operation of evolving service oriented systems and a coherent view of the quality status of the system which evolves together with the running systems.} } @article{BRIGUEZ20146467, title = {Argument-Based Mixed Recommenders and Their Application to Movie Suggestion}, author = {Briguez, Cristian E. and Budán, Maximiliano C.D. and Deagustini, Cristhian A.D. and Maguitman, Ana G. and Capobianco, Marcela and Simari, Guillermo R.}, date = {2014}, journaltitle = {Expert Syst. Appl.}, volume = {41}, number = {14}, pages = {6467--6482}, issn = {0957-4174}, url = {http://www.sciencedirect.com/science/article/pii/S0957417414001845}, nodoi = {https://doi.org/10.1016/j.eswa.2014.03.046}, keywords = {Defeasible argumentation,Qualitative vs quantitative recommendations} } @article{brockman2016openai, title = {Openai Gym}, author = {Brockman, Greg and Cheung, Vicki and Pettersson, Ludwig and Schneider, Jonas and Schulman, John and Tang, Jie and Zaremba, Wojciech}, date = {2016}, journaltitle = {ArXiv Prepr. ArXiv160601540}, eprint = {1606.01540}, eprinttype = {arXiv} } @article{broringEnablingIoTEcosystems2017, entrysubtype = {magazine}, title = {Enabling {{IoT Ecosystems}} through {{Platform Interoperability}}}, author = {Broring, Arne and Schmid, Stefan and Schindhelm, Corina-Kim and Khelil, Abdelmajid and Kabisch, Sebastian and Kramer, Denis and Phuoc, Danh Le and Mitic, Jelena and Anicic, Darko and Teniente, Ernest}, date = {2017}, journaltitle = {IEEE Software}, volume = {34}, number = {1}, pages = {54--61}, issn = {0740-7459}, abstract = {Today, the Internet of Things (IoT) comprises vertically oriented platforms for things. Developers who want to use them need to negotiate access individually and adapt to the platform-specific API and information models. Having to perform these actions for each platform often outweighs the possible gains from adapting applications to multiple platforms. This fragmentation of the IoT and the missing interoperability result in high entry barriers for developers and prevent the emergence of broadly accepted IoT ecosystems. The BIG IoT (Bridging the Interoperability Gap of the IoT) project aims to ignite an IoT ecosystem as part of the European Platforms Initiative. As part of the project, researchers have devised an IoT ecosystem architecture. It employs five interoperability patterns that enable cross-platform interoperability and can help establish successful IoT ecosystems.}, keywords = {internet of things,software engineering}, note = {TL;DR \par The BIG IoT (Bridging the Interoperability Gap of the IoT) project aims to ignite an IoT ecosystem as part of the European Platforms Initiative and employs five interoperability patterns that enable cross-platform interoperability and can help establish successful IoT ecosystems.} } @article{broyYesterdayTodayTomorrow2018, title = {Yesterday, {{Today}}, and {{Tomorrow}}: 50 {{Years}} of {{Software Engineering}}}, shorttitle = {Yesterday, {{Today}}, and {{Tomorrow}}}, author = {Broy, M.}, date = {2018}, journaltitle = {IEEE Softw.}, volume = {35}, number = {5}, pages = {38--43}, issn = {0740-7459}, doi = {10.1109/MS.2018.290111138}, abstract = {In 2018, we’re now 50 years after the famous groundbreaking conference on software engineering in Garmisch, organized by its chairman F.L. Bauer and his cochairs L. Bolliet and H.J. Helms. This conference introduced the notion and discipline of software engineering. This is a moment to look back at what we’ve achieved, what we haven’t achieved, where we are today, and what challenges lie ahead. This article is part of a theme issue on software engineering’s 50th anniversary.}, keywords = {software engineering} } @inproceedings{bruchEvaluatingRecommenderSystems2008, title = {On Evaluating Recommender Systems for {{API}} Usages}, booktitle = {Proc. 2008 {{Int}}. {{Workshop Recomm}}. {{Syst}}. {{Softw}}. {{Eng}}.}, author = {Bruch, Marcel and Schäfer, Thorsten and Mezini, Mira}, date = {2008}, series = {{{RSSE}} '08}, pages = {16--20}, publisher = {ACM}, location = {New York, NY, USA}, url = {http://doi.acm.org/10.1145/1454247.1454254}, acmid = {1454254}, isbn = {978-1-60558-228-3}, nodoi = {10.1145/1454247.1454254}, numpages = {5}, note = {TL;DR \par An automated evaluation process is presented which enables the validation of recommendation systems with large test beds in an objective manner by means of precision and recall measures and demonstrates the applicability of this approach by evaluating an improvement of an existing API recommender tool that takes into account the framework-method context for recommendations.} } @article{bruelModelTransformationReuse, title = {Model {{Transformation Reuse}} across {{Metamodels}}}, author = {Bruel, Jean-Michel and Combemale, Benoit and Guerra, Esther and Jézéquel, Jean-Marc}, pages = {15}, abstract = {Model transformations (MTs) are essential elements of model-driven engineering (MDE) solutions. MDE promotes the creation of domain-specific metamodels, but without proper reuse mechanisms, MTs need to be developed from scratch for each new metamodel. In this paper, we classify reuse approaches for MTs across different metamodels and compare a sample of specific approaches – model types, concepts, a-posteriori typing, multilevel modeling, and design patterns for MTs – with the help of a feature model developed for this purpose, as well as a common example. We discuss strengths and weaknesses of each approach, provide a reading grid used to compare their features, and identify gaps in current reuse approaches.}, langid = {english} } @article{brun2007emf, title = {{{EMF}} Compare}, author = {Brun, Cedric and Musset, Jonathan and Toulme, Antoine}, date = {2007} } @inproceedings{Brun2008MODELDI, title = {Model Differences in the Eclipse Modeling Framework}, author = {Brun, Cédric and {Obeo} and Pierantonio, Alfonso}, date = {2008}, url = {https://api.semanticscholar.org/CorpusID:16702546}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found} } @article{bruneliere2014modisco, title = {Modisco: {{A}} Model Driven Reverse Engineering Framework}, author = {Bruneliere, Hugo and Cabot, Jordi and Dupé, Grégoire and Madiot, Frédéric}, date = {2014}, journaltitle = {Inf. Softw. Technol.}, volume = {56}, number = {8}, pages = {1012--1032}, publisher = {Elsevier} } @inproceedings{BruneliereCJM10, title = {{{MoDisco}}: A Generic and Extensible Framework for Model Driven Reverse Engineering}, booktitle = {{{ASE}} 2010 25th {{IEEEACM Int}}. {{Conf}}. {{Autom}}. {{Softw}}. {{Eng}}. {{Antwerp Belg}}. {{Sept}}. 20-24 2010}, author = {Bruneliere, Hugo and Cabot, Jordi and Jouault, Frédéric and Madiot, Frédéric}, editor = {Pecheur, Charles and Andrews, Jamie and Nitto, Elisabetta Di}, date = {2010}, pages = {173--174}, publisher = {ACM}, doi = {10.1145/1858996.1859032}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {TL;DR \par MoDisco intensively uses MDE principles and techniques to improve existing approaches for reverse engineering, and is introduced as a generic and extensible open source reverse engineering solution.}, timestamp = {Sun, 20 Nov 2022 22:43:32 +0100} } @inproceedings{bruneliereIndustrializationResearchTools2010, title = {Industrialization of Research Tools: {{The ATL}} Case}, shorttitle = {Industrialization of Research Tools}, booktitle = {Third {{Int}}. {{Workshop Acad}}. {{Softw}}. {{Dev}}. {{Tools Tech}}.-{{WASDeTT-3 Co-Located}} 25th {{IEEEACM Int}}. {{Conf}}. {{Autom}}. {{Softw}}. {{Eng}}.-{{ASE2010}}}, author = {Bruneliere, Hugo and Cabot, Jordi and Jouault, Frédéric and Tisi, Massimo and Bézivin, Jean}, date = {2010}, url = {https://hal.inria.fr/hal-00539173/}, urldate = {2016-10-10}, note = {TL;DR \par It is argued in this paper that the best solution for research teams aiming to create high-quality and widely-used tools is to industrialize their research prototypes through a partnership with a technology provider.} } @inproceedings{bruneliereLightweightMetamodelExtension, title = {On {{Lightweight Metamodel Extension}} to {{Support Modeling Tools Agility}}}, booktitle = {11th {{Eur}}. {{Conf}}. {{Model}}. {{Found}}. {{Appl}}. {{ECMFA}} 2015a {{STAF}} 2015 {{Conf}}.}, author = {Bruneliere, Hugo and Garcia, Jokin and Desfray, Philippe and Khelladi, Djamel Eddine and Hebig, Regina and Bendraou, Reda and Cabot, Jordi}, url = {https://hal.inria.fr/hal-01146802/}, urldate = {2015-06-24}, note = {TL;DR \par Following the ongoing OMG MOF Extension Facility (MEF) RFP, this paper proposes a generic lightweight metamodel extension mechanism developed as part of the MoNoGe collaborative project, with two different implementations of this extension mechanism based on Eclipse/EMF and the Modelio modeling environment.} } @incollection{brunEngineeringSelfadaptiveSystems2009, title = {Engineering Self-Adaptive Systems through Feedback Loops}, booktitle = {Software Engineering for Self-Adaptive Systems}, author = {Brun, Yuriy and Serugendo, Giovanna Di Marzo and Gacek, Cristina and Giese, Holger and Kienle, Holger and Litoiu, Marin and Müller, Hausi and Pezzè, Mauro and Shaw, Mary}, date = {2009}, pages = {48--70}, publisher = {Springer}, url = {http://link.springer.com/chapter/10.1007/978-3-642-02161-9_3}, urldate = {2016-11-03}, note = {TL;DR \par The state-of-the-art in engineering self-adaptive systems is explored and the critical challenges the community must address to enable systematic and well-organized engineering of self- Adaptive and self-managing software systems are identified.} } @article{brunschwigModellingMobileDevices, title = {Modelling on Mobile Devices}, author = {Brunschwig, Lea and Guerra, Esther and family=Lara, given=Juan, prefix=de, useprefix=true}, pages = {27}, abstract = {Modelling is central to many disciplines in engineering and the natural and social sciences. A wide variety of modelling languages and tools have been proposed along the years, traditionally for static environments such as desktops and laptops. However, the availability of increasingly powerful mobile devices makes it possible to profit from their embedded sensors and components (e.g., camera, microphone, GPS, accelerometer, gyroscope) for modelling. This has promoted a new range of modelling tools specially designed for their use in mobility. Such tools open the door to modelling in dynamic scenarios that go beyond the capabilities of traditional desktop tools. For example, modelling in mobility can be useful to design smart factories on-site, or to create models of hiking routes while walking along the routes, among many other scenarios.}, langid = {english}, note = {TL;DR \par A systematic mapping study is reported on to identify the state of the art and trends in modelling on mobile devices, and derive a classification for mobile modelling tools along three orthogonal dimensions, discuss current gaps, and propose avenues for further research.} } @article{brynjulfsenXAITextDomainSpecificLanguage, title = {{{XAIText}}: {{A Domain-Specific Language}} for {{Developing}} an {{AI Pipeline}}}, author = {Brynjulfsen, Håvard and Rabbi, Fazle}, pages = {105}, langid = {english}, keywords = {GOAL\_MDE4AI} } @article{bucaioniFunctionalSoftwareReference, title = {A Functional Software Reference Architecture for {{LLM-integrated}} Systems}, author = {Bucaioni, Alessio and Weyssow, Martin and He, Junda and Lyu, Yunbo and Lo, David}, langid = {english} } @article{bucchiaroneAutonomousShuttleasaServiceASaaS2020, title = {Autonomous {{Shuttle-as-a-Service}} ({{ASaaS}}): {{Challenges}}, {{Opportunities}}, and {{Social Implications}}}, shorttitle = {Autonomous {{Shuttle-as-a-Service}} ({{ASaaS}})}, author = {Bucchiarone, Antonio and Battisti, Sandro and Marconi, Annapaola and Maldacea, Roberto and Ponce, Diego Cardona}, date = {2020}, journaltitle = {IEEE Trans. Intell. Transport. Syst.}, pages = {1--10}, issn = {1524-9050, 1558-0016}, doi = {10.1109/TITS.2020.3025670}, abstract = {Providing mobility services effectively to residents and visitors is a complex socio-technical system task to city public managers. Smart mobility systems aim to support the efficient exploitation of city transport facilities and sustainable mobility within the urban environment. People need to travel quickly and conveniently between locations at different scales, ranging from a few blocks within a city to a journey across cities. At the same time, goods need to be timely delivered, considering both the users and the businesses’ needs. Several cities indicated an interest in using Autonomous Vehicles (AV) for the “last-mile” mobility services in the last few years. With them, it seems to be easier to get people and goods around using fewer vehicles. In this context, Autonomous Shuttles (AS) are beginning to be thought of as a new mobility/delivery service into the city center where narrow streets are not easily served by traditional buses. They allow them to perform critical areas with minimal new infrastructure and reduce noise and pollution. The article analyses the state-of-art on autonomous shuttles by proposing four application scenarios targeting the last-mile delivery of goods, the tourist experiences, and the shared and integrated mobility. Furthermore, we contribute with the proposition of the Autonomous Shuttles-as-a service (ASaaS) concept as the key pillar for the realization of innovative and sustainable proximity mobility. Our research proposed new research challenges for ASaaS, and we discuss social implications and governance challenges that consider user engagement and sustainability. It also recommended extending new research to focus on simulation and machine learning techniques for last-mile mobility planning and explore the journeys tracking certification via artificial intelligence and blockchain-based techniques.}, langid = {english} } @article{BucchiaroneCPP20, title = {Grand Challenges in Model-Driven Engineering: An Analysis of the State of the Research}, author = {Bucchiarone, Antonio and Cabot, Jordi and Paige, Richard F. and Pierantonio, Alfonso}, date = {2020}, journaltitle = {Software Syst. Model.}, volume = {19}, number = {1}, pages = {5--13}, doi = {10.1007/S10270-019-00773-6}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {TL;DR \par The events brought together experts from industry, academia, and the open-source community to assess what has changed in research in MDE over the last 10 years, what challenges remain, and what new challenges have arisen.}, timestamp = {Sun, 25 Jul 2021 11:40:23 +0200} } @article{bucchiaroneWhatFutureModeling2021, title = {What {{Is}} the {{Future}} of {{Modeling}}?}, author = {Bucchiarone, Antonio and Ciccozzi, Federico and Lambers, Leen and Pierantonio, Alfonso and Tichy, Matthias and Tisi, Massimo and Wortmann, Andreas and Zaytsev, Vadim}, date = {2021-03}, journaltitle = {IEEE Softw.}, volume = {38}, number = {2}, pages = {119--127}, issn = {0740-7459, 1937-4194}, doi = {10.1109/MS.2020.3041522}, langid = {english}, note = {TL;DR \par It is the right time to define what should be the future of modeling technologies, especially the requirements for the next generation of modeling frameworks and languages.} } @article{buchmannWhiteboxLLMsupportedLowcode2018, title = {White-Box {{LLM-supported Low-code Engineering}}: {{A Vision}} and {{First Insights}}}, author = {Buchmann, Thomas and Peinl, René and Schwägerl, Felix}, date = {2018}, abstract = {Low-code development platforms promise to empower citizen developers to define core domain models and rules for business applications. However, as domain rules grow complex, LCD platforms may fail to do so effectively. Generative AI, driven by large language models (LLMs), offers source code generation from natural language but suffers from its blackbox nature. Therefore, rather than having LLMs generate entire applications from single prompts, we advocate for a white-box approach allowing citizen developers to specify domain models, attaching constraints and operations as natural language annotations. These are fed incrementally into an LLM contextualized with the generated application stub. This results in precise, explainable, and deterministic generation of static application components, while offering citizen developers an appropriate level of abstraction. We report on a case study in manufacturing execution systems, where the implementation of the approach provides first insights.}, langid = {english}, keywords = {LOGSEQ}, note = {\section{Annotazioni\\ (27/7/2024, 12:18:08)} \par - “grow complex” (Buchmann et al., 2018, p. 1) \#ff6666 \par - “offers source code generation from natural language but suffers from its blackbox nature.” (Buchmann et al., 2018, p. 1) \#5fb236 \par - “white-box approach” (Buchmann et al., 2018, p. 1) \#ffd400\\ \mkbibemph{What do you mean?} \par - “We report on a case study in manufacturing execution systems, where the implementation of the approach provides first insights.” (Buchmann et al., 2018, p. 1) \#5fb236 \par - “prompt” (Buchmann et al., 2018, p. 1) \#ffd400\\ \mkbibemph{THere are too many papers in CORR. I would replace them with papers appeared in peer-reviewed venues!} \par - “All further constraints and operations are phrased as natural-language annotations and remain attached to the domain model until the application stub has been generated.” (Buchmann et al., 2018, p. 1) \#ffd400\\ \mkbibemph{It's a chain-of-though approach. What's the novelty?} \par - “well-known weakness in doing maths can be overcome by teaching them how to use a calculator” (Buchmann et al., 2018, p. 2) \#5fb236 \par - “tool-use” of LLMs” (Buchmann et al., 2018, p. 2) \#5fb236 \par - “,” (Buchmann et al., 2018, p. 2) \#ff6666 \par - “do few-shot learning by presenting them some examples to be able to generate the desired output.” (Buchmann et al., 2018, p. 2) \#5fb236 \par - “One of the first to evaluate the use of LLMs in model driven development is [3].” (Buchmann et al., 2018, p. 2) \#a28ae5 \par - “applicability of LLMs to Business Process Management” (Buchmann et al., 2018, p. 2) \#a28ae5 \par - “identification of processes based on documents” (Buchmann et al., 2018, p. 2) \#5fb236 \par - “Vidgof et al.” (Buchmann et al., 2018, p. 2) \#ff6666\\ \mkbibemph{Missing ref.} \par - ““which tasks can be achieved with already existing models” and that data sets, benchmarks and new LLMs specialized on process management are required.” (Buchmann et al., 2018, p. 2) \#5fb236 \par - “[4] use” (Buchmann et al., 2018, p. 2) \#ffd400\\ \mkbibemph{Avoid using references as subject of sentences. Write instead Forell et. al [4] use...} \par - “mining declarative process models from textual descriptions,” (Buchmann et al., 2018, p. 2) \#ff6666\\ \mkbibemph{Repeatead !!!} \par - “Design of an initial domain model” (Buchmann et al., 2018, p. 2) \#5fb236 \par - “Partial code generation” (Buchmann et al., 2018, p. 2) \#5fb236 \par - “sage of a LLM contextualized with the existing source code in order to fill in the generated stubs one after another” (Buchmann et al., 2018, p. 2) \#5fb236 \par - “LLM GitHub Copilot 3.” (Buchmann et al., 2018, p. 4) \#5fb236 \par - “Creating the resulting source code requires manual copyand-paste in our experimentation, but will be automated in our envisioned approach.” (Buchmann et al., 2018, p. 4) \#5fb236 \par - “seven of the nine operation bodies were provided correctly by the LLM, while we had to re-phrase two of them.” (Buchmann et al., 2018, p. 4) \#5fb236 \par - “conversational nature of LLMs.” (Buchmann et al., 2018, p. 4) \#5fb236 \par - “We expect, however, that the here envisioned approach is applicable, also to input field validation and the like.” (Buchmann et al., 2018, p. 4) \#ffd400\\ \mkbibemph{too high level, too preliminary. I understand this is a workshop submission, but I think the propometed ideas are very high level. Aspects like generalizability,ways to mitigate hallucinations, etc are not mentioned at all. I see the proposed approach as a kind of chain of thoughs, and as a such I don't see clearly explained the research contribution of the envisioned work. Moreover, I'm missing to see the envisioned approach contextualized in some modeling/software engineering process. The white-box of the proposed approach is limited, because it relies on LLMs that by nature are black box and as users we can "touch" the inputs and analyze the outputs without any control of the inner behaviour of the model.} \par - “deterministic generation of applications” (Buchmann et al., 2018, p. 5) \#ffd400\\ \mkbibemph{I'm not convinced about the deterministic characteristcs of the generation phase!}} } @article{buckleyTaxonomySoftwareChange2003, title = {Towards a Taxonomy of Software Change}, author = {Buckley, Jim and Mens, Tom and Zenger, Matthias and Rashid, Awais and Kniesel, Gunter}, date = {2003}, abstract = {Previous taxonomies of software change have focused on the purpose of the change (i.e. the why ) rather than the underlying mechanisms. This paper proposes a taxonomy of software change based on characterizing the mechanisms of change and the factors that influence these mechanisms.}, langid = {english} } @article{buczakSurveyDataMining2016, title = {A {{Survey}} of {{Data Mining}} and {{Machine Learning Methods}} for {{Cyber Security Intrusion Detection}}}, author = {Buczak, Anna L. and Guven, Erhan}, date = {2016-22}, journaltitle = {IEEE Commun. Surv. Tutorials}, volume = {18}, number = {2}, pages = {1153--1176}, issn = {1553-877X, 2373-745X}, doi = {10.1109/COMST.2015.2494502}, note = {TL;DR \par A targeted literature survey of machine learning (ML) and data processing (DM) strategies for cyber analytics in support of intrusion detection as it applies to wired networks.} } @book{budinskyEclipseModelingFramework2003, title = {Eclipse {{Modeling Framework}}}, author = {Budinsky, F. and Steinberg, D. and Merks, E. and Ellersick, R. and {T.J. Grose}}, date = {2003}, publisher = {Addison Wesley}, note = {TL;DR \par The authoritative guide to the Eclipse Modeling Framework (EMF)--written by the lead EMF designers! shows how EMF unifies three important technologies: Java, XML, and UML.} } @article{bugiottiComparisonDataModels, title = {A {{Comparison}} of {{Data Models}} and {{APIs}} of {{NoSQL Datastores}}}, author = {Bugiotti, Francesca and Cabibbo, Luca}, pages = {12}, abstract = {NoSQL datastore systems are a new generation of non-relational databases. More than fifty NoSQL systems have been already implemented, each with different characteristics — especially, with different data models and different APIs to access the data. In this paper we describe and compare the data models and operations offered by a number of representative NoSQL datastores, which we have directly used while developing the SOS (Save Our Systems) and ONDM (Object-NoSQL Datastore Mapper) frameworks. We discuss how these NoSQL systems can be used to manage a database consisting of collections of objects. Furthermore, we report on some experimental results concerning the use of the various systems and the implementation of the data representations described in this paper.}, langid = {english} } @incollection{bugiottiDatabaseDesignNoSQL2014, ids = {bugiottiDatabaseDesignNoSQL2014a}, title = {Database {{Design}} for {{NoSQL Systems}}}, booktitle = {Conceptual {{Modeling}}}, author = {Bugiotti, Francesca and Cabibbo, Luca and Atzeni, Paolo and Torlone, Riccardo}, editor = {Yu, Eric and Dobbie, Gillian and Jarke, Matthias and Purao, Sandeep}, date = {2014}, volume = {8824}, pages = {223--231}, publisher = {Springer International Publishing}, location = {Cham}, doi = {10.1007/978-3-319-12206-9_18}, abstract = {We propose a database design methodology for NoSQL systems. The approach is based on NoAM (NoSQL Abstract Model), a novel abstract data model for NoSQL databases, which exploits the commonalities of various NoSQL systems and is used to specify a system-independent representation of the application data. This intermediate representation can be then implemented in target NoSQL databases, taking into account their specific features. Overall, the methodology aims at supporting scalability, performance, and consistency, as needed by next-generation web applications.}, isbn = {978-3-319-12205-2 978-3-319-12206-9}, langid = {english}, note = {TL;DR \par The approach is based on NoAM (NoSQL Abstract Model), a novel abstract data model for NoSQL databases, which exploits the commonalities of various NoSQL systems and is used to specify a system-independent representation of the application data.} } @online{BuildingAutomatedMachine, title = {Building an {{Automated Machine Learning Pipeline}}: {{Part One}} | by {{Ceren Iyim}} | {{Towards Data Science}}}, url = {https://towardsdatascience.com/building-an-automated-machine-learning-pipeline-part-one-5c70ae682f35}, urldate = {2021-04-21} } @online{BuildingIoTOntologies, title = {Building {{IoT}} Ontologies and Integrating Them with {{Eclipse}} Projects | {{EclipseCon Europe}} 2016}, url = {https://www.eclipsecon.org/europe2016/session/building-iot-ontologies-and-integrating-them-eclipse-projects}, urldate = {2016-09-27} } @online{BuildingRaspberryPi, title = {Building {{A Raspberry Pi VPN Part One}}: {{How And Why To Build A Server}} - {{ReadWrite}}}, url = {http://readwrite.com/2014/04/10/raspberry-pi-vpn-tutorial-server-secure-web-browsing}, urldate = {2015-04-17} } @online{BuildingSmarterEclipse, title = {Building a {{Smarter Eclipse IoT Greenhouse}} with {{Eclipse Vorto}}, {{Kura}}, {{Californium}} and {{Paho}} | {{EclipseCon Europe}} 2016}, url = {https://www.eclipsecon.org/europe2016/session/building-smarter-eclipse-iot-greenhouse-eclipse-vorto-kura-californium-and-paho}, urldate = {2016-09-27} } @online{BuildingYourFirst, title = {Building Your First Agentic Framework with Python | by Amber Teng | Dec, 2024 | {{AI}} Advances}, url = {https://ai.gopubby.com/building-your-first-agentic-framework-with-python-8f374fdc1bb6?gi=a6ee426bc2d9}, urldate = {2024-12-30}, langid = {english}, keywords = {source\_mobile} } @online{BuildYourOwn, title = {Build Your Own {{OpenAI}} Agent - {{LlamaIndex}}}, url = {https://docs.llamaindex.ai/en/stable/examples/agent/openai_agent/}, urldate = {2025-01-22}, langid = {english} } @inproceedings{Bunne20191374, title = {Learning Generative Models across Incomparable Spaces}, author = {Bunne, C. and Alvarez-Melis, D. and Krause, A. and Jegelka, S.}, date = {2019}, series = {36th {{International Conference}} on {{Machine Learning}}, {{ICML}} 2019}, volume = {2019-June}, pages = {1374--1389}, publisher = {International Machine Learning Society (IMLS)}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077966344&partnerID=40&md5=28c6da710bb574e41803cd5cf2d19ea2}, abstract = {Generative Adversarial Networks have shown remarkable success in learning a distribution that faithfully recovers a reference distribution in its entirety. However, in some cases, we may want to only learn some aspects (e.g., cluster or manifold structure), while modifying others (e.g., style, orientation or dimension). In this work, we propose an approach to learn generative models across such incomparable spaces, and demonstrate how to steer the learned distribution towards target properties. A key component of our model is the Gromov-Wasserstein distance, a notion of discrepancy that compares distributions relationally rather than absolutely. While this framework subsumes current generative models in identically reproducing distributions, its inherent flexibility allows application to tasks in manifold learning, relational learning and cross-domain learning. © 36th International Conference on Machine Learning, ICML 2019. All rights reserved.}, document_type = {Conference Paper}, isbn = {978-1-5108-8698-8}, source = {Scopus}, keywords = {Adversarial networks,Artificial intelligence,Cross-domain learning,Generative model,Inherent flexibility,Machine learning,Manifold learning,Manifold structures,Relational learning,Software engineering,Wasserstein distance}, note = {cited By 20} } @article{Burattin2018322, title = {Who Is behind the Model? Classifying Modelers Based on Pragmatic Model Features}, author = {Burattin, A. and Soffer, P. and Fahland, D. and Mendling, J. and Reijers, H.A. and Vanderfeesten, I. and Weidlich, M. and Weber, B.}, editor = {Montali M., Weber I., vom Brocke J., Weske M.}, date = {2018}, journaltitle = {Lect. Notes Comput. Sci. Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinforma.}, volume = {11080 LNCS}, pages = {322--338}, publisher = {Springer Verlag}, issn = {03029743}, doi = {10.1007/978-3-319-98648-7_19}, abbrev_source_title = {Lect. Notes Comput. Sci.}, affiliation = {Technical University of Denmark, Kgs. Lyngby, Denmark; University of Haifa, Haifa, Israel; Eindhoven University of Technology, Eindhoven, Netherlands; Vienna University of Economics and Business, Vienna, Austria; Vrije Universiteit Amsterdam, Amsterdam, Netherlands; Humboldt-University, Berlin, Germany; University of Innsbruck, Innsbruck, Austria}, correspondence_address1 = {Burattin, A.; Technical University of DenmarkDenmark; email: andbur@dtu.dk}, document_type = {Conference Paper}, isbn = {9783319986470}, langid = {english}, source = {Scopus}, keywords = {GOAL\_Model-Classification,notion,TECHNIQUE\_FFNN}, note = {cited By 2} } @inproceedings{BurduselZ018, title = {{{MDEoptimiser}}: A Search Based Model Engineering Tool}, booktitle = {Proc. 21st {{ACMIEEE Int}}. {{Conf}}. {{Model Driven Eng}}. {{Lang}}. {{Syst}}. {{Companion Proc}}. {{MODELS}}}, author = {Burdusel, Alexandru and Zschaler, Steffen and Strüber, Daniel}, date = {2018}, pages = {12--16}, publisher = {ACM}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {TL;DR \par This tool demostration presents MDEOptimiser (MDEO), a tool for specifying and solving optimisation problems using MDE, and shows that with MDEO, specifying Optimisation problems becomes a less complex task compared to custom implementations.} } @article{buresSoftwareEngineeringSmart2015, title = {Software {{Engineering}} for {{Smart Cyber-Physical Systems}} -- {{Towards}} a {{Research Agenda}}: {{Report}} on the {{First International Workshop}} on {{Software Engineering}} for {{Smart CPS}}}, shorttitle = {Software {{Engineering}} for {{Smart Cyber-Physical Systems}} -- {{Towards}} a {{Research Agenda}}}, author = {Bures, Tomas and Krikava, Filip and Mordinyi, Richard and Pronios, Nikos and Weyns, Danny and Berger, Christian and Biffl, Stefan and Daun, Marian and Gabor, Thomas and Garlan, David and Gerostathopoulos, Ilias and Julien, Christine}, date = {2015-11-11}, journaltitle = {ACM SIGSOFT Softw. Eng. Notes}, volume = {40}, number = {6}, pages = {28--32}, issn = {01635948}, doi = {10.1145/2830719.2830736}, langid = {english}, note = {TL;DR \par This paper reports on the results of the First International Workshop on Software Engineering of Smart Cyber-Physical Systems (SEsCPS 2015), where participants discussed characteristics, challenges and opportunities of SE for smart CPS, with the aim to outline an agenda for future research in this important area.} } @article{Burgueño20191, title = {The Future of Model Transformation Languages: {{An}} Open Community Discussion}, author = {Burgueño, L. and Cabot, J. and Gérard, S.}, date = {2019}, journaltitle = {J. Object Technol.}, volume = {18}, number = {3}, pages = {1--11}, publisher = {Association Internationale pour les Technologies Objets}, issn = {16601769}, doi = {10.5381/JOT.2019.18.3.A7}, abbrev_source_title = {J. Object Technol.}, affiliation = {IN3, Open University of Catalonia, Spain; Institut LIST, CEA, Université Paris-Saclay, France; ICREA, Spain}, art_number = {A7}, document_type = {Article}, langid = {english}, source = {Scopus}, keywords = {GOAL\_Model-Transformation-Development,notion}, note = {cited By 19} } @inproceedings{Burgueno2019168, title = {Preface to {{MDE}} Intelligence 2019: 1st Workshop on Artificial Intelligence and Model-Driven Engineering}, author = {Burgueno, L. and Burdusel, A. and Gerard, S. and Wimmer, M.}, editor = {Burgueno L., Burgueno L., Voss S., Chaudron M., Kienzle J., Volter M., Gerard S., Zahedi M., Bousse E., Rensink A., Polack F., Engels G., Kappel G., Pretschner A.}, date = {2019}, series = {Proceedings - 2019 {{ACM}}/{{IEEE}} 22nd {{International Conference}} on {{Model Driven Engineering Languages}} and {{Systems Companion}}, {{MODELS-C}} 2019}, pages = {168--169}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/MODELS-C.2019.00028}, abbrev_source_title = {Proc. - ACM/IEEE Int. Conf. Model Driven Eng. Lang. Syst. Companion, MODELS-C}, affiliation = {IN3, Open University of Catalonia, Spain; Institut LIST, CEA, Université Paris-Saclay, France; King's College London, United Kingdom; CDL-MINT, Johannes Kepler Universit t Linz, Austria}, art_number = {8904820}, document_type = {Conference Paper}, isbn = {978-1-7281-5125-0}, langid = {english}, source = {Scopus}, note = {cited By 2 \par TL;DR \par The 1st edition of the Workshop on Artificial Intelligence and Model-driven Engineering (MDE Intelligence) was held on September 16, 2019 in Munich, Germany, as part of the satellite events of the IEEE/ACM 22th International Conference on Model-Driven Engineering Languages and Systems (MODELS 2019).} } @inproceedings{Burgueño2019294, title = {An {{LSTM-Based}} Neural Network Architecture for Model Transformations}, author = {Burgueño, L. and Cabot, J. and Gérard, S.}, editor = {Kessentini M., Yue T., Pretschner A., Voss S., Burgueno L., Burgueno L., Yue T.}, date = {2019}, series = {Proceedings - 2019 {{ACM}}/{{IEEE}} 22nd {{International Conference}} on {{Model Driven Engineering Languages}} and {{Systems}}, {{MODELS}} 2019}, pages = {294--299}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/MODELS.2019.00013}, abbrev_source_title = {Proc. - ACM/IEEE Int. Conf. Model Driven Eng. Lang. Syst., MODELS}, affiliation = {IN3, Open University of Catalonia, Institut LIST, CEA, Université Paris-Saclay, France; ICREA, IN3, Open University of Catalonia, Spain}, art_number = {8906971}, document_type = {Conference Paper}, isbn = {978-1-7281-2535-0}, langid = {english}, source = {Scopus}, keywords = {GOAL\_Model-Transformation-Development,notion,TECHNIQUE\_LSTM}, note = {cited By 22} } @inproceedings{Burgueno2021148, title = {{{MDE}} Intelligence 2021: {{3rdWorkshop}} on Artificial Intelligence and Model-Driven Engineering}, author = {Burgueno, L. and Kessentini, M. and Wimmer, M. and Zschaler, S.}, date = {2021}, series = {Companion {{Proceedings}} - 24th {{International Conference}} on {{Model-Driven Engineering Languages}} and {{Systems}}, {{MODELS-C}} 2021}, pages = {148--149}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/MODELS-C53483.2021.00026}, abbrev_source_title = {Companion Proc. - Int. Conf. Model-Driven Eng. Lang. Syst., MODELS-C}, affiliation = {IN3, Open University of Catalonia, Spain; University of Michigan, Dearborn, United States; CDL-MINT, Johannes Kepler Universität Linz, Austria; King's College, London, United Kingdom}, document_type = {Conference Paper}, isbn = {978-1-6654-2484-4}, langid = {english}, source = {Scopus}, note = {cited By 0} } @article{BurguenoCLG22, ids = {burgueno2022generic}, title = {A Generic {{LSTM}} Neural Network Architecture to Infer Heterogeneous Model Transformations}, author = {Burgueño, Loli and Cabot, Jordi and Li, Shuai and Gérard, Sébastien}, date = {2022}, journaltitle = {Software Syst. Model.}, volume = {21}, number = {1}, pages = {139--156}, doi = {10.1007/S10270-021-00893-Y}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Sun, 02 Oct 2022 15:49:27 +0200} } @article{burguenoContentsModelBasedSoftware2019, ids = {BurguenoCFKLMPP19}, title = {Contents for a {{Model-Based Software Engineering Body}} of {{Knowledge}}}, author = {Burgueño, Loli and Ciccozzi, Federico and Famelis, Michalis and Kappel, Gerti and Lambers, Leen and Mosser, Sebastien and Paige, Richard F. and Pierantonio, Alfonso and Rensink, Arend and Salay, Rick and Taentzer, Gabriele and Vallecillo, Antonio and Wimmer, Manuel}, date = {2019-12}, journaltitle = {Softw Syst Model}, volume = {18}, number = {6}, pages = {3193--3205}, issn = {1619-1366, 1619-1374}, doi = {10.1007/s10270-019-00746-9}, abstract = {Although Model-Based Software Engineering (MBE) is a widely accepted Software Engineering (SE) discipline, no agreedupon core set of concepts and practices (i.e., a Body of Knowledge) has been defined for it yet. With the goals of characterizing the contents of the MBE discipline, promoting a global consistent view of it, clarifying its scope with regard to other SE disciplines, and defining a foundation for the development of educational curricula on MBE, this paper proposes the contents for a Body of Knowledge for MBE. We also describe the methodology that we have used to come up with the proposed list of contents, as well as the results of a survey study that we conducted to sound out the opinion of the community on the importance of the proposed topics and their level of coverage in the existing SE curricula.}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {TL;DR \par This paper proposes the contents for a Body of Knowledge for MBE, and describes the methodology that was used to come up with the proposed list of contents, as well as the results of a survey study conducted to sound out the opinion of the community.}, timestamp = {Sat, 09 Apr 2022 12:28:53 +0200} } @article{burguenoGuestEditorialTheme2022, ids = {burguenoGuestEditorialTheme2022a}, title = {Guest Editorial to the Theme Section on {{AI-enhanced}} Model-Driven Engineering}, author = {Burgueño, Lola and Cabot, Jordi and Wimmer, Manuel and Zschaler, Steffen}, date = {2022-06}, journaltitle = {Softw Syst Model}, volume = {21}, number = {3}, pages = {963--965}, issn = {1619-1366, 1619-1374}, doi = {10.1007/s10270-022-00988-0}, abstract = {This theme section brings together the latest research at the intersection of artificial intelligence (AI) and model-driven engineering (MDE). Over the past years, we have witnessed a substantial rise of AI successfully applied to different domains, including software development and MDE. Dedicated events at the intersection of AI and MDE have been created, too, such as the MDE Intelligence workshop series co-located with the MODELS conference. This theme section covers research contributions integrating AI components into MDE approaches—increasing the current benefits of MDE processes and tools and pushing the limits of “classic” MDE with the goal to provide software and systems engineers with the right techniques to develop the next generation of highly complex model-based systems—and applications of MDE to the development of AI components. In total, nine submissions were accepted in the theme section after a thorough peer-reviewing process.}, langid = {english} } @article{burguenoMDEIntelligence6th, title = {{{MDE Intelligence}}: 6th {{Workshop}} on {{Artificial Intelligence}} and {{Model-driven Engineering}}}, author = {Burgueno, Lola and Bork, Dominik and Cabot, Jordi and Gerard, Sebastien and Ramırez, Aurora}, abstract = {Artificial Intelligence (AI) has become part of everyone’s life. More recently, AI has started to impact all aspects of the system and software development lifecycle, from specification to design, testing, deployment, and maintenance, with the main goal of helping engineers produce higher-quality systems and software more efficiently while being able to handle ever more complex systems. We believe there is a clear need for AI-empowered MDE, which will push the limits of “classic” MDE and provide the proper techniques to develop the next generation of highly complex software systems engineers will have to design tomorrow.}, langid = {english} } @article{burguenoNLPbasedArchitectureAutocompletion, title = {A {{NLP-based}} Architecture for the Autocompletion of Partial Domain Models}, author = {Burgueño, Loli and Clarisó, Robert and Li, Shuai and Gérard, Sébastien and Cabot, Jordi}, pages = {16}, abstract = {Domain models capture the key concepts and relationships of a business domain. Typically, domain models are manually defined by software designers in the initial phases of a software development cycle, based on their interactions with the client and their own domain expertise. Given the key role of domain models in the quality of the final system, it is important that they properly reflect the reality of the business. To facilitate the definition of domain models and improve their quality, we propose to move towards a more assisted domain modeling building process where an NLP-based assistant will provide autocomplete suggestions for the partial model under construction based on the automatic analysis of the textual information available for the project (contextual knowledge) and/or its related business domain (general knowledge). The process will also take into account the feedback collected from the designer’s interaction with the assistant. We have developed a proof-of-concept tool and have performed a preliminary evaluation that shows promising results.}, langid = {english}, note = {TL;DR \par This work proposes to move towards a more assisted domain modeling building process where an NLP-based assistant will provide autocomplete suggestions for the partial model under construction based on the automatic analysis of the textual information available for the project and/or its related business domain.} } @article{burguenoStaticFaultLocalization2015, title = {Static {{Fault Localization}} in {{Model Transformations}}}, author = {Burgueno, Loli and Troya, Javier and Wimmer, Manuel and Vallecillo, Antonio}, date = {2015-05-01}, journaltitle = {IEEE Trans. Softw. Eng.}, volume = {41}, number = {5}, pages = {490--506}, issn = {0098-5589, 1939-3520}, doi = {10.1109/TSE.2014.2375201}, note = {TL;DR \par This paper presents a light-weight and static approach for locating the faulty rules in model transformations, based on matching functions that automatically establish these alignments using the metamodel footprints, i.e., the metAModel elements used.} } @article{BurguenoWV16, title = {A Linda-Based Platform for the Parallel Execution of out-Place Model Transformations}, author = {Burgueño, Loli and Wimmer, Manuel and Vallecillo, Antonio}, date = {2016}, journaltitle = {Inf. Softw. Technol.}, volume = {79}, pages = {17--35}, doi = {10.1016/J.INFSOF.2016.06.001}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Tue, 16 Aug 2022 23:05:33 +0200} } @article{buttingModelingReusablePlatformIndependent2016, title = {Modeling {{Reusable}}, {{Platform-Independent Robot Assembly Processes}}}, author = {Butting, Arvid and Rumpe, Bernhard and Schulze, Christoph and Thomas, Ulrike and Wortmann, Andreas}, date = {2016}, journaltitle = {ArXiv Prepr. ArXiv160102452}, eprint = {1601.02452}, eprinttype = {arXiv}, url = {http://arxiv.org/abs/1601.02452}, urldate = {2016-01-18}, note = {TL;DR \par A new domain-specific language and toolchain for robot assembly tasks for compliant manipulators with the LightRocks toolchain, allowing a separation of concerns between domain experts and robotics experts.} } @book{buyyaInternetThingsPrinciples2016, title = {Internet of {{Things}}: Principles and Paradigms}, shorttitle = {Internet of {{Things}}}, author = {Buyya, Rajkumar and Dastjerdi, Amir Vahid}, date = {2016}, publisher = {Morgan Kaufmann}, location = {Amsterdam Boston Heidelberg}, isbn = {978-0-12-805395-9}, langid = {english}, pagetotal = {354}, note = {TL;DR \par Internet of Things: Principles and Paradigms captures the state-of-the-art research in Internet of Things, its applications, architectures, and technologies and identifies potential future directions and technologies that facilitate insight into numerous scientific, business, and consumer applications.} } @inproceedings{BWMB06, title = {Securing Collaborative Filtering against Malicious Attacks through Anomaly Detection}, booktitle = {Proc. {{ITWP06}}}, author = {Bhaumik, Runa and Williams, Chad and Mobasher, Bamshad and Burke, Robin}, date = {2006-07}, location = {Held at AAAI 2006, Boston, Massachusetts}, url = {http://www.aaai.org/Press/Reports/Workshops/ws-06-10.php}, note = {TL;DR \par This paper examines approaches for detecting suspicious rating trends based on statistical anomaly detection and empirically shows these techniques can be highly successful at detecting items under attack and time intervals when an attack occurred.} } @article{CA7F13858DC9A0A2F2B68A7CEA562E672, title = {{{CA7F13858DC9A0A2F2B68A7CEA562E67-2}}} } @inproceedings{CabotCR07, title = {{{UMLtoCSP}}: A Tool for the Formal Verification of {{UML}}/{{OCL}} Models Using Constraint Programming}, booktitle = {22nd {{IEEEACM Int}}. {{Conf}}. {{Autom}}. {{Softw}}. {{Eng}}. {{ASE}} 2007 {{Novemb}}. 5-9 2007 {{Atlanta Ga}}. {{USA}}}, author = {Cabot, Jordi and Clarisó, Robert and Riera, Daniel}, editor = {Stirewalt, R. E. Kurt and Egyed, Alexander and Fischer, Bernd}, date = {2007}, pages = {547--548}, publisher = {ACM}, doi = {10.1145/1321631.1321737}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {TL;DR \par UMLtoCSP is a tool for the formal verification of UML/OCL models that is able to automatically check several correctness properties, such as the strong and weak satisfiability of the model or the lack of redundant constraints.}, timestamp = {Wed, 14 Nov 2018 10:58:39 +0100} } @article{Cacheda:2011:CCF:1921591.1921593, title = {Comparison of Collaborative Filtering Algorithms: {{Limitations}} of Current Techniques and Proposals for Scalable, High-Performance Recommender Systems}, author = {Cacheda, Fidel and Carneiro, Víctor and Fernández, Diego and Formoso, Vreixo}, date = {2011-02}, journaltitle = {ACM Trans. Web}, volume = {5}, number = {1}, pages = {2:1-2:33}, publisher = {ACM}, address = {New York, NY, USA}, issn = {1559-1131}, url = {http://doi.acm.org/10.1145/1921591.1921593}, acmid = {1921593}, articleno = {2}, issue_date = {February 2011}, nodoi = {10.1145/1921591.1921593}, numpages = {33}, keywords = {Collaborative filtering,recommender systems} } @article{cadavidAnalysisMetamodelingPractices2015, title = {An Analysis of Metamodeling Practices for {{MOF}} and {{OCL}}}, author = {Cadavid, Juan José and Combemale, Benoit and Baudry, Benoit}, date = {2015-03}, journaltitle = {Comput. Lang. Syst. Struct.}, issn = {14778424}, doi = {10.1016/j.cl.2015.02.002}, langid = {english} } @article{calegariVerificationModelTransformations2013, title = {Verification of {{Model Transformations}}: {{A Survey}} of the {{State-of-the-Art}}}, shorttitle = {Verification of {{Model Transformations}}}, author = {Calegari, Daniel and Szasz, Nora}, date = {2013-03-05}, journaltitle = {Electronic Notes in Theoretical Computer Science}, series = {Proceedings of the {{XXXVIII Latin American Conference}} in {{Informatics}} ({{CLEI}})}, volume = {292}, pages = {5--25}, issn = {1571-0661}, doi = {10.1016/j.entcs.2013.02.002}, abstract = {Within the Model-Driven Engineering paradigm, software development is based on the definition of models providing different views of the system to be constructed and model transformations supporting a (semi)automatic development process. The verification of models and model transformations is crucial in order to improve the quality and the reliability of the products developed using this paradigm. In this context, the verification of a model transformation has three main components: the transformation itself, the properties of interest addressed, and the verification techniques used to establish the properties. In this paper we present an exhaustive review of the literature on the verification of model transformations analyzing these three components. We also take a problem-based approach exemplifying those aspects of interest that could be verified on a model transformation and show how this can be done. Finally, we conclude the need of an integrated environment for addressing the heterogeneous verification of model transformations.} } @inproceedings{callowAddressingSystemsVerification2011, title = {Addressing Systems Verification of Autonomous Systems through {{Bi-directional}} Model Transformations: {{A}} Systems Model Driven Architecture Approach}, shorttitle = {Addressing Systems Verification of Autonomous Systems through {{Bi-directional}} Model Transformations}, booktitle = {Syst. {{Syst}}. {{Eng}}. {{SoSE}} 2011 6th {{Int}}. {{Conf}}. {{On}}}, author = {Callow, Glenn and Kalawsky, Roy and Watson, Graham and Okuda, Yuki}, date = {2011}, pages = {311--316}, publisher = {IEEE}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5966616}, urldate = {2016-08-21}, note = {TL;DR \par A domain modeling approach that employs novel bi-directional model transformations to enable the verification of an autonomous system's functions and performance and can be applied to other types of complex system.} } @inproceedings{Çam2018372, title = {Supporting Simulation Experiments with Megamodeling}, author = {Çam, S. and Dayibaş, O. and Görür, B.K. and Oǧuztüzün, H. and Yilmaz, L. and Chakladar, S. and Doud, K. and Smith, A.E. and Teran-Somohano, A.}, editor = {Hammoudi S., Pires L.F., Selic B.}, date = {2018}, series = {{{MODELSWARD}} 2018 - {{Proceedings}} of the 6th {{International Conference}} on {{Model-Driven Engineering}} and {{Software Development}}}, volume = {2018-January}, pages = {372--378}, publisher = {SciTePress}, doi = {10.5220/0006586703720378}, abstract = {Recent developments in computational science and engineering allow a great deal of experimental work to be conducted through computer simulation. In a simulation experiment, a model of the phenomena to be studied is run in a computing environment under varying model and environment settings. As models are adjusted to experimental procedures and execution environments, variations arise. Models also evolve in time. Thus, models must be managed. We propose to bring Global Model Management (GMM) to bear on simulation experiment management by using techniques and tools from megamodeling. The proposed approach will facilitate model management tasks by providing an interface to query the model repository, relate models with each other, and apply model transformations from/to simulation models. Our proposed Megamodel for Simulation Experiments is based on SED-ML (Simulation Experiment Description Markup Language). Copyright © 2018 by SCITEPRESS-Science and Technology Publications, Lda. All rights reserved.}, document_type = {Conference Paper}, isbn = {978-989-758-283-7}, source = {Scopus}, note = {cited By 1 \par TL;DR \par The proposed Megamodel for Simulation Experiments is based on SED-ML (Simulation Experiment Description Markup Language) and will facilitate model management tasks by providing an interface to query the model repository, relate models with each other, and apply model transformations from/to simulation models.} } @article{camara2023assessment, title = {On the Assessment of Generative {{AI}} in Modeling Tasks: An Experience Report with {{ChatGPT}} and {{UML}}}, author = {Cámara, Javier and Troya, Javier and Burgueño, Lola and Vallecillo, Antonio}, date = {2023}, journaltitle = {Software Syst. Model.}, volume = {22}, number = {3}, pages = {781--793}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {TL;DR \par The findings show that, in contrast to code generation, the performance of the current version of ChatGPT for software modeling is limited, with various syntactic and semantic deficiencies, lack of consistency in responses and scalability issues.} } @article{camaraBridgingGapControl2020, title = {Towards {{Bridging}} the {{Gap}} between {{Control}} and {{Self-Adaptive System Properties}}}, author = {Cámara, Javier and Papadopoulos, Alessandro V. and Vogel, Thomas and Weyns, Danny and Garlan, David and Huang, Shihong and Tei, Kenji}, date = {2020-06-29}, journaltitle = {Proc. IEEEACM 15th Int. Symp. Softw. Eng. Adapt. Self-Manag. Syst.}, eprint = {2004.11846}, eprinttype = {arXiv}, pages = {78--84}, doi = {10.1145/3387939.3391568}, abstract = {Two of the main paradigms used to build adaptive software employ different types of properties to capture relevant aspects of the system's run-time behavior. On the one hand, control systems consider properties that concern static aspects like stability, as well as dynamic properties that capture the transient evolution of variables such as settling time. On the other hand, self-adaptive systems consider mostly non-functional properties that capture concerns such as performance, reliability, and cost. In general, it is not easy to reconcile these two types of properties or identify under which conditions they constitute a good fit to provide run-time guarantees. There is a need of identifying the key properties in the areas of control and self-adaptation, as well as of characterizing and mapping them to better understand how they relate and possibly complement each other. In this paper, we take a first step to tackle this problem by: (1) identifying a set of key properties in control theory, (2) illustrating the formalization of some of these properties employing temporal logic languages commonly used to engineer self-adaptive software systems, and (3) illustrating how to map key properties that characterize self-adaptive software systems into control properties, leveraging their formalization in temporal logics. We illustrate the different steps of the mapping on an exemplar case in the cloud computing domain and conclude with identifying open challenges in the area.}, note = {TL;DR \par This paper identifies a set of key properties in control theory and illustrates the formalization of some of these properties employing temporal logic languages commonly used to engineer self-adaptive software systems, and illustrates how to map key properties that characterize self- Adaptive software Systems into control properties, leveraging their formalization in temporal logics.} } @article{camaraUncertaintyInteractionProblem2022, title = {The Uncertainty Interaction Problem in Self-Adaptive Systems}, author = {Cámara, Javier and Troya, Javier and Vallecillo, Antonio and Bencomo, Nelly and Calinescu, Radu and Cheng, Betty H. C. and Garlan, David and Schmerl, Bradley}, date = {2022-08}, journaltitle = {Softw Syst Model}, volume = {21}, number = {4}, pages = {1277--1294}, issn = {1619-1366, 1619-1374}, doi = {10.1007/s10270-022-01037-6}, abstract = {The problem of mitigating uncertainty in self-adaptation has driven much of the research proposed in the area of software engineering for self-adaptive systems in the last decade. Although many solutions have already been proposed, most of them tend to tackle specific types, sources, and dimensions of uncertainty (e.g., in goals, resources, adaptation functions) in isolation. A special concern are the aspects associated with uncertainty modeling in an integrated fashion. Different uncertainties are rarely independent and often compound, affecting the satisfaction of goals and other system properties in subtle and often unpredictable ways. Hence, there is still limited understanding about the specific ways in which uncertainties from various sources interact and ultimately affect the properties of self-adaptive, software-intensive systems. In this SoSym expert voice, we introduce the Uncertainty Interaction Problem as a way to better qualify the scope of the challenges with respect to representing different types of uncertainty while capturing their interaction in models employed to reason about self-adaptation. We contribute a characterization of the problem and discuss its relevance in the context of case studies taken from two representative application domains. We posit that the Uncertainty Interaction Problem should drive future research in software engineering for autonomous and self-adaptive systems, and therefore, contribute to evolving uncertainty modeling towards holistic approaches that would enable the construction of more resilient self-adaptive systems.}, langid = {english}, note = {TL;DR \par It is proposed that the Uncertainty Interaction Problem should drive future research in software engineering for autonomous and self- Adaptive systems, and therefore, contribute to evolving uncertainty modeling towards holistic approaches that would enable the construction of more resilient self-adaptive systems.} } @article{Can2012424, title = {A Comparison of Genetic Programming and Artificial Neural Networks in Metamodeling of Discrete-Event Simulation Models}, author = {Can, B. and Heavey, C.}, date = {2012}, journaltitle = {Comput. Oper. Res.}, volume = {39}, number = {2}, pages = {424--436}, issn = {03050548}, doi = {10.1016/j.cor.2011.05.004}, abbrev_source_title = {Comp. Oper. Res.}, abstract = {Genetic programming (GP) and artificial neural networks (ANNs) can be used in the development of surrogate models of complex systems. The purpose of this paper is to provide a comparative analysis of GP and ANNs for metamodeling of discrete-event simulation (DES) models. Three stochastic industrial systems are empirically studied: an automated material handling system (AMHS) in semiconductor manufacturing, an (s,S) inventory model and a serial production line. The results of the study show that GP provides greater accuracy in validation tests, demonstrating a better generalization capability than ANN. However, GP when compared to ANN requires more computation in metamodel development. Even given this increased computational requirement, the results presented indicate that GP is very competitive in metamodeling of DES models. © 2011 Elsevier Ltd.}, affiliation = {Enterprise Research Centre, ERB, University of Limerick, Limerick, Ireland}, coden = {CMORA}, correspondence_address1 = {Can, B.; Enterprise Research Centre, , Limerick, Ireland; email: birkan.can@ul.ie}, document_type = {Article}, langid = {english}, source = {Scopus}, keywords = {Automated material handling systems,Comparative analysis,Computational requirements,Computer simulation,Decision support systems,Decision support tools,Design of experiments,Discrete events,Discrete-event simulation model,Generalization capability,Genetic programming,Industrial systems,Inventory models,Manufacture,Materials handling,Materials handling equipment,Meta model,Metamodel development,Metamodeling,Neural networks,notion,Semiconductor device manufacture,Semiconductor manufacturing,Serial production line,Stochastic models,Surrogate model,Symbolic regression,Validation test}, note = {cited By 79 \par cited By 81} } @article{candelUnifiedMetamodelNoSQL2021, title = {A {{Unified Metamodel}} for {{NoSQL}} and {{Relational Databases}}}, author = {Candel, Carlos J. Fernández and Ruiz, Diego Sevilla and García-Molina, Jesús J.}, date = {2021-05-17}, journaltitle = {ArXiv210506494 Cs}, eprint = {2105.06494}, eprinttype = {arXiv}, eprintclass = {cs}, url = {http://arxiv.org/abs/2105.06494}, urldate = {2021-06-27}, abstract = {The Database field is undergoing significant changes. Although relational systems are still predominant, the interest in NoSQL systems is continuously increasing. In this scenario, polyglot persistence is envisioned as the database architecture to be prevalent in the future. Multi-model database tools normally use a generic or unified metamodel to represent schemas of the data model that they support. Such metamodels facilitate developing utilities, as they can be built on a common representation. Also, the number of mappings required to migrate databases from a data model to another is reduced, and integrability is favored. In this paper, we present the U-Schema unified metamodel able to represent logical schemas for the four most popular NoSQL paradigms (columnar, document, key-value, and graph) as well as relational schemas. We will formally define the mappings between U-Schema and the data model defined for each paradigm. How these mappings have been implemented and validated will be discussed, and some applications of U-Schema will be shown. To achieve flexibility to respond to data changes, most of NoSQL systems are "schema-on-write," and the declaration of schemas is not required. Such an absence of schema declaration makes structural variability possible, i.e., stored data of the same entity type can have different structure. Moreover, data relationships supported by each data model are different. We will show how all these issues have been tackled in our approach. Our metamodel goes beyond the existing proposals by distinguishing entity types and relationship types, representing aggregation and reference relationships, and including the notion of structural variability. Our contributions also include developing schema extraction strategies for schemaless systems of each NoSQL data model, and tackling performance and scalability in the implementation for each store.}, keywords = {Computer Science - Databases}, note = {Comment: 31 pages, 18 figures} } @article{canoHybridRecommenderSystems2017, title = {Hybrid Recommender Systems: {{A}} Systematic Literature Review}, shorttitle = {Hybrid Recommender Systems}, author = {Çano, Erion and Morisio, Maurizio}, date = {2017-11-15}, journaltitle = {Intell. Data Anal.}, volume = {21}, number = {6}, pages = {1487--1524}, issn = {1088467X, 15714128}, doi = {10.3233/IDA-163209}, abstract = {Recommender systems are software tools used to generate and provide suggestions for items and other entities to the users by exploiting various strategies. Hybrid recommender systems combine two or more recommendation strategies in different ways to benefit from their complementary advantages. This systematic literature review presents the state of the art in hybrid recommender systems of the last decade. It is the first quantitative review work completely focused in hybrid recommenders. We address the most relevant problems considered and present the associated data mining and recommendation techniques used to overcome them. We also explore the hybridization classes each hybrid recommender belongs to, the application domains, the evaluation process and proposed future research directions. Based on our findings, most of the studies combine collaborative filtering with another technique often in a weighted way. Also cold-start and data sparsity are the two traditional and top problems being addressed in 23 and 22 studies each, while movies and movie datasets are still widely used by most of the authors. As most of the studies are evaluated by comparisons with similar methods using accuracy metrics, providing more credible and user oriented evaluations remains a typical challenge. Besides this, newer challenges were also identified such as responding to the variation of user context, evolving user tastes or providing cross-domain recommendations. Being a hot topic, hybrid recommenders represent a good basis with which to respond accordingly by exploring newer opportunities such as contextualizing recommendations, involving parallel hybrid algorithms, processing larger datasets, etc.}, langid = {english} } @inproceedings{cao_adversarial_2020, title = {Adversarial {{Attacks}} and {{Detection}} on {{Reinforcement Learning-Based Interactive Recommender Systems}}}, booktitle = {Proc. 43rd {{ACM SIGIR}}}, author = {Cao, Yuanjiang and Chen, Xiaocong and Yao, Lina and Wang, Xianzhi and Zhang, Wei Emma}, date = {2020-07}, pages = {1669--1672}, publisher = {ACM}, location = {Virtual Event China}, doi = {10.1145/3397271.3401196}, abstract = {Adversarial attacks pose significant challenges for detecting adversarial attacks at an early stage. We propose attack-agnostic detection on reinforcement learning-based interactive recommendation systems. We first craft adversarial examples to show their diverse distributions and then augment recommendation systems by detecting potential attacks with a deep learning-based classifier based on the crafted data. Finally, we study the attack strength and frequency of adversarial examples and evaluate our model on standard datasets with multiple crafting methods. Our extensive experiments show that most adversarial attacks are effective, and both attack strength and attack frequency impact the attack performance. The strategically-timed attack achieves comparative attack performance with only 1/3 to 1/2 attack frequency. Besides, our black-box detector trained with one crafting method has the generalization ability over several crafting methods.}, isbn = {978-1-4503-8016-4}, langid = {english} } @inproceedings{Cao2020, title = {Network-Level System Performance Prediction Using Deep Neural Networks with Cross-Layer Information}, author = {Cao, Q. and Zeng, S. and Pun, M.-O. and Chen, Y.}, date = {2020}, series = {{{IEEE International Conference}} on {{Communications}}}, volume = {2020-June}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {15503607}, doi = {10.1109/ICC40277.2020.9149189}, abstract = {How to predict the wireless network level performance such as the network capacity, the average user data rate, and the 5-tile user data rate is a million-dollar question. In the literature, some pioneering works have been proposed by exploiting either the information theoretic techniques on the physical layer (PHY) information or the Markov chain techniques on the multiple access control (MAC) layer information. However, since these mathematical model-driven approaches usually focus on a small part of the network structure, they cannot characterize the whole network performance. In this paper, we propose to utilize a data-driven machine learning approach to tackle this problem. More specifically, both PHY and MAC information is fed into a deep neural network (DNN) specifically designed for network-level performance prediction. Simulation results show that the network level performance can be accurately predicted at the cost of higher computational complexity. © 2020 IEEE.}, art_number = {9149189}, document_type = {Conference Paper}, isbn = {978-1-7281-5089-5}, source = {Scopus}, note = {cited By 0 \par TL;DR \par This paper proposes to utilize a data-driven machine learning approach to tackle the wireless network level performance prediction by fed into a deep neural network specifically designed for network-level performance prediction.} } @inproceedings{Cao2021, title = {Simulation Optimization for a Digital Twin Using a Multi-Fidelity Framework}, author = {Cao, Y. and Currie, C. and Onggo, B.S. and Higgins, M.}, date = {2021}, series = {Proceedings - {{Winter Simulation Conference}}}, volume = {2021-December}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {08917736}, doi = {10.1109/WSC52266.2021.9715498}, abbrev_source_title = {Proc. Winter Simul. Conf.}, affiliation = {Centre for Operational Research, Management Science and Information Systems, University of Southampton, Southampton, SO17 1BJ, United Kingdom; PowerTrain Manufacturing Engineering, Ford Motor Company, Dunton Research Centre, Basildon, SS15 6EE, United Kingdom}, coden = {WSCPD}, document_type = {Conference Paper}, isbn = {978-1-6654-3311-2}, langid = {english}, source = {Scopus}, keywords = {notion}, note = {cited By 1} } @misc{cao2023study, title = {A Study on Prompt Design, Advantages and Limitations of {{ChatGPT}} for Deep Learning Program Repair}, author = {Cao, Jialun and Li, Meiziniu and Wen, Ming and Cheung, Shing-chi}, date = {2023}, eprint = {2304.08191}, eprinttype = {arXiv}, eprintclass = {cs.SE} } @article{Capiluppi:2019:JSS:Clustering, title = {The {{Effects}} of {{Clustering}} on the {{Characteristics}} of {{Java Software}} - Manuscript under Revision}, author = {Capiluppi, Andrea and Di Ruscio, Davide and Di Rocco, Juri and Nguyen, Phuong T. and Ajienka, Nemitari}, date = {2019}, journaltitle = {J. Syst. Softw.} } @article{capiluppiDetectingJavaSoftware2019, title = {Detecting {{Java Software Similarities}} by Using {{Different Clustering Techniques}}}, author = {Capiluppi, Andrea and Di Ruscio, Davide and Di Rocco, Juri and Nguyen, Phuong T and Ajienka, Nemitari}, date = {2019}, journaltitle = {Elsevier Inf. Softw. Technol. IST J.}, pages = {40}, abstract = {Research on empirical software engineering has increasingly been conducted by analysing and measuring vast amounts of software systems. Hundreds, thousands and even millions of systems have been (and are) considered by researchers, and often within the same study, in order to test theories, demonstrate approaches or run prediction models. A much less investigated aspect is whether the collected metrics might be context-specific, or whether systems should be better analysed in clusters.}, langid = {english} } @inproceedings{cardaioliItsMatterStyle2021, ids = {cardaioliItMatterStyle2021}, title = {It’s a {{Matter}} of {{Style}}: {{Detecting Social Bots}} through {{Writing Style Consistency}}}, shorttitle = {It’s a {{Matter}} of {{Style}}}, booktitle = {2021 {{Int}}. {{Conf}}. {{Comput}}. {{Commun}}. {{Netw}}. {{ICCCN}}}, author = {Cardaioli, Matteo and Conti, Mauro and Sorbo, Andrea Di and Fabrizio, Enrico and Laudanna, Sonia and Visaggio, Corrado A.}, date = {2021-07}, pages = {1--9}, publisher = {IEEE}, location = {Athens, Greece}, issn = {2637-9430}, doi = {10.1109/ICCCN52240.2021.9522339}, abstract = {Social bots are computer algorithms able to produce content and interact with other users on social media autonomously, trying to emulate and possibly influence humans’ behavior. Indeed, bots are largely employed for malicious purposes, like spreading disinformation and conditioning electoral campaigns. Nowadays, bots’ capability of emulating human behaviors has become increasingly sophisticated, making their detection harder. In this paper, we aim at recognizing bot-driven accounts by evaluating the consistency of users’ writing style over time. In particular, we leverage the intuition that while bots compose posts according to fairly deterministic processes, humans are influenced by subjective factors (e.g., emotions) that can alter their writing style. To verify this assumption, by using stylistic consistency indicators, we characterize the writing style of more than 12,000 among bot-driven and human-operated Twitter accounts and find that statistically significant differences can be observed between the different types of users. Thus, we evaluate the effectiveness of different machine learning (ML) algorithms based on stylistic consistency features in discerning between human-operated and bot-driven Twitter accounts and show that the experimented ML algorithms can achieve high performance (i.e., F-measure values up to 98\%) in social bot detection tasks.}, eventtitle = {2021 {{International Conference}} on {{Computer Communications}} and {{Networks}} ({{ICCCN}})}, isbn = {978-1-6654-1278-0}, langid = {english}, keywords = {Blogs,Bot detection,Machine learning,Machine learning algorithms,Measurement,Social bot,Social networking (online),Stylometry,Support vector machines,Writing} } @article{cardelliUnderstandingTypesData1985, title = {On Understanding Types, Data Abstraction, and Polymorphism}, author = {Cardelli, Luca and Wegner, Peter}, date = {1985-12-10}, journaltitle = {ACM Comput. Surv.}, volume = {17}, number = {4}, pages = {471--523}, issn = {0360-0300, 1557-7341}, doi = {10.1145/6041.6042}, abstract = {Our objective is to understand the notion of type in programming languages, present a model of typed, polymorphic programming languages that reflects recent research in type theory, and examine the relevance of recent research to the design of practical programming languages.}, langid = {english} } @book{cardonNewAutonomousSystems2016, title = {New Autonomous Systems}, author = {Cardon, Alain and Itmi, Mhamed}, date = {2016-04-28}, edition = {1}, publisher = {Wiley}, doi = {10.1002/9781119287858}, isbn = {978-1-84821-935-9 978-1-119-28785-8}, langid = {english}, keywords = {MOSAICO,P1} } @inproceedings{cardososilvaBenchmarkingMachineLearning2020, title = {Benchmarking {{Machine Learning Solutions}} in {{Production}}}, booktitle = {2020 19th {{IEEE Int}}. {{Conf}}. {{Mach}}. {{Learn}}. {{Appl}}. {{ICMLA}}}, author = {Cardoso Silva, Lucas and Rezende Zagatti, Fernando and Silva Sette, Bruno and Nildaimon Dos Santos Silva, Lucas and Lucredio, Daniel and Furtado Silva, Diego and De Medeiros Caseli, Helena}, date = {2020-12}, pages = {626--633}, publisher = {IEEE}, location = {Miami, FL, USA}, doi = {10.1109/ICMLA51294.2020.00104}, eventtitle = {2020 19th {{IEEE International Conference}} on {{Machine Learning}} and {{Applications}} ({{ICMLA}})}, isbn = {978-1-7281-8470-8}, keywords = {LOGSEQ}, note = {TL;DR \par This paper presents an approach, based on a simple API and set of tools, to monitor ML solutions, and indicates that the approach can deliver useful information to help in decision making, proper resource provision and operation of ML systems.} } @article{carletonAIEffectWorking2020, ids = {carletonAIEffectWorking}, title = {The {{AI Effect}}: {{Working}} at the {{Intersection}} of {{AI}} and {{SE}}}, shorttitle = {The {{AI Effect}}}, author = {Carleton, Anita D. and Harper, Erin and Menzies, Tim and Xie, Tao and Eldh, Sigrid and Lyu, Michael R.}, date = {2020-07}, journaltitle = {IEEE Softw.}, volume = {37}, number = {4}, pages = {26--35}, issn = {0740-7459, 1937-4194}, doi = {10.1109/MS.2020.2987666}, langid = {english} } @inproceedings{caruanaEmpiricalComparisonSupervised2006, title = {An Empirical Comparison of Supervised Learning Algorithms}, booktitle = {Proc. 23rd {{Int}}. {{Conf}}. {{Mach}}. {{Learn}}. - {{ICML}} 06}, author = {Caruana, Rich and Niculescu-Mizil, Alexandru}, date = {2006}, pages = {161--168}, publisher = {ACM Press}, location = {Pittsburgh, Pennsylvania}, doi = {10.1145/1143844.1143865}, abstract = {A number of supervised learning methods have been introduced in the last decade. Unfortunately, the last comprehensive empirical evaluation of supervised learning was the Statlog Project in the early 90’s. We present a large-scale empirical comparison between ten supervised learning methods: SVMs, neural nets, logistic regression, naive bayes, memory-based learning, random forests, decision trees, bagged trees, boosted trees, and boosted stumps. We also examine the effect that calibrating the models via Platt Scaling and Isotonic Regression has on their performance. An important aspect of our study is the use of a variety of performance criteria to evaluate the learning methods.}, eventtitle = {The 23rd International Conference}, isbn = {978-1-59593-383-6}, langid = {english}, note = {TL;DR \par A large-scale empirical comparison between ten supervised learning methods: SVMs, neural nets, logistic regression, naive bayes, memory-based learning, random forests, decision trees, bagged trees, boosted trees, and boosted stumps is presented.} } @article{carverExtractingRequirementsModeling2021, title = {Extracting {{Requirements}} and {{Modeling Information}} and {{Controlling Risk}}}, author = {Carver, Jeffrey C. and Abrahao, Silvia and Penzenstadler, Birgit}, date = {2021-05-01}, journaltitle = {IEEE Softw.}, volume = {38}, number = {03}, pages = {121--124}, publisher = {IEEE Computer Society}, issn = {0740-7459}, doi = {10.1109/MS.2021.3056989}, abstract = {Presents papers from the 2020 IEEE Conference on Requirements Engineering and the ACM/ IEEE 23rd International Conference on Model Driven Engineering Languages and Systems (MODELS 2020).}, langid = {english} } @article{carverIndustryAcademiaCollaboration2018, title = {Industry–{{Academia Collaboration}} in {{Software Engineering}}}, author = {Carver, J. C. and Prikladnicki, R.}, date = {2018-09}, journaltitle = {IEEE Softw.}, volume = {35}, number = {5}, pages = {120--124}, issn = {0740-7459}, doi = {10.1109/MS.2018.3571250}, abstract = {This article aims to encourage more industry–academia collaborations by highlighting examples of successful collaborations. Through these examples, the authors hope to help practitioners and researchers understand the breadth of options available for such interactions. This article is part of a theme issue on software engineering’s 50th anniversary.}, keywords = {software engineering}, note = {TL;DR \par This article aims to encourage more industry–academia collaborations by highlighting examples of successful collaborations and to help practitioners and researchers understand the breadth of options available for such interactions.} } @inproceedings{carverIssuesUsingStudents2003, title = {Issues in Using Students in Empirical Studies in Software Engineering Education}, booktitle = {Proc. 5th {{Int}}. {{Workshop Enterp}}. {{Netw}}. {{Comput}}. {{Healthc}}. {{Ind}}. {{IEEE Cat No03EX717}}}, author = {Carver, J. and Jaccheri, L. and Morasca, S. and Shull, F.}, date = {2003}, pages = {239--249}, publisher = {IEEE Comput. Soc}, location = {Sydney, NSW, Australia}, doi = {10.1109/METRIC.2003.1232471}, abstract = {Past research suggests that Computer Science (CS) undergraduate students are not equipped to manage quality characteristics such as security, reliability, and maintainability. Filling such a gap should allow CS undergraduates an easier integration into the labor market after graduation. To make students more ready for such a market, we introduced a training intervention in our Software Technologies for the Web (STW ) course in the academic year (a.y.) 2022-23. Our intervention focused on security, i.e., students were trained on secure development and were asked to use SonarCloud. To assess this intervention, we compared the web apps developed in a.y. 2021-22 and a.y. 2022-23 and observed that the security significantly improved in the a.y. 2022-23 web apps. To understand whether and to what extent our training intervention triggered autonomous motivation in the students (a.y. 2022-23) on reliability and maintainability, we also compared the web apps of a.y. 2021-22 and a.y. 2022-23 on these issues. To that end, we did not ask students to deal with reliability and maintainability. This part of our research is presented in this paper for the first time and revealed that the web apps of a.y. 2022-23 are more reliable and maintainable than those of a.y. 2021-22.}, eventtitle = {Ninth {{International Software Metrics Symposium}}}, isbn = {978-0-7695-1987-6}, langid = {english} } @article{Casalaro202219, title = {Model-Driven Engineering for Mobile Robotic Systems: A Systematic Mapping Study}, author = {Casalaro, G.L. and Cattivera, G. and Ciccozzi, F. and Malavolta, I. and Wortmann, A. and Pelliccione, P.}, date = {2022}, journaltitle = {Softw. Syst. Model.}, volume = {21}, number = {1}, pages = {19--49}, publisher = {{Springer Science and Business Media Deutschland GmbH}}, issn = {16191366}, doi = {10.1007/s10270-021-00908-8}, abbrev_source_title = {Softw. Syst. Model.}, affiliation = {Department of Information Engineering, Computer Science and Mathematics, University of L’Aquila, L’Aquila, Italy; School of Innovation, Design and Engineering, Mälardalen University, Västerås, Sweden; Department of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, Netherlands; Chair of Software Engineering, RWTH Aachen University, Aachen, Germany; Chalmers | University of Gothenburg, Gothenburg, Sweden; University of L’Aquila, L’Aquila, Italy}, correspondence_address1 = {Ciccozzi, F.; School of Innovation, Sweden; email: federico.ciccozzi@mdh.se}, document_type = {Article}, langid = {english}, source = {Scopus}, note = {cited By 0} } @book{cassell_essential_2022, title = {Essential {{Guide}} to {{Qualitative Methods}} in {{Organizational Research}}}, author = {Cassell, Catherine and Symon, Gillian}, date = {2022-11}, location = {London}, doi = {10.4135/9781446280119} } @inproceedings{castanoExploringCarbonFootprint2023, title = {Exploring the {{Carbon Footprint}} of {{Hugging Face}}'s {{ML Models}}: {{A Repository Mining Study}}}, shorttitle = {Exploring the {{Carbon Footprint}} of {{Hugging Face}}'s {{ML Models}}}, booktitle = {2023 {{ACMIEEE Int}}. {{Symp}}. {{Empir}}. {{Softw}}. {{Eng}}. {{Meas}}. {{ESEM}}}, author = {Castaño, Joel and Martínez-Fernández, Silverio and Franch, Xavier and Bogner, Justus}, date = {2023-10-26}, eprint = {2305.11164}, eprinttype = {arXiv}, eprintclass = {cs, stat}, pages = {1--12}, doi = {10.1109/ESEM56168.2023.10304801}, abstract = {The rise of machine learning (ML) systems has exacerbated their carbon footprint due to increased capabilities and model sizes. However, there is scarce knowledge on how the carbon footprint of ML models is actually measured, reported, and evaluated. In light of this, the paper aims to analyze the measurement of the carbon footprint of 1,417 ML models and associated datasets on Hugging Face, which is the most popular repository for pretrained ML models. The goal is to provide insights and recommendations on how to report and optimize the carbon efficiency of ML models. The study includes the first repository mining study on the Hugging Face Hub API on carbon emissions. This study seeks to answer two research questions: (1) how do ML model creators measure and report carbon emissions on Hugging Face Hub?, and (2) what aspects impact the carbon emissions of training ML models? The study yielded several key findings. These include a stalled proportion of carbon emissions-reporting models, a slight decrease in reported carbon footprint on Hugging Face over the past 2 years, and a continued dominance of NLP as the main application domain. Furthermore, the study uncovers correlations between carbon emissions and various attributes such as model size, dataset size, and ML application domains. These results highlight the need for software measurements to improve energy reporting practices and promote carbon-efficient model development within the Hugging Face community. In response to this issue, two classifications are proposed: one for categorizing models based on their carbon emission reporting practices and another for their carbon efficiency. The aim of these classification proposals is to foster transparency and sustainable model development within the ML community.}, keywords = {Computer Science - Computers and Society,Computer Science - Information Retrieval,Computer Science - Machine Learning,Statistics - Machine Learning}, note = {Comment: Accepted at the 2023 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM) \par TL;DR \par This paper analyzes the measurement of the carbon footprint of 1,417 ML models and associated datasets on Hugging Face and proposes two classifications: one for categorizing models based on their carbon emission reporting practices and another for their carbon efficiency.} } @inproceedings{Castells_noveltyand, ids = {Castells_novelty}, title = {Novelty and Diversity Metrics for Recommender Systems: {{Choice}}, Discovery and Relevance}, booktitle = {Proc. {{Int}}. {{Workshop Divers}}. {{Doc}}. {{Retr}}. {{DDR}}}, author = {Castells, Pablo and Vargas, Saúl}, pages = {29--37}, citeulike-article-id = {9136077}, posted-at = {2011-04-11 15:46:44}, priority = {2}, keywords = {diversity,novelty,project-mavir,recommender,uam} } @article{castelnovoResponsibleAIBanking, title = {Towards {{Responsible AI}} in {{Banking}}: {{Addressing Bias}} for {{Fair Decision-Making}}}, author = {Castelnovo, Alessandro}, langid = {english}, note = {TL;DR \par This thesis embarks on a comprehensive exploration of bias and fairness, with a particular emphasis on their ramifications within the banking sector, where AI-driven decisions bear substantial societal consequences.} } @article{castroRiseServerlessComputing2019, title = {The Rise of Serverless Computing}, author = {Castro, Paul and Ishakian, Vatche and Muthusamy, Vinod and Slominski, Aleksander}, date = {2019-11-21}, journaltitle = {Commun. ACM}, volume = {62}, number = {12}, pages = {44--54}, issn = {0001-0782, 1557-7317}, doi = {10.1145/3368454}, abstract = {The server is dead, long live the server.}, langid = {english}, keywords = {LOGSEQ} } @online{CatedrasaesumuNoSQLDataEngineeringNoSQL, title = {Catedrasaes-Umu/{{NoSQLDataEngineering}}: {{NoSQL Data Engineering}}}, url = {https://github.com/catedrasaes-umu/NoSQLDataEngineering#schema-models}, urldate = {2018-05-07} } @article{cedeno-mielesDataAnalysisModeling2020, title = {Data Analysis and Modeling Pipelines for Controlled Networked Social Science Experiments}, author = {Cedeno-Mieles, Vanessa and Hu, Zhihao and Ren, Yihui and Deng, Xinwei and Contractor, Noshir and Ekanayake, Saliya and Epstein, Joshua M. and Goode, Brian J. and Korkmaz, Gizem and Kuhlman, Chris J. and Machi, Dustin and Macy, Michael and Marathe, Madhav V. and Ramakrishnan, Naren and Saraf, Parang and Self, Nathan}, editor = {Cai, Ning}, date = {2020-11-24}, journaltitle = {PLoS ONE}, volume = {15}, number = {11}, pages = {e0242453}, issn = {1932-6203}, doi = {10.1371/journal.pone.0242453}, abstract = {There is large interest in networked social science experiments for understanding human behavior at-scale. Significant effort is required to perform data analytics on experimental outputs and for computational modeling of custom experiments. Moreover, experiments and modeling are often performed in a cycle, enabling iterative experimental refinement and data modeling to uncover interesting insights and to generate/refute hypotheses about social behaviors. The current practice for social analysts is to develop tailor-made computer programs and analytical scripts for experiments and modeling. This often leads to inefficiencies and duplication of effort. In this work, we propose a pipeline framework to take a significant step towards overcoming these challenges. Our contribution is to describe the design and implementation of a software system to automate many of the steps involved in analyzing social science experimental data, building models to capture the behavior of human subjects, and providing data to test hypotheses. The proposed pipeline framework consists of formal models, formal algorithms, and theoretical models as the basis for the design and implementation. We propose a formal data model, such that if an experiment can be described in terms of this model, then our pipeline software can be used to analyze data efficiently. The merits of the proposed pipeline framework is elaborated by several case studies of networked social science experiments.}, langid = {english}, note = {TL;DR \par This work describes the design and implementation of a software system to automate many of the steps involved in analyzing social science experimental data, building models to capture the behavior of human subjects, and providing data to test hypotheses.} } @article{celebiFAIRProtocolsWorkflows2020, title = {Towards {{FAIR}} Protocols and Workflows: The {{OpenPREDICT}} Use Case}, shorttitle = {Towards {{FAIR}} Protocols and Workflows}, author = {Celebi, Remzi and Rebelo Moreira, Joao and Hassan, Ahmed A. and Ayyar, Sandeep and Ridder, Lars and Kuhn, Tobias and Dumontier, Michel}, date = {2020-09-21}, journaltitle = {PeerJ Comput. Sci.}, volume = {6}, pages = {e281}, issn = {2376-5992}, doi = {10.7717/peerj-cs.281}, abstract = {It is essential for the advancement of science that researchers share, reuse and reproduce each other’s workflows and protocols. The FAIR principles are a set of guidelines that aim to maximize the value and usefulness of research data, and emphasize the importance of making digital objects findable and reusable by others. The question of how to apply these principles not just to data but also to the workflows and protocols that consume and produce them is still under debate and poses a number of challenges. In this paper we describe a two-fold approach of simultaneously applying the FAIR principles to scientific workflows as well as the involved data. We apply and evaluate our approach on the case of the PREDICT workflow, a highly cited drug repurposing workflow. This includes FAIRification of the involved datasets, as well as applying semantic technologies to represent and store data about the detailed versions of the general protocol, of the concrete workflow instructions, and of their execution traces. We propose a semantic model to address these specific requirements and was evaluated by answering competency questions. This semantic model consists of classes and relations from a number of existing ontologies, including Workflow4ever, PROV, EDAM, and BPMN. This allowed us then to formulate and answer new kinds of competency questions. Our evaluation shows the high degree to which our FAIRified OpenPREDICT workflow now adheres to the FAIR principles and the practicality and usefulness of being able to answer our new competency questions.}, langid = {english}, note = {TL;DR \par This paper describes a two-fold approach of simultaneously applying the FAIR principles to scientific workflows as well as the involved data, and proposes a semantic model to address these specific requirements.} } @article{celisseOptimalCrossvalidationDensity2014, title = {Optimal Cross-Validation in Density Estimation with the \${{L}}\textasciicircum\{2\}\$-Loss}, author = {Celisse, Alain}, date = {2014-10-01}, journaltitle = {Ann. Statist.}, volume = {42}, number = {5}, issn = {0090-5364}, doi = {10.1214/14-AOS1240} } @article{Celms2020205, title = {Towards Dsl for Dl Lifecycle Data Management}, author = {Celms, E. and Barzdins, J. and Kalnins, A. and Sprogis, A. and Grasmanis, M. and Rikacovs, S. and Barzdins, P.}, editor = {Robal T., Haav H.-M., Matulevicius R., Penjam J.}, date = {2020}, journaltitle = {Commun. Comput. Inf. Sci.}, volume = {1243 CCIS}, pages = {205--218}, publisher = {Springer}, issn = {18650929}, doi = {10.1007/978-3-030-57672-1_16}, abbrev_source_title = {Commun. Comput. Info. Sci.}, affiliation = {Institute of Mathematics and Computer Science, University of Latvia, Riga, Latvia; Innovation Labs LETA, Riga, Latvia}, correspondence_address1 = {Celms, E.; Institute of Mathematics and Computer Science, Latvia; email: edgars.celms@lumii.lv}, document_type = {Conference Paper}, isbn = {9783030576714}, langid = {english}, source = {Scopus}, note = {cited By 2} } @article{Celms2021597, title = {{{DSL}} Approach to Deep Learning Lifecycle Data Management}, author = {Celms, E. and Barzdins, J. and Kalnins, A. and Barzdins, P. and Sprogis, A. and Grasmanis, M. and Rikacovs, S.}, date = {2021}, journaltitle = {Balt. J. Mod. Comput.}, volume = {8}, number = {4}, pages = {597--617}, publisher = {University of Latvia}, issn = {22558942}, doi = {10.22364/BJMC.2020.8.4.09}, abbrev_source_title = {Baltic J. Mod. Comp.}, affiliation = {Institute of Mathematics and Computer Science, University of Latvia, Raiņa bulvaris 29, Riga, LV-1459, Latvia; Innovation Labs LETA, Latvia, Riga, Marijas iela 2, Riga, LV 1050, Latvia}, document_type = {Article}, langid = {english}, source = {Scopus}, note = {cited By 1} } @online{cemriWhyMultiagentLLM2025, title = {Why Do Multi-Agent {{LLM}} Systems Fail?}, author = {Cemri, Mert and Pan, Melissa Z. and Yang, Shuyi and Agrawal, Lakshya A. and Chopra, Bhavya and Tiwari, Rishabh and Keutzer, Kurt and Parameswaran, Aditya and Klein, Dan and Ramchandran, Kannan and Zaharia, Matei and Gonzalez, Joseph E. and Stoica, Ion}, date = {2025-03-17}, eprint = {2503.13657}, eprinttype = {arXiv}, eprintclass = {cs}, doi = {10.48550/arXiv.2503.13657}, abstract = {Despite growing enthusiasm for Multi-Agent Systems (MAS), where multiple LLM agents collaborate to accomplish tasks, their performance gains across popular benchmarks remain minimal compared to single-agent frameworks. This gap highlights the need to analyze the challenges hindering MAS effectiveness. In this paper, we present the first comprehensive study of MAS challenges. We analyze five popular MAS frameworks across over 150 tasks, involving six expert human annotators. We identify 14 unique failure modes and propose a comprehensive taxonomy applicable to various MAS frameworks. This taxonomy emerges iteratively from agreements among three expert annotators per study, achieving a Cohen's Kappa score of 0.88. These fine-grained failure modes are organized into 3 categories, (i) specification and system design failures, (ii) inter-agent misalignment, and (iii) task verification and termination. To support scalable evaluation, we integrate MASFT with LLM-as-a-Judge. We also explore if identified failures could be easily prevented by proposing two interventions: improved specification of agent roles and enhanced orchestration strategies. Our findings reveal that identified failures require more complex solutions, highlighting a clear roadmap for future research. We open-source our dataset and LLM annotator.}, langid = {english}, pubstate = {prepublished}, keywords = {/unread,Computer Science - Artificial Intelligence} } @article{cesalEstablishingInteroperabilityEMF, title = {Establishing Interoperability between {{EMF}} and {{MSDKVS}}: An {{M3-level-bridge}} to Transform Metamodels and Models}, author = {Cesal, Florian and Bork, Dominik}, abstract = {Many powerful metamodeling platforms enabling Model-Driven Software Engineering (MDSE) exist, each with its strengths, weaknesses, functionalities, programming language(s), and developer community. Platform interoperability would enable users to exploit their mutual benefits. Such interoperability would allow the transformation of metamodels and models created in one platform into equivalent metamodels and models in other platforms. Language engineers could then freely choose the metamodeling platform without risking a lock-in effect. Two well-documented and publicly available metamodeling platforms are the Eclipse Modeling Framework (EMF) and the Modeling SDK for Visual Studio (MSDKVS). In this paper, we propose an M3-Level-Bridge (M3B) that establishes interoperability between EMF and MSDKVS on the abstract syntax level and on the graphical concrete syntax level. To establish such interoperability we i) compare the two platforms, ii) present a conceptual mapping between them, and iii) implement a bidirectional transformation bridge including both the metamodel and model layer. We evaluate our approach by transforming a collection of publicly available metamodels and automatically generated or manually created models thereof. The transformation outcomes are then used to quantitatively and qualitatively evaluate the transformation’s validity, executability, and expressiveness.}, langid = {english} } @book{CEURWorkshopProceedings2015, title = {{{CEUR Workshop Proceedings}}}, date = {2015}, volume = {1406}, publisher = {CEUR-WS}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84992533486&partnerID=40&md5=bd42da7c52ba8ecd02ca81bcc01132c9} } @inproceedings{chaaben2023towards, title = {Towards Using Few-Shot Prompt Learning for Automating Model Completion}, booktitle = {2023 {{IEEEACM}} 45th {{Int}}. {{Conf}}. {{Softw}}. {{Eng}}. {{New Ideas Emerg}}. {{Results ICSE-NIER}}}, author = {Chaaben, Meriem Ben and Burgueño, Lola and Sahraoui, Houari}, date = {2023}, pages = {7--12}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found} } @article{Chabanet2021, title = {Coupling Digital Simulation and Machine Learning Metamodel through an Active Learning Approach in {{Industry}} 4.0 Context}, author = {Chabanet, S. and Bril El-Haouzi, H. and Thomas, P.}, date = {2021}, journaltitle = {Comput. Ind.}, volume = {133}, publisher = {Elsevier B.V.}, issn = {01663615}, doi = {10.1016/j.compind.2021.103529}, abbrev_source_title = {Comput Ind}, affiliation = {Université de Lorraine, CNRS, CRAN, Epinal, F-88000, France}, art_number = {103529}, coden = {CINUD}, correspondence_address1 = {Chabanet, S.; Université de Lorraine, France; email: sylvain.chabanet@univ-lorraine.fr}, document_type = {Article}, langid = {english}, source = {Scopus}, keywords = {notion}, note = {cited By 3} } @article{Chabanet2021573, title = {Dissimilarity to Class Medoids as Features for {{3D}} Point Cloud Classification}, author = {Chabanet, S. and Chazelle, V. and Thomas, P. and El-Haouzi, H.B.}, editor = {Dolgui A., Bernard A., von Cieminski G., Romero D., Lemoine D.}, date = {2021}, journaltitle = {IFIP Adv. Inf. Commun. Technol.}, volume = {632 IFIP}, pages = {573--581}, publisher = {{Springer Science and Business Media Deutschland GmbH}}, issn = {18684238}, doi = {10.1007/978-3-030-85906-0_62}, abbrev_source_title = {IFIP Advances in Information and Communication Technology}, affiliation = {Université de Lorraine, CNRS, CRAN, Epinal, 88000, France}, correspondence_address1 = {Chabanet, S.; Université de Lorraine, France; email: sylvain.chabanet@univ-lorraine.fr}, document_type = {Conference Paper}, isbn = {9783030859053}, langid = {english}, source = {Scopus}, note = {cited By 0} } @article{Chai2022, title = {Data Management for Machine Learning: {{A}} Survey}, author = {Chai, C. and Wang, J. and Luo, Y. and Niu, Z. and Li, G.}, date = {2022}, journaltitle = {IEEE Trans. Knowl. Data Eng.}, publisher = {IEEE Computer Society}, issn = {10414347}, doi = {10.1109/TKDE.2022.3148237}, abstract = {Machine learning(ML) has widespread applications and has revolutionized many industries, but suffers from several challenges. First, sufficient high-quality training data is inevitable for producing a well-performed model, but the data is always human expensive to acquire.Second, a large amount of training data and complicated model structures lead to the inefficiency of training and inference. Third, given an ML task, one always needs to train lots of models, which are hard to manage in real applications. Fortunately, database techniques can benefit ML by addressing the above three challenges. In this paper, we review existing studies from the following three aspects along with the pipeline highly related to ML. (1) Data preparation(Pre-ML): it focuses on preparing high-quality training data that can improve the performance of the ML model, where we review data discovery, data cleaning and data labeling. (2) Model training \& inference(In-ML): researchers in ML community focus on improving the model performance during training, while in this survey we mainly study how to accelerate the entire training process, also including feature selection and model selection. (3) Model management(Post-ML): in this part, we survey how to store, query, deploy and debug the models after training. Finally, we provide research challenges and future directions. IEEE}, coden = {ITKEE}, document_type = {Article}, source = {Scopus}, keywords = {GOAL\_MDE4AI}, note = {cited By 0 \par TL;DR \par This paper reviews existing studies from the following three aspects along with the pipeline highly related to ML, and surveys how to store, query, deploy and debug the models after training.} } @inproceedings{ChakrabortyM0M20, title = {Fairway: A Way to Build Fair {{ML}} Software}, booktitle = {{{ESECFSE}} 20 28th {{ACM Jt}}. {{Eur}}. {{Softw}}. {{Eng}}. {{Conf}}. {{Symp}}. {{Found}}. {{Softw}}. {{Eng}}. {{Virtual Event USA Novemb}}. 8-13 2020}, author = {Chakraborty, Joymallya and Majumder, Suvodeep and Yu, Zhe and Menzies, Tim}, date = {2020}, pages = {654--665}, publisher = {ACM}, note = {TL;DR \par This work explains how ground-truth bias in training data affects machine learning model fairness and how to find that bias in AI software, and proposes a method Fairway which combines pre-processing and in-processing approach to remove ethical bias from training data and trained model.} } @inproceedings{ChakrabortyMM21, title = {Bias in Machine Learning Software: Why? How? What to Do?}, booktitle = {{{ESECFSE}} 21 29th {{ACM Jt}}. {{Eur}}. {{Softw}}. {{Eng}}. {{Conf}}. {{Symp}}. {{Found}}. {{Softw}}. {{Eng}}. {{Athens Greece August}} 23-28 2021}, author = {Chakraborty, Joymallya and Majumder, Suvodeep and Menzies, Tim}, date = {2021}, pages = {429--440}, publisher = {ACM} } @inproceedings{ChakrabortyPM20, title = {Making Fair {{ML}} Software Using Trustworthy Explanation}, booktitle = {35th {{IEEEACM Int}}. {{Conf}}. {{Autom}}. {{Softw}}. {{Eng}}. {{ASE}} 2020 {{Melb}}. {{Aust}}. {{Sept}}. 21-25 2020}, author = {Chakraborty, Joymallya and Peng, Kewen and Menzies, Tim}, date = {2020}, pages = {1229--1233}, publisher = {IEEE}, note = {TL;DR \par This work shows how the proposed method based on K nearest neighbors can overcome shortcomings and find the underlying bias of black box models and describes the future framework combining explanation and planning to build fair software.} } @misc{chatgpt, title = {{{ChatGPT}} {{https://openai.com/blog/chatgpt}}} } @online{ChatGPTOperator, title = {{{ChatGPT Operator}}}, url = {https://www.linkedin.com/posts/conorgrennan_breaking-openai-has-released-ai-agents-ugcPost-7288287296993255424-6ndO/?utm_source=share&utm_medium=member_android}, urldate = {2025-01-25}, langid = {english}, keywords = {/unread} } @inproceedings{Chatzimparmpas20211, title = {Empirical Study: {{Visual}} Analytics for Comparing Stacking to Blending Ensemble Learning}, author = {Chatzimparmpas, A. and Martins, R.M. and Kucher, K. and Kerren, A.}, date = {2021}, series = {Proceedings - 2021 23rd {{International Conference}} on {{Control Systems}} and {{Computer Science Technologies}}, {{CSCS}} 2021}, pages = {1--8}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/CSCS52396.2021.00008}, abbrev_source_title = {Proc. - Int. Conf. Control Syst. Comput. Sci. Technol., CSCS}, affiliation = {Linnaeus University, Department of Computer Science and Media Technology, Växjö, Sweden; Department of Computer Science and Media Technology, Linnaeus University, Växjö, Sweden; Department of Science and Technology, Linköping University, Norrköping, Sweden}, art_number = {9481023}, document_type = {Conference Paper}, isbn = {978-1-6654-3939-8}, langid = {english}, source = {Scopus}, note = {cited By 4} } @article{Chatzimparmpas20211547, title = {{{StackGenVis}}: {{Alignment}} of Data, Algorithms, and Models for Stacking Ensemble Learning Using Performance Metrics}, author = {Chatzimparmpas, A. and Martins, R.M. and Kucher, K. and Kerren, A.}, date = {2021}, journaltitle = {IEEE Trans. Vis. Comput. Graph.}, volume = {27}, number = {2}, pages = {1547--1557}, publisher = {IEEE Computer Society}, issn = {10772626}, doi = {10.1109/TVCG.2020.3030352}, abbrev_source_title = {IEEE Trans Visual Comput Graphics}, affiliation = {Linnaeus University, Växjö, Sweden}, art_number = {9222343}, coden = {ITVGE}, document_type = {Article}, langid = {english}, pubmed_id = {33048687}, source = {Scopus}, note = {cited By 13 \par TL;DR \par A knowledge generation model, which supports ensemble learning with the use of visualization, and a visual analytics system for stacked generalization, which helps users to decide between distinct models and to reduce the complexity of the resulting stack by removing overpromising and underperforming models.} } @inproceedings{Chatzimparmpas2022161, title = {Evaluating {{StackGenVis}} with a Comparative User Study}, author = {Chatzimparmpas, A. and Park, V. and Kerren, A.}, date = {2022}, series = {{{IEEE Pacific Visualization Symposium}}}, volume = {2022-April}, pages = {161--165}, publisher = {IEEE Computer Society}, issn = {21658765}, doi = {10.1109/PacificVis53943.2022.00025}, abbrev_source_title = {IEEE Pacific Visual. Symp.}, affiliation = {Linnaeus University, Linköping University, Sweden}, correspondence_address1 = {Chatzimparmpas, A.; Linnaeus University, Sweden; email: angelos.chatzimparmpas@lnu.se}, document_type = {Conference Paper}, isbn = {978-1-6654-2335-9}, langid = {english}, source = {Scopus}, note = {cited By 0 \par TL;DR \par The results indicate that StackGen Vis is significantly more powerful than OVS based on the qualitative feedback provided by the participants, and the average completion time for all tasks was comparable between both tools.} } @book{chaudronProceedings40thInternational2018, title = {Proceedings of the 40th International Conference on Software Engineering, {{ICSE}} 2018, Gothenburg, Sweden, May 27 - June 03, 2018}, editor = {Chaudron, Michel and Crnkovic, Ivica and Chechik, Marsha and Harman, Mark}, date = {2018}, publisher = {ACM}, doi = {10.1145/3180155}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/bib/conf/icse/2018}, isbn = {978-1-4503-5638-1}, timestamp = {Wed, 21 Nov 2018 12:43:58 +0100} } @online{CheCosaFurono, title = {Che Cosa Furono i Massacri Delle Foibe}, url = {https://www.focus.it/cultura/storia/che-cosa-furono-i-massacri-delle-foibe}, urldate = {2025-02-17}, abstract = {La storia dimenticata dei massacri delle foibe, della questione triestina e del giorno del ricordo che commemora tutte le vittime italiane e slave.}, langid = {english}, organization = {Focus.it}, keywords = {/unread} } @inproceedings{chen_adversarial_2017, title = {Adversarial {{Machine Learning}} in {{Malware Detection}}: {{Arms Race}} between {{Evasion Attack}} and {{Defense}}}, shorttitle = {Adversarial {{Machine Learning}} in {{Malware Detection}}}, booktitle = {2017 {{Eur}}. {{Intell}}. {{Secur}}. {{Inform}}. {{Conf}}. {{EISIC}}}, author = {Chen, L. and Ye, Y. and Bourlai, T.}, date = {2017-09}, pages = {99--106}, doi = {10.1109/EISIC.2017.21}, abstract = {Since malware has caused serious damages and evolving threats to computer and Internet users, its detection is of great interest to both anti-malware industry and researchers. In recent years, machine learning-based systems have been successfully deployed in malware detection, in which different kinds of classifiers are built based on the training samples using different feature representations. Unfortunately, as classifiers become more widely deployed, the incentive for defeating them increases. In this paper, we explore the adversarial machine learning in malware detection. In particular, on the basis of a learning-based classifier with the input of Windows Application Programming Interface (API) calls extracted from the Portable Executable (PE) files, we present an effective evasion attack model (named EvnAttack) by considering different contributions of the features to the classification problem. To be resilient against the evasion attack, we further propose a secure-learning paradigm for malware detection (named SecDefender), which not only adopts classifier retraining technique but also introduces the security regularization term which considers the evasion cost of feature manipulations by attackers to enhance the system security. Comprehensive experimental results on the real sample collections from Comodo Cloud Security Center demonstrate the effectiveness of our proposed methods.}, keywords = {Adversarial Machine Learning,Computer security,Data mining,Data models,Evasion Attack and Defense,Feature extraction,Malware,Malware Detection}, note = {00048} } @inproceedings{Chen:2005:CCF:2154509.2154540, title = {Context-Aware Collaborative Filtering System: {{Predicting}} the User's Preference in the Ubiquitous Computing Environment}, booktitle = {Proc. {{First Int}}. {{Conf}}. {{Locat}}. {{Context-Aware}}.}, author = {Chen, Annie}, date = {2005}, series = {{{LoCA}}'05}, pages = {244--253}, publisher = {Springer-Verlag}, location = {Berlin, Heidelberg}, url = {http://dx.doi.org/10.1007/11426646_23}, acmid = {2154540}, isbn = {3-540-25896-5 978-3-540-25896-4}, nodoi = {10.1007/11426646₂3}, numpages = {10}, note = {TL;DR \par A context-aware collaborative filtering system that predicts a user's preference in different context situations based on past experiences and can help predict the user's behavior in different situations without the user actively defining it is presented.} } @online{chen$textbfEMOS$$textbfE$mbodimentawareHeterogeneous2024, title = {\$\textbackslash textbf\{\vphantom\}{{EMOS}}\vphantom\{\}\$: \$\textbackslash textbf\{\vphantom\}{{E}}\vphantom\{\}\$mbodiment-Aware Heterogeneous \$\textbackslash textbf\{\vphantom\}{{M}}\vphantom\{\}\$ulti-Robot \$\textbackslash textbf\{\vphantom\}{{O}}\vphantom\{\}\$perating \$\textbackslash textbf\{\vphantom\}{{S}}\vphantom\{\}\$ystem with {{LLM}} Agents}, shorttitle = {\$\textbackslash textbf\{\vphantom\}{{EMOS}}\vphantom\{\}\$}, author = {Chen, Junting and Yu, Checheng and Zhou, Xunzhe and Xu, Tianqi and Mu, Yao and Hu, Mengkang and Shao, Wenqi and Wang, Yikai and Li, Guohao and Shao, Lin}, date = {2024-10-30}, eprint = {2410.22662}, eprinttype = {arXiv}, eprintclass = {cs}, doi = {10.48550/arXiv.2410.22662}, abstract = {Heterogeneous multi-robot systems (HMRS) have emerged as a powerful approach for tackling complex tasks that single robots cannot manage alone. Current large-language-model-based multi-agent systems (LLM-based MAS) have shown success in areas like software development and operating systems, but applying these systems to robot control presents unique challenges. In particular, the capabilities of each agent in a multi-robot system are inherently tied to the physical composition of the robots, rather than predefined roles. To address this issue, we introduce a novel multi-agent framework designed to enable effective collaboration among heterogeneous robots with varying embodiments and capabilities, along with a new benchmark named Habitat-MAS. One of our key designs is \$\textbackslash textit\{Robot Resume\}\$: Instead of adopting human-designed role play, we propose a self-prompted approach, where agents comprehend robot URDF files and call robot kinematics tools to generate descriptions of their physics capabilities to guide their behavior in task planning and action execution. The Habitat-MAS benchmark is designed to assess how a multi-agent framework handles tasks that require embodiment-aware reasoning, which includes 1) manipulation, 2) perception, 3) navigation, and 4) comprehensive multi-floor object rearrangement. The experimental results indicate that the robot's resume and the hierarchical design of our multi-agent system are essential for the effective operation of the heterogeneous multi-robot system within this intricate problem context.}, langid = {english}, pubstate = {prepublished}, keywords = {/unread,Computer Science - Artificial Intelligence,Computer Science - Multiagent Systems,Computer Science - Robotics}, note = {Comment: 10 pages of main content, 3 pages of references, 5 pages of appendix, 7 figures in total} } @inproceedings{Chen20191901, title = {Multivariate-Information Adversarial Ensemble for Scalable Joint Distribution Matching}, author = {Chen, Z. and Yang, Z. and Wang, X. and Liang, X. and Yan, X. and Li, G. and Lin, L.}, date = {2019}, series = {36th {{International Conference}} on {{Machine Learning}}, {{ICML}} 2019}, volume = {2019-June}, pages = {1901--1910}, publisher = {International Machine Learning Society (IMLS)}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078043028&partnerID=40&md5=0e04ea8110817d757bfa4fdda4d16605}, abstract = {A broad range of cross-m-domain generation researches boil down to matching a joint distribution by deep generative models (DGMs). Hitherto algorithms excel in pairwise domains while as m increases, remain struggling to scale themselves to fit a joint distribution. In this paper, we propose a domain-scalable DGM, i.e., MMI-ALI for m-domain joint distribution matching. As an m-domain ensemble model of ALIs (Dumoulin et al., 2016), MMI-ALI is adversarially trained with maximizing Multivariate Mutual Information (MMI) w.r.t. joint variables of each pair of domains and their shared feature. The negative MMIs are upper bounded by a series of feasible losses that provably lead to matching m-domain joint distributions. MMI-ALI linearly scales as m increases and thus, strikes a right balance between efficacy and scalability. We evaluate MMI-ALI in diverse challenging m-domain scenarios and verify its superiority. © 2019 by the Author(S).}, document_type = {Conference Paper}, isbn = {978-1-5108-8698-8}, source = {Scopus}, keywords = {Artificial intelligence,Ensemble modeling,Generative model,Joint distributions,Machine learning,Multivariate mutual information,Software engineering}, note = {cited By 0 \par TL;DR \par A domain-scalable DGM, i.e., MMI-ALI for \$m-domain joint distribution matching, which linearly scales as \$m\$ increases and thus, strikes a right balance between efficacy and scalability.} } @inproceedings{Chen201978, title = {{{VISION}}: {{Evaluating}} Scenario Suitableness for {{DNN}} Models by Mirror Synthesis}, author = {Chen, Z. and Wang, H. and Xu, C. and Ma, X. and Cao, C.}, date = {2019}, series = {Proceedings - {{Asia-Pacific Software Engineering Conference}}, {{APSEC}}}, volume = {2019-December}, pages = {78--85}, publisher = {IEEE Computer Society}, issn = {15301362}, doi = {10.1109/APSEC48747.2019.00020}, abstract = {Software systems assisted with deep neural networks (DNNs) are gaining increasing popularities. However, one outstanding problem is to judge whether a given application scenario suits a DNN model, whose answer highly affects its concerned system's performance. Existing work indirectly addressed this problem by seeking for higher test coverage or generating adversarial inputs. One pioneering work is SynEva, which exactly addressed this problem by synthesizing mirror programs for scenario suitableness evaluation of general machine learning programs, but fell short in supporting DNN models. In this paper, we propose VISION to eValuatIng Scenario suItableness fOr DNN models, specially catered for DNN characteristics. We conducted experiments on a real-world self-driving dataset Udacity, and the results show that VISION was effective in evaluating scenario suitableness for DNN models with an accuracy of 75.6-89.0\% as compared to that of SynEva, 50.0-81.8\%. We also explored different meta-models in VISION, and found out that the decision tree logic learner meta-model could be the best one for balancing VISION's effectiveness and efficiency. © 2019 IEEE.}, art_number = {8945698}, author_keywords = {DNN model; mirror synthesis; scenario suitableness evaluation}, document_type = {Conference Paper}, isbn = {978-1-7281-4648-5}, source = {Scopus}, keywords = {Application scenario,Balancing,Decision tree logic,Decision trees,Deep neural networks,Effectiveness and efficiencies,Mirror synthesis,Mirrors,scenario suitableness evaluation,Software engineering,Software systems,System's performance,Test coverage}, note = {cited By 1} } @inproceedings{chen2023automated, title = {Automated Domain Modeling with Large Language Models: A Comparative Study}, booktitle = {2023 {{ACMIEEE}} 26th {{Int}}. {{Conf}}. {{Model Driven Eng}}. {{Lang}}. {{Syst}}. {{MODELS}}}, author = {Chen, Kua and Yang, Yujing and Chen, Boqi and López, José Antonio Hernández and Mussbacher, Gunter and Varró, Dániel}, date = {2023}, pages = {162--172}, publisher = {IEEE}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found} } @inproceedings{chen2023ontheuse, title = {On the Use of {{GPT-4}} for Creating Goal Models: An Exploratory Study}, booktitle = {2023 {{IEEE}} 31st {{Int}}. {{Requir}}. {{Eng}}. {{Conf}}. {{Workshop REW}}}, author = {Chen, Boqi and Chen, Kua and Hassani, Shabnam and Yang, Yujing and Amyot, Daniel and Lessard, Lysanne and Mussbacher, Gunter and Sabetzadeh, Mehrdad and Varró, Dániel}, date = {2023}, pages = {262--271}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found} } @online{chenFairnessTestingComprehensive2022, title = {Fairness {{Testing}}: {{A Comprehensive Survey}} and {{Analysis}} of {{Trends}}}, shorttitle = {Fairness {{Testing}}}, author = {Chen, Zhenpeng and Zhang, Jie M. and Hort, Max and Sarro, Federica and Harman, Mark}, date = {2022-08-05}, eprint = {2207.10223}, eprinttype = {arXiv}, eprintclass = {cs}, url = {http://arxiv.org/abs/2207.10223}, urldate = {2023-03-09}, abstract = {Software systems are vulnerable to fairness bugs and frequently exhibit unfair behaviors, making software fairness an increasingly important concern for software engineers. Research has focused on helping software engineers to detect fairness bugs automatically. This paper provides a comprehensive survey of existing research on fairness testing. We collect 122 papers and organise them based on the testing workflow (i.e., the testing activities) and the testing components (i.e., where to find fairness bugs) for conducting fairness testing. We also analyze the research focus, trends, promising directions, as well as widely-adopted datasets and open source tools for fairness testing.}, pubstate = {prepublished}, keywords = {Computer Science - Software Engineering,LOGSEQ}, note = {TL;DR \par A comprehensive survey of existing studies in fairness testing of ML software is offered, and the research focus, trends, and promising directions in the realm of fairness testing are analyzed.} } @article{Cheng2019472, title = {Deep Neighbor Embedding for Evaluation of Large Portfolios of Variable Annuities}, author = {Cheng, X. and Luo, W. and Gan, G. and Li, G.}, editor = {Douligeris C., Apostolou D., Karagiannis D.}, date = {2019}, journaltitle = {Lect. Notes Comput. Sci. Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinforma.}, volume = {11775 LNAI}, pages = {472--480}, publisher = {Springer}, issn = {03029743}, doi = {10.1007/978-3-030-29551-6_42}, abbrev_source_title = {Lect. Notes Comput. Sci.}, affiliation = {Xi’an Shiyou University, Shaanxi, China; Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, China; Deakin University, Geelong, Australia; University of Connecticut, Storrs, CT, United States}, correspondence_address1 = {Luo, W.; Deakin UniversityAustralia; email: wei.luo@deakin.edu.au}, document_type = {Conference Paper}, isbn = {9783030295509}, langid = {english}, source = {Scopus}, note = {cited By 3 \par TL;DR \par A novel and highly effective method for selecting representative contracts using a deep neighbor embedding that supports robust clustering of the contracts in a portfolio and achieves significant improvement in valuation accuracy.} } @online{chengExploringLargeLanguage2024, title = {Exploring Large Language Model Based Intelligent Agents: Definitions, Methods, and Prospects}, shorttitle = {Exploring Large Language Model Based Intelligent Agents}, author = {Cheng, Yuheng and Zhang, Ceyao and Zhang, Zhengwen and Meng, Xiangrui and Hong, Sirui and Li, Wenhao and Wang, Zihao and Wang, Zekai and Yin, Feng and Zhao, Junhua and He, Xiuqiang}, date = {2024-01-07}, eprint = {2401.03428}, eprinttype = {arXiv}, eprintclass = {cs}, doi = {10.48550/arXiv.2401.03428}, abstract = {Intelligent agents stand out as a potential path toward artificial general intelligence (AGI). Thus, researchers have dedicated significant effort to diverse implementations for them. Benefiting from recent progress in large language models (LLMs), LLM-based agents that use universal natural language as an interface exhibit robust generalization capabilities across various applications -- from serving as autonomous general-purpose task assistants to applications in coding, social, and economic domains, LLM-based agents offer extensive exploration opportunities. This paper surveys current research to provide an in-depth overview of LLM-based intelligent agents within single-agent and multi-agent systems. It covers their definitions, research frameworks, and foundational components such as their composition, cognitive and planning methods, tool utilization, and responses to environmental feedback. We also delve into the mechanisms of deploying LLM-based agents in multi-agent systems, including multi-role collaboration, message passing, and strategies to alleviate communication issues between agents. The discussions also shed light on popular datasets and application scenarios. We conclude by envisioning prospects for LLM-based agents, considering the evolving landscape of AI and natural language processing.}, langid = {english}, pubstate = {prepublished}, keywords = {Computer Science - Artificial Intelligence,Computer Science - Multiagent Systems} } @book{chengSoftwareEngineeringSelfadaptive2009, title = {Software Engineering for Self-Adaptive Systems}, editor = {Cheng, Betty H. C.}, date = {2009}, series = {Lecture Notes in Computer Science}, number = {5525}, publisher = {Springer}, location = {Berlin ; New York}, isbn = {978-3-642-02160-2}, pagetotal = {260} } @article{chenouardAutomaticallyDiscoveringHidden2009, title = {Automatically {{Discovering Hidden Transformation Chaining Constraints}}}, author = {Chenouard, Raphaël and Jouault, Frédéric}, date = {2009}, journaltitle = {Model Driven Eng. Lang. Syst.}, volume = {5795}, pages = {92--106}, doi = {10.1007/978-3-642-04425-0_8} } @inproceedings{chenSimAppFrameworkDetecting2015, ids = {Chen:2015:SFD:2684822.2685305}, title = {{{SimApp}}: {{A Framework}} for {{Detecting Similar Mobile Applications}} by {{Online Kernel Learning}}}, shorttitle = {{{SimApp}}}, author = {Chen, Ning and Hoi, Steven C.H. and Li, Shaohua and Xiao, Xiaokui}, date = {2015}, pages = {305--314}, publisher = {ACM Press}, location = {Shanghai, China}, doi = {10.1145/2684822.2685305}, acmid = {2685305}, isbn = {978-1-4503-3317-7}, langid = {english}, nodoi = {10.1145/2684822.2685305}, numpages = {10}, keywords = {mobile applications,multi-modal data,multiple kernels,online kernel learning,similarity function}, note = {TL;DR \par This paper explores multi-modal heterogeneous data in app markets (e.g., description text, images, user reviews, etc.), and presents "SimApp" -- a novel framework for detecting similar apps using machine learning.} } @inproceedings{ChenSN05, title = {Toward a Semantic Anchoring Infrastructure for Domain-Specific Modeling Languages}, booktitle = {{{EMSOFT}} 2005 {{Sept}}. 18-22 2005 {{Jersey City NJ USA}} 5th {{ACM Int}}. {{Conf}}. {{Embed}}. {{Softw}}. {{Proc}}.}, author = {Chen, Kai and Sztipanovits, Janos and Neema, Sandeep}, editor = {Wolf, Wayne H.}, date = {2005}, pages = {35--43}, publisher = {ACM}, doi = {10.1145/1086228.1086236}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {TL;DR \par This paper uses a mathematical model, Abstract State Machines, a common semantic framework to define the semantic domains of DSML-s, and proposes a formal well founded methodology with supporting tools to anchor the semantics of DS ML to precisely defined and validated "semantic units".}, timestamp = {Mon, 05 Feb 2024 20:35:04 +0100} } @article{cheungModelbasedSystemsEngineering, title = {Model-Based Systems Engineering Toolchains for Software Development of Robotic Autonomous Systems}, author = {Cheung, Calvin and Pfiel, Andrew and Petrotta, Mark and Haggerty, Catherine}, abstract = {Robotic autonomous system development requires a system-of-systems approach to manage the complexity inherent in integrating disparate fields of engineering into a single system. While model-based systems engineering (MBSE) provides the tools and processes needed in the management of complex robotics system development, there has traditionally been challenges in leveraging MBSE in rapidly changing software-intensive domains such as autonomy. To bridge the gap between MBSE and software engineering, we developed model import toolchains that automate the generation of SysML models. These toolchains are tailored for the Robot Operating System and leverage model import files that are maintained by the software developer, ensuring alignment between the models and codebase. By leveraging the model import toolchain, we have been able to successfully integrate an MBSE approach with our software engineering processes, improving efficiency in the development process and the quality of robotic systems.}, langid = {english}, keywords = {LOGSEQ}, note = {\section{Annotazioni\\ (3/12/2024, 22:47:54)} \par - “there has traditionally been challenges in leveraging MBSE in rapidly changing software-intensive domains such as autonomy.” (Cheung et al., p. 1) \#a28ae5 \par - “model import toolchains that automate the generation of SysML models” (Cheung et al., p. 1) \#a28ae5 \par - “By leveraging the model import toolchain, we have been able to successfully integrate an MBSE approach with our software engineering processes, improving efficiency in the development process and the quality of robotic systems.” (Cheung et al., p. 1) \#ffd400\\ \mkbibemph{The goal of the paper is not clear by looking at the abstract.} \par - “combined to create systems that integrate together towards the common goal of a mobile autonomous system” (Cheung et al., p. 1) \#a28ae5 \par - “reliable systems engineering approach is vital to the overall success of RAS development.” (Cheung et al., p. 1) \#5fb236 \par - “improving the processes used in integrating robotic software engineering and model-based systems engineering (MBSE) via the development of a systems modeling language (SysML) import toolchain.” (Cheung et al., p. 1) \#a28ae5\\ \mkbibemph{This is supposed to be the novelty of the paper even though it is not clear what are the "competitors" and their limitations that authors want to address with this paper.} \par - “reducing the friction experienced by software engineers when working within a systems engineering framework and ensuring synchronicity between the software and the overall system models.” (Cheung et al., p. 1) \#ffd400\\ \mkbibemph{it's not clear what are the considered processes their limitations and how authors are addressing them!!} \par - “the formalized application of modeling to support system requirements, design, analysis, and verification and validation activities beginning in the conceptual design phase and continuing throughout development and later lifecycle phases” (Cheung et al., p. 1) \#5fb236 \par - “The overall purpose of models is to aid in understanding, decision making, and predict events.” (Cheung et al., p. 1) \#5fb236 \par - “specification of the structure, behavior, requirements, and parametrics of a system.” (Cheung et al., p. 1) \#5fb236 \par - “Fig. 1 shows the nine diagram types defined by SysML, each of which conveys information about different aspects of a system. Model elements across each diagram type can be linked together to describe the interrelations amongst the different parts of a system.” (Cheung et al., p. 1) \#ffd400\\ \mkbibemph{What is the need for this figure, or in general show the different types of diagrams in SysML} \par - “challenges in integrating MBSE with software engineering methodologies exists. M” (Cheung et al., p. 1) \#a28ae5 \par - “toolchains that reduced the burden placed upon software engineers in the creation of models” (Cheung et al., p. 2) \#5fb236 \par - “we wanted to be able to allow for the automated generation of SysML models based on structured, text-based, and machine-readable files managed by the developers” (Cheung et al., p. 2) \#ffd400\\ \mkbibemph{AUtomated generationof SysML models starting from textual descriptions? How do they look like?} \par - “ROS is a framework for the development of robotic software, leveraging a collection of libraries, tools, and conventions to enable collaborative software development and reuse” (Cheung et al., p. 2) \#5fb236 \par - “The instantiation import files on the other hand define the structure of a ROS system as collection of individual components.” (Cheung et al., p. 2) \#ffd400\\ \mkbibemph{In the end we are condiguring a ROS based system by means of XML files. Isn't it?} \par - “Definition Phase: Import ROS constructs and components into SysML model elements” (Cheung et al., p. 2) \#ffd400\\ \mkbibemph{Here it seems that we are generating SysML models starting from XML specifications.} \par - “Applying these toolchains to our ROS development efforts enabled us to apply MBSE to our software engineering efforts in a number of different ways, increasing efficiency of processes and consistency of the system” (Cheung et al., p. 3) \#ffd400\\ \mkbibemph{The process is not clear at all!} \par - “he model elements and diagrams generated by our model import toolchains have enabled integration between MBSE and software engineering processes in a synchronized fashion, ensuring consistency between the two domains” (Cheung et al., p. 3) \#ffd400\\ \mkbibemph{What do you mean?} \par - “In order to perform these reviews, accurate diagrams of both the overall RAS architecture and the architecture of the components are required to compare interfaces towards integration.” (Cheung et al., p. 3) \#5fb236 \par - “This linkage between requirements and capabilities allows for greater system consistency and reduction in errors” (Cheung et al., p. 3) \#5fb236 \par - “Changes to capabilities can immediately flag the affected requirements and vice versa, allowing for rapid response in ensuring that overall system needs are met” (Cheung et al., p. 3) \#ffd400 \par - “MBSE utilization in RAS development, there may still be situations where more traditional forms of documentation are preferred.” (Cheung et al., p. 3) \#5fb236 \par - “XML was decided upon due to the prevalence of the XML file format in both ROS and Doxygen, a code documentation generator” (Cheung et al., p. 3) \#5fb236 \par - “TABLE I. ROS CONSTRUCTS” (Cheung et al., p. 3) \#5fb236 \par - “s on the benefits of different technologies cannot be in a bubble. The entire development environment and ecosystem must be taken into consideration for design choices related to selection of competing technologies.” (Cheung et al., p. 4) \#5fb236 \par - “inclusion of MBSE” (Cheung et al., p. 4) \#ffd400\\ \mkbibemph{what kind of integration has been done? How was before and how is it after the adoption of the presented approach?} \par - “From the perspective of a software developer, they may see their current practices as sufficient, and any additional effort adds no value to their workflow” (Cheung et al., p. 4) \#5fb236 \par - “Incremental adaptation of new MBSE workflows that meet developers within their comfort zone yielded better results than trying to add new processes.” (Cheung et al., p. 4) \#a28ae5 \par - “needed a ROS profile to use in our toolchain to generate SysML diagrams.” (Cheung et al., p. 4) \#5fb236 \par - “we developed a ROS profile for our toolchain that defines common ROS constructs in SysML.” (Cheung et al., p. 4) \#5fb236 \par - “increasing the variety of ROS constructs that the model import toolchain can represen” (Cheung et al., p. 4) \#5fb236 \par - “While the constructs described in Table I can be used to provide a basic overview of a ROS system, there are additional constructs that can be represented that would give a more detailed understanding of the system.” (Cheung et al., p. 4) \#ffd400\\ \mkbibemph{If you keep adding ROS constructs, you risk to get too complex. It is important to keep and leverage the concept of abstractsions when possible.} \par - “we would be able to increase the fidelity at which a ROS system can be modeled and represented in MBSE” (Cheung et al., p. 4) \#ffd400\\ \mkbibemph{Yes, at the price of model complexity. What would be the related benefit then?} \par - “we developed SysML model import toolchains that automated the generation of SysML model elements based on ROS constructs and model import files.” (Cheung et al., p. 4) \#5fb236 \par - “utility of MBSE in RAS development to provide a starting point for the greater robotics community to follow suite in their own efforts.” (Cheung et al., p. 4) \#5fb236} } @inproceedings{Chowdhury2017, title = {Optimal Metamodeling to Interpret Activity-Based Health Sensor Data}, author = {Chowdhury, S. and Mehmani, A.}, date = {2017}, series = {Proceedings of the {{ASME Design Engineering Technical Conference}}}, volume = {3}, publisher = {American Society of Mechanical Engineers (ASME)}, doi = {10.1115/DETC2017-68385}, abbrev_source_title = {Proc. ASME Des. Eng. Tech. Conf.}, affiliation = {University at Buffalo, Buffalo, NY 14260, United States; Columbia University, New York, NY 10027, United States; Department of Mechanical and Aerospace Engineering, ASME, United States}, correspondence_address1 = {Chowdhury, S.; Department of Mechanical and Aerospace Engineering, United States; email: soumacho@buffalo.edu}, document_type = {Conference Paper}, isbn = {978-0-7918-5815-8}, langid = {english}, source = {Scopus}, note = {cited By 2} } @inproceedings{Christen20194124, title = {Cybernetical Concepts for Cellular Automaton and Artificial Neural Network Modelling and Implementation}, author = {Christen, P. and Fabbro, O.D.}, date = {2019}, series = {Conference {{Proceedings}} - {{IEEE International Conference}} on {{Systems}}, {{Man}} and {{Cybernetics}}}, volume = {2019-October}, pages = {4124--4130}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {1062922X}, doi = {10.1109/SMC.2019.8913839}, abbrev_source_title = {Conf. Proc. IEEE Int. Conf. Syst. Man Cybern.}, affiliation = {Institute for Information Systems, FHNW, Olten, Switzerland; ETH Zurich, Zurich, Switzerland}, art_number = {8913839}, coden = {PICYE}, correspondence_address1 = {Christen, P.; Institute for Information Systems, Switzerland; email: patrik.christen@fhnw.ch}, document_type = {Conference Paper}, isbn = {978-1-7281-4569-3}, langid = {english}, source = {Scopus}, note = {cited By 2 \par TL;DR \par This paper emphasises this allagmatic theory by showing how Simondon’s philosophical concepts can be used to formulate a generic computer model or metamodel for complex systems modelling and its implementation in program code, according to generic programming.} } @article{chughSurveyHandlingComputationally2019, title = {A Survey on Handling Computationally Expensive Multiobjective Optimization Problems with Evolutionary Algorithms}, author = {Chugh, T. and Sindhya, K. and Hakanen, J. and Miettinen, K.}, date = {2019}, journaltitle = {Soft Comput.}, volume = {23}, number = {9}, pages = {3137--3166}, publisher = {Springer Verlag}, issn = {14327643}, doi = {10.1007/s00500-017-2965-0}, abstract = {Evolutionary algorithms are widely used for solving multiobjective optimization problems but are often criticized because of a large number of function evaluations needed. Approximations, especially function approximations, also referred to as surrogates or metamodels are commonly used in the literature to reduce the computation time. This paper presents a survey of 45 different recent algorithms proposed in the literature between 2008 and 2016 to handle computationally expensive multiobjective optimization problems. Several algorithms are discussed based on what kind of an approximation such as problem, function or fitness approximation they use. Most emphasis is given to function approximation-based algorithms. We also compare these algorithms based on different criteria such as metamodeling technique and evolutionary algorithm used, type and dimensions of the problem solved, handling constraints, training time and the type of evolution control. Furthermore, we identify and discuss some promising elements and major issues among algorithms in the literature related to using an approximation and numerical settings used. In addition, we discuss selecting an algorithm to solve a given computationally expensive multiobjective optimization problem based on the dimensions in both objective and decision spaces and the computation budget available. © 2017, Springer-Verlag GmbH Germany, part of Springer Nature.}, keywords = {Approximation algorithms,Budget control,Computational costs,Evolutionary algorithms,Learning systems,Meta model,Multicriteria optimization,Multiobjective optimization,Pareto principle,Pareto-optimality,Problem solving,Response surface approximation,Surrogate,Surveys}, note = {cited By 93 \par TL;DR \par A survey of 45 different recent algorithms proposed in the literature between 2008 and 2016 to handle computationally expensive multiobjective optimization problems and identifies and discusses some promising elements and major issues among algorithms in the Literature related to using an approximation and numerical settings used.} } @article{chuiInternetThings2010, title = {The Internet of Things}, author = {Chui, Michael and Löffler, Markus and Roberts, Roger}, date = {2010}, journaltitle = {McKinsey Q.}, volume = {2}, number = {2010}, pages = {1--9}, url = {https://realyze.in/downloads/TheInternetofThings.pdf}, urldate = {2016-08-21} } @article{cicchettiManagingEvolutionDataintensive2013, title = {Managing the Evolution of Data-Intensive Web Applications by Model-Driven Techniques}, author = {Cicchetti, Antonio and Di Ruscio, Davide and Iovino, Ludovico and Pierantonio, Alfonso}, date = {2013-02}, journaltitle = {Software Syst. Model.}, volume = {12}, number = {1}, pages = {53--83}, issn = {1619-1366, 1619-1374}, doi = {10.1007/s10270-011-0193-0}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {TL;DR \par A model-driven approach for supporting the migration and evolution of data-intensive Web applications and is validated by considering applications described with the beContent and WebML modeling languages.} } @inproceedings{ciccozziAutomaticSynthesisHeterogeneous2013, title = {Automatic Synthesis of Heterogeneous {{CPU-GPU}} Embedded Applications from a {{UML}} Profile}, booktitle = {{{CEUR Workshop Proc}}.}, author = {Ciccozzi, F.}, editor = {Graf S., Ober I., Noyrit F., Karsai G.}, date = {2013}, volume = {1084}, publisher = {CEUR-WS}, issn = {16130073}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84924051199&partnerID=40&md5=70ef85d012e33a637026195a4787b475}, abstract = {Modern embedded systems present an ever increasing complexity and model-driven engineering has been shown to be helpful in mitigating it. In our previous works we exploited the power of model-driven engineering to develop a round-trip approach for aiding the evaluation and assessment of extra-functional properties preservation from models to code. In addition, we showed how the round-trip approach could be employed to evaluate different deployment strategies, and the focus was on homogeneous CPUbased platforms. Due to the fact that the assortment of target-platforms in the embedded domain is inevitably shifting to heterogeneous solutions, our goal is to broaden the scope of the round-trip approach towards mixed CPU-GPU configurations. In this work we focus on the modelling of heterogeneous deployment and the enhancement of the current automatic code generator to synthesize code targeting such heterogeneous configurations.}, keywords = {ALF,CHESS-ML,Code synthesis,Codes (symbols),Computational linguistics,Embedded systems,Heterogeneous systems,MARTE,Model-driven Engineering,UML}, note = {cited By 0} } @inproceedings{ciccozziBodyKnowledgeModelbased2018, title = {Towards a Body of Knowledge for Model-Based Software Engineering}, booktitle = {Proc. 21st {{ACMIEEE Int}}. {{Conf}}. {{Model Driven Eng}}. {{Lang}}. {{Syst}}. {{Companion Proc}}.}, author = {Ciccozzi, Federico and Famelis, Michalis and Kappel, Gerti and Lambers, Leen and Mosser, Sebastien and Paige, Richard F. and Pierantonio, Alfonso and Rensink, Arend and Salay, Rick and Taentzer, Gabi and Vallecillo, Antonio and Wimmer, Manuel}, date = {2018-10-14}, pages = {82--89}, publisher = {ACM}, location = {Copenhagen Denmark}, doi = {10.1145/3270112.3270121}, abstract = {Model-based Software Engineering (MBSE) is now accepted as a Software Engineering (SE) discipline and is being taught as part of more general SE curricula. However, an agreed core of concepts, mechanisms and practices — which constitutes the Body of Knowledge of a discipline — has not been captured anywhere, and is only partially covered by the SE Body of Knowledge (SWEBOK). With the goals of characterizing the contents of the MBSE discipline, promoting a consistent view of it worldwide, clarifying its scope with regard to other SE disciplines, and defining a foundation for a curriculum development on MBSE, this paper provides a proposal for an extension of the contents of SWEBOK with the set of fundamental concepts, terms and mechanisms that should constitute the MBSE Body of Knowledge.}, eventtitle = {{{MODELS}} '18: {{ACM}}/{{IEEE}} 21th {{International Conference}} on {{Model Driven Engineering Languages}} and {{Systems}}}, isbn = {978-1-4503-5965-8}, langid = {english}, note = {TL;DR \par This paper provides a proposal for an extension of the contents of SWEBOK with the set of fundamental concepts, terms and mechanisms that should constitute the MBSE Body of Knowledge.} } @article{ciccozziMessageRoSE20182018, ids = {ciccozziMessageRoSE20182018a}, title = {Message from the {{RoSE}} 2018 {{Co-Organizers}}}, author = {Ciccozzi, F. and Di Ruscio, D. and Malavolta, I. and Pelliccione, P. and Wortmann, A.}, date = {2018}, journaltitle = {Proc. - Int. Conf. Softw. Eng.}, volume = {Part F137815}, pages = {x}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85051185469&partnerID=40&md5=58d98116887df216a6a4dfdf5660ffdd}, note = {cited By 0 \par cited By 0} } @misc{ciniselli2021empirical, title = {An Empirical Study on the Usage of {{BERT}} Models for Code Completion}, author = {Ciniselli, Matteo and Cooper, Nathan and Pascarella, Luca and Poshyvanyk, Denys and Penta, Massimiliano Di and Bavota, Gabriele}, date = {2021}, eprint = {2103.07115}, eprinttype = {arXiv}, eprintclass = {cs.SE}, note = {TL;DR \par A large-scale empirical study aimed at exploring the capabilities of state-of-the-art deep learning (DL) models in supporting code completion at different granularity levels, including single tokens, one or multiple entire statements, up to entire code blocks.} } @article{CiniselliCPMAPP22, title = {An Empirical Study on the Usage of Transformer Models for Code Completion}, author = {Ciniselli, Matteo and Cooper, Nathan and Pascarella, Luca and Mastropaolo, Antonio and Aghajani, Emad and Poshyvanyk, Denys and Di Penta, Massimiliano and Bavota, Gabriele}, date = {2022}, journaltitle = {IEEE Trans Softw. Eng}, volume = {48}, number = {12}, pages = {4818--4837}, note = {TL;DR \par A large-scale study exploring the capabilities of state-of-the-art Transformer-based models in supporting code completion at different granularity levels, including single tokens, one or multiple entire statements, up to entire code blocks (e.g., the iterated block of a \mkbibemph{for}.} } @article{ciniselliTodaysCodeTomorrows2030, title = {From {{Today}}’s {{Code}} to {{Tomorrow}}’s {{Symphony}}: {{The AI Transformation}} of {{Developer}}’s {{Routine}} by 2030}, author = {Ciniselli, Matteo and Puccinelli, Niccolò and Qiu, Ketai}, date = {2030}, abstract = {In the rapidly evolving landscape of software engineering, the integration of Artificial Intelligence (AI) into the Software Development Life-Cycle (SDLC) heralds a transformative era for developers. Recently, we have assisted to a pivotal shift towards AI-assisted programming, exemplified by tools like GitHub Copilot and OpenAI’s ChatGPT, which have become a crucial element for coding, debugging, and software design. In this paper we provide a comparative analysis between the current state of AI-assisted programming in 2024 and our projections for 2030, by exploring how AI advancements are set to enhance the implementation phase, fundamentally altering developers’ roles from manual coders to orchestrators of AI-driven development ecosystems. We envision HyperAssistant, an augmented AI tool that offers comprehensive support to 2030 developers, addressing current limitations in mental health support, fault detection, code optimization, team interaction, and skill development. We emphasize AI as a complementary force, augmenting developers’ capabilities rather than replacing them, leading to the creation of sophisticated, reliable, and secure software solutions. Our vision seeks to anticipate the evolution of programming practices, challenges, and future directions, shaping a new paradigm where developers and AI collaborate more closely, promising a significant leap in SE efficiency, security and creativity.}, langid = {english}, keywords = {LOGSEQ} } @article{cioffiArtificialIntelligenceMachine2020, title = {Artificial {{Intelligence}} and {{Machine Learning Applications}} in {{Smart Production}}: {{Progress}}, {{Trends}}, and {{Directions}}}, shorttitle = {Artificial {{Intelligence}} and {{Machine Learning Applications}} in {{Smart Production}}}, author = {Cioffi, Raffaele and Travaglioni, Marta and Piscitelli, Giuseppina and Petrillo, Antonella and De Felice, Fabio}, date = {2020-01-08}, journaltitle = {Sustainability}, volume = {12}, number = {2}, pages = {492}, issn = {2071-1050}, doi = {10.3390/su12020492}, abstract = {Adaptation and innovation are extremely important to the manufacturing industry. This development should lead to sustainable manufacturing using new technologies. To promote sustainability, smart production requires global perspectives of smart production application technology. In this regard, thanks to intensive research efforts in the field of artificial intelligence (AI), a number of AI-based techniques, such as machine learning, have already been established in the industry to achieve sustainable manufacturing. Thus, the aim of the present research was to analyze, systematically, the scientific literature relating to the application of artificial intelligence and machine learning (ML) in industry. In fact, with the introduction of the Industry 4.0, artificial intelligence and machine learning are considered the driving force of smart factory revolution. The purpose of this review was to classify the literature, including publication year, authors, scientific sector, country, institution, and keywords. The analysis was done using the Web of Science and SCOPUS database. Furthermore, UCINET and NVivo 12 software were used to complete them. A literature review on ML and AI empirical studies published in the last century was carried out to highlight the evolution of the topic before and after Industry 4.0 introduction, from 1999 to now. Eighty-two articles were reviewed and classified. A first interesting result is the greater number of works published by the USA and the increasing interest after the birth of Industry 4.0.}, langid = {english}, keywords = {artificial intelligence,DONE,machine learning,smart manufacturing}, note = {\textbf{Green Annotations (18/12/2020, 16:35:02)} \par "Table 5. Main areas in sustainable manufacturing." (\href{zotero://open-pdf/library/items/YUS586FA?page=16}{Cioffi et al 2020:507}) \par \mkbibemph{MAYBE USEFUL FOR THE OBJECTIVES (\href{zotero://open-pdf/library/items/YUS586FA?page=16}{note on p.507})}} } @inproceedings{citoInteractiveProductionPerformance2019, title = {Interactive {{Production Performance Feedback}} in the {{IDE}}}, booktitle = {2019 {{IEEEACM}} 41st {{Int}}. {{Conf}}. {{Softw}}. {{Eng}}. {{ICSE}}}, author = {Cito, Jurgen and Leitner, Philipp and Rinard, Martin and Gall, Harald C.}, date = {2019-05}, pages = {971--981}, publisher = {IEEE}, location = {Montreal, QC, Canada}, doi = {10.1109/ICSE.2019.00102}, eventtitle = {2019 {{IEEE}}/{{ACM}} 41st {{International Conference}} on {{Software Engineering}} ({{ICSE}})}, isbn = {978-1-7281-0869-8}, note = {TL;DR \par PerformanceHat, a new system that uses profiling information from production executions to develop a global performance model suitable for integration into interactive development environments, is presented and results indicate that developers using PerformanceHat were significantly faster in detecting the performance problem, and finding the root-cause of the problem.} } @inproceedings{clarisoApplyingGraphKernels2018, ids = {Clarisó20181}, title = {Applying Graph Kernels to Model-Driven Engineering Problems}, booktitle = {{{MASES}} 2018 - {{Proc}}. 1st {{Int}}. {{Workshop Mach}}. {{Learn}}. {{Softw}}. {{Eng}}. {{Symbiosis Co-Located ASE}} 2018}, author = {Clarisó, R. and Cabot, J.}, editor = {Perrouin G., Acher M., Devroey X., Cordy M., Cordy M.}, date = {2018}, pages = {1--5}, publisher = {Association for Computing Machinery, Inc}, doi = {10.1145/3243127.3243128}, abbrev_source_title = {MASES - Proc. Int. Workshop Mach. Learn. Soft. Eng. Symbiosis, co-located ASE}, abstract = {Machine Learning (ML) can be used to analyze and classify large collections of graph-based information, e.g. images, location information, the structure of molecules and proteins, . . . Graph kernels is one of the ML techniques typically used for such tasks. In a software engineering context, models of a system such as structural or architectural diagrams can be viewed as labeled graphs. Thus, in this paper we propose to employ graph kernels for clustering software modeling artifacts. Among other benefits, this would improve the efficiency and usability of a variety of software modeling activities, e.g., design space exploration, testing or verification and validation. © 2018 Association for Computing Machinery.}, affiliation = {Multimedia and Telecommunication Dept. Barcelona, Universitat Oberta de Catalunya (UOC) IT, Spain; SOM Research Lab, ICREA, Barcelona, Spain}, document_type = {Conference Paper}, isbn = {978-1-4503-5972-6}, source = {Scopus}, keywords = {Artificial intelligence,Classification (of information),Clustering,Design space exploration,GOAL\_Model-Classification,Graph kernels,Graphic methods,Labeled graphs,Learning systems,Location information,Model-driven Engineering,notion,Software testing,Structure of molecules,Systems analysis,TECHNIQUE\_GRAPH-KERNELS,Verification,Verification-and-validation}, note = {cited By 10 \par cited By 10 \par TL;DR \par This paper proposes to employ graph kernels for clustering software modeling artifacts to improve the efficiency and usability of a variety of software modeling activities, e.g., design space exploration, testing or verification and validation.} } @article{clarisoBackwardsReasoningModel2015, title = {Backwards Reasoning for Model Transformations: {{Method}} and Applications}, shorttitle = {Backwards Reasoning for Model Transformations}, author = {Clarisó, Robert and Cabot, Jordi and Guerra, Esther and family=Lara, given=Juan, prefix=de, useprefix=true}, date = {2015-08}, journaltitle = {J. Syst. Softw.}, issn = {01641212}, doi = {10.1016/j.jss.2015.08.017}, langid = {english} } @inproceedings{clarisoModelDrivenPromptEngineering2023, title = {Model-{{Driven Prompt Engineering}}}, booktitle = {2023 {{ACMIEEE}} 26th {{Int}}. {{Conf}}. {{Model Driven Eng}}. {{Lang}}. {{Syst}}. {{MODELS}}}, author = {Clarisó, Robert and Cabot, Jordi}, date = {2023-10}, pages = {47--54}, doi = {10.1109/MODELS58315.2023.00020}, abstract = {Generative artificial intelligence (AI) systems are capable of synthesizing complex content such as text, source code or images according to the instructions described in a natural language prompt. The quality of the output depends on crafting a suitable prompt. This has given rise to prompt engineering, the process of designing natural language prompts to best take advantage of the capabilities of generative AI systems.Through experimentation, the creative and research communities have created guidelines and strategies for creating good prompts. However, even for the same task, these best practices vary depending on the particular system receiving the prompt. Moreover, some systems offer additional features using a custom platform-specific syntax, e.g., assigning a degree of relevance to specific concepts within the prompt.In this paper, we propose applying model-driven engineering to support the prompt engineering process. Using a domain-specific language (DSL), we define platform-independent prompts that can later be adapted to provide good quality outputs in a target AI system. The DSL also facilitates managing prompts by providing mechanisms for prompt versioning and prompt chaining. Tool support is available thanks to a Langium-based Visual Studio Code plugin.}, eventtitle = {2023 {{ACM}}/{{IEEE}} 26th {{International Conference}} on {{Model Driven Engineering Languages}} and {{Systems}} ({{MODELS}})}, keywords = {Artificial intelligence,domain-specific language,DSL,generative AI,large language models,Model driven engineering,model-driven engineering,Natural languages,prompt engineering,Source coding,Syntactics,Visualization} } @online{clavieItsAllMASK2025, title = {It's All in the [{{MASK}}]: Simple Instruction-Tuning Enables {{BERT-like}} Masked Language Models {{As}} Generative Classifiers}, shorttitle = {It's All in the [{{MASK}}]}, author = {Clavié, Benjamin and Cooper, Nathan and Warner, Benjamin}, date = {2025-02-10}, eprint = {2502.03793}, eprinttype = {arXiv}, eprintclass = {cs}, doi = {10.48550/arXiv.2502.03793}, abstract = {While encoder-only models such as BERT and ModernBERT are ubiquitous in real-world NLP applications, their conventional reliance on task-specific classification heads can limit their applicability compared to decoder-based large language models (LLMs). In this work, we introduce ModernBERT-Large-Instruct, a 0.4B-parameter encoder model that leverages its masked language modelling (MLM) head for generative classification. Our approach employs an intentionally simple training loop and inference mechanism that requires no heavy pre-processing, heavily engineered prompting, or architectural modifications. ModernBERT-Large-Instruct exhibits strong zero-shot performance on both classification and knowledge-based tasks, outperforming similarly sized LLMs on MMLU and achieving 93\% of Llama3-1B's MMLU performance with 60\% less parameters. We also demonstrate that, when fine-tuned, the generative approach using the MLM head matches or even surpasses traditional classification-head methods across diverse NLU tasks.This capability emerges specifically in models trained on contemporary, diverse data mixes, with models trained on lower volume, less-diverse data yielding considerably weaker performance. Although preliminary, these results demonstrate the potential of using the original generative masked language modelling head over traditional task-specific heads for downstream tasks. Our work suggests that further exploration into this area is warranted, highlighting many avenues for future improvements.}, langid = {english}, pubstate = {prepublished}, keywords = {/unread,Computer Science - Artificial Intelligence,Computer Science - Computation and Language} } @online{ClosedloopSystemClosedloop, title = {Closed-Loop {{System}} and {{Closed-loop Control Systems}}}, url = {http://www.electronics-tutorials.ws/systems/closed-loop-system.html}, urldate = {2016-11-01} } @online{ClusteringIntroduction, title = {Clustering - {{Introduction}}}, url = {http://home.deib.polimi.it/matteucc/Clustering/tutorial_html/}, urldate = {2015-04-23} } @online{ClusteringValidationTechniques, title = {On {{Clustering Validation Techniques}} - {{Springer}}}, url = {http://link.springer.com/article/10.1023/A:1012801612483}, urldate = {2015-05-07} } @online{ClusterSummarizationDense, title = {Cluster {{Summarization}} with {{Dense Region Detection}} - {{Springer}}}, url = {http://link.springer.com/chapter/10.1007/978-3-319-25840-9_5?wt_mc=alerts.TOCseries}, urldate = {2015-11-02} } @article{codabuxTeachingMiningSoftware, title = {Teaching {{Mining Software Repositories}}}, author = {Codabux, Zadia and Fard, Fatemeh and Verdecchia, Roberto and Palomba, Fabio and Nucci, Di and Recupito, Gilberto}, abstract = {Mining Software Repositories (MSR) has become a popular research area recently. MSR analyzes different sources of data, such as version control systems, code repositories, defect tracking systems, archived communication, deployment logs, and so on, to uncover interesting and actionable insights from the data for improved software development, maintenance, and evolution. This chapter provides an overview of MSR and how to conduct an MSR study, including setting up a study, formulating research goals and questions, identifying repositories, extracting and cleaning the data, performing data analysis and synthesis, and discussing MSR study limitations. Furthermore, the chapter discusses MSR as part of a mixed method study, how to mine data ethically, and gives an overview of recent trends in MSR as well as reflects on the future. As a teaching aid, the chapter provides tips for educators, exercises for students at all levels, and a list of repositories that can be used as a starting point for an MSR study.}, langid = {english} } @inproceedings{Cohen:1995:FER:3091622.3091637, title = {Fast Effective Rule Induction}, booktitle = {Proc. {{Twelfth Int}}. {{Conf}}. {{Int}}. {{Conf}}. {{Mach}}. {{Learn}}.}, author = {Cohen, William W.}, date = {1995}, series = {{{ICML}}'95}, pages = {115--123}, publisher = {Morgan Kaufmann Publishers Inc.}, location = {San Francisco, CA, USA}, url = {http://dl.acm.org/citation.cfm?id=3091622.3091637}, acmid = {3091637}, isbn = {1-55860-377-8}, numpages = {9} } @article{cohenFourPillarsResearch2021, title = {The {{Four Pillars}} of {{Research Software Engineering}}}, author = {Cohen, Jeremy and Katz, Daniel S. and Barker, Michelle and Chue Hong, Neil and Haines, Robert and Jay, Caroline}, date = {2021-01}, journaltitle = {IEEE Softw.}, volume = {38}, number = {1}, pages = {97--105}, issn = {0740-7459, 1937-4194}, doi = {10.1109/MS.2020.2973362}, note = {TL;DR \par Four elements are presented that are key to providing a comprehensive and sustainable support for research software engineering: software development, community, training, and policy.} } @misc{COLAD2400156PDF, title = {{{COLA-D-24-00156}}.{{PDF}}}, langid = {english}, keywords = {LOGSEQ}, note = {\section{Annotazioni\\ (6/3/2025, 14:47:58)} \par - “Context-based Word Embedding” (“COLA-D-24-00156.PDF”, p. 3) \#5fb236 \par - “Software Defect Prediction” (“COLA-D-24-00156.PDF”, p. 3) \#5fb236 \par - “software defect prediction” (“COLA-D-24-00156.PDF”, p. 3) \#5fb236 \par - “PROMISE” (“COLA-D-24-00156.PDF”, p. 3) \#2ea8e5 \par - “5 deep learning models” (“COLA-D-24-00156.PDF”, p. 3) \#5fb236 \par - “eighteen out of twenty-five scenarios” (“COLA-D-24-00156.PDF”, p. 3) \#5fb236 \par - “seven out of twenty-five cases” (“COLA-D-24-00156.PDF”, p. 3) \#5fb236 \par - “The supremacy of RoBERTa is accredited to its large training data, advanced pre-training strategies, and distinctive architecture.” (“COLA-D-24-00156.PDF”, p. 3) \#a28ae5 \par - “SDP has emerged as a valuable technique, alleviating the burden on developers by early detection and prevention of defects.” (“COLA-D-24-00156.PDF”, p. 3) \#5fb236 \par - “A software defect or a software bug denotes a mistake or defect in a software program that affects it such that it acts in an unforeseen or undesirable manner” (“COLA-D-24-00156.PDF”, p. 4) \#5fb236 \par - “SDP is the progression which utilizes data mining and machine learning methods to spot possible software defects before they occur.” (“COLA-D-24-00156.PDF”, p. 4) \#a28ae5 \par - “Predictive models are constructed by analyzing historical data on software deficiencies (Khalid et al., 2023) like bug reports, code changes, and testing results to detect distinct behaviors and patterns that would suggest future defects.” (“COLA-D-24-00156.PDF”, p. 4) \#5fb236 \par - “SDP is a technique that aims to identify and fix defects early in the development process so that they don’t lead to delays.” (“COLA-D-24-00156.PDF”, p. 4) \#5fb236 \par - “SDP attempts to use data mining and machine learning methods to predict possible defects well before they happen.” (“COLA-D-24-00156.PDF”, p. 4) \#ffd400\\ \mkbibemph{Quite repetitive} \par - “It could be used to prioritize testing efforts and allocate resources toward improving the quality of the entire software” (“COLA-D-24-00156.PDF”, p. 4) \#a28ae5 \par - “extracting semantic information using ASTs” (“COLA-D-24-00156.PDF”, p. 4) \#2ea8e5 \par - “word embedding technique” (“COLA-D-24-00156.PDF”, p. 4) \#2ea8e5 \par - “deep-learning classifier that predicts possible defects within the software” (“COLA-D-24-00156.PDF”, p. 4) \#2ea8e5 \par - “as for the semantic features inherent in the coding, it became apparent that static code metrics had a drawback.” (“COLA-D-24-00156.PDF”, p. 4) \#5fb236 \par - “Javalang is used to parse Java source code and generate ASTs in this paper.” (“COLA-D-24-00156.PDF”, p. 4) \#ffd400\\ \mkbibemph{Is it necessary to give this details at this stage when it is not clear yet what the contribution of this paper is?} \par - “SDP were based on static code metrics to evaluate code quality. These metrics cannot capture semantic aspects of the code to predict defects accurately.” (“COLA-D-24-00156.PDF”, p. 5) \#5fb236 \par - “limitations have been further increased by recent improvements in software development process.” (“COLA-D-24-00156.PDF”, p. 5) \#5fb236 \par - “(” (“COLA-D-24-00156.PDF”, p. 5) \#ff6666 \par - “understand code semantics” (“COLA-D-24-00156.PDF”, p. 5) \#a28ae5 \par - “Bidirectional Encoder Representations from Transformers (BERT)” (“COLA-D-24-00156.PDF”, p. 5) \#2ea8e5 \par - “Code Bidirectional Encoder Representations from Transformers (CodeBERT)” (“COLA-D-24-00156.PDF”, p. 5) \#2ea8e5 \par - “Robustly optimized Bidirectional Encoder Representations from Transformers approach (RoBERTa)” (“COLA-D-24-00156.PDF”, p. 5) \#2ea8e5 \par - “XLNet” (“COLA-D-24-00156.PDF”, p. 5) \#2ea8e5 \par - “identifying the optimal word embedding technique for SDP” (“COLA-D-24-00156.PDF”, p. 5) \#a28ae5 \par - “The lack of comparative analysis of these techniques for SDP presents a challenge for researchers in determining the most effective method.” (“COLA-D-24-00156.PDF”, p. 5) \#a28ae5 \par - “This research study aims to bridge this gap through comparative analysis of BERT, CodeBERT, RoBERTa and XLNet to find the best word embedding technique for SDP based on their relative performance. The study will help to identify more accurate and efficient defect prediction models by evaluating relative performance and applicability of these techniques.” (“COLA-D-24-00156.PDF”, p. 5) \#e56eee\\ \mkbibemph{That's important and represents the goal of this paper.} \par - “fine-tune particular tasks or datasets” (“COLA-D-24-00156.PDF”, p. 5) \#5fb236 \par - “The motivation behind this study is to investigate which advanced word embedding techniques can be considered optimal for SDP.” (“COLA-D-24-00156.PDF”, p. 5) \#a28ae5 \par - “Perform a comparative analysis” (“COLA-D-24-00156.PDF”, p. 5) \#2ea8e5 \par - “Identify the best-performing word embedding technique for SDP,” (“COLA-D-24-00156.PDF”, p. 5) \#2ea8e5 \par - “Provide statistical support for the determination of the superior context-based word embedding technique among those evaluated.” (“COLA-D-24-00156.PDF”, p. 5) \#2ea8e5 \par - “selected word embedding techniques” (“COLA-D-24-00156.PDF”, p. 5) \#2ea8e5 \par - “overview of related work” (“COLA-D-24-00156.PDF”, p. 5) \#2ea8e5 \par - “three methodological phases” (“COLA-D-24-00156.PDF”, p. 6) \#2ea8e5 \par - “Word embeddings techniques are used to represent words as numerical vectors in NL” (“COLA-D-24-00156.PDF”, p. 6) \#5fb236 \par - “two categories of word embeddings” (“COLA-D-24-00156.PDF”, p. 6) \#2ea8e5 \par - “non-context-based” (“COLA-D-24-00156.PDF”, p. 6) \#5fb236 \par - “context-based.” (“COLA-D-24-00156.PDF”, p. 6) \#5fb236 \par - “context-based techniques dynamically represent words by incorporating the surrounding context in which they are used” (“COLA-D-24-00156.PDF”, p. 6) \#5fb236 \par - “BERT (Devlin et al., 2019) is trained on a massive dataset of 3.3 billion words, including Wikipedia and BooksCorpus from Google.” (“COLA-D-24-00156.PDF”, p. 6) \#5fb236 \par - “This specialized model undergoes training on large-scale code corpora, aiming to capture contextual information and semantic relationships within programming languages. CodeBERT exhibits a similar model architecture to BERT, making it adept at understanding both programming languages (PL) and natural language (NL).” (“COLA-D-24-00156.PDF”, p. 7) \#5fb236 \par - “The optimization strategy adopted for RoBERTa involves Adam, employing specific parameters such as β1 = 0.9, β2 = 0.999, ɛ = 0.000001, and using an L2 weight decay of 0.01. Noteworthy adjustments in the training of RoBERTa include the learning rate warming up over the initial 10,000 steps to a maximum value of 0.0001, followed by a linear decay. Key modifications during the training of RoBERTa involve gradually increasing the learning rate over the initial 10,000 steps to reach a maximum value of 0.0001, followed by linear decay. Additionally, RoBERTa incorporates a dropout rate of 0.1 on all layers and attention weights, along with a GELU activation function. The pretraining phase consists of 1,000,000 updates, with minibatches containing 256 sequences, each having a” (“COLA-D-24-00156.PDF”, p. 7) \#ffd400\\ \mkbibemph{Are these details relevant for the sake of this work here?} \par - “maximizing the expected value of the sum of the log probabilities of predicting each token in the sequence, given the tokens that precede it, over all possible permutations of the token sequence” (“COLA-D-24-00156.PDF”, p. 8) \#5fb236 \par - “The exploration of effective techniques in this domain has been a persistent area of interest within the research community. Some common ways explored by the researchers are mentioned in this section.” (“COLA-D-24-00156.PDF”, p. 8) \#5fb236 \par - “Tested on ten open-source projects, this model achieved superior F1-scores compared to state-of-the-art models.” (“COLA-D-24-00156.PDF”, p. 8) \#5fb236 \par - “Softmax neural network” (“COLA-D-24-00156.PDF”, p. 8) \#5fb236 \par - “Defect Prediction via Self-Attention mechanism (DPSAM)” (“COLA-D-24-00156.PDF”, p. 8) \#5fb236 \par - “Cross-Project Defect Prediction (CPDP)” (“COLA-D-24-00156.PDF”, p. 8) \#e56eee \par - “Within-Project Defect Prediction (WPDP)” (“COLA-D-24-00156.PDF”, p. 8) \#a28ae5 \par - “comprehensive comparative analysis of context-based word embeddings.” (“COLA-D-24-00156.PDF”, p. 9) \#a28ae5 \par - “Addressing this gap in the research landscape holds significant importance as it can potentially guide practitioners and researchers towards selecting the most suitable context-based word embeddings for improved accuracy and generalizability in SDP.” (“COLA-D-24-00156.PDF”, p. 10) \#5fb236 \par - “extracting word tokens from AST” (“COLA-D-24-00156.PDF”, p. 10) \#5fb236 \par - “The methodology begins with extracting semantic features from designated open-source Java projects using javalang (https://pypi.org/project/javalang), an open-source Python tool for parsing Java source code. This tool enables the conversion of code into AST format, focusing on three categories of AST nodes: controlflow type, declaration type, and method invocation type as determined by existing studies” (“COLA-D-24-00156.PDF”, p. 10) \#5fb236 \par - “This selective approach was intended to mitigate potential noise introduced by specific AST node types, such as intrinsic type declarations.” (“COLA-D-24-00156.PDF”, p. 10) \#5fb236 \par - “The application of multiple models (Xu et al., 2021) enables a comprehensive evaluation of the embedding techniques' effectiveness within the realm of defect prediction.” (“COLA-D-24-00156.PDF”, p. 12) \#5fb236 \par - “Attention Mechanism (ANN-AM)” (“COLA-D-24-00156.PDF”, p. 12) \#5fb236 \par - “Self-attention was chosen because it allows the model to dynamically weigh the importance of different parts of the input sequence, capturing long-range dependencies effectively” (“COLA-D-24-00156.PDF”, p. 12) \#5fb236 \par - “softmax activation” (“COLA-D-24-00156.PDF”, p. 12) \#5fb236 \par - “WPDP” (“COLA-D-24-00156.PDF”, p. 12) \#5fb236 \par - “This section provides a thorough examination of the experimental setting used to assess the performance of various word embedding algorithms for defect prediction.” (“COLA-D-24-00156.PDF”, p. 12) \#5fb236 \par - “It covers the dataset description, outlines the proposed methodology, and details the evaluation metrics.” (“COLA-D-24-00156.PDF”, p. 12) \#a28ae5 \par - “y Sp” (“COLA-D-24-00156.PDF”, p. 12) \#ff6666 \par - “The second and third models utilize an LSTM layer and a GRU layer, respectively. Operating on input sequences with a length of 768, both models process the data through their respective recurrent layers and generate predictions via a fully connected layer.” (“COLA-D-24-00156.PDF”, p. 14) \#5fb236 \par - “The output size is set to 2, corresponding to the binary classification task. The BiLSTM model (Uddin et al., 2022) follows a similar structure, with the LSTM layer replaced by a Bidirectional LSTM layer. The size of the fully connected layer is doubled (200) to accommodate bidirectionality” (“COLA-D-24-00156.PDF”, p. 14) \#5fb236 \par - “The paper concludes that RoBERTa is the most effective word embedding technique in predicting software defects, outperforming others in accuracy” (“COLA-D-24-00156.PDF”, p. 19) \#5fb236} } @inproceedings{colantoniDevOpsMLModelingDevOps2020, title = {{{DevOpsML}}: Towards Modeling {{DevOps}} Processes and Platforms}, shorttitle = {{{DevOpsML}}}, booktitle = {Proc. 23rd {{ACMIEEE Int}}. {{Conf}}. {{Model Driven Eng}}. {{Lang}}. {{Syst}}. {{Companion Proc}}.}, author = {Colantoni, Alessandro and Berardinelli, Luca and Wimmer, Manuel}, date = {2020-10-16}, pages = {1--10}, publisher = {ACM}, location = {Virtual Event Canada}, doi = {10.1145/3417990.3420203}, abstract = {DevOps and Model Driven Engineering (MDE) provide differently skilled IT stakeholders with methodologies and tools for organizing and automating continuous software engineering activities–from development to operations, and using models as key engineering artifacts, respectively. Both DevOps and MDE aim at shortening the development life-cycle, dealing with complexity, and improve software process and product quality.}, eventtitle = {{{MODELS}} '20: {{ACM}}/{{IEEE}} 23rd {{International Conference}} on {{Model Driven Engineering Languages}} and {{Systems}}}, isbn = {978-1-4503-8135-2}, langid = {english}, keywords = {LOGSEQ} } @article{colinaInternetThingsIoT, title = {Internet of {{Things}} ({{IoT}}) in 5 Days}, author = {Colina, Antonio Liñán and Vives, Alvaro and Bagula, Antoine and Zennaro, Marco and Pietrosemoli, Ermanno}, pages = {227} } @article{collobertNaturalLanguageProcessing2011, title = {Natural Language Processing (Almost) from Scratch}, author = {Collobert, Ronan and Weston, Jason and Bottou, Léon and Karlen, Michael and Kavukcuoglu, Koray and Kuksa, Pavel}, date = {2011-11}, journaltitle = {J. Mach. Learn. Res.}, volume = {12}, pages = {2493--2537}, publisher = {JMLR.org}, issn = {1532-4435}, url = {http://dl.acm.org/citation.cfm?id=1953048.2078186}, acmid = {2078186}, issue_date = {2/1/2011}, numpages = {45}, note = {TL;DR \par A unified neural network architecture and learning algorithm that can be applied to various natural language processing tasks including part-of-speech tagging, chunking, named entity recognition, and semantic role labeling is proposed.} } @inproceedings{colnagoInformingDesignPersonalized2020, title = {Informing the {{Design}} of a {{Personalized Privacy Assistant}} for the {{Internet}} of {{Things}}}, booktitle = {Proc. 2020 {{CHI Conf}}. {{Hum}}. {{Factors Comput}}. {{Syst}}.}, author = {Colnago, Jessica and Feng, Yuanyuan and Palanivel, Tharangini and Pearman, Sarah and Ung, Megan and Acquisti, Alessandro and Cranor, Lorrie Faith and Sadeh, Norman}, date = {2020-04-21}, pages = {1--13}, publisher = {ACM}, location = {Honolulu HI USA}, doi = {10.1145/3313831.3376389}, eventtitle = {{{CHI}} '20: {{CHI Conference}} on {{Human Factors}} in {{Computing Systems}}}, isbn = {978-1-4503-6708-0}, langid = {english} } @article{Combemale202171, title = {A Hitchhiker's Guide to Model-Driven Engineering for Data-Centric Systems}, author = {Combemale, B. and Kienzle, J. and Mussbacher, G. and Ali, H. and Amyot, D. and Bagherzadeh, M. and Batot, E. and Bencomo, N. and Benni, B. and Bruel, J.-M. and Cabot, J. and Cheng, B.H.C. and Collet, P. and Engels, G. and Heinrich, R. and Jezequel, J.-M. and Koziolek, A. and Mosser, S. and Reussner, R. and Sahraoui, H. and Saini, R. and Sallou, J. and Stinckwich, S. and Syriani, E. and Wimmer, M.}, date = {2021}, journaltitle = {IEEE Softw.}, volume = {38}, number = {4}, pages = {71--84}, publisher = {IEEE Computer Society}, issn = {07407459}, doi = {10.1109/MS.2020.2995125}, abbrev_source_title = {IEEE Software}, affiliation = {University of Toulouse, Toulouse, F-35042, France; McGill University, Montréal, QC H3A 0E9, Canada; University of Ottawa, Ottawa, ON K1N 5N5, Canada; Open University of Catalonia, Barcelona, H3C 3J7, Spain; Aston University, Birmingham, B4 7ET, United Kingdom; Concordia University, Montréal, QC F-06103, Canada; Department of Business Informatics-Software Engineering, Johannes Kepler University Linz, Linz, Austria; Michigan State University, East Lansing, MI 48824, United States; University Côte d'Azur, Biot, F-06103, France; Paderborn University, Paderborn, D-33100, Germany; Karlsruhe Institute of Technology, Karlsruhe, D-76128, Germany; University of Rennes, Rennes, F-35042, France; University of Québec Montréal, Montréal, QC H3C 3P8, Canada; Montréal University, Montréal, QC H3C 3J7, Canada; United Nations University Institute, Macau, 150-8925, Macau}, art_number = {9094197}, coden = {IESOE}, document_type = {Article}, langid = {english}, source = {Scopus}, note = {cited By 8} } @inproceedings{CombemaleBW17, title = {Language Engineering with the {{GEMOC}} Studio}, booktitle = {2017 {{IEEE Int}}. {{Conf}}. {{Softw}}. {{Archit}}. {{Workshop ICSA Workshop}} 2017 {{Gothenbg}}. {{Swed}}. {{April}} 5-7 2017}, author = {Combemale, Benoît and Barais, Olivier and Wortmann, Andreas}, date = {2017}, pages = {189--191}, publisher = {IEEE Computer Society}, doi = {10.1109/ICSAW.2017.61}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {TL;DR \par From such a specification, the ability of the GEMOC studio to automatically support model execution, graphical animation, omniscient debugging, concurrency analysis and concurrent execution of heterogeneous models is demonstrated.}, timestamp = {Fri, 24 Mar 2023 00:02:32 +0100} } @article{combemaleChatGPTSoftwareModeling2023, title = {{{ChatGPT}} in Software Modeling}, author = {Combemale, Benoit and Gray, Jeff and Rumpe, Bernhard}, date = {2023-05-11}, journaltitle = {Softw Syst Model}, pages = {s10270-023-01106-4}, issn = {1619-1366, 1619-1374}, doi = {10.1007/s10270-023-01106-4}, langid = {english}, keywords = {LOGSEQ}, note = {TL;DR \par This editorial focuses more positively and asks the main question: How will ChatGPT be able to help in a development process, especially in the tasks of developing or using models for analysis, production or understanding of software and systems.} } @incollection{combemaleFormallyDefiningIterating2012, title = {Formally {{Defining}} and {{Iterating Infinite Models}}}, booktitle = {Model {{Driven Engineering Languages}} and {{Systems}}}, author = {Combemale, Benoit and Thirioux, Xavier and Baudry, Benoit}, editor = {France, Robert B. and Kazmeier, Jürgen and Breu, Ruth and Atkinson, Colin}, editora = {Hutchison, David and Kanade, Takeo and Kittler, Josef and Kleinberg, Jon M. and Mattern, Friedemann and Mitchell, John C. and Naor, Moni and Nierstrasz, Oscar and Pandu Rangan, C. and Steffen, Bernhard and Sudan, Madhu and Terzopoulos, Demetri and Tygar, Doug and Vardi, Moshe Y. and Weikum, Gerhard}, editoratype = {redactor}, date = {2012}, volume = {7590}, pages = {119--133}, publisher = {Springer Berlin Heidelberg}, location = {Berlin, Heidelberg}, doi = {10.1007/978-3-642-33666-9_9}, isbn = {978-3-642-33665-2 978-3-642-33666-9}, langid = {english} } @book{combemaleGlobalizingDomainSpecificLanguages2015, title = {Globalizing {{Domain-Specific Languages}}}, editor = {Combemale, Benoit and Cheng, Betty H.C. and France, Robert B. and Jézéquel, Jean-Marc and Rumpe, Bernhard}, date = {2015}, series = {Lecture {{Notes}} in {{Computer Science}}}, volume = {9400}, publisher = {Springer International Publishing}, location = {Cham}, url = {http://link.springer.com/10.1007/978-3-319-26172-0}, urldate = {2016-01-26}, isbn = {978-3-319-26171-3 978-3-319-26172-0} } @article{combemaleGlobalizingModelingLanguages2014, title = {Globalizing Modeling Languages}, author = {Combemale, Benoit and Deantoni, Julien and Baudry, Benoit and France, Robert B. and Jézéquel, Jean-Marc and Gray, Jordan}, date = {2014}, journaltitle = {Computer}, volume = {47}, number = {6}, pages = {68--71}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6839148}, urldate = {2015-09-23}, note = {TL;DR \par A research initiative is described that broadens the DSML research focus beyond independent DSML development to one that supports globalized DSMLs-that is, DS MLs that facilitate coordination of work across different domains of expertise.} } @thesis{combemaleLanguageOrientedModeling2015, title = {Towards {{Language-Oriented Modeling}}}, author = {Combemale, Benoit}, date = {2015}, institution = {Université de Rennes 1}, url = {https://hal.inria.fr/tel-01238817/}, urldate = {2016-01-26}, note = {TL;DR \par A decade of research work in the fields of Model-Driven Engineering (MDE) and Software Language Engineering (SLE) is reviewed, and contributions to support a language-oriented modeling are proposed, with the particular focus on enabling early validation \& verification of software-intensive systems.} } @article{combemaleSLEBOKSoftwareLanguage2018, title = {{{SLEBOK}}: {{The Software Language Engineering Body}} of {{Knowledge}} ({{Dagstuhl Seminar}} 17342)}, shorttitle = {{{SLEBOK}}}, author = {Combemale, Benoît and Lämmel, Ralf and Van Wyk, Eric}, namea = {Herbstritt, Marc}, nameatype = {collaborator}, date = {2018}, pages = {10 pages}, publisher = {Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik GmbH, Wadern/Saarbruecken, Germany}, doi = {10.4230/DAGREP.7.8.45}, abstract = {This report documents the program and the outcomes of Dagstuhl Seminar 17342 "SLEBOK: The Software Language Engineering Body of Knowledge". Software Language Engineering (SLE) has emerged as a scientific field, with a strong motivation to connect and integrate different research disciplines such as compiler construction, reverse engineering, software transformation, model-driven engineering, and ontologies. This seminar supported further integration of said communities with the clear objective of assembling a Body of Knowledge on SLE (SLEBoK). The BoK features artifacts, definitions, methods, techniques, best practices, open challenges, case studies, teaching material, and other components that will afterwards help students, researchers, teachers, and practitioners to learn from, to better leverage, to better contribute to, and to better disseminate the intellectual contributions and practical tools and techniques coming from the SLE field.}, langid = {english}, keywords = {{000 Computer science, knowledge, general works},Computer Science}, note = {\subsection{Other} \par This report documents the program and the outcomes of Dagstuhl Seminar 17342 "SLEBOK: The Software Language Engineering Body of Knowledge". Software Language Engineering (SLE) has emerged as a scientific field, with a strong motivation to connect and integrate different research disciplines such as compiler construction, reverse engineering, software transformation, model-driven engineering, and ontologies. This seminar supported further integration of said communities with the clear objective of assembling a Body of Knowledge on SLE (SLEBoK). The BoK features artifacts, definitions, methods, techniques, best practices, open challenges, case studies, teaching material, and other components that will afterwards help students, researchers, teachers, and practitioners to learn from, to better leverage, to better contribute to, and to better disseminate the intellectual contributions and practical tools and techniques coming from the SLE field.} } @misc{Commissionwhitepaperartificialintelligencefeb2020_en, title = {Commission-White-Paper-Artificial-Intelligence-Feb2020\_en}, langid = {english} } @article{CommunicationsACMFebruary, title = {Communications of the {{ACM}} - {{February}} 2022}, pages = {124}, langid = {english} } @article{CommunicationsACMJuly, title = {Communications of the {{ACM}} - {{July}} 2021}, pages = {116}, langid = {english} } @article{CommunicationsACMJulya, title = {Communications of the {{ACM}} - {{July}} 2020}, pages = {116}, langid = {english} } @article{CommunicationsACMJune, title = {Communications of the {{ACM}} - {{June}} 2021}, pages = {124}, langid = {english} } @article{CommunicationsACMJunea, title = {Communications of the {{ACM}} - {{June}} 2020}, pages = {100}, langid = {english} } @article{CommunicationsACMMay, title = {Communications of the {{ACM}} - {{May}} 2020}, pages = {116}, langid = {english} } @article{CommunicationsACMOctober, title = {Communications of the {{ACM}} - {{October}} 2020}, pages = {112}, langid = {english} } @article{ComparingEfficiencySemantic2017, title = {Comparing the Efficiency of Semantic Code Clones}, date = {2017}, abstract = {Two programs are said to be semantic code clones if they produce the same output for all inputs. But there is far more to code than its semantics; for instance a pair of semantic code clones can have more or fewer vulnerabilities, can be more or less readable, or can be more or less efficient with respect to run time, memory usage, etc. In this paper, we propose a framework and approach to systematically and automatically compare the efficiency of such semantic code clones. Our approach distills program inputs into multiple numeric input properties, leverages statistical and numerical methods to model and analyze the relationships between these inputs and various efficiency metrics related to program execution, and finally reports the best performing clones w.r.t. all efficiency metrics on the studied ranges of property values, as well as the models and visualizations of efficiency as it relates to the input properties. We implement this approach in a tool called PMA (for Property/Metric Analyzer), with support for programs written in Python, JavaScript, Rust, and R, and evaluate PMA in 8 subject areas, showing that PMA generates useful and sometimes surprising insights about semantic code clones across many languages and applications; insights that we believe developers could leverage to better understand and improve the efficiency of their applications.}, langid = {english}, keywords = {LOGSEQ}, note = {I'm reviewing a research paper and I took the following notes: \par \section{Annotazioni\\ (17/5/2025, 10:05:19)} \par - “Two programs are said to be semantic code clones if they produce the same output for all inputs.” (“Comparing the efficiency of semantic code clones”, 2017, p. 1) \#5fb236 \par - “In this paper, we propose a framework and approach to systematically and automatically compare the efficiency of such semantic code clones.” (“Comparing the efficiency of semantic code clones”, 2017, p. 1) \#5fb236 \par - “Our approach distills program inputs into multiple numeric input properties, leverages statistical and numerical methods to model and analyze the relationships between these inputs and various efficiency metrics related to program execution, and finally reports the best performing clones w.r.t. all efficiency metrics on the studied ranges of property values, as well as the models and visualizations of efficiency as it relates to the input properties” (“Comparing the efficiency of semantic code clones”, 2017, p. 1) \#5fb236 \par - “PMA (for Property/Metric Analyzer)” (“Comparing the efficiency of semantic code clones”, 2017, p. 1) \#5fb236 \par - “Two programs are said to be semantic code clones if they produce the same output for all inputs, and semantic code clones are everywhere.” (“Comparing the efficiency of semantic code clones”, 2017, p. 1) \#5fb236 \par - “These kinds of clones appear frequently in the context of differential testing, a strategy to test some program P by also executing other, semantically-equivalent programs, e.g., P′, with the same inputs as P and using the other executions as oracles.” (“Comparing the efficiency of semantic code clones”, 2017, p. 1) \#5fb236 \par - “if for some input i, P (i) crashes but P′ (i) does not, there is probably an issue with P.” (“Comparing the efficiency of semantic code clones”, 2017, p. 1) \#ffd400\\ \mkbibemph{or the other way round.... the issue might be with P' and not with P} \par - “in this work we present an approach that automatically and systematically compares the efficiency of semantic code clones by exercising them with the same inputs and observing efficiency metrics related to execution.” (“Comparing the efficiency of semantic code clones”, 2017, p. 1) \#a28ae5 \par - “the approach can automatically generate inputs and distills them into multiple numeric input properties in order to relate them to the collected efficiency metrics.” (“Comparing the efficiency of semantic code clones”, 2017, p. 1) \#a28ae5 \par - “our approach can leverage statistical and numerical methods to build, analyze, and compare models of program efficiency, and we develop novel modeling and analysis techniques that account for the probabilistic nature of recording the efficiency of code.” (“Comparing the efficiency of semantic code clones”, 2017, p. 1) \#a28ae5 \par - “PMA (for Property/Metric Analyzer), and evaluate it on 20 subject applications spanning eight application areas and four programming languages.” (“Comparing the efficiency of semantic code clones”, 2017, p. 1) \#a28ae5 \par - “PMA can discover which input properties contribute to efficiency and which do not, and find which application is most efficient on which ranges of input property values, as well as overall.” (“Comparing the efficiency of semantic code clones”, 2017, p. 1) \#5fb236 \par - “an approach for profiling and analyzing the efficiency of semantic code clones, w.r.t. several properties of inputs;” (“Comparing the efficiency of semantic code clones”, 2017, p. 1) \#2ea8e5 \par - “a prototype implementation of this approach in a tool called PMA,” (“Comparing the efficiency of semantic code clones”, 2017, p. 1) \#2ea8e5 \par - “evaluation where we applied PMA in various domains” (“Comparing the efficiency of semantic code clones”, 2017, p. 1) \#2ea8e5 \par - “quicksort, mergesort” (“Comparing the efficiency of semantic code clones”, 2017, p. 2) \#5fb236 \par - “bubblesort, and insertionsort” (“Comparing the efficiency of semantic code clones”, 2017, p. 2) \#5fb236 \par - “there’s quite a bit of variability in both runtime and memory consumed for lists of the same length, which implies that another property of the input lists may be playing a role in the efficiency of the algorithms;” (“Comparing the efficiency of semantic code clones”, 2017, p. 2) \#5fb236 \par - “sortedness”, which we compute as the \% of adjacent elements in the list that are out of order.” (“Comparing the efficiency of semantic code clones”, 2017, p. 2) \#5fb236 \par - “considering sortedness explains the anomalous extremely high run time and peak memory values for quicksort:” (“Comparing the efficiency of semantic code clones”, 2017, p. 2) \#ffd400\\ \mkbibemph{I appreciate the effort of producing Fig. 1b. However, it is not clear for some of the situations. For instance, quicksort is not visible for sotedness values {$<$}0.8 Thus the message that authrors want to convey at the end of Sec 2.2 is not fully clear.} \par - “At its core, our approach aims to investigate the relationship between inputs and various efficiency metrics related to program execution” (“Comparing the efficiency of semantic code clones”, 2017, p. 2) \#a28ae5 \par - “computing numeric properties of inputs” (“Comparing the efficiency of semantic code clones”, 2017, p. 2) \#5fb236 \par - “Input Properties.” (“Comparing the efficiency of semantic code clones”, 2017, p. 2) \#2ea8e5 \par - “In the sorting example, these are the length of the input list, as well as the \% of adjacent pairs of elements in the list that are out-of-order; length is a default property, and the latter is a custom property. Our approach also makes the distinction between primary and other input properties; by default the primary input property is the first supplied by the user, and forms the basis of the initial exploratory data analysis which will, among other things, determine which other properties are significant predictors of efficiency.” (“Comparing the efficiency of semantic code clones”, 2017, p. 3) \#ffd400\\ \mkbibemph{These properties are very specific to the program at hand. Who is going to define them? Still not clear at this stage the automation level of the proposed approach.} \par - “Efficiency Metrics.” (“Comparing the efficiency of semantic code clones”, 2017, p. 3) \#2ea8e5 \par - “during the execution of a program.” (“Comparing the efficiency of semantic code clones”, 2017, p. 3) \#5fb236 \par - “observing with some observer M the execution of a program S on some inputs i0, ..., in, i.e., M (S (i0, ..., in)) = m” (“Comparing the efficiency of semantic code clones”, 2017, p. 3) \#5fb236 \par - “profile the programs under test S0, ..., Sk , collecting various metrics m0, ..., m j by observing them during program execution” (“Comparing the efficiency of semantic code clones”, 2017, p. 3) \#5fb236 \par - “every input” (“Comparing the efficiency of semantic code clones”, 2017, p. 3) \#ffd400\\ \mkbibemph{Every input needs to be further elaborated! Every is strong.} \par - “Profiling produces a summary containing b entries for each of the k programs, and each entry contains the values of all r input properties and j metrics. This summary serves as the basis for all future analysis steps, which are discussed next.” (“Comparing the efficiency of semantic code clones”, 2017, p. 3) \#5fb236 \par - “to identify and discard true clones, which we define as clones with identical observable semantics and observable efficiency” (“Comparing the efficiency of semantic code clones”, 2017, p. 3) \#ffd400\\ \mkbibemph{Why discard?} \par - “statistically significant differences between the programs for each metric.” (“Comparing the efficiency of semantic code clones”, 2017, p. 3) \#5fb236 \par - “t x1 ∈ X1 will be greater than an element x2 ∈ X2” (“Comparing the efficiency of semantic code clones”, 2017, p. 3) \#5fb236 \par - “hen the programs are determined to be equivalent and one of the clones is discarded at random.” (“Comparing the efficiency of semantic code clones”, 2017, p. 3) \#ffd400\\ \mkbibemph{Why is it discarded? It's not clear the discarding phase with respect to the overall goal of the approach as presented in the introduction "....compares the efficiency of semantic code clones...."} \par - “In this stage, the analysis determines and reports all significant differences between the remaining “false clones”.” (“Comparing the efficiency of semantic code clones”, 2017, p. 3) \#ffd400\\ \mkbibemph{Now it becoming a bit clear. So essentially during the profiling phase you aim at removing all the programs that according to the £detect true clones" phase are confirmed to be clones. This needs to be clearly stated. Why do you need to do so? It is necessary to introduce early in Section 3 a figure depicting all the phases of the proposed approach so that the reader can have a high level view of the approach and better follow the details.} \par - “3.3.1 Relative Efficiency Analysis.” (“Comparing the efficiency of semantic code clones”, 2017, p. 3) \#2ea8e5 \par - “in Section 2 we find the turning range where insertionsort and mergesort are equally efficient is [108, 140], while [0, 108] is optimal for insertionsort and [140, 500] is optimal for mergesort.” (“Comparing the efficiency of semantic code clones”, 2017, p. 4) \#5fb236 \par - “3.3.2 Anomaly Investigation.” (“Comparing the efficiency of semantic code clones”, 2017, p. 4) \#2ea8e5 \par - “20 subject applications across eight subject areas spanning four programming languages.” (“Comparing the efficiency of semantic code clones”, 2017, p. 5) \#5fb236 \par - “Setup. We aim to find the optimal list length at which to switch between quicksort and insertion sort, and see if the developers were correct in setting it to 20.” (“Comparing the efficiency of semantic code clones”, 2017, p. 5) \#ffd400 \par - “For our input properties, we chose the length of the list, since this is the metric over which the sorting algorithm switch threshold is computed, and also sortedness (recall that sortedness referes to the \% of adjacent elements that are in order).” (“Comparing the efficiency of semantic code clones”, 2017, p. 5) \#ffd400\\ \mkbibemph{I'm not convinced about the generalizability of the work. Moreover, the paper is related to many works on non-functional aspects of software systems, including performance analysis that are completely neglected in the paper. The focus of the paper is not clear. In the end it seems that the work is on supporting the analysis of alternative implementations of similar tasks with respect to efficiency criteria. Programs under analysis that are supposed to be clones, might be not and the different on performance might due on several aspects that might go beyond the given input and on characteristics that might even be related to the execution environment.} \par - “It is possible that our evaluation is not representative of real-world scenarios.” (“Comparing the efficiency of semantic code clones”, 2017, p. 9) \#ffd400\\ \mkbibemph{Exactly. See my previous comment.} \par - “Efficiency metrics are tied to the system on which a program is being run, and so it is possible that running our evaluation on a different system would yield different results.” (“Comparing the efficiency of semantic code clones”, 2017, p. 9) \#ffd400\\ \mkbibemph{This is also related to my previous points. In my opinion the considered settings for comparison makes semplifications that need to be convincingly supported!} \par - “The approach proposed in this paper automatically builds and analyzes performance profiles of the programs under test, closely related to profiling” (“Comparing the efficiency of semantic code clones”, 2017, p. 10) \#ffd400\\ \mkbibemph{There is no comparison of the proposed approach with existing profiling mechanisms and tools.} \par - “In contrast, our approach systematically compares multiple equivalent implementations of a function or system, rather than focusing only on one, and incorporates multiple input properties in its analysis.” (“Comparing the efficiency of semantic code clones”, 2017, p. 10) \#ffd400\\ \mkbibemph{This is said in the related work and claimed to highlight the difference with respect to existing work. This is not convincing in my opinion. First of all because the definition of code clones is blurred in my opinion. There are theoretical limitation on identifying clones. The proposed approach is kind of approximation, and as a such it is necessary to consider potential errors while discussing the subsequent phases of the process that rely on such approximations.} \par - “Typically, these tools are complex to use, in contrast to our approach. Their goal is also complementary as they are designed to operate at a huge scale; in contrast, our method can be applied at a small scale” (“Comparing the efficiency of semantic code clones”, 2017, p. 10) \#ffd400\\ \mkbibemph{This is also a claim in the related work section. This is a critical point for comparison. To make the paper strong it is required to demonstrate that the proposed approach is on the one hand easier than existing approaches and on the other hand demonstrate that existing tools cannot be operated at small scale.} \par - “we proposed a novel automated profiling and analysis approach that leverages multiple input properties and models and analyzes their relationship with efficiency metrics related to the execution of multiple semantically equivalent programs” (“Comparing the efficiency of semantic code clones”, 2017, p. 10) \#5fb236 \par - “With this approach, developers can compare functionally-equivalent programs in a single framework, visually examine the relative effects of multiple input properties on a given efficiency metric, and automated statistical and numerical analysis determines optimal ranges for programs and builds multi-dimensional models of efficiency.” (“Comparing the efficiency of semantic code clones”, 2017, p. 10) \#5fb236 \par - “we implemented our approach in a tool called PMA, and evaluated its ability to discern efficiency differences in eight subject areas across four programming languages.” (“Comparing the efficiency of semantic code clones”, 2017, p. 10) \#5fb236 \par COnsider that those that are tagget with \#5fb236 are just highlights, those that are tagged with \#e56eee and \#a28ae5 are imporant sentences. Please pay attention instead to the notes that are tagged with \#ffd400. Those that are tagged with \#ff6666 are typos or errors. Could you please draft a review by organizing it as follows: SUMMARY: Just a few sentence to summarize the work STRENGHTS: \par WEAKNESSES: \par COMMENTS: Organize the notes with respect to the following criteria: \par \begin{itemize} \item \texttt{Novelty} \item \texttt{Rigor} \item \texttt{Relevance (of the contribution)} \item \texttt{Verifiability and Transparency} \item \texttt{Presentation} \end{itemize} \par And then add a Detailed Comments section to report the notes that contain issues or typos. \\ Can you also formulate three explicit questions by considering the comments above?} } @online{ComparisonModelMigration, title = {A {{Comparison}} of {{Model Migration Tools}} - {{Springer}}}, url = {http://link.springer.com/chapter/10.1007/978-3-642-16145-2_5}, urldate = {2015-03-24} } @online{ComplexNatureMDE, title = {On the Complex Nature of {{MDE}} Evolution and Its Impact on Changeability - {{Online First}} - {{Springer}}}, url = {http://link.springer.com/article/10.1007%2Fs10270-015-0464-2}, urldate = {2015-12-08} } @inproceedings{Compton2020243, title = {Embedding {{Java Classes}} with Code2vec: {{Improvements}} from {{Variable Obfuscation}}}, shorttitle = {Embedding {{Java Classes}} with Code2vec}, booktitle = {Proc. 17th {{Int}}. {{Conf}}. {{Min}}. {{Softw}}. {{Repos}}.}, author = {Compton, Rhys and Frank, Eibe and Patros, Panos and Koay, Abigail}, date = {2020-06}, series = {Proceedings - 2020 {{IEEE}}/{{ACM}} 17th {{International Conference}} on {{Mining Software Repositories}}, {{MSR}} 2020}, pages = {243--253}, publisher = {Association for Computing Machinery, Inc}, location = {New York, NY, USA}, doi = {10.1145/3379597.3387445}, abstract = {Automatic source code analysis in key areas of software engineering, such as code security, can benefit from Machine Learning (ML). However, many standard ML approaches require a numeric representation of data and cannot be applied directly to source code. Thus, to enable ML, we need to embed source code into numeric feature vectors while maintaining the semantics of the code as much as possible. code2vec is a recently released embedding approach that uses the proxy task of method name prediction to map Java methods to feature vectors. However, experimentation with code2vec shows that it learns to rely on variable names for prediction, causing it to be easily fooled by typos or adversarial attacks. Moreover, it is only able to embed individual Java methods and cannot embed an entire collection of methods such as those present in a typical Java class, making it difficult to perform predictions at the class level (e.g., for the identification of malicious Java classes). Both shortcomings are addressed in the research presented in this paper. We investigate the effect of obfuscating variable names during training of a code2vec model to force it to rely on the structure of the code rather than specific names and consider a simple approach to creating class-level embeddings by aggregating sets of method embeddings. Our results, obtained on a challenging new collection of source-code classification problems, indicate that obfuscating variable names produces an embedding model that is both impervious to variable naming and more accurately reflects code semantics. The datasets, models, and code are shared1 for further ML research on source code. © 2020 ACM.}, author_keywords = {code obfuscation; code2vec; machine learning; neural networks; source code}, document_type = {Conference Paper}, isbn = {978-1-4503-7517-7}, source = {Scopus}, keywords = {Class level,code obfuscation,Code security,Code semantics,code2vec,Embeddings,Feature vectors,Forecasting,Java methods,Java programming language,machine learning,neural networks,Semantics,Simple approach,Software engineering,source code,Source code analysis,Source codes}, note = {cited By 19 \par TL;DR \par The effect of obfuscating variable names during training of a code2vec model is investigated to force it to rely on the structure of the code rather than specific names and a simple approach to creating class-level embeddings by aggregating sets of method embeddeddings is considered.} } @inproceedings{conf:iscis:MadylovaO09, title = {A Taxonomy Based Semantic Similarity of Documents Using the Cosine Measure.}, booktitle = {{{ISCIS}}}, author = {Madylova, Ainura and Ögüducü, Sule Gündüz}, year = {2009-12-30, 2009}, pages = {129--134}, publisher = {IEEE}, url = {http://dblp.uni-trier.de/db/conf/iscis/iscis2009.html#MadylovaO09}, added-at = {2009-12-30T00:00:00.000+0100}, biburl = {https://www.bibsonomy.org/bibtex/2b6f37cbf44daa243cf2b91e00181806f/dblp}, description = {dblp}, ee = {http://dx.doi.org/10.1109/ISCIS.2009.5291865}, interhash = {17227ba19186316517f52ffa39fa8568}, intrahash = {b6f37cbf44daa243cf2b91e00181806f}, keywords = {dblp}, note = {TL;DR \par A new method based on cosine similarity calculation between concept vectors of documents obtained from a taxonomy of words that captures IS-A relations which results in faster computational time.}, timestamp = {2009-12-31T11:34:52.000+0100} } @inproceedings{conf/cvpr/NguyenYC15, title = {Deep Neural Networks Are Easily Fooled: {{High}} Confidence Predictions for Unrecognizable Images.}, booktitle = {{{CVPR}}}, author = {Nguyen, Anh Mai and Yosinski, Jason and Clune, Jeff}, date = {2015}, pages = {427--436}, publisher = {IEEE Computer Society}, url = {http://dblp.uni-trier.de/db/conf/cvpr/cvpr2015.html#NguyenYC15}, added-at = {2016-04-28T00:00:00.000+0200}, biburl = {https://www.bibsonomy.org/bibtex/26672da9a292a16afa346ca2d8e3c181a/dblp}, ee = {http://doi.ieeecomputersociety.org/10.1109/CVPR.2015.7298640}, interhash = {c8af26890ac9aa947cf1485879db901f}, intrahash = {6672da9a292a16afa346ca2d8e3c181a}, isbn = {978-1-4673-6964-0}, keywords = {dblp}, timestamp = {2016-04-29T11:54:14.000+0200} } @inproceedings{conf/stids/OlssonPSP11, title = {Finding and Explaining Similarities in Linked Data.}, booktitle = {{{STIDS}}}, author = {Olsson, Catherine and Petrov, Plamen and Sherman, Jeff and Perez-Lopez, Andrew}, editor = {family=Costa, given=Paulo Cesar G., prefix=da, useprefix=true and Laskey, Kathryn B.}, date = {2011}, series = {{{CEUR}} Workshop Proceedings}, volume = {808}, pages = {52--59}, publisher = {CEUR-WS.org}, url = {http://dblp.uni-trier.de/db/conf/stids/stids2011.html#OlssonPSP11}, added-at = {2013-07-22T00:00:00.000+0200}, biburl = {http://www.bibsonomy.org/bibtex/29536bc182f305da5dad27f51b28a779e/dblp}, ee = {http://ceur-ws.org/Vol-808/STIDS2011\textsubscript{C}R\textsubscript{T}7\textsubscript{O}lssonEtAl.pdf}, interhash = {a078f5c01f90adea0e5067f8a3d88106}, intrahash = {9536bc182f305da5dad27f51b28a779e}, keywords = {dblp}, note = {TL;DR \par A definition of an “explanation” of similarity is formulated, a system that can produce such explanations efficiently is described, and a methodology is presented to allow the user to tailor how “obvious” or ”obscure” the provided explanations are.}, timestamp = {2013-07-22T00:00:00.000+0200} } @article{connollyWhyComputingBelongs2020, title = {Why Computing Belongs within the Social Sciences}, author = {Connolly, Randy}, date = {2020-07-22}, journaltitle = {Commun. ACM}, volume = {63}, number = {8}, pages = {54--59}, issn = {0001-0782, 1557-7317}, doi = {10.1145/3383444}, abstract = {Fully appreciating the overarching scope of CS requires weaving more than ethics into the reigning curricula.}, langid = {english}, note = {TL;DR \par Fully appreciating the overarching scope of CS requires weaving more than ethics into the reigning curricula.} } @article{conselInternetThingsChallenge, title = {Internet of {{Things}}: {{A Challenge}} for {{Software Engineering}}}, author = {Consel, Charles and Kabáč, Milan}, pages = {3}, langid = {english}, note = {TL;DR \par The Internet of Things (IoT) has become a reality with the emergence of Smart Cities, populated with large amounts of smart objects which are used to deliver a range of citizen services.} } @online{ConstructingAutonomousSystems, title = {Constructing {{Autonomous Systems}}}, url = {http://aosgrp.com/featured-research/autonomy_and_agents/autonomous_systems/constructing_autonomous_sys.html}, urldate = {2016-08-24} } @online{ContinuousDeliveryMap, title = {Continuous {{Delivery Map}} | {{Continuous Delivery Map}}}, url = {https://assessment-tools.ca.com/tools/continuous-delivery-tools/en?embed}, urldate = {2018-04-30} } @online{ContrattiDiRicerca, title = {Contratti di ricerca - Università di Bologna}, url = {https://www.unibo.it/it/ateneo/lavora-con-noi/contratti-di-ricerca}, urldate = {2025-04-17}, abstract = {Sono contratti stipulati con soggetti dotati di adeguata qualificazione scientifica ai fini dello svolgimento di specifici progetti di ricerca.}, langid = {italian}, keywords = {/unread} } @online{ContrattoDiRicerca, title = {Il Contratto Di Ricerca Nelle Università: A Proposito Dei Regolamenti Negli Atenei}, shorttitle = {Il Contratto Di Ricerca Nelle Università}, url = {https://www.flcgil.it/universita/il-contratto-di-ricerca-nelle-universita-a-proposito-dei-regolamenti-negli-atenei.flc}, urldate = {2025-04-17}, abstract = {Alcune considerazioni e linee guida sull’attivazione delle nuove figure pre-ruolo.}, langid = {english}, keywords = {/unread} } @online{ControlSystemsFeedback, title = {Control {{Systems}}/{{Feedback Loops}} - {{Wikibooks}}, Open Books for an Open World}, url = {https://en.wikibooks.org/wiki/Control_Systems/Feedback_Loops}, urldate = {2016-11-01} } @online{ControlTheory101, title = {Control {{Theory}} 101 for {{Beginners}} | {{Nuvation}}}, url = {http://www.nuvation.com/blog/electronic-design-services/control-theory-101-beginners}, urldate = {2016-09-20} } @online{ControlTheory1012013, title = {Control {{Theory}} 101 for {{Beginners}}}, date = {2013-09-24T22:02:15+00:00}, url = {http://www.nuvation.com/blog/electronic-design-services/control-theory-101-beginners}, urldate = {2016-09-20}, abstract = {While not as ubiquitous as electric power or microelectronics, control theory is applied everywhere in our daily lives but it is rarely noticed.}, organization = {Nuvation} } @online{conwayEnterprisesOnboardAI2025, title = {Enterprises Onboard {{AI}} Teammates Faster with {{NVIDIA NeMo}} Tools to Scale Employee Productivity}, author = {Conway, Joey}, date = {2025-04-23}, url = {https://blogs.nvidia.com/blog/nemo-enterprises-ai-teammates-employee-productivity/}, urldate = {2025-04-23}, abstract = {Now generally available, NVIDIA NeMo microservices are helping enterprise IT quickly build AI teammates that tap into data flywheels to scale employee productivity.}, langid = {english}, organization = {NVIDIA Blog} } @book{COPEWorkbenchCoupled2011, title = {{{COPE}} – {{A Workbench}} for the {{Coupled Evolution}} of {{Metamodels}} and {{Models}}}, date = {2011}, volume = {6563}, publisher = {Springer Berlin Heidelberg}, location = {Berlin, Heidelberg}, doi = {10.1007/978-3-642-19440-5_18}, isbn = {978-3-642-19439-9}, langid = {english}, keywords = {linter/error}, note = {TL;DR \par This paper presents COPE, a tool to automate the coupled evolution of metamodels and models and provides advanced tool support to inspect, refactor and recover the coupling evolution.} } @article{corbelliniPersistingBigdataNoSQL2017, title = {Persisting Big-Data: {{The NoSQL}} Landscape}, shorttitle = {Persisting Big-Data}, author = {Corbellini, Alejandro and Mateos, Cristian and Zunino, Alejandro and Godoy, Daniela and Schiaffino, Silvia}, date = {2017-01}, journaltitle = {Inf. Syst.}, volume = {63}, pages = {1--23}, issn = {03064379}, doi = {10.1016/j.is.2016.07.009}, abstract = {The growing popularity of massively accessed Web applications that store and analyze large amounts of data, being Facebook, Twitter and Google Search some prominent examples of such applications, have posed new requirements that greatly challenge traditional RDBMS. In response to this reality, a new way of creating and manipulating data stores, known as NoSQL databases, has arisen. This paper reviews implementations of NoSQL databases in order to provide an understanding of current tools and their uses. First, NoSQL databases are compared with traditional RDBMS and important concepts are explained. Only databases allowing to persist data and distribute them along different computing nodes are within the scope of this review. Moreover, NoSQL databases are divided into different types: Key-Value, Wide-Column, Document-oriented and Graphoriented. In each case, a comparison of available databases is carried out based on their most important features.}, langid = {english} } @article{corradiniFloWareModeldrivenApproach2023, title = {{{FloWare}}: A Model-Driven Approach Fostering Reuse and Customisation in {{IoT}} Applications Modelling and Development}, shorttitle = {{{FloWare}}}, author = {Corradini, Flavio and Fedeli, Arianna and Fornari, Fabrizio and Polini, Andrea and Re, Barbara}, date = {2023-02}, journaltitle = {Softw Syst Model}, volume = {22}, number = {1}, pages = {131--158}, issn = {1619-1366, 1619-1374}, doi = {10.1007/s10270-022-01026-9}, abstract = {The relevance of IoT-based solutions in everyday life is continuously increasing. The capability to sense the world, activate computation based on data gathered by sensors, and possibly produce reactions on the world itself results in an almost neverending identification of novel IoT solutions and application scenarios. Nonetheless, IoT’s intrinsic nature, which includes a high degree of variability in used devices, data formats, resources, and communication protocols, complicates the design, development, reuse and customisation of IoT-based software systems. In addition, customers require personalised solutions strongly based on their specific requirements. Reducing the complexity of building customised solutions and increasing the reusability of developed artefacts are among the topmost challenges for enterprises and IoT application developers. Upon these challenges, we propose a model-driven approach organising the modelling and development of IoT applications in different steps, handling the complexity in representing the IoT domain variability, and empowering the reusability of design decisions and artefacts to simplify the derivation of customised IoT applications. Our proposal is named FloWare. It follows the typical path of an MDE solution, providing modelling support through feature models to fully represent and handle the possible variability of devices in a specific IoT application domain. Once a specific configuration has been selected, this will be complemented with specific information about the deployment context to automatically derive fragments of the IoT applications, that will be successively combined by the developer within a low-code development environment. The approach is fully supported by a toolchain that has been released for public use.}, langid = {english}, keywords = {LOGSEQ} } @article{correaCoupledEvolutionMetamodels2013, title = {Towards {{Coupled Evolution}} of {{Metamodels}}, {{Models}}, {{Graph-Based Transformations}} and {{Traceability Links}}}, author = {Correa, Chessman and Toacy, Oliveira and Claudia, Werner}, date = {2013} } @article{cortellessaModeldrivenQualityAnalysis, title = {Model-Driven Quality Analysis of Cyber-Physical Systems: State of the Art and Perspectives}, author = {Cortellessa, Vittorio and Pierantonio, Alfonso and Ruscio, Davide Di and Lombardi, Tiziano}, abstract = {The analysis of quality attributes in Cyber-Physical Systems (CPS) is crucial for ensuring their efficiency and resilience. This paper presents an exploratory study on the state of the art regarding CPS quality analysis, through annotated models, while focusing on the role of Digital Twins (DTs) in these analyses. A methodic selection and filtering process lead to identify 11 papers from the last six years, which can be considered foundational for further investigations in this field. The selected studies provide insights into methodologies, challenges, and emerging trends, highlighting how annotated models and DTs contribute to improving CPS assessment and management. This work enables future research on more effective model-driven methodologies and techniques for analyzing the quality attributes of CPS, possibly supported by DTs.}, langid = {english} } @article{cosentinoSystematicMappingStudy2017, title = {A {{Systematic Mapping Study}} of {{Software Development With GitHub}}}, author = {Cosentino, Valerio and Canovas Izquierdo, Javier L. and Cabot, Jordi}, date = {2017}, journaltitle = {IEEE Access}, volume = {5}, pages = {7173--7192}, issn = {2169-3536}, doi = {10.1109/ACCESS.2017.2682323}, note = {TL;DR \par The high activity of research work around the field of Open Source collaboration, especially in the software domain, revealed a set of shortcomings and proposed some actions to mitigate them.} } @article{cosmoSoftwareHeritageWhy, title = {Software {{Heritage}}: {{Why}} and {{How}} to {{Preserve Software Source Code}}}, author = {Cosmo, Roberto Di and Zacchiroli, Stefano}, pages = {10}, abstract = {Software is now a key component present in all aspects of our society. Its preservation has attracted growing attention over the past years within the digital preservation community. We claim that source code—the only representation of software that contains human readable knowledge—is a precious digital object that needs special handling: it must be a first class citizen in the preservation landscape and we need to take action immediately, given the increasingly more frequent incidents that result in permanent losses of source code collections. In this paper we present Software Heritage, an ambitious initiative to collect, preserve, and share the entire corpus of publicly accessible software source code. We discuss the archival goals of the project, its use cases and role as a participant in the broader digital preservation ecosystem, and detail its key design decisions. We also report on the project road map and the current status of the Software Heritage archive that, as of early 2017, has collected more than 3 billion unique source code files and 700 million commits coming from more than 50 million software development projects. ACM Reference Format: Roberto Di Cosmo and Stefano Zacchiroli. 2017. Software Heritage: Why and How to Preserve Software Source Code. In Proceedings of 14th International Conference on Digital Preservation (iPRES2017). ACM, New York, NY, USA, 10 pages.}, langid = {english}, note = {TL;DR \par This paper presents Software Heritage, an ambitious initiative to collect, preserve, and share the entire corpus of publicly accessible software source code, and discusses the archival goals, use cases and role as a participant in the broader digital preservation ecosystem, and detail its key design decisions.} } @inproceedings{cossette_seeking_2012, title = {Seeking the Ground Truth: A Retroactive Study on the Evolution and Migration of Software Libraries}, shorttitle = {Seeking the Ground Truth}, booktitle = {Procs {{ACM SIGSOFT}} 20th {{Int Symp}}. {{Found}}. {{Softw}}. {{Eng}}. - {{FSE}} 12}, author = {Cossette, Bradley E. and Walker, Robert J.}, date = {2012}, pages = {1}, location = {Cary, North Carolina}, doi = {10.1145/2393596.2393661}, abstract = {Application programming interfaces (APIs) are a common and industrially-relevant means for third-party software developers to reuse external functionality. Several techniques have been proposed to help migrate client code between library versions with incompatible APIs, but it is not clear how well these perform in an absolute sense. We present a retroactive study into the presence and nature of API incompatibilities between several versions of a set of Java-based software libraries; for each, we perform a detailed, manual analysis to determine what the correct adaptations are to migrate from the older to the newer version. In addition, we investigate whether any of a set of adaptation recommender techniques is capable of identifying the correct adaptations for library migration. We find that a given API incompatibility can typically be addressed by only one or two recommender techniques, but sometimes none serve. Furthermore, those techniques give correct recommendations, on average, in only about 20\% of cases.}, isbn = {978-1-4503-1614-9}, langid = {english}, nopublisher = {ACM Press}, nourl = {http://dl.acm.org/citation.cfm?doid=2393596.2393661} } @inproceedings{costaModelingIoTApplications2016, title = {Modeling {{IoT Applications}} with {{SysML4IoT}}}, booktitle = {2016 42th {{Euromicro Conf}}. {{Softw}}. {{Eng}}. {{Adv}}. {{Appl}}. {{SEAA}}}, author = {Costa, Bruno and Pires, Paulo F. and Delicato, Flavia C.}, date = {2016-08}, pages = {157--164}, publisher = {IEEE}, location = {Limassol, Cyprus}, doi = {10.1109/SEAA.2016.19}, eventtitle = {2016 42th {{Euromicro Conference}} on {{Software Engineering}} and {{Advanced Applications}} ({{SEAA}})}, isbn = {978-1-5090-2820-7}, note = {TL;DR \par A Model-Based Systems Engineering methodology for IoT application development, focusing on the design phase, is introduced, which aims at helping organizations to develop IoT applications to fully achieve the benefits of this new paradigm.} } @inproceedings{costaModelMateRecommenderTextual2024, title = {{{ModelMate}}: {{A}} Recommender for Textual Modeling Languages Based on Pre-Trained Language Models}, shorttitle = {{{ModelMate}}}, booktitle = {Proc. {{ACMIEEE}} 27th {{Int}}. {{Conf}}. {{Model Driven Eng}}. {{Lang}}. {{Syst}}.}, author = {Costa, Carlos Durá and López, José Antonio Hernández and Cuadrado, Jesús Sánchez}, date = {2024-09-22}, series = {{{MODELS}} '24}, pages = {183--194}, publisher = {Association for Computing Machinery}, location = {New York, NY, USA}, doi = {10.1145/3640310.3674089}, abstract = {Current DSL environments lack smart editing facilities intended to enhance modeler productivity and cannot keep pace of current developments of integrated development environments based on AI. In this paper, we propose an approach to address this shortcoming through a recommender system specifically tailored for textual DSLs based on the fine-tuning of pre-trained language models. We identify three main tasks: identifier suggestion, line completion, and block completion, which we implement over the same fine-tuned model and we propose a workflow to apply these tasks to any textual DSL. We have evaluated our approach with different pre-trained models for three DSLs: Emfatic, Xtext and a DSL to specify domain entities, showing that the system performs well and provides accurate suggestions. We compare it against existing approaches in the feature name recommendation task showing that our system outperforms the alternatives. Moreover, we evaluate the inference time of our approach obtaining low latencies, which makes the system adequate for live assistance. Finally, we contribute a concrete recommender, named ModelMate, which implements the training, evaluation and inference steps of the workflow as well as providing integration into Eclipse-based textual editors.}, isbn = {979-8-4007-0504-5} } @online{CoupledEvolutionModelDriven, title = {Coupled {{Evolution}} in {{Model-Driven Engineering}}}, url = {https://ieeexplore-ieee-org.univaq.idm.oclc.org/stamp/stamp.jsp?tp=&arnumber=6336727}, urldate = {2023-10-03}, note = {TL;DR \par The authors provide an overview of coupled evolution methods and tools to handle dependencies of model-driven engineering metamodels and how to deal with invalid artifacts.} } @article{Coutinho2014AnalysisOD, title = {Analysis of Distance Functions for Similarity-Based Test Suite Reduction in the Context of Model-Based Testing}, author = {Coutinho, Ana Emília Victor Barbosa and Cartaxo, Emanuela Gadelha and family=Lima Machado, given=Patrícia Duarte, prefix=de, useprefix=true}, date = {2014}, journaltitle = {Softw. Qual. J.}, volume = {24}, pages = {407--445}, note = {TL;DR \par The effectiveness of distance functions in the scope of a MBT reduction strategy based on the similarity degree of test cases is investigated and it is shown that the choice of a distance function has little influence on the size of the reduced test suite.} } @inproceedings{Covington:2016:DNN:2959100.2959190, title = {Deep Neural Networks for {{YouTube}} Recommendations}, booktitle = {Proc. 10th {{ACM Conf}}. {{Recomm}}. {{Syst}}.}, author = {Covington, Paul and Adams, Jay and Sargin, Emre}, date = {2016}, series = {{{RecSys}} '16}, pages = {191--198}, publisher = {ACM}, location = {New York, NY, USA}, url = {http://doi.acm.org/10.1145/2959100.2959190}, acmid = {2959190}, isbn = {978-1-4503-4035-9}, nodoi = {10.1145/2959100.2959190}, numpages = {8}, keywords = {deep learning,recommender system,scalability}, note = {TL;DR \par This paper details a deep candidate generation model and then describes a separate deep ranking model and provides practical lessons and insights derived from designing, iterating and maintaining a massive recommendation system with enormous user-facing impact.} } @article{coyleEthicalConcernsUnmanned, title = {Ethical {{Concerns}} of {{Unmanned}} and {{Autonomous Systems}} in {{Engineering Programs}}}, author = {Coyle, Eric Joe}, journaltitle = {age}, volume = {24}, pages = {1}, url = {https://www.asee.org/file_server/papers/attachment/file/0004/3811/ASEE-2014-UNMANNED-ETHICS-final.pdf}, urldate = {2016-08-21}, note = {TL;DR \par This paper provides an overview of a new graduate program in unmanned and autonomous systems engineering, and addresses three major ethical issues to be addressed for such a program.} } @inproceedings{Cremonesi:2008:EMC:1468165.1468327, title = {An Evaluation Methodology for Collaborative Recommender Systems}, booktitle = {Proc. 2008 {{Int}}. {{Conf}}. {{Autom}}. {{Solut}}. {{Cross Media Content Multi-Channel Distrib}}.}, author = {Cremonesi, Paolo and Turrin, Roberto and Lentini, Eugenio and Matteucci, Matteo}, date = {2008}, series = {{{AXMEDIS}} '08}, pages = {224--231}, publisher = {IEEE Computer Society}, location = {Washington, DC, USA}, url = {https://doi.org/10.1109/AXMEDIS.2008.13}, acmid = {1468327}, isbn = {978-0-7695-3406-0}, nodoi = {10.1109/AXMEDIS.2008.13}, numpages = {8}, keywords = {Collaborative,evaluation,knowledge discovery,methodology,naive bayesian networks,recommender systems,svd} } @inproceedings{cremonesiPerformanceRecommenderAlgorithms2010, title = {Performance of Recommender Algorithms on Top-n Recommendation Tasks}, booktitle = {Proc. {{Fourth ACM Conf}}. {{Recomm}}. {{Syst}}.}, author = {Cremonesi, Paolo and Koren, Yehuda and Turrin, Roberto}, date = {2010}, series = {{{RecSys}} '10}, pages = {39--46}, publisher = {ACM}, location = {New York, NY, USA}, url = {http://doi.acm.org/10.1145/1864708.1864721}, acmid = {1864721}, isbn = {978-1-60558-906-0}, nodoi = {10.1145/1864708.1864721}, numpages = {8}, keywords = {evaluation,precision,recall,top-n recommendations}, note = {TL;DR \par An extensive evaluation of several state-of-the art recommender algorithms suggests that algorithms optimized for minimizing RMSE do not necessarily perform as expected in terms of top-N recommendation task, and new variants of two collaborative filtering algorithms are offered.} } @article{criadoEnablingReuseStored, title = {Enabling the Reuse of Stored Model Transformations through Annotations}, author = {Criado, Javier and Martınez, Salvador and Iribarne, Luis and Cabot, Jordi}, url = {http://modeling-languages.com/wp-content/uploads/2015/04/icmt2015.pdf}, urldate = {2015-05-26} } @online{CROSSMETERQuestionsBegel, title = {{{CROSSMETER}} - {{Questions}} from {{Begel}}/{{Zimmermann}}'s {{ICSE}} 2014 Paper - {{Google Docs}}}, url = {https://docs.google.com/document/d/1jyZJE4xIUsRLHqMqsGjpZDGt9RBGmZAZLh5S_ueTsQQ/edit}, urldate = {2016-01-22} } @article{CROSSREC-DATA, title = {{{CrossRec}} Tool and Evaluation Data}, author = {Di Rocco, Juri and Nguyen, Phuong T. and Di Ruscio, Davide}, date = {2018}, note = {https://doi.org/10.5281/zenodo.1252848 \par https://doi.org/10.5281/zenodo.1252848 \par https://doi.org/10.5281/zenodo.1252848 \par https://doi.org/10.5281/zenodo.1252848 \par https://doi.org/10.5281/zenodo.1252848} } @article{Crussell2015AnDarwinSD, title = {{{AnDarwin}}: {{Scalable}} Detection of Android Application Clones Based on Semantics}, author = {Crussell, Jonathan and Gibler, Clint and Chen, Hao}, date = {2015}, journaltitle = {IEEE Trans. Mob. Comput.}, volume = {14}, pages = {2007--2019} } @inproceedings{crussellAndarwinScalableDetection2013, ids = {Crussell2013}, title = {Andarwin: {{Scalable}} Detection of Semantically Similar Android Applications}, shorttitle = {Andarwin}, booktitle = {Eur. {{Symp}}. {{Res}}. {{Comput}}. {{Secur}}.}, author = {Crussell, Jonathan and Gibler, Clint and Chen, Hao}, date = {2013}, pages = {182--199}, publisher = {Springer}, url = {http://link.springer.com/chapter/10.1007/978-3-642-40203-6_11}, urldate = {2017-09-25} } @online{CS886Recent, title = {{{CS}} 886: {{Recent Advances}} on {{Foundation Models}}}, url = {https://cs.uwaterloo.ca/~wenhuche/teaching/cs886/}, urldate = {2025-05-25} } @online{CSGSSISEAICourse, ids = {CSGSSISEAIa,CSGSSISEAIb}, title = {{{CS}}@{{GSSI}} - {{SE-AI Course}} 2021}, url = {https://sites.google.com/gssi.it/csgssi/ph-d-program/se-ai-course-2021}, urldate = {2021-05-07} } @article{CuadradoBWV22, title = {Efficient Execution of {{ATL}} Model Transformations Using Static Analysis and Parallelism}, author = {Cuadrado, Jesús Sánchez and Burgueño, Loli and Wimmer, Manuel and Vallecillo, Antonio}, date = {2022}, journaltitle = {IEEE Trans, Softw. Eng,}, volume = {48}, number = {4}, pages = {1097--1114}, doi = {10.1109/TSE.2020.3011388}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Thu, 23 Jun 2022 20:04:59 +0200} } @article{cuadradoModelFindingEMF2020, title = {Model {{Finding}} in the {{EMF Ecosystem}}.}, author = {Cuadrado, Jesús Sánchez and Gogolla, Martin}, date = {2020}, journaltitle = {JOT}, volume = {19}, number = {2}, pages = {10:1}, issn = {1660-1769}, doi = {10.5381/jot.2020.19.2.a10}, abstract = {The EMF framework is the main meta-modelling framework used nowadays. It has a rich ecosystem of plug-ins and tools built with and for it, including the option of enriching meta-models with OCL constraints. However, the EMF ecosystem lacks usable model finding approaches. Given a meta-model, a model finder automatically searches for models that satisfy a given set of formulas (e.g., OCL constraints). This feature can be used for a number of purposes, including model verification and model synthesis. In this paper, we present an approach to support model finding in the EMF ecosystem that is designed to realize several scenarios including model consistency, example generation, partial solution completion and scrolling. Moreover, it allows several OCL variants to be plugged-in via an intermediate representation. This approach has been realized in a tool called EFinder. We have assessed the usability of the approach by implementing three advanced application scenarios and evaluated its verification capabilities by analyzing OCL constraints from an external OCL dataset containing about 300 valid EMF/OCL specifications. Our model finder is able to process about 65\% of these EMF/OCL models.}, langid = {english} } @article{cuadradoModularizationModelTransformations2009, title = {Modularization of Model Transformations through a Phasing Mechanism}, author = {Cuadrado, Jesús Sánchez and Molina, Jesús García}, date = {2009-07}, journaltitle = {Softw Syst Model}, volume = {8}, number = {3}, pages = {325--345}, issn = {1619-1366, 1619-1374}, doi = {10.1007/s10270-008-0093-0}, abstract = {In recent years a great effort has been devoted to understanding the nature of model transformations. As a result, several mechanisms to improve model transformation languages have been proposed. Phasing has been mentioned in some works as a rule scheduling or organization mechanism, but without any detail. In this paper, we present a phasing mechanism in the context of rule-based transformation languages. We explain the structure and the behavior of the mechanism, and how it can be integrated in a language. We also analyze how the mechanism promotes modularity, internal transformation composition and helps to solve usual transformation problems. Besides, we show several examples of application to illustrate the usefulness of the mechanism.}, langid = {english} } @article{cuadradoVerifiedCatalogueOCL2019, title = {A Verified Catalogue of {{OCL}} Optimisations}, author = {Cuadrado, Jesús Sánchez}, date = {2019-07-02}, journaltitle = {Softw Syst Model}, issn = {1619-1374}, doi = {10.1007/s10270-019-00740-1}, abstract = {OCL is widely used by model-driven engineering tools with different purposes like writing integrity constraints for meta-models, as a navigation language in model transformation languages or to define transformation specifications. Another scenario is the automatic generation of OCL code by a repair system. These generated expressions tend to be complex and unreadable due to the nature of the generative process. However, to be useful this code should be simple and resemble manually written code as much as possible when a developer must manually maintain it. There exists refactorings approaches for manually written OCL code, but there is no tool targeted to the optimisation of OCL expressions which have been automatically synthesised. Moreover, there is no available catalogue of OCL refactorings which can be integrated seamlessly into a tool. In this work, we contribute a set of refactorings intended to optimise OCL expressions, notably covering cases likely to arise in generated OCL code. We also contribute the implementation of these refactorings, built as a generic transformation catalogue using bentō, a transformation reuse tool for ATL. This makes it possible to specialise the catalogue for any OCL variant based on Ecore. Moreover, we propose a method to verify the correctness of the implemented catalogue based on translation validation and model finding. We describe the design and implementation of the catalogue and evaluate it by optimising a large amount of OCL expressions and proving the correctness of each optimisation execution. We also derive working implementations of the catalogue for ATL, EMF/OCL and SimpleOCL made available in a tool called BeautyOCL.}, langid = {english}, note = {TL;DR \par A set of refactorings intended to optimise OCL expressions, notably covering cases likely to arise in generated OCL code, are contributed and a method to verify the correctness of the implemented catalogue based on translation validation and model finding is proposed.} } @article{Cui20211223, title = {Recommending best suitable metaheuristic based on landmarking feature and meta-learning approach [基于地标特征和元学习方法推荐最适用优化算法]}, author = {Cui, J.-S. and Lyu, Y. and Xu, Z.-H.}, date = {2021}, journaltitle = {Kongzhi Yu JueceControl Decis.}, volume = {36}, number = {5}, pages = {1223--1231}, publisher = {Northeast University}, issn = {10010920}, doi = {10.13195/j.kzyjc.2019.0993}, abbrev_source_title = {Kongzhi yu Juece Control Decis}, affiliation = {Dolinks School of Economics and Management, University of Science and Technology Beijing, Beijing, 100083, China}, coden = {KYJUE}, correspondence_address1 = {Cui, J.-S.; Dolinks School of Economics and Management, China; email: cuijs@manage.ustb.edu.cn}, document_type = {Article}, langid = {chinese}, source = {Scopus}, note = {cited By 1} } @article{Cui2021788, title = {Jointly Sparse Signal Recovery and Support Recovery via Deep Learning with Applications in {{MIMO-Based}} Grant-Free Random Access}, author = {Cui, Y. and Li, S. and Zhang, W.}, date = {2021}, journaltitle = {IEEE J. Sel. Areas Commun.}, volume = {39}, number = {3}, pages = {788--803}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {07338716}, doi = {10.1109/JSAC.2020.3018802}, abstract = {In this article, we investigate jointly sparse signal recovery and jointly sparse support recovery in Multiple Measurement Vector (MMV) models for complex signals, which arise in many applications in communications and signal processing. Recent key applications include channel estimation and device activity detection in MIMO-based grant-free random access which is proposed to support massive machine-type communications (mMTC) for Internet of Things (IoT). Utilizing techniques in compressive sensing, optimization and deep learning, we propose two model-driven approaches, based on the standard auto-encoder structure for real numbers. One is to jointly design the common measurement matrix and jointly sparse signal recovery method, and the other aims to jointly design the common measurement matrix and jointly sparse support recovery method. The proposed model-driven approaches can effectively utilize features of sparsity patterns in designing common measurement matrices and adjusting model-driven decoders, and can greatly benefit from the underlying state-of-the-art recovery methods with theoretical guarantee. Hence, the obtained common measurement matrices and recovery methods can significantly outperform the underlying advanced recovery methods. We conduct extensive numerical results on channel estimation and device activity detection in MIMO-based grant-free random access. The numerical results show that the proposed approaches provide pilot sequences and channel estimation or device activity detection methods which can achieve higher estimation or detection accuracy with shorter computation time than existing ones. Furthermore, the numerical results explain how such gains are achieved via the proposed approaches. © 1983-2012 IEEE.}, art_number = {9174792}, coden = {ISACE}, document_type = {Article}, source = {Scopus}, note = {cited By 8 \par TL;DR \par The numerical results show that the proposed approaches provide pilot sequences and channel estimation or device activity detection methods which can achieve higher estimation or detection accuracy with shorter computation time than existing ones.} } @inproceedings{Cunningham2018, title = {A Validation Neural Network ({{VNN}}) Metamodel for Predicting the Performance of Deep Generative Designs}, author = {Cunningham, J. and Tucker, C.S.}, date = {2018}, series = {Proceedings of the {{ASME Design Engineering Technical Conference}}}, volume = {2B-2018}, publisher = {American Society of Mechanical Engineers (ASME)}, doi = {10.1115/DETC201886299}, abbrev_source_title = {Proc. ASME Des. Eng. Tech. Conf.}, affiliation = {Department of Computer Science and Engineering, Penn State University University, Park, PA 16802, United States; Department of Engineering Design and Industrial and Manufacturing Engineering, Penn State University University, Park, PA 16802, United States}, document_type = {Conference Paper}, isbn = {978-0-7918-5176-0}, langid = {english}, source = {Scopus}, keywords = {notion}, note = {cited By 3 \par TL;DR \par This work presents a deep neural network method for approximating the performance of generated design concepts by discovering the visual features of a design that correlated to good and bad performance by simply observing the pixels of images of many candidate designs and their corresponding performance in a simulation environment.} } @article{cusumanoSelfdrivingVehicleTechnology2020, title = {Self-Driving Vehicle Technology: Progress and Promises}, shorttitle = {Self-Driving Vehicle Technology}, author = {Cusumano, Michael A.}, date = {2020-09-23}, journaltitle = {Commun. ACM}, volume = {63}, number = {10}, pages = {20--22}, issn = {0001-0782, 1557-7317}, doi = {10.1145/3417074}, langid = {english} } @online{CyberPhysicalSystemsConcept, title = {Cyber-{{Physical Systems}} - a {{Concept Map}}}, url = {http://cyberphysicalsystems.org/}, urldate = {2015-10-09} } @inproceedings{D'Aloisio2022291, title = {Quality-Driven Machine Learning-Based Data Science Pipeline Realization: A Software Engineering Approach}, author = {D'Aloisio, G.}, date = {2022}, series = {Proceedings - {{International Conference}} on {{Software Engineering}}}, pages = {291--293}, publisher = {IEEE Computer Society}, issn = {02705257}, doi = {10.1109/ICSE-Companion55297.2022.9793779}, abbrev_source_title = {Proc Int Conf Software Eng}, affiliation = {University of l'Aquila, Italy}, coden = {PCSED}, correspondence_address1 = {D'aloisio, G.; University of l'AquilaItaly; email: giordano.daloisio@graduate.univaq.it}, document_type = {Conference Paper}, isbn = {978-1-6654-9598-1}, langid = {english}, source = {Scopus}, note = {cited By 0 \par TL;DR \par This research project will define a new software engineering approach for data-science systems development that assures compliance with quality requirements and implement tools that guide IT professionals and researchers in the realization of ML-based data science pipelines since the requirement engineering.} } @inproceedings{daFonseca2017950, title = {The Potential of Artificial Neural Networks to Model Daylight Harvesting in Buildings Located in Different Climate Zones}, author = {family=Fonseca, given=R.W., prefix=da, useprefix=true and Pereira, F.O.R. and Papamichael, K.}, editor = {Barnaby C.S., Wetter M.}, date = {2017}, series = {Building {{Simulation Conference Proceedings}}}, volume = {2}, pages = {950--959}, publisher = {International Building Performance Simulation Association}, issn = {25222708}, doi = {10.26868/25222708.2017.570}, abbrev_source_title = {Build. Simul. Conf. Proc.}, affiliation = {Environmental Comfort Laboratory, Federal University of Santa Catarina, Brazil; California Lighting Technology Center, UC Davis, United States}, document_type = {Conference Paper}, isbn = {978-1-5108-7067-3}, langid = {english}, source = {Scopus}, note = {cited By 0} } @inproceedings{dagenaisMovingNewSoftware2010, ids = {Dagenais:2010:MNS:1806799.1806842}, title = {Moving into a New Software Project Landscape}, booktitle = {Proc. {{32Nd ACMIEEE Int}}. {{Conf}}. {{Softw}}. {{Eng}}. - {{Vol}}. 1}, author = {Dagenais, Barthélémy and Ossher, Harold and Bellamy, Rachel K. E. and Robillard, Martin P. and family=Vries, given=Jacqueline P., prefix=de, useprefix=true}, date = {2010}, series = {{{ICSE}} '10}, pages = {275--284}, publisher = {ACM}, location = {New York, NY, USA}, url = {http://doi.acm.org/10.1145/1806799.1806842}, acmid = {1806842}, isbn = {978-1-60558-719-6}, nodoi = {10.1145/1806799.1806842}, numpages = {10}, pagetotal = {10}, note = {TL;DR \par It is theorized that there are three primary factors that impact the integration experience of newcomers: early experimentation, internalizing structures and cultures, and progress validation.} } @article{Dai2017344, title = {A Wavelet Support Vector Machine-Based Neural Network Metamodel for Structural Reliability Assessment}, author = {Dai, H. and Cao, Z.}, date = {2017}, journaltitle = {Comput.-Aided Civ. Infrastruct. Eng.}, volume = {32}, number = {4}, pages = {344--357}, publisher = {Blackwell Publishing Inc.}, issn = {10939687}, doi = {10.1111/mice.12257}, abbrev_source_title = {Comput.-Aided Civ. Infrastruct. Eng.}, affiliation = {School of Civil Engineering, Harbin Institute of Technology, Harbin, China and, Key Lab of Structures Dynamic Behavior and Control (Harbin Institute of Technology), Ministry of Education, Harbin, 150090, China}, coden = {CCIEF}, correspondence_address1 = {Dai, H.; School of Civil Engineering, China; email: hzdai@hit.edu.cn}, document_type = {Article}, langid = {english}, source = {Scopus}, note = {cited By 95} } @incollection{Dalianis2018, title = {Evaluation Metrics and Evaluation}, booktitle = {Clinical Text Mining: {{Secondary}} Use of Electronic Patient Records}, author = {Dalianis, Hercules}, date = {2018}, pages = {45--53}, publisher = {Springer International Publishing}, location = {Cham}, doi = {10.1007/978-3-319-78503-5_6}, abstract = {This chapter describes the metrics for the evaluation of information retrieval and natural language processing systems, the annotation techniques and evaluation metrics and the concepts of training, development and evaluations sets for information retrieval systems.}, isbn = {978-3-319-78503-5}, note = {TL;DR \par This chapter describes the metrics for the evaluation of information retrieval and natural language processing systems, the annotation techniques and evaluation metrics and the concepts of training, development and evaluations sets for information retrieval systems.} } @article{daliborGeneratingCustomizedLowcode2022, title = {Generating Customized Low-Code Development Platforms for Digital Twins}, author = {Dalibor, Manuela and Heithoff, Malte and Michael, Judith and Netz, Lukas and Pfeiffer, Jérôme and Rumpe, Bernhard and Varga, Simon and Wortmann, Andreas}, date = {2022-06}, journaltitle = {Journal of Computer Languages}, volume = {70}, pages = {101117}, issn = {25901184}, doi = {10.1016/j.cola.2022.101117}, abstract = {A digital twin improves our use of a cyber–physical system and understanding of its emerging behavior. To this effect, a digital twin is to be developed and configured and potentially also operated by domain experts, who rarely have a professional software engineering background and for whom easy access and support, e.g., in form of low-code platforms are missing. In this paper, we report on an integrated method for the modeldriven engineering of low-code development platforms for digital twins that enables domain experts to create and operate digital twins for cyber–physical systems using the most appropriate modeling languages. The foundation of this method is (1) a code generation infrastructure for information systems combined with (2) an extensible base architecture for self-adaptive digital twins and (3) reusable language components for their configuration. Using this method, software engineers first configure the information system with the required modeling languages to generate the low-code development platform for digital twins before domain experts leverage the generated platform to create digital twins. This two-step method facilitates creating tailored low-code development platforms as well as creating and operating customized digital twins for a variety of applications.}, langid = {english}, keywords = {LOGSEQ} } @article{DALOISIO2023103226, title = {Debiaser for {{Multiple Variables}} to Enhance Fairness in Classification Tasks}, author = {family=Aloisio, given=Giordano, prefix=d', useprefix=true and D'Angelo, Andrea and Di Marco, Antinisca and Stilo, Giovanni}, date = {2023}, journaltitle = {Inf. Process. Manag.}, volume = {60}, number = {2}, pages = {103226}, issn = {0306-4573}, doi = {10.1016/j.ipm.2022.103226}, abstract = {Nowadays assuring that search and recommendation systems are fair and do not apply discrimination among any kind of population has become of paramount importance. This is also highlighted by some of the sustainable development goals proposed by the United Nations. Those systems typically rely on machine learning algorithms that solve the classification task. Although the problem of fairness has been widely addressed in binary classification, unfortunately, the fairness of multi-class classification problem needs to be further investigated lacking well-established solutions. For the aforementioned reasons, in this paper, we present the Debiaser for Multiple Variables (DEMV), an approach able to mitigate unbalanced groups bias (i.e., bias caused by an unequal distribution of instances in the population) in both binary and multi-class classification problems with multiple sensitive variables. The proposed method is compared, under several conditions, with a set of well-established baselines using different categories of classifiers. At first we conduct a specific study to understand which is the best generation strategies and their impact on DEMVs ability to improve fairness. Then, we evaluate our method on a heterogeneous set of datasets and we show how it overcomes the established algorithms of the literature in the multi-class classification setting and in the binary classification setting when more than two sensitive variables are involved. Finally, based on the conducted experiments, we discuss strengths and weaknesses of our method and of the other baselines.}, keywords = {Bias and Fairness,Equality,Machine learning,Multi-class classification,Preprocessing algorithm} } @article{dalpiazNaturalLanguageProcessing2018, title = {Natural {{Language Processing}} for {{Requirements Engineering}}: {{The Best Is Yet}} to {{Come}}}, shorttitle = {Natural {{Language Processing}} for {{Requirements Engineering}}}, author = {Dalpiaz, F. and Ferrari, A. and Franch, X. and Palomares, C.}, date = {2018-09}, journaltitle = {IEEE Softw.}, volume = {35}, number = {5}, pages = {115--119}, issn = {0740-7459}, doi = {10.1109/MS.2018.3571242}, abstract = {As part of the growing interest in natural language processing for requirements engineering (RE), RE researchers, computational linguists, and industry practitioners met at the First Workshop on Natural Language Processing for Requirements Engineering (NLP4RE 18). This article summarizes the workshop and presents an overview of the discussion held on the field’s future. This article is part of a theme issue on software engineering’s 50th anniversary.}, keywords = {software engineering}, note = {TL;DR \par This article summarizes the workshop and presents an overview of the discussion held on the field’s future at the First Workshop on Natural Language Processing for Requirements Engineering (NLP4RE 18).} } @article{damevskiMiningSequencesDeveloper2017, title = {Mining {{Sequences}} of {{Developer Interactions}} in {{Visual Studio}} for {{Usage Smells}}}, author = {Damevski, Kostadin and Shepherd, David C. and Schneider, Johannes and Pollock, Lori}, date = {2017-04-01}, journaltitle = {IEEE Trans. Softw. Eng.}, volume = {43}, number = {4}, pages = {359--371}, issn = {0098-5589, 1939-3520}, doi = {10.1109/TSE.2016.2592905}, note = {TL;DR \par This paper identifies usage patterns and smells that contribute to the understanding of the usability of Visual Studio for debugging, code search, and active file navigation, and, more broadly, to theUnderstanding of developer behavior during these software development activities.} } @incollection{dammFormalSemanticsTraffic2018, title = {A {{Formal Semantics}} for {{Traffic Sequence Charts}}}, booktitle = {Principles of {{Modeling}}}, author = {Damm, Werner and Möhlmann, Eike and Peikenkamp, Thomas and Rakow, Astrid}, editor = {Lohstroh, Marten and Derler, Patricia and Sirjani, Marjan}, date = {2018}, volume = {10760}, pages = {182--205}, publisher = {Springer International Publishing}, location = {Cham}, doi = {10.1007/978-3-319-95246-8_11}, isbn = {978-3-319-95245-1 978-3-319-95246-8}, keywords = {⛔ No INSPIRE recid found}, note = {TL;DR \par This paper paves the way for a future scenario catalog-based approach to acceptance testing for highly autonomous vehicles by providing a rigorous formal semantics for a visual specification language of traffic sequence charts to be used for building the scenario catalog.} } @article{Dang201415, title = {General Frameworks for Optimization of Plastic Injection Molding Process Parameters}, author = {Dang, X.-P.}, date = {2014}, journaltitle = {Simul. Model. Pract. Theory}, volume = {41}, pages = {15--27}, issn = {1569190X}, doi = {10.1016/j.simpat.2013.11.003}, abbrev_source_title = {Simul. Model. Pract. Theory}, affiliation = {Faculty of Mechanical Engineering, Nha Trang University, 2 Nguyen Dinh Chieu Street, Nha Trang City, Khanh Hoa 57000, Viet Nam}, correspondence_address1 = {Dang, X.-P.; Faculty of Mechanical Engineering, 2 Nguyen Dinh Chieu Street, Nha Trang City, Khanh Hoa 57000, Viet Nam; email: phuongdx@ntu.edu.vn}, document_type = {Article}, langid = {english}, source = {Scopus}, note = {cited By 99} } @inproceedings{Dang202015, title = {Data Poisoning Attack on Deep Neural Network and Some Defense Methods}, author = {Dang, T.K. and Truong, P.T.T. and Tran, P.T.}, editor = {Le L.-S., Marchese M., Toulouse M., Tran K.D., Dao B.}, date = {2020}, series = {Proceedings - 2020 {{International Conference}} on {{Advanced Computing}} and {{Applications}}, {{ACOMP}} 2020}, pages = {15--22}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/ACOMP50827.2020.00010}, abstract = {In recent years, Artificial Intelligence has disruptively changed information technology and software engineering with a proliferation of technologies and applications based-on it. However, recent researches show that AI models in general and the most greatest invention since sliced bread-Deep Learning models in particular, are vulnerable to being hacked and can be misused for bad purposes. In this paper, we carry out a brief review of data poisoning attack-one of the two recently dangerous emerging attacks-and the state-of-The-Art defense methods for this problem. Finally, we discuss current challenges and future developments. © 2020 IEEE.}, art_number = {9353086}, author_keywords = {Adversarial Machine Learning; Poisoning Attack; Secure Learning; Security in Deep Learning}, document_type = {Conference Paper}, isbn = {978-1-7281-8167-7}, source = {Scopus}, keywords = {Application programs,Deep learning,Deep neural networks,Learning models,Network security,Neural networks,Poisoning attacks,Recent researches,State of the art,Technologies and applications}, note = {cited By 4 \par TL;DR \par A brief review of data poisoning attack - one of the two recently dangerous emerging attacks - and the state-of-the-art defense methods for this problem are carried out.} } @inproceedings{DaniaC16, title = {{{OCL2MSFOL}}: A Mapping to Many-Sorted First-Order Logic for Efficiently Checking the Satisfiability of {{OCL}} Constraints}, booktitle = {Proc. {{ACMIEEE}} 19th {{Int}}. {{Conf}}. {{Model Driven Eng}}. {{Lang}}. {{Syst}}. {{St}}.-{{Malo Fr}}. {{Oct}}. 2-7 2016}, author = {Dania, Carolina and Clavel, Manuel}, editor = {Baudry, Benoit and Combemale, Benoît}, date = {2016}, pages = {65--75}, publisher = {ACM}, url = {http://dl.acm.org/citation.cfm?id=2976774}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Tue, 06 Nov 2018 16:57:17 +0100} } @incollection{danielUMLtoGraphDBMappingConceptual2016, title = {{{UMLtoGraphDB}}: {{Mapping Conceptual Schemas}} to {{Graph Databases}}}, shorttitle = {{{UMLtoGraphDB}}}, booktitle = {Conceptual {{Modeling}}}, author = {Daniel, Gwendal and Sunyé, Gerson and Cabot, Jordi}, editor = {Comyn-Wattiau, Isabelle and Tanaka, Katsumi and Song, Il-Yeol and Yamamoto, Shuichiro and Saeki, Motoshi}, date = {2016}, volume = {9974}, pages = {430--444}, publisher = {Springer International Publishing}, location = {Cham}, doi = {10.1007/978-3-319-46397-1_33}, abstract = {The need to store and manipulate large volume of (unstructured) data has led to the development of several NoSQL databases for better scalability. Graph databases are a particular kind of NoSQL databases that have proven their efficiency to store and query highly interconnected data, and have become a promising solution for multiple applications. While the mapping of conceptual schemas to relational databases is a well-studied field of research, there are only few solutions that target conceptual modeling for NoSQL databases and even less focusing on graph databases. This is specially true when dealing with the mapping of business rules and constraints in the conceptual schema. In this article we describe a mapping from UML/OCL conceptual schemas to Blueprints, an abstraction layer on top of a variety of graph databases, and Gremlin, a graph traversal language, via an intermediate Graph metamodel. Tool support is fully available.}, isbn = {978-3-319-46396-4 978-3-319-46397-1}, langid = {english}, note = {TL;DR \par This article describes a mapping from UML/OCL conceptual schemas to Blueprints, an abstraction layer on top of a variety of graph databases, and Gremlin, a graph traversal language, via an intermediate Graph metamodel.} } @inproceedings{Danvin2019, title = {Laminar to Turbulent Transition Prediction in Hypersonic Flows with Neural Networks Committee}, author = {Danvin, F. and Olazabal-Loume, M. and Pinna, F.}, date = {2019}, series = {{{AIAA Aviation}} 2019 {{Forum}}}, publisher = {{American Institute of Aeronautics and Astronautics Inc, AIAA}}, doi = {10.2514/6.2019-2837}, abbrev_source_title = {AIAA Aviation Forum}, affiliation = {CEA-CESTA, 15 avenue des Sablières, CS 60001, Le Barp Cedex, 33114, France; Von Karman Institute for Fluid Dynamics, 72 Chaussée de Waterloo B-1640, Rhode-Saint-Genèse, Belgium}, document_type = {Conference Paper}, isbn = {978-1-62410-589-0}, langid = {english}, page_count = {14}, source = {Scopus}, note = {cited By 3} } @article{Daosabah2021324, title = {Dynamic Composition of Services: An Approach Driven by the User’s Intention and Context}, author = {Daosabah, A. and Guermah, H. and Nassar, M.}, date = {2021}, journaltitle = {Int. J. Web Eng. Technol.}, volume = {16}, number = {4}, pages = {324--354}, publisher = {Inderscience Publishers}, issn = {14761289}, doi = {10.1504/IJWET.2021.122768}, abbrev_source_title = {Int. J. Web Eng. Technol.}, affiliation = {IMS Team, ADMIR Laboratory, Rabat IT Centre, ENSIAS, Mohammed V University, Rabat, Morocco}, correspondence_address1 = {Daosabah, A.; IMS Team, Morocco; email: a.daosabah@gmail.com}, document_type = {Article}, langid = {english}, source = {Scopus}, note = {cited By 1} } @inproceedings{daSilva201915, title = {A Conceptual Vision toward the Management of Machine Learning Models}, author = {family=Silva, given=D.N.R., prefix=da, useprefix=true and Simões, A. and Cardoso, C. and family=Oliveira, given=D.E.M., prefix=de, useprefix=true and Rittmeyer, J.N. and Wehmuth, K. and Lustosa, H. and Pereira, R.S. and Souto, Y. and Vignoli, L.E.G. and Salles, R. and family=Heleno, S.C., given=Jr., given-i={{Jr}}, prefix=de, useprefix=true and Ziviani, A. and Ogasawara, E. and Delicato, F.C. and family=Pires, given=P.F., prefix=de, useprefix=true and family=Pinto, given=H.L.C.P., prefix=da, useprefix=true and Maia, L. and Porto, F.}, editor = {Panach J.I., Guizzardi R., Claro D.B.}, date = {2019}, series = {{{CEUR Workshop Proceedings}}}, volume = {2469}, pages = {15--27}, publisher = {CEUR-WS}, issn = {16130073}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074090321&partnerID=40&md5=71c78c0900e41656c1b6f88703cb4f35}, abstract = {To turn big data into actionable knowledge, the adoption of machine learning (ML) methods has proven to be one of the de facto approaches. When elaborating an appropriate ML model for a given task, one typically builds many models and generates several data artifacts. Given the amount of information associated with the developed models performance, their appropriate selection is often difficult. Therefore, appropriately comparing a set of competitive ML models and choosing one according to an arbitrary set of user metrics require systematic solutions. In particular, ML model management is a promising research direction for a more systematic and comprehensive approach for machine learning model selection. Therefore, in this paper, we introduce a conceptual model for ML development. Based on this conceptualization, we introduce our vision toward a knowledge-based model management system oriented to model selection. Copyright © 2019 for this paper by its authors.}, document_type = {Conference Paper}, source = {Scopus}, note = {cited By 0} } @video{DataDistributionService, entrysubtype = {video}, title = {Data {{Distribution Service}}™ ({{DDS}}™)}, url = {https://www.youtube.com/watch?v=6iICap5G7rw}, urldate = {2019-10-20}, abstract = {The Data Distribution Service™ (DDS™) is a middleware protocol and API standard for data-centric connectivity from the OMG®. This presentation will cover the use cases of DDS and share example implementations of the DDS standard. DDS integrates the components of a system together, providing low-latency data connectivity, extreme reliability, and a scalable architecture required by business and mission-critical Internet of Things (IoT) applications. In a distributed system, middleware is the software layer that lies between the operating system and applications. It enables the various components of a system to more easily communicate and share data. It simplifies the development of a distributed system by letting software developers focus on the specific purpose of their applications rather than the mechanics of passing information between applications and systems. Presenter: Dr. Gerardo Pardo-Castellote, Co-Chair OMG DDS Special Interest Group, OMG Board of Directors, and CTO, RTI} } @online{DataistsTaxonomyData, title = {Dataists » {{A Taxonomy}} of {{Data Science}}}, url = {http://www.dataists.com/2010/09/a-taxonomy-of-data-science/}, urldate = {2021-03-18}, langid = {american} } @online{DataMiningCluster, title = {Data {{Mining Cluster Analysis}}}, url = {http://www.tutorialspoint.com/data_mining/dm_cluster_analysis.htm}, urldate = {2015-04-22} } @online{DataModelDesign, title = {Data {{Model Design}} and {{Best Practices}} ({{Part}} 1) - {{Talend}}}, url = {https://www.talend.com/blog/2017/05/05/data-model-design-best-practices-part-1/}, urldate = {2018-04-30} } @online{DataModelingAge, title = {Data {{Modeling In The Age Of NoSQL And Big Data}}}, url = {http://www.dataversity.net/data-modeling-age-nosql-big-data/}, urldate = {2015-03-26}, abstract = {by Jennifer Zaino Hadoop Hbase. MongoDB. Cassandra. Couchbase. Neo4J. Riak. Those are just a few of the sprawling community of NoSQL databases, a category that originally sprang up in response to the internal needs of companies such as Google, Amazon, Facebook, LinkedIn, Yahoo and more – needs for better scalability, lower latency, greater flexibility, and a better price/performance ratio in an age of Big Data and Cloud computing.}, organization = {DATAVERSITY} } @online{DataModelingDeadLong, title = {Data {{Modeling}} Is {{Dead}}...{{Long Live Schema Design}}! - {{DATAVERSITY}}}, url = {https://www.dataversity.net/data-modeling-dead-long-live-schema-design/}, urldate = {2019-11-11} } @online{DataModelingGuidelines, title = {Data {{Modeling Guidelines}} for {{NoSQL JSON Document Databases}} | {{MapR}}}, url = {https://mapr.com/blog/data-modeling-guidelines-nosql-json-document-databases/}, urldate = {2018-05-07} } @online{DataModelingKey, title = {Data {{Modeling}} with {{Key Value NoSQL Data Stores}} – {{Interview}} with {{Casey Rosenthal}}}, url = {http://www.infoq.com/articles/data-modeling-with-key-value-nosql-data-stores}, urldate = {2015-03-26}, abstract = {In Key Value data stores, data is represented as a collection of key–value pairs. The key–value model is one of the simplest non-trivial data models, and richer data models are implemented on top of it. InfoQ spoke with Casey Rosenthal from Basho team about the data modeling concepts and best practices when using these NoSQL databases for data management.}, organization = {InfoQ} } @online{DataModelsInternet, title = {Data Models for the {{Internet}} of {{Things}}}, url = {http://iot-datamodels.blogspot.it/}, urldate = {2016-09-27} } @article{DataPoisoningLLMs, title = {Data {{Poisoning}} in {{LLMs}}}, langid = {english}, keywords = {LOGSEQ}, note = {\section{Annotazioni\\ (5/3/2024, 16:02:24)} \par - “particular” (“Data Poisoning in LLMs”, p. 1) \#ff6666\\ \mkbibemph{You just started, it is not clear what respect to what! } \par - “simple to contaminate small bits of data” (“Data Poisoning in LLMs”, p. 1) \#ffd400\\ \mkbibemph{In what context? } \par - “Small amounts of poisoned data, which are inputs with triggers (poisoned inputs) combined with attacker-specified outputs (targeted outputs), are injected by the attacker in a data poisoning-based backdoor attack. When the same trigger(s) occur in test inputs during inference, a model trained on a poisoned dataset generates attacker-specified outputs while continuing to function properly on clean inputs” (“Data Poisoning in LLMs”, p. 1) \#ffd400\\ \mkbibemph{Can you be more specific with the help of an explanatory example? } \par - “interfere with the internal learning process that a machine learning model undergoes in order to render it unreliable or incapable of generating the desired output that the system is intended to produce.” (“Data Poisoning in LLMs”, p. 1) \#ffd400\\ \mkbibemph{ } \par - “Research Challenges:” (“Data Poisoning in LLMs”, p. 1) \#ff6666\\ \mkbibemph{Can you elaborate and refine these challenges by presenting concrete examples e.g., based on ChatGPT? } \par - “id ti identify the data which can poisoned to manipulate the outcomes” (“Data Poisoning in LLMs”, p. 1) \#ff6666\\ \mkbibemph{ } \par - “In case of LLMs, unlike the DL/ML models, the input of the data is not clear.” (“Data Poisoning in LLMs”, p. 1) \#ffd400\\ \mkbibemph{What do you think? } \par - “Identifying the input data that can lead to data poisoning in Large Language Models (LLMs) poses a significant research challenge due to the vast and diverse nature of the datasets used to train LLMs.” (“Data Poisoning in LLMs”, p. 2) \#ffd400\\ \mkbibemph{If you are referring to ChatGPT or Bird, the problem is even worse: you don't have any access to the training data. } \par - “He et al. [] r” (“Data Poisoning in LLMs”, p. 2) \#ff6666\\ \mkbibemph{All there references are missing. } \par - “Proposed Methodology” (“Data Poisoning in LLMs”, p. 3) \#ff6666\\ \mkbibemph{Can you do a presentation by adding figures distilling the proposed solution's main components? It is necessary to refer to the examples I suggested to add earlier in the document. }} } @online{DataStreamingIoT, title = {Data {{Streaming}} in {{IoT}} and {{Big Data Analytics}}}, url = {https://www2.slideshare.net/VincenzoGulisano/data-streaming-in-iot-and-big-data-analytics?qid=9a707bc0-4c0e-41ae-a551-1c8462c82314&v=&b=&from_search=21}, urldate = {2021-01-05}, keywords = {Data analysis,data streaming,DONE,internet of things} } @article{dautovStreamProcessingClustered2022, title = {Stream {{Processing}} on {{Clustered Edge Devices}}}, author = {Dautov, Rustem and Distefano, Salvatore}, date = {2022-04-01}, journaltitle = {IEEE Trans. Cloud Comput.}, volume = {10}, number = {2}, pages = {885--898}, issn = {2168-7161, 2372-0018}, doi = {10.1109/TCC.2020.2983402}, abstract = {The Internet of Things continuously generates avalanches of raw sensor data to be transferred to the Cloud for processing and storage. Due to network latency and limited bandwidth, this vertical offloading model, however, fails to meet requirements of time-critical data-intensive applications which must act upon generated data with minimum time delays. To address such a limitation, this article proposes a novel distributed architecture enabling stream data processing at the edge of the network, broadening the principle of enabling processing closer to data sources adopted by Fog and Edge Computing. Specifically, this architecture extends the Apache NiFi stream processing middleware with support for run-time clustering of heterogeneous edge devices, such that computational tasks can be horizontally offloaded to peer devices and executed in parallel. As opposed to vertical offloading on the Cloud, the proposed solution does not suffer from increased network latency and is thus able to offer 5-25 times faster response time, as demonstrated by the experiments on a run-time license plate recognition system.}, langid = {english}, keywords = {LOGSEQ}, note = {TL;DR \par This architecture extends the Apache NiFi stream processing middleware with support for run-time clustering of heterogeneous edge devices, such that computational tasks can be horizontally offloaded to peer devices and executed in parallel.} } @article{david2ndInternationalWorkshop, title = {2nd International Workshop on Sustainability and Modeling ({{SusMod}}’25)}, author = {David, Istvan and Fedeli, Arianna and Stoico, Vincenzo}, abstract = {Sustainability is becoming a key characteristic of modern systems. While this trend has been long recognized, rigorous formal methods for assessing sustainability, reasoning about often contradicting sustainability properties, and involving the human in this process are missing. This workshop aims to unearth visceral links between sustainability and MDE, and that, in both directions: MDE in support of sustainable systems engineering, and sustainability of MDE techniques.}, langid = {english}, keywords = {LOGSEQ}, note = {\textbf{REVIEW} \par \textbf{(1. Workshop title, 2. Motivation, 3. Organization, 4. Workshop format, 5. Additional material).} \par The workshop is about a relevant topic. The event is properly motivated, and the proponents have the required expertise to organize a potentially successful workshop. I like the proposed topics of interest. The number of submissions that have been received for the SoSym journal issue on the same theme as the workshop is a sign of the growing interest in the topic of sustainability by the modeling community.} } @inproceedings{davidediruscioManagingEvolutionFree2011, title = {Managing the {{Evolution}} of {{Free}} and {{Open Source Software Complex Systems}}}, booktitle = {V {{Conf}}. {{Ital}}. {{Sul Softw}}. {{Lib}}. - {{Milano}} 23-24 {{Giugno}} 2011}, author = {Davide Di Ruscio and Pelliccione, P}, date = {2011} } @article{davidEvaluatingCapabilitiesEnterprise2015, title = {Evaluating the Capabilities of {{Enterprise Architecture}} Modeling Tools for {{Visual Analysis}}.}, author = {David, Naranjo and Sánchez, Mario and Villalobos, Jorge}, date = {2015}, journaltitle = {J. Object Technol.}, volume = {14}, number = {1}, pages = {3:1}, issn = {1660-1769}, doi = {10.5381/jot.2015.14.1.a3}, langid = {english} } @online{DavidParnasKeynote, title = {David {{Parnas Keynote ICSE2025}}: {{Regulation}} of {{AI}} and {{Other Untrustworthy}}… | {{Qurat}} Ul Ain {{Ali}}}, shorttitle = {David {{Parnas Keynote ICSE2025}}}, url = {https://www.linkedin.com/posts/quratulain-ali_david-parnas-keynote-icse2025-regulation-activity-7328397336538214400-iRJg}, urldate = {2025-05-14}, abstract = {A highly recommended keynote by software engineering pioneer David Parnas — a true legend in software engineering. He brings a thoughtful, no-hype perspective on AI, asking the tough questions engineers should be asking. Insightful, grounded, and more relevant than ever. https://lnkd.in/ekgj8p\_b} } @incollection{davidStreamingModelTransformations2014, title = {Streaming {{Model Transformations By Complex Event Processing}}}, booktitle = {Model-{{Driven Engineering Languages}} and {{Systems}}}, author = {Dávid, István and Ráth, István and Varró, Dániel}, editor = {Dingel, Juergen and Schulte, Wolfram and Ramos, Isidro and Abrahão, Silvia and Insfran, Emilio}, date = {2014}, volume = {8767}, pages = {68--83}, publisher = {Springer International Publishing}, location = {Cham}, doi = {10.1007/978-3-319-11653-2_5}, abstract = {Streaming model transformations represent a novel class of transformations dealing with models whose elements are continuously produced or modified by a background process [1]. Executing streaming transformations requires efficient techniques to recognize the activated transformation rules on a potentially infinite input stream. Detecting a series of events triggered by compound structural changes is especially challenging for a high volume of rapid modifications, a characteristic of an emerging class of applications built on runtime models.}, isbn = {978-3-319-11652-5 978-3-319-11653-2}, langid = {english}, note = {TL;DR \par This work has shown that detecting a series of events triggered by compound structural changes is especially challenging for a high volume of rapid modifications, a characteristic of an emerging class of applications built on runtime models.} } @inproceedings{davisRelationshipPrecisionrecallROC2006, title = {The Relationship between Precision-Recall and {{ROC}} Curves}, booktitle = {Proc. 23rd {{Int}}. {{Conf}}. {{Mach}}. {{Learn}}.}, author = {Davis, Jesse and Goadrich, Mark}, date = {2006}, series = {{{ICML}} '06}, pages = {233--240}, publisher = {ACM}, location = {New York, NY, USA}, url = {http://doi.acm.org/10.1145/1143844.1143874}, acmid = {1143874}, isbn = {1-59593-383-2}, nodoi = {10.1145/1143844.1143874}, numpages = {8}, note = {TL;DR \par It is shown that a deep connection exists between ROC space and PR space, such that a curve dominates in R OC space if and only if it dominates in PR space.} } @incollection{DBLP:books/sp/rsse14/MensL14, title = {Source Code-Based Recommendation Systems}, booktitle = {Recommendation Systems in Software Engineering}, author = {Mens, Kim and Lozano, Angela}, editor = {Robillard, Martin P. and Maalej, Walid and Walker, Robert J. and Zimmermann, Thomas}, date = {2014}, pages = {93--130}, publisher = {Springer}, doi = {10.1007/978-3-642-45135-5\_5}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/books/sp/rsse14/MensL14.bib}, note = {TL;DR \par This chapter highlights relevant decisions involved in developing source code-based recommendation systems and an in-depth presentation of a particular system the authors developed serves as a concrete illustration of some of the issues that can be encountered and of the development choices that need to be made when building such a system.}, timestamp = {Mon, 29 May 2017 13:41:07 +0200} } @inproceedings{DBLP:conf/caise/BascianiRRIP16, title = {Automated Clustering of Metamodel Repositories}, booktitle = {Adv. {{Inf}}. {{Syst}}. {{Eng}}. - 28th {{Int}}. {{Conf}}. {{CAiSE}} 2016 {{Ljubl}}. {{Slov}}. {{June}} 13-17 2016 {{Proc}}.}, author = {Basciani, Francesco and Rocco, Juri Di and Ruscio, Davide Di and Iovino, Ludovico and Pierantonio, Alfonso}, editor = {Nurcan, Selmin and Soffer, Pnina and Bajec, Marko and Eder, Johann}, date = {2016}, series = {Lecture Notes in Computer Science}, volume = {9694}, pages = {342--358}, publisher = {Springer}, doi = {10.1007/978-3-319-39696-5\_21}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Mon, 15 Jun 2020 01:00:00 +0200} } @inproceedings{DBLP:conf/caise/NguyenRR19, title = {Building Information Systems Using Collaborative-Filtering Recommendation Techniques}, booktitle = {Adv. {{Inf}}. {{Syst}}. {{Eng}}. {{Workshop}} - {{CAiSE}} 2019 {{Int}}. {{Workshop Rome Italy June}} 3-7 2019 {{Proc}}.}, author = {Nguyen, Phuong Thanh and Rocco, Juri Di and Ruscio, Davide Di}, editor = {Proper, Henderik A. and Stirna, Janis}, date = {2019}, series = {Lecture Notes in Business Information Processing}, volume = {349}, pages = {214--226}, publisher = {Springer}, doi = {10.1007/978-3-030-20948-3\_19}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Thu, 24 Mar 2022 00:00:00 +0100} } @inproceedings{DBLP:conf/caise/RuscioP05, title = {Model Transformations in the Development of Data-Intensive Web Applications}, booktitle = {Adv. {{Inf}}. {{Syst}}. {{Eng}}. 17th {{Int}}. {{Conf}}. {{CAiSE}} 2005 {{Porto Port}}. {{June}} 13-17 2005 {{Proc}}.}, author = {Ruscio, Davide Di and Pierantonio, Alfonso}, editor = {Pastor, Oscar and family=Cunha, given=João Falcão, prefix=e, useprefix=false}, date = {2005}, series = {Lecture Notes in Computer Science}, volume = {3520}, pages = {475--490}, publisher = {Springer}, doi = {10.1007/11431855\_33}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Mon, 18 Jan 2021 08:56:37 +0100} } @inproceedings{DBLP:conf/dagstuhl/AutiliRIPT11, title = {{{ModelLAND}}: Where Do Models Come From?}, booktitle = {Model. - {{Found}}. {{Appl}}. {{Roadmaps Dagstuhl Semin}}. 11481 {{Novemb}}. 27 - {{Dec}}. 2 2011}, author = {Autili, Marco and Ruscio, Davide Di and Inverardi, Paola and Pelliccione, Patrizio and Tivoli, Massimo}, editor = {Bencomo, Nelly and France, Robert B. and Cheng, Betty H. C. and Aßmann, Uwe}, date = {2011}, series = {Lecture Notes in Computer Science}, volume = {8378}, pages = {162--187}, publisher = {Springer}, doi = {10.1007/978-3-319-08915-7\_6}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Sat, 09 Apr 2022 01:00:00 +0200} } @inproceedings{DBLP:conf/dsd/AfzalBRSMCTCFPS17, title = {The {{MegaM}}@{{Rt2 ECSEL}} Project: {{MegaModelling}} at Runtime - Scalable Model-Based Framework for Continuous Development and Runtime Validation of Complex Systems}, booktitle = {Euromicro {{Conf}}. {{Digit}}. {{Syst}}. {{Des}}. {{DSD}} 2017 {{Vienna Austria August}} 30 - {{Sept}} 1 2017}, author = {Afzal, Wasif and Bruneliere, Hugo and Ruscio, Davide Di and Sadovykh, Andrey and Mazzini, Silvia and Cariou, Eric and Truscan, Dragos and Cabot, Jordi and Field, Daniel and Pomante, Luigi and Smrz, Pavel}, editor = {Kubátová, Hana and Novotný, Martin and Skavhaug, Amund}, date = {2017}, pages = {494--501}, publisher = {IEEE Computer Society}, doi = {10.1109/DSD.2017.50}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Thu, 23 Mar 2023 00:00:00 +0100} } @inproceedings{DBLP:conf/ease/Doan0RR23, title = {Too Long; Didn't Read: Automatic Summarization of {{GitHub README}}.{{MD}} with Transformers}, booktitle = {Proc. 27th {{Int}}. {{Conf}}. {{Eval}}. {{Assess}}. {{Softw}}. {{Eng}}. {{EASE}} 2023 {{Oulu Finl}}. {{June}} 14-16 2023}, author = {Doan, Thu Thu Ha and Nguyen, Phuong T. and Rocco, Juri Di and Ruscio, Davide Di}, date = {2023}, pages = {267--272}, publisher = {ACM}, doi = {10.1145/3593434.3593448}, bibsource = {dblp computer science bibliography, https://dblp.org}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 0 \par TL;DR \par GitSum is built on top of BART and T5, two cutting-edge deep learning techniques, learning from existing data to perform recommendations for repositories with a missing description, and shows that it can generate relevant predictions, outperforming a well-established baseline.}, timestamp = {Thu, 15 Jun 2023 01:00:00 +0200} } @inproceedings{DBLP:conf/ease/NguyenRR19, title = {Enabling Heterogeneous Recommendations in {{OSS}} Development: What's Done and What's next in {{CROSSMINER}}}, booktitle = {Proc. {{Eval}}. {{Assess}}. {{Softw}}. {{Eng}}. {{EASE}} 2019 {{Cph}}. {{Den}}. {{April}} 15-17 2019}, author = {Nguyen, Phuong Thanh and Rocco, Juri Di and Ruscio, Davide Di}, editor = {Ali, Shaukat and Garousi, Vahid}, date = {2019}, pages = {326--331}, publisher = {ACM}, doi = {10.1145/3319008.3319353}, bibsource = {dblp computer science bibliography, https://dblp.org}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 4 \par cited By 4 \par TL;DR \par A context-aware recommender system to recommend API function calls and usage patterns and a graph-based representation to encode in a homogeneous manner different aspects of OSS ecosystems as well as to incorporate various well-founded recommendation techniques are described.}, timestamp = {Thu, 24 Mar 2022 00:00:00 +0100} } @inproceedings{DBLP:conf/ease/NguyenRRSP21, title = {Adversarial Machine Learning: On the Resilience of Third-Party Library Recommender Systems}, booktitle = {{{EASE}} 2021 {{Eval}}. {{Assess}}. {{Softw}}. {{Eng}}. {{Trondheim Nor}}. {{June}} 21-24 2021}, author = {Nguyen, Phuong Thanh and Ruscio, Davide Di and Rocco, Juri Di and Sipio, Claudio Di and Penta, Massimiliano Di}, editor = {Chitchyan, Ruzanna and Li, Jingyue and Weber, Barbara and Yue, Tao}, date = {2021}, pages = {247--253}, publisher = {ACM}, doi = {10.1145/3463274.3463809}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 1 \par cited By 1 \par cited By 1 \par cited By 1 \par cited By 1 \par TL;DR \par The extent to which the presence of manipulated data can have a negative impact on the outcomes of two state-of-the-art recommender systems which suggest third-party libraries to developers is shown.}, timestamp = {Tue, 21 Mar 2023 00:00:00 +0100} } @inproceedings{DBLP:conf/ease/SipioRRN20, title = {A Multinomial Naïve Bayesian ({{MNB}}) Network to Automatically Recommend Topics for {{GitHub}} Repositories}, booktitle = {{{EASE}} 20 {{Eval}}. {{Assess}}. {{Softw}}. {{Eng}}. {{Trondheim Nor}}. {{April}} 15-17 2020}, author = {Sipio, Claudio Di and Rubei, Riccardo and Ruscio, Davide Di and Nguyen, Phuong Thanh}, editor = {Li, Jingyue and Jaccheri, Letizia and Dingsøyr, Torgeir and Chitchyan, Ruzanna}, date = {2020}, pages = {71--80}, publisher = {ACM}, doi = {10.1145/3383219.3383227}, bibsource = {dblp computer science bibliography, https://dblp.org}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 14 \par cited By 14}, timestamp = {Thu, 24 Mar 2022 00:00:00 +0100} } @inproceedings{DBLP:conf/ease/Wohlin14, title = {Guidelines for Snowballing in Systematic Literature Studies and a Replication in Software Engineering}, booktitle = {18th {{Int}}. {{Conf}}. {{Eval}}. {{Assess}}. {{Softw}}. {{Eng}}. {{EASE}} 14 {{Lond}}. {{Engl}}. {{U}}. {{K}}. {{May}} 13-14 2014}, author = {Wohlin, Claes}, editor = {Shepperd, Martin J. and Hall, Tracy and Myrtveit, Ingunn}, date = {2014}, pages = {38:1--38:10}, publisher = {ACM}, doi = {10.1145/2601248.2601268}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/ease/Wohlin14.bib}, note = {TL;DR \par It is concluded that using snowballing, as a first search strategy, may very well be a good alternative to the use of database searches.}, timestamp = {Sat, 31 Jul 2021 17:22:31 +0200} } @inproceedings{DBLP:conf/ecmdafa/RuscioEIPS16, title = {Supporting Variability Exploration and Resolution during Model Migration}, booktitle = {Model. {{Found}}. {{Appl}}. - 12th {{Eur}}. {{Conf}}. {{ECMFASTAF}} 2016 {{Vienna Austria July}} 6-7 2016 {{Proc}}.}, author = {Ruscio, Davide Di and Etzlstorfer, Juergen and Iovino, Ludovico and Pierantonio, Alfonso and Schwinger, Wieland}, editor = {Wasowski, Andrzej and Lönn, Henrik}, date = {2016}, series = {Lecture Notes in Computer Science}, volume = {9764}, pages = {231--246}, publisher = {Springer}, doi = {10.1007/978-3-319-42061-5\_15}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Fri, 20 Nov 2020 13:15:18 +0100} } @inproceedings{DBLP:conf/ecmdafa/RuscioEIPS17, title = {A Feature-Based Approach for Variability Exploration and Resolution in Model Transformation Migration}, booktitle = {Model. {{Found}}. {{Appl}}. - 13th {{Eur}}. {{Conf}}. {{ECMFASTAF}} 2017 {{Marburg Ger}}. {{July}} 19-20 2017 {{Proc}}.}, author = {Ruscio, Davide Di and Etzlstorfer, Juergen and Iovino, Ludovico and Pierantonio, Alfonso and Schwinger, Wieland}, editor = {Anjorin, Anthony and Espinoza, Huáscar}, date = {2017}, series = {Lecture Notes in Computer Science}, volume = {10376}, pages = {71--89}, publisher = {Springer}, doi = {10.1007/978-3-319-61482-3\_5}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Fri, 20 Nov 2020 13:14:49 +0100} } @book{DBLP:conf/ecoop/2013acme, title = {Proceedings of the Workshop on {{ACadeMics}} Tooling with Eclipse, {{ACME}}@{{ECOOP}} 2013, Montpellier, France, July 2, 2013}, editor = {Ruscio, Davide Di and Kolovos, Dimitris S. and Rose, Louis M. and Al-Hilank, Samir}, date = {2013}, publisher = {ACM}, doi = {10.1145/2491279}, bibsource = {dblp computer science bibliography, https://dblp.org}, isbn = {978-1-4503-2036-8}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Tue, 06 Nov 2018 00:00:00 +0100} } @inproceedings{DBLP:conf/ecoop/RoccoRIP13, title = {Traceability Visualization in Metamodel Change Impact Detection}, booktitle = {Proc. {{Second Workshop Graph}}. {{Model}}. {{Lang}}. {{Dev}}. {{GMLD}} 13 {{Montp}}. {{Fr}}. {{July}} 2 2013}, author = {Rocco, Juri Di and Ruscio, Davide Di and Iovino, Ludovico and Pierantonio, Alfonso}, editor = {Kern, Heiko and Tolvanen, Juha-Pekka and Bottoni, Paolo}, date = {2013}, pages = {51--62}, publisher = {ACM}, doi = {10.1145/2489820.2489824}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 10 \par cited By 10 \par TL;DR \par How to generate and visualize traceability information about the dependencies between artifacts in a ecosystem and their related metamodel and how this affects the ecosystem by means of intuitive and straightforward visualization techniques is discussed.}, timestamp = {Sat, 09 Apr 2022 01:00:00 +0200} } @inproceedings{DBLP:conf/ecoop/RuscioKRA13, title = {{{ACadeMics}} Tooling with Eclipse: {{ACME}} 2013 Workshop Summmary}, booktitle = {Proc. {{Workshop Acad}}. {{Tool}}. {{Eclipse ACMEECOOP}} 2013 {{Montp}}. {{Fr}}. {{July}} 2 2013}, author = {Ruscio, Davide Di and Kolovos, Dimitrios S. and Rose, Louis M. and Al-Hilank, Samir}, editor = {Ruscio, Davide Di and Kolovos, Dimitris S. and Rose, Louis M. and Al-Hilank, Samir}, date = {2013}, pages = {1:1--1:2}, publisher = {ACM}, doi = {10.1145/2491279.2491280}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {⛔ No INSPIRE recid found}, note = {cited By 0 \par cited By 0 \par TL;DR \par The Eclipse platform has played a very significant role in the evolution of software engineering research over the last few years as it has provided mature infrastructure for the development of orthogonal but interoperable prototypes in areas including model driven engineering, code analysis, data visualisation, software measurement and testing and language development.}, timestamp = {Sun, 02 Jun 2019 01:00:00 +0200} } @inproceedings{DBLP:conf/ecsa/RuscioMMPP10, title = {{{ByADL}}: An {{MDE}} Framework for Building Extensible Architecture Description Languages}, booktitle = {Softw. {{Archit}}. 4th {{Eur}}. {{Conf}}. {{ECSA}} 2010 {{Cph}}. {{Den}}. {{August}} 23-26 2010 {{Proc}}.}, author = {Ruscio, Davide Di and Malavolta, Ivano and Muccini, Henry and Pelliccione, Patrizio and Pierantonio, Alfonso}, editor = {Babar, Muhammad Ali and Gorton, Ian}, date = {2010}, series = {Lecture Notes in Computer Science}, volume = {6285}, pages = {527--531}, publisher = {Springer}, doi = {10.1007/978-3-642-15114-9\_56}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 3 \par cited By 3 \par TL;DR \par The BYADL (Build Your ADL), a framework which allows software architects to extend existent ADLs with domain specificities, new architectural views, or analysis aspects, and integrate an ADL with development processes and methodologies, is presented.}, timestamp = {Mon, 05 Feb 2024 00:00:00 +0100} } @inproceedings{DBLP:conf/edoc/CicchettiRE06, title = {Towards Propagation of Changes by Model Approximations}, booktitle = {Tenth {{IEEE Int}}. {{Enterp}}. {{Distrib}}. {{Object Comput}}. {{Conf}}. {{EDOC}} 2006 16-20 {{Oct}}. 2006 {{Hong Kong China Workshop}}}, author = {Cicchetti, Antonio and Ruscio, Davide Di and Eramo, Romina}, date = {2006}, pages = {24}, publisher = {IEEE Computer Society}, doi = {10.1109/EDOCW.2006.68}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {⛔ No INSPIRE recid found}, note = {cited By 14 \par cited By 14}, timestamp = {Mon, 05 Feb 2024 00:00:00 +0100} } @inproceedings{DBLP:conf/edoc/CicchettiREP08, ids = {cicchettiAutomatingCoevolutionModelDriven2008,cicchettiAutomatingCoevolutionModelDriven2008a,cicchettiAutomatingCoevolutionModeldriven2008,cicchettiAutomatingCoevolutionModeldriven2008a}, title = {Automating Co-Evolution in Model-Driven Engineering}, booktitle = {12th {{Int}}. {{IEEE Enterp}}. {{Distrib}}. {{Object Comput}}. {{Conf}}. {{ECOC}} 2008 15-19 {{Sept}}. 2008 {{Munich Ger}}.}, author = {Cicchetti, Antonio and Ruscio, Davide Di and Eramo, Romina and Pierantonio, Alfonso}, date = {2008}, pages = {222--231}, publisher = {IEEE Computer Society}, doi = {10.1109/EDOC.2008.44}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 218 \par cited By 218}, timestamp = {Mon, 05 Feb 2024 00:00:00 +0100} } @inproceedings{DBLP:conf/enase/CicchettiRPPZ09, title = {Towards a Model Driven Approach to Upgrade Complex Software Systems}, booktitle = {{{ENASE}} 2009 - {{Proc}}. 4th {{Int}}. {{Conf}}. {{Eval}}. {{Nov}}. {{Approaches Softw}}. {{Eng}}. {{Milan Italy May}} 2009}, author = {Cicchetti, Antonio and Ruscio, Davide Di and Pelliccione, Patrizio and Pierantonio, Alfonso and Zacchiroli, Stefano}, editor = {Jablonski, Stefan and Maciaszek, Leszek A.}, date = {2009}, pages = {121--133}, publisher = {INSTICC Press}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 4 \par cited By 4}, timestamp = {Thu, 06 Jan 2011 10:26:54 +0100} } @inproceedings{DBLP:conf/enase/CicchettiRPPZ09a, title = {A Model Driven Approach to Upgrade Package-Based Software Systems}, booktitle = {Eval. {{Nov}}. {{Approaches Softw}}. {{Eng}}. - 3rd 4th {{Int}}. {{Conf}}. {{ENASE}} 20082009 {{Funchal Madeira Port}}. {{May}} 4-7 2008 {{Milan Italy May}} 9-10 2009 {{Revis}}. {{Sel}}. {{Pap}}.}, author = {Cicchetti, Antonio and Ruscio, Davide Di and Pelliccione, Patrizio and Pierantonio, Alfonso and Zacchiroli, Stefano}, editor = {Maciaszek, Leszek A. and González-Pérez, César and Jablonski, Stefan}, date = {2009}, series = {Communications in Computer and Information Science}, volume = {69}, pages = {262--276}, doi = {10.1007/978-3-642-14819-4\_19}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Wed, 25 Sep 2019 01:00:00 +0200} } @inproceedings{DBLP:conf/epew/ArcelliCR13, title = {Applying Model Differences to Automate Performance-Driven Refactoring of Software Models}, booktitle = {Comput. {{Perform}}. {{Eng}}. - 10th {{Eur}}. {{Workshop EPEW}} 2013 {{Venice Italy Sept}}. 16-17 2013 {{Proc}}.}, author = {Arcelli, Davide and Cortellessa, Vittorio and Ruscio, Davide Di}, editor = {Balsamo, Maria Simonetta and Knottenbelt, William J. and Marin, Andrea}, date = {2013}, series = {Lecture Notes in Computer Science}, volume = {8168}, pages = {312--324}, publisher = {Springer}, doi = {10.1007/978-3-642-40725-3\_24}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Tue, 21 Mar 2023 00:00:00 +0100} } @inproceedings{DBLP:conf/esem/RoccoRSNR20, title = {{{TopFilter}}: An Approach to Recommend Relevant {{GitHub}} Topics}, booktitle = {{{ESEM}} 20 {{ACM IEEE Int}}. {{Symp}}. {{Empir}}. {{Softw}}. {{Eng}}. {{Meas}}. {{Bari Italy Oct}}. 5-7 2020}, author = {Rocco, Juri Di and Ruscio, Davide Di and Sipio, Claudio Di and Nguyen, Phuong Thanh and Rubei, Riccardo}, editor = {Baldassarre, Maria Teresa and Lanubile, Filippo and Kalinowski, Marcos and Sarro, Federica}, date = {2020}, pages = {21:1--21:11}, publisher = {ACM}, doi = {10.1145/3382494.3410690}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {⛔ No INSPIRE recid found}, note = {cited By 7 \par cited By 7}, timestamp = {Tue, 21 Mar 2023 00:00:00 +0100} } @inproceedings{DBLP:conf/euromicro/BascianiRIP21, title = {Automated Quality Assessment of Interrelated Modeling Artifacts}, booktitle = {47th {{Euromicro Conf}}. {{Softw}}. {{Eng}}. {{Adv}}. {{Appl}}. {{SEAA}} 2021 {{Palermo Italy Sept}}. 1-3 2021}, author = {Basciani, Francesco and Ruscio, Davide Di and Iovino, Ludovico and Pierantonio, Alfonso}, editor = {Baldassarre, Maria Teresa and Scanniello, Giuseppe and Skavhaug, Amund}, date = {2021}, pages = {234--243}, publisher = {IEEE}, doi = {10.1109/SEAA53835.2021.00037}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 0 \par cited By 0 \par cited By 0 \par cited By 0 \par TL;DR \par A quality assessment approach is proposed, which considers the relationships among the artifacts under analysis as part of the quality measurement process and can generate Epsilon Object Language (EOL) programs by means of a model-to-code transformation to perform the specified quality assessment process.}, timestamp = {Tue, 21 Mar 2023 00:00:00 +0100} } @inproceedings{DBLP:conf/euromicro/NguyenRRR18, title = {{{CrossSim}}: Exploiting Mutual Relationships to Detect Similar {{OSS}} Projects}, booktitle = {44th {{Euromicro Conf}}. {{Softw}}. {{Eng}}. {{Adv}}. {{Appl}}. {{SEAA}} 2018 {{Prague Czech Repub}}. {{August}} 29-31 2018}, author = {Nguyen, Phuong Thanh and Rocco, Juri Di and Rubei, Riccardo and Ruscio, Davide Di}, editor = {Bures, Tomás and Angelis, Lefteris}, date = {2018}, pages = {388--395}, publisher = {IEEE Computer Society}, doi = {10.1109/SEAA.2018.00069}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {⛔ No INSPIRE recid found}, note = {cited By 15 \par cited By 15 \par TL;DR \par CrossSim is proposed as a novel approach to model open source software projects and related artifacts and to compute similarities among them and shows that CrossSim outperforms an existing technique, which has been proven to have a good performance in detecting similar GitHub repositories.}, timestamp = {Fri, 24 Mar 2023 00:00:00 +0100} } @inproceedings{DBLP:conf/euromicro/RuscioIP13, title = {Managing the Coupled Evolution of Metamodels and Textual Concrete Syntax Specifications}, booktitle = {39th {{Euromicro Conf}}. {{Softw}}. {{Eng}}. {{Adv}}. {{Appl}}. {{SEAA}} 2013 {{Santander Spain Sept}}. 4-6 2013}, author = {Ruscio, Davide Di and Iovino, Ludovico and Pierantonio, Alfonso}, editor = {Demirörs, Onur and Türetken, Oktay}, date = {2013}, pages = {114--121}, publisher = {IEEE Computer Society}, doi = {10.1109/SEAA.2013.22}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 12 \par cited By 12}, timestamp = {Fri, 24 Mar 2023 00:00:00 +0100} } @inproceedings{DBLP:conf/euromicro/SahayIRP20, title = {Supporting the Understanding and Comparison of Low-Code Development Platforms}, booktitle = {46th {{Euromicro Conf}}. {{Softw}}. {{Eng}}. {{Adv}}. {{Appl}}. {{SEAA}} 2020 {{Portoroz Slov}}. {{August}} 26-28 2020}, author = {Sahay, Apurvanand and Indamutsa, Arsene and Ruscio, Davide Di and Pierantonio, Alfonso}, date = {2020}, pages = {171--178}, publisher = {IEEE}, doi = {10.1109/SEAA51224.2020.00036}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 50 \par cited By 50 \par TL;DR \par A technical survey of different LCDPs is presented by relying on a proposed conceptual comparative framework and a corresponding set of features have been identified to distil the functionalities and the services that each considered platform can support.}, timestamp = {Sun, 12 Nov 2023 00:00:00 +0100} } @inproceedings{DBLP:conf/ewsa/CaporuscioRIPP05, title = {Engineering {{MDA}} into Compositional Reasoning for Analyzing Middleware-Based Applications}, booktitle = {Softw. {{Archit}}. 2nd {{Eur}}. {{Workshop EWSA}} 2005 {{Pisa Italy June}} 13-14 2005 {{Proc}}.}, author = {Caporuscio, Mauro and Ruscio, Davide Di and Inverardi, Paola and Pelliccione, Patrizio and Pierantonio, Alfonso}, editor = {Morrison, Ronald and Oquendo, Flávio}, date = {2005}, series = {Lecture Notes in Computer Science}, volume = {3527}, pages = {130--145}, publisher = {Springer}, doi = {10.1007/11494713\_9}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Tue, 14 May 2019 10:00:49 +0200} } @inproceedings{DBLP:conf/fase/AutiliRSIT13, title = {A Model-Based Synthesis Process for Choreography Realizability Enforcement}, booktitle = {Fundam. {{Approaches Softw}}. {{Eng}}. - 16th {{Int}}. {{Conf}}. {{FASE}} 2013 {{Held Part Eur}}. {{Jt}}. {{Conf}}. {{Theory Pract}}. {{Softw}}. {{ETAPS}} 2013 {{Rome Italy March}} 16-24 2013 {{Proc}}.}, author = {Autili, Marco and Ruscio, Davide Di and Salle, Amleto Di and Inverardi, Paola and Tivoli, Massimo}, editor = {Cortellessa, Vittorio and Varró, Dániel}, date = {2013}, series = {Lecture Notes in Computer Science}, volume = {7793}, pages = {37--52}, publisher = {Springer}, doi = {10.1007/978-3-642-37057-1\_4}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Mon, 05 Feb 2024 00:00:00 +0100} } @inproceedings{DBLP:conf/fase/LaraRRGIPC17, title = {Reusing Model Transformations through Typing Requirements Models}, booktitle = {Fundam. {{Approaches Softw}}. {{Eng}}. - 20th {{Int}}. {{Conf}}. {{FASE}} 2017 {{Held Part Eur}}. {{Jt}}. {{Conf}}. {{Theory Pract}}. {{Softw}}. {{ETAPS}} 2017 {{Upps}}. {{Swed}}. {{April}} 22-29 2017 {{Proc}}.}, author = {family=Lara, given=Juan, prefix=de, useprefix=true and Rocco, Juri Di and Ruscio, Davide Di and Guerra, Esther and Iovino, Ludovico and Pierantonio, Alfonso and Cuadrado, Jesús Sánchez}, editor = {Huisman, Marieke and Rubin, Julia}, date = {2017}, series = {Lecture Notes in Computer Science}, volume = {10202}, pages = {264--282}, publisher = {Springer}, doi = {10.1007/978-3-662-54494-5\_15}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Mon, 03 Jan 2022 00:00:00 +0100} } @inproceedings{DBLP:conf/fase/RuscioMMPP12, title = {Model-Driven Techniques to Enhance Architectural Languages Interoperability}, booktitle = {Fundam. {{Approaches Softw}}. {{Eng}}. - 15th {{Int}}. {{Conf}}. {{FASE}} 2012 {{Held Part Eur}}. {{Jt}}. {{Conf}}. {{Theory Pract}}. {{Softw}}. {{ETAPS}} 2012 {{Tallinn Est}}. {{March}} 24 - {{April}} 1 2012 {{Proc}}.}, author = {Ruscio, Davide Di and Malavolta, Ivano and Muccini, Henry and Pelliccione, Patrizio and Pierantonio, Alfonso}, editor = {family=Lara, given=Juan, prefix=de, useprefix=true and Zisman, Andrea}, date = {2012}, series = {Lecture Notes in Computer Science}, volume = {7212}, pages = {26--42}, publisher = {Springer}, doi = {10.1007/978-3-642-28872-2\_2}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Mon, 05 Feb 2024 00:00:00 +0100} } @inproceedings{DBLP:conf/flairs/AbdollahpouriBM19, title = {Managing Popularity Bias in Recommender Systems with Personalized Re-Ranking}, booktitle = {Proc. {{Thirty-Second Int}}. {{Fla}}. {{Artif}}. {{Intell}}. {{Res}}. {{Soc}}. {{Conf}}. {{Sarasota Fla}}. {{USA May}} 19-22 2019}, author = {Abdollahpouri, Himan and Burke, Robin and Mobasher, Bamshad}, editor = {Barták, Roman and Brawner, Keith W.}, date = {2019}, pages = {413--418}, publisher = {AAAI Press}, url = {https://aaai.org/ocs/index.php/FLAIRS/FLAIRS19/paper/view/18199}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/flairs/AbdollahpouriBM19.bib}, note = {TL;DR \par This paper introduces a personalized diversification re-ranking approach to increase the representation of less popular items in recommendations while maintaining acceptable recommendation accuracy.}, timestamp = {Wed, 26 Oct 2022 08:35:09 +0200} } @inproceedings{DBLP:conf/gg/RuscioIP12, title = {Evolutionary Togetherness: How to Manage Coupled Evolution in Metamodeling Ecosystems}, booktitle = {Graph {{Transform}}. - 6th {{Int}}. {{Conf}}. {{ICGT}} 2012 {{Brem}}. {{Ger}}. {{Sept}}. 24-29 2012 {{Proc}}.}, author = {Ruscio, Davide Di and Iovino, Ludovico and Pierantonio, Alfonso}, editor = {Ehrig, Hartmut and Engels, Gregor and Kreowski, Hans-Jörg and Rozenberg, Grzegorz}, date = {2012}, series = {Lecture Notes in Computer Science}, volume = {7562}, pages = {20--37}, publisher = {Springer}, doi = {10.1007/978-3-642-33654-6\_2}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Wed, 25 Sep 2019 01:00:00 +0200} } @book{DBLP:conf/icmt/2010iwmcp, title = {Proceedings of the 1st International Workshop on Model Comparison in Practice, {{IWMCP}} '10, Malaga, Spain, July 1, 2010}, editor = {Ruscio, Davide Di and Kolovos, Dimitris S.}, date = {2010}, publisher = {ACM}, doi = {10.1145/1826147}, bibsource = {dblp computer science bibliography, https://dblp.org}, isbn = {978-1-60558-960-2}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Fri, 17 Dec 2021 00:00:00 +0100} } @book{DBLP:conf/icmt/2011iwmcp, title = {Proceedings of the 2nd International Workshop on Model Comparison in Practice, {{IWMCP}} '11, Zurich, Switzerland, June 30, 2011}, editor = {Ruscio, Davide Di and Kolovos, Dimitris S.}, date = {2011}, publisher = {ACM}, doi = {10.1145/2000410}, bibsource = {dblp computer science bibliography, https://dblp.org}, isbn = {978-1-4503-0668-3}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Fri, 17 Dec 2021 00:00:00 +0100} } @book{DBLP:conf/icmt/2014, title = {Theory and Practice of Model Transformations - 7th International Conference, {{ICMT}}@{{STAF}} 2014, York, {{UK}}, July 21-22, 2014. {{Proceedings}}}, editor = {Ruscio, Davide Di and Varró, Dániel}, date = {2014}, series = {Lecture Notes in Computer Science}, volume = {8568}, publisher = {Springer}, doi = {10.1007/978-3-319-08789-4}, bibsource = {dblp computer science bibliography, https://dblp.org}, isbn = {978-3-319-08788-7}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Wed, 24 Jun 2020 01:00:00 +0200} } @inproceedings{DBLP:conf/icmt/CicchettiRP09, title = {Managing Dependent Changes in Coupled Evolution}, booktitle = {Theory {{Pract}}. {{Model Transform}}. - 2nd {{Int}}. {{Conf}}. {{ICMTTOOLS}} 2009 {{Zurich Switz}}. {{June}} 29-30 2009 {{Proc}}.}, author = {Cicchetti, Antonio and Ruscio, Davide Di and Pierantonio, Alfonso}, editor = {Paige, Richard F.}, date = {2009}, series = {Lecture Notes in Computer Science}, volume = {5563}, pages = {35--51}, publisher = {Springer}, doi = {10.1007/978-3-642-02408-5\_4}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Wed, 24 Jun 2020 13:02:21 +0200} } @inproceedings{DBLP:conf/icmt/RoccoRHILP18, title = {Systematic Recovery of {{MDE}} Technology Usage}, booktitle = {Theory {{Pract}}. {{Model Transform}}. - 11th {{Int}}. {{Conf}}. {{ICMTSTAF}} 2018 {{Toulouse Fr}}. {{June}} 25-26 2018 {{Proc}}.}, author = {Rocco, Juri Di and Ruscio, Davide Di and Härtel, Johannes and Iovino, Ludovico and Lämmel, Ralf and Pierantonio, Alfonso}, editor = {Rensink, Arend and Cuadrado, Jesús Sánchez}, date = {2018}, series = {Lecture Notes in Computer Science}, volume = {10888}, pages = {110--126}, publisher = {Springer}, doi = {10.1007/978-3-319-93317-7\_5}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Tue, 29 Dec 2020 00:00:00 +0100} } @inproceedings{DBLP:conf/icmt/RoccoRPCLG16, title = {Using {{ATL}} Transformation Services in the {{MDEForge}} Collaborative Modeling Platform}, booktitle = {Theory {{Pract}}. {{Model Transform}}. - 9th {{Int}}. {{Conf}}. {{ICMTSTAF}} 2016 {{Vienna Austria July}} 4-5 2016 {{Proc}}.}, author = {Rocco, Juri Di and Ruscio, Davide Di and Pierantonio, Alfonso and Cuadrado, Jesús Sánchez and family=Lara, given=Juan, prefix=de, useprefix=true and Guerra, Esther}, editor = {Gorp, Pieter Van and Engels, Gregor}, date = {2016}, series = {Lecture Notes in Computer Science}, volume = {9765}, pages = {70--78}, publisher = {Springer}, doi = {10.1007/978-3-319-42064-6\_5}, bibsource = {dblp computer science bibliography, https://dblp.org}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 6 \par cited By 6 \par TL;DR \par This paper proposes MDEForge: an extensible, collaborative modeling platform that provides remote model management facilities and prevents the user from focussing on time-consuming, and less creative procedures.}, timestamp = {Wed, 07 Dec 2022 23:13:49 +0100} } @inproceedings{DBLP:conf/icmt/RuscioIP11, title = {What Is Needed for Managing Co-Evolution in {{MDE}}?}, booktitle = {Proc. 2nd {{Int}}. {{Workshop Model Comp}}. {{Pract}}. {{IWMCP}} 11 {{Zurich Switz}}. {{June}} 30 2011}, author = {Ruscio, Davide Di and Iovino, Ludovico and Pierantonio, Alfonso}, editor = {Ruscio, Davide Di and Kolovos, Dimitris S.}, date = {2011}, pages = {30--38}, publisher = {ACM}, doi = {10.1145/2000410.2000416}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 45 \par cited By 45}, timestamp = {Wed, 16 Mar 2022 00:00:00 +0100} } @inproceedings{DBLP:conf/icmt/RuscioIP13, title = {A Methodological Approach for the Coupled Evolution of Metamodels and {{ATL}} Transformations}, booktitle = {Theory {{Pract}}. {{Model Transform}}. - 6th {{Int}}. {{Conf}}. {{ICMTSTAF}} 2013 {{Bp}}. {{Hung}}. {{June}} 18-19 2013 {{Proc}}.}, author = {Ruscio, Davide Di and Iovino, Ludovico and Pierantonio, Alfonso}, editor = {Duddy, Keith and Kappel, Gerti}, date = {2013}, series = {Lecture Notes in Computer Science}, volume = {7909}, pages = {60--75}, publisher = {Springer}, doi = {10.1007/978-3-642-38883-5\_9}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Wed, 24 Jun 2020 13:02:21 +0200} } @inproceedings{DBLP:conf/icmt/WagelaarIRP12, title = {Translational Semantics of a Co-Evolution Specific Language with the {{EMF}} Transformation Virtual Machine}, booktitle = {Theory {{Pract}}. {{Model Transform}}. - 5th {{Int}}. {{Conf}}. {{ICMTTOOLS}} 2012 {{Prague Czech Repub}}. {{May}} 28-29 2012 {{Proc}}.}, author = {Wagelaar, Dennis and Iovino, Ludovico and Ruscio, Davide Di and Pierantonio, Alfonso}, editor = {Hu, Zhenjiang and family=Lara, given=Juan, prefix=de, useprefix=true}, date = {2012}, series = {Lecture Notes in Computer Science}, volume = {7307}, pages = {192--207}, publisher = {Springer}, doi = {10.1007/978-3-642-30476-7\_13}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Wed, 24 Jun 2020 13:02:21 +0200} } @book{DBLP:conf/icse/2018rose, title = {Proceedings of the 1st International Workshop on Robotics Software Engineering, {{RoSE}}@{{ICSE}} 2018, Gothenburg, Sweden, May 28, 2018}, editor = {Ciccozzi, Federico and Ruscio, Davide Di and Malavolta, Ivano and Pelliccione, Patrizio and Wortmann, Andreas}, date = {2018}, publisher = {ACM}, url = {http://dl.acm.org/citation.cfm?id=3196558}, bibsource = {dblp computer science bibliography, https://dblp.org}, isbn = {978-1-4503-5760-9}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Fri, 27 Nov 2020 00:00:00 +0100} } @inproceedings{DBLP:conf/icse/AutiliBRT12, title = {Providing Lightweight and Adaptable Service Technology for Information and Communication ({{PLASTIC}}) in the Mobile Ehealth Case Study}, booktitle = {4th {{Int}}. {{ICSE Workshop Princ}}. {{Eng}}. {{Serv}}.-{{Oriented Syst}}. {{PESOS}} 2012 {{June}} 4 2012 {{Zurich Switz}}.}, author = {Autili, Marco and Berardinelli, Luca and Ruscio, Davide Di and Trubiani, Catia}, editor = {Lago, Patricia and Lewis, Grace A. and Metzger, Andreas and Tosic, Vladimir}, date = {2012}, pages = {69--70}, publisher = {IEEE}, doi = {10.1109/PESOS.2012.6225946}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {⛔ No INSPIRE recid found}, note = {cited By 2 \par cited By 2 \par TL;DR \par The goal of the case study is to show how to model a service-based application and to demonstrate that model-based solutions are suitable to generate Quality of Service (QoS) models and adaptable code from service models.}, timestamp = {Thu, 23 Mar 2023 00:00:00 +0100} } @inproceedings{DBLP:conf/icse/CiccozziRMPT17, title = {Engineering the Software of Robotic Systems}, booktitle = {Proc. 39th {{Int}}. {{Conf}}. {{Softw}}. {{Eng}}. {{ICSE}} 2017 {{B}}. {{Aires Argent}}. {{May}} 20-28 2017 - {{Companion Vol}}.}, author = {Ciccozzi, Federico and Ruscio, Davide Di and Malavolta, Ivano and Pelliccione, Patrizio and Tumova, Jana}, editor = {Uchitel, Sebastián and Orso, Alessandro and Robillard, Martin P.}, date = {2017}, pages = {507--508}, publisher = {IEEE Computer Society}, doi = {10.1109/ICSE-C.2017.167}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 13 \par cited By 13}, timestamp = {Thu, 23 Mar 2023 00:00:00 +0100} } @inproceedings{DBLP:conf/icse/KolovosMPRSSZ19, title = {Domain-Specific Languages for the Design, Deployment and Manipulation of Heterogeneous Databases}, booktitle = {Proc. 11th {{Int}}. {{Workshop Model}}. {{Softw}}. {{Eng}}. {{MiSEICSE}} 2019 {{Montr}}. {{QC Can}}. {{May}} 26-27 2019}, author = {Kolovos, Dimitrios S. and Medhat, Fady and Paige, Richard F. and Ruscio, Davide Di and family=Storm, given=Tijs, prefix=van der, useprefix=true and Scholze, Sebastian and Zolotas, Athanasios}, editor = {Chechik, Marsha and Strüber, Daniel and Varró, Dániel}, date = {2019}, pages = {89--92}, publisher = {ACM}, doi = {10.1109/MISE.2019.00021}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 8 \par cited By 8 \par TL;DR \par A model-based methodology developed in the context of the EC-funded H2020 TYPHON project for designing, developing, querying and evolving such scalable architectures for persistence, analytics and monitoring of large volumes of hybrid data, in a systematic and disciplined manner is outlined.}, timestamp = {Sat, 09 Apr 2022 01:00:00 +0200} } @inproceedings{DBLP:conf/icse/KolovosRPP09a, ids = {10.1109/CVSM.2009.5071714,5071714,kolovosCollaborativeModeldrivenSoftware2009,kolovosDifferentModelsModel2009a,kolovosDifferentModelsModel2009b,kolovosDifferentModelsModel2009c}, title = {Different Models for Model Matching: An Analysis of Approaches to Support Model Differencing}, booktitle = {Proc. 2009 {{ICSE Workshop Comp}}. {{Versioning Softw}}. {{Models CVSM}} 09 {{Vanc}}. {{BC Can}}. {{May}} 2009}, author = {Kolovos, Dimitrios S. and Ruscio, Davide Di and Pierantonio, Alfonso and Paige, Richard F.}, date = {2009}, pages = {1--6}, publisher = {IEEE Computer Society}, issn = {null}, doi = {10.1109/CVSM.2009.5071714}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, numpages = {6}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 159 \par cited By 159 \par TL;DR \par Researchers and practitioners can use the results for identifying existing research/technical gaps to attack, better scoping their own contributions, or understanding existing ones for identifying, classifying, and understanding existing collaborative MDSE approaches.}, timestamp = {Sat, 09 Apr 2022 01:00:00 +0200} } @inproceedings{DBLP:conf/icse/NguyenPVN16, title = {Learning {{API}} Usages from Bytecode: A Statistical Approach}, booktitle = {Proc. 38th {{Int}}. {{Conf}}. {{Softw}}. {{Eng}}. {{ICSE}} 2016 {{Austin TX USA May}} 14-22 2016}, author = {Nguyen, Tam The and Pham, Hung Viet and Vu, Phong Minh and Nguyen, Tung Thanh}, date = {2016}, pages = {416--427}, doi = {10.1145/2884781.2884873}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/icse/NguyenPVN16.bib}, note = {TL;DR \par The empirical evaluation shows that the prototype tool can effectively learn API usages from 200 thousand apps containing 350 million method sequences and recommends next method calls with top-3 accuracy and outperforms baseline approaches on average 10-20\%.}, timestamp = {Mon, 07 Sep 2020 10:46:58 +0200} } @inproceedings{DBLP:conf/icse/NguyenRRODP19, title = {{{FOCUS}}: A Recommender System for Mining {{API}} Function Calls and Usage Patterns}, booktitle = {Proc. 41st {{Int}}. {{Conf}}. {{Softw}}. {{Eng}}. {{ICSE}} 2019 {{Montr}}. {{QC Can}}. {{May}} 25-31 2019}, author = {Nguyen, Phuong Thanh and Rocco, Juri Di and Ruscio, Davide Di and Ochoa, Lina and Degueule, Thomas and Penta, Massimiliano Di}, editor = {Atlee, Joanne M. and Bultan, Tevfik and Whittle, Jon}, date = {2019}, pages = {1050--1060}, publisher = {IEEE / ACM}, doi = {10.1109/ICSE.2019.00109}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {⛔ No INSPIRE recid found}, note = {cited By 52 \par cited By 52 \par TL;DR \par A new tool is presented, FOCUS, which mines open-source project repositories to recommend API method invocations and usage patterns by analyzing how APIs are used in projects similar to the current project, and results indicate the suitability of context-aware collaborative-filtering recommender systems to provide API usage patterns.}, timestamp = {Sun, 02 Oct 2022 01:00:00 +0200} } @inproceedings{DBLP:conf/icse/RigbyR13, title = {Discovering Essential Code Elements in Informal Documentation}, booktitle = {35th {{Int}}. {{Conf}}. {{Softw}}. {{Eng}}. {{ICSE}} 13 {{San Franc}}. {{CA USA May}} 18-26 2013}, author = {Rigby, Peter C. and Robillard, Martin P.}, date = {2013}, pages = {832--841}, doi = {10.1109/ICSE.2013.6606629}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/bib/conf/icse/RigbyR13}, timestamp = {Tue, 23 May 2017 01:11:52 +0200} } @inproceedings{DBLP:conf/icse/RoccoRIP14, title = {Mining Metrics for Understanding Metamodel Characteristics}, booktitle = {6th {{Int}}. {{Workshop Model}}. {{Softw}}. {{Eng}}. {{MiSE}} 2014 {{Hyderabad India June}} 2-3 2014}, author = {Rocco, Juri Di and Ruscio, Davide Di and Iovino, Ludovico and Pierantonio, Alfonso}, editor = {Atlee, Joanne M. and Kulkarni, Vinay and Clark, Tony and France, Robert B. and Rumpe, Bernhard}, date = {2014}, pages = {55--60}, publisher = {ACM}, doi = {10.1145/2593770.2593774}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {⛔ No INSPIRE recid found}, note = {cited By 39 \par cited By 39 \par TL;DR \par A number of metrics are used to quantify and measure metamodels and cross-link different aspects in order to provide additional information about how meetamodel characteristics are related.}, timestamp = {Tue, 03 Nov 2020 08:17:22 +0100} } @inproceedings{DBLP:conf/icse/RoccoRIP15, title = {Mining Correlations of {{ATL}} Model Transformation and Metamodel Metrics}, booktitle = {7th {{IEEEACM Int}}. {{Workshop Model}}. {{Softw}}. {{Eng}}. {{MiSE}} 2015 {{Florence Italy May}} 16-17 2015}, author = {Rocco, Juri Di and Ruscio, Davide Di and Iovino, Ludovico and Pierantonio, Alfonso}, editor = {Gray, Jeff and Chechik, Marsha and Kulkarni, Vinay and Paige, Richard F.}, date = {2015}, pages = {54--59}, publisher = {IEEE Computer Society}, doi = {10.1109/MISE.2015.17}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {⛔ No INSPIRE recid found}, note = {cited By 10 \par cited By 10 \par TL;DR \par This paper proposes a process to analyze model transformations with the aim of identifying to what extent their characteristics depend on the corresponding input and target met models.}, timestamp = {Thu, 23 Mar 2023 00:00:00 +0100} } @inproceedings{DBLP:conf/icse/RuscioCR17, title = {9th Workshop on Modelling in Software Engineering ({{MiSE}} 2017)}, booktitle = {9th {{IEEEACM Int}}. {{Workshop Model}}. {{Softw}}. {{Eng}}. {{MiSEICSE}} 2017 {{B}}. {{Aires Argent}}. {{May}} 21-22 2017}, author = {Ruscio, Davide Di and Chechik, Marsha and Rumpe, Bernhard}, date = {2017}, pages = {1}, publisher = {IEEE}, doi = {10.1109/MISE.2017.15}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 0 \par cited By 0}, timestamp = {Sat, 30 Sep 2023 01:00:00 +0200} } @inproceedings{DBLP:conf/icse/RuscioFMM17, title = {Envisioning the Future of Collaborative Model-Driven Software Engineering}, booktitle = {Proc. 39th {{Int}}. {{Conf}}. {{Softw}}. {{Eng}}. {{ICSE}} 2017 {{B}}. {{Aires Argent}}. {{May}} 20-28 2017 - {{Companion Vol}}.}, author = {Ruscio, Davide Di and Franzago, Mirco and Muccini, Henry and Malavolta, Ivano}, editor = {Uchitel, Sebastián and Orso, Alessandro and Robillard, Martin P.}, date = {2017}, pages = {219--221}, publisher = {IEEE Computer Society}, doi = {10.1109/ICSE-C.2017.143}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 16 \par cited By 16 \par TL;DR \par The adoption of Model-driven Software Engineering to develop complex software systems in application domains like automotive and aerospace is being supported by the maturation of model-driven platforms and tools, but empirical studies show that a wider adoption is still an issue.}, timestamp = {Mon, 05 Feb 2024 00:00:00 +0100} } @inproceedings{DBLP:conf/icse/RuscioFMM18, title = {Collaborative Model-Driven Software Engineering: A Classification Framework and a Research Map}, booktitle = {Proc. 40th {{Int}}. {{Conf}}. {{Softw}}. {{Eng}}. {{ICSE}} 2018 {{Gothenbg}}. {{Swed}}. {{May}} 27 - {{June}} 03 2018}, author = {Ruscio, Davide Di and Franzago, Mirco and Muccini, Henry and Malavolta, Ivano}, editor = {Chaudron, Michel and Crnkovic, Ivica and Chechik, Marsha and Harman, Mark}, date = {2018}, pages = {535}, publisher = {ACM}, doi = {10.1145/3180155.3182543}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {⛔ No INSPIRE recid found}, note = {TL;DR \par Researchers and practitioners can use the results for identifying existing research/technical gaps to attack, better scoping their own contributions, or understanding existing ones for identifying, classifying, and understanding existing collaborative MDSE approaches.}, timestamp = {Tue, 21 Mar 2023 00:00:00 +0100} } @inproceedings{DBLP:conf/icse/RuscioMMPP10, title = {Developing next Generation {{ADLs}} through {{MDE}} Techniques}, booktitle = {Proc. 32nd {{ACMIEEE Int}}. {{Conf}}. {{Softw}}. {{Eng}}. - {{Vol}}. 1 {{ICSE}} 2010 {{Cape Town South Afr}}. 1-8 {{May}} 2010}, author = {Ruscio, Davide Di and Malavolta, Ivano and Muccini, Henry and Pelliccione, Patrizio and Pierantonio, Alfonso}, editor = {Kramer, Jeff and Bishop, Judith and Devanbu, Premkumar T. and Uchitel, Sebastián}, date = {2010}, pages = {85--94}, publisher = {ACM}, doi = {10.1145/1806799.1806816}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 33 \par cited By 33}, timestamp = {Mon, 05 Feb 2024 00:00:00 +0100} } @inproceedings{DBLP:conf/icse/RuscioPP12, title = {{{EVOSS}}: A Tool for Managing the Evolution of Free and Open Source Software Systems}, booktitle = {34th {{Int}}. {{Conf}}. {{Softw}}. {{Eng}}. {{ICSE}} 2012 {{June}} 2-9 2012 {{Zurich Switz}}.}, author = {Ruscio, Davide Di and Pelliccione, Patrizio and Pierantonio, Alfonso}, editor = {Glinz, Martin and Murphy, Gail C. and Pezzè, Mauro}, date = {2012}, pages = {1415--1418}, publisher = {IEEE Computer Society}, doi = {10.1109/ICSE.2012.6227234}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 2 \par cited By 2}, timestamp = {Thu, 23 Mar 2023 00:00:00 +0100} } @inproceedings{DBLP:conf/icse/SalleRNRFS22, title = {{{PILOT}}: Synergy between Text Processing and Neural Networks to Detect Self-Admitted Technical Debt}, booktitle = {{{TechDebt}} 22 {{Int}}. {{Conf}}. {{Tech}}. {{Debt Pittsburgh Pa}}. {{May}} 17-18 2022}, author = {Salle, Amleto Di and Rota, Alessandra and Nguyen, Phuong T. and Ruscio, Davide Di and Fontana, Francesca Arcelli and Sala, Irene}, editor = {Ernst, Neil A. and Lenarduzzi, Valentina and Sharma, Tushar}, date = {2022}, pages = {41--45}, publisher = {ACM}, doi = {10.1145/3524843.3528093}, bibsource = {dblp computer science bibliography, https://dblp.org}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 0 \par cited By 0 \par cited By 0 \par cited By 0 \par TL;DR \par This work introduces PILOT, a technical debt detector built on top of a combination of different natural language processing (NLP) and machine learning (ML) techniques, and shows that it obtains an encouraging performance and outperforms a well-established baseline.}, timestamp = {Mon, 05 Feb 2024 00:00:00 +0100} } @inproceedings{DBLP:conf/icsoc/AutiliBCMRIT07, title = {A Development Process for Self-Adapting Service Oriented Applications}, booktitle = {Serv.-{{Oriented Comput}}. - {{ICSOC}} 2007 {{Fifth Int}}. {{Conf}}. {{Vienna Austria Sept}}. 17-20 2007 {{Proc}}.}, author = {Autili, Marco and Berardinelli, Luca and Cortellessa, Vittorio and Marco, Antinisca Di and Ruscio, Davide Di and Inverardi, Paola and Tivoli, Massimo}, editor = {Krämer, Bernd J. and Lin, Kwei-Jay and Narasimhan, Priya}, date = {2007}, series = {Lecture Notes in Computer Science}, volume = {4749}, pages = {442--448}, publisher = {Springer}, doi = {10.1007/978-3-540-74974-5\_41}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Tue, 21 Mar 2023 00:00:00 +0100} } @inproceedings{DBLP:conf/icwe/CicchettiREMP09, title = {{{beContent}}: A Model-Driven Platform for Designing and Maintaining Web Applications}, booktitle = {Web {{Eng}}. 9th {{Int}}. {{Conf}}. {{ICWE}} 2009 {{San Sebastián Spain June}} 24-26 2009 {{Proc}}.}, author = {Cicchetti, Antonio and Ruscio, Davide Di and Eramo, Romina and Maccarrone, Francesco and Pierantonio, Alfonso}, editor = {Gaedke, Martin and Grossniklaus, Michael and Díaz, Oscar}, date = {2009}, series = {Lecture Notes in Computer Science}, volume = {5648}, pages = {518--522}, publisher = {Springer}, doi = {10.1007/978-3-642-02818-2\_52}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Mon, 05 Feb 2024 00:00:00 +0100} } @inproceedings{DBLP:conf/ifip5-7/IndamutsaRP21, title = {A Low-Code Development Environment to Orchestrate Model Management Services}, booktitle = {Adv. {{Prod}}. {{Manag}}. {{Syst}}. {{Artif}}. {{Intell}}. {{Sustain}}. {{Resilient Prod}}. {{Syst}}. - {{IFIP WG}} 57 {{Int}}. {{Conf}}. {{APMS}} 2021 {{Nantes Fr}}. {{Sept}}. 5-9 2021 {{Proc}}. {{Part I}}}, author = {Indamutsa, Arsene and Ruscio, Davide Di and Pierantonio, Alfonso}, editor = {Dolgui, Alexandre and Bernard, Alain and Lemoine, David and family=Cieminski, given=Gregor, prefix=von, useprefix=true and Romero, David}, date = {2021}, series = {{{IFIP}} Advances in Information and Communication Technology}, volume = {630}, pages = {342--350}, publisher = {Springer}, doi = {10.1007/978-3-030-85874-2\_36}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Tue, 21 Mar 2023 00:00:00 +0100} } @inproceedings{DBLP:conf/ifip5-7/TisiBLRK21, title = {Towards Twin-Driven Engineering: Overview of the State-of-the-Art and Research Directions}, booktitle = {Adv. {{Prod}}. {{Manag}}. {{Syst}}. {{Artif}}. {{Intell}}. {{Sustain}}. {{Resilient Prod}}. {{Syst}}. - {{IFIP WG}} 57 {{Int}}. {{Conf}}. {{APMS}} 2021 {{Nantes Fr}}. {{Sept}}. 5-9 2021 {{Proc}}. {{Part I}}}, author = {Tisi, Massimo and Bruneliere, Hugo and family=Lara, given=Juan, prefix=de, useprefix=true and Ruscio, Davide Di and Kolovos, Dimitris S.}, editor = {Dolgui, Alexandre and Bernard, Alain and Lemoine, David and family=Cieminski, given=Gregor, prefix=von, useprefix=true and Romero, David}, date = {2021}, series = {{{IFIP}} Advances in Information and Communication Technology}, volume = {630}, pages = {351--359}, publisher = {Springer}, doi = {10.1007/978-3-030-85874-2\_37}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Mon, 26 Jun 2023 01:00:00 +0200} } @inproceedings{DBLP:conf/iir/NguyenRR18, title = {Mining Software Repositories to Support {{OSS}} Developers: A Recommender Systems Approach}, booktitle = {Proc. 9th {{Ital}}. {{Inf}}. {{Retr}}. {{Workshop Rome Italy May}} 28-30 2018}, author = {Nguyen, Phuong Thanh and Rocco, Juri Di and Ruscio, Davide Di}, editor = {Tonellotto, Nicola and Becchetti, Luca and Tkalcic, Marko}, date = {2018}, series = {{{CEUR}} Workshop Proceedings}, volume = {2140}, publisher = {CEUR-WS.org}, url = {https://ceur-ws.org/Vol-2140/paper9.pdf}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 1 \par cited By 1 \par TL;DR \par The main research problems are presented as well the proposed approach together with some preliminary results, and cutting-edge technologies are applied, such as information retrieval and recommender systems to solve the problem of mining the rich metadata available at OSS repositories.}, timestamp = {Fri, 10 Mar 2023 16:22:48 +0100} } @inproceedings{DBLP:conf/iir/NguyenRRSR21, title = {Recommending Third-Party Library Updates with {{LSTM}} Neural Networks}, booktitle = {Proc. 11th {{Ital}}. {{Inf}}. {{Retr}}. {{Workshop}} 2021 {{Bari Italy Sept}}. 13-15 2021}, author = {Nguyen, Phuong Thanh and Rocco, Juri Di and Rubei, Riccardo and Sipio, Claudio Di and Ruscio, Davide Di}, editor = {Anelli, Vito Walter and Noia, Tommaso Di and Ferro, Nicola and Narducci, Fedelucio}, date = {2021}, series = {{{CEUR}} Workshop Proceedings}, volume = {2947}, publisher = {CEUR-WS.org}, url = {https://ceur-ws.org/Vol-2947/paper7.pdf}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 0 \par cited By 0 \par cited By 0 \par cited By 0}, timestamp = {Fri, 10 Mar 2023 16:22:47 +0100} } @inproceedings{DBLP:conf/kbse/AutiliBRIT08, title = {A Development Process for Context-Aware Adaptive Services}, booktitle = {23rd {{IEEEACM Int}}. {{Conf}}. {{Autom}}. {{Softw}}. {{Eng}}. - {{Workshop Proc}}. {{ASE Workshop}} 2008 15-16 {{Sept}}. 2008 {{Aquila Italy}}}, author = {Autili, Marco and Benedetto, Paolo Di and Ruscio, Davide Di and Inverardi, Paola and Tivoli, Massimo}, date = {2008}, pages = {9--16}, publisher = {IEEE}, doi = {10.1109/ASEW.2008.4686288}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Fri, 24 Mar 2023 00:00:00 +0100} } @inproceedings{DBLP:conf/kbse/BozhinoskiRMPT15, title = {{{FLYAQ}}: Enabling Non-Expert Users to Specify and Generate Missions of Autonomous Multicopters}, booktitle = {30th {{IEEEACM Int}}. {{Conf}}. {{Autom}}. {{Softw}}. {{Eng}}. {{ASE}} 2015 {{Linc}}. {{NE USA Novemb}}. 9-13 2015}, author = {Bozhinoski, Darko and Ruscio, Davide Di and Malavolta, Ivano and Pelliccione, Patrizio and Tivoli, Massimo}, editor = {Cohen, Myra B. and Grunske, Lars and Whalen, Michael}, date = {2015}, pages = {801--806}, publisher = {IEEE Computer Society}, doi = {10.1109/ASE.2015.104}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 31 \par cited By 31}, timestamp = {Fri, 24 Mar 2023 00:00:00 +0100} } @inproceedings{DBLP:conf/kbse/Gu0019, title = {{{CodeKernel}}: {{A}} Graph Kernel Based Approach to the Selection of {{API}} Usage Examples}, booktitle = {34th {{IEEEACM Int}}. {{Conf}}. {{Autom}}. {{Softw}}. {{Eng}}. {{ASE}} 2019 {{San Diego CA USA Novemb}}. 11-15 2019}, author = {Gu, Xiaodong and Zhang, Hongyu and Kim, Sunghun}, date = {2019}, pages = {590--601}, doi = {10.1109/ASE.2019.00061}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/kbse/Gu0019.bib}, note = {TL;DR \par This work proposes CodeKernel, a graph kernel based approach to the selection of API usage examples that represents source code as object usage graphs and selects more accurate code examples than the related work (MUSE and eXoaDocs).}, timestamp = {Sun, 19 Jan 2020 15:19:48 +0100} } @inproceedings{DBLP:conf/kbse/NguyenSRPR21, title = {Adversarial Attacks to {{API}} Recommender Systems: Time to Wake up and Smell the Coffee(f)}, booktitle = {36th {{IEEEACM Int}}. {{Conf}}. {{Autom}}. {{Softw}}. {{Eng}}. {{ASE}} 2021 {{Melb}}. {{Aust}}. {{Novemb}}. 15-19 2021}, author = {Nguyen, Phuong T. and Sipio, Claudio Di and Rocco, Juri Di and Penta, Massimiliano Di and Ruscio, Davide Di}, date = {2021}, pages = {253--265}, publisher = {IEEE}, doi = {10.1109/ASE51524.2021.9678946}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {⛔ No INSPIRE recid found}, note = {cited By 1 \par cited By 1 \par cited By 1 \par cited By 4 \par cited By 4}, timestamp = {Thu, 20 Apr 2023 01:00:00 +0200} } @book{DBLP:conf/models/2012xm, title = {Proceedings of the 2012 Extreme Modeling Workshop, {{XM}} '12, Innsbruck, Austria, October 1, 2012}, editor = {Ruscio, Davide Di and Pierantonio, Alfonso and family=Lara, given=Juan, prefix=de, useprefix=true}, date = {2012}, publisher = {ACM}, doi = {10.1145/2467307}, bibsource = {dblp computer science bibliography, https://dblp.org}, isbn = {978-1-4503-1804-4}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Thu, 06 Jan 2022 00:00:00 +0100} } @book{DBLP:conf/models/2013jp, title = {Joint Proceedings of {{MODELS}}'13 Invited Talks, Demonstration Session, Poster Session, and {{ACM}} Student Research Competition Co-Located with the 16th International Conference on Model Driven Engineering Languages and Systems ({{MODELS}} 2013), Miami, {{USA}}, September 29 - October 4, 2013}, editor = {Liu, Yan and Zschaler, Steffen and Baudry, Benoit and Ghosh, Sudipto and Ruscio, Davide Di and Jackson, Ethan K. and Wimmer, Manuel}, date = {2014}, series = {{{CEUR}} Workshop Proceedings}, volume = {1115}, publisher = {CEUR-WS.org}, url = {https://ceur-ws.org/Vol-1115}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, urn = {urn:nbn:de:0074-1115-4}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Fri, 10 Mar 2023 00:00:00 +0100} } @book{DBLP:conf/models/2013xm, title = {Proceedings of the Workshop on Extreme Modeling Co-Located with {{ACM}}/{{IEEE}} 16th International Conference on Model Driven Engineering Languages \& Systems ({{MoDELS}} 2013), Miami, Florida, {{USA}}, September 29, 2013}, editor = {family=Lara, given=Juan, prefix=de, useprefix=true and Ruscio, Davide Di and Pierantonio, Alfonso}, date = {2013}, series = {{{CEUR}} Workshop Proceedings}, volume = {1089}, publisher = {CEUR-WS.org}, url = {https://ceur-ws.org/Vol-1089}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, urn = {urn:nbn:de:0074-1089-2}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Fri, 10 Mar 2023 00:00:00 +0100} } @book{DBLP:conf/models/2014xm, title = {Proceedings of the 3rd Workshop on Extreme Modeling Co-Located with {{ACM}}/{{IEEE}} 17th International Conference on Model Driven Engineering Languages \& Systems, {{XM}}@{{MoDELS}} 2014, Valencia, Spain, September 29, 2014}, editor = {Ruscio, Davide Di and family=Lara, given=Juan, prefix=de, useprefix=true and Pierantonio, Alfonso}, date = {2014}, series = {{{CEUR}} Workshop Proceedings}, volume = {1239}, publisher = {CEUR-WS.org}, url = {https://ceur-ws.org/Vol-1239}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, urn = {urn:nbn:de:0074-1239-2}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Fri, 10 Mar 2023 00:00:00 +0100} } @book{DBLP:conf/models/2015flexmde, title = {Proceedings of the Workshop on Flexible Model Driven Engineering Co-Located with {{ACM}}/{{IEEE}} 18th International Conference on Model Driven Engineering Languages \& Systems ({{MoDELS}} 2015), Ottawa, Canada, September 29, 2015}, editor = {Ruscio, Davide Di and family=Lara, given=Juan, prefix=de, useprefix=true and Pierantonio, Alfonso}, date = {2015}, series = {{{CEUR}} Workshop Proceedings}, volume = {1470}, publisher = {CEUR-WS.org}, url = {https://ceur-ws.org/Vol-1470}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, urn = {urn:nbn:de:0074-1470-7}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Fri, 10 Mar 2023 00:00:00 +0100} } @book{DBLP:conf/models/2016flexmde, title = {Proceedings of the 2nd Workshop on Flexible Model Driven Engineering Co-Located with {{ACM}}/{{IEEE}} 19th International Conference on Model Driven Engineering Languages \& Systems ({{MoDELS}} 2016), Saint-Malo, France, October 2, 2016}, editor = {Ruscio, Davide Di and family=Lara, given=Juan, prefix=de, useprefix=true and Pierantonio, Alfonso}, date = {2016}, series = {{{CEUR}} Workshop Proceedings}, volume = {1694}, publisher = {CEUR-WS.org}, url = {https://ceur-ws.org/Vol-1694}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, urn = {urn:nbn:de:0074-1694-9}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Fri, 10 Mar 2023 00:00:00 +0100} } @book{DBLP:conf/models/2017s, title = {Proceedings of {{MODELS}} 2017 Satellite Event: Workshops ({{ModComp}}, {{ME}}, {{EXE}}, {{COMMitMDE}}, {{MRT}}, {{MULTI}}, {{GEMOC}}, {{MoDeVVa}}, {{MDETools}}, {{FlexMDE}}, {{MDEbug}}), Posters, Doctoral Symposium, Educator Symposium, {{ACM}} Student Research Competition, and Tools and Demonstrations Co-Located with {{ACM}}/{{IEEE}} 20th International Conference on Model Driven Engineering Languages and Systems ({{MODELS}} 2017), Austin, {{TX}}, {{USA}}, September, 17, 2017}, editor = {Burgueño, Loli and Corley, Jonathan and Bencomo, Nelly and Clarke, Peter J. and Collet, Philippe and Famelis, Michalis and Ghosh, Sudipto and Gogolla, Martin and Greenyer, Joel and Guerra, Esther and Kokaly, Sahar and Pierantonio, Alfonso and Rubin, Julia and Ruscio, Davide Di}, date = {2017}, series = {{{CEUR}} Workshop Proceedings}, volume = {2019}, publisher = {CEUR-WS.org}, url = {https://ceur-ws.org/Vol-2019}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, urn = {urn:nbn:de:0074-2019-2}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Fri, 10 Mar 2023 00:00:00 +0100} } @inproceedings{DBLP:conf/models/AddaziCRRIP16, title = {Semantic-Based Model Matching with {{EMFCompare}}}, booktitle = {Proc. 10th {{Workshop Models Evol}}. {{Co-Located ACMIEEE}} 19th {{Int}}. {{Conf}}. {{Model Driven Eng}}. {{Lang}}. {{Syst}}. {{MODELS}} 2016 {{St}}.-{{Malo Fr}}. {{Oct}}. 2 2016}, author = {Addazi, Lorenzo and Cicchetti, Antonio and Rocco, Juri Di and Ruscio, Davide Di and Iovino, Ludovico and Pierantonio, Alfonso}, editor = {Mayerhofer, Tanja and Pierantonio, Alfonso and Schätz, Bernhard and Tamzalit, Dalila}, date = {2016}, series = {{{CEUR}} Workshop Proceedings}, volume = {1706}, pages = {40--49}, publisher = {CEUR-WS.org}, url = {https://ceur-ws.org/Vol-1706/paper6.pdf}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 12 \par cited By 12}, timestamp = {Fri, 10 Mar 2023 16:22:21 +0100} } @inproceedings{DBLP:conf/models/AutiliRSI14, title = {Synthesizing an Automata-Based Representation of {{BPMN2}} Choreography Diagrams}, booktitle = {Proc. 1st {{Int}}. {{Workshop Model-Driven Eng}}. {{Compon}}.-{{Based Softw}}. {{Syst}}. {{Co-Located ACMIEEE}} 17th {{Int}}. {{Conf}}. {{Model Driven Eng}}. {{Lang}}. {{Syst}}. {{MoDELS}} 2014 {{Valencia Spain Sept}}. 29 2014}, author = {Autili, Marco and Ruscio, Davide Di and Salle, Amleto Di and Inverardi, Paola}, editor = {Ciccozzi, Federico and Tivoli, Massimo and Carlson, Jan}, date = {2014}, series = {{{CEUR}} Workshop Proceedings}, volume = {1281}, pages = {67--77}, publisher = {CEUR-WS.org}, url = {https://ceur-ws.org/Vol-1281/7.pdf}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 1 \par cited By 1}, timestamp = {Fri, 10 Mar 2023 16:22:20 +0100} } @inproceedings{DBLP:conf/models/BarrigaRINP20, title = {An Extensible Tool-Chain for Analyzing Datasets of Metamodels}, booktitle = {{{MODELS}} 20 {{ACMIEEE}} 23rd {{Int}}. {{Conf}}. {{Model Driven Eng}}. {{Lang}}. {{Syst}}. {{Virtual Event Can}}. 18-23 {{Oct}}. 2020 {{Companion Proc}}.}, author = {Barriga, Angela and Ruscio, Davide Di and Iovino, Ludovico and Nguyen, Phuong Thanh and Pierantonio, Alfonso}, editor = {Guerra, Esther and Iovino, Ludovico}, date = {2020}, pages = {50:1--50:8}, publisher = {ACM}, doi = {10.1145/3417990.3419626}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 4 \par cited By 4 \par TL;DR \par A dataset of metamodels has been collected for experimenting with different approaches conceived by the authors and has been automatically curated using a toolchain, which has been re-designed post-ante the definition of the proposed approaches to foster its future reuse.}, timestamp = {Sat, 09 Apr 2022 01:00:00 +0200} } @inproceedings{DBLP:conf/models/BascianiRDFPI18, title = {A Tool for Automatically Selecting Optimal Model Transformation Chains}, booktitle = {Proc. 21st {{ACMIEEE Int}}. {{Conf}}. {{Model Driven Eng}}. {{Lang}}. {{Syst}}. {{Companion Proc}}. {{MODELS}} 2018 {{Cph}}. {{Den}}. {{Oct}}. 14-19 2018}, author = {Basciani, Francesco and Ruscio, Davide Di and D'Emidio, Mattia and Frigioni, Daniele and Pierantonio, Alfonso and Iovino, Ludovico}, editor = {Babur, Önder and Strüber, Daniel and Abrahão, Silvia and Burgueño, Loli and Gogolla, Martin and Greenyer, Joel and Kokaly, Sahar and Kolovos, Dimitris S. and Mayerhofer, Tanja and Zahedi, Mansooreh}, date = {2018}, pages = {2--6}, publisher = {ACM}, doi = {10.1145/3270112.3270123}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 1 \par cited By 1 \par TL;DR \par The CITRIC tool is presented as a solution to mitigate the problem of characterizing the multitude of transformation chains that can be defined by composing existing model transformations to bridge source and target metamodels that are of interest for the modeler.}, timestamp = {Wed, 23 Feb 2022 12:16:51 +0100} } @inproceedings{DBLP:conf/models/BascianiRIP14, title = {Automated Chaining of Model Transformations with Incompatible Metamodels}, booktitle = {Model-{{Driven Eng}}. {{Lang}}. {{Syst}}. - 17th {{Int}}. {{Conf}}. {{MODELS}} 2014 {{Valencia Spain Sept}}. 28 - {{Oct}}. 3 2014 {{Proc}}.}, author = {Basciani, Francesco and Ruscio, Davide Di and Iovino, Ludovico and Pierantonio, Alfonso}, editor = {Dingel, Jürgen and Schulte, Wolfram and Ramos, Isidro and Abrahão, Silvia and Insfrán, Emilio}, date = {2014}, series = {Lecture Notes in Computer Science}, volume = {8767}, pages = {602--618}, publisher = {Springer}, doi = {10.1007/978-3-319-11653-2\_37}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Wed, 07 Dec 2022 00:00:00 +0100} } @inproceedings{DBLP:conf/models/BascianiRRIP15, title = {Model Repositories: Will They Become Reality?}, booktitle = {Proc. 3rd {{Int}}. {{Workshop Model-Driven Eng}}. {{Cloud}} 18th {{Int}}. {{Conf}}. {{Model Driven Eng}}. {{Lang}}. {{Syst}}. {{MoDELS}} 2015 {{Ott}}. {{Can}}. {{Sept}}. 29 2015}, author = {Basciani, Francesco and Rocco, Juri Di and Ruscio, Davide Di and Iovino, Ludovico and Pierantonio, Alfonso}, editor = {Paige, Richard F. and Cabot, Jordi and Brambilla, Marco and Hill, James H.}, date = {2015}, series = {{{CEUR}} Workshop Proceedings}, volume = {1563}, pages = {37--42}, publisher = {CEUR-WS.org}, url = {https://ceur-ws.org/Vol-1563/paper7.pdf}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {TL;DR \par The opportunities related to the adoption of model repositories are discussed and the research issues that have to be addressed are identified in order to make model repositories a reality in MDE.}, timestamp = {Fri, 10 Mar 2023 16:22:20 +0100} } @inproceedings{DBLP:conf/models/BascianiRRIP18, title = {Exploring Model Repositories by Means of Megamodel-Aware Search Operators}, booktitle = {Proc. {{MODELS}} 2018 {{Workshop ModComp MRT OCL FlexMDE EXE COMMitMDE MDETools GEMOC MORSE MDE4IoT MDEbug MoDeVVa ME MULTI HuFaMo AMMoRe PAINS Co-Located ACMIEEE}} 21st {{Int}}. {{Conf}}. {{Model Driven Eng}}. {{Lang}}. {{Syst}}. {{MODELS}} 2018 {{Cph}}. {{Den}}. {{Oct}}. 14 2018}, author = {Basciani, Francesco and Rocco, Juri Di and Ruscio, Davide Di and Iovino, Ludovico and Pierantonio, Alfonso}, editor = {Hebig, Regina and Berger, Thorsten}, date = {2018}, series = {{{CEUR}} Workshop Proceedings}, volume = {2245}, pages = {793--798}, publisher = {CEUR-WS.org}, url = {https://ceur-ws.org/Vol-2245/ammore_paper_3.pdf}, bibsource = {dblp computer science bibliography, https://dblp.org}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 4 \par cited By 4}, timestamp = {Fri, 10 Mar 2023 00:00:00 +0100} } @inproceedings{DBLP:conf/models/BascianiRRPI15, title = {A Tool for Clustering Metamodel Repositories}, booktitle = {Proc. {{MoDELS}} 2015 {{Demo Poster Sess}}. {{Co-Located ACMIEEE}} 18th {{Int}}. {{Conf}}. {{Model Driven Eng}}. {{Lang}}. {{Syst}}. {{MoDELS}} 2015 {{Ott}}. {{Can}}. {{Sept}}. 27 2015}, author = {Basciani, Francesco and Ruscio, Davide Di and Rocco, Juri Di and Pierantonio, Alfonso and Iovino, Ludovico}, editor = {Kulkarni, Vinay and Badreddin, Omar}, date = {2015}, series = {{{CEUR}} Workshop Proceedings}, volume = {1554}, pages = {1--4}, publisher = {CEUR-WS.org}, url = {https://ceur-ws.org/Vol-1554/PD_MoDELS_2015_paper_2.pdf}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 0 \par cited By 0 \par TL;DR \par A clustering tool for automatically organizing stored metamodels and provide users with repository overviews as, for instance, the application domains covered by the available metamadels, which has been implemented and integrated in the MDEForge repository.}, timestamp = {Fri, 10 Mar 2023 16:22:21 +0100} } @inproceedings{DBLP:conf/models/BascianiRRPI20, ids = {bascianiTyphonMLModelingEnvironment2020,bascianiTyphonMLModelingEnvironment2020a,bascianiTyphonMLModelingEnvironment2020b}, title = {{{TyphonML}}: A Modeling Environment to Develop Hybrid Polystores}, booktitle = {{{MODELS}} 20 {{ACMIEEE}} 23rd {{Int}}. {{Conf}}. {{Model Driven Eng}}. {{Lang}}. {{Syst}}. {{Virtual Event Can}}. 18-23 {{Oct}}. 2020 {{Companion Proc}}.}, author = {Basciani, Francesco and Rocco, Juri Di and Ruscio, Davide Di and Pierantonio, Alfonso and Iovino, Ludovico}, editor = {Guerra, Esther and Iovino, Ludovico}, date = {2020}, pages = {2:1--2:5}, publisher = {ACM}, doi = {10.1145/3417990.3421999}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 8 \par cited By 8 \par TL;DR \par TyphonML is proposed, a modeling language and supporting environment, which permits modelers to specify data that need to be persisted in hybrid architectures, by abstracting over the specificities of the underlying technologies.}, timestamp = {Sat, 09 Apr 2022 01:00:00 +0200} } @inproceedings{DBLP:conf/models/BascianiRRSIP14, title = {{{MDEForge}}: An Extensible Web-Based Modeling Platform}, booktitle = {Proc. 2nd {{Int}}. {{Workshop Model-Driven Eng}}. {{Cloud Co-Located}} 17th {{Int}}. {{Conf}}. {{Model Driven Eng}}. {{Lang}}. {{Syst}}. {{CloudMDEMoDELS}} 2014 {{Valencia Spain Sept}}. 30 2014}, author = {Basciani, Francesco and Rocco, Juri Di and Ruscio, Davide Di and Salle, Amleto Di and Iovino, Ludovico and Pierantonio, Alfonso}, editor = {Paige, Richard F. and Cabot, Jordi and Brambilla, Marco and Rose, Louis M. and Hill, James H.}, date = {2014}, series = {{{CEUR}} Workshop Proceedings}, volume = {1242}, pages = {66--75}, publisher = {CEUR-WS.org}, url = {https://ceur-ws.org/Vol-1242/paper10.pdf}, bibsource = {dblp computer science bibliography, https://dblp.org}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 68 \par cited By 68 \par TL;DR \par MDEForge is proposed, a novel extensible Web-based modeling platform specifically conceived to foster a community- based modeling repository that enables the adoption of model management tools as software-as-a-service that can be remotely used without overwhelming the users with intricate and error-prone installation and configuration procedures.}, timestamp = {Fri, 10 Mar 2023 16:22:20 +0100} } @inproceedings{DBLP:conf/models/BettiniRIP17, title = {Edelta: An Approach for Defining and Applying Reusable Metamodel Refactorings}, booktitle = {Proc. {{MODELS}} 2017 {{Satell}}. {{Event Workshop ModComp ME EXE COMMitMDE MRT MULTI GEMOC MoDeVVa MDETools FlexMDE MDEbug Posters Dr}}. {{Symp}}. {{Educ}}. {{Symp}}. {{ACM Stud}}. {{Res}}. {{Compet}}. {{Tools Demonstr}}. {{Co-Located ACMIEEE}} 20th {{Int}}. {{Conf}}. {{Model Driven Eng}}. {{Lang}}. {{Syst}}. {{MODELS}} 2017 {{Austin TX USA Sept}}. 17 2017}, author = {Bettini, Lorenzo and Ruscio, Davide Di and Iovino, Ludovico and Pierantonio, Alfonso}, editor = {Burgueño, Loli and Corley, Jonathan and Bencomo, Nelly and Clarke, Peter J. and Collet, Philippe and Famelis, Michalis and Ghosh, Sudipto and Gogolla, Martin and Greenyer, Joel and Guerra, Esther and Kokaly, Sahar and Pierantonio, Alfonso and Rubin, Julia and Ruscio, Davide Di}, date = {2017}, series = {{{CEUR}} Workshop Proceedings}, volume = {2019}, pages = {71--80}, publisher = {CEUR-WS.org}, url = {https://ceur-ws.org/Vol-2019/me_4.pdf}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 13 \par cited By 13 \par TL;DR \par Edelta is proposed, a domain specific language for specifying reusable libraries of metamodel refactorings that allows both atomic and complex changes and it is supported by an Eclipse-based IDE.}, timestamp = {Fri, 10 Mar 2023 16:22:20 +0100} } @inproceedings{DBLP:conf/models/BettiniRIP20, title = {Edelta 2.0: Supporting Live Metamodel Evolutions}, booktitle = {{{MODELS}} 20 {{ACMIEEE}} 23rd {{Int}}. {{Conf}}. {{Model Driven Eng}}. {{Lang}}. {{Syst}}. {{Virtual Event Can}}. 18-23 {{Oct}}. 2020 {{Companion Proc}}.}, author = {Bettini, Lorenzo and Ruscio, Davide Di and Iovino, Ludovico and Pierantonio, Alfonso}, editor = {Guerra, Esther and Iovino, Ludovico}, date = {2020}, pages = {51:1--51:10}, publisher = {ACM}, doi = {10.1145/3417990.3419501}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 1 \par cited By 1 \par TL;DR \par The new version of Edelta is presented, which provides EMF modelers with linguistic constructs for specifying both basic and complex refactorings and allows the users to easily introduce additional validation checks in their own Edelta programs, which are taken into consideration by the Edelta compiler and the IDE.}, timestamp = {Sat, 09 Apr 2022 01:00:00 +0200} } @inproceedings{DBLP:conf/models/CicchettiRP08, title = {Managing Model Conflicts in Distributed Development}, booktitle = {Model {{Driven Eng}}. {{Lang}}. {{Syst}}. 11th {{Int}}. {{Conf}}. {{MoDELS}} 2008 {{Toulouse Fr}}. {{Sept}}. 28 - {{Oct}}. 3 2008 {{Proc}}.}, author = {Cicchetti, Antonio and Ruscio, Davide Di and Pierantonio, Alfonso}, editor = {Czarnecki, Krzysztof and Ober, Ileana and Bruel, Jean-Michel and Uhl, Axel and Völter, Markus}, date = {2008}, series = {Lecture Notes in Computer Science}, volume = {5301}, pages = {311--325}, publisher = {Springer}, doi = {10.1007/978-3-540-87875-9\_23}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Fri, 30 Dec 2022 23:08:54 +0100} } @inproceedings{DBLP:conf/models/CicchettiRP09, title = {Model Patches in Model-Driven Engineering}, booktitle = {Models {{Softw}}. {{Eng}}. {{Workshop Symp}}. {{MODELS}} 2009 {{Denver CO USA Oct}}. 4-9 2009 {{Rep}}. {{Revis}}. {{Sel}}. {{Pap}}.}, author = {Cicchetti, Antonio and Ruscio, Davide Di and Pierantonio, Alfonso}, editor = {Ghosh, Sudipto}, date = {2009}, series = {Lecture Notes in Computer Science}, volume = {6002}, pages = {190--204}, publisher = {Springer}, doi = {10.1007/978-3-642-12261-3\_19}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Wed, 23 Feb 2022 12:58:06 +0100} } @inproceedings{DBLP:conf/models/ClerissiRRSIMMR23, title = {Supporting Early-Safety Analysis of {{IoT}} Systems by Exploiting Testing Techniques}, booktitle = {{{ACMIEEE Int}}. {{Conf}}. {{Model Driven Eng}}. {{Lang}}. {{Syst}}. {{MODELS}} 2023 {{Companion Västerås Swed}}. {{Oct}}. 1-6 2023}, author = {Clerissi, Diego and Rocco, Juri Di and Ruscio, Davide Di and Sipio, Claudio Di and Ihirwe, Felicien and Mariani, Leonardo and Micucci, Daniela and Rossi, Maria Teresa and Rubei, Riccardo}, date = {2023}, pages = {520--529}, publisher = {IEEE}, doi = {10.1109/MODELS-C59198.2023.00089}, bibsource = {dblp computer science bibliography, https://dblp.org}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Fri, 05 Jan 2024 00:00:00 +0100} } @inproceedings{DBLP:conf/models/IhirweIRMP21, title = {Cloud-Based Modeling in {{IoT}} Domain: A Survey, Open Challenges and Opportunities}, booktitle = {{{ACMIEEE Int}}. {{Conf}}. {{Model Driven Eng}}. {{Lang}}. {{Syst}}. {{Companion MODELS}} 2021 {{Companion Fukuoka Jpn}}. {{Oct}}. 10-15 2021}, author = {Ihirwe, Felicien and Indamutsa, Arsene and Ruscio, Davide Di and Mazzini, Silvia and Pierantonio, Alfonso}, date = {2021}, pages = {73--82}, publisher = {IEEE}, doi = {10.1109/MODELS-C53483.2021.00018}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 1 \par cited By 1 \par cited By 1 \par cited By 4 \par TL;DR \par A thorough review is conducted to investigate where the IoT domain community stands concerning the current trend of moving traditional modeling infrastructures to the cloud and highlights various opportunities and challenges related to the adoption of cloud-based modeling tools and platforms in the IoTdomain.}, timestamp = {Tue, 21 Mar 2023 00:00:00 +0100} } @inproceedings{DBLP:conf/models/IndamutsaRRP21, title = {{{MDEForgeWL}}: Towards Cloud-Based Discovery and Composition of Model Management Services}, booktitle = {{{ACMIEEE Int}}. {{Conf}}. {{Model Driven Eng}}. {{Lang}}. {{Syst}}. {{Companion MODELS}} 2021 {{Companion Fukuoka Jpn}}. {{Oct}}. 10-15 2021}, author = {Indamutsa, Arsene and Rocco, Juri Di and Ruscio, Davide Di and Pierantonio, Alfonso}, date = {2021}, pages = {118--127}, publisher = {IEEE}, doi = {10.1109/MODELS-C53483.2021.00023}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 0 \par cited By 0 \par cited By 0 \par cited By 0 \par TL;DR \par MDEForgeWL is presented, a complete infrastructure to support the execution of MMO workflows that are remotely available as dedicated services and consists of a DSL and supporting engine for defining and executing user-defined workflows of model management services.}, timestamp = {Tue, 21 Mar 2023 00:00:00 +0100} } @inproceedings{DBLP:conf/models/IovinoSRP20, title = {Metamodel Deprecation to Manage Technical Debt in Model Co-Evolution}, booktitle = {{{MODELS}} 20 {{ACMIEEE}} 23rd {{Int}}. {{Conf}}. {{Model Driven Eng}}. {{Lang}}. {{Syst}}. {{Virtual Event Can}}. 18-23 {{Oct}}. 2020 {{Companion Proc}}.}, author = {Iovino, Ludovico and Salle, Amleto Di and Ruscio, Davide Di and Pierantonio, Alfonso}, editor = {Guerra, Esther and Iovino, Ludovico}, date = {2020}, pages = {49:1--49:10}, publisher = {ACM}, doi = {10.1145/3417990.3419625}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 0 \par cited By 0 \par TL;DR \par This paper proposes using deprecation in metamodeling to mitigate the difficulties in performing a class of adaptations that must be operated manually and shows the feasibility of the methods.}, timestamp = {Mon, 05 Feb 2024 00:00:00 +0100} } @inproceedings{DBLP:conf/models/LopezRCR22, title = {Machine Learning Methods for Model Classification: A Comparative Study}, booktitle = {Proc. 25th {{Int}}. {{Conf}}. {{Model Driven Eng}}. {{Lang}}. {{Syst}}. {{MODELS}} 2022 {{Montr}}. {{Quebec Can}}. {{Oct}}. 23-28 2022}, author = {López, José Antonio Hernández and Rubei, Riccardo and Cuadrado, Jesús Sánchez and Ruscio, Davide Di}, editor = {Syriani, Eugene and Sahraoui, Houari A. and Bencomo, Nelly and Wimmer, Manuel}, date = {2022}, pages = {165--175}, publisher = {ACM}, doi = {10.1145/3550355.3552461}, bibsource = {dblp computer science bibliography, https://dblp.org}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 0 \par cited By 0 \par cited By 0}, timestamp = {Sat, 30 Sep 2023 01:00:00 +0200} } @inproceedings{DBLP:conf/models/NguyenRRPI19, ids = {Nguyen2019272,nguyen2019automated,nguyenAutomatedClassificationMetamodel2019,nguyenAutomatedClassificationMetamodel2019a,nguyenAutomatedClassificationMetamodel2019b,nguyenAutomatedClassificationMetamodel2019c}, title = {Automated Classification of Metamodel Repositories: A Machine Learning Approach}, booktitle = {22nd {{ACMIEEE Int}}. {{Conf}}. {{Model Driven Eng}}. {{Lang}}. {{Syst}}. {{MODELS}} 2019 {{Munich Ger}}. {{Sept}}. 15-20 2019}, author = {Nguyen, Phuong Thanh and Rocco, Juri Di and Ruscio, Davide Di and Pierantonio, Alfonso and Iovino, Ludovico}, editor = {Kessentini, Marouane and Yue, Tao and Pretschner, Alexander and Voss, Sebastian and Burgueño, Loli}, date = {2019}, pages = {272--282}, publisher = {IEEE}, doi = {10.1109/MODELS.2019.00011}, abbrev_source_title = {Proc. - ACM/IEEE Int. Conf. Model Driven Eng. Lang. Syst., MODELS}, affiliation = {Università degli Studi dell'Aquila, L'Aquila, Italy; Gran Sasso Science Institute, L'Aquila, Italy}, art_number = {8906979}, bibsource = {dblp computer science bibliography, https://dblp.org}, document_type = {Conference Paper}, source = {Scopus}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 27 \par cited By 29 \par cited By 29 \par TL;DR \par An experimental evaluation over a dataset of 555 metamodels demonstrates that the technique permits to learn from manually classified data and effectively categorize incoming unlabeled data with a considerably high prediction rate.}, timestamp = {Thu, 24 Mar 2022 00:00:00 +0100} } @inproceedings{DBLP:conf/models/RoccoRHILP17, title = {Consistency Recovery in Interactive Modeling}, booktitle = {Proc. {{MODELS}} 2017 {{Satell}}. {{Event Workshop ModComp ME EXE COMMitMDE MRT MULTI GEMOC MoDeVVa MDETools FlexMDE MDEbug Posters Dr}}. {{Symp}}. {{Educ}}. {{Symp}}. {{ACM Stud}}. {{Res}}. {{Compet}}. {{Tools Demonstr}}. {{Co-Located ACMIEEE}} 20th {{Int}}. {{Conf}}. {{Model Driven Eng}}. {{Lang}}. {{Syst}}. {{MODELS}} 2017 {{Austin TX USA Sept}}. 17 2017}, author = {Rocco, Juri Di and Ruscio, Davide Di and Heinz, Marcel and Iovino, Ludovico and Lämmel, Ralf and Pierantonio, Alfonso}, editor = {Burgueño, Loli and Corley, Jonathan and Bencomo, Nelly and Clarke, Peter J. and Collet, Philippe and Famelis, Michalis and Ghosh, Sudipto and Gogolla, Martin and Greenyer, Joel and Guerra, Esther and Kokaly, Sahar and Pierantonio, Alfonso and Rubin, Julia and Ruscio, Davide Di}, date = {2017}, series = {{{CEUR}} Workshop Proceedings}, volume = {2019}, pages = {116--122}, publisher = {CEUR-WS.org}, url = {https://ceur-ws.org/Vol-2019/exe_6.pdf}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 4 \par cited By 4 \par TL;DR \par This paper captures the types of artifacts and the relevant relationships in a megamodelingbased manner for the purpose of monitoring and recovering a MDE project’s consistency in response to changes that users may apply to the project within an interactive modeling platform.}, timestamp = {Fri, 10 Mar 2023 00:00:00 +0100} } @inproceedings{DBLP:conf/models/RoccoRIP14, title = {Dealing with the Coupled Evolution of Metamodels and Model-to-Text Transformations}, booktitle = {Proc. {{Workshop Models Evol}}. {{Co-Located ACMIEEE}} 17th {{Int}}. {{Conf}}. {{Model Driven Eng}}. {{Lang}}. {{Syst}}. {{MoDELS}} 2014 {{Valencia Spain Sept}} 28 2014}, author = {Rocco, Juri Di and Ruscio, Davide Di and Iovino, Ludovico and Pierantonio, Alfonso}, editor = {Pierantonio, Alfonso and Schätz, Bernhard and Tamzalit, Dalila}, date = {2014}, series = {{{CEUR}} Workshop Proceedings}, volume = {1331}, pages = {22--31}, publisher = {CEUR-WS.org}, url = {https://ceur-ws.org/Vol-1331/p3.pdf}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 12 \par cited By 12 \par TL;DR \par This paper presents an approach for the coupled evolution of Acceleo-based templating including the OCL embedded in its notation and has been implemented and illustrated by means of a running example.}, timestamp = {Fri, 10 Mar 2023 16:22:20 +0100} } @inproceedings{DBLP:conf/models/RoccoRNP18, title = {Resilience in Sirius Editors: Understanding the Impact of Metamodel Changes}, booktitle = {Proc. {{MODELS}} 2018 {{Workshop ModComp MRT OCL FlexMDE EXE COMMitMDE MDETools GEMOC MORSE MDE4IoT MDEbug MoDeVVa ME MULTI HuFaMo AMMoRe PAINS Co-Located ACMIEEE}} 21st {{Int}}. {{Conf}}. {{Model Driven Eng}}. {{Lang}}. {{Syst}}. {{MODELS}} 2018 {{Cph}}. {{Den}}. {{Oct}}. 14 2018}, author = {Rocco, Juri Di and Ruscio, Davide Di and Narayanankutty, Hrishikesh and Pierantonio, Alfonso}, editor = {Hebig, Regina and Berger, Thorsten}, date = {2018}, series = {{{CEUR}} Workshop Proceedings}, volume = {2245}, pages = {620--630}, publisher = {CEUR-WS.org}, url = {https://ceur-ws.org/Vol-2245/me_paper_6.pdf}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {TL;DR \par A study is presented that analyzes the impact of meta-model changes over visual editors based on the Sir-ius framework to provide designers with the possibility to perform an early assessment of the early assessment of the editor consistency needed to restore the editor consistency.}, timestamp = {Fri, 10 Mar 2023 16:22:20 +0100} } @inproceedings{DBLP:conf/models/RoccoRSVPT23, title = {Jjodel - a Reflective Cloud-Based Modeling Framework}, booktitle = {{{ACMIEEE Int}}. {{Conf}}. {{Model Driven Eng}}. {{Lang}}. {{Syst}}. {{MODELS}} 2023 {{Companion Västerås Swed}}. {{Oct}}. 1-6 2023}, author = {Rocco, Juri Di and Ruscio, Davide Di and Salle, Amleto Di and Vincenzo, Damiano Di and Pierantonio, Alfonso and Tinella, Giordano}, date = {2023}, pages = {55--59}, publisher = {IEEE}, doi = {10.1109/MODELS-C59198.2023.00019}, bibsource = {dblp computer science bibliography, https://dblp.org}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Fri, 05 Jan 2024 16:35:45 +0100} } @inproceedings{DBLP:conf/models/RoccoSNRP22, title = {Finding with {{NEMO}}: A Recommender System to Forecast the next Modeling Operations}, booktitle = {Proc. 25th {{Int}}. {{Conf}}. {{Model Driven Eng}}. {{Lang}}. {{Syst}}. {{MODELS}} 2022 {{Montr}}. {{Quebec Can}}. {{Oct}}. 23-28 2022}, author = {Rocco, Juri Di and Sipio, Claudio Di and Nguyen, Phuong T. and Ruscio, Davide Di and Pierantonio, Alfonso}, editor = {Syriani, Eugene and Sahraoui, Houari A. and Bencomo, Nelly and Wimmer, Manuel}, date = {2022}, pages = {154--164}, publisher = {ACM}, doi = {10.1145/3550355.3552459}, bibsource = {dblp computer science bibliography, https://dblp.org}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 0 \par cited By 0 \par cited By 0}, timestamp = {Thu, 20 Apr 2023 01:00:00 +0200} } @inproceedings{DBLP:conf/models/RoccoSRN21, ids = {DiRocco202170,di2021gnn,diroccoGNNbasedRecommenderSystem2021,diroccoGNNbasedRecommenderSystem2021a,diroccoGNNbasedRecommenderSystem2021b,roccoGNNbasedRecommenderSystem2021}, title = {A {{GNN-based}} Recommender System to Assist the Specification of Metamodels and Models}, booktitle = {24th {{Int}}. {{Conf}}. {{Model Driven Eng}}. {{Lang}}. {{Syst}}. {{MODELS}} 2021 {{Fukuoka Jpn}}. {{Oct}}. 10-15 2021}, author = {Rocco, Juri Di and Sipio, Claudio Di and Ruscio, Davide Di and Nguyen, Phuong Thanh}, date = {2021}, pages = {70--81}, publisher = {IEEE}, doi = {10.1109/MODELS50736.2021.00016}, abbrev_source_title = {Proc. - Int. Conf. Model-Driven Eng. Lang. Syst., MODELS}, affiliation = {Università degli Studi dell'Aquila, L'Aquila, Italy}, bibsource = {dblp computer science bibliography, https://dblp.org}, document_type = {Conference Paper}, langid = {english}, source = {Scopus}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 2 \par cited By 4 \par cited By 6 \par cited By 6 \par cited By 14 \par TL;DR \par MORGAN, a recommender system based on a graph neural network (GNN) to assist modelers in performing the specification of metamodels and models and evaluated on real-world datasets using various quality metrics, i.e., precision, recall, and F-measures.}, timestamp = {Tue, 21 Mar 2023 00:00:00 +0100} } @inproceedings{DBLP:conf/models/RubeiRRNP21, title = {A Lightweight Approach for the Automated Classification and Clustering of Metamodels}, booktitle = {{{ACMIEEE Int}}. {{Conf}}. {{Model Driven Eng}}. {{Lang}}. {{Syst}}. {{Companion MODELS}} 2021 {{Companion Fukuoka Jpn}}. {{Oct}}. 10-15 2021}, author = {Rubei, Riccardo and Rocco, Juri Di and Ruscio, Davide Di and Nguyen, Phuong T. and Pierantonio, Alfonso}, date = {2021}, pages = {477--482}, publisher = {IEEE}, doi = {10.1109/MODELS-C53483.2021.00074}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 0 \par cited By 0 \par cited By 1 \par cited By 1 \par cited By 1 \par TL;DR \par This paper investigates the adoption of general-purpose indexing and search features provided by Apache Lucene to support the classification and clustering of metamodel repositories and shows thatapache Lucene allows us to get accurate results whenever the mandatory requirements of more appropriate techniques, such as hierarchical clustering or neural networks cannot be met.}, timestamp = {Thu, 20 Apr 2023 01:00:00 +0200} } @inproceedings{DBLP:conf/models/RuscioKLTW21, title = {{{LowCode}} 2021: 2(ⁿᵈ) Workshop on Modeling in Low-Code Development Platforms}, booktitle = {{{ACMIEEE Int}}. {{Conf}}. {{Model Driven Eng}}. {{Lang}}. {{Syst}}. {{Companion MODELS}} 2021 {{Companion Fukuoka Jpn}}. {{Oct}}. 10-15 2021}, author = {Ruscio, Davide Di and Kolovos, Dimitris S. and family=Lara, given=Juan, prefix=de, useprefix=true and Tisi, Massimo and Wimmer, Manuel}, date = {2021}, pages = {45--46}, publisher = {IEEE}, doi = {10.1109/MODELS-C53483.2021.00014}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {⛔ No INSPIRE recid found}, note = {cited By 0 \par cited By 0 \par cited By 0 \par cited By 1}, timestamp = {Sat, 30 Sep 2023 01:00:00 +0200} } @inproceedings{DBLP:conf/models/RuscioMPT16, title = {Automatic Generation of Detailed Flight Plans from High-Level Mission Descriptions}, booktitle = {Proc. {{ACMIEEE}} 19th {{Int}}. {{Conf}}. {{Model Driven Eng}}. {{Lang}}. {{Syst}}. {{St}}.-{{Malo Fr}}. {{Oct}}. 2-7 2016}, author = {Ruscio, Davide Di and Malavolta, Ivano and Pelliccione, Patrizio and Tivoli, Massimo}, editor = {Baudry, Benoit and Combemale, Benoît}, date = {2016}, pages = {45--55}, publisher = {ACM}, url = {http://dl.acm.org/citation.cfm?id=2976794}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 23 \par cited By 23 \par TL;DR \par This paper considers mission specifications expressed through a domain-specific modeling language which can be effectively used by end-users with no technical expertise, e.g., firefighters and rescue workers, and generates the lower level logic that each drone must perform to accomplish the specified mission.}, timestamp = {Tue, 06 Nov 2018 16:57:17 +0100} } @inproceedings{DBLP:conf/models/RuscioPL12, title = {Summary of the Extreme Modeling Workshop ({{XM}} 2012)}, booktitle = {Proc. 2012 {{Extreme Model}}. {{Workshop XM}} 12 {{Innsbr}}. {{Austria Oct}}. 1 2012}, author = {Ruscio, Davide Di and Pierantonio, Alfonso and family=Lara, given=Juan, prefix=de, useprefix=true}, editor = {Ruscio, Davide Di and Pierantonio, Alfonso and family=Lara, given=Juan, prefix=de, useprefix=true}, date = {2012}, pages = {1--2}, publisher = {ACM}, doi = {10.1145/2467307.2467308}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 0 \par cited By 0 \par TL;DR \par This volume includes 8 papers from the Extreme Modeling workshop (XM'2012), a satellite event of MoDELS 2012 held on October, 1st 2012, in Innsbruck, Austria.}, timestamp = {Sat, 09 Apr 2022 01:00:00 +0200} } @inproceedings{DBLP:conf/models/SahayRP20, title = {Understanding the Role of Model Transformation Compositions in Low-Code Development Platforms}, booktitle = {{{MODELS}} 20 {{ACMIEEE}} 23rd {{Int}}. {{Conf}}. {{Model Driven Eng}}. {{Lang}}. {{Syst}}. {{Virtual Event Can}}. 18-23 {{Oct}}. 2020 {{Companion Proc}}.}, author = {Sahay, Apurvanand and Ruscio, Davide Di and Pierantonio, Alfonso}, editor = {Guerra, Esther and Iovino, Ludovico}, date = {2020}, pages = {63:1--63:5}, publisher = {ACM}, doi = {10.1145/3417990.3420197}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 3 \par cited By 3 \par TL;DR \par The adoption of concepts and tools related to the composition of model transformations to support the specification of complex workflows in LCDPs are proposed.}, timestamp = {Sun, 12 Nov 2023 00:00:00 +0100} } @inproceedings{DBLP:conf/models/SipioRN20, title = {Democratizing the Development of Recommender Systems by Means of Low-Code Platforms}, booktitle = {{{MODELS}} 20 {{ACMIEEE}} 23rd {{Int}}. {{Conf}}. {{Model Driven Eng}}. {{Lang}}. {{Syst}}. {{Virtual Event Can}}. 18-23 {{Oct}}. 2020 {{Companion Proc}}.}, author = {Sipio, Claudio Di and Ruscio, Davide Di and Nguyen, Phuong Thanh}, editor = {Guerra, Esther and Iovino, Ludovico}, date = {2020}, pages = {68:1--68:9}, publisher = {ACM}, doi = {10.1145/3417990.3420202}, bibsource = {dblp computer science bibliography, https://dblp.org}, keywords = {⛔ No INSPIRE recid found}, note = {cited By 11 \par cited By 11 \par TL;DR \par This paper proposes a low-code development approach to engineering recommender systems by means of a metamodel to represent the peculiar components and dedicated supporting tools are proposed to help developers easily model and build their custom recommender system.}, timestamp = {Thu, 24 Mar 2022 00:00:00 +0100} } @inproceedings{DBLP:conf/models/Stevens18, title = {Towards Sound, Optimal, and Flexible Building from Megamodels}, booktitle = {Proc. 21th {{ACMIEEE Int}}. {{Conf}}. {{Model Driven Eng}}. {{Lang}}. {{Syst}}. {{MODELS}} 2018 {{Cph}}. {{Den}}. {{Oct}}. 14-19 2018}, author = {Stevens, Perdita}, date = {2018}, pages = {301--311}, doi = {10.1145/3239372.3239378}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/bib/conf/models/Stevens18}, timestamp = {Wed, 21 Nov 2018 12:44:12 +0100} } @inproceedings{DBLP:conf/mompes/RuscioMPP06, title = {{{TowardsWeaving}} Software Architecture Models}, booktitle = {Proc. {{Jt}}. {{Meet}}. {{Fourth Workshop Model-Based Dev}}. {{Comput}}.-{{Based Syst}}. {{Third Int}}. {{Workshop Model-Based Methodol}}. {{Pervasive Embed}}. {{Softw}}. {{MBDMOMPES}} 2006 {{Potsdam Ger}}. {{March}} 30 2006 {{Proc}}.}, author = {Ruscio, Davide Di and Muccini, Henry and Pierantonio, Alfonso and Pelliccione, Patrizio}, editor = {Machado, Ricardo Jorge and Fernandes, João M. and Riebisch, Matthias and Schätz, Bernhard}, date = {2006}, pages = {103--112}, publisher = {IEEE Computer Society}, doi = {10.1109/MBD-MOMPES.2006.24}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 4 \par cited By 4 \par TL;DR \par The coexistence and integration of different analysis techniques at the architectural level is reduced to the problem of enriching multi-view descriptions with proper UML elements by means of directed weaving operations.}, timestamp = {Mon, 05 Feb 2024 00:00:00 +0100} } @inproceedings{DBLP:conf/monterey/AutiliCRIPT12, title = {Integration Architecture Synthesis for Taming Uncertainty in the Digital Space}, booktitle = {Large-{{Scale Complex IT Syst}}. {{Dev}}. {{Oper}}. {{Manag}}. - 17th {{Monterey Workshop}} 2012 {{Oxf}}. {{UK March}} 19-21 2012 {{Revis}}. {{Sel}}. {{Pap}}.}, author = {Autili, Marco and Cortellessa, Vittorio and Ruscio, Davide Di and Inverardi, Paola and Pelliccione, Patrizio and Tivoli, Massimo}, editor = {Calinescu, Radu and Garlan, David}, date = {2012}, series = {Lecture Notes in Computer Science}, volume = {7539}, pages = {118--131}, publisher = {Springer}, doi = {10.1007/978-3-642-34059-8\_6}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Sat, 09 Apr 2022 01:00:00 +0200} } @inproceedings{DBLP:conf/msr/NguyenRRSRP23, title = {Dealing with Popularity Bias in Recommender Systems for Third-Party Libraries: How Far Are We?}, booktitle = {20th {{IEEEACM Int}}. {{Conf}}. {{Min}}. {{Softw}}. {{Repos}}. {{MSR}} 2023 {{Melb}}. {{Aust}}. {{May}} 15-16 2023}, author = {Nguyen, Phuong T. and Rubei, Riccardo and Rocco, Juri Di and Sipio, Claudio Di and Ruscio, Davide Di and Penta, Massimiliano Di}, date = {2023}, pages = {12--24}, publisher = {IEEE}, doi = {10.1109/MSR59073.2023.00016}, bibsource = {dblp computer science bibliography, https://dblp.org}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Mon, 05 Feb 2024 00:00:00 +0100} } @inproceedings{DBLP:conf/msr/WilliamsRMRK14, title = {Models of {{OSS}} Project Meta-Information: A Dataset of Three Forges}, booktitle = {11th {{Work}}. {{Conf}}. {{Min}}. {{Softw}}. {{Repos}}. {{MSR}} 2014 {{Proc}}. {{May}} 31 - {{June}} 1 2014 {{Hyderabad India}}}, author = {Williams, James R. and Ruscio, Davide Di and Matragkas, Nicholas Drivalos and Rocco, Juri Di and Kolovos, Dimitris S.}, editor = {Devanbu, Premkumar T. and Kim, Sung and Pinzger, Martin}, date = {2014}, pages = {408--411}, publisher = {ACM}, doi = {10.1145/2597073.2597132}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 7 \par cited By 7}, timestamp = {Sun, 02 Jun 2019 01:00:00 +0200} } @inproceedings{DBLP:conf/ndss/WuGCHCD19, title = {Understanding Open Ports in Android Applications: {{Discovery}}, Diagnosis, and Security Assessment}, booktitle = {26th {{Annu}}. {{Netw}}. {{Distrib}}. {{Syst}}. {{Secur}}. {{Symp}}. {{NDSS}} 2019 {{San Diego Calif}}. {{USA Febr}}. 24-27 2019}, author = {Wu, Daoyuan and Gao, Debin and Chang, Rocky K. C. and He, En and Cheng, Eric K. T. and Deng, Robert H.}, date = {2019}, publisher = {The Internet Society}, url = {https://bit.ly/3e3enkJ}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/ndss/WuGCHCD19.bib}, timestamp = {Thu, 02 May 2019 15:52:50 +0200} } @inproceedings{DBLP:conf/oopsla/RoccoRPI15, title = {Supporting Users to Manage Breaking and Unresolvable Changes in Coupled Evolution}, booktitle = {Proc. {{Workshop Domain-Specif}}. {{Model}}. {{DSMSPLASH}} 2015 {{Pittsburgh PA USA Oct}}. 27 2015}, author = {Rocco, Juri Di and Ruscio, Davide Di and Pierantonio, Alfonso and Iovino, Ludovico}, editor = {Gray, Jeff and Sprinkle, Jonathan and Tolvanen, Juha-Pekka and Rossi, Matti}, date = {2015}, pages = {47--54}, publisher = {ACM}, doi = {10.1145/2846696.2846703}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 3 \par cited By 3 \par TL;DR \par This paper proposes an approach supporting users during the adaptation steps that cannot be fully automated, applied to cope with the coupled evolution of metamodels and model-to-text transformations.}, timestamp = {Mon, 21 Jun 2021 12:26:28 +0200} } @inproceedings{DBLP:conf/qrs/IhirweRGP22, title = {Assessing the Quality of Low-Code and Model-Driven Engineering Platforms for Engineering {{IoT}} Systems}, booktitle = {22nd {{IEEE Int}}. {{Conf}}. {{Softw}}. {{Qual}}. {{Reliab}}. {{Secur}}. {{QRS}} 2022 {{Guangzhou China Dec}}. 5-9 2022}, author = {Ihirwe, Felicien and Ruscio, Davide Di and Gianfranceschi, Simone and Pierantonio, Alfonso}, date = {2022}, pages = {583--594}, publisher = {IEEE}, doi = {10.1109/QRS57517.2022.00065}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 0}, timestamp = {Sat, 30 Sep 2023 01:00:00 +0200} } @inproceedings{DBLP:conf/quatic/BascianiRRIP16, title = {A Customizable Approach for the Automated Quality Assessment of Modelling Artifacts}, booktitle = {10th {{Int}}. {{Conf}}. {{Qual}}. {{Inf}}. {{Commun}}. {{Technol}}. {{QUATIC}} 2016 {{Lisbon Port}}. {{Sept}}. 6-9 2016}, author = {Basciani, Francesco and Rocco, Juri Di and Ruscio, Davide Di and Iovino, Ludovico and Pierantonio, Alfonso}, editor = {Paulk, Mark C. and Machado, Ricardo J. and Brito, Miguel A. and Goulão, Miguel and Amaral, Vasco}, date = {2016}, pages = {88--93}, publisher = {IEEE Computer Society}, doi = {10.1109/QUATIC.2016.025}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 15 \par cited By 15}, timestamp = {Wed, 09 Aug 2023 08:39:51 +0200} } @inproceedings{DBLP:conf/quatic/RuscioKKMV16, title = {Supporting Custom Quality Models to Analyse and Compare Open-Source Software}, booktitle = {10th {{Int}}. {{Conf}}. {{Qual}}. {{Inf}}. {{Commun}}. {{Technol}}. {{QUATIC}} 2016 {{Lisbon Port}}. {{Sept}}. 6-9 2016}, author = {Ruscio, Davide Di and Kolovos, Dimitrios S. and Korkontzelos, Yannis and Matragkas, Nicholas and Vinju, Jurgen J.}, editor = {Paulk, Mark C. and Machado, Ricardo J. and Brito, Miguel A. and Goulão, Miguel and Amaral, Vasco}, date = {2016}, pages = {94--99}, publisher = {IEEE Computer Society}, doi = {10.1109/QUATIC.2016.026}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {⛔ No INSPIRE recid found}, note = {cited By 1 \par cited By 1}, timestamp = {Fri, 24 Mar 2023 00:00:00 +0100} } @inproceedings{DBLP:conf/re/AutiliRILT11, title = {A Development Process for Requirements Based Service Choreography}, booktitle = {Requir. {{Eng}}. {{Syst}}. {{Serv}}. {{Syst}}.--{{Syst}}. {{RESS}} 2011 {{Workshop Trento Italy August}} 30 2011}, author = {Autili, Marco and Ruscio, Davide Di and Inverardi, Paola and Lockerbie, James and Tivoli, Massimo}, date = {2011}, pages = {59--62}, publisher = {IEEE Computer Society}, doi = {10.1109/RESS.2011.6043925}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Sat, 30 Sep 2023 01:00:00 +0200} } @inproceedings{DBLP:conf/recsys/AbdollahpouriMB19, title = {The Unfairness of Popularity Bias in Recommendation}, booktitle = {Proc. {{Workshop Recomm}}. {{Multi-Stakehold}}. {{Environ}}. {{Co-Located}} 13th {{ACM Conf}}. {{Recomm}}. {{Syst}}. {{RecSys}} 2019 {{Cph}}. {{Den}}. {{Sept}}. 20 2019}, author = {Abdollahpouri, Himan and Mansoury, Masoud and Burke, Robin and Mobasher, Bamshad}, editor = {Burke, Robin and Abdollahpouri, Himan and Malthouse, Edward C. and Thai, K. P. and Zhang, Yongfeng}, date = {2019}, series = {{{CEUR}} Workshop Proceedings}, volume = {2440}, publisher = {CEUR-WS.org}, url = {http://ceur-ws.org/Vol-2440/paper4.pdf}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/recsys/AbdollahpouriMB19.bib}, note = {TL;DR \par The experimental results on a movie dataset show that in many recommendation algorithms the recommendations the users get are extremely concentrated on popular items even if a user is interested in long-tail and non-popular items showing an extreme bias disparity.}, timestamp = {Wed, 12 Feb 2020 16:45:17 +0100} } @inproceedings{DBLP:conf/recsys/NguyenRR18, title = {Knowledge-Aware Recommender System for Software Development}, booktitle = {Proc. {{Workshop Knowl}}.-{{Aware Conversational Recomm}}. {{Syst}}. 2018 {{Co-Located}} 12th {{ACM Conf}}. {{Recomm}}. {{Syst}}. {{KaRSRecSys}} 2018 {{Vanc}}. {{Can}}. {{Oct}}. 7 2018}, author = {Nguyen, Phuong Thanh and Rocco, Juri Di and Ruscio, Davide Di}, editor = {Anelli, Vito Walter and Noia, Tommaso Di and Lops, Pasquale and Musto, Cataldo and Zanker, Markus and Basile, Pierpaolo and Bridge, Derek G. and Narducci, Fedelucio}, date = {2018}, series = {{{CEUR}} Workshop Proceedings}, volume = {2290}, pages = {16--22}, publisher = {CEUR-WS.org}, url = {https://ceur-ws.org/Vol-2290/kars2018_paper4.pdf}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 1 \par cited By 1}, timestamp = {Fri, 10 Mar 2023 16:22:14 +0100} } @inproceedings{DBLP:conf/recsys/RoccoRSNP21, title = {On the Need for a Body of Knowledge on Recommender Systems (Short Paper)}, booktitle = {Jt. {{Workshop Proc}}. 3rd {{Ed}}. {{Knowl}}.-{{Aware Conversational Recomm}}. {{Syst}}. {{KaRS}} 5th {{Ed}}. {{Recomm}}. {{Complex Environ}}. {{ComplexRec Co-Located}} 15th {{ACM Conf}}. {{Recomm}}. {{Syst}}. {{RecSys}} 2021 {{Virtual Event Amst}}. {{Neth}}. {{Sept}}. 25 2021}, author = {Rocco, Juri Di and Ruscio, Davide Di and Sipio, Claudio Di and Nguyen, Phuong Thanh and Pomo, Claudio}, editor = {Anelli, Vito Walter and Basile, Pierpaolo and Noia, Tommaso Di and Donini, Francesco Maria and Musto, Cataldo and Narducci, Fedelucio and Zanker, Markus and Abdollahpouri, Himan and Bogers, Toine and Mobasher, Bamshad and Petersen, Casper and Pera, Maria Soledad}, date = {2021}, series = {{{CEUR}} Workshop Proceedings}, volume = {2960}, publisher = {CEUR-WS.org}, url = {https://ceur-ws.org/Vol-2960/paper5.pdf}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 0 \par TL;DR \par This work motivates a BOK for recommender systems and proposes a methodology that can be employed to support the definition of an RSBOK, a precisely curated and organized core set of concepts and practices.}, timestamp = {Fri, 10 Mar 2023 16:22:14 +0100} } @inproceedings{DBLP:conf/recsys/SipioRRN21, title = {A Low-Code Tool Supporting the Development of Recommender Systems}, booktitle = {{{RecSys}} 21 {{Fifteenth ACM Conf}}. {{Recomm}}. {{Syst}}. {{Amst}}. {{Neth}}. 27 {{Sept}}. 2021 - 1 {{Oct}}. 2021}, author = {Sipio, Claudio Di and Rocco, Juri Di and Ruscio, Davide Di and Nguyen, Phuong Thanh}, editor = {Pampín, Humberto Jesús Corona and Larson, Martha A. and Willemsen, Martijn C. and Konstan, Joseph A. and McAuley, Julian J. and Garcia-Gathright, Jean and Huurnink, Bouke and Oldridge, Even}, date = {2021}, pages = {741--744}, publisher = {ACM}, doi = {10.1145/3460231.3478885}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {⛔ No INSPIRE recid found}, note = {cited By 0 \par cited By 0 \par cited By 0 \par cited By 4 \par TL;DR \par LEV4REC, a low-code environment to assist developers in designing, configuring, and delivering recommender systems, is proposed and used to specify two existing RSs built on top of two different recommendation algorithms, i.e., collaborative filtering and supervised machine learning.}, timestamp = {Tue, 21 Mar 2023 00:00:00 +0100} } @inproceedings{DBLP:conf/recsys/WuSCTP14, title = {The Browsemaps: {{Collaborative}} Filtering at {{LinkedIn}}}, booktitle = {{{RSWeb}}@{{RecSys}}}, author = {Wu, Lili and Shah, Sam and Choi, Sean and Tiwari, Mitul and Posse, Christian}, date = {2014}, series = {{{CEUR}} Workshop Proceedings}, volume = {1271}, publisher = {CEUR-WS.org}, note = {TL;DR \par This paper presents LinkedIn’s horizontal collaborative filtering infrastructure, known as browsemaps, which enables rapid development, deployment, and computation of collaborative filtering recommendations for almost any use case on LinkedIn.} } @inproceedings{DBLP:conf/sac/BalzeraniRPA05, title = {A Product Line Architecture for Web Applications}, booktitle = {Proc. 2005 {{ACM Symp}}. {{Appl}}. {{Comput}}. {{SAC St}}. {{Fe N}}. {{M}}. {{USA March}} 13-17 2005}, author = {Balzerani, Luca and Ruscio, Davide Di and Pierantonio, Alfonso and Angelis, Guglielmo De}, editor = {Haddad, Hisham and Liebrock, Lorie M. and Omicini, Andrea and Wainwright, Roger L.}, date = {2005}, pages = {1689--1693}, publisher = {ACM}, doi = {10.1145/1066677.1067059}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 15 \par cited By 15}, timestamp = {Wed, 25 Sep 2019 01:00:00 +0200} } @inproceedings{DBLP:conf/sac/BascianiPRIP21, title = {Integrating Semantic Reasoning in Information Loss-Based Transformation Chain Rankers}, booktitle = {{{SAC}} 21 36th {{ACMSIGAPP Symp}}. {{Appl}}. {{Comput}}. {{Virtual Event Repub}}. {{Korea March}} 22-26 2021}, author = {Basciani, Francesco and Pompeo, Daniele Di and Ruscio, Davide Di and Iovino, Ludovico and Pierantonio, Alfonso}, editor = {Hung, Chih-Cheng and Hong, Jiman and Bechini, Alessio and Song, Eunjee}, date = {2021}, pages = {1494--1503}, publisher = {ACM}, doi = {10.1145/3412841.3442024}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 0 \par cited By 0 \par cited By 0 \par cited By 0}, timestamp = {Tue, 21 Mar 2023 00:00:00 +0100} } @inproceedings{DBLP:conf/sac/CicchettiRP06, title = {Weaving Concerns in Model Based Development of Data-Intensive Web Applications}, booktitle = {Proc. 2006 {{ACM Symp}}. {{Appl}}. {{Comput}}. {{SAC Dijon Fr}}. {{April}} 23-27 2006}, author = {Cicchetti, Antonio and Ruscio, Davide Di and Pierantonio, Alfonso}, editor = {Haddad, Hisham}, date = {2006}, pages = {1256--1261}, publisher = {ACM}, doi = {10.1145/1141277.1141571}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {⛔ No INSPIRE recid found}, note = {cited By 4 \par cited By 4 \par TL;DR \par This paper proposes explicit weaving models to define rigorous connections between the different artifacts produced during a system development, in order to enhance their reuse and maintenance and perform operations based on the connection semantics.}, timestamp = {Tue, 06 Nov 2018 11:06:49 +0100} } @inproceedings{DBLP:conf/sac/CicchettiRS07, title = {Software Customization in Model Driven Development of Web Applications}, booktitle = {Proc. 2007 {{ACM Symp}}. {{Appl}}. {{Comput}}. {{SAC Seoul Korea March}} 11-15 2007}, author = {Cicchetti, Antonio and Ruscio, Davide Di and Salle, Amleto Di}, editor = {Cho, Yookun and Wainwright, Roger L. and Haddad, Hisham and Shin, Sung Y. and Koo, Yong Wan}, date = {2007}, pages = {1025--1030}, publisher = {ACM}, doi = {10.1145/1244002.1244224}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 15 \par cited By 15 \par TL;DR \par This paper discusses and attempt to hand-tune the generated code by providing an approach supporting its merging with hand written modifications by considering the behaviour model of the system under study to graphically specify the injection points where the modifications have to occur.}, timestamp = {Mon, 05 Feb 2024 00:00:00 +0100} } @book{DBLP:conf/sattose/2014, title = {Post-Proceedings of the Seventh Seminar on Advanced Techniques and Tools for Software Evolution, {{SATToSE}} 2014, {{L}}'aquila, Italy, 9-11 July 2014}, editor = {Ruscio, Davide Di and Zaytsev, Vadim}, date = {2015}, series = {{{CEUR}} Workshop Proceedings}, volume = {1354}, publisher = {CEUR-WS.org}, url = {https://ceur-ws.org/Vol-1354}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, urn = {urn:nbn:de:0074-1354-1}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Fri, 10 Mar 2023 00:00:00 +0100} } @inproceedings{DBLP:conf/sattose/BascianiRRIP14, title = {Qualifying Chains of Transformation with Coverage-Based Evaluation Criteria}, booktitle = {Post-{{Proc}}. {{Seventh Semin}}. {{Adv}}. {{Tech}}. {{Tools Softw}}. {{Evol}}. {{SATToSE}} 2014 {{Aquila Italy}} 9-11 {{July}} 2014}, author = {Basciani, Francesco and Rocco, Juri Di and Ruscio, Davide Di and Iovino, Ludovico and Pierantonio, Alfonso}, editor = {Ruscio, Davide Di and Zaytsev, Vadim}, date = {2014}, series = {{{CEUR}} Workshop Proceedings}, volume = {1354}, pages = {79--89}, publisher = {CEUR-WS.org}, url = {https://ceur-ws.org/Vol-1354/paper-07.pdf}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {⛔ No INSPIRE recid found}, note = {cited By 0 \par cited By 0 \par TL;DR \par This paper proposes an approach to classify these suitable chains with respect to the coverage of the metamodels involved in the transformation with an evaluation criteria which gives as an indication of how much information a transformation chain covers over another.}, timestamp = {Fri, 10 Mar 2023 16:23:14 +0100} } @inproceedings{DBLP:conf/sattose/RoccoRIP14, title = {Describing the Correlations between Metamodels and Transformations Aspects}, booktitle = {Post-{{Proc}}. {{Seventh Semin}}. {{Adv}}. {{Tech}}. {{Tools Softw}}. {{Evol}}. {{SATToSE}} 2014 {{Aquila Italy}} 9-11 {{July}} 2014}, author = {Rocco, Juri Di and Ruscio, Davide Di and Iovino, Ludovico and Pierantonio, Alfonso}, editor = {Ruscio, Davide Di and Zaytsev, Vadim}, date = {2014}, series = {{{CEUR}} Workshop Proceedings}, volume = {1354}, pages = {90--101}, publisher = {CEUR-WS.org}, url = {https://ceur-ws.org/Vol-1354/paper-08.pdf}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 1 \par cited By 1}, timestamp = {Fri, 10 Mar 2023 00:00:00 +0100} } @inproceedings{DBLP:conf/scc2/Fletcher19, title = {A Quality-Aware Web {{API}} Recommender System for Mashup Development}, booktitle = {Serv. {{Comput}}. - {{SCC}} 2019 - 16th {{Int}}. {{Conf}}. {{Held Part Serv}}. {{Conf}}. {{Fed}}. {{SCF}} 2019 {{San Diego CA USA June}} 25-30 2019 {{Proc}}.}, author = {Fletcher, Kenneth K.}, editor = {Ferreira, João Eduardo and Musaev, Aibek and Zhang, Liang-Jie}, date = {2019}, series = {Lecture Notes in Computer Science}, volume = {11515}, pages = {1--15}, publisher = {Springer}, doi = {10.1007/978-3-030-23554-3\_1}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/scc2/Fletcher19.bib}, timestamp = {Fri, 05 Jul 2019 09:41:11 +0200} } @inproceedings{DBLP:conf/serene/RuscioMP13, title = {Engineering a Platform for Mission Planning of Autonomous and Resilient Quadrotors}, booktitle = {Softw. {{Eng}}. {{Resilient Syst}}. 5th {{Int}}. {{Workshop SERENE}} 2013 {{Kiev Ukr}}. {{Oct}}. 3-4 2013 {{Proc}}.}, author = {Ruscio, Davide Di and Malavolta, Ivano and Pelliccione, Patrizio}, editor = {Gorbenko, Anatoliy and Romanovsky, Alexander B. and Kharchenko, Vyacheslav S.}, date = {2013}, series = {Lecture Notes in Computer Science}, volume = {8166}, pages = {33--47}, publisher = {Springer}, doi = {10.1007/978-3-642-40894-6\_3}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Mon, 05 Feb 2024 20:33:35 +0100} } @inproceedings{DBLP:conf/serene/RuscioMP14, title = {The Role of Parts in the System Behaviour}, booktitle = {Softw. {{Eng}}. {{Resilient Syst}}. - 6th {{Int}}. {{Workshop SERENE}} 2014 {{Bp}}. {{Hung}}. {{Oct}}. 15-16 2014 {{Proc}}.}, author = {Ruscio, Davide Di and Malavolta, Ivano and Pelliccione, Patrizio}, editor = {Majzik, István and Vieira, Marco}, date = {2014}, series = {Lecture Notes in Computer Science}, volume = {8785}, pages = {24--39}, publisher = {Springer}, doi = {10.1007/978-3-319-12241-0\_3}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Tue, 29 Dec 2020 00:00:00 +0100} } @inproceedings{DBLP:conf/serene/RuscioP13, title = {Supporting the Evolution of Free and Open Source Software Distributions}, booktitle = {Softw. {{Eng}}. {{Resilient Syst}}. 5th {{Int}}. {{Workshop SERENE}} 2013 {{Kiev Ukr}}. {{Oct}}. 3-4 2013 {{Proc}}.}, author = {Ruscio, Davide Di and Pelliccione, Patrizio}, editor = {Gorbenko, Anatoliy and Romanovsky, Alexander B. and Kharchenko, Vyacheslav S.}, date = {2013}, series = {Lecture Notes in Computer Science}, volume = {8166}, pages = {56--63}, publisher = {Springer}, doi = {10.1007/978-3-642-40894-6\_5}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Sun, 02 Jun 2019 01:00:00 +0200} } @inproceedings{DBLP:conf/sfm/RuscioEP12, title = {Model Transformations}, booktitle = {Form. {{Methods Model-Driven Eng}}. - 12th {{Int}}. {{Sch}}. {{Form}}. {{Methods Des}}. {{Comput}}. {{Commun}}. {{Softw}}. {{Syst}}. {{SFM}} 2012 {{Bertinoro Italy June}} 18-23 2012 {{Adv}}. {{Lect}}.}, author = {Ruscio, Davide Di and Eramo, Romina and Pierantonio, Alfonso}, editor = {Bernardo, Marco and Cortellessa, Vittorio and Pierantonio, Alfonso}, date = {2012}, series = {Lecture Notes in Computer Science}, volume = {7320}, pages = {91--136}, publisher = {Springer}, doi = {10.1007/978-3-642-30982-3\_4}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Mon, 05 Feb 2024 00:00:00 +0100} } @book{DBLP:conf/sigsoft/2005, title = {Proceedings of the 10th European Software Engineering Conference Held Jointly with 13th {{ACM SIGSOFT}} International Symposium on Foundations of Software Engineering, 2005, Lisbon, Portugal, September 5-9, 2005}, editor = {Wermelinger, Michel and Gall, Harald C.}, date = {2005}, publisher = {ACM}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/bib/conf/sigsoft/2005}, isbn = {1-59593-014-0}, note = {TL;DR \par ESEC/FSE'05 features three exciting keynote addresses by Oscar Nierstrasz (Professor of Computer Science at the University of Bern, Switzerland), Antonio Câmara (CEO of YDreams and Professor at the New University of Lisbon, Portugal), and the two ACM SIGSOFT Outstanding Research Award recipients.}, timestamp = {Wed, 01 Apr 2015 20:06:24 +0200} } @inproceedings{DBLP:conf/sigsoft/AutiliCRIPT11, title = {{{EAGLE}}: Engineering Software in the Ubiquitous Globe by Leveraging {{uncErtainty}}}, booktitle = {{{SIGSOFTFSE11}} 19th {{ACM SIGSOFT Symp}}. {{Found}}. {{Softw}}. {{Eng}}. {{FSE-19 ESEC11}} 13th {{Eur}}. {{Softw}}. {{Eng}}. {{Conf}}. {{ESEC-13 Szeged Hung}}. {{Sept}}. 5-9 2011}, author = {Autili, Marco and Cortellessa, Vittorio and Ruscio, Davide Di and Inverardi, Paola and Pelliccione, Patrizio and Tivoli, Massimo}, editor = {Gyimóthy, Tibor and Zeller, Andreas}, date = {2011}, pages = {488--491}, publisher = {ACM}, doi = {10.1145/2025113.2025199}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {⛔ No INSPIRE recid found}, note = {cited By 15 \par cited By 15 \par TL;DR \par A novel paradigm of software production process is introduced that explores available software and assesses its degree of uncertainty in relation to the opportunistic goal G, assists the producer in creating the appropriate integration means towards G, and validates the quality of the integrated system with respect to G and the current context.}, timestamp = {Sat, 09 Apr 2022 01:00:00 +0200} } @inproceedings{DBLP:conf/sigsoft/AutiliRSP14, title = {{{CHOReOSynt}}: Enforcing Choreography Realizability in the Future Internet}, booktitle = {Proc. 22nd {{ACM SIGSOFT Int}}. {{Symp}}. {{Found}}. {{Softw}}. {{Eng}}. {{FSE-22 Hong Kong China Novemb}}. 16 - 22 2014}, author = {Autili, Marco and Ruscio, Davide Di and Salle, Amleto Di and Perucci, Alexander}, editor = {Cheung, Shing-Chi and Orso, Alessandro and Storey, Margaret-Anne D.}, date = {2014}, pages = {723--726}, publisher = {ACM}, doi = {10.1145/2635868.2661667}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {⛔ No INSPIRE recid found}, note = {cited By 13 \par cited By 13 \par TL;DR \par This paper describes the CHOReOSynt tool, which has been conceived to deal with an additional problem, namely, automated choreography enforcement, and solves this problem by automatically synthesizing additional software entities that, when interposed among the services, allow for preventing undesired interactions.}, timestamp = {Mon, 05 Feb 2024 00:00:00 +0100} } @inproceedings{DBLP:conf/sigsoft/InverardiARPT13, title = {Producing Software by Integration: Challenges and Research Directions (Keynote)}, booktitle = {Jt. {{Meet}}. {{Eur}}. {{Softw}}. {{Eng}}. {{Conf}}. {{ACM SIGSOFT Symp}}. {{Found}}. {{Softw}}. {{Eng}}. {{ESECFSE13 St}}. {{Petersburg Russ}}. {{Fed}}. {{August}} 18-26 2013}, author = {Inverardi, Paola and Autili, Marco and Ruscio, Davide Di and Pelliccione, Patrizio and Tivoli, Massimo}, editor = {Meyer, Bertrand and Baresi, Luciano and Mezini, Mira}, date = {2013}, pages = {2--12}, publisher = {ACM}, doi = {10.1145/2491411.2505428}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {⛔ No INSPIRE recid found}, note = {cited By 5 \par cited By 5}, timestamp = {Sat, 09 Apr 2022 01:00:00 +0200} } @inproceedings{DBLP:conf/sigsoft/RuscioKKMV15, title = {{{OSSMETER}}: A Software Measurement Platform for Automatically Analysing Open Source Software Projects}, booktitle = {Proc. 2015 10th {{Jt}}. {{Meet}}. {{Found}}. {{Softw}}. {{Eng}}. {{ESECFSE}} 2015 {{Bergamo Italy August}} 30 - {{Sept}}. 4 2015}, author = {Ruscio, Davide Di and Kolovos, Dimitris S. and Korkontzelos, Ioannis and Matragkas, Nicholas Drivalos and Vinju, Jurgen J.}, editor = {Nitto, Elisabetta Di and Harman, Mark and Heymans, Patrick}, date = {2015}, pages = {970--973}, publisher = {ACM}, doi = {10.1145/2786805.2803186}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {⛔ No INSPIRE recid found}, note = {cited By 7 \par cited By 7 \par TL;DR \par OSSMETER is an extensible and scalable platform that can monitor and incrementally analyse a large number of OSS projects and can directly compare different O SS projects with each other.}, timestamp = {Sat, 09 Apr 2022 01:00:00 +0200} } @book{DBLP:conf/sle/2015, title = {Proceedings of the 2015 {{ACM SIGPLAN}} International Conference on Software Language Engineering, {{SLE}} 2015, Pittsburgh, {{PA}}, {{USA}}, October 25-27, 2015}, editor = {Paige, Richard F. and Ruscio, Davide Di and Völter, Markus}, date = {2015}, publisher = {ACM}, doi = {10.1145/2814251}, bibsource = {dblp computer science bibliography, https://dblp.org}, isbn = {978-1-4503-3686-4}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Thu, 21 Apr 2022 01:00:00 +0200} } @inproceedings{DBLP:conf/sle/CicchettiREP10, title = {{{JTL}}: A Bidirectional and Change Propagating Transformation Language}, booktitle = {Softw. {{Lang}}. {{Eng}}. - {{Third Int}}. {{Conf}}. {{SLE}} 2010 {{Eindh}}. {{Neth}}. {{Oct}}. 12-13 2010 {{Revis}}. {{Sel}}. {{Pap}}.}, author = {Cicchetti, Antonio and Ruscio, Davide Di and Eramo, Romina and Pierantonio, Alfonso}, editor = {Malloy, Brian A. and Staab, Steffen and family=Brand, given=Mark, prefix=van den, useprefix=true}, date = {2010}, series = {Lecture Notes in Computer Science}, volume = {6563}, pages = {183--202}, publisher = {Springer}, doi = {10.1007/978-3-642-19440-5\_11}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Mon, 05 Feb 2024 00:00:00 +0100} } @book{DBLP:conf/staf/2013bigmde, title = {Proceedings of the Workshop on Scalability in Model Driven Engineering, Budapest, Hungary, June 17, 2013}, editor = {Ruscio, Davide Di and Kolovos, Dimitris S. and Matragkas, Nicholas}, date = {2013}, publisher = {ACM}, url = {http://dl.acm.org/citation.cfm?id=2487766}, bibsource = {dblp computer science bibliography, https://dblp.org}, isbn = {978-1-4503-2165-5}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Fri, 20 Jan 2017 00:00:00 +0100} } @book{DBLP:conf/staf/2014bigmde, title = {Proceedings of the 2nd Workshop on Scalability in Model Driven Engineering Co-Located with the Software Technologies: Applications and Foundations Conference, {{BigMDE}}@{{STAF2014}}, York, {{UK}}, July 24, 2014}, editor = {Kolovos, Dimitris S. and Ruscio, Davide Di and Matragkas, Nicholas Drivalos and family=Lara, given=Juan, prefix=de, useprefix=true and Ráth, István and Tisi, Massimo}, date = {2014}, series = {{{CEUR}} Workshop Proceedings}, volume = {1206}, publisher = {CEUR-WS.org}, url = {https://ceur-ws.org/Vol-1206}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, urn = {urn:nbn:de:0074-1206-7}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Fri, 10 Mar 2023 00:00:00 +0100} } @book{DBLP:conf/staf/2015bigmde, title = {Proceedings of the 3rd Workshop on Scalable Model Driven Engineering Part of the Software Technologies: Applications and Foundations ({{STAF}} 2015) Federation of Conferences, {{L}}'aquila, Italy, July 23, 2015}, editor = {Kolovos, Dimitris S. and Ruscio, Davide Di and Matragkas, Nicholas Drivalos and Cuadrado, Jesús Sánchez and Ráth, István and Tisi, Massimo}, date = {2015}, series = {{{CEUR}} Workshop Proceedings}, volume = {1406}, publisher = {CEUR-WS.org}, url = {https://ceur-ws.org/Vol-1406}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, urn = {urn:nbn:de:0074-1406-4}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Fri, 10 Mar 2023 00:00:00 +0100} } @book{DBLP:conf/staf/2016bigmde, title = {Proceedings of the 4rd Workshop on Scalable Model Driven Engineering Part of the Software Technologies: Applications and Foundations ({{STAF}} 2016) Federation of Conferences, Vienna, Austria, July 8, 2016}, editor = {Kolovos, Dimitris S. and Ruscio, Davide Di and Matragkas, Nicholas Drivalos and Cuadrado, Jesús Sánchez and Ráth, István and Tisi, Massimo}, date = {2016}, series = {{{CEUR}} Workshop Proceedings}, volume = {1652}, publisher = {CEUR-WS.org}, url = {https://ceur-ws.org/Vol-1652}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, urn = {urn:nbn:de:0074-1652-C}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Fri, 10 Mar 2023 00:00:00 +0100} } @inproceedings{DBLP:conf/staf/AlmeidaABBRRKKH15, title = {{{OSSMETER}}: Automated Measurement and Analysis of Open Source Software}, booktitle = {Proc. {{Proj}}. {{Showc}}. {{Part Softw}}. {{Technol}}. {{Appl}}. {{Found}}. 2015 {{Fed}}. {{Conf}}. {{STAF}} 2015 {{Aquila Italy July}} 22 2015}, author = {Almeida, Bruno and Ananiadou, Sophia and Bagnato, Alessandra and Barbero, Alberto Berreteaga and Rocco, Juri Di and Ruscio, Davide Di and Kolovos, Dimitrios S. and Korkontzelos, Ioannis and Hansen, Scott and Maló, Pedro and Drivalos, Nikolaos and Paige, Richard F. and Vinju, Jurgen J.}, editor = {Autili, Marco and Ritter, Tom and Sadovykh, Andrey and Tivoli, Massimo}, date = {2015}, series = {{{CEUR}} Workshop Proceedings}, volume = {1400}, pages = {36--43}, publisher = {CEUR-WS.org}, url = {https://ceur-ws.org/Vol-1400/paper9.pdf}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 4 \par cited By 4}, timestamp = {Fri, 10 Mar 2023 16:23:10 +0100} } @inproceedings{DBLP:conf/staf/BagnatoBBCRRGHK17, title = {Developer-Centric Knowledge Mining from Large Open-Source Software Repositories ({{CROSSMINER}})}, booktitle = {Softw. {{Technol}}. {{Appl}}. {{Found}}. - {{STAF}} 2017 {{Collocated Workshop Marburg Ger}}. {{July}} 17-21 2017 {{Revis}}. {{Sel}}. {{Pap}}.}, author = {Bagnato, Alessandra and Barmpis, Konstantinos and Bessis, Nik and Cabrera-Diego, Luis Adrián and Rocco, Juri Di and Ruscio, Davide Di and Gergely, Tamás and Hansen, Scott and Kolovos, Dimitris S. and Krief, Philippe and Korkontzelos, Ioannis and Laurière, Stéphane and family=Fuente, given=Jose Manrique Lopez, prefix=de la, useprefix=true and Maló, Pedro and Paige, Richard F. and Spinellis, Diomidis and Thomas, Cedric and Vinju, Jurgen J.}, editor = {Seidl, Martina and Zschaler, Steffen}, date = {2017}, series = {Lecture Notes in Computer Science}, volume = {10748}, pages = {375--384}, publisher = {Springer}, doi = {10.1007/978-3-319-74730-9\_33}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Sat, 30 Sep 2023 01:00:00 +0200} } @inproceedings{DBLP:conf/staf/BoudeffaABRMA19, title = {Integrating and Deploying Heterogeneous Components by Means of a Microservices Architecture in the {{CROSSMINER}} Project}, booktitle = {{{STAF}} 2019 {{Co-Located Events Jt}}. {{Proc}}. 1st {{Jr}}. {{Res}}. {{Community Event}} 2nd {{Int}}. {{Workshop Model-Driven Eng}}. {{Des}}.-{{Runtime Interact}}. {{Complex Syst}}. 1st {{Res}}. {{Proj}}. {{Showc}}. {{Workshop Co-Located Softw}}. {{Technol}}. {{Appl}}. {{Found}}. {{STAF}} 2019 {{Eindh}}. {{Neth}}. {{July}} 15 - 19 2019}, author = {Boudeffa, Amin and Abherve, Antonin and Bagnato, Alessandra and Ruscio, Davide Di and Mateus, Márcio and Almeida, Bruno}, editor = {Bagnato, Alessandra and Bruneliere, Hugo and Burgueño, Loli and Eramo, Romina and Gómez, Abel}, date = {2019}, series = {{{CEUR}} Workshop Proceedings}, volume = {2405}, pages = {67--72}, publisher = {CEUR-WS.org}, url = {https://ceur-ws.org/Vol-2405/12_paper.pdf}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 1 \par cited By 1 \par TL;DR \par This paper presents a microservice architecture, which is implemented by relying on Docker to support the integration and deployment of the CROSSMINER components.}, timestamp = {Fri, 10 Mar 2023 16:23:10 +0100} } @inproceedings{DBLP:conf/staf/RuscioKM13, title = {Scalability in Model Driven Engineering: {{BigMDE}}'13 Workshop Summary}, booktitle = {Proc. {{Workshop Scalability Model Driven Eng}}. {{Bp}}. {{Hung}}. {{June}} 17 2013}, author = {Ruscio, Davide Di and Kolovos, Dimitrios S. and Matragkas, Nicholas Drivalos}, editor = {Ruscio, Davide Di and Kolovos, Dimitris S. and Matragkas, Nicholas}, date = {2013}, pages = {1}, publisher = {ACM}, doi = {10.1145/2487766.2487767}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {⛔ No INSPIRE recid found}, note = {cited By 1 \par cited By 1}, timestamp = {Sun, 02 Jun 2019 01:00:00 +0200} } @inproceedings{DBLP:conf/staf/RuscioMP14, title = {A Family of Domain-Specific Languages for Specifying Civilian Missions of Multi-Robot Systems}, booktitle = {Proc. 1st {{Int}}. {{Workshop Model-Driven Robot Softw}}. {{Eng}}. {{Co-Located Int}}. {{Conf}}. {{Softw}}. {{Technol}}. {{Appl}}. {{Found}}. {{MORSESTAF}} 2014 {{York UK July}} 21 2014}, author = {Ruscio, Davide Di and Malavolta, Ivano and Pelliccione, Patrizio}, editor = {Aßmann, Uwe and Wagner, Gerd}, date = {2014}, series = {{{CEUR}} Workshop Proceedings}, volume = {1319}, pages = {16--29}, publisher = {CEUR-WS.org}, url = {https://ceur-ws.org/Vol-1319/morse14_paper_02.pdf}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 12 \par cited By 12}, timestamp = {Fri, 10 Mar 2023 16:23:10 +0100} } @inproceedings{DBLP:conf/staf/TisiMKLGRPW19, title = {Lowcomote: Training the next Generation of Experts in Scalable Low-Code Engineering Platforms}, booktitle = {{{STAF}} 2019 {{Co-Located Events Jt}}. {{Proc}}. 1st {{Jr}}. {{Res}}. {{Community Event}} 2nd {{Int}}. {{Workshop Model-Driven Eng}}. {{Des}}.-{{Runtime Interact}}. {{Complex Syst}}. 1st {{Res}}. {{Proj}}. {{Showc}}. {{Workshop Co-Located Softw}}. {{Technol}}. {{Appl}}. {{Found}}. {{STAF}} 2019 {{Eindh}}. {{Neth}}. {{July}} 15 - 19 2019}, author = {Tisi, Massimo and Mottu, Jean-Marie and Kolovos, Dimitrios S. and family=Lara, given=Juan, prefix=de, useprefix=true and Guerra, Esther and Ruscio, Davide Di and Pierantonio, Alfonso and Wimmer, Manuel}, editor = {Bagnato, Alessandra and Bruneliere, Hugo and Burgueño, Loli and Eramo, Romina and Gómez, Abel}, date = {2019}, series = {{{CEUR}} Workshop Proceedings}, volume = {2405}, pages = {73--78}, publisher = {CEUR-WS.org}, url = {https://ceur-ws.org/Vol-2405/13_paper.pdf}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 13 \par cited By 13}, timestamp = {Fri, 10 Mar 2023 00:00:00 +0100} } @inproceedings{DBLP:conf/tsp/IhirweIR21, title = {Towards an {{MQTT5}} Geo-Location Extension for Location-Aware Applications}, booktitle = {44th {{Int}}. {{Conf}}. {{Telecommun}}. {{Signal Process}}. {{TSP}} 2021 {{Brno Czech Repub}}. {{July}} 26-28 2021}, author = {Ihirwe, Felicien and Iovino, Giovanni and Ruscio, Davide Di}, date = {2021}, pages = {100--105}, publisher = {IEEE}, doi = {10.1109/TSP52935.2021.9522590}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 1 \par cited By 1 \par cited By 1 \par cited By 1 \par TL;DR \par A novel extension of the recently released MQTT5 protocol is proposed that will permit message delivery by not only respecting the topics of interest but also in addition to geo-referenced information provided by both publishers and subscribers.}, timestamp = {Tue, 21 Mar 2023 00:00:00 +0100} } @inproceedings{DBLP:conf/wcre/AsyrofiT0J20, title = {{{AUSearch}}: {{Accurate API}} Usage Search in {{GitHub}} Repositories with Type Resolution}, booktitle = {27th {{IEEE Int}}. {{Conf}}. {{Softw}}. {{Anal}}. {{Evol}}. {{Reengineering SANER}} 2020 {{Lond}}. {{Can}}. {{Febr}}. 18-21 2020}, author = {Asyrofi, Muhammad Hilmi and Thung, Ferdian and Lo, David and Jiang, Lingxiao}, date = {2020}, pages = {637--641}, doi = {10.1109/SANER48275.2020.9054809}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/wcre/AsyrofiT0J20.bib}, note = {TL;DR \par Given an API query that allows type constraints, AUSearch finds code examples in GitHub that contain usages of the specific APIs in the query and highlights the relevant lines of code in the files for easier reference.}, timestamp = {Thu, 16 Apr 2020 16:52:52 +0200} } @inproceedings{DBLP:conf/wcre/OguraMHK18, title = {Bring Your Own Coding Style}, booktitle = {25th {{Int}}. {{Conf}}. {{Softw}}. {{Anal}}. {{Evol}}. {{Reengineering SANER}} 2018 {{Campobasso Italy March}} 20-23 2018}, author = {Ogura, Naoto and Matsumoto, Shinsuke and Hata, Hideaki and Kusumoto, Shinji}, editor = {Oliveto, Rocco and Penta, Massimiliano Di and Shepherd, David C.}, date = {2018}, pages = {527--531}, publisher = {IEEE Computer Society}, doi = {10.1109/SANER.2018.8330253}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/wcre/OguraMHK18.bib}, note = {TL;DR \par A novel tool is proposed, called StyleCoordinator, to solve both of the following problems, which would appear to contradict each other: ensuring a consistent coding style for all source codes managed in a repository and ensuring the ability of developers to use their own coding styles in a local environment.}, timestamp = {Fri, 24 Mar 2023 00:04:44 +0100} } @inproceedings{DBLP:conf/wcre/RubeiSRRN22, title = {Endowing Third-Party Libraries Recommender Systems with Explicit User Feedback Mechanisms}, booktitle = {{{IEEE Int}}. {{Conf}}. {{Softw}}. {{Anal}}. {{Evol}}. {{Reengineering SANER}} 2022 {{Honol}}. {{HI USA March}} 15-18 2022}, author = {Rubei, Riccardo and Sipio, Claudio Di and Rocco, Juri Di and Ruscio, Davide Di and Nguyen, Phuong T.}, date = {2022}, pages = {817--821}, publisher = {IEEE}, doi = {10.1109/SANER53432.2022.00099}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 0 \par cited By 0 \par cited By 0 \par cited By 1 \par TL;DR \par This paper proposes an approach to handle explicit user feedback, including positive, negative, and additive, and shows how user feedback generated by the proposed mechanism can change the outcome of a real TPLs recommender system.}, timestamp = {Thu, 20 Apr 2023 01:00:00 +0200} } @article{DBLP:journals/access/AutiliRIPT19, title = {A Software Exoskeleton to Protect and Support Citizen's Ethics and Privacy in the Digital World}, author = {Autili, Marco and Ruscio, Davide Di and Inverardi, Paola and Pelliccione, Patrizio and Tivoli, Massimo}, date = {2019}, journaltitle = {IEEE Access}, volume = {7}, pages = {62011--62021}, doi = {10.1109/ACCESS.2019.2916203}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Mon, 28 Aug 2023 01:00:00 +0200} } @article{DBLP:journals/access/BettiniRIP19, title = {Quality-Driven Detection and Resolution of Metamodel Smells}, author = {Bettini, Lorenzo and Ruscio, Davide Di and Iovino, Ludovico and Pierantonio, Alfonso}, date = {2019}, journaltitle = {IEEE Access}, volume = {7}, pages = {16364--16376}, doi = {10.1109/ACCESS.2019.2891357}, bibsource = {dblp computer science bibliography, https://dblp.org}, keywords = {⛔ No INSPIRE recid found}, note = {cited By 21 \par TL;DR \par This paper presents an approach to defining extensible catalogues of metamodel smells that automatically select only those smells that have to be necessarily resolved for enhancing the quality factors that are of interest for the modeler.}, timestamp = {Wed, 25 Sep 2019 01:00:00 +0200} } @article{DBLP:journals/access/CiccozziRMP16, title = {Adopting {{MDE}} for Specifying and Executing Civilian Missions of Mobile Multi-Robot Systems}, author = {Ciccozzi, Federico and Ruscio, Davide Di and Malavolta, Ivano and Pelliccione, Patrizio}, date = {2016}, journaltitle = {IEEE Access}, volume = {4}, pages = {6451--6466}, doi = {10.1109/ACCESS.2016.2613642}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Sun, 02 Jun 2019 01:00:00 +0200} } @article{DBLP:journals/air/SiL20, ids = {si_shilling_2020}, title = {Shilling Attacks against Collaborative Recommender Systems: A Review}, author = {Si, Mingdan and Li, Qingshan}, date = {2020}, journaltitle = {Artif. Intell. Rev.}, volume = {53}, number = {1}, pages = {291--319}, doi = {10.1007/s10462-018-9655-x}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/journals/air/SiL20.bib}, note = {TL;DR \par This paper briefly discusses the related survey papers about shilling attacks in CFRSs, explains profile injection attack strategies, shilling attack detection schemes and robust recommendation algorithms proposed so far in detail, and briefly explains evaluation metrics of the proposed schemes.}, timestamp = {Tue, 16 Jun 2020 17:16:04 +0200} } @article{DBLP:journals/apin/RoccoRSNR23, ids = {diroccoHybridRecRecommenderSystem2022,diroccoHybridRecRecommenderSystem2022a,diroccoHybridRecRecommenderSystem2023}, title = {{{HybridRec}}: A Recommender System for Tagging {{GitHub}} Repositories}, author = {Rocco, Juri Di and Ruscio, Davide Di and Sipio, Claudio Di and Nguyen, Phuong T. and Rubei, Riccardo}, date = {2023}, journaltitle = {Appl. Intell.}, volume = {53}, number = {8}, pages = {9708--9730}, publisher = {Springer}, doi = {10.1007/S10489-022-03864-Y}, bibsource = {dblp computer science bibliography, https://dblp.org}, keywords = {/unread,⛔ No INSPIRE recid found,Bayesia n networks,Bayesian networks,Bug reports,Codes (symbols),Collaborative filtering,Github tagging,Mining software,Mining software repository,Program debugging,Reachability,Recommender systems,Short texts,Software repositories,Source codes,Stochastic networks,Stochastic systems}, note = {cited By 0 \par cited By 0 \par cited By 0 \par TL;DR \par HybridRec, a recommender system based on stochastic and collaborative-filtering techniques to generate more relevant topics for GitHub topics, is built and it is concluded that the conceived framework can be used to help developers increase their projects’ visibility.}, timestamp = {Wed, 17 May 2023 01:00:00 +0200} } @article{DBLP:journals/apin/RubeiRSRN22, ids = {DBLP:journals/corr/abs-2201-08201}, title = {Providing Upgrade Plans for Third-Party Libraries: A Recommender System Using Migration Graphs}, author = {Rubei, Riccardo and Ruscio, Davide Di and Sipio, Claudio Di and Rocco, Juri Di and Nguyen, Phuong T.}, date = {2022}, journaltitle = {Appl. Intell.}, volume = {52}, number = {10}, eprint = {2201.08201}, eprinttype = {arXiv}, pages = {12000--12015}, doi = {10.1007/S10489-021-02911-4}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 1 \par cited By 1 \par cited By 2}, timestamp = {Thu, 20 Apr 2023 01:00:00 +0200} } @article{DBLP:journals/ase/RuscioIMN24, title = {Leveraging Privacy Profiles to Empower Users in the Digital Society}, author = {Ruscio, Davide Di and Inverardi, Paola and Migliarini, Patrizio and Nguyen, Phuong T.}, date = {2024}, journaltitle = {Autom. Softw. Eng.}, volume = {31}, number = {1}, pages = {16}, doi = {10.1007/S10515-024-00415-2}, bibsource = {dblp computer science bibliography, https://dblp.org}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Fri, 08 Mar 2024 00:00:00 +0100} } @article{DBLP:journals/cea/DuongNSR20, title = {Automated Fruit Recognition Using {{EfficientNet}} and {{MixNet}}}, author = {Duong, Linh T. and Nguyen, Phuong Thanh and Sipio, Claudio Di and Ruscio, Davide Di}, date = {2020}, journaltitle = {Comput. Electron. Agric.}, volume = {171}, pages = {105326}, doi = {10.1016/J.COMPAG.2020.105326}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 37}, timestamp = {Mon, 05 Feb 2024 00:00:00 +0100} } @article{DBLP:journals/cl/RuscioLP17, title = {Special Issue on Flexible Model Driven Engineering}, author = {Ruscio, Davide Di and family=Lara, given=Juan, prefix=de, useprefix=true and Pierantonio, Alfonso}, date = {2017}, journaltitle = {Comput. Lang. Syst. Struct.}, volume = {49}, pages = {174--175}, doi = {10.1016/J.CL.2016.12.003}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 4 \par cited By 4}, timestamp = {Tue, 11 Feb 2020 00:00:00 +0100} } @article{DBLP:journals/corr/abs-0909-5087, ids = {11697_38176,diruscioMaintainerScriptModernization2009,diruscioMaintainerScriptModernization2009a,ruscioMaintainerScriptModernization2009}, title = {Towards Maintainer Script Modernization in {{FOSS}} Distributions}, author = {Ruscio, Davide Di and Pelliccione, Patrizio and Pierantonio, Alfonso and Zacchiroli, Stefano}, date = {2009}, journaltitle = {CoRR}, volume = {abs/909.5087}, eprint = {0909.5087}, eprinttype = {arXiv}, publisher = {Association for Computing Machinery, Inc. (ACM)}, location = {NEW YORK, NY, USA}, url = {http://arxiv.org/abs/0909.5087}, bibsource = {dblp computer science bibliography, https://dblp.org}, isbn = {978-1-60558-677-9}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 10 \par cited By 10 \par TL;DR \par This paper presents a process to define meta-models that enable dealing with upgrade failures and help rolling back from them, taking into account maintainer scripts, applied to FOSS distributions.}, timestamp = {Mon, 13 Aug 2018 01:00:00 +0200} } @article{DBLP:journals/corr/abs-0910-0493, ids = {bucchiaroneRequirementsCodeArchitecturecentric2009,bucchiaroneRequirementsCodeArchitecturecentric2009a}, title = {From Requirements to Code: An Architecture-Centric Approach for Producing Quality Systems}, author = {Bucchiarone, Antonio and Ruscio, Davide Di and Muccini, Henry and Pelliccione, Patrizio}, date = {2009}, journaltitle = {CoRR}, volume = {abs/910.493}, eprint = {0910.0493}, eprinttype = {arXiv}, url = {http://arxiv.org/abs/0910.0493}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {⛔ No INSPIRE recid found}, timestamp = {Mon, 13 Aug 2018 01:00:00 +0200} } @article{DBLP:journals/corr/abs-0911-5046, title = {Integrating the Probabilistic Models {{BM25}}/{{BM25F}} into Lucene}, author = {Pérez-Iglesias, Joaquín and Pérez-Agüera, José R. and Fresno, Víctor and Feinstein, Yuval Z.}, date = {2009}, journaltitle = {CoRR}, volume = {abs/0911.5046}, eprint = {0911.5046}, eprinttype = {arXiv}, url = {http://arxiv.org/abs/0911.5046}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/bib/journals/corr/abs-0911-5046}, note = {TL;DR \par This document describes the BM25 and BM25F implementation using the Lucene Java Framework, both of which have stood out at TREC by their performance and are considered as state-of-the-art in the IR community.}, timestamp = {Mon, 13 Aug 2018 16:46:38 +0200} } @article{DBLP:journals/corr/abs-1006-5761, title = {Automated Co-Evolution of {{GMF}} Editor Models}, author = {Ruscio, Davide Di and Lämmel, Ralf and Pierantonio, Alfonso}, date = {2010}, journaltitle = {CoRR}, volume = {abs/1006.5761}, eprint = {1006.5761}, eprinttype = {arXiv}, url = {http://arxiv.org/abs/1006.5761}, bibsource = {dblp computer science bibliography, https://dblp.org}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 42 \par cited By 42 \par TL;DR \par This work analyzes the resulting co-evolution challenge, and provides a solution by means of GMF model adapters, which automate the propagation of domain-model changes and special model-to-model transformations that are driven by difference models for domain- model changes.}, timestamp = {Mon, 13 Aug 2018 01:00:00 +0200} } @article{DBLP:journals/corr/abs-1207-4525, title = {{{SiGMa}}: {{Simple}} Greedy Matching for Aligning Large Knowledge Bases}, author = {Lacoste-Julien, Simon and Palla, Konstantina and Davies, Alex and Kasneci, Gjergji and Graepel, Thore and Ghahramani, Zoubin}, date = {2012}, journaltitle = {CoRR}, volume = {abs/1207.4525}, eprint = {1207.4525}, eprinttype = {arXiv}, url = {http://arxiv.org/abs/1207.4525}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/journals/corr/abs-1207-4525.bib}, note = {TL;DR \par Simple Greedy Matching (SiGMa), a simple algorithm for aligning knowledge bases with millions of entities and facts, which is an iterative propagation algorithm that leverages both the structural information from the relationship graph and flexible similarity measures between entity properties in a greedy local search, which makes it scalable.}, timestamp = {Mon, 13 Aug 2018 16:46:21 +0200} } @article{DBLP:journals/corr/abs-1812-04894, title = {A4: {{Automatically}} Assisting Android {{API}} Migrations Using Code Examples}, author = {Lamothe, Maxime and Shang, Weiyi and Chen, Tse-Hsun}, date = {2018}, journaltitle = {CoRR}, volume = {abs/1812.04894}, eprint = {1812.04894}, eprinttype = {arXiv}, url = {http://arxiv.org/abs/1812.04894}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/bib/journals/corr/abs-1812-04894}, note = {TL;DR \par An approach that automatically learns API migration patterns from code examples and applies these patterns to the source code of Android apps for API migration is proposed and can be adopted by Android developers to reduce the effort they spend on regularly migrating Android APIs.}, timestamp = {Tue, 01 Jan 2019 15:01:25 +0100} } @article{DBLP:journals/corr/abs-1909-09436, title = {{{CodeSearchNet}} Challenge: {{Evaluating}} the State of Semantic Code Search}, author = {Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc}, date = {2019}, journaltitle = {CoRR}, volume = {abs/1909.09436}, eprint = {1909.09436}, eprinttype = {arXiv}, url = {http://arxiv.org/abs/1909.09436}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/journals/corr/abs-1909-09436.bib}, note = {TL;DR \par The methodology used to obtain the corpus and expert labels, as well as a number of simple baseline solutions for the task are described.}, timestamp = {Tue, 24 Sep 2019 11:33:51 +0200} } @article{DBLP:journals/corr/abs-2101-11149, title = {In-{{IDE}} Code Generation from Natural Language: {{Promise}} and Challenges}, author = {Xu, Frank F. and Vasilescu, Bogdan and Neubig, Graham}, date = {2021}, journaltitle = {CoRR}, volume = {abs/2101.11149}, eprint = {2101.11149}, eprinttype = {arXiv}, bibsource = {dblp computer science bibliography, https://dblp.org}, note = {TL;DR \par This article develops a plugin for the PyCharm IDE that implements a hybrid of code generation and code retrieval functionality and asks developers with various backgrounds to complete 7 varieties of 14 Python programming tasks ranging from basic file manipulation to machine learning or data visualization, with or without the help of the plugin.}, timestamp = {Sun, 31 Jan 2021 17:23:50 +0100} } @article{DBLP:journals/corr/abs-2103-06987, title = {Development of Recommendation Systems for Software Engineering: The {{CROSSMINER}} Experience}, author = {Rocco, Juri Di and Ruscio, Davide Di and Sipio, Claudio Di and Nguyen, Phuong Thanh and Rubei, Riccardo}, date = {2021}, journaltitle = {CoRR}, volume = {abs/2103.6987}, eprint = {2103.06987}, eprinttype = {arXiv}, url = {https://arxiv.org/abs/2103.06987}, bibsource = {dblp computer science bibliography, https://dblp.org}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 10 \par cited By 10 \par cited By 12 \par TL;DR \par This paper is an experience report to present the knowledge pertinent to the set of recommendation systems developed through the CROSSMINER project, and explains in detail the challenges the team had to deal with, together with the related lessons learned when developing and evaluating these systems.}, timestamp = {Thu, 24 Mar 2022 00:00:00 +0100} } @article{DBLP:journals/corr/abs-2105-14136, ids = {DBLP:conf/staf/IhirweRMP21}, title = {Towards a Modeling and Analysis Environment for Industrial {{IoT}} Systems}, author = {Ihirwe, Felicien and Ruscio, Davide Di and Mazzini, Silvia and Pierantonio, Alfonso}, date = {2021}, journaltitle = {CoRR}, volume = {abs/2105.14136}, eprint = {2105.14136}, eprinttype = {arXiv}, publisher = {CEUR-WS.org}, url = {https://arxiv.org/abs/2105.14136}, bibsource = {dblp computer science bibliography, https://dblp.org}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 0 \par cited By 0 \par cited By 0 \par cited By 2}, timestamp = {Wed, 02 Jun 2021 01:00:00 +0200} } @article{DBLP:journals/corr/abs-2109-09244, title = {A Domain-Specific Modeling and Analysis Environment for Complex {{IoT}} Applications}, author = {Ihirwe, Felicien and Ruscio, Davide Di and Mazzini, Silvia and Pierantonio, Alfonso}, date = {2021}, journaltitle = {CoRR}, volume = {abs/2109.9244}, eprint = {2109.09244}, eprinttype = {arXiv}, url = {https://arxiv.org/abs/2109.09244}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {TL;DR \par CHESSIoT, a domain-specific modeling environment for complex IoT applications, is introduced and the existing supported real-time analysis mechanism, as well as a proposed code generation approach, are presented.}, timestamp = {Mon, 27 Sep 2021 01:00:00 +0200} } @article{DBLP:journals/corr/abs-2111-14453, ids = {DBLP:conf/models/VincenzoRRP21}, title = {Enhancing Syntax Expressiveness in Domain-Specific Modelling}, author = {Vincenzo, Damiano Di and Rocco, Juri Di and Ruscio, Davide Di and Pierantonio, Alfonso}, date = {2021}, journaltitle = {CoRR}, volume = {abs/2111.14453}, eprint = {2111.14453}, eprinttype = {arXiv}, publisher = {IEEE}, url = {https://arxiv.org/abs/2111.14453}, bibsource = {dblp computer science bibliography, https://dblp.org}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 1 \par cited By 1 \par cited By 1 \par cited By 1}, timestamp = {Tue, 28 Dec 2021 00:00:00 +0100} } @article{DBLP:journals/corr/abs-2203-06068, title = {{{MemoRec}}: A Recommender System for Assisting Modelers in Specifying Metamodels}, author = {Rocco, Juri Di and Ruscio, Davide Di and Sipio, Claudio Di and Nguyen, Phuong T. and Pierantonio, Alfonso}, date = {2022}, journaltitle = {CoRR}, volume = {abs/2203.6068}, eprint = {2203.06068}, eprinttype = {arXiv}, doi = {10.48550/ARXIV.2203.06068}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {⛔ No INSPIRE recid found}, note = {TL;DR \par The results demonstrate that MemoRec is capable of suggesting relevant items given a partial metamodel and supporting modelers in their task, and the quality of the work is assessed with respect to different metrics, i.e., success rate, precision, and recall.}, timestamp = {Thu, 20 Apr 2023 01:00:00 +0200} } @article{DBLP:journals/corr/abs-2204-00011, title = {Leveraging Privacy Profiles to Empower Users in the Digital Society}, author = {Ruscio, Davide Di and Inverardi, Paola and Migliarini, Patrizio and Nguyen, Phuong T.}, date = {2022}, journaltitle = {CoRR}, volume = {abs/2204.11}, eprint = {2204.00011}, eprinttype = {arXiv}, doi = {10.48550/ARXIV.2204.00011}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {TL;DR \par This work focuses on characterizing/collecting users’ privacy preferences and contributes a step in this direction through an empirical study on an existing dataset collected from the fitness domain, and implements a recommender system to provide users with suitable recommendations related to privacy choices.}, timestamp = {Thu, 20 Apr 2023 01:00:00 +0200} } @article{DBLP:journals/corr/abs-2205-09379, title = {{{GitRanking}}: A Ranking of {{GitHub}} Topics for Software Classification Using Active Sampling}, author = {Sas, Cezar and Capiluppi, Andrea and Sipio, Claudio Di and Rocco, Juri Di and Ruscio, Davide Di}, date = {2022}, journaltitle = {CoRR}, volume = {abs/2205.9379}, eprint = {2205.09379}, eprinttype = {arXiv}, doi = {10.48550/ARXIV.2205.09379}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Tue, 16 Aug 2022 01:00:00 +0200} } @article{DBLP:journals/corr/abs-2304-10409, title = {Dealing with Popularity Bias in Recommender Systems for Third-Party Libraries: How Far Are We?}, author = {Nguyen, Phuong T. and Rubei, Riccardo and Rocco, Juri Di and Sipio, Claudio Di and Ruscio, Davide Di and Penta, Massimiliano Di}, date = {2023}, journaltitle = {CoRR}, volume = {abs/2304.10409}, eprint = {2304.10409}, eprinttype = {arXiv}, doi = {10.48550/ARXIV.2304.10409}, bibsource = {dblp computer science bibliography, https://dblp.org}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Wed, 26 Apr 2023 01:00:00 +0200} } @article{DBLP:journals/corr/abs-2307-09381, title = {Is This Snippet Written by {{ChatGPT}}? {{An}} Empirical Study with a {{CodeBERT-based}} Classifier}, author = {Nguyen, Phuong T. and Rocco, Juri Di and Sipio, Claudio Di and Rubei, Riccardo and Ruscio, Davide Di and Penta, Massimiliano Di}, date = {2023}, journaltitle = {CoRR}, volume = {abs/2307.9381}, eprint = {2307.09381}, eprinttype = {arXiv}, doi = {10.48550/ARXIV.2307.09381}, bibsource = {dblp computer science bibliography, https://dblp.org}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Tue, 25 Jul 2023 01:00:00 +0200} } @article{DBLP:journals/corr/abs-2309-02985, title = {Supporting Early-Safety Analysis of {{IoT}} Systems by Exploiting Testing Techniques}, author = {Clerissi, Diego and Rocco, Juri Di and Ruscio, Davide Di and Sipio, Claudio Di and Ihirwe, Felicien and Mariani, Leonardo and Micucci, Daniela and Rossi, Maria Teresa and Rubei, Riccardo}, date = {2023}, journaltitle = {CoRR}, volume = {abs/2309.2985}, eprint = {2309.02985}, eprinttype = {arXiv}, doi = {10.48550/ARXIV.2309.02985}, bibsource = {dblp computer science bibliography, https://dblp.org}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Mon, 05 Feb 2024 00:00:00 +0100} } @article{DBLP:journals/corr/abs-2312-12492, title = {{{CodeLL}}: A Lifelong Learning Dataset to Support the Co-Evolution of Data and Language Models of Code}, author = {Weyssow, Martin and Sipio, Claudio Di and Ruscio, Davide Di and Sahraoui, Houari A.}, date = {2023}, journaltitle = {CoRR}, volume = {abs/2312.12492}, eprint = {2312.12492}, eprinttype = {arXiv}, doi = {10.48550/ARXIV.2312.12492}, bibsource = {dblp computer science bibliography, https://dblp.org}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Wed, 17 Jan 2024 00:00:00 +0100} } @article{DBLP:journals/corr/FranzagoRMM16, ids = {11697_128217,franzagoProtocolSystematicMapping2016,franzagoProtocolSystematicMapping2016a}, title = {Protocol for a Systematic Mapping Study on Collaborative Model-Driven Software Engineering}, author = {Franzago, Mirco and Ruscio, Davide Di and Malavolta, Ivano and Muccini, Henry}, date = {2016}, journaltitle = {CoRR}, volume = {abs/1611.2619}, eprint = {1611.02619}, eprinttype = {arXiv}, publisher = {arXiv}, url = {http://arxiv.org/abs/1611.02619}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {⛔ No INSPIRE recid found}, note = {TL;DR \par A systematic mapping study (SMS) can help researchers and practitioners in having a complete, comprehensive and valid picture of the state of the art about collaborative MDSE, and identifying potential gaps in current research and future research directions.}, timestamp = {Mon, 13 Aug 2018 01:00:00 +0200} } @inproceedings{DBLP:journals/corr/GoodfellowSS14, title = {Explaining and Harnessing Adversarial Examples}, booktitle = {{{ICLR}} 2015 {{San Diego CA USA May}} 7-9 2015 {{Conf}}. {{Track Proc}}.}, author = {Goodfellow, Ian J. and Shlens, Jonathon and Szegedy, Christian}, editor = {Bengio, Yoshua and LeCun, Yann}, date = {2015}, url = {http://arxiv.org/abs/1412.6572}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/journals/corr/GoodfellowSS14.bib}, note = {TL;DR \par It is argued that the primary cause of neural networks' vulnerability to adversarial perturbation is their linear nature, supported by new quantitative results while giving the first explanation of the most intriguing fact about them: their generalization across architectures and training sets.}, timestamp = {Thu, 25 Jul 2019 14:25:38 +0200} } @article{DBLP:journals/corr/IzmaylovaKSV13, title = {M3: {{An}} Open Model for Measuring Code Artifacts}, author = {Izmaylova, Anastasia and Klint, Paul and Shahi, Ashim and Vinju, Jurgen J.}, date = {2013}, journaltitle = {CoRR}, volume = {abs/1312.1188}, eprint = {1312.1188}, eprinttype = {arXiv}, url = {http://arxiv.org/abs/1312.1188}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/bib/journals/corr/IzmaylovaKSV13}, timestamp = {Wed, 07 Jun 2017 14:42:09 +0200} } @article{DBLP:journals/ercim/RuscioPP12, title = {Managing the Evolution of {{FOSS}} Systems}, author = {Ruscio, Davide Di and Pelliccione, Patrizio and Pierantonio, Alfonso}, date = {2012}, journaltitle = {ERCIM News}, volume = {2012}, number = {88}, url = {http://ercim-news.ercim.eu/en88/special/managing-the-evolution-of-foss-systems}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Wed, 22 May 2019 01:00:00 +0200} } @article{DBLP:journals/ese/LoriotMM22, title = {Styler: Learning Formatting Conventions to Repair {{Checkstyle}} Violations}, author = {Loriot, Benjamin and Madeiral, Fernanda and Monperrus, Martin}, date = {2022}, journaltitle = {Empir. Softw. Eng.}, volume = {27}, number = {6}, pages = {149}, doi = {10.1007/s10664-021-10107-0}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/journals/ese/LoriotMM22.bib}, timestamp = {Mon, 24 Oct 2022 20:51:22 +0200} } @article{DBLP:journals/ese/PascarellaBB19, title = {Classifying Code Comments in {{Java}} Software Systems}, author = {Pascarella, Luca and Bruntink, Magiel and Bacchelli, Alberto}, date = {2019}, journaltitle = {Empir. Softw. Eng.}, volume = {24}, number = {3}, pages = {1499--1537}, doi = {10.1007/s10664-019-09694-w}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/journals/ese/PascarellaBB19.bib}, note = {TL;DR \par This paper investigates how 14 diverse Java open and closed source software projects use code comments, with the aim of understanding their purpose; through analysis, a taxonomy of source code comments is produced and how often each category occur is investigated.}, timestamp = {Tue, 21 Mar 2023 21:09:18 +0100} } @article{DBLP:journals/eswa/NguyenRRSR22, title = {{{DeepLib}}: Machine Translation Techniques to Recommend Upgrades for Third-Party Libraries}, author = {Nguyen, Phuong T. and Rocco, Juri Di and Rubei, Riccardo and Sipio, Claudio Di and Ruscio, Davide Di}, date = {2022}, journaltitle = {Expert Syst. Appl.}, volume = {202}, pages = {117267}, doi = {10.1016/J.ESWA.2022.117267}, bibsource = {dblp computer science bibliography, https://dblp.org}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 2 \par cited By 2 \par cited By 3}, timestamp = {Thu, 20 Apr 2023 01:00:00 +0200} } @article{DBLP:journals/eswa/NguyenSRRP23, ids = {nguyenFittingMissingAPI2023}, title = {Fitting Missing {{API}} Puzzles with Machine Translation Techniques}, author = {Nguyen, Phuong T. and Sipio, Claudio Di and Rocco, Juri Di and Ruscio, Davide Di and Penta, Massimiliano Di}, date = {2023}, journaltitle = {Expert Syst. Appl.}, volume = {216}, pages = {119477}, publisher = {Elsevier Ltd}, doi = {10.1016/J.ESWA.2022.119477}, bibsource = {dblp computer science bibliography, https://dblp.org}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 0}, timestamp = {Thu, 20 Apr 2023 01:00:00 +0200} } @article{DBLP:journals/ijswis/HliaoutakisVVPM06, title = {Information Retrieval by Semantic Similarity}, author = {Hliaoutakis, Angelos and Varelas, Giannis and Voutsakis, Epimenidis and Petrakis, Euripides G. M. and Milios, Evangelos E.}, date = {2006}, journaltitle = {Int. J. Semantic Web Inf. Syst.}, volume = {2}, number = {3}, pages = {55--73}, doi = {10.4018/jswis.2006070104}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/bib/journals/ijswis/HliaoutakisVVPM06}, timestamp = {Sat, 20 May 2017 00:24:06 +0200} } @article{DBLP:journals/ijwet/RuscioMP04, title = {A Data-Modelling Approach to Web Application Synthesis}, author = {Ruscio, Davide Di and Muccini, Henry and Pierantonio, Alfonso}, date = {2004}, journaltitle = {Int. J. Web Eng. Technol.}, volume = {1}, number = {3}, pages = {320--337}, doi = {10.1504/IJWET.2004.005236}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Mon, 05 Feb 2024 00:00:00 +0100} } @article{DBLP:journals/infsof/CapiluppiRRNA20, title = {Detecting Java Software Similarities by Using Different Clustering Techniques}, author = {Capiluppi, Andrea and Ruscio, Davide Di and Rocco, Juri Di and Nguyen, Phuong Thanh and Ajienka, Nemitari}, date = {2020}, journaltitle = {Inf. Software Technol.}, volume = {122}, pages = {106279}, doi = {10.1016/J.INFSOF.2020.106279}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 6}, timestamp = {Tue, 16 Aug 2022 01:00:00 +0200} } @article{DBLP:journals/infsof/RubeiSNRR20, title = {{{PostFinder}}: Mining Stack Overflow Posts to Support Software Developers}, author = {Rubei, Riccardo and Sipio, Claudio Di and Nguyen, Phuong Thanh and Rocco, Juri Di and Ruscio, Davide Di}, date = {2020}, journaltitle = {Inf. Software Technol.}, volume = {127}, pages = {106367}, doi = {10.1016/J.INFSOF.2020.106367}, bibsource = {dblp computer science bibliography, https://dblp.org}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 13}, timestamp = {Tue, 16 Aug 2022 01:00:00 +0200} } @article{DBLP:journals/infsof/RuscioP14, title = {Simulating Upgrades of Complex Systems: The Case of Free and Open Source Software}, author = {Ruscio, Davide Di and Pelliccione, Patrizio}, date = {2014}, journaltitle = {Inf. Software Technol.}, volume = {56}, number = {4}, pages = {438--462}, doi = {10.1016/J.INFSOF.2014.01.006}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {⛔ No INSPIRE recid found}, note = {cited By 7}, timestamp = {Thu, 20 Feb 2020 00:00:00 +0100} } @article{DBLP:journals/infsof/RuscioSIP23, ids = {diruscioModelingAssistantManage2023,diruscioModelingAssistantManage2023a}, title = {A Modeling Assistant to Manage Technical Debt in Coupled Evolution}, author = {Ruscio, Davide Di and Salle, Amleto Di and Iovino, Ludovico and Pierantonio, Alfonso}, date = {2023}, journaltitle = {Inf. Softw. Technol.}, volume = {156}, pages = {107146}, publisher = {Elsevier B.V.}, doi = {10.1016/J.INFSOF.2022.107146}, bibsource = {dblp computer science bibliography, https://dblp.org}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 0}, timestamp = {Sat, 29 Apr 2023 01:00:00 +0200} } @article{DBLP:journals/ir/BelloginCC17, title = {Statistical Biases in {{Information Retrieval}} Metrics for Recommender Systems}, author = {Bellogín, Alejandro and Castells, Pablo and Cantador, Iván}, date = {2017}, journaltitle = {Inf. Retr. J.}, volume = {20}, number = {6}, pages = {606--634}, doi = {10.1007/s10791-017-9312-z}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/journals/ir/BelloginCC17.bib}, timestamp = {Sun, 02 Oct 2022 15:39:26 +0200} } @article{DBLP:journals/jot/BascianiRIP21, title = {Uncertainty Management with Extra-Functional Qualities in Multi-Artefact Co-Evolution}, author = {Basciani, Francesco and Ruscio, Davide Di and Iovino, Ludovico and Pierantonio, Alfonso}, date = {2021}, journaltitle = {J. Object Technol,}, volume = {20}, number = {3}, pages = {2:1--15}, doi = {10.5381/JOT.2021.20.3.A2}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {⛔ No INSPIRE recid found}, timestamp = {Sat, 09 Apr 2022 01:00:00 +0200} } @article{DBLP:journals/jot/BettiniRIP20, ids = {bettiniDetectingMetamodelEvolutions2020,bettiniDetectingMetamodelEvolutions2020a,bettiniDetectingMetamodelEvolutions2020b}, title = {Detecting Metamodel Evolutions in Repositories of Model-Driven Projects}, author = {Bettini, Lorenzo and Ruscio, Davide Di and Iovino, Ludovico and Pierantonio, Alfonso}, date = {2020}, journaltitle = {J. Object Technol,}, volume = {19}, number = {2}, pages = {14:1--22}, doi = {10.5381/JOT.2020.19.2.A14}, bibsource = {dblp computer science bibliography, https://dblp.org}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 1}, timestamp = {Sat, 09 Apr 2022 01:00:00 +0200} } @article{DBLP:journals/jot/CicchettiRP07, ids = {cicchettiMetamodelIndependentApproach2007}, title = {A Metamodel Independent Approach to Difference Representation}, author = {Cicchetti, Antonio and Ruscio, Davide Di and Pierantonio, Alfonso}, date = {2007}, journaltitle = {J. Object Technol,}, volume = {6}, number = {9}, pages = {165--185}, doi = {10.5381/JOT.2007.6.9.A9}, bibsource = {dblp computer science bibliography, https://dblp.org}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Mon, 05 Feb 2024 00:00:00 +0100} } @article{DBLP:journals/jot/EramoKCTOBR19, ids = {eramoModeldrivenDesignRuntimeInteraction2019,eramoModeldrivenDesignRuntimeInteraction2019a,eramoModeldrivenDesignRuntimeInteraction2019b,eramoModeldrivenDesignruntimeInteraction2019}, title = {Model-Driven Design-Runtime Interaction in Safety Critical System Development: An Experience Report}, author = {Eramo, Romina and family=Kerchove, given=Florent Marchand, prefix=de, useprefix=true and Colange, Maximilien and Tucci, Michele and Ouy, Julien and Bruneliere, Hugo and Ruscio, Davide Di}, date = {2019}, journaltitle = {J. Object Technol,}, volume = {18}, number = {2}, pages = {1:1--22}, doi = {10.5381/JOT.2019.18.2.A1}, bibsource = {dblp computer science bibliography, https://dblp.org}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 1 \par TL;DR \par This paper presents a model-based approach that has been conceived to analyze runtime data, to produce corresponding traceability models and to automatically infer from them potential design issues that might need to be fixed in order to solve detected system malfunctionings.}, timestamp = {Mon, 05 Feb 2024 00:00:00 +0100} } @article{DBLP:journals/jot/KokalyR22, ids = {kokalyPrefaceJOTIssue2022}, title = {Preface to the {{JOT}} Issue on 18th European Conference on Modelling Foundations and Applications ({{ECMFA}} 2022)}, author = {Kokaly, Sahar and Ruscio, Davide Di}, date = {2022}, journaltitle = {J. Object Technol.}, volume = {21}, number = {3}, pages = {1--4}, doi = {10.5381/JOT.2022.21.3.E1}, bibsource = {dblp computer science bibliography, https://dblp.org}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Sat, 02 Jul 2022 01:00:00 +0200} } @article{DBLP:journals/jot/RuscioK12, ids = {ruscioInternationalWorkshopModel2012,ruscioInternationalWorkshopModel2012a}, title = {International Workshop on Model Comparison}, author = {Ruscio, Davide Di and Kolovos, Dimitris S.}, date = {2012}, journaltitle = {J. Object Technol,}, volume = {11}, number = {3}, doi = {10.5381/JOT.2012.11.3.E1}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Wed, 17 Feb 2021 00:00:00 +0100} } @article{DBLP:journals/jot/RuscioPL14, ids = {ruscioExtremeModellingXM2014,ruscioExtremeModellingXM2014a}, title = {Extreme Modelling ({{XM}}) 2012 Special Section}, author = {Ruscio, Davide Di and Pierantonio, Alfonso and family=Lara, given=Juan, prefix=de, useprefix=true}, date = {2014}, journaltitle = {J. Object Technol,}, volume = {13}, number = {3}, doi = {10.5381/JOT.2014.13.3.E1}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {⛔ No INSPIRE recid found}, timestamp = {Wed, 17 Feb 2021 00:00:00 +0100} } @article{DBLP:journals/jss/BozhinoskiRMPC19, title = {Safety for Mobile Robotic Systems: A Systematic Mapping Study from a Software Engineering Perspective}, author = {Bozhinoski, Darko and Ruscio, Davide Di and Malavolta, Ivano and Pelliccione, Patrizio and Crnkovic, Ivica}, date = {2019}, journaltitle = {J. Syst. Software}, volume = {151}, pages = {150--179}, doi = {10.1016/J.JSS.2019.02.021}, bibsource = {dblp computer science bibliography, https://dblp.org}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 23}, timestamp = {Thu, 14 Oct 2021 01:00:00 +0200} } @article{DBLP:journals/jss/NguyenRPRI21, title = {Convolutional Neural Networks for Enhanced Classification Mechanisms of Metamodels}, author = {Nguyen, Phuong Thanh and Ruscio, Davide Di and Pierantonio, Alfonso and Rocco, Juri Di and Iovino, Ludovico}, date = {2021}, journaltitle = {J. Syst. Software}, volume = {172}, pages = {110860}, doi = {10.1016/J.JSS.2020.110860}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {⛔ No INSPIRE recid found}, note = {cited By 13 \par cited By 14 \par cited By 15 \par cited By 19}, timestamp = {Sat, 09 Apr 2022 01:00:00 +0200} } @article{DBLP:journals/jss/NguyenRRP20, title = {{{CrossRec}}: Supporting Software Developers by Recommending Third-Party Libraries}, author = {Nguyen, Phuong Thanh and Rocco, Juri Di and Ruscio, Davide Di and Penta, Massimiliano Di}, date = {2020}, journaltitle = {J. Syst. Softw. - Elsevier}, volume = {161}, doi = {10.1016/J.JSS.2019.110460}, bibsource = {dblp computer science bibliography, https://dblp.org}, keywords = {⛔ No INSPIRE recid found}, note = {cited By 35}, timestamp = {Thu, 24 Mar 2022 00:00:00 +0100} } @article{DBLP:journals/jwe/BalzeraniARP06, title = {Supporting Web Applications Development with a {{PLA}}}, author = {Balzerani, Luca and Angelis, Guglielmo De and Ruscio, Davide Di and Pierantonio, Alfonso}, date = {2006}, journaltitle = {J. Web Eng.}, volume = {5}, number = {1}, pages = {25--42}, url = {http://www.rintonpress.com/xjwe5/jwe-5-1/025-042.pdf}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Thu, 12 Mar 2020 00:00:00 +0100} } @article{DBLP:journals/mam/AfzalBRSMCTCGGP18, title = {The {{MegaM}}@{{Rt2 ECSEL}} Project: {{MegaModelling}} at Runtime - Scalable Model-Based Framework for Continuous Development and Runtime Validation of Complex Systems}, author = {Afzal, Wasif and Bruneliere, Hugo and Ruscio, Davide Di and Sadovykh, Andrey and Mazzini, Silvia and Cariou, Eric and Truscan, Dragos and Cabot, Jordi and Gómez, Abel and Gorroñogoitia, Jesús and Pomante, Luigi and Smrz, Pavel}, date = {2018}, journaltitle = {Microprocess, Microsyst.}, volume = {61}, pages = {86--95}, doi = {10.1016/J.MICPRO.2018.05.010}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {⛔ No INSPIRE recid found}, note = {cited By 6 \par cited By 6 \par cited By 25}, timestamp = {Sun, 20 Nov 2022 00:00:00 +0100} } @article{DBLP:journals/scp/BrandRKR15, title = {Guest Editors' Introduction to the Fifth Issue of Experimental Software and Toolkits ({{EST}}): A Special Issue on Academics Modelling with Eclipse ({{ACME2012}})}, author = {family=Brand, given=Mark, prefix=van den, useprefix=true and Ruscio, Davide Di and Kolovos, Dimitrios S. and Rose, Louis M.}, date = {2015}, journaltitle = {Sci. Comput. Program.}, volume = {98}, pages = {1--2}, doi = {10.1016/J.SCICO.2014.11.001}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 1}, timestamp = {Wed, 17 Feb 2021 00:00:00 +0100} } @article{DBLP:journals/scp/CicchettiR08, title = {Decoupling Web Application Concerns through Weaving Operations}, author = {Cicchetti, Antonio and Ruscio, Davide Di}, date = {2008}, journaltitle = {Sci. Comput. Program.}, volume = {70}, number = {1}, pages = {62--86}, doi = {10.1016/J.SCICO.2007.10.002}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Wed, 17 Feb 2021 00:00:00 +0100} } @article{DBLP:journals/scp/CosmoRPPZ11, title = {Supporting Software Evolution in Component-Based {{FOSS}} Systems}, author = {Cosmo, Roberto Di and Ruscio, Davide Di and Pelliccione, Patrizio and Pierantonio, Alfonso and Zacchiroli, Stefano}, date = {2011}, journaltitle = {Sci. Comput. Program.}, volume = {76}, number = {12}, pages = {1144--1160}, doi = {10.1016/J.SCICO.2010.11.001}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Wed, 17 Feb 2021 00:00:00 +0100} } @article{DBLP:journals/scp/RuscioPP14, title = {Guest Editorial to the Special Issue on Success Stories in Model Driven Engineering}, author = {Ruscio, Davide Di and Paige, Richard F. and Pierantonio, Alfonso}, date = {2014}, journaltitle = {Sci. Comput. Program.}, volume = {89}, pages = {69--70}, doi = {10.1016/J.SCICO.2013.12.006}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {⛔ No INSPIRE recid found}, note = {cited By 24}, timestamp = {Wed, 17 Feb 2021 00:00:00 +0100} } @article{DBLP:journals/sigmobile/Shannon01, title = {A Mathematical Theory of Communication}, author = {Shannon, Claude E.}, date = {2001}, journaltitle = {Mob. Comput. Commun. Rev.}, volume = {5}, number = {1}, pages = {3--55}, doi = {10.1145/584091.584093}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/bib/journals/sigmobile/Shannon01}, note = {TL;DR \par It is proved that the authors can get some positive data rate that has the same small error probability and also there is an upper bound of the data rate, which means they cannot achieve the data rates with any encoding scheme that has small enough error probability over the upper bound.}, timestamp = {Wed, 28 Nov 2018 12:57:16 +0100} } @article{DBLP:journals/sigsoft/ChechikR17, ids = {11697_121408,chechikReport9thWorkshop2017,chechikReport9thWorkshop2017a,chechikReport9thWorkshop2017b}, title = {Report from the 9th Workshop on Modelling in Software Engineering({{MiSE}} 2017)}, author = {Chechik, Marsha and Ruscio, Davide Di}, date = {2017}, journaltitle = {ACM SIGSOFT Softw, Eng, Notes}, volume = {42}, number = {4}, pages = {21--24}, doi = {10.1145/3149485.3149520}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {TL;DR \par The 9th edition of the MiSE workshop provided a forum to discuss successful applications of software-modeling techniques and to gain insights into challenging modeling techniques, including uncertainty management, model heterogeneity, model reuse and evolution, testing, and the adoption of mod- els in critical application domains like self-adaptive and real-time systems.}, timestamp = {Tue, 21 Mar 2023 00:00:00 +0100} } @article{DBLP:journals/smr/RuscioP15, title = {A Model-Driven Approach to Detect Faults in {{FOSS}} Systems}, author = {Ruscio, Davide Di and Pelliccione, Patrizio}, date = {2015}, journaltitle = {J. Softw. Evol. Process.}, volume = {27}, number = {4}, pages = {294--318}, doi = {10.1002/SMR.1716}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Fri, 06 Mar 2020 00:00:00 +0100} } @article{DBLP:journals/software/CiccozziCRMPS17, ids = {11697_106779,ciccozziModelDrivenEngineeringMissionCritical2017,ciccozziModelDrivenEngineeringMissionCritical2017a,ciccozziModeldrivenEngineeringMissionCritical2017}, title = {Model-Driven Engineering for Mission-Critical {{IoT}} Systems}, author = {Ciccozzi, Federico and Crnkovic, Ivica and Ruscio, Davide Di and Malavolta, Ivano and Pelliccione, Patrizio and Spalazzese, Romina}, date = {2017}, journaltitle = {IEEE Softw,}, volume = {34}, number = {1}, pages = {46--53}, doi = {10.1109/MS.2017.1}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 69 \par TL;DR \par Model-driven engineering can potentially meet the challenges of mission-critical Internet of Things systems and better enable the adoption of MC-IoTs.}, timestamp = {Sat, 30 Sep 2023 01:00:00 +0200} } @article{DBLP:journals/software/RoccoRIP15, title = {Collaborative Repositories in Model-Driven Engineering}, author = {Rocco, Juri Di and Ruscio, Davide Di and Iovino, Ludovico and Pierantonio, Alfonso}, date = {2015}, journaltitle = {IEEE Softw.}, volume = {32}, number = {3}, pages = {28--34}, doi = {10.1109/MS.2015.61}, bibsource = {dblp computer science bibliography, https://dblp.org}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 59}, timestamp = {Mon, 08 Jun 2020 01:00:00 +0200} } @article{DBLP:journals/software/RuscioIP12, ids = {DIRUSCIO12}, title = {Coupled Evolution in Model-Driven Engineering}, author = {Ruscio, Davide Di and Iovino, Ludovico and Pierantonio, Alfonso}, date = {2012}, journaltitle = {IEEE Softw.}, volume = {29}, number = {6}, pages = {78--84}, doi = {10.1109/MS.2012.153}, bibsource = {dblp computer science bibliography, https://dblp.org}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Mon, 08 Jun 2020 01:00:00 +0200} } @article{DBLP:journals/sosym/BettiniRIP22, title = {An Executable Metamodel Refactoring Catalog}, author = {Bettini, Lorenzo and Ruscio, Davide Di and Iovino, Ludovico and Pierantonio, Alfonso}, date = {2022}, journaltitle = {Software Syst. Model.}, volume = {21}, number = {5}, pages = {1689--1709}, doi = {10.1007/S10270-022-01034-9}, bibsource = {dblp computer science bibliography, https://dblp.org}, keywords = {/unread,⛔ No INSPIRE recid found,Metamodels,Refactoring}, note = {cited By 0 \par TL;DR \par The Edelta framework is proposed as an operative environment to provide modelers with constructs for specifying basic refactorings and evolution operators, to define a complete metamodel refactoring catalog.}, timestamp = {Mon, 28 Aug 2023 01:00:00 +0200} } @article{DBLP:journals/sosym/NguyenRIRP21, title = {Evaluation of a Machine Learning Classifier for Metamodels}, author = {Nguyen, Phuong Thanh and Rocco, Juri Di and Iovino, Ludovico and Ruscio, Davide Di and Pierantonio, Alfonso}, date = {2021}, journaltitle = {Software Syst. Model.}, volume = {20}, number = {6}, pages = {1797--1821}, doi = {10.1007/S10270-021-00913-X}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 1 \par cited By 1 \par cited By 2 \par cited By 4 \par TL;DR \par A thorough evaluation of AURORA as a machine learning classifier for metamodel repositories is presented by taking into consideration different settings as well as evaluation metrics, and it is seen that AurORA outperforms the baselines with respect to various quality metrics.}, timestamp = {Tue, 28 Feb 2023 00:00:00 +0100} } @article{DBLP:journals/sosym/NguyenRIRP21a, ids = {nguyenCorrectionEvaluationMachine2021,nguyenCorrectionEvaluationMachine2021a,nguyenCorrectionEvaluationMachine2021b,nguyenCorrectionEvaluationMachine2021d}, title = {Correction to: Evaluation of a Machine Learning Classifier for Metamodels}, author = {Nguyen, Phuong Thanh and Rocco, Juri Di and Iovino, Ludovico and Ruscio, Davide Di and Pierantonio, Alfonso}, date = {2021}, journaltitle = {Software Syst. Model.}, volume = {20}, number = {6}, pages = {1823}, publisher = {{Springer Science and Business Media Deutschland GmbH}}, doi = {10.1007/S10270-021-00944-4}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 0 \par cited By 0 \par cited By 0}, timestamp = {Thu, 24 Mar 2022 00:00:00 +0100} } @article{DBLP:journals/sosym/RoccoRHILP20, title = {Understanding {{MDE}} Projects: Megamodels to the Rescue for Architecture Recovery}, author = {Rocco, Juri Di and Ruscio, Davide Di and Härtel, Johannes and Iovino, Ludovico and Lämmel, Ralf and Pierantonio, Alfonso}, date = {2020}, journaltitle = {Software Syst. Model.}, volume = {19}, number = {2}, pages = {401--423}, doi = {10.1007/S10270-019-00748-7}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 7}, timestamp = {Fri, 09 Apr 2021 01:00:00 +0200} } @article{DBLP:journals/sosym/RoccoRSNP23, title = {{{MemoRec}}: A Recommender System for Assisting Modelers in Specifying Metamodels}, author = {Rocco, Juri Di and Ruscio, Davide Di and Sipio, Claudio Di and Nguyen, Phuong T. and Pierantonio, Alfonso}, date = {2023}, journaltitle = {Software Syst. Model.}, volume = {22}, number = {1}, pages = {203--223}, doi = {10.1007/S10270-022-00994-2}, bibsource = {dblp computer science bibliography, https://dblp.org}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 1 \par cited By 2 \par cited By 2 \par TL;DR \par The results demonstrate that MemoRec is capable of suggesting relevant items given a partial metamodel and supporting modelers in their task, and the quality of the work is assessed with respect to different metrics, i.e., success rate, precision, and recall.}, timestamp = {Thu, 20 Apr 2023 01:00:00 +0200} } @article{DBLP:journals/sosym/RuscioGT22, title = {Editorial to Theme Section on Modeling in Low-Code Development Platforms}, author = {Ruscio, Davide Di and Guerra, Esther and Tisi, Massimo}, date = {2022}, journaltitle = {Software Syst. Model.}, volume = {21}, number = {5}, pages = {1957--1958}, doi = {10.1007/S10270-022-01045-6}, bibsource = {dblp computer science bibliography, https://dblp.org}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 0 \par cited By 0 \par TL;DR \par The growing need for secure, trustworthy, and cost-efficient software, as well as recent developments in cloud computing technologies, and the shortage of highly skilled professional software developers have given rise to a new generation of low-code software development platforms, such as Google AppSheet and Microsoft PowerApps.}, timestamp = {Sat, 30 Sep 2023 01:00:00 +0200} } @article{DBLP:journals/sosym/RuscioKLPTW22, ids = {RuscioKLPTW22,diruscioLowcodeDevelopmentModeldriven2022,diruscioLowcodeDevelopmentModeldriven2022b,diruscioLowcodeDevelopmentModeldriven2022c,diruscioLowcodeDevelopmentModeldriven2022e,diruscioLowcodeDevelopmentModeldriven2022f,ruscioLowcodeDevelopmentModeldriven2022}, title = {Low-Code Development and Model-Driven Engineering: Two Sides of the Same Coin?}, author = {Ruscio, Davide Di and Kolovos, Dimitrios S. and family=Lara, given=Juan, prefix=de, useprefix=true and Pierantonio, Alfonso and Tisi, Massimo and Wimmer, Manuel}, date = {2022}, journaltitle = {Software Syst. Model.}, volume = {21}, number = {2}, pages = {437--446}, publisher = {{Springer Science and Business Media Deutschland GmbH}}, doi = {10.1007/S10270-021-00970-2}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 9 \par cited By 11 \par cited By 28 \par TL;DR \par This expert-voice paper compares and contrast low-code and model-driven approaches, identifying their differences and commonalities, analysing their strong and weak points, and proposing directions for cross-pollination.}, timestamp = {Thu, 23 Jun 2022 01:00:00 +0200} } @article{DBLP:journals/sosym/RuscioKLPTW22a, title = {Correction to: Low-Code Development and Model-Driven Engineering: Two Sides of the Same Coin?}, author = {Ruscio, Davide Di and Kolovos, Dimitrios S. and family=Lara, given=Juan, prefix=de, useprefix=true and Pierantonio, Alfonso and Tisi, Massimo and Wimmer, Manuel}, date = {2022}, journaltitle = {Software Syst. Model.}, volume = {21}, number = {5}, pages = {1687}, doi = {10.1007/S10270-022-01038-5}, bibsource = {dblp computer science bibliography, https://dblp.org}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 0 \par cited By 0 \par cited By 1}, timestamp = {Mon, 28 Aug 2023 01:00:00 +0200} } @article{DBLP:journals/sosym/SipioRRN23, title = {{{MORGAN}}: A Modeling Recommender System Based on Graph Kernel}, author = {Sipio, Claudio Di and Rocco, Juri Di and Ruscio, Davide Di and Nguyen, Phuong T.}, date = {2023}, journaltitle = {Software Syst. Model.}, volume = {22}, number = {5}, pages = {1427--1449}, doi = {10.1007/S10270-023-01102-8}, bibsource = {dblp computer science bibliography, https://dblp.org}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 0 \par TL;DR \par The proposed MORGAN recommender system is equipped with the ability to support recommendations for JSON schema that provides a model representation of data handling operations, and additional preprocessing steps and a kernel similarity function based on item frequency are introduced, aiming to enhance the capabilities, providing more precise recommendations.}, timestamp = {Wed, 01 Nov 2023 00:00:00 +0100} } @article{DBLP:journals/spe/SahayRIP23, title = {Analyzing Business Process Management Capabilities of Low-Code Development Platforms}, author = {Sahay, Apurvanand and Ruscio, Davide Di and Iovino, Ludovico and Pierantonio, Alfonso}, date = {2023}, journaltitle = {Softw. Pract. Exp.}, volume = {53}, number = {4}, pages = {1036--1060}, doi = {10.1002/SPE.3177}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found,business process management,low-code development platforms,model-driven engineering}, timestamp = {Sat, 30 Sep 2023 01:00:00 +0200} } @article{DBLP:journals/spe/SasCSRR23, title = {{{GitRanking}}: A Ranking of {{GitHub}} Topics for Software Classification Using Active Sampling}, author = {Sas, Cezar and Capiluppi, Andrea and Sipio, Claudio Di and Rocco, Juri Di and Ruscio, Davide Di}, date = {2023}, journaltitle = {Softw. Pract. Exp.}, volume = {53}, number = {10}, pages = {1982--2006}, doi = {10.1002/SPE.3238}, bibsource = {dblp computer science bibliography, https://dblp.org}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Fri, 17 Nov 2023 00:00:00 +0100} } @article{DBLP:journals/sqj/NguyenRRR20, title = {An Automated Approach to Assess the Similarity of {{GitHub}} Repositories}, author = {Nguyen, Phuong Thanh and Rocco, Juri Di and Rubei, Riccardo and Ruscio, Davide Di}, date = {2020}, journaltitle = {Software Qual. J.}, volume = {28}, number = {2}, pages = {595--631}, doi = {10.1007/S11219-019-09483-0}, bibsource = {dblp computer science bibliography, https://dblp.org}, keywords = {⛔ No INSPIRE recid found}, note = {cited By 12 \par TL;DR \par This paper addresses the issue of mining open source software repositories to detect similar projects, which can be eventually reused by developers and proposes CrossSim as a novel approach to model the OSS ecosystem and to compute similarities among software projects.}, timestamp = {Mon, 28 Aug 2023 01:00:00 +0200} } @article{DBLP:journals/sttt/BettiniRIP22, title = {Supporting Safe Metamodel Evolution with Edelta}, author = {Bettini, Lorenzo and Ruscio, Davide Di and Iovino, Ludovico and Pierantonio, Alfonso}, date = {2022}, journaltitle = {Int. J. Softw. Tools Technol. Transf.}, volume = {24}, number = {2}, pages = {247--260}, doi = {10.1007/S10009-022-00646-2}, bibsource = {dblp computer science bibliography, https://dblp.org}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 1 \par cited By 1 \par cited By 2 \par TL;DR \par This paper presents a tool-supported approach that can automatically analyze the available metamodels and alert modelers in case of change operations that can give place to invalid situations like dangling references.}, timestamp = {Tue, 21 Mar 2023 00:00:00 +0100} } @article{DBLP:journals/tosem/LaraGRRCIP19, ids = {delaraAutomatedReuseModel2019,delaraAutomatedReuseModel2019a,laraAutomatedReuseModel2019,laraAutomatedReuseModel2019a}, title = {Automated Reuse of Model Transformations through Typing Requirements Models}, author = {family=Lara, given=Juan, prefix=de, useprefix=true and Guerra, Esther and Ruscio, Davide Di and Rocco, Juri Di and Cuadrado, Jesús Sánchez and Iovino, Ludovico and Pierantonio, Alfonso}, date = {2019}, journaltitle = {ACM Trans, Softw, Eng, Methodol,}, volume = {28}, number = {4}, pages = {21:1--21:62}, doi = {10.1145/3340108}, bibsource = {dblp computer science bibliography, https://dblp.org}, keywords = {⛔ No INSPIRE recid found}, note = {cited By 4 \par cited By 4}, timestamp = {Mon, 03 Jan 2022 00:00:00 +0100} } @article{DBLP:journals/tse/AutiliBARS16, title = {A Tool-Supported Methodology for Validation and Refinement of Early-Stage Domain Models}, author = {Autili, Marco and Bertolino, Antonia and Angelis, Guglielmo De and Ruscio, Davide Di and Sandro, Alessio Di}, date = {2016}, journaltitle = {IEEE Trans, Softw. Eng,}, volume = {42}, number = {1}, pages = {2--25}, doi = {10.1109/TSE.2015.2449319}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Mon, 26 Oct 2020 00:00:00 +0100} } @article{DBLP:journals/tse/BascianiDRFIP20, ids = {bascianiAutomatedSelectionOptimal2020,bascianiAutomatedSelectionOptimal2020a}, title = {Automated Selection of Optimal Model Transformation Chains via Shortest-Path Algorithms}, author = {Basciani, Francesco and D'Emidio, Mattia and Ruscio, Davide Di and Frigioni, Daniele and Iovino, Ludovico and Pierantonio, Alfonso}, date = {2020}, journaltitle = {IEEE Trans, Softw. Eng,}, volume = {46}, number = {3}, pages = {251--279}, doi = {10.1109/TSE.2018.2846223}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {⛔ No INSPIRE recid found}, note = {cited By 8 \par TL;DR \par This paper proposes an approach, based on well-established algorithms, to support modellers when multiple transformation chains are available to bridge a source metamodel with a target one.}, timestamp = {Thu, 09 Apr 2020 01:00:00 +0200} } @article{DBLP:journals/tse/FranzagoRMM18, title = {Collaborative Model-Driven Software Engineering: A Classification Framework and a Research Map}, author = {Franzago, Mirco and Ruscio, Davide Di and Malavolta, Ivano and Muccini, Henry}, date = {2018}, journaltitle = {IEEE Trans, Softw. Eng,}, volume = {44}, number = {12}, pages = {1146--1175}, doi = {10.1109/TSE.2017.2755039}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 49 \par TL;DR \par Researchers and practitioners can use the results for identifying existing research/technical gaps to attack, better scoping their own contributions, or understanding existing ones for identifying, classifying, and understanding existing collaborative MDSE approaches.}, timestamp = {Sun, 02 Jun 2019 01:00:00 +0200} } @article{DBLP:journals/tse/NguyenRSRP22, ids = {9359479,DBLP:journals/corr/abs-2102-07508,nguyenRecommendingAPIFunction2021,nguyenRecommendingAPIFunction2021a,nguyenRecommendingAPIFunction2022,nguyenRecommendingAPIFunction2022a,nguyenRecommendingAPIFunction2022b,nguyenRecommendingAPIFunction2022c}, title = {Recommending {{API}} Function Calls and Code Snippets to Support Software Development}, author = {Nguyen, Phuong T. and Rocco, Juri Di and Sipio, Claudio Di and Ruscio, Davide Di and Penta, Massimiliano Di}, date = {2022}, journaltitle = {IEEE Trans, Softw. Eng,}, volume = {48}, number = {7}, eprint = {2102.07508}, eprinttype = {arXiv}, pages = {2417--2438}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/TSE.2021.3059907}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {cited By 5 \par cited By 5 \par cited By 8}, timestamp = {Thu, 20 Apr 2023 01:00:00 +0200} } @article{DBLP:journals/vlc/BascianiRRIP19, ids = {bascianiToolsupportedApproachAssessing2019}, title = {A Tool-Supported Approach for Assessing the Quality of Modeling Artifacts}, author = {Basciani, Francesco and Rocco, Juri Di and Ruscio, Davide Di and Iovino, Ludovico and Pierantonio, Alfonso}, date = {2019}, journaltitle = {J. Comput. Lang.}, volume = {51}, pages = {173--192}, doi = {10.1016/J.COLA.2019.02.003}, bibsource = {dblp computer science bibliography, https://dblp.org}, keywords = {/unread,⛔ No INSPIRE recid found,Assessment of metamodel quality,Assessment of model transformation quality,Automated quality assessment,Model driven engineering}, timestamp = {Mon, 15 Jun 2020 01:00:00 +0200} } @article{DBLP:journals/vlc/IhirweRBGP24, title = {Supporting Model-Based Safety Analysis for Safety-Critical {{IoT}} Systems}, author = {Ihirwe, Felicien and Ruscio, Davide Di and Blasio, Katia Di and Gianfranceschi, Simone and Pierantonio, Alfonso}, date = {2024}, journaltitle = {J. Comput. Lang.}, volume = {78}, pages = {101243}, doi = {10.1016/J.COLA.2023.101243}, bibsource = {dblp computer science bibliography, https://dblp.org}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Sat, 10 Feb 2024 00:00:00 +0100} } @article{DBLP:journals/vlc/IhirweRGP24, title = {{{CHESSIoT}}: A Model-Driven Approach for Engineering Multi-Layered {{IoT}} Systems}, author = {Ihirwe, Felicien and Ruscio, Davide Di and Gianfranceschi, Simone and Pierantonio, Alfonso}, date = {2024}, journaltitle = {J. Comput. Lang.}, volume = {78}, pages = {101254}, doi = {10.1016/J.COLA.2023.101254}, bibsource = {dblp computer science bibliography, https://dblp.org}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Sat, 10 Feb 2024 00:00:00 +0100} } @article{DBLP:journals/vlc/SipioRRN24, title = {{{LEV4REC}}: A Feature-Based Approach to Engineering {{RSSEs}}}, author = {Sipio, Claudio Di and Rocco, Juri Di and Ruscio, Davide Di and Nguyen, Phuong T.}, date = {2024}, journaltitle = {J. Comput. Lang.}, volume = {78}, pages = {101256}, doi = {10.1016/J.COLA.2023.101256}, bibsource = {dblp computer science bibliography, https://dblp.org}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Mon, 05 Feb 2024 00:00:00 +0100} } @book{DBLP:reference/rsh/2011, title = {Recommender Systems Handbook}, editor = {Ricci, Francesco and Rokach, Lior and Shapira, Bracha and Kantor, Paul B.}, date = {2011}, publisher = {Springer}, doi = {10.1007/978-0-387-85820-3}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/reference/rsh/2011.bib}, isbn = {978-0-387-85819-7}, timestamp = {Mon, 16 Sep 2019 15:22:30 +0200} } @inproceedings{Dean:2012:LSD:2999134.2999271, title = {Large Scale Distributed Deep Networks}, booktitle = {Proc. 25th {{Int Conf Neural Inf}}. {{Process}}. {{Syst}}. - {{Vol}}. 1}, author = {Dean, Jeffrey and Corrado, Greg S. and Monga, Rajat and Chen, Kai and Devin, Matthieu and Le, Quoc V. and Mao, Mark Z. and Ranzato, Marc'Aurelio and Senior, Andrew and Tucker, Paul and Yang, Ke and Ng, Andrew Y.}, date = {2012}, series = {{{NIPS}}'12}, pages = {1223--1231}, publisher = {Curran Associates Inc.}, location = {USA}, acmid = {2999271}, numpages = {9} } @article{Deb:2002:FEM:2221359.2221582, ids = {DBLP:journals/tec/DebAPM02}, title = {A Fast and Elitist Multiobjective Genetic Algorithm: {{NSGA-II}}}, author = {Deb, K. and Pratap, A. and Agarwal, S. and Meyarivan, T.}, date = {2002-04}, journaltitle = {Trans. Evol. Comp}, volume = {6}, number = {2}, pages = {182--197}, publisher = {IEEE Press}, address = {Piscataway, NJ, USA}, issn = {1089-778X}, url = {http://dx.doi.org/10.1109/4235.996017}, acmid = {2221582}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/journals/tec/DebAPM02.bib}, issue_date = {April 2002}, nodoi = {10.1109/4235.996017}, numpages = {16}, note = {TL;DR \par This paper suggests a non-dominated sorting-based MOEA, called NSGA-II (Non-dominated Sorting Genetic Algorithm II), which alleviates all of the above three difficulties, and modify the definition of dominance in order to solve constrained multi-objective problems efficiently.}, timestamp = {Tue, 12 May 2020 16:51:01 +0200} } @article{debieAutomatingDataScience2022, title = {Automating Data Science}, author = {De Bie, Tijl and De Raedt, Luc and Hernández-Orallo, José and Hoos, Holger H. and Smyth, Padhraic and Williams, Christopher K. I.}, date = {2022-03}, journaltitle = {Commun. ACM}, volume = {65}, number = {3}, pages = {76--87}, issn = {0001-0782, 1557-7317}, doi = {10.1145/3495256}, abstract = {Given the complexity of data science projects and related demand for human expertise, automation has the potential to transform the data science process.}, langid = {english}, keywords = {STAR}, note = {TL;DR \par Given the complexity of data science projects and related demand for human expertise, automation has the potential to transform the data science process.} } @book{deconciniLeggereSapere, title = {Leggere per {{Sapere}}}, author = {DE CONCINI, Alessandro}, url = {https://d1ysz50cxb9zwl.cloudfront.net/nWyzM3XH0hJ4vIuhHYLNVg0WKjOGYKsr2aaLkQRn2_0BQ0RGaTtLRAK_wruIb4gH/by/4646762/as/file.pdf?Expires=1705862343&Signature=aXo5~ZVrOyAsiHg5~F9z9nYa38tSSbnuWO4MF1pH6qRDesP3TFtgMOknkRaJTGklNhAc-ZEflzTaW1MVO16qo66pltzM8XqFRbMWKLTw-AT8RnfU3nmDM8Imy7DMn8cJh9xYBG6x2HgL~9BBCTIOxg14vqwnmGmw1SHrAdHPd9D48XaQSspxQK1cb1zQDefW2BPDKF9cLP6fwJ0lCxGru8KDRvgxFqiiGaO8j01KFKQqeOYIXZ-uJQ1E6ft2~IV4IHhev-NX-iBoimjpeTDraDn6xouBAmiQ1DxaMMWHq56ENDabV~xdEuPSo6t8tvlD~1T2cF2ecYHB5jFbemKmTA__&Key-Pair-Id=APKAJAERRT46LD6FN4NA}, urldate = {2024-01-21} } @online{DeerFlow, title = {🦌 {{DeerFlow}}}, url = {https://deerflow.tech/}, urldate = {2025-05-15} } @book{degyurkyAutonomousSystemFoundational2014, title = {The Autonomous System a Foundational Synthesis of the Sciences of the Mind}, author = {De Gyurky, Szabolcs Michael and Tarbell, Mark A}, date = {2014}, publisher = {Wiley}, location = {Hoboken. N.J.}, url = {http://public.eblib.com/choice/publicfullrecord.aspx?p=1465945}, urldate = {2016-08-21}, abstract = {This book describes-in modern computer science terms-the Level II architecture of the meaning and definition of the process referred to as "thinking". It applies the basis of early cognitive science research to the creation of autonomous system architectures-connecting philosophical findings of the past with cutting-edge progress in artificial intelligence. Providing an in-depth introduction to the classical, philosophical theories of cognitive scientists like Immanuel Kant, Arthur Schopenhauer, and G.W.F. Hegel, the book examines the Will System, Reason System, Imagination System, and the C.}, isbn = {978-1-118-75749-9 978-1-118-75995-0 978-1-118-29424-6 978-1-299-98883-5}, langid = {english} } @article{dehghaniFacilitatingMigrationMicroservice2022, title = {Facilitating the Migration to the Microservice Architecture via Model-Driven Reverse Engineering and Reinforcement Learning}, author = {Dehghani, MohammadHadi and Kolahdouz-Rahimi, Shekoufeh and Tisi, Massimo and Tamzalit, Dalila}, date = {2022-06}, journaltitle = {Softw Syst Model}, volume = {21}, number = {3}, pages = {1115--1133}, issn = {1619-1366, 1619-1374}, doi = {10.1007/s10270-022-00977-3}, langid = {english}, keywords = {GOAL\_System-Modernization,notion,TECHNIQUE\_ReinforcementLearning} } @article{dehuryTOSCAdataModelingData2022, title = {{{TOSCAdata}}: {{Modeling}} Data Pipeline Applications in {{TOSCA}}}, shorttitle = {{{TOSCAdata}}}, author = {Dehury, Chinmaya Kumar and Jakovits, Pelle and Srirama, Satish Narayana and Giotis, Giorgos and Garg, Gaurav}, date = {2022-04}, journaltitle = {Journal of Systems and Software}, volume = {186}, pages = {111164}, issn = {01641212}, doi = {10.1016/j.jss.2021.111164}, abstract = {The serverless platform allows a customer to effectively use cloud resources and pay for the exact amount of used resources. A number of dedicated open source and commercial cloud data management tools are available to handle the massive amount of data. Such modern cloud data management tools are not enough matured to integrate the generic cloud application with the serverless platform due to the lack of mature and stable standards. One of the most popular and mature standards, TOSCA (Topology and Orchestration Specification for Cloud Applications), mainly focuses on application and service portability and automated management of the generic cloud application components. This paper proposes the extension of the TOSCA standard, TOSCAdata, that focuses on the modeling of data pipeline-based cloud applications. Keeping the requirements of modern data pipeline cloud applications, TOSCAdata provides a number of TOSCA models that are independently deployable, schedulable, scalable, and re-usable, while effectively handling the flow and transformation of data in a pipeline manner. We also demonstrate the applicability of proposed TOSCAdata models by taking a web-based cloud application in the context of tourism promotion as a use case scenario.}, langid = {english} } @article{delara3rdworkshopFlexibleModel2017, title = {3rdworkshop on Flexible Model Driven Engineering ({{FlexMDE}} 2017)}, author = {De Lara, J. and Ruscio, D.D. and Pierantonio, A.}, date = {2017}, journaltitle = {CEUR Workshop Proc.}, volume = {2019}, pages = {385--386}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85041447069&partnerID=40&md5=66fef2d9e76c891d78b2ae52d58a6d0a}, note = {cited By 0 \par cited By 0} } @article{delaraPreface1stInternational2020, ids = {delaraPreface1stInternational2020a}, title = {Preface to 1st {{International Workshop}} on {{Modeling}} in {{Low-Code Development Platforms}} ({{LowCode}} 2020)}, author = {De Lara, J. and Di Ruscio, D. and Kolovos, D. and Tisi, M. and Wimmer, M.}, date = {2020}, journaltitle = {Proc. - 23rd ACMIEEE Int. Conf. Model Driven Eng. Lang. Syst. MODELS-C 2020 - Companion Proc.}, pages = {XXII-XXIII}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096765618&partnerID=40&md5=4a22ec01b9bd336223ac732c915f3aef}, note = {cited By 0 \par cited By 0} } @article{delaraReusableAbstractionsModeling2013, title = {Reusable Abstractions for Modeling Languages}, author = {family=Lara, given=Juan, prefix=de, useprefix=true and Guerra, Esther and Sánchez Cuadrado, Jesús}, date = {2013-11}, journaltitle = {Inf. Syst.}, volume = {38}, number = {8}, pages = {1128--1149}, issn = {03064379}, doi = {10.1016/j.is.2013.06.001}, langid = {english}, note = {TL;DR \par This paper has devised some techniques, based on generic programming and domain-specific meta-modelling, to define generic abstraction operations that can be reused over families of modelling languages sharing certain characteristics, and developed a catalogue of reusable abstractions.} } @article{delavegaLavoisierDSLIncreasing2020, ids = {DELAVEGA2020100987}, title = {Lavoisier: {{A DSL}} for Increasing the Level of Abstraction of Data Selection and Formatting in Data Mining}, shorttitle = {Lavoisier}, author = {family=Vega, given=Alfonso, prefix=de la, useprefix=true and García-Saiz, Diego and Zorrilla, Marta and Sánchez, Pablo}, date = {2020-10}, journaltitle = {Journal of Computer Languages}, volume = {60}, pages = {100987}, issn = {25901184}, doi = {10.1016/j.cola.2020.100987}, langid = {english}, keywords = {Data formatting,Data mining,Data selection,Domain-specific languages} } @article{deldjooAdversarialMachineLearning, ids = {DDM20a}, title = {Adversarial {{Machine Learning}} in {{Recommender Systems}}: {{State}} of the Art and {{Challenges}}}, author = {Deldjoo, Yashar and Noia, Tommaso Di and Merra, Felice Antonio}, pages = {35}, langid = {english}, keywords = {adversarial machine learning,Adversarial Machine Learning,Literature Review,Recommender System}, note = {TL;DR \par An exhaustive literature review of 60 articles published in major RS and ML journals and conferences is provided to present recent advances on AML-RS for the security of RS and to show another successful application of AML in generative adversarial networks (GANs), which use the core concept of learning in AML for generative applications. \par Under Review} } @article{deldjooSurveyAdversarialRecommender2021, ids = {10.1145/3439729}, title = {A {{Survey}} on {{Adversarial Recommender Systems}}: {{From Attack}}/{{Defense Strategies}} to {{Generative Adversarial Networks}}}, shorttitle = {A {{Survey}} on {{Adversarial Recommender Systems}}}, author = {Deldjoo, Yashar and Noia, Tommaso Di and Merra, Felice Antonio}, date = {2021-03}, journaltitle = {ACM Comput. Surv.}, volume = {54}, number = {2}, pages = {1--38}, publisher = {Association for Computing Machinery}, location = {New York, NY, USA}, issn = {0360-0300, 1557-7341}, doi = {10.1145/3439729}, abstract = {Latent-factor models (LFM) based on collaborative filtering (CF), such as matrix factorization (MF) and deep CF methods, are widely used in modern recommender systems (RS) due to their excellent performance and recommendation accuracy. However, success has been accompanied with a major new arising challenge: Many applications of machine learning (ML) are adversarial in nature [146]. In recent years, it has been shown that these methods are vulnerable to adversarial examples, i.e., subtle but non-random perturbations designed to force recommendation models to produce erroneous outputs. The goal of this survey is two-fold: (i) to present recent advances on adversarial machine learning (AML) for the security of RS (i.e., attacking and defense recommendation models) and (ii) to show another successful application of AML in generative adversarial networks (GANs) for generative applications, thanks to their ability for learning (high-dimensional) data distributions. In this survey, we provide an exhaustive literature review of 76 articles published in major RS and ML journals and conferences. This review serves as a reference for the RS community working on the security of RS or on generative models using GANs to improve their quality.}, articleno = {35}, issue_date = {March 2021}, langid = {english}, pagetotal = {38}, keywords = {adversarial machine learning,adversarial perturbation,generative adversarial network,min-max game,privacy,Recommender systems,robustness,security} } @incollection{delemosSoftwareEngineeringSelfadaptive2013, title = {Software Engineering for Self-Adaptive Systems: {{A}} Second Research Roadmap}, shorttitle = {Software Engineering for Self-Adaptive Systems}, booktitle = {Software {{Engineering}} for {{Self-Adaptive Systems II}}}, author = {De Lemos, Rogério and Giese, Holger and Müller, Hausi A. and Shaw, Mary and Andersson, Jesper and Litoiu, Marin and Schmerl, Bradley and Tamura, Gabriel and Villegas, Norha M. and Vogel, Thomas and others}, date = {2013}, pages = {1--32}, publisher = {Springer}, url = {http://link.springer.com/chapter/10.1007/978-3-642-35813-5_1}, urldate = {2016-08-21} } @inproceedings{delimaWorkloaddrivenLogicalDesign2015, title = {A Workload-Driven Logical Design Approach for {{NoSQL}} Document Databases}, booktitle = {Proc. 17th {{Int}}. {{Conf}}. {{Inf}}. {{Integr}}. {{Web-Based Appl}}. {{Serv}}.}, author = {family=Lima, given=Claudio, prefix=de, useprefix=true and family=Santos Mello, given=Ronaldo, prefix=dos, useprefix=true}, date = {2015-12-11}, pages = {1--10}, publisher = {ACM}, location = {Brussels Belgium}, doi = {10.1145/2837185.2837218}, eventtitle = {{{iiWAS}} '15: {{The}} 17th {{International Conference}} on {{Information Integration}} and {{Web-based Application}} \& {{Services}}}, isbn = {978-1-4503-3491-4}, langid = {english}, note = {TL;DR \par This work converts a conceptual modeling into efficient logical representations for a NoSQL document database and demonstrates that the NoSQL logical structure generated by this approach reduces the amount of items accessed by the application queries.} } @article{dellannaEvaluatingClassifiersSE2023, title = {Evaluating Classifiers in {{SE}} Research: The {{ECSER}} Pipeline and Two Replication Studies}, shorttitle = {Evaluating Classifiers in {{SE}} Research}, author = {Dell’Anna, Davide and Aydemir, Fatma Başak and Dalpiaz, Fabiano}, date = {2023-02}, journaltitle = {Empir Software Eng}, volume = {28}, number = {1}, pages = {3}, issn = {1382-3256, 1573-7616}, doi = {10.1007/s10664-022-10243-1}, abstract = {Objective The lack of guidelines for applying and reporting classification techniques for SE research leads to studies in which important research steps may be skipped, key findings might not be identified and shared, and the readers may find reported results (e.g., precision or recall above 90\%) that are not a credible representation of the performance in operational contexts. The goal of this paper is to advance ML4SE research by proposing rigorous ways of conducting and reporting research. Results We introduce the ECSER (Evaluating Classifiers in Software Engineering Research) pipeline, which includes a series of steps for conducting and evaluating automated classification research in SE. Then, we conduct two replication studies where we apply ECSER to recent research in requirements engineering and in software testing. Conclusions In addition to demonstrating the applicability of the pipeline, the replication studies demonstrate ECSER’s usefulness: not only do we confirm and strengthen some findings identified by the original authors, but we also discover additional ones. Some of these findings contradict the original ones.}, langid = {english} } @article{dellaquilaDETERMINAZIONEDIRIGENZIALE67, title = {{{DETERMINAZIONE DIRIGENZIALE}} n. 67 Del 01/12/2009}, author = {Dell’Aquila, Comune}, langid = {english} } @incollection{demuthSupportingCoevolutionMetamodels2013, title = {Supporting the {{Co-evolution}} of {{Metamodels}} and {{Constraints}} through {{Incremental Constraint Management}}}, booktitle = {Model-{{Driven Engineering Languages}} and {{Systems}}}, author = {Demuth, Andreas and Lopez-Herrejon, Roberto E. and Egyed, Alexander}, editor = {Moreira, Ana and Schätz, Bernhard and Gray, Jeff and Vallecillo, Antonio and Clarke, Peter}, date = {2013}, series = {Lecture {{Notes}} in {{Computer Science}}}, number = {8107}, pages = {287--303}, publisher = {Springer Berlin Heidelberg}, url = {http://link.springer.com/chapter/10.1007/978-3-642-41533-3_18}, urldate = {2015-03-24}, abstract = {Design models must abide by constraints that can come from diverse sources, like metamodels, requirements, or the problem domain. Modelers intent to live by these constraints and thus desire automated mechanism that provide instant feedback on constraint violations. However, typical approaches assume that constraints do not evolve over time, which, unfortunately, is becoming increasingly unrealistic. For example, the co-evolution of metamodels and models requires corresponding constraints to be co-evolved continuously. This demands efficient constraint adaptation mechanisms to ensure that validated constraints are up-to-date. This paper presents an approach based on constraint templates that tackles this evolution scenario by automatically updating constraints. We developed the Cross-Layer Modeler (XLM) approach which relies on incremental consistency-checking. As a case study, we performed evolutions of the UML-metamodel and 21 design models. Our approach is sound and the empirical evaluation shows that it is near instant and scales with increasing model sizes.}, isbn = {978-3-642-41532-6 978-3-642-41533-3}, langid = {english}, keywords = {software engineering}, note = {TL;DR \par This paper developed the Cross-Layer Modeler XLM approach which relies on incremental consistency-checking and is sound and the empirical evaluation shows that it is near instant and scales with increasing model sizes.} } @inproceedings{DenilJVV14, title = {Search-Based Model Optimization Using Model Transformations}, booktitle = {Syst. {{Anal}}. {{Model}}. {{Models Reusability}} - 8th {{Int}}. {{Conf}}. {{SAM}}}, author = {Denil, Joachim and Jukss, Maris and Verbrugge, Clark and Vangheluwe, Hans}, date = {2014}, series = {Lecture Notes in Computer Science}, volume = {8769}, pages = {80--95}, publisher = {Springer}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found} } @misc{DeplanoPesStoriaColonialismo, title = {Deplano-{{Pes}} -{{Storia}} Del Colonialismo Italiano[1]} } @article{derakhshanmaneshModelintegratingDevelopmentSoftware2018, title = {Model-Integrating Development of Software Systems: A Flexible Component-Based Approach}, shorttitle = {Model-Integrating Development of Software Systems}, author = {Derakhshanmanesh, Mahdi and Ebert, Jürgen and Grieger, Marvin and Engels, Gregor}, date = {2018-06-16}, journaltitle = {Softw. Syst. Model.}, issn = {1619-1366, 1619-1374}, doi = {10.1007/s10270-018-0682-5}, langid = {english} } @article{derlerModelingCyberx2013PhysicalSystems2012, title = {Modeling {{Cyber}}\&\#x2013;{{Physical Systems}}}, author = {Derler, P. and Lee, E. A. and Vincentelli, A. S.}, date = {2012-01}, journaltitle = {Proc. IEEE}, volume = {100}, number = {1}, pages = {13--28}, issn = {0018-9219, 1558-2256}, doi = {10.1109/JPROC.2011.2160929} } @inproceedings{deServiceModellingInternet2011, title = {Service Modelling for the {{Internet}} of {{Things}}}, booktitle = {Comput. {{Sci}}. {{Inf}}. {{Syst}}. {{FedCSIS}} 2011 {{Fed}}. {{Conf}}. {{On}}}, author = {De, Suparna and Barnaghi, Payam and Bauer, Martin and Meissner, Stefan}, date = {2011}, pages = {949--955}, publisher = {IEEE}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6078180}, urldate = {2016-02-09}, note = {TL;DR \par This paper presents a semantic modeling approach for different components in an IoT framework and discusses how the model can be integrated into the IoT framework by using automated association mechanisms with physical entities and how the data can be discovered using semantic search and reasoning mechanisms.} } @inproceedings{desouzaRankingCrowdKnowledge2014, title = {Ranking Crowd Knowledge to Assist Software Development}, booktitle = {Proc. {{22Nd Int}}. {{Conf}}. {{Program Comprehension}}}, author = {family=Souza, given=Lucas B. L., prefix=de, useprefix=true and Campos, Eduardo C. and Maia, Marcelo de A.}, date = {2014}, series = {{{ICPC}} 2014}, pages = {72--82}, publisher = {ACM}, location = {New York, NY, USA}, url = {http://doi.acm.org/10.1145/2597008.2597146}, acmid = {2597146}, isbn = {978-1-4503-2879-1}, nodoi = {10.1145/2597008.2597146}, numpages = {11}, keywords = {crowd knowledge,Q\&A services,recommendation systems} } @misc{DevelopersBenefitRecommendations, title = {Do Developers Benefit from Recommendations When Repairing Inconsistent Design Models? {{A}} Controlled Experiment}, langid = {english} } @misc{dex2jar, title = {Dex2jar}, date = {2020-03-23}, url = {https://tools.kali.org/reverse-engineering/dex2jar}, urldate = {2020-03-23}, abstract = {dex2jar contains multiple components to work with .dex files.}, langid = {american}, note = {Library Catalog: tools.kali.org} } @inproceedings{dhouibRobotmlDomainspecificLanguage2012, title = {Robotml, a Domain-Specific Language to Design, Simulate and Deploy Robotic Applications}, booktitle = {Int. {{Conf}}. {{Simul}}. {{Model}}. {{Program}}. {{Auton}}. {{Robots}}}, author = {Dhouib, Saadia and Kchir, Selma and Stinckwich, Serge and Ziadi, Tewfik and Ziane, Mikal}, date = {2012}, pages = {149--160}, publisher = {Springer}, url = {http://link.springer.com/chapter/10.1007/978-3-642-34327-8_16}, urldate = {2016-08-21} } @article{Díaz-Manríquez20175647, title = {Comparison of Metamodeling Techniques in Evolutionary Algorithms}, author = {Díaz-Manríquez, A. and Toscano, G. and Coello Coello, C.A.}, date = {2017}, journaltitle = {Soft Comput.}, volume = {21}, number = {19}, pages = {5647--5663}, publisher = {Springer Verlag}, issn = {14327643}, doi = {10.1007/s00500-016-2140-z}, abbrev_source_title = {Soft Comput.}, affiliation = {Facultad de Ingeniería y Ciencias, Centro Universitario Victoria, Universidad Autónoma de Tamaulipas, Cd. Victoria, Tamaulipas 87000, Mexico; CINVESTAV-IPN, Unidad Tamaulipas, Parque Científico y Tecnológico TECNOTAM, Km. 5.5 carretera Cd. Victoria-Soto La Marina, Cd. Victoria, Tamaulipas 87130, Mexico; Departamento de Computación, CINVESTAV-IPN, Av. IPN No. 2508, Col. San Pedro Zacatenco, Mexico, DF 07360, Mexico}, correspondence_address1 = {Díaz-Manríquez, A.; Facultad de Ingeniería y Ciencias, Mexico; email: amanriquez@uat.edu.mx}, document_type = {Article}, langid = {english}, source = {Scopus}, note = {cited By 32 \par TL;DR \par An in-depth comparison study over four of the most popular metamodeling techniques: polynomial response surface, Kriging, radial basis function neural network (RBF), and support vector regression shows that the precision, measured with the ranking preservation indicator, gives a more valuable information for selecting purposes.} } @article{diceMeasuresAmountEcologic1945, title = {Measures of the Amount of Ecologic Association between Species}, author = {Dice, Lee R}, date = {1945}, journaltitle = {Ecology}, volume = {26}, number = {3}, pages = {297--302}, publisher = {Wiley Online Library} } @article{Did32Waterfall2017, entrysubtype = {magazine}, title = {Did 32\% {{Waterfall Surprise You}}?}, date = {2017-01}, journaltitle = {IEEE Software}, volume = {34}, number = {1}, pages = {7--7}, issn = {0740-7459}, url = {http://ieeexplore.ieee.org/document/7819417/}, langid = {english} } @article{didonetdelfabroEfficientDevelopmentModel2009, title = {Towards the Efficient Development of Model Transformations Using Model Weaving and Matching Transformations}, author = {Didonet Del Fabro, Marcos and Valduriez, Patrick}, date = {2009-07}, journaltitle = {Softw Syst Model}, volume = {8}, number = {3}, pages = {305--324}, issn = {1619-1366, 1619-1374}, doi = {10.1007/s10270-008-0094-z}, abstract = {Model transformations can be used in many different application scenarios, for instance, to provide interoperability between models of different size and complexity. As a consequence, they are becoming more and more complex. However, model transformations are typically developed manually. Several code patterns are implemented repetitively, thus increasing the probability of programming errors and reducing code reusability. There is not yet a complete solution that automates the development of model transformations. In this paper, we present a novel approach that uses matching transformations and weaving models to semi-automate the development of transformations. Weaving models are models that contain different kinds of relationships between model elements. These relationships capture different transformation patterns. Matching transformations are a special kind of transformations that implement methods that create weaving models. We present a practical solution that enables the creation and the customization of different creation methods in an efficient way. We combine different methods, and present a metamodel-based method that exploits metamodel data to automatically produce weaving models. The weaving models are derived into model integration transformations. To validate our approach, we present an experiment using metamodels with distinct size and complexity, which show the feasibility and scalability of our solution.}, langid = {english} } @inproceedings{DIFFERENCINGMERGINGSOFTWARE2007, title = {{{DIFFERENCING AND MERGING OF SOFTWARE DIAGRAMS}} - {{State}} of the {{Art}} and {{Challenges}}:}, shorttitle = {{{DIFFERENCING AND MERGING OF SOFTWARE DIAGRAMS}} - {{State}} of the {{Art}} and {{Challenges}}}, booktitle = {Proc. {{Second Int}}. {{Conf}}. {{Softw}}. {{Data Technol}}.}, date = {2007}, pages = {90--99}, publisher = {{SciTePress - Science and and Technology Publications}}, location = {Barcelona, Spain}, doi = {10.5220/0001342900900099}, abstract = {For long, fine-grained version control for software documents has been neglected severely. Typically, software configuration management systems support the management of text or binary files. Unfortunately, text-based tools for fine-grained version control are not adequate for software documents produced in earlier phases in the software life cycle. Frequently, these documents have a graphical syntax; therefore we will call them software diagrams. This paper discusses the current state of the art in fine-grained version control (differencing and merging) for software diagrams with an emphasis on UML diagrams.}, eventtitle = {2nd {{International Conference}} on {{Software}} and {{Data Technologies}}}, isbn = {978-989-8111-05-0 978-989-8111-06-7 978-989-8111-07-4}, langid = {english} } @inproceedings{dig_role_2005, title = {The Role of Refactorings in {{API}} Evolution}, booktitle = {21st {{IEEE Int Conf Softw}}. {{Maint}}. {{ICSM05}}}, author = {Dig, D. and Johnson, R.}, date = {2005}, pages = {389--398}, doi = {10.1109/ICSM.2005.90}, abstract = {Frameworks and libraries change their APIs. Migrating an application to the new API is tedious and disrupts the development process. Although some tools and ideas have been proposed to solve the evolution of APIs, most updates are done manually. To better understand the requirements for migration tools we studied the API changes of three frameworks and one library. We discovered that the changes that break existing applications are not random, but they tend to fall into particular categories. Over 80\% of these changes are refactorings. This suggests that refactoring-based migration tools should be used to update applications.}, keywords = {API evolution,application program interfaces,Application software,Computer languages,Computer science,Costs,Operating systems,Programming profession,refactoring-based migration tools,software libraries,Software libraries,software maintenance,Software maintenance,software prototyping,Software systems,software tools}, note = {TL;DR \par It is discovered that the changes that break existing applications are not random, but they tend to fall into particular categories and refactorings, which suggests that refactoring-based migration tools should be used to update applications.} } @article{dimartinoInternetThingsReference2018, title = {Internet of Things Reference Architectures, Security and Interoperability: {{A}} Survey}, shorttitle = {Internet of Things Reference Architectures, Security and Interoperability}, author = {Di Martino, B. and Rak, M. and Ficco, M. and Esposito, A. and Maisto, S.A. and Nacchia, S.}, date = {2018-09}, journaltitle = {Internet Things}, volume = {1--2}, pages = {99--112}, issn = {25426605}, doi = {10.1016/j.iot.2018.08.008}, abstract = {The term Internet of Things (IoT) is used as an umbrella that covers several topics, related to the application of technological means to monitor, measure and act upon the environment. As a result, it is difficult to determine a univocal architecture to identify as a reference and several scenarios, involving different sensors, smart devices, networks or gateways, can unfold. The data exchanged within and among IoT frameworks are growing exponentially, and the pervasiveness of such systems brings them to come in possession of very sensitive information: as a consequence, Security and Privacy have become a hot topic on the IoT scenery. Furthermore, due to the great variety of technological solutions which are currently available, interoperability issues are bound to arise, especially when no standard API interface, or communication protocol, has been officially adopted.}, langid = {english} } @inproceedings{dingSwoogleSearchMetadata2004, title = {Swoogle: {{A}} Search and Metadata Engine for the Semantic Web}, booktitle = {Proc. {{Thirteen}}. {{ACM Int}}. {{Conf}}. {{Inf}}. {{Knowl}}. {{Manag}}.}, author = {Ding, Li and Finin, Tim and Joshi, Anupam and Pan, Rong and Cost, R. Scott and Peng, Yun and Reddivari, Pavan and Doshi, Vishal and Sachs, Joel}, date = {2004}, series = {{{CIKM}} '04}, pages = {652--659}, publisher = {ACM}, location = {New York, NY, USA}, url = {http://doi.acm.org/10.1145/1031171.1031289}, acmid = {1031289}, isbn = {1-58113-874-1}, nodoi = {10.1145/1031171.1031289}, numpages = {8}, keywords = {crawler,metadata,rank,search,semantic web}, note = {TL;DR \par Swoogle is a crawler-based indexing and retrieval system for the Semantic Web that extracts metadata for each discovered document, and computes relations between documents.} } @inproceedings{DiNoia:2012:LOD:2362499.2362501, title = {Linked Open Data to Support Content-Based Recommender Systems}, booktitle = {Proc. 8th {{Int}}. {{Conf}}. {{Semantic Syst}}.}, author = {Di Noia, Tommaso and Mirizzi, Roberto and Ostuni, Vito Claudio and Romito, Davide and Zanker, Markus}, date = {2012}, series = {I-Semantics '12}, pages = {1--8}, publisher = {ACM}, location = {New York, NY, USA}, url = {http://doi.acm.org/10.1145/2362499.2362501}, acmid = {2362501}, isbn = {978-1-4503-1112-0}, nodoi = {10.1145/2362499.2362501}, numpages = {8}, keywords = {content-based recommender systems,DBpedia,freebase,linked data,LinkedMDB,movielens,precision,recall,semantic web,vector space model}, note = {TL;DR \par This paper implemented a content-based RS that leverages the data available within Linked Open Data datasets (in particular DBpedia, Freebase and LinkedMDB) in order to recommend movies to the end users.} } @incollection{DiNoia2014, title = {Linked Open Data-Enabled Recommender Systems: {{ESWC}} 2014 Challenge on Book Recommendation}, booktitle = {Semantic Web Evaluation Challenge: {{SemWebEval}} 2014 at {{ESWC}} 2014, Anissaras, Crete, Greece, May 25-29, 2014, Revised Selected Papers}, author = {Di Noia, Tommaso and Cantador, Iván and Ostuni, Vito Claudio}, editor = {Presutti, Valentina and Stankovic, Milan and Cambria, Erik and Cantador, Iván and Di Iorio, Angelo and Di Noia, Tommaso and Lange, Christoph and Reforgiato Recupero, Diego and Tordai, Anna}, date = {2014}, pages = {129--143}, publisher = {Springer International Publishing}, location = {Cham}, doi = {10.1007/978-3-319-12024-9₁7}, abstract = {In this chapter we present a report of the ESWC 2014 Challenge on Linked Open Data-enabled Recommender Systems, which consisted of three tasks in the context of book recommendation: rating prediction in cold-start situations, top N recommendations from binary user feedback, and diversity in content-based recommendations. Participants were requested to address the tasks by means of recommendation approaches that made use of Linked Open Data and semantic technologies. In the chapter we describe the challenge motivation, goals and tasks, summarize and compare the nine final participant recommendation approaches, and discuss their experimental results and lessons learned. Finally, we end with some conclusions and potential lines of future research.}, isbn = {978-3-319-12024-9}, note = {TL;DR \par This chapter presents a report of the ESWC 2014 Challenge on Linked Open Data-enabled Recommender Systems, which consisted of three tasks in the context of book recommendation: rating prediction in cold-start situations, top N recommendations from binary user feedback, and diversity in content-based recommendations.} } @article{dinoiaRecommenderSystemsEuropean2022, title = {Recommender Systems under {{European AI}} Regulations}, author = {Di Noia, Tommaso and Tintarev, Nava and Fatourou, Panagiota and Schedl, Markus}, date = {2022-04}, journaltitle = {Commun. ACM}, volume = {65}, number = {4}, pages = {69--73}, issn = {0001-0782, 1557-7317}, doi = {10.1145/3512728}, langid = {english}, note = {TL;DR \par The EC aims at introducing the first comprehensive legal framework on AI, which will identify specific risks for AI, provide a collection of high-risk application domains, propose specific requirements that AI systems should meet when used in such domains, and define obligations for users and providers.} } @inproceedings{diroccoNeedBodyKnowledge2021, ids = {diroccoNeedBodyKnowledge2021b,diroccoNeedBodyKnowledge2021c}, title = {On the Need for a Body of Knowledge on Recommender Systems}, booktitle = {{{CEUR Workshop Proc}}.}, author = {Di Rocco, J. and Di Ruscio, D. and Di Sipio, C. and Nguyen, P.T. and Pomo, C.}, editor = {Anelli V.W., Basile P., Di Noia T., Donini F.M., Musto C., Narducci F., Zanker M., Abdollahpouri H., Bogers T., Mobasher B., Petersen C., Pera M.S.}, date = {2021}, volume = {2960}, publisher = {CEUR-WS}, issn = {16130073}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116904185&partnerID=40&md5=0959deaf4805904d6d7e00eeac1b162f}, langid = {english}, keywords = {Applications domains,Body of knowledge,Core set,Model-driven Engineering,Recommender systems,Software engineering,Teachers'}, note = {cited By 0 \par cited By 0 \par cited By 0} } @article{diruscio4ThWorkshop2018, ids = {diruscioThWorkshopFlexible2018a}, title = {4 Th Workshop on Flexible Model-Driven Engineering ({{FlexMDE}} 2018)}, author = {Di Ruscio, D. and De Lara, J. and Pierantonio, A.}, date = {2018}, journaltitle = {CEUR Workshop Proc.}, volume = {2245}, pages = {201}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85063110971&partnerID=40&md5=705fc127b25951907174123b6397bb90}, note = {cited By 0 \par cited By 0} } @article{diruscioACMInternationalConference2010, ids = {diruscioACMInternationalConference2010a}, title = {{{ACM International Conference Proceeding Series}}: {{Foreword}}}, author = {Di Ruscio, D. and Kolovos, D.}, date = {2010}, journaltitle = {ACM Int. Conf. Proceeding Ser.}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-77955140503&partnerID=40&md5=26e00eed22f2ba08e5832f36cd6e8f6d}, note = {cited By 0 \par cited By 0} } @article{diruscioForeward2011, ids = {diruscioForeward2011a}, title = {Foreward}, author = {Di Ruscio, D. and Kolovos, D.}, date = {2011}, journaltitle = {ACM Int. Conf. Proceeding Ser.}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-80051525717&partnerID=40&md5=1803be81852072c0e1a2ad4bc4157532}, note = {cited By 0 \par cited By 0} } @inproceedings{diruscioPreface2014, ids = {diruscioPreface2014a,diruscioPreface2014b,diruscioPreface2014c,diruscioPreface2014d,diruscioPreface2014e,diruscioPreface2014f,diruscioPreface2015,diruscioPreface2015a,kolovosPreface2015,kolovosPreface2015a}, title = {Preface}, booktitle = {{{CEUR Workshop Proc}}.}, author = {DI RUSCIO, Davide and Varro, Daniel}, date = {2014}, volume = {8568}, pages = {VII--VIII}, publisher = {Association for Computing Machinery, Inc}, url = {http://springerlink.com/content/0302-9743/copyright/2005/}, isbn = {978-3-319-08788-7}, keywords = {Computer Science (all),Computer Science Applications1707 Computer Vision and Pattern Recognition,Software,Theoretical Computer Science}, note = {cited By 0 \par cited By 0 \par cited By 0 \par cited By 0 \par cited By 0 \par cited By 0 \par cited By 0 \par cited By 0 \par cited By 0 \par cited By 0 \par TL;DR \par Some of the field’s leaders in atmospheric chemistry, in both the gas and the aerosol phases, provide insights in this volume of Topics in Current Chemistry.} } @article{diruscioPreface2017, ids = {diruscioPreface2017a}, title = {Preface}, author = {Di Ruscio, D. and Koenig, B.}, date = {2017}, journaltitle = {CEUR Workshop Proc.}, volume = {1955}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85033449020&partnerID=40&md5=f48c65bca87745d014af6cc28eb005e9}, note = {cited By 0 \par cited By 0 \par TL;DR \par Some of the field’s leaders in atmospheric chemistry, in both the gas and the aerosol phases, provide insights in this volume of Topics in Current Chemistry.} } @incollection{diskinTraceabilityMappingsFundamental2017, title = {Traceability {{Mappings}} as a {{Fundamental Instrument}} in {{Model Transformations}}}, booktitle = {Fundamental {{Approaches}} to {{Software Engineering}}}, author = {Diskin, Zinovy and Gómez, Abel and Cabot, Jordi}, editor = {Huisman, Marieke and Rubin, Julia}, date = {2017}, volume = {10202}, pages = {247--263}, publisher = {Springer Berlin Heidelberg}, location = {Berlin, Heidelberg}, doi = {10.1007/978-3-662-54494-5_14}, isbn = {978-3-662-54493-8 978-3-662-54494-5} } @article{doAmaral20225205, title = {Metamodeling-Based Simulation Optimization in Manufacturing Problems: A Comparative Study}, author = {family=Amaral, given=J.V.S., prefix=do, useprefix=true and family=Carvalho Miranda, given=R., prefix=de, useprefix=true and Montevechi, J.A.B. and family=Santos, given=C.H., prefix=dos, useprefix=true and Gabriel, G.T.}, date = {2022}, journaltitle = {Int. J. Adv. Manuf. Technol.}, volume = {120}, number = {7--8}, pages = {5205--5224}, publisher = {{Springer Science and Business Media Deutschland GmbH}}, issn = {02683768}, doi = {10.1007/s00170-022-09072-9}, abstract = {In the context of modern industry, optimization emerges as one of the most powerful tools, allowing decision-makers to allocate their resources more assertively and deal with complex manufacturing problems. Moreover, manufacturing systems usually involve activities’ interdependency and high stochastic levels, which are necessary to associate optimization and simulation techniques to solve problems. Although simulation optimization is a powerful technique, it can converge on a good solution, which often limits its use in day-to-day operations. As an alternative, metamodels may be used to replace simulation models in the optimization process. In recent years, with the development in the machine learning area, algorithms with high learning capacity have emerged, making the metamodel-based simulation optimization (MBSO) a promising study field. Based on the latest theoretical research on the theme, MBSO techniques have been widely used to solve manufacturing problems. However, there is still no consensus about the experimental design, the learning algorithms, and the importance of the hyperparameter optimization step. Then, the article evaluates the performance of six machine learning algorithms trained with and without hyperparameter optimization, two experimental designs, and five different sample sizes to build metamodels in three real manufacturing cases. Based on the results, the random forest algorithm and the random design with 40 × sample size expressed the better performance to metamodels’ development. Furthermore, the hyperparameter optimization step reduced the metamodels’ error in about 32.83\%. © 2022, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.}, coden = {IJATE}, document_type = {Article}, source = {Scopus}, note = {cited By 0 \par TL;DR \par The article evaluates the performance of six machine learning algorithms trained with and without hyperparameter optimization, two experimental designs, and five different sample sizes to build metamodels in three real manufacturing cases and finds the random forest algorithm and the random design expressed the better performance to metamadels’ development.} } @article{doi:10.1080/21693277.2016.1192517, title = {Machine Learning in Manufacturing: Advantages, Challenges, and Applications}, author = {Wuest, Thorsten and Weimer, Daniel and Irgens, Christopher and Thoben, Klaus-Dieter}, date = {2016}, journaltitle = {Prod. Manuf. Res.}, volume = {4}, number = {1}, pages = {23--45}, publisher = {Taylor \& Francis}, nodoi = {10.1080/21693277.2016.1192517}, note = {TL;DR \par This paper contributes in presenting an overview of available machine learning techniques and structuring this rather complicated area with a special focus on the potential benefit, and examples of successful applications in a manufacturing environment.} } @article{doi:10.1089/big.2016.0048, title = {Conscientious Classification: {{A}} Data Scientist's Guide to Discrimination-Aware Classification}, author = {family=Alessandro, given=Brian, prefix=d', useprefix=true and O'Neil, Cathy and LaGatta, Tom}, date = {2017}, journaltitle = {Big Data}, volume = {5}, number = {2}, eprint = {https://doi.org/10.1089/big.2016.0048}, pages = {120--134}, doi = {10.1089/big.2016.0048}, abstract = {Abstract Recent research has helped to cultivate growing awareness that machine-learning systems fueled by big data can create or exacerbate troubling disparities in society. Much of this research comes from outside of the practicing data science community, leaving its members with little concrete guidance to proactively address these concerns. This article introduces issues of discrimination to the data science community on its own terms. In it, we tour the familiar data-mining process while providing a taxonomy of common practices that have the potential to produce unintended discrimination. We also survey how discrimination is commonly measured, and suggest how familiar development processes can be augmented to mitigate systems' discriminatory potential. We advocate that data scientists should be intentional about modeling and reducing discriminatory outcomes. Without doing so, their efforts will result in perpetuating any systemic discrimination that may exist, but under a misleading veil of data-driven objectivity.}, note = {PMID: 28632437 \par TL;DR \par It is advocated that data scientists should be intentional about modeling and reducing discriminatory outcomes, without doing so, their efforts will result in perpetuating any systemic discrimination that may exist, but under a misleading veil of data-driven objectivity.} } @article{dolques2009transformation, title = {From Transformation Traces to Transformation Rules: Assisting Model Driven Engineering Approach with Formal Concept Analysis}, author = {Dolques, Xavier and Huchard, Marianne and Nebut, Clémentine}, date = {2009}, journaltitle = {Suppl. Proc. ICCS}, volume = {9}, pages = {15--29}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {TL;DR \par This paper is proposing a method based on FCA using relational descriptions of objects to find transformation rules between two par- ticular syntaxes using transformation examples (transformation traces) as input data.} } @article{DomainspecificSemanticrichSoftware2026, title = {Domain-Specific Semantic-Rich Software Knowledge Graph Construction through Human-{{LLM}} Team Working}, date = {2026}, abstract = {In software engineering (SE), while graph-based tools such as user models and control flow graphs effectively capture behavioral and structural aspects of systems, they inadequately represent the semantic information essential for discerning complex relationships within software artifacts. Recognizing that Knowledge Graphs (KGs) excel in modeling semantic data, we introduce the Software Knowledge Graph (SKG), which adapts KGs to encapsulate entities, relationships, and categories pertinent to SE. Conventional KG construction approaches fall into two primary paradigms: the first one is the predefined-schema-guided method, which achieves high precision through strict adherence to established schema, but suffers from limited completeness, as such predefined schema often fail to capture all relevant entities and relationships; the second one is schema-free method, which leverages Large Language Models (LLMs) to enhance completeness but consequently introduces noise and inconsistency in the absence of schematic constraints. To reconcile these inherent trade-offs, this paper introduces the Do-While Human-LLM Team Working (DHTW) method, an evolutionary schema exploration paradigm that integrates LLM-driven autonomy with expert validation to balance precision and completeness in KG construction. In the “Do” phase, LLMs autonomously extract candidate schema elements, such as entity classes, attributes, and relationships, from domain-specific corpora based on learned user preferences rather than rigid predefined schema, thereby fostering expansive knowledge discovery. Subsequently, in the “While” phase, domain experts iteratively refine and validate these elements to ensure semantic consistency and precise alignment with domain requirements; this process continues until the corpus is exhaustively explored. By synergistically combining LLM-driven exploration with human-guided validation, the DHTW method effectively overcomes the rigidity and incompleteness of predefinedschema-guided methods while mitigating the noise and irrelevance of schema-free approaches. The comprehensive experimental evaluations underscore the effectiveness of the DHTW method. Furthermore, the generality of this method provides a blueprint for Human-LLM interaction in complex domains.}, langid = {english}, keywords = {LOGSEQ}, note = {I'm reviewing a research paper and I took the following notes: \par \section{Annotazioni\\ (17/5/2025, 15:28:22)} \par - “Software Knowledge Graph Constructio” (“Domain-specific semantic-rich software knowledge graph construction through human-LLM team working”, 2026, p. 1) \#ffd400\\ \mkbibemph{Software Knowledge Graphs should be constructed with a goal in mind, isn't it? How the goal is given and taken into account during the graph construction process?} \par - “Human-LLM Team Working” (“Domain-specific semantic-rich software knowledge graph construction through human-LLM team working”, 2026, p. 1) \#5fb236 \par - “Recognizing that Knowledge Graphs (KGs) excel in modeling semantic data, we introduce the Software Knowledge Graph (SKG), which adapts KGs to encapsulate entities, relationships, and categories pertinent to SE.” (“Domain-specific semantic-rich software knowledge graph construction through human-LLM team working”, 2026, p. 1) \#5fb236 \par - “predefined-schema-guided method, which achieves high precision through strict adherence to established schema, but suffers from limited completeness, as such predefined schema often fail to capture all relevant entities and relationships” (“Domain-specific semantic-rich software knowledge graph construction through human-LLM team working”, 2026, p. 1) \#5fb236 \par - “schema-free method, which leverages Large Language Models (LLMs) to enhance completeness but consequently introduces noise and inconsistency in the absence of schematic constraints” (“Domain-specific semantic-rich software knowledge graph construction through human-LLM team working”, 2026, p. 1) \#5fb236 \par - “Do-While Human-LLM Team Working (DHTW) method, an evolutionary schema exploration paradigm that integrates LLM-driven autonomy with expert validation to balance precision and completeness in KG construction” (“Domain-specific semantic-rich software knowledge graph construction through human-LLM team working”, 2026, p. 1) \#5fb236 \par - “These traditional graph representations primarily focus on capturing the behavioral and structural properties of systems” (“Domain-specific semantic-rich software knowledge graph construction through human-LLM team working”, 2026, p. 1) \#5fb236 \par - “represent entities, their relationships, and categorical information pertinent to SE.” (“Domain-specific semantic-rich software knowledge graph construction through human-LLM team working”, 2026, p. 1) \#ffd400\\ \mkbibemph{How do you embed or take into account the goal of the encoding of software systems in terms of KG? Knowledge Graphs can be done to capture different levels of abstraction!} \par - “Do-While Human-LLM” (“Domain-specific semantic-rich software knowledge graph construction through human-LLM team working”, 2026, p. 1) \#5fb236 \par - “Team Working (DHTW)” (“Domain-specific semantic-rich software knowledge graph construction through human-LLM team working”, 2026, p. 2) \#5fb236 \par - “In the "Do" phase, LLMs autonomously explore candidate schema elements, such as entity classes, attributes, and relationships, from specific corpora, guided by learned user preferences rather than rigid, predefined schema, thus facilitating expansive domain knowledge acquisition with LLM’s inherent divergence.” (“Domain-specific semantic-rich software knowledge graph construction through human-LLM team working”, 2026, p. 2) \#5fb236 \par - “"While" phase, users, typically domain experts, iteratively refine, validate, and adjust these elements, ensuring semantic consistency, domain alignment, and fidelity to user-specified requirements through targeted corrections and adjustments.” (“Domain-specific semantic-rich software knowledge graph construction through human-LLM team working”, 2026, p. 2) \#5fb236 \par - “To evaluate the effectiveness of the proposed DHTW method, we conducted experiments in two domains, focusing first on the API” (“Domain-specific semantic-rich software knowledge graph construction through human-LLM team working”, 2026, p. 2) \#ffd400\\ \mkbibemph{It's not clear if the API domain is just a domain that has been used to evaluate the approach or if it is the main target of the work. This is not clarified in the paper. It seems that the approach is API domain specific.} \par - “API domain, where extensive schema can be derived from the widely adopted Unified Modeling Language (UML)” (“Domain-specific semantic-rich software knowledge graph construction through human-LLM team working”, 2026, p. 2) \#5fb236 \par - “but also precisely aligned with the specific requirements of the domain.” (“Domain-specific semantic-rich software knowledge graph construction through human-LLM team working”, 2026, p. 2) \#ffd400\\ \mkbibemph{How is this given to the elicitation process? This is not clear to me.} \par - “balancing completeness, precision, and contextual relevance.” (“Domain-specific semantic-rich software knowledge graph construction through human-LLM team working”, 2026, p. 2) \#ffd400\\ \mkbibemph{Exaclty, these are all relevant characteristics even though it is not clear at this stage how they are managed and given as input to the elicitation process.} \par - “the absence of guidance frequently generates noise and leads to deviations from domain-specific standards, undermining the precision and alignment of the resulting KGs with user requirements” (“Domain-specific semantic-rich software knowledge graph construction through human-LLM team working”, 2026, p. 2) \#5fb236 \par - “DHTW method presents a disciplined yet flexible alternative, leveraging an iterative process to address these limitations.” (“Domain-specific semantic-rich software knowledge graph construction through human-LLM team working”, 2026, p. 2) \#5fb236 \par - “In the method, the "Do" phase employs LLMs to identify and extract diverse schema elements from heterogeneous data sources, while the "While" phase incorporates expert validation to refine and organize these elements, ensuring iterative improvement.” (“Domain-specific semantic-rich software knowledge graph construction through human-LLM team working”, 2026, p. 2) \#ffd400\\ \mkbibemph{This is a repetition. This concept has been already said earlier in the paper.} \par - “This work introduces the DHTW method for implementing "Human-in-the-Loop" by decomposing tasks into iterative cycles.” (“Domain-specific semantic-rich software knowledge graph construction through human-LLM team working”, 2026, p. 2) \#a28ae5 \par - “In each iteration, LLMs execute subtasks (Do phase) using human-derived feedback (e.g., operational optimizations, clarified requirements), while domain experts refine outputs (While phase) to ensure precision and inject actionable guidance for subsequent rounds.” (“Domain-specific semantic-rich software knowledge graph construction through human-LLM team working”, 2026, p. 2) \#a28ae5 \par - “This closedloop process harmonizes human expertise with LLM ability, fostering continuous improvement in both precision and completeness.” (“Domain-specific semantic-rich software knowledge graph construction through human-LLM team working”, 2026, p. 2) \#ffd400\\ \mkbibemph{This is a kind of chain of thoughts process, isn't it?} \par - “DHTW method take the advantages of both predefinedschema-guided method and schema-free method, through Do-While schema exploration and schema-guided KG construction.” (“Domain-specific semantic-rich software knowledge graph construction through human-LLM team working”, 2026, p. 2) \#a28ae5 \par - “The former iteratively discovers schema that adheres to domain standards while transcending predefined schema” (“Domain-specific semantic-rich software knowledge graph construction through human-LLM team working”, 2026, p. 2) \#5fb236 \par - “the latter leverages refined schema to construct KG from large-scale data, resolving the tension between rigidity and over-generalization in traditional approaches” (“Domain-specific semantic-rich software knowledge graph construction through human-LLM team working”, 2026, p. 2) \#5fb236 \par - “KGs for APIs (Application Programming Interface) can capture complex relationships and dependencies among large number of APIs, which can help developers better manage, optimize, and automate within API ecosystems” (“Domain-specific semantic-rich software knowledge graph construction through human-LLM team working”, 2026, p. 2) \#5fb236 \par - “reflecting domainspecific nuance” (“Domain-specific semantic-rich software knowledge graph construction through human-LLM team working”, 2026, p. 2) \#a28ae5 \par - “Figure 1 illustrates their differences, highlighting limitations and the need for a new approach.” (“Domain-specific semantic-rich software knowledge graph construction through human-LLM team working”, 2026, p. 2) \#5fb236 \par - “KW\&H, by Huang et al. [13], is a rule-based method with a predefined schema. It achieves high precision by analyzing software documentation but struggles with evolving needs, missing relationships outside predefined rules” (“Domain-specific semantic-rich software knowledge graph construction through human-LLM team working”, 2026, p. 2) \#5fb236 \par - “Reflecting on these methods, KW\&H ensures precision but sacrifices completeness, while GraphRAG and EDC excel in completeness but compromise precision and semantic richness.” (“Domain-specific semantic-rich software knowledge graph construction through human-LLM team working”, 2026, p. 2) \#ffd400\\ \mkbibemph{It's still vague.} \par - “o API functional dependencies or interactions,” (“Domain-specific semantic-rich software knowledge graph construction through human-LLM team working”, 2026, p. 3) \#ffd400\\ \mkbibemph{If this is the interest of the study, it has to be clearly stated upfront in the paper.} \par - “Figure 1: Comparison of Knowledge Graph Construction Methods in the API Domain” (“Domain-specific semantic-rich software knowledge graph construction through human-LLM team working”, 2026, p. 3) \#ffd400\\ \mkbibemph{Why not reusing existing approaches based on static analysis and just filter out those relationship triples that are not relevant for the goal at hand?} \par - “Collections.sort(), relies on, Arrays.asList” (“Domain-specific semantic-rich software knowledge graph construction through human-LLM team working”, 2026, p. 3) \#ffd400\\ \mkbibemph{This example gives some hints on the wanted granularity of the poposed approach even though it is not explicitely mentioned. It is important to specify upfront what is the granularity of the proposed approach. Is it at method level?Class level? Component level? What are the entities of interest? What are they relationships of interest?} \par - “avoiding pitfalls like (Files.readAllLines(), throws, IOException),” (“Domain-specific semantic-rich software knowledge graph construction through human-LLM team working”, 2026, p. 3) \#ffd400\\ \mkbibemph{Why this case are not of interest at all? It depens of the usage of the KGs, isn't it?} \par - “3.1.1 Seed Text Preparation and Data Chunking.” (“Domain-specific semantic-rich software knowledge graph construction through human-LLM team working”, 2026, p. 3) \#2ea8e5 \par - “In the initial phase of schema exploration, we first extract a small but representative set of seed texts (e.g., API documentation, code snippets, or Stack Overflow posts) from the target domain [21]. These seed texts serve as input data, providing domain-specific context for schema exploration” (“Domain-specific semantic-rich software knowledge graph construction through human-LLM team working”, 2026, p. 3) \#ffd400\\ \mkbibemph{Ok this answers my comments about the domain previously given. It's important to add clarifications about this aspect earlier in the paper. By the way it is necessary to clarify what are the characteristics that need to be satisfied by the text and data that need to be prepared at this stage. Is data chunking automated? Depending on it, the subsequent phases are affected.} \par - “[specific criteria TBD by user].” (“Domain-specific semantic-rich software knowledge graph construction through human-LLM team working”, 2026, p. 3) \#ff6666\\ \mkbibemph{I guess this is a sentence to be completed. isn't it?} \par - “The chunking strategy is based on size, ensuring each chunk is suitable for processing” (“Domain-specific semantic-rich software knowledge graph construction through human-LLM team working”, 2026, p. 3) \#ffd400\\ \mkbibemph{Details are needed here.} \par - “3.1.2 Do Phase: LLM-Based Schema Extraction and Definition.” (“Domain-specific semantic-rich software knowledge graph construction through human-LLM team working”, 2026, p. 3) \#2ea8e5 \par - “In the Do phase, the LLM extracts schema from the current data chunk, aiming to ensure the completeness of the generated schema.” (“Domain-specific semantic-rich software knowledge graph construction through human-LLM team working”, 2026, p. 3) \#ffd400\\ \mkbibemph{Completeness with respect to what?} \par - “The LLM first performs entity extraction to identify domain-related entities in the data chunk, such as a function name or variable from a code snippet.” (“Domain-specific semantic-rich software knowledge graph construction through human-LLM team working”, 2026, p. 3) \#ffd400\\ \mkbibemph{Again, it is necessary to clarify what's the target domain of the approach. Is it for generating KGs for APIs? Can it be applied to other kinds of artifacts? The envisioned target use of the approach is not clear to me.} \par - “Subsequently, the LLM inputs the extracted entities into the entity type labeling unit, which aggregates entities into preliminary labels, for example, grouping function-related terms under a “function” category. These labels are then input into the entity type fusion unit, which further abstracts low-dimensional entity types into high-dimensional types, such as combining “function” and “procedure” into a broader “callable” type. Relation types are abstracted through the relation type fusion unit, such as merging “calls” and “invokes” into a “call” category. Through this fusion mechanism, the method unifies and standardizes diverse entity and relation types, ensuring higher abstraction and consistency in the generated schema, unlike purely automated methods that may lack such refinement.” (“Domain-specific semantic-rich software knowledge graph construction through human-LLM team working”, 2026, p. 3) \#ffd400\\ \mkbibemph{This is related to my previous points. Authors seem to have specific categories of interest that should be enumerated and justified in the paper.} \par - “3.1.3 While Phase: Human Review and Feedback.” (“Domain-specific semantic-rich software knowledge graph construction through human-LLM team working”, 2026, p. 4) \#2ea8e5 \par - “LLM generates two types of suggestions to optimize the next iteration. First, the LLM generates operational suggestions, such as suggesting not to extract temporary variables in the next iteration if the user does not need them,” (“Domain-specific semantic-rich software knowledge graph construction through human-LLM team working”, 2026, p. 4) \#5fb236 \par - “Second, the LLM generates clarification suggestions, such as suggesting to clarify if the user needs dependency relations, ensuring the schema meets specific needs.” (“Domain-specific semantic-rich software knowledge graph construction through human-LLM team working”, 2026, p. 4) \#5fb236 \par - “3.1.4 Iterative Loop (Do-While Loop).” (“Domain-specific semantic-rich software knowledge graph construction through human-LLM team working”, 2026, p. 4) \#2ea8e5 \par - “until all chunks are processed” (“Domain-specific semantic-rich software knowledge graph construction through human-LLM team working”, 2026, p. 4) \#5fb236 \par - “which involves extracting API KGs from large-scale data based on validated schema.” (“Domain-specific semantic-rich software knowledge graph construction through human-LLM team working”, 2026, p. 4) \#5fb236 \par - “the LLM identifies domainrelated entities in the data based on the entity types defined in the schema, such as functions or classes.” (“Domain-specific semantic-rich software knowledge graph construction through human-LLM team working”, 2026, p. 4) \#5fb236 \par - “LLM identifies relationships between entities based on the predefined relation types in the schema, such as a “call” relation.” (“Domain-specific semantic-rich software knowledge graph construction through human-LLM team working”, 2026, p. 4) \#5fb236 \par - “Through schema constraints, strict quality control is implemented, filtering out noise and correcting inconsistencies.” (“Domain-specific semantic-rich software knowledge graph construction through human-LLM team working”, 2026, p. 4) \#5fb236 \par - “The final output of high-quality API KGs can be directly applied to practical domain tasks, such as code generation, code search, and vulnerability detection [25].” (“Domain-specific semantic-rich software knowledge graph construction through human-LLM team working”, 2026, p. 4) \#ffd400\\ \mkbibemph{This statement needs to be substantiated.} \par - “code generation, code search, and vulnerability detection” (“Domain-specific semantic-rich software knowledge graph construction through human-LLM team working”, 2026, p. 4) \#5fb236 \par - “balance precision, adaptability, and completeness,” (“Domain-specific semantic-rich software knowledge graph construction through human-LLM team working”, 2026, p. 4) \#ffd400\\ \mkbibemph{I expect to see an experiment section that clearly show how the approach outperforms existing techniques with respect to three peculiar characteristics.} \par - “we designed the method of DHTW which combines human expertise with the scalability of LLMs to deliver a KG that captures accurate relationships while avoiding noise and errors.” (“Domain-specific semantic-rich software knowledge graph construction through human-LLM team working”, 2026, p. 4) \#5fb236 \par - “alignment with user needs and domainspecific nuances.” (“Domain-specific semantic-rich software knowledge graph construction through human-LLM team working”, 2026, p. 5) \#5fb236 \par - “For instance, they assess whether “NullPointerException” is relevant to the user’s goals, and if deemed redundant, revise the schema by marking triples like “(Arrays.asList, throws, NullPointerException)” for exclusion.” (“Domain-specific semantic-rich software knowledge graph construction through human-LLM team working”, 2026, p. 5) \#ffd400\\ \mkbibemph{I?m wondering how scalable is this manual process with respect to the size of the input data. Indeed this can be an error-prone and strenuous task.} \par - “Predefined-schema-guided methods ensure precision but often lack completeness, as predefined schemas may miss relevant entities and relationships, leading to incomplete domain representations.” (“Domain-specific semantic-rich software knowledge graph construction through human-LLM team working”, 2026, p. 5) \#ffd400\\ \mkbibemph{This is not properly supported. At the end, with the proposed approach, human implicitely defines the schema of interest. Why not giving this upfront to an algortihmic approach?} \par - “Our research questions aim to investigate the indispensable role of Do-While schema exploration and definition coupled with the Human-LLM team working mechanism, in enhancing the applicability of constructed KGs.” (“Domain-specific semantic-rich software knowledge graph construction through human-LLM team working”, 2026, p. 5) \#ffd400\\ \mkbibemph{Repeated manh times.} \par - “thereby establishing a robust framework for effectively capturing the intricate semantics inherent in SE artifacts.” (“Domain-specific semantic-rich software knowledge graph construction through human-LLM team working”, 2026, p. 6) \#ffd400\\ \mkbibemph{Te paper is missing an explicit definition of the SE artifact of interest or in other words that are managed by the proposed approach.} \par - “RQ1: How does DHTW method outperform predefinedschema-guided and schema-free methods in constructing domain-specific KGs?” (“Domain-specific semantic-rich software knowledge graph construction through human-LLM team working”, 2026, p. 6) \#2ea8e5 \par - “RQ2: How does Human-LLM team working mechanism enhance the precision and completeness of the constructed domain-specific KGs?” (“Domain-specific semantic-rich software knowledge graph construction through human-LLM team working”, 2026, p. 6) \#2ea8e5 \par - “RQ3: How does KG schema exploration and definition strategy enhance the precision and completeness of the constructed domain-specific KGs?” (“Domain-specific semantic-rich software knowledge graph construction through human-LLM team working”, 2026, p. 6) \#ffd400\\ \mkbibemph{This seems to be covered by the previous two RQs, isn't it?} \par - “There are two datasets used in the experiments” (“Domain-specific semantic-rich software knowledge graph construction through human-LLM team working”, 2026, p. 6) \#5fb236 \par - “The first dataset focuses on the API domain. It includes two parts, the first part includes 206 Stack Overflow texts about Java obtained from work [13] (via GitHub [1]), which were used to summarize API entities and relationship types. The second part comprises 32,505 Java tutorial documents [14] provided by the same work [13]. However, not all Java tutorial documents contain relevant API entities and relationships. For example, some documents discuss general programming concepts without specific API references, such as: "This article focuses on two common operations: adding/removing elements...".” (“Domain-specific semantic-rich software knowledge graph construction through human-LLM team working”, 2026, p. 6) \#ffd400\\ \mkbibemph{The usage of the term domain is not clear. Both datasets are for creating KGs related to APIs. What do you mean with domain?~ \#question} \par - “Applying these criteria, we extracted API entities and relationships from 5,047 texts to construct the API KG” (“Domain-specific semantic-rich software knowledge graph construction through human-LLM team working”, 2026, p. 6) \#ffd400\\ \mkbibemph{Right, it seems your goal is generating API KGs. Do you support other kinds of software artifacts? \#question} \par - “The second dataset is the WebNLG+2020 (v3.0) [5] semantic parsing task test set, which is also used in the EDC method [31]. WebNLG+2020 includes 1,165 pairs of texts and triplets, focusing on urban information, with reference triplet patterns covering 159 unique relationship types. We sampled 333 texts from the WebNLG+2020 training set to construct seed texts. However, we observed that the reference triplets in WebNLG+2020 are often non-exhaustive and may include information external to the text. This issue may lead to inaccuracies in evaluation results. To address this problem, we arranged for annotators to construct reference answers for 367 sampled data points in the urban information domain, following the same method as the first dataset, as an additional test set. The Cohen’s Kappa coefficients for this annotation process were 0.83 and 0.81, demonstrating high inter-annotator agreement.” (“Domain-specific semantic-rich software knowledge graph construction through human-LLM team working”, 2026, p. 6) \#ffd400\\ \mkbibemph{What is the wanted KG supposed to represent with this dataset?} \par - “4.4 Experimental Evaluation Metrics” (“Domain-specific semantic-rich software knowledge graph construction through human-LLM team working”, 2026, p. 7) \#ffd400\\ \mkbibemph{The confusion about the scope of the paper is also testified in this section. Even though in the previous section mentions that two different "domains" are considered, the evaluation metrics that are presented in section 4.4. all refer ti the API knowledge extraction problem.} \par - “5.1 Evaluation of RQ1” (“Domain-specific semantic-rich software knowledge graph construction through human-LLM team working”, 2026, p. 7) \#ffd400\\ \mkbibemph{Also in this section, only the API KG extraction problem is considered. No trace about the second application domain mentioned in section 4.3. Actually the same problem occurs for the whole experimental evaluation section.} \par - “While they excel in breadth, they fall short in delivering a semantic-rich schema.” (“Domain-specific semantic-rich software knowledge graph construction through human-LLM team working”, 2026, p. 7) \#ffd400\\ \mkbibemph{Can you elaborate more on this? It is not clear the problem that~ you want to address. The challenges that are caused~ by existing approaches and that this paper aims to address are not clear \#question} \par COnsider that those that are tagget with \#5fb236 are just highlights, those that are tagged with \#e56eee and \#a28ae5 are imporant sentences. Please pay attention instead to the notes that are tagged with \#ffd400. Those that are tagged with \#ff6666 are typos or errors. Could you please draft a review by organizing it as follows: SUMMARY: Just a few sentence to summarize the work STRENGHTS: \par WEAKNESSES: \par COMMENTS: Organize the notes with respect to the following criteria: \par \begin{itemize} \item \texttt{Novelty} \item \texttt{Rigor} \item \texttt{Relevance (of the contribution)} \item \texttt{Verifiability and Transparency} \item \texttt{Presentation} \end{itemize} \par And then add a Detailed Comments section to report the notes that contain issues or typos. \\ Can you also formulate three explicit questions by considering the comments that are tagged with \#question ?} } @article{Domingos:2012:FUT:2347736.2347755, title = {A Few Useful Things to Know about Machine Learning}, author = {Domingos, Pedro}, date = {2012-10}, journaltitle = {Commun. ACM}, volume = {55}, number = {10}, pages = {78--87}, publisher = {ACM}, address = {New York, NY, USA}, issn = {0001-0782}, acmid = {2347755}, issue_date = {October 2012}, nodoi = {10.1145/2347736.2347755}, numpages = {10} } @article{Dong2022326, title = {{{SA-CGAN}}: {{An}} Oversampling Method Based on Single Attribute Guided Conditional {{GAN}} for Multi-Class Imbalanced Learning}, author = {Dong, Y. and Xiao, H. and Dong, Y.}, date = {2022}, journaltitle = {Neurocomputing}, volume = {472}, pages = {326--337}, publisher = {Elsevier B.V.}, issn = {09252312}, doi = {10.1016/j.neucom.2021.04.135}, abstract = {Imbalanced data can always be observed in our daily life and various practical tasks. A lot of well-constructed machine learning methodologies may produce ineffective performance, when conducted on this kind of data. This originates from the produced high training biases that towards the majority class instances. Among all the solutions of this problem, data generation of the minority class is always considered the most effective approach. However, in all the previous works, data are always processed sample-wisely and the distribution of each single data attribute is never noticed. So, in this paper, to estimate the mechanism of how each attribute contributes to its label, we explore the potential connection between the two items by Conditional Generative Adversarial Networks (CGAN) separately and individually. Then, the constructed new instances are purified by a designed attribute-based minimax filter and the survivors are concatenated to form the eventual generated data. In other words, different from the CGAN based data generation way, the proposed approach improves it by additionally considering all the single attribute patterns of the data that to construct new instances. In addition, we extend the binary class imbalanced learning framework to multiple class one. In the experimental part, the improved model is compared against GAN, CGAN and some other standard multiple-class oversampling algorithms on several widely used datasets. Results, in terms of four common measurements, have shown that the proposed approach can produce comparable and always superior performance when compared with the competitors. © 2021 Elsevier B.V.}, author_keywords = {Attribute/feature pattern learning; Data generation; Generative adversarial network; Multi-class uneven/imbalanced data}, coden = {NRCGE}, document_type = {Article}, source = {Scopus}, keywords = {algorithm,Article,attribute feature pattern learning,Attribute/feature pattern learning,conceptual framework,conditional generative adversarial network,Data generation,data processing,Feature pattern,Generative adversarial networks,Imbalanced data,Imbalanced Learning,machine learning,measurement accuracy,measurement precision,multi class imbalanced learning,Multi-class uneven/imbalanced data,Multiple class,Neural networks,Over sampling,Pattern Learning,perception,Performance,random sample,Software engineering,statistical significance}, note = {cited By 2} } @article{dornenburgPathDevOps2018, title = {The {{Path}} to {{DevOps}}}, author = {Dörnenburg, E.}, date = {2018-09}, journaltitle = {IEEE Softw.}, volume = {35}, number = {5}, pages = {71--75}, issn = {0740-7459}, doi = {10.1109/MS.2018.290110337}, abstract = {IT’s role in the business world has changed dramatically over the past decades. New technologies and techniques let enterprises get much more out of IT, while increasingly sophisticated business models have pushed IT to investigate and deliver novel solutions. Agile development led the way, and now the DevOps and DesignOps movements are hitting the mainstream. IT in businesses is now entirely a team activity. While we still need experts with deep technical knowledge, we must focus on how to get people from all disciplines working together effectively. This article is part of a theme issue on software engineering’s 50th anniversary.}, keywords = {software engineering}, note = {TL;DR \par This article is part of a theme issue on software engineering’s 50th anniversary, which focuses on how to get people from all disciplines working together effectively in IT.} } @inproceedings{Dorodnykh202160, title = {End-User Development of Knowledge Bases for Semi-Automated Formation of Task Cards}, author = {Dorodnykh, N.O. and Kotlov, Y.V. and Nikolaychuk, O.A. and Popov, V.M. and Yurin, A.Y.}, editor = {Bychkov I., Tchernykh A., Feoktistov A.}, date = {2021}, series = {{{CEUR Workshop Proceedings}}}, volume = {2913}, pages = {60--73}, publisher = {CEUR-WS}, issn = {16130073}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111360450&partnerID=40&md5=0dfb7b7b307c3f0687f5911003a61f5b}, abbrev_source_title = {CEUR Workshop Proc.}, affiliation = {Matrosov Institute for System Dynamics and Control Theory, Siberian Branch of Russian Academy of Sciences (ISDCT SB RAS), 134, Lermontov str., Irkutsk, 664033, Russian Federation; Moscow State Technical University of Civil Aviation, Irkutsk Branch (MSTUCA), 3, Kommunarov str., Irkutsk, 664003, Russian Federation}, correspondence_address1 = {Yurin, A.Y.; Matrosov Institute for System Dynamics and Control Theory, 134, Lermontov str., Russian Federation; email: iskander@icc.ru}, document_type = {Conference Paper}, langid = {english}, source = {Scopus}, note = {cited By 1 \par TL;DR \par An extension of the Prototyping expert systems based on transformations technology, which implements the End-user development, is proposed in the context of the problem to be solved for Sukhoi Superjet aircraft.} } @article{dsouzaWorkshopSoftwareArchitectures, title = {Workshop on {{Software Architectures}} for {{Adaptive Autonomous Systems}} ({{SAAAS}})}, author = {D’Souza, Meenakshi and Mohalik, Swarup Kumar and Jayaraman, Mahesh Babu}, url = {https://pdfs.semanticscholar.org/b1d6/f9387fdefc8d4eb0054162cb1c040de8d69f.pdf}, urldate = {2016-08-21} } @inproceedings{Duala-Ekoko:2012:AAQ:2337223.2337255, title = {Asking and Answering Questions about Unfamiliar {{APIs}}: {{An}} Exploratory Study}, booktitle = {Proc. 34th {{Int}}. {{Conf}}. {{Softw}}. {{Eng}}.}, author = {Duala-Ekoko, Ekwa and Robillard, Martin P.}, date = {2012}, series = {{{ICSE}} '12}, pages = {266--276}, publisher = {IEEE Press}, location = {Piscataway, NJ, USA}, url = {http://dl.acm.org/citation.cfm?id=2337223.2337255}, acmid = {2337255}, isbn = {978-1-4673-1067-3}, numpages = {11}, note = {TL;DR \par Twenty different types of questions programmers ask when working with unfamiliar APIs are identified, and new insights are provided to the cause of the difficulties programmers encounter when answering questions about the use of APIs.} } @inproceedings{Dubey2020, title = {{{HACO}}: {{A}} Framework for Developing Human-{{AI}} Teaming}, author = {Dubey, A. and Abhinav, K. and Jain, S. and Arora, V. and Puttaveerana, A.}, date = {2020}, series = {{{ACM International Conference Proceeding Series}}}, publisher = {Association for Computing Machinery}, doi = {10.1145/3385032.3385044}, abbrev_source_title = {ACM Int. Conf. Proc. Ser.}, affiliation = {Accenture Labs, Bangalore, India}, art_number = {3385044}, document_type = {Conference Paper}, isbn = {978-1-4503-7594-8}, langid = {english}, source = {Scopus}, note = {cited By 6 \par TL;DR \par A solution framework, Human-AI Collaboration (HACO), enables a model-driven development of human-AI teaming systems through graphical user interface and a user study performed to assess the usefulness of HACO, shows that HacO is a promising framework.} } @article{Dunke2020, title = {Neural Networks for the Metamodeling of Simulation Models with Online Decision Making}, author = {Dunke, F. and Nickel, S.}, date = {2020}, journaltitle = {Simul. Model. Pract. Theory}, volume = {99}, publisher = {Elsevier B.V.}, issn = {1569190X}, doi = {10.1016/j.simpat.2019.102016}, abbrev_source_title = {Simul. Model. Pract. Theory}, affiliation = {Karlsruhe Institute of Technology, Institute of Operations Research, Discrete Optimization and Logistics, Kaiserstr. 12, Karlsruhe, 76131, Germany}, art_number = {102016}, correspondence_address1 = {Dunke, F.; Karlsruhe Institute of Technology, Kaiserstr. 12, Germany; email: fabian.dunke@kit.edu}, document_type = {Article}, langid = {english}, source = {Scopus}, keywords = {GOAL\_Model-simulation,notion}, note = {cited By 12} } @inproceedings{dunlapStudyApplicationSandbox2022, title = {A {{Study}} of {{Application Sandbox Policies}} in {{Linux}}}, booktitle = {Proc. 27th {{ACM Symp}}. {{Access Control Models Technol}}.}, author = {Dunlap, Trevor and Enck, William and Reaves, Bradley}, date = {2022-06-07}, pages = {19--30}, publisher = {ACM}, location = {New York NY USA}, doi = {10.1145/3532105.3535016}, abstract = {Desktop operating systems, including macOS, Windows 10, and Linux, are adopting the application-based security model pervasive in mobile platforms. In Linux, this transition is part of the movement towards two distribution-independent application platforms: Flatpak and Snap. This paper provides the first analysis of sandbox policies defined for Flatpak and Snap applications, covering 283 applications contained in both platforms. First, we find that 90.1\% of Snaps and 58.3\% of Flatpak applications studied are contained by tamperproof sandboxes. Further, we find evidence that package maintainers actively attempt to define least-privilege application policies. However, defining policy is difficult and error-prone. When studying the set of matching applications that appear in both Flatpak and Snap app stores, we frequently found policy mismatches: e.g., the Flatpak version has a broad privilege (e.g., file access) that the Snap version does not, or vice versa. This work provides confidence that Flatpak and Snap improve Linux platform security while highlighting opportunities for improvement.}, eventtitle = {{{SACMAT}} '22: {{The}} 27th {{ACM Symposium}} on {{Access Control Models}} and {{Technologies}}}, isbn = {978-1-4503-9357-7}, langid = {english} } @article{Duran22, title = {Rewriting Logic and Maude for the Formalization and Analysis of {{DSMLs}}, and the Prototyping of {{MDSE}} Tools}, author = {Durán, Francisco}, date = {2022-10}, journaltitle = {J. Object Technol.}, volume = {21}, number = {4}, pages = {4:1-12}, issn = {1660-1769}, doi = {10.5381/jot.2022.21.4.a2}, langid = {english}, x-editor = {Lola Burgueno and Martin Gogolla and Richard Paige}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {Special Issue dedicated to Antonio Vallecillo on his 60th Birthday \par TL;DR \par This paper summarizes the main contributions by Antonio and myself on the possibilities of analysing models, long before they were implemented, and graphically defining the behavior of systems and DSMLs using Maude as back-end tool.} } @article{dustdarDistributedComputingContinuum2022, title = {On Distributed Computing Continuum Systems}, author = {Dustdar, Schahram and Casamajor Pujol, Victor and Donta, Praveen Kumar}, date = {2022}, journaltitle = {IEEE Trans. Knowl. Data Eng.}, pages = {1--1}, issn = {1041-4347, 1558-2191, 2326-3865}, doi = {10.1109/TKDE.2022.3142856}, keywords = {LOGSEQ} } @inproceedings{duttSelfAwarenessCyberPhysicalSystems2016, title = {Self-{{Awareness}} in {{Cyber-Physical Systems}}}, author = {Dutt, Nikil and TaheriNejad, Nima}, date = {2016-01}, pages = {5--6}, publisher = {IEEE}, doi = {10.1109/VLSID.2016.129}, isbn = {978-1-4673-8700-2}, note = {TL;DR \par This tutorial will introduce the concepts surrounding self-awareness in the context of Cyber-Physical Systems (CPS), which is the inherent control function where the environment is sensed, the system is analyzed, and adaptions are applied to respect constraints and achieve desired goals.} } @inproceedings{EASE2021VP, title = {Article Hidden to Conform with the Double Blind Review Policy}, author = {{AnonymousAuthors}}, date = {2021}, url = {https://bit.ly/3eqNUMG}, note = {To appear in conference proceedings} } @book{ebertGlobalSoftwareIT2011, title = {Global Software and {{IT}}: A Guide to Distributed Development, Projects, and Outsourcing}, shorttitle = {Global Software and {{IT}}}, author = {Ebert, Christof}, date = {2011}, publisher = {John Wiley \& Sons}, url = {http://books.google.com/books?hl=en&lr=&id=Bj7poEQLZOUC&oi=fnd&pg=PT11&dq=%22Time-to-pro%EF%AC%81t+means+that+you+must+cut+out+delays+from+the+introduction%22+%22complexity.+Open+source+software+only+delivers+core+features+and%22+%22For+that+very+reason,+security+breaches+are+typically+%EF%AC%81xed%22+&ots=l5lCIeb6BB&sig=mK3SgFCs5N3Rvnu70r_9cOw0l5I}, urldate = {2017-06-23}, note = {TL;DR \par This book will provide a more balanced framework for planning globalDevelopment, covering topics such as managing people in distributed sites, managing a project across locations, mitigating the risk of offshoring, processes for global development, practical outsourcing guidelines, collaboration, and communication.} } @online{echelonBuildingIoTIndustrial, title = {Building an {{IoT}} for Industrial Control: {{Part}} 1 – {{What}} Is {{Industrial IoT}}?}, shorttitle = {Building an {{IoT}} for Industrial Control}, author = {Echelon, Robert Dolin}, url = {http://www.embedded.com/design/real-world-applications/4426952/1/Building-an-IoT-for-industrial-control--Part-1--What-is-Industrial-IoT-}, urldate = {2016-11-01}, abstract = {In the first in a two-part series, Echelon’s Robert Dolin describes the requirements that the IP-enabled “Internet of Things” (IoT) must meet to be suitable for use in industrial control network environments.}, organization = {Embedded} } @inproceedings{ECL, ids = {10.1007/978-3-642-02674-4_11}, title = {Establishing Correspondences between Models with the Epsilon Comparison Language}, booktitle = {Model {{Driven Archit}}. - {{Found}}. {{Appl}}.}, author = {Kolovos, Dimitrios S.}, editor = {Paige, Richard F. and Hartman, Alan and Rensink, Arend}, date = {2009}, pages = {146--157}, publisher = {Springer Berlin Heidelberg}, location = {Berlin, Heidelberg}, isbn = {978-3-642-02674-4}, note = {TL;DR \par The Epsilon Comparison Language (ECL) is presented, a hybrid rule-based language, built atop the Epsilon platform, which enables developers to implement comparison algorithms at a high level of abstraction and execute them in order to identify matches between elements belonging to models of diverse metamodels and modelling technologies.} } @online{EclipseSmartHomeFlexible, title = {Eclipse {{SmartHome}} - {{A Flexible Framework}} for the {{Smart Home}} - {{Binding}} Development}, url = {http://www.eclipse.org/smarthome/documentation/development/bindings/how-to.html}, urldate = {2016-12-08} } @online{EclipseZoneGettingStarted, title = {{{EclipseZone}} - {{Getting}} Started with {{OSGi}}: {{Interacting}} ...}, url = {http://www.eclipsezone.com/eclipse/forums/m92131032.html}, urldate = {2016-12-04} } @article{edmunds_focus_1999, title = {The {{Focus Group Research Handbook}}}, author = {Edmunds, Holly}, date = {1999-01}, journaltitle = {Bottom Line}, volume = {12}, number = {3}, pages = {46--46}, issn = {0888-045X}, doi = {10.1108/bl.1999.12.3.46.1}, note = {Publisher: Emerald Group Publishing Limited} } @article{efremovIntegratedApproachCommon2015, title = {An {{Integrated Approach}} to {{Common Problems}} in the {{Internet}} of {{Things}}}, author = {Efremov, Sergey and Pilipenko, Nikolay and Voskov, Leonid}, date = {2015}, journaltitle = {Procedia Engineering}, volume = {100}, pages = {1215--1223}, issn = {18777058}, doi = {10.1016/j.proeng.2015.01.486}, abstract = {The recent advances in technology enabled transition to the Internet of Things (IoT), in which physical objects around us become an integral part of the global information system. A major technical challenge however is to make these numerous objects interact seamlessly with each other. The latest works related to concepts, such as Web of Things or Social Web of Things, partly address the issue. In our paper we further investigate the topic and point out several problems that need to be efficiently solved for the Internet of Things to work on large scale numbers. One of the main tasks is to make devices easily discoverable. Thus, an efficient way to handle and store their metadata is required. Another problem is connected with providing different models of inter-device communication, asynchronous being the most important, as many of today’s widely used web standards were not designed for it. Finally, we propose a general cloud-based IoT architecture aimed at solving the above-described problems.}, langid = {english}, note = {TL;DR \par This paper points out several problems that need to be efficiently solved for the Internet of Things to work on large scale numbers and proposes a general cloud-based IoT architecture aimed at solving these problems.} } @article{EhrigKT09, title = {Generating Instance Models from Meta Models}, author = {Ehrig, Karsten and Küster, Jochen Malte and Taentzer, Gabriele}, date = {2009}, journaltitle = {Software Syst. Model.}, volume = {8}, number = {4}, pages = {479--500}, doi = {10.1007/S10270-008-0095-Y}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Fri, 18 Sep 2020 11:19:18 +0200} } @article{Eichhoff2015333, title = {A Survey on Automating Configuration and Parameterization in Evolutionary Design Exploration}, author = {Eichhoff, J.R. and Roller, D.}, date = {2015}, journaltitle = {Artif. Intell. Eng. Des. Anal. Manuf. AIEDAM}, volume = {29}, number = {4}, pages = {333--350}, publisher = {Cambridge University Press}, issn = {08900604}, doi = {10.1017/S0890060415000372}, abbrev_source_title = {Artif Intell Eng Des Anal Manuf}, affiliation = {Institute of Computer-Aided Product Development Systems, University of Stuttgart, Stuttgart, Germany}, coden = {AIEME}, correspondence_address1 = {Eichhoff, J.R.; Institut für Rechnergestützte Ingenieursysteme, Universitätsstrasse 38, Germany; email: julian.eichhoff@informatik.uni-stuttgart.de}, document_type = {Conference Paper}, langid = {english}, source = {Scopus}, note = {cited By 3 \par TL;DR \par The authors further review promising machine learning and natural language processing methods for automatic knowledge elicitation and formalization with respect to their implementation for evolutionary design optimization.} } @inproceedings{einarssonSmartHomeMLDomainSpecificModeling2017, title = {{{SmartHomeML}}: {{Towards}} a {{Domain-Specific Modeling Language}} for {{Creating Smart Home Applications}}}, shorttitle = {{{SmartHomeML}}}, author = {Einarsson, Atli F. and Patreksson, Patrekur and Hamdaqa, Mohammad and Hamou-Lhadj, Abdelwahab}, date = {2017-06}, pages = {82--88}, publisher = {IEEE}, doi = {10.1109/IEEE.ICIOT.2017.35}, isbn = {978-1-5386-2011-3}, note = {TL;DR \par This paper shows through an example how to use SmartHomeML to model asmart home service independently from the target smart home provider and then generate Amazon Alexa Skill Adapters and SmartThings SmartApps automatically.} } @article{eisenbergMultiobjectiveModelTransformation2024, title = {Multi-Objective Model Transformation Chain Exploration with {{MOMoT}}}, author = {Eisenberg, Martin and Sahay, Apurvanand and Di Ruscio, Davide and Iovino, Ludovico and Wimmer, Manuel and Pierantonio, Alfonso}, date = {2024-05}, journaltitle = {Information and Software Technology}, pages = {107500}, issn = {09505849}, doi = {10.1016/j.infsof.2024.107500}, abstract = {Context: The increasing complexity of modern systems leads to an increasing amount of artifacts that are used along the model-based software and systems development lifecycle. This also includes model transformations, which serve for mapping models between representations, e.g., for verification and validation purposes.}, langid = {english} } @inproceedings{EisenbergPGW21, title = {Towards Reinforcement Learning for In-Place Model Transformations}, booktitle = {24th {{Int}}. {{Conf}}. {{Model Driven Eng}}. {{Lang}}. {{Syst}}. {{MODELS}}}, author = {Eisenberg, Martin and Pichler, Hans-Peter and Garmendia, Antonio and Wimmer, Manuel}, date = {2021}, pages = {82--88}, publisher = {IEEE}, doi = {10.1109/MODELS50736.2021.00017}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {TL;DR \par This paper applies for the first time reinforcement learning for in-place model transformations with value-based and policy-based techniques and investigates several case studies for validating the feasibility of using reinforcementLearning for model-driven optimization and compares the performance against existing approaches.}, timestamp = {Wed, 23 Feb 2022 10:19:30 +0100} } @article{eisenbergSearchingModelsHybrid2021, title = {Searching for {{Models}} with {{Hybrid AI Techniques}}}, author = {Eisenberg, Martin and Pichler, Hans-Peter and Garmendia, Antonio}, date = {2021}, pages = {2}, abstract = {The Model-Driven Engineering (MDE) [3] paradigm advocates for the use of models as an abstraction layer to represent complex systems. Model transformations are a central technique within MDE [10]. They either modify existing models or create new ones from scratch. Generally, these models should represent an optimal state of the system that has to be found within a large space of possible solutions. Model-driven optimization [1, 2, 4–6, 9] is a research area within MDE that proposes to automatically find optimal solutions which are constructed by a set of transformation rules given certain objectives. In order to search into large solution spaces, model-driven optimization approaches combine the expressiveness of models and domain-specific modeling languages, with the computational effectiveness of Artificial Intelligence (AI) methods to find the best model for a particular scenario.}, langid = {english}, keywords = {GOAL-Model\_Search,notion,TECHNIQUE\_ReinforcementLearning} } @incollection{ekaputraOntologyChangeOntologyBased2015, title = {Ontology {{Change}} in {{Ontology-Based Information Integration Systems}}}, booktitle = {The {{Semantic Web}}. {{Latest Advances}} and {{New Domains}}}, author = {Ekaputra, Fajar Juang}, editor = {Gandon, Fabien and Sabou, Marta and Sack, Harald and family=Amato, given=Claudia, prefix=d’, useprefix=true and Cudré-Mauroux, Philippe and Zimmermann, Antoine}, date = {2015}, volume = {9088}, pages = {711--720}, publisher = {Springer International Publishing}, location = {Cham}, url = {http://link.springer.com/10.1007/978-3-319-18818-8_44}, urldate = {2015-06-24}, isbn = {978-3-319-18817-1 978-3-319-18818-8}, note = {TL;DR \par This work discusses the research goals, methods, and evaluation options to address the challenge of ontology change in OBII contexts, and plans to adapt successful techniques proposed both by Semantic Web and Model-Driven Engineering communities.} } @inproceedings{ekstrandLensKitPythonNextGeneration2020, title = {{{LensKit}} for {{Python}}: {{Next-Generation Software}} for {{Recommender Systems Experiments}}}, shorttitle = {{{LensKit}} for {{Python}}}, booktitle = {Proc. 29th {{ACM Int}}. {{Conf}}. {{Inf}}. {{Knowl}}. {{Manag}}.}, author = {Ekstrand, Michael D.}, date = {2020-10-19}, pages = {2999--3006}, publisher = {ACM}, location = {Virtual Event Ireland}, doi = {10.1145/3340531.3412778}, eventtitle = {{{CIKM}} '20: {{The}} 29th {{ACM International Conference}} on {{Information}} and {{Knowledge Management}}}, isbn = {978-1-4503-6859-9}, langid = {english}, note = {TL;DR \par The next generation of the LensKit project is presented, re-envisioning the original tool's objectives as flexible Python package for supporting recommender systems research and development.} } @article{Elnagar2020383, title = {Towards Applying Deep Learning to the Internet of Things: {{A}} Model and a Framework}, author = {Elnagar, S. and Osei-Bryson, K.-M.}, editor = {Themistocleous M., Papadaki M., Kamal M.M.}, date = {2020}, journaltitle = {Lect. Notes Bus. Inf. Process.}, volume = {402}, pages = {383--398}, publisher = {{Springer Science and Business Media Deutschland GmbH}}, issn = {18651348}, doi = {10.1007/978-3-030-63396-7_26}, abstract = {Deep Learning (DL) modeling has been a recent topic of interest. With the accelerating need to embed Deep Learning Networks (DLNs) to the Internet of Things (IoT) applications, many DL optimization techniques were developed to enable applying DL to IoTs. However, despite the plethora of DL optimization techniques, there is always a trade-off between accuracy, latency, and cost. Moreover, there are no specific criteria for selecting the best optimization model for a specific scenario. Therefore, this research aims at providing a DL optimization model that eases the selection and re-using DLNs on IoTs. In addition, the research presents an initial design for a DL optimization model management framework. This framework would help organizations choose the optimal DL optimization model that maximizes performance without sacrificing quality. The research would add to the IS design science knowledge as well as the industry by providing insights to many IT managers to apply DLNs to IoTs such as machines and robots. © 2020, Springer Nature Switzerland AG.}, document_type = {Conference Paper}, isbn = {9783030633950}, source = {Scopus}, note = {cited By 0} } @misc{emf, title = {Eclipse {{EMF}}}, url = {https://www.eclipse.org/modeling/emf/} } @online{EMFFacet, title = {{{EMF Facet}}}, url = {http://www.eclipse.org/facet/}, urldate = {2015-09-24} } @online{EmpiricalStandards, title = {Empirical Standards}, url = {https://www2.sigsoft.org/EmpiricalStandards/}, urldate = {2025-04-17}, abstract = {Empirical standards for conducting and evaluating research in software engineering}, langid = {english}, organization = {Empirical Standards}, keywords = {/unread} } @online{EnablingAutonomousApplications, title = {Enabling {{Autonomous Applications}} for {{IoT}} - {{Alta Devices Alta Devices}}}, url = {http://www.altadevices.com/energy-harvesting/enabling-autonomous-applications-for-iot/}, urldate = {2016-09-03} } @inproceedings{EncodingConceptualModels2021, title = {Encoding {{Conceptual Models}} for {{Machine Learning}}: {{A Systematic Review}}}, booktitle = {2021 {{ACMIEEE Int}}. {{Conf}}. {{Model Driven Eng}}. {{Lang}}. {{Syst}}. {{Companion MODELS-C}}}, date = {2021}, publisher = {IEEE}, location = {Fukuoka, Japan}, abstract = {Conceptual models are essential in Software and Information Systems Engineering to meet many purposes since they explicitly represent the subject domains. Machine Learning (ML) approaches have recently been used in conceptual modeling to realize, among others, intelligent modeling assistance, model transformation, and metamodel classification. These works encode models in various ways, making the encoded models suitable for applying ML algorithms. The encodings capture the models’ structure and/or semantics, making this information available to the ML model during training. Therefore, the choice of the encoding for any ML-driven task is crucial for the ML model to learn the relevant contextual information. In this paper, we report findings from a systematic literature review which yields insights into the current research in machine learning for conceptual modeling (ML4CM). The review focuses on the various encodings used in existing ML4CM solutions and provides insights into i) which are the information sources, ii) how is the conceptual model’s structure and/or semantics encoded, iii) why is the model encoded, i.e., for which conceptual modeling task and, iv) which ML algorithms are applied. The results aim to structure the state of the art in encoding conceptual models for ML.}, eventtitle = {2021 {{ACM}}/{{IEEE International Conference}} on {{Model Driven Engineering Languages}} and {{Systems Companion}} ({{MODELS-C}})}, isbn = {978-1-6654-2484-4}, langid = {english}, keywords = {LOGSEQ} } @article{enetModelingLanguageEngineering, title = {Modeling Language Engineering ({{MLE}})}, author = {Enet, Josselin and Kurtev, Ivan and Wortmann, Andreas}, abstract = {Modeling is a key paradigm to successfully engineer softwareintensive systems in collaboration with experts from diverse domains. These experts use domain-specific concepts, notations, and paradigms, that need to be expressed in suitable, domain-specific modeling languages. Conceiving, engineering, evolving, and maintaining domain-specific modeling languages, hence, is crucial to handle increasing complexity of software-intensive systems. Consequently, modeling language engineering is vital to the success of modeling in particular and the engineering of these systems in general. The proposed MLE workshop will be a full-day workshop aiming at bringing together researchers and practitioners in the language and modeling language engineering communities to discuss the challenges associated with the engineering of modeling languages as well as their integration. Following five editions of the GEMOC workshop, four of the EXE workshop, and six joint editions as the MLE workshop, this edition will continue the success of MLE and, again, attract a large number of interested participants from the MODELS and the SLE community.}, langid = {english}, keywords = {LOGSEQ}, note = {\textbf{REVIEW} \par (1. Workshop title, 2. Motivation, 3. Organization, 4. Workshop format, 5. Additional material).} } @inproceedings{EngelsHHS00, title = {Dynamic Meta Modeling: A Graphical Approach to the Operational Semantics of Behavioral Diagrams in {{UML}}}, booktitle = {«{{UML}}» 2000 - {{Unified Model}}. {{Lang}}. {{Adv}}. {{Stand}}. {{Third Int}}. {{Conf}}. {{York UK Oct}}. 2-6 2000 {{Proc}}.}, author = {Engels, Gregor and Hausmann, Jan Hendrik and Heckel, Reiko and Sauer, Stefan}, editor = {Evans, Andy and Kent, Stuart and Selic, Bran}, date = {2000}, series = {Lecture Notes in Computer Science}, volume = {1939}, pages = {323--337}, publisher = {Springer}, doi = {10.1007/3-540-40011-7\_23}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {TL;DR \par The dynamic meta model extends the well-known static meta model by a specification of the system's dynamics by means of collaboration diagrams, which can be both mathematically rigorous so as to enable formal specifications and proofs and understandable without prior knowledge of heavy mathematic machinery.}, timestamp = {Mon, 24 Jun 2019 12:03:37 +0200} } @article{ennsManagementHeterogeneousData2024, title = {Management of {{Heterogeneous Data}} in {{Digital Editions}}: {{A Model-Driven Approach}}}, author = {Enns, Sebastian}, date = {2024}, abstract = {The increasing use of web technologies in the Digital Humanities presents new opportunities for handling domain-specific content but also introduces challenges, particularly due to the heterogeneity of data models. Users of this domain often encounter difficulties in finding or configuring systems for their Digital Editions that can manage complex documents and perform necessary adjustments. This frequently results in self-management, where users must set up and maintain intricate technical infrastructures. To address these issues, a multi-level modeling architecture is proposed for integrating and managing heterogeneous data in Digital Editions. This model-driven infrastructure abstracts elements and relationships across various projects, enhancing interoperability and customization. Key contributions include the development of a meta-model, the implementation of multi-level modeling techniques, and the creation of tools such as a modeling tool, data importer, text editor, and web page generator. These tools are going to improve the efficiency, flexibility, and sustainability of essential processes in Digital Editions, encompassing the conception, elaboration, and publication phases. The approach is going to evaluate, how a modeldriven infrastructure based on a platform-independent multi-level modeling approach will improve the efficiency and applicability of the technology in the aforementioned steps of Digital Editions.}, langid = {english}, keywords = {LOGSEQ}, note = {\section{Annotazioni\\ (18/7/2024, 16:58:01)} \par - “Heterogeneous Data in Digital Editions” (Enns, 2024, p. 1) \#5fb236 \par - “Digital Editions” (Enns, 2024, p. 1) \#ffd400\\ \mkbibemph{What do you mean? What are they?} \par - “multi-level modeling architecture is proposed for integrating and managing heterogeneous data in Digital Editions.” (Enns, 2024, p. 1) \#a28ae5 \par - “across various projects, enhancing interoperability and customization” (Enns, 2024, p. 1) \#a28ae5 \par - “multi-level modeling techniques, and the creation of tools such as a modeling tool, data importer, text editor, and web page generator.” (Enns, 2024, p. 1) \#ffd400\\ \mkbibemph{Check the related work.} \par - “Digital Editions are scholarly publications that provide foundational material for research in the humanities and cultural studies in digital form” (Enns, 2024, p. 1) \#a28ae5 \par - “conception phase” (Enns, 2024, p. 1) \#5fb236 \par - “conception phase, the data model is developed, the subject of the edition is determined, contents and questions are defined, and the relationships within and outside the edition are established.” (Enns, 2024, p. 1) \#a28ae5 \par - “Elaboration” (Enns, 2024, p. 1) \#5fb236 \par - “involves exploring the contents through the description and transcription of documents, as well as establishing metadata standards.” (Enns, 2024, p. 1) \#a28ae5 \par - “Publication” (Enns, 2024, p. 1) \#5fb236 \par - “includes the release of the edited subjects in a suitable environment” (Enns, 2024, p. 1) \#a28ae5 \par - “data model was developed” (Enns, 2024, p. 1) \#a28ae5 \par - “model transformations of the actual data model of the edition” (Enns, 2024, p. 1) \#5fb236 \par - “ther, the variety and complexity of technologies in the Digital Humanities often lead to inadequate and inefficient tools that do not meet specific research requirements [41]. Additionally, these tools often lack user-friendliness and demand significant dedication to master [35]. Large, standardized infrastructures are often too rigid, costly, and unable to adequately support the dynamic, innovative needs of Humanities research” (Enns, 2024, p. 1) \#ffd400\\ \mkbibemph{All of these motivations are peculiar and need to be presented by complementing the text with reference to some concrete examples.} \par - “heterogeneous data, obsolete tools and complex technical infrastructures,” (Enns, 2024, p. 1) \#5fb236 \par - “Using multi-level modeling, further model layers can be created based on a meta-model, which can extend the existing model with additional conditions and aspects” (Enns, 2024, p. 2) \#5fb236 \par - “abstraction of elements and relations from data models of existing Digital Editions” (Enns, 2024, p. 2) \#a28ae5 \par - “model-driven infrastructure that includes an additional domain-specific model layer based on a meta-model.” (Enns, 2024, p. 2) \#ffd400 \par - “This infrastructure will enable the integration and management of heterogeneous data for Digital Editions in the Digital Humanities.” (Enns, 2024, p. 2) \#ffd400\\ \mkbibemph{It's not clear what's the contribution of this work with respect to existing techniques to manage hybrid polystores. What are the specificities of the considered application domain that make existing solutions unsuitable for it?} \par - “Enhancing the interoperability of various elements within Digital Editions by providing a common meta-model that can be adapted to different projects” (Enns, 2024, p. 2) \#ffd400\\ \mkbibemph{What are the technical challenges that you foresee?} \par - “Allow domain experts to tailor the domain-specific layer and tools to fit the specific needs of their projects, ensuring that unique aspects of their data are adequately addressed.” (Enns, 2024, p. 2) \#ffd400\\ \mkbibemph{There are low-code tools that permit to create custom data management systems. What are the novelties that you see from this aspect?} \par - “While the specific attributes and models are tailored to the needs of the CensusIRL project, they are based on generic principles of data modeling and process definition that could be reused in other similar projects” (Enns, 2024, p. 2) \#ffd400\\ \mkbibemph{What are the limitations of such tools that justify and require the development of another approach?} \par - “However, the current lack of a cohesive framework to manage heterogeneous data models hinders the efficiency and applicability of technologies” (Enns, 2024, p. 2) \#ffd400\\ \mkbibemph{A motivating example is required here otherwise it is not clear what are the peculiar requirements of the quested approach. Without such discussions, it seems that the author is talking about typical data-intensive applications, which can be engineered with several existing tools.} \par - “6 Current status The recent research focused on identifying the topic and the necessary preliminary work. This includes an initial literature review in the field of platform-independent modeling and the examination of existing Digital Editions in the Digital Humanities.” (Enns, 2024, p. 4) \#ffd400\\ \mkbibemph{The paper is missing a clear description of the peculiarities of the considered application domain. Moreover, it is not clear if the perspective work mainly consists of applying existing technologies, without envisioning novelties, further than the implementation of a framework managing Digital Editions.}} } @article{enriquezUnifiedModelRepresentation2020, title = {A Unified Model Representation of Machine Learning Knowledge}, author = {Enríquez, J.G. and Martínez-Rojas, A. and Lizcano, D. and Jiménez-Ramírez, A.}, date = {2020}, journaltitle = {J. Web Eng.}, volume = {19}, number = {2}, pages = {319--340}, publisher = {River Publishers}, issn = {15409589}, doi = {10.13052/jwe1540-9589.1929}, abstract = {Nowadays, Machine Learning (ML) algorithms are being widely applied in virtually all possible scenarios. However, developing a ML project entails the effort of many ML experts who have to select and configure the appropriate algorithm to process the data to learn from, between other things. Since there exist thousands of algorithms, it becomes a time-consuming and challenging task. To this end, recently, AutoML emerged to provide mechanisms to automate parts of this process. However, most of the efforts focus on applying brute force procedures to try different algorithms or configuration and select the one which gives better results. To make a smarter and more efficient selection, a repository of knowledge is necessary. To this end, this paper proposes (1) an approach towards a common language to consolidate the current distributed knowledge sources related the algorithm selection in ML, and (2) a method to join the knowledge gathered through this language in a unified store that can be exploited later on, and (3) a traceability links maintenance. The preliminary evaluations of this approach allow to create a unified store collecting the knowledge of 13 different sources and to identify a bunch of research lines to conduct. © 2020 River Publishers.}, note = {cited By 3 \par TL;DR \par An approach towards a common language to consolidate the current distributed knowledge sources related the algorithm selection in ML and a method to join the knowledge gathered through this language in a unified store that can be exploited later on, and a traceability links maintenance.} } @misc{EnterpriseRestaurantCompute, title = {Enterprise {{Restaurant Compute}}.Pdf}, url = {https://medium.com/chick-fil-atech/enterprise-restaurant-compute-f5e2fd63d20f}, keywords = {LOGSEQ} } @inproceedings{Eramo2021303, title = {{{AIdoArt}}: {{AI-augmented}} Automation for {{DevOps}}, a Model-Based Framework for Continuous Development in Cyber-Physical Systems}, author = {Eramo, R. and Muttillo, V. and Berardinelli, L. and Bruneliere, H. and Gomez, A. and Bagnato, A. and Sadovykh, A. and Cicchetti, A.}, editor = {Leporati F., Vitabile S., Skavhaug A.}, date = {2021}, series = {Proceedings - 2021 24th {{Euromicro Conference}} on {{Digital System Design}}, {{DSD}} 2021}, pages = {303--310}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/DSD53832.2021.00053}, abbrev_source_title = {Proc. - Euromicro Conf. Digit. Syst. Des., DSD}, affiliation = {University of L’Aquila, L’Aquila, Italy; Johannes Kepler University, Linz, Austria; IMT Atlantique, LS2N (UMR CNRS 6004), Nantes, France; Universitat Oberta de Catalunya, Barcelona, Spain; Softeam, Paris, France; Mälardalen University, Västerås, Sweden}, document_type = {Conference Paper}, isbn = {978-1-6654-2703-6}, langid = {english}, source = {Scopus}, keywords = {GOAL\_Model-Assistance,notion}, note = {cited By 1 \par HIGH LEVEL. NO SPECIFIC TECHNIQUES ARE MENTIONED.} } @article{erdogmus50YearsSoftware2018, title = {50 {{Years}} of {{Software Engineering}}}, author = {Erdogmus, H. and Medvidović, N. and Paulisch, F.}, date = {2018}, journaltitle = {IEEE Softw.}, volume = {35}, number = {5}, pages = {20--24}, issn = {0740-7459}, doi = {10.1109/MS.2018.3571240}, abstract = {This theme issue on software engineering’s 50th anniversary presents a range of contributions—from pioneers and well-established software engineers, to younger contributors whose imprint on the field is perhaps yet to come. These contributions come in a variety of formats that provide a balanced look at our field’s past, present, and likely future. The topics include both timeless ideas that appeared to fade for a while, only to pop up again in a new incarnation, and entirely new paradigms that have disrupted the field.}, keywords = {software engineering} } @article{erdogmusConversationBarryBoehm2018, title = {A {{Conversation}} with {{Barry Boehm}}: {{Recollections}} from 50 {{Years}} of {{Software Engineering}}}, shorttitle = {A {{Conversation}} with {{Barry Boehm}}}, author = {Erdogmus, Hakan and Medvidovic, Nenad}, date = {2018-09}, journaltitle = {IEEE Softw.}, volume = {35}, number = {5}, pages = {14--19}, issn = {0740-7459, 1937-4194}, doi = {10.1109/MS.2018.3571249}, langid = {english} } @inproceedings{erdweg2013state, title = {The State of the Art in Language Workbenches: {{Conclusions}} from the {{Language Workbench Challenge}}}, booktitle = {Int. {{Conf}}. {{Softw}}. {{Lang}}. {{Eng}}.}, author = {Erdweg, Sebastian and family=Storm, given=Tijs, prefix=van der, useprefix=true and Völter, Markus and Boersma, Markus and Bosman, Remi and Cook, William R and Gerritsen, Ake and Hulshout, Angelo and Kelly, Steven and Loh, Alex and others}, date = {2013}, pages = {197--217}, publisher = {Springer} } @incollection{erginLanguageGraphBasedModel2014, title = {Towards a {{Language}} for {{Graph-Based Model Transformation Design Patterns}}}, booktitle = {Theory and {{Practice}} of {{Model Transformations}}}, author = {Ergin, Hüseyin and Syriani, Eugene}, date = {2014}, pages = {91--105}, publisher = {Springer}, url = {http://link.springer.com/chapter/10.1007/978-3-319-08789-4_7}, urldate = {2015-09-15}, note = {TL;DR \par DelTa is a language for expressing design patterns for model transformations that is more abstract than and independent from any existing model transformation language, yet it is expressive enough to define design patterns as guidelines transformation developers can follow.} } @inproceedings{erlenhovCurrentFutureBots2019, title = {Current and {{Future Bots}} in {{Software Development}}}, booktitle = {2019 {{IEEEACM}} 1st {{Int}}. {{Workshop Bots Softw}}. {{Eng}}. {{BotSE}}}, author = {Erlenhov, Linda and Gomes de Oliveira Neto, Francisco and Scandariato, Riccardo and Leitner, Philipp}, date = {2019-05}, pages = {7--11}, publisher = {IEEE}, location = {Montreal, QC, Canada}, doi = {10.1109/BotSE.2019.00009}, eventtitle = {2019 {{IEEE}}/{{ACM}} 1st {{International Workshop}} on {{Bots}} in {{Software Engineering}} ({{BotSE}})}, isbn = {978-1-7281-2262-5} } @article{ernstAIDrivenDevelopmentHere2022, title = {{{AI-Driven Development Is Here}}: {{Should You Worry}}?}, shorttitle = {{{AI-Driven Development Is Here}}}, author = {Ernst, Neil A. and Bavota, Gabriele and Menzies, Tim}, date = {2022-03}, journaltitle = {IEEE Softw.}, volume = {39}, number = {2}, pages = {106--110}, issn = {0740-7459, 1937-4194}, doi = {10.1109/MS.2021.3133805}, langid = {english}, note = {TL;DR \par Artificial Intelligence Driven Development Environments integrate the power of modern AI into IDEs like Visual Studio Code and JetBrains IntelliJ and come with new challenges to think about, such as bias, legal compliance, security vulnerabilities, and their impact on learn programming.} } @inproceedings{escobar-avilaUnsupervisedSoftwareCategorization2015, title = {Unsupervised Software Categorization Using Bytecode}, booktitle = {2015 {{IEEE}} 23rd {{Int}}. {{Conf}}. {{Program Comprehension}}}, author = {Escobar-Avila, J. and Linares-Vásquez, M. and Haiduc, S.}, date = {2015-05}, pages = {229--239}, issn = {1092-8138}, doi = {10.1109/ICPC.2015.33}, keywords = {Accuracy,Apache foundation repository,automatic software categorization,bytecode,clustering,Clustering algorithms,Data mining,dirichlet process,Java,Java libraries,learning (artificial intelligence),program compilers,semantic information,Software,software categorization,Software libraries,software profiles,source code,source code (software),supervised machine learning,training data,unsupervised algorithm,unsupervised software categorization}, note = {TL;DR \par This work proposes a novel approach, which overcomes limitations by using semantic information recovered from byte code and an unsupervised algorithm to assign categories to software systems.} } @article{escottContinuousModernisationPlaybook, title = {The {{Continuous Modernisation Playbook}}}, author = {Escott, Eban and Tansey, Indi}, pages = {96}, langid = {english} } @misc{ESEM24_paper_205pdf, title = {{{ESEM24}}\_paper\_205.Pdf}, keywords = {LOGSEQ}, note = {\section{Annotazioni\\ (15/7/2024, 11:13:13)} \par - “we report the preliminary results of a study in which we are exploring the use of ChatGPT to support API migration tasks” (“ESEM24\_paper\_205.pdf”, p. 1) \#5fb236 \par - “the use of ChatGPT to migrate a client application to use a newer version of SQLAlchemy” (“ESEM24\_paper\_205.pdf”, p. 1) \#5fb236 \par - “evolve rapidly” (“ESEM24\_paper\_205.pdf”, p. 1) \#ffd400\\ \mkbibemph{How can the rapid evolution managed by LLMs without any dedicated training?} \par - “this task is manual since we lack established and consolidated tools to support it, at least in production software” (“ESEM24\_paper\_205.pdf”, p. 1) \#5fb236 \par - “we use this version as a ground-truth, particularly for analysing the quality scores generated by the mentioned static analysis tools.” (“ESEM24\_paper\_205.pdf”, p. 1) \#5fb236 \par - “we also evaluated and presented the results of the migration of the application’s tests” (“ESEM24\_paper\_205.pdf”, p. 1) \#ffd400\\ \mkbibemph{so, also the tests of the original application have been migrated.} \par - “set of prompts” (“ESEM24\_paper\_205.pdf”, p. 1) \#ffd400\\ \mkbibemph{the issue here is that we don't know how generalizable prompts are and to what extent they are robust with respect to the evolution of the underlying LLMs.} \par - “analysis of the different prompting methods” (“ESEM24\_paper\_205.pdf”, p. 1) \#5fb236 \par - “migrating from version 1 to version 2 of the SQLAlchemy” (“ESEM24\_paper\_205.pdf”, p. 2) \#5fb236 \par - “Python’s asyncio was introduced in version 1.4, version 2.0 includes performance improvements and optimizations for asynchronous operations.” (“ESEM24\_paper\_205.pdf”, p. 2) \#5fb236 \par - “after migration” (“ESEM24\_paper\_205.pdf”, p. 2) \#ffd400\\ \mkbibemph{Is it fully automated? I'm not sure, I think developers have to take care of integrating the output produced by the used LLM with the original source code that needs to be updated.} \par - “The first step consists on migrating only the application code with the assistance of GPT” (“ESEM24\_paper\_205.pdf”, p. 2) \#5fb236 \par - “assess the migrated tests by running them on the manually migrated application” (“ESEM24\_paper\_205.pdf”, p. 2) \#5fb236 \par - “yielded slightly different results each time, even though the temperature setting was set to 0.” (“ESEM24\_paper\_205.pdf”, p. 2) \#ffd400\\ \mkbibemph{This is related to one of my previous comments about the robustness of the LLM-based solution.} \par - “9. Make sure the code works.” (“ESEM24\_paper\_205.pdf”, p. 3) \#ffd400\\ \mkbibemph{What's the role of this? Have you checked the quality of the results produced without such a line?} \par - “Application Migration Results” (“ESEM24\_paper\_205.pdf”, p. 4) \#ffd400\\ \mkbibemph{Some details about the migration and execution environment is needed. Moreover, it is necessary to present the context that is given together with the source code to be migrated. How is it passed? To make the approach applicable in practice, it is necessary to show such details.} \par - “Runs Successfully” (“ESEM24\_paper\_205.pdf”, p. 4) \#ffd400\\ \mkbibemph{How many executions have you done? By considering the nature of the considered tools, it is necessary to perform several executions for each query and then calculate the average of the considered metrics.} \par - “a fixture is a function that runs before each test)” (“ESEM24\_paper\_205.pdf”, p. 5) \#5fb236 \par - “It is interesting to point out that all three approaches managed to migrate the tests in the same way, including making the same mistakes while migrating the mentioned fixture.” (“ESEM24\_paper\_205.pdf”, p. 5) \#5fb236 \par - “The first threat that we address in this study is related to the selection of only one repository as target client for migration.” (“ESEM24\_paper\_205.pdf”, p. 6) \#5fb236 \par - “To mitigate this threat, we have set to 0 the temperature option in its settings, which should make it less "nondeterministic"” (“ESEM24\_paper\_205.pdf”, p. 6) \#5fb236 \par - “with a Few-Shot approach positively influences the results.” (“ESEM24\_paper\_205.pdf”, p. 6) \#5fb236 \par - “CoCoNut and Codex” (“ESEM24\_paper\_205.pdf”, p. 6) \#ffd400\\ \mkbibemph{What about OpenRewrite?} \par - “We concluded that LLMs are able to correctly migrate the project when provided with at least one example of an already migrated application, making only minor mistakes such as leaving unused imports or incorrectly typed variables, which are errors than can be later fixed by a developer.” (“ESEM24\_paper\_205.pdf”, p. 6) \#5fb236} } @misc{ESEM24_paper_220pdf, title = {{{ESEM24}}\_paper\_220.Pdf}, keywords = {LOGSEQ}, note = {\section{Annotazioni\\ (15/7/2024, 15:59:52)} \par - “issue-commit link recovery (ILR)” (“ESEM24\_paper\_220.pdf”, p. 1) \#5fb236 \par - “due to inconsistencies between the ILR task and PLMs, these methods not fully leverage the semantic information in PLMs.” (“ESEM24\_paper\_220.pdf”, p. 1) \#5fb236 \par - “Multi-template prompt learning method with adversarial training for issue-commit link recovery” (“ESEM24\_paper\_220.pdf”, p. 1) \#ffd400\\ \mkbibemph{What does it mean?} \par - “trace links between issues and commits (issue-commit link recovery (ILR)) play a key role in bug localization[1], bug prediction[2], and other software maintenance tasks.” (“ESEM24\_paper\_220.pdf”, p. 1) \#a28ae5 \par - “However, these methods require large-scale data and not apply to projects with poor quality or small scales.” (“ESEM24\_paper\_220.pdf”, p. 1) \#5fb236 \par - “Although these methods demonstrate good performance, they are not consistent with the training objectives of PLMs, which may result in generalized domain knowledge in PLMs not being well used.” (“ESEM24\_paper\_220.pdf”, p. 1) \#a28ae5 \par - “prompt learning” (“ESEM24\_paper\_220.pdf”, p. 1) \#e56eee \par - “prompting templates and label words” (“ESEM24\_paper\_220.pdf”, p. 1) \#5fb236 \par - “Multi-template prompt learning method” (“ESEM24\_paper\_220.pdf”, p. 1) \#5fb236 \par - “adversarial training” (“ESEM24\_paper\_220.pdf”, p. 1) \#ffd400\\ \mkbibemph{why? What's the role of the adversarial training?} \par - “mitigate the model overfitting” (“ESEM24\_paper\_220.pdf”, p. 1) \#5fb236 \par - “Single and Multi-template PromptLink for the ILR?” (“ESEM24\_paper\_220.pdf”, p. 1) \#5fb236 \par - “adversarial training contribute to the PromptLink” (“ESEM24\_paper\_220.pdf”, p. 1) \#5fb236 \par - “PromptLink significantly enhances ILR performance, revealing the potential of prompt learning in ILR tasks.” (“ESEM24\_paper\_220.pdf”, p. 2) \#a28ae5 \par - “However, these approaches necessitate extensive feature engineering, and the feature selection needs researchers to have a substantial reserve of prior knowledge.” (“ESEM24\_paper\_220.pdf”, p. 2) \#5fb236 \par - “minimize the need for manual feature engineering and leverage more automated techniques to enhance the performance of the ILR model.” (“ESEM24\_paper\_220.pdf”, p. 2) \#5fb236 \par - “\vphantom\{\} .” (“ESEM24\_paper\_220.pdf”, p. 3) \#ff6666 \par - “right side introduces adversarial training to mitigate the model overfitting of the training process” (“ESEM24\_paper\_220.pdf”, p. 3) \#5fb236 \par - “extracted from issues and commits to improving the model’s comprehension.” (“ESEM24\_paper\_220.pdf”, p. 3) \#5fb236 \par - “I Di represents the issue description” (“ESEM24\_paper\_220.pdf”, p. 3) \#5fb236 \par - “"p” (“ESEM24\_paper\_220.pdf”, p. 3) \#ff6666 \par - “entropy loss function to train the model:” (“ESEM24\_paper\_220.pdf”, p. 3) \#5fb236 \par - “Different templates may have distinct advantages and be suited to different dataset scenarios. Existing research lacks prior knowledge of which prompt template is most effective.” (“ESEM24\_paper\_220.pdf”, p. 3) \#ffd400\\ \mkbibemph{SO far the paper is not easy to ready. I would have preferred to read some explanatory examples, on the quest of finding a right tradeoff between completeness and readability/understandability of the proposed approach.} \par - “six open-source projects” (“ESEM24\_paper\_220.pdf”, p. 4) \#5fb236 \par - “calculated means and standard deviation of each metri” (“ESEM24\_paper\_220.pdf”, p. 4) \#5fb236 \par - “a degree of stability and consistency in their performance across projects. I” (“ESEM24\_paper\_220.pdf”, p. 4) \#5fb236 \par - “RoBERTa, GPT-2, and BERT exhibit good stability. Notably, RoBERTa achieves the best average performance and the slightest fluctuations across all measures, indicating excellent generalisation capability.” (“ESEM24\_paper\_220.pdf”, p. 5) \#5fb236 \par - “PromptLink will default to using RoBERTa for artifact representation and mask prediction.” (“ESEM24\_paper\_220.pdf”, p. 5) \#5fb236 \par - “adversarial training is effective in preventing model overfitting” (“ESEM24\_paper\_220.pdf”, p. 6) \#5fb236 \par - “template types and their applicability in different scenario” (“ESEM24\_paper\_220.pdf”, p. 6) \#5fb236} } @online{EserciziDiMemoria, title = {Esercizi Di Memoria | {{CIMEC}} - {{Centro Interdipartimentale Mente}}/{{Cervello}}}, url = {https://www.cimec.unitn.it/1172/esercizi-di-memoria}, urldate = {2022-07-15} } @article{espinazopaganQueryingLargeModels2014, title = {Querying Large Models Efficiently}, author = {Espinazo Pagán, Javier and García Molina, Jesús}, date = {2014-06}, journaltitle = {Inf. Softw. Technol.}, volume = {56}, number = {6}, pages = {586--622}, issn = {09505849}, doi = {10.1016/j.infsof.2014.01.005}, langid = {english} } @book{Essaidi2013240, title = {Model-Driven Data Warehouse Automation: {{A}} Dependent-Concept Learning Approach}, author = {Essaidi, M. and Osmani, A. and Rouveirol, C.}, date = {2013}, pages = {240--267}, publisher = {IGI Global}, doi = {10.4018/978-1-4666-4494-6.ch011}, abstract = {Transformation design is a key step in model-driven engineering, and it is a very challenging task, particularly in context of the model-driven data warehouse. Currently, this process is ensured by human experts. The authors propose a new methodology using machine learning techniques to automatically derive these transformation rules. The main goal is to automatically derive the transformation rules to be applied in the model-driven data warehouse process. The proposed solution allows for a simple design of the decision support systems and the reduction of time and costs of development. The authors use the inductive logic programming framework to learn these transformation rules from examples of previous projects. Then, they find that in model-driven data warehouse application, dependencies exist between transformations. Therefore, the authors investigate a new machine learning methodology, learning dependent-concepts, that is suitable to solve this kind of problem. The experimental evaluation shows that the dependent-concept learning approach gives significantly better results. © 2014 by IGI Global. All rights reserved.}, document_type = {Book Chapter}, isbn = {978-1-4666-4495-3 1-4666-4494-X 978-1-4666-4494-6}, source = {Scopus}, note = {cited By 0} } @book{Essaidi2014151, title = {Learning Dependent-Concepts in {{ILP}}: {{Application}} to Model-Driven Data Warehouses}, author = {Essaidi, M. and Osmani, A. and Rouveirol, C.}, date = {2014}, pages = {151--172}, publisher = {Imperial College Press}, doi = {10.1142/9781783265091_0017}, abstract = {This chapter studies a new machine learning application with a possible challenging benchmark for relational learning systems. We are interested in the automation of a model-driven data warehouse using machine learning techniques. The main goal is to automatically derive the transformation rules to be applied in the model-driven process. This aims to reduce the contribution of transformation designers, thereby reducing the time and cost of development. We propose to express the model transformation problem as an Inductive Logic Programming (ILP) one: existing project traces (or project experiences) are used to define the background knowledge and examples. The Aleph ILP engine is used to learn best transformation rules. In our application, we need to deal with several dependent-concepts. Taking into account the work in Predicate Invention, Layered Learning, Cascade Learning and Context Learning, we propose a new methodology that automatically updates the background knowledge of concepts to be learned. Experimental results support the conclusion that our approach is suitable to solve this kind of problem. © 2015 Imperial College Press. All rights reserved.}, document_type = {Book Chapter}, isbn = {978-1-78326-509-1 978-1-78326-508-4}, source = {Scopus}, note = {cited By 1 \par TL;DR \par This paper proposes to express the model transformation problem as an Inductive Logic Programming one: existing project traces are used to define the background knowledge and examples, and the aleph ILP engine is used to learn best transformation rules.} } @book{Essaidi20162730, title = {Model-Driven Data Warehouse Automation: {{A}} Dependent-Concept Learning Approach}, author = {Essaidi, M. and Osmani, A. and Rouveirol, C.}, date = {2016}, volume = {4}, pages = {2730--2758}, publisher = {IGI Global}, doi = {10.4018/978-1-5225-1759-7.ch113}, abstract = {Transformation design is a key step in model-driven engineering, and it is a very challenging task, particularly in context of the model-driven data warehouse. Currently, this process is ensured by human experts. The authors propose a new methodology using machine learning techniques to automatically derive these transformation rules. The main goal is to automatically derive the transformation rules to be applied in the model-driven data warehouse process. The proposed solution allows for a simple design of the decision support systems and the reduction of time and costs of development. The authors use the inductive logic programming framework to learn these transformation rules from examples of previous projects. Then, they find that in model-driven data warehouse application, dependencies exist between transformations. Therefore, the authors investigate a new machine learning methodology, learning dependent-concepts, that is suitable to solve this kind of problem. The experimental evaluation shows that the dependent-concept learning approach gives significantly better results. © 2017 by IGI Global. All rights reserved.}, document_type = {Book Chapter}, isbn = {978-1-5225-1760-3 1-5225-1759-6 978-1-5225-1759-7}, source = {Scopus}, note = {cited By 0} } @article{etienChainingModelTransformations2012, title = {Chaining Model Transformations}, author = {Etien, Anne and Aranega, Vincent and Blanc, Xavier and Paige, Richard F.}, date = {2012}, journaltitle = {Proc. First Workshop Anal. Model Transform. - AMT 12}, pages = {9--14}, doi = {10.1145/2432497.2432500} } @inproceedings{etienCombiningIndependentModel2010, title = {Combining {{Independent Model Transformations}}}, booktitle = {Proc. 2010 {{ACM Symp}}. {{Appl}}. {{Comput}}.}, author = {Etien, Anne and Muller, Alexis and Legrand, Thomas and Blanc, Xavier}, date = {2010}, series = {{{SAC}} '10}, pages = {2237--2243}, publisher = {ACM}, location = {New York, NY, USA}, doi = {10.1145/1774088.1774557}, abstract = {Model transformation is one of the key principles of Model Driven Engineering. Many approaches have been proposed to design and realize them. However, for all the approaches, model transformations are considered as single entities that can only be chained if their input and output metamodels are compatible. This approach has the major drawback to focus on the satisfaction of the compliance property when building a transformation chain. In this paper we propose a mechanism for combining independent model transformations which jointly work towards a common objective but do not initially handle compatible metamodels. Our proposal is independent of any model transformation approach. It has been validated using Gaspard, an environment dedicated to code generation for embedded systems.}, isbn = {978-1-60558-639-7}, note = {TL;DR \par This paper proposes a mechanism for combining independent model transformations which jointly work towards a common objective but do not initially handle compatible metamodels.} } @article{etienLocalizedModelTransformations2013, title = {Localized Model Transformations for Building Large-Scale Transformations}, author = {Etien, Anne and Muller, Alexis and Legrand, Thomas and Paige, Richard F.}, date = {2013}, journaltitle = {Softw. Syst. Model.}, doi = {10.1007/s10270-013-0379-8}, keywords = {Model transformation,Reusable transformation,software engineering,Transformation chaining} } @inproceedings{etzlstorferEvolutionModelingEcosystems2017, title = {On the {{Evolution}} of {{Modeling Ecosystems}}: {{An Evaluation}} of {{Co-Evolution Approaches}}:}, shorttitle = {On the {{Evolution}} of {{Modeling Ecosystems}}}, author = {Etzlstorfer, Juergen and Kapsammer, Elisabeth and Schwinger, Wieland}, date = {2017}, pages = {90--99}, publisher = {{SCITEPRESS - Science and Technology Publications}}, doi = {10.5220/0006167900900099}, isbn = {978-989-758-210-3}, note = {TL;DR \par A dedicated evaluation framework for coevolution approaches focusing on aspects relevant in the context of modeling ecosystems, and its application to a representative set of recent approaches is presented.} } @article{Evans2009, title = {Clone Detection via Structural Abstraction}, author = {Evans, William S. and Fraser, Christopher W. and Ma, Fei}, date = {2009-12-01}, journaltitle = {Softw. Qual. J.}, volume = {17}, number = {4}, pages = {309--330}, issn = {1573-1367}, doi = {10.1007/s11219-009-9074-y}, abstract = {This paper describes the design, implementation, and application of a new algorithm to detect cloned code. It operates on the abstract syntax trees formed by many compilers as an intermediate representation. It extends prior work by identifying clones even when arbitrary subtrees have been changed. These subtrees may represent structural rather than simply lexical code differences. In several hundred thousand lines of Java and C\# code, 20–50\% of the clones that we find involve these structural changes, which are not accounted for by previous methods. Our method also identifies cloning in declarations, so it is somewhat more general than conventional procedural abstraction.}, note = {TL;DR \par A new algorithm to detect cloned code is described that operates on the abstract syntax trees formed by many compilers as an intermediate representation and extends prior work by identifying clones even when arbitrary subtrees have been changed.} } @article{EvolutionaryPromptEngineering2017, title = {Evolutionary Prompt Engineering for Cost-Effective Code Generation with Large Language Models}, date = {2017}, abstract = {Large Language Models (LLMs) have seen increasing use in various software development tasks, especially in code generation. The most advanced recent methods attempt to incorporate feedback from code execution into prompts to help guide LLMs in generating correct code in an iterative process. While effective, these methods could be costly due to numerous interactions with the LLM and extensive token usage. To address this issue, we propose an alternative approach named Evolutionary Prompt Engineering for Code (EPiC), which leverages a lightweight evolutionary algorithm to refine the original prompts into improved versions that generate high-quality code, with minimal interactions with the LLM. Our evaluation against state-of-the-art (SOTA) LLM-based code generation agents shows that EPiC not only achieves up to 6\% improvement in pass@k but is also 2–10 times more cost-effective than the baselines.}, langid = {english}, keywords = {LOGSEQ}, note = {I'm reviewing a research paper and I took the following notes: \par \section{Annotazioni\\ (16/5/2025, 11:18:31)} \par - “Cost-Effective Code Generation” (“Evolutionary prompt engineering for cost-effective code generation with large language models”, 2017, p. 1) \#5fb236 \par - “feedback from code execution into prompts” (“Evolutionary prompt engineering for cost-effective code generation with large language models”, 2017, p. 1) \#ffd400\\ \mkbibemph{What kind of code execution information are fed into prompts?} \par - “6\% improvement in pass@k” (“Evolutionary prompt engineering for cost-effective code generation with large language models”, 2017, p. 1) \#5fb236 \par - “cost-effective” (“Evolutionary prompt engineering for cost-effective code generation with large language models”, 2017, p. 1) \#ffd400\\ \mkbibemph{what kind of cost is considered here?} \par - “MBPP dataset” (“Evolutionary prompt engineering for cost-effective code generation with large language models”, 2017, p. 1) \#ffd400\\ \mkbibemph{what is it?} \par - “LATS” (“Evolutionary prompt engineering for cost-effective code generation with large language models”, 2017, p. 1) \#ffd400\\ \mkbibemph{what is it?} \par - “One key limitation of these approaches is that the initial prompt fed to the LLM is often suboptimal.” (“Evolutionary prompt engineering for cost-effective code generation with large language models”, 2017, p. 1) \#a28ae5 \par - “If we had an approach that systematically improves the prompt with minimal LLM calls, we could find the optimal prompt.” (“Evolutionary prompt engineering for cost-effective code generation with large language models”, 2017, p. 1) \#5fb236\\ \mkbibemph{this is s a kind of teacher-student pattern.} \par - “if we automatically mutate the initial prompt, systematically test it, and then finalize an improved version, we can converge on correct code more efficiently.” (“Evolutionary prompt engineering for cost-effective code generation with large language models”, 2017, p. 1) \#5fb236 \par - “Evolutionary Prompt Engineering for Code (EPiC)” (“Evolutionary prompt engineering for cost-effective code generation with large language models”, 2017, p. 1) \#a28ae5 \par - “to refine prompts in a structured and costeffective way.” (“Evolutionary prompt engineering for cost-effective code generation with large language models”, 2017, p. 1) \#e56eee \par - “augmented” (“Evolutionary prompt engineering for cost-effective code generation with large language models”, 2017, p. 1) \#ffd400\\ \mkbibemph{why necessarely augmented? withb respect to what?} \par - “The mutation is carried out using two approaches: one utilizes an LLM 1 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 Conference’17, July 2017, Washington, DC, USA Anon. 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 guided by a prompt that specifies how to perform the mutation, and the other employs vector embeddings of words to find and replace similar words.” (“Evolutionary prompt engineering for cost-effective code generation with large language models”, 2017, p. 1) \#a28ae5 \par - “pass@k metric,” (“Evolutionary prompt engineering for cost-effective code generation with large language models”, 2017, p. 2) \#a28ae5 \par - “as introduced in [3], for accuracy (effectiveness), which estimates the probability that at least one of the top k generated code samples is correct” (“Evolutionary prompt engineering for cost-effective code generation with large language models”, 2017, p. 2) \#a28ae5 \par - “Additional Token Usage per Solved Problem” (“Evolutionary prompt engineering for cost-effective code generation with large language models”, 2017, p. 2) \#e56eee \par - “code generation task from the cost-effectiveness perspective” (“Evolutionary prompt engineering for cost-effective code generation with large language models”, 2017, p. 2) \#e56eee \par - “novel framework EPiC that leverages a lightweight evolutionary algorithm to evolve the original prompts toward better ones that produce high-quality code” (“Evolutionary prompt engineering for cost-effective code generation with large language models”, 2017, p. 2) \#e56eee \par - “cost-effectiveness of EPiC in code generation compared to baseline methods” (“Evolutionary prompt engineering for cost-effective code generation with large language models”, 2017, p. 2) \#e56eee \par - “we briefly explain prompt engineering for LLMs in general and report the most related work in the context of prompt engineering of LLM for code.” (“Evolutionary prompt engineering for cost-effective code generation with large language models”, 2017, p. 2) \#5fb236 \par - “Prompt engineering refers to the process of designing and refining prompts to achieve desired outcomes when using LLMs1” (“Evolutionary prompt engineering for cost-effective code generation with large language models”, 2017, p. 2) \#5fb236 \par - “approaches without training” (“Evolutionary prompt engineering for cost-effective code generation with large language models”, 2017, p. 2) \#5fb236 \par - “reasoning and logic” (“Evolutionary prompt engineering for cost-effective code generation with large language models”, 2017, p. 2) \#5fb236 \par - “reducing hallucination” (“Evolutionary prompt engineering for cost-effective code generation with large language models”, 2017, p. 2) \#5fb236 \par - “evolutionary-based methods” (“Evolutionary prompt engineering for cost-effective code generation with large language models”, 2017, p. 2) \#5fb236 \par - “reasoning and logic-based methods” (“Evolutionary prompt engineering for cost-effective code generation with large language models”, 2017, p. 2) \#5fb236 \par - “Few-shot prompting [1] provides limited examples to guide understanding but requires more tokens, making it less practical for long texts and susceptible to bias from example selection.” (“Evolutionary prompt engineering for cost-effective code generation with large language models”, 2017, p. 2) \#5fb236 \par - “EvoPrompt [34] automates this by iteratively refining prompts using mutation and crossover.” (“Evolutionary prompt engineering for cost-effective code generation with large language models”, 2017, p. 2) \#a28ae5 \par - “Using Parsel, they break down algorithmic tasks into structured descriptions written in natural language, then explore various combinations of function implementations using tests” (“Evolutionary prompt engineering for cost-effective code generation with large language models”, 2017, p. 2) \#5fb236 \par - “Reflexion [29] is a reinforcement-based framework in which language agents learn from linguistic feedback.” (“Evolutionary prompt engineering for cost-effective code generation with large language models”, 2017, p. 2) \#5fb236 \par - “EPiC is the first evolutionary-based prompt engineering method for code generation.” (“Evolutionary prompt engineering for cost-effective code generation with large language models”, 2017, p. 3) \#e56eee \par - “It employs a lightweight process to identify the optimal solution in a cost-effective manner.” (“Evolutionary prompt engineering for cost-effective code generation with large language models”, 2017, p. 3) \#e56eee \par - “EPiC utilizes a local embedding function to implement mutation operators on text to reduce the cost of iterative prompt engineering for code generation.” (“Evolutionary prompt engineering for cost-effective code generation with large language models”, 2017, p. 3) \#a28ae5 \par - “guides the search over iterations using the fitness function in Section 4.4, which helps in finding the best prompts.” (“Evolutionary prompt engineering for cost-effective code generation with large language models”, 2017, p. 3) \#a28ae5 \par - “without requiring gradient information” (“Evolutionary prompt engineering for cost-effective code generation with large language models”, 2017, p. 3) \#a28ae5 \par - “where the pass rate of generated code is a discrete and non-differentiable function.” (“Evolutionary prompt engineering for cost-effective code generation with large language models”, 2017, p. 3) \#e56eee \par - “initialization, evaluation, selection, variation, and iteration.” (“Evolutionary prompt engineering for cost-effective code generation with large language models”, 2017, p. 3) \#2ea8e5 \par - “evaluating fitness is relatively straightforward” (“Evolutionary prompt engineering for cost-effective code generation with large language models”, 2017, p. 3) \#ffd400\\ \mkbibemph{How can you say that? You are supposed to measure the quality of the output produced with the prompt at hand, and this can require some time/effort! \#question} \par - “optimized” (“Evolutionary prompt engineering for cost-effective code generation with large language models”, 2017, p. 3) \#ffd400\\ \mkbibemph{Do you know if each iteration improves the previous ones? \#question} \par - “sufficient to generate the correct implementatio” (“Evolutionary prompt engineering for cost-effective code generation with large language models”, 2017, p. 3) \#ffd400\\ \mkbibemph{Here we have "sufficient" and "correct" that need to be concretize. They refer crucial aspects that need to be materialized otherwise it's not clear how they can be achieved in practice.} \par - “In” (“Evolutionary prompt engineering for cost-effective code generation with large language models”, 2017, p. 3) \#ffd400\\ \mkbibemph{Maybe you can start the paragraph by saying, "By referring to Fig. 2 ..."} \par - “muCandidates ← chooseCandidates (candidates, N − 1)” (“Evolutionary prompt engineering for cost-effective code generation with large language models”, 2017, p. 4) \#5fb236 \par - “test cases.” (“Evolutionary prompt engineering for cost-effective code generation with large language models”, 2017, p. 4) \#ffd400\\ \mkbibemph{It seems that also test cases are generated. What if generated test cases are semantically wrong? This is a possible "point of failure" of the whole process.} \par - “If any test case fails on the code, we continue with the EPE phase. If all test cases pass, we report the generated code as the final answer” (“Evolutionary prompt engineering for cost-effective code generation with large language models”, 2017, p. 4) \#ffd400\\ \mkbibemph{See my previous comment. Test cases might pass because tests are wrong if those are not given as input and instead also part of the generation.} \par - “Note that test cases can be provided in various ways. One approach is to use developer-provided test cases for evaluation, while another is to generate test cases using the LLM.” (“Evolutionary prompt engineering for cost-effective code generation with large language models”, 2017, p. 4) \#ffd400\\ \mkbibemph{See my previous point. This can be an important point of failure of the process.} \par - “we also opted to use LLMs for test case generation to ensure a fully automated approach, assuming no developer-provided test cases. To ensure the functional correctness of these test cases, we validated them by parsing their Abstract Syntax Trees (AST)” (“Evolutionary prompt engineering for cost-effective code generation with large language models”, 2017, p. 4) \#ffd400\\ \mkbibemph{This is a critical point. It is not clear how it works. When test cases are generated, starting from what? From the same prompt used to generate code? \#question} \par - “prompts for code and test case generation” (“Evolutionary prompt engineering for cost-effective code generation with large language models”, 2017, p. 4) \#ffd400\\ \mkbibemph{These look like completely disconnected. How can you be sure that test cases are semantically connected to the wanted generated code? \#question} \par - “assert add3Numbers(1, 2, 3) == 6 assert add3Numbers(-1, 2, 3) == 4 assert add3Numbers(1, -2, 3) == 2 assert add3Numbers(1, 2, -3) == 0 assert add3Numbers(-3, -2, -1) == -6 assert add3Numbers(0, 0, 0) == 0” (“Evolutionary prompt engineering for cost-effective code generation with large language models”, 2017, p. 4) \#ffd400\\ \mkbibemph{I'm skeptical about the semantic correctness of the generated test cases.} \par - “we generate multiple prompts by modifying the initial prompt using an LLM agent. Generating this prompt population forms an important part of our evolutionary algorithm” (“Evolutionary prompt engineering for cost-effective code generation with large language models”, 2017, p. 5) \#ffd400\\ \mkbibemph{What are the mutation operators to generate multiple prompts?} \par - “We define a fitness function based on the ratio of test cases passed.” (“Evolutionary prompt engineering for cost-effective code generation with large language models”, 2017, p. 5) \#ffd400\\ \mkbibemph{See my previous content.} \par - “randomly mutates” (“Evolutionary prompt engineering for cost-effective code generation with large language models”, 2017, p. 5) \#ffd400\\ \mkbibemph{This triggers many questions related to the way mutants are generated.} \par - “adding the elite prompt to the pool of mutated prompts.” (“Evolutionary prompt engineering for cost-effective code generation with large language models”, 2017, p. 5) \#5fb236 \par - “LLM\_as\_mutator , we provide LLMs with predefined instructions on how to implement the prompt mutation.” (“Evolutionary prompt engineering for cost-effective code generation with large language models”, 2017, p. 6) \#2ea8e5 \par - “alter the prompts by substituting words with their synonyms (sim\_words\_as\_mutator )” (“Evolutionary prompt engineering for cost-effective code generation with large language models”, 2017, p. 6) \#2ea8e5 \par - “You are a mutation tool. This is a Python function and its description. Please change the description by enhancing its clarity and comprehensibility for sophisticated language models. Please put the changed description between \#Explanation and \#End. Use at most 600 words.” (“Evolutionary prompt engineering for cost-effective code generation with large language models”, 2017, p. 6) \#ffd400\\ \mkbibemph{Sometimes LLMs even fail in producing outputs respecting the given number of words limit!?!?!?} \par - “selected words” (“Evolutionary prompt engineering for cost-effective code generation with large language models”, 2017, p. 6) \#ffd400\\ \mkbibemph{What's the criteria that is used to select words to changed for mutating prompts? \#question} \par - “[21],” (“Evolutionary prompt engineering for cost-effective code generation with large language models”, 2017, p. 6) \#ff6666\\ \mkbibemph{It should be [3], isn't it?} \par - “For a single problem, this metric estimates the probability that at least one of the top k samples is correct” (“Evolutionary prompt engineering for cost-effective code generation with large language models”, 2017, p. 7) \#2ea8e5 \par - “(Pm − Pb ) × N” (“Evolutionary prompt engineering for cost-effective code generation with large language models”, 2017, p. 7) \#ffd400\\ \mkbibemph{The denominator of the ATSP equation can be 0, isn't it?} \par - “Total token usage for method m and baseline b” (“Evolutionary prompt engineering for cost-effective code generation with large language models”, 2017, p. 7) \#5fb236 \par - “pass@1” (“Evolutionary prompt engineering for cost-effective code generation with large language models”, 2017, p. 7) \#5fb236\\ \mkbibemph{So this is the probability that the first generated sample is correct.} \par - “The goal of the EPiC framework is to optimize the fitness function F based on the provided test cases T by identifying the optimal input x within the prompt space X.” (“Evolutionary prompt engineering for cost-effective code generation with large language models”, 2017, p. 7) \#5fb236 \par - “their cost is not disproportionately high.” (“Evolutionary prompt engineering for cost-effective code generation with large language models”, 2017, p. 7) \#ffd400\\ \mkbibemph{What do you mean? What's the threshold that you considered for this? \#question} \par - “baseline configurations” (“Evolutionary prompt engineering for cost-effective code generation with large language models”, 2017, p. 7) \#ffd400\\ \mkbibemph{Can you say something more about about the used baseline configurations? Are they coming from the original works and presented as those that permit the corresponding approaches at their best? \#question} \par - “To enable a fairer comparison among the agents, we adopted a consistent test generation approach to produce test cases rather than using the ground-truth tests for internal evaluation.” (“Evolutionary prompt engineering for cost-effective code generation with large language models”, 2017, p. 8) \#5fb236 \par - “For the ablation study in Section 6,” (“Evolutionary prompt engineering for cost-effective code generation with large language models”, 2017, p. 8) \#ffd400\\ \mkbibemph{The reference to the performed ablation study suddenly appears without a proper motivation and introduction.} \par - “temperature of 0.0” (“Evolutionary prompt engineering for cost-effective code generation with large language models”, 2017, p. 8) \#ffd400\\ \mkbibemph{How this temperature setting is connected to the temperature value given as 0.6 for the initial population builder phase described in Sec 3.2.1? \#question} \par - “RQ1: How does EPiC perform across different SOTA LLMs?” (“Evolutionary prompt engineering for cost-effective code generation with large language models”, 2017, p. 8) \#ffd400\\ \mkbibemph{Do you have any estimation of the costs of the considered baselines? \#question} \par - “achieves higher functional correctness at the cost of increased token usage.” (“Evolutionary prompt engineering for cost-effective code generation with large language models”, 2017, p. 8) \#5fb236 \par - “RQ2: How does EPiC compare to other iterative-based agents?” (“Evolutionary prompt engineering for cost-effective code generation with large language models”, 2017, p. 9) \#ffd400\\ \mkbibemph{What is the LLM that is used to get the results shown in Table 3?} \par - “To alleviate the threat, we have shown that the LLM-generated tests are not that far off from the results using the original (developer-written) tests.” (“Evolutionary prompt engineering for cost-effective code generation with large language models”, 2017, p. 10) \#ffd400\\ \mkbibemph{How can you be sure about that? \#question} \par COnsider that those that are tagget with \#5fb236 are just highlights, those that are tagged with \#e56eee and \#a28ae5 are imporant sentences. Please pay attention instead to the notes that are tagged with \#ffd400. Those that are tagged with \#ff6666 are typos or errors. Could you please draft a review by organizing it as follows: SUMMARY: Just a few sentence to summarize the work STRENGHTS: \par WEAKNESSES: \par COMMENTS: Organize the notes with respect to the following criteria: \par \begin{itemize} \item \texttt{Novelty} \item \texttt{Rigor} \item \texttt{Relevance (of the contribution)} \item \texttt{Verifiability and Transparency} \item \texttt{Presentation} \end{itemize} \par And then add a Detailed Comments section to report the notets that contain issues or typos.} } @online{ExemplarsSoftwareEngineering, title = {Exemplars | {{Software Engineering}} for {{Self-Adaptive Systems}}}, url = {https://www.hpi.uni-potsdam.de/giese/public/selfadapt/exemplars/}, urldate = {2016-09-24} } @online{ExploreEclipsesOSGi, title = {Explore {{Eclipse}}'s {{OSGi}} Console}, url = {https://www.ibm.com/developerworks/library/os-ecl-osgiconsole/}, urldate = {2016-12-04} } @online{ExploringEffectivenessLLMs, title = {Exploring the {{Effectiveness}} of {{LLMs}} in {{Automated Logging Statement Generation}}: {{An Empirical Study}}}, url = {https://www.computer.org/csdl/journal/ts/2024/12/10707668/20SyPzyeSxq}, keywords = {source\_mobile} } @inproceedings{ExtractingDomainModels2021, title = {Extracting {{Domain Models}} from {{Textual Requirements}} in the {{Era}} of {{Large Language Models}}}, booktitle = {2021 {{ACMIEEE Int}}. {{Conf}}. {{Model Driven Eng}}. {{Lang}}. {{Syst}}. {{Companion MODELS-C}}}, date = {2021}, publisher = {IEEE}, location = {Fukuoka, Japan}, abstract = {Conceptual models are essential in Software and Information Systems Engineering to meet many purposes since they explicitly represent the subject domains. Machine Learning (ML) approaches have recently been used in conceptual modeling to realize, among others, intelligent modeling assistance, model transformation, and metamodel classification. These works encode models in various ways, making the encoded models suitable for applying ML algorithms. The encodings capture the models’ structure and/or semantics, making this information available to the ML model during training. Therefore, the choice of the encoding for any ML-driven task is crucial for the ML model to learn the relevant contextual information. In this paper, we report findings from a systematic literature review which yields insights into the current research in machine learning for conceptual modeling (ML4CM). The review focuses on the various encodings used in existing ML4CM solutions and provides insights into i) which are the information sources, ii) how is the conceptual model’s structure and/or semantics encoded, iii) why is the model encoded, i.e., for which conceptual modeling task and, iv) which ML algorithms are applied. The results aim to structure the state of the art in encoding conceptual models for ML.}, eventtitle = {2021 {{ACM}}/{{IEEE International Conference}} on {{Model Driven Engineering Languages}} and {{Systems Companion}} ({{MODELS-C}})}, isbn = {978-1-6654-2484-4}, langid = {english}, keywords = {LOGSEQ} } @inproceedings{ExtremeDataManagement2019, title = {Extreme {{Data Management Analysis}} and {{Visualization}} for {{Exascale Supercomputers}} and {{Experimental Facilities}}}, booktitle = {2019 {{Amity Int}}. {{Conf}}. {{Artif}}. {{Intell}}. {{AICAI}}}, date = {2019-02}, pages = {i-i}, publisher = {IEEE}, location = {Dubai, United Arab Emirates}, doi = {10.1109/AICAI.2019.8701403}, eventtitle = {2019 {{Amity International Conference}} on {{Artificial Intelligence}} ({{AICAI}})}, isbn = {978-1-5386-9346-9} } @unpublished{fabiofumarola5DataModeling14:24:10UTC, type = {Data \& Analytics}, title = {5 {{Data Modeling}} for {{NoSQL}} 1/2}, author = {Fabio Fumarola}, year = {14:24:10 UTC}, url = {https://www.slideshare.net/fabiofumarola1/data-modeling-for-nosql-12}, urldate = {2018-04-30}, abstract = {The Information Technology have led us into an era where the production,} } @online{FacebookOpenSourced, title = {Facebook {{Open Sourced}} This {{Architecture}} for {{Personalized Neural Recommendation Systems}} | by {{Jesus Rodriguez}} | {{DataSeries}} | {{May}}, 2021 | {{Medium}}}, url = {https://medium.com/dataseries/facebook-open-sourced-this-architecture-for-personalized-neural-recommendation-systems-97db3fef35bb}, urldate = {2021-06-07} } @inproceedings{fagadauAnalyzingPromptInfluence2024, title = {Analyzing {{Prompt Influence}} on {{Automated Method Generation}}: {{An Empirical Study}} with {{Copilot}}}, shorttitle = {Analyzing {{Prompt Influence}} on {{Automated Method Generation}}}, booktitle = {Proc. 32nd {{IEEEACM Int}}. {{Conf}}. {{Program Comprehension}}}, author = {Fagadau, Ionut Daniel and Mariani, Leonardo and Micucci, Daniela and Riganelli, Oliviero}, date = {2024-04-15}, pages = {24--34}, publisher = {ACM}, location = {Lisbon Portugal}, doi = {10.1145/3643916.3644409}, eventtitle = {{{ICPC}} '24: 32nd {{IEEE}}/{{ACM International Conference}} on {{Program Comprehension}}}, isbn = {979-8-4007-0586-1}, langid = {english} } @article{falessiApplyingEmpiricalSoftware2010, title = {Applying Empirical Software Engineering to Software Architecture: Challenges and Lessons Learned}, shorttitle = {Applying Empirical Software Engineering to Software Architecture}, author = {Falessi, Davide and Babar, Muhammad Ali and Cantone, Giovanni and Kruchten, Philippe}, date = {2010-06}, journaltitle = {Empir Software Eng}, volume = {15}, number = {3}, pages = {250--276}, issn = {1382-3256, 1573-7616}, doi = {10.1007/s10664-009-9121-0}, abstract = {In the last 15 years, software architecture has emerged as an important software engineering field for managing the development and maintenance of large, software-intensive systems. Software architecture community has developed numerous methods, techniques, and tools to support the architecture process (analysis, design, and review). Historically, most advances in software architecture have been driven by talented people and industrial experience, but there is now a growing need to systematically gather empirical evidence about the advantages or otherwise of tools and methods rather than just rely on promotional anecdotes or rhetoric. The aim of this paper is to promote and facilitate the application of the empirical paradigm to software architecture. To this end, we describe the challenges and lessons learned when assessing software architecture research that used controlled experiments, replications, expert opinion, systematic literature reviews, observational studies, and surveys. Our research will support the emergence of a body of knowledge consisting of the more widely-accepted and well-formed software architecture. theories.}, langid = {english} } @incollection{falzoneModelBasedRapid2018, title = {Model {{Based Rapid Prototyping}} and {{Evolution}} of {{Web Application}}}, booktitle = {Web {{Engineering}}}, author = {Falzone, Emanuele and Bernaschina, Carlo}, editor = {Mikkonen, Tommi and Klamma, Ralf and Hernández, Juan}, date = {2018}, volume = {10845}, pages = {496--500}, publisher = {Springer International Publishing}, location = {Cham}, doi = {10.1007/978-3-319-91662-0_43}, isbn = {978-3-319-91661-3 978-3-319-91662-0}, note = {TL;DR \par The demonstrated approach solves the well-know problem of model driven forward engineering of breaking the automated development cycle when features that cannot be modelled are added manually to the generated code.} } @article{families2persons, title = {A Simple Illustration of Model to Model Transformation}, author = {Allilaire, Freddy and Jouault, Fr�d�ric}, date = {2007}, url = {https://www.eclipse.org/atl/documentation/old/ATLUseCase_Families2Persons.pdf} } @article{Fang20221151, title = {Facial Expression {{GAN}} for Voice-Driven Face Generation}, author = {Fang, Z. and Liu, Z. and Liu, T. and Hung, C.-C. and Xiao, J. and Feng, G.}, date = {2022}, journaltitle = {Vis. Comput.}, volume = {38}, number = {3}, pages = {1151--1164}, publisher = {{Springer Science and Business Media Deutschland GmbH}}, issn = {01782789}, doi = {10.1007/s00371-021-02074-w}, abstract = {Cross-modal audiovisual generation is an emerging topic in machine learning. In particular, voice-to-face is one of the most popular research branches, which aims to generate faces from human voice clips. Most recent works in voice-to-face generation do not take emotion information into account. However, it could be widely observed that expressions are the key face attributes to reconstruct sharper and more discriminative faces. In this paper, we propose a novel facial expression GAN (FE-GAN) which takes emotion and expressions into account in face generation. To achieve this goal, we use two auxiliary classifiers to learn more emotion and identity representations between different modalities, respectively. Moreover, we design two discriminators, each focusing on a different aspect of the faces, to measure identity and emotion semantic relevance in generating. The triple loss is designed to make FE-GAN robust to voice variety and keep balance in two different modalities. Extensive experiments are conducted on two real datasets to demonstrate the effectiveness of FE-GAN in both quantitative and qualitative perspectives. The experimental results show that FE-GAN can not only outperform the previous models in terms of FID and IS values, but also generate more realistic face images compared with previous models. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature.}, author_keywords = {Cross-model generation; Expression reconstruction; Generative adversarial networks; Voice-to-face generation}, coden = {VICOE}, document_type = {Article}, source = {Scopus}, keywords = {Cross-modal,Emerging topics,Emotion semantics,Face generation,Face images,Facial Expressions,Human voice,Interfaces (computer),Real data sets,Semantics,Software engineering}, note = {cited By 8 \par TL;DR \par This paper proposes a novel facial expression GAN (FE-GAN) which takes emotion and expressions into account in face generation and can not only outperform the previous models in terms of FID and IS values, but also generate more realistic face images compared with previous models.} } @online{fanLargeLanguageModels2023, title = {Large {{Language Models}} for {{Software Engineering}}: {{Survey}} and {{Open Problems}}}, shorttitle = {Large {{Language Models}} for {{Software Engineering}}}, author = {Fan, Angela and Gokkaya, Beliz and Harman, Mark and Lyubarskiy, Mitya and Sengupta, Shubho and Yoo, Shin and Zhang, Jie M.}, date = {2023-11-11}, eprint = {2310.03533}, eprinttype = {arXiv}, eprintclass = {cs}, url = {http://arxiv.org/abs/2310.03533}, urldate = {2024-01-17}, abstract = {This paper provides a survey of the emerging area of Large Language Models (LLMs) for Software Engineering (SE). It also sets out open research challenges for the application of LLMs to technical problems faced by software engineers. LLMs' emergent properties bring novelty and creativity with applications right across the spectrum of Software Engineering activities including coding, design, requirements, repair, refactoring, performance improvement, documentation and analytics. However, these very same emergent properties also pose significant technical challenges; we need techniques that can reliably weed out incorrect solutions, such as hallucinations. Our survey reveals the pivotal role that hybrid techniques (traditional SE plus LLMs) have to play in the development and deployment of reliable, efficient and effective LLM-based SE.}, pubstate = {prepublished}, keywords = {Computer Science - Software Engineering,LOGSEQ}, note = {TL;DR \par This survey reveals the pivotal role that hybrid techniques (traditional SE plus LLMs) have to play in the development and deployment of reliable, efficient and effective LLM-based SE.} } @inproceedings{fanSYnergyFinegrainedEnergyEfficient2023, title = {{{SYnergy}}: {{Fine-grained Energy-Efficient Heterogeneous Computing}} for {{Scalable Energy Saving}}}, shorttitle = {{{SYnergy}}}, booktitle = {Proc. {{Int}}. {{Conf}}. {{High Perform}}. {{Comput}}. {{Netw}}. {{Storage Anal}}.}, author = {Fan, Kaijie and D'Antonio, Marco and Carpentieri, Lorenzo and Cosenza, Biagio and Ficarelli, Federico and Cesarini, Daniele}, date = {2023-11-12}, pages = {1--13}, publisher = {ACM}, location = {Denver CO USA}, doi = {10.1145/3581784.3607055}, eventtitle = {{{SC}} '23: {{International Conference}} for {{High Performance Computing}}, {{Networking}}, {{Storage}} and {{Analysis}}}, isbn = {979-8-4007-0109-2}, langid = {english} } @inproceedings{Fard2020755, title = {Vertica-{{ML}}: {{Distributed}} Machine Learning in Vertica Database}, author = {Fard, A. and Le, A. and Larionov, G. and Dhillon, W. and Bear, C.}, date = {2020}, series = {Proceedings of the {{ACM SIGMOD International Conference}} on {{Management}} of {{Data}}}, pages = {755--768}, publisher = {Association for Computing Machinery}, issn = {07308078}, doi = {10.1145/3318464.3386137}, abstract = {A growing number of companies rely on machine learning as a key element for gaining a competitive edge from their collected Big Data. An in-database machine learning system can provide many advantages in this scenario, e.g., eliminating the overhead of data transfer, avoiding the maintenance costs of a separate analytical system, and addressing data security and provenance concerns. In this paper, we present our distributed machine learning subsystem within the Vertica database. This subsystem, Vertica-ML, includes machine learning functionalities with SQL API which cover a complete data science workflow as well as model management. We treat machine learning models in Vertica as first-class database objects like tables and views; therefore, they enjoy a similar mechanism for archiving and managing. We explain the architecture of the subsystem, and present a set of experiments to evaluate the performance of the machine learning algorithms implemented on top of it. © 2020 Association for Computing Machinery.}, document_type = {Conference Paper}, isbn = {978-1-4503-6735-6}, source = {Scopus}, note = {cited By 5} } @inproceedings{faunes2013genetic, title = {Genetic-Programming Approach to Learn Model Transformation Rules from Examples}, booktitle = {Theory {{Pract}}. {{Model Transform}}. {{ICMT}}}, author = {Faunes, Martin and Sahraoui, Houari and Boukadoum, Mounir}, date = {2013}, pages = {17--32}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {TL;DR \par A genetic programming-based approach to automatically learn model transformation rules from prior transformation pairs of source-target models used as examples, that does not need fine-grained transformation traces to produce many-to-many rules.} } @article{favreMegamodelModelSoftware2005, title = {Towards a {{Megamodel}} to {{Model Software Evolution Through Transformations}}}, author = {Favre, Jean-Marie and NGuyen, Tam}, date = {2005-04}, journaltitle = {Electronic Notes in Theoretical Computer Science}, volume = {127}, number = {3}, pages = {59--74}, issn = {15710661}, doi = {10.1016/j.entcs.2004.08.034}, abstract = {Model Driven Engineering is a promizing approach that could lead to the emergence of a new paradigm for software evolution, namely Model Driven Software Evolution. Models, Metamodels and Transformations are the cornerstones of this approach. Combining these concepts leads to very complex structures which revealed to be very difficult to understand especially when different technological spaces are considered such as XMLWare (the technology based on XML), Grammarware and BNF, Modelware and UML, Dataware and SQL, etc. The concepts of model, metamodel and transformation are usually ill-defined in industrial standards like the MDA or XML. This paper provides a conceptual framework, called a megamodel, that aims at modelling large-scale software evolution processes. Such processes are modeled as graphs of systems linked with well-defined set of relations such as RepresentationOf (µ), ConformsTo (χ) and IsTransformedIn (τ ).}, langid = {english} } @inproceedings{Fayyad201731, title = {Benchmarks and Process Management in Data Science: {{Will}} We Ever Get over the Mess?}, author = {Fayyad, U.M. and Candel, A. and De La Rubia, E.A. and Pafka, S. and Chong, A. and Lee, J.-Y.}, date = {2017}, series = {Proceedings of the {{ACM SIGKDD International Conference}} on {{Knowledge Discovery}} and {{Data Mining}}}, volume = {Part F129685}, pages = {31--32}, publisher = {Association for Computing Machinery}, doi = {10.1145/3097983.3120998}, abstract = {This panel aims to address areas that are widely acknowledged to be of critical importance to the success of Data Science projects and to the healthy growth of KDD/Data Science as a field of scientific research. However, despite this acknowledgement of their criticality, these areas receive insufficient attention in the major conferences in the field. Furthermore, there is a lack of actual actions and tools to address these areas in actual practice. These areas are summarized as follows: 1. Ask any data scientist or machine learning practitioner what they spend the majority of their time working on, and you will most likely get an answer that indicates that 80\% to 90\% of their time is spent on "Data Chasing", "Data Sourcing", "Data Wrangling", "Data Cleaning" and generally what researchers would refer to-often dismissively-as "Data Preparation". The process of producing statistical or data mining models from data is typically "messy" and certainly lacks management tools to help manage, replicate, reconstruct, and capture all the knowledge that goes in 90\% of activities of a Data Scientists. The intensive Data Engineering work that goes into exploring and determining the representation of problem and the significant amount of "data cleaning" that ensues creates a plethora of extracts, files, and many artifacts that are only meaningful to the data scientist. 2. The severe lack of Benchmarks in the field, especially ones at big data scale is an impediment to true, objective, measurable progress on performance. The results of each paper are highly dependent on the large degree of freedom an author has on configuring competitive models and on determining which data sets to use (often Data that is not available to others to replicate results on) 3. Monitoring the health of models in production, and deploying models into production environments efficiently and effectively is a black art and often an ignored area. Many models are effectively "orphans" with no means of getting appropriate health monitoring. The task of deploying a built model to production is frequently beyond the capabilities of a Data Scientists and the understanding of the IT team. For a typical company, a Machine Learning or Data Science expert is a major investment; yet these people are in such hot demand, that likelihood of churn is high. Typically, when a data scientist is replaced, the process pretty much starts over with a tabula rasa⋯ In fact, I would argue most data scientists coming back to tasks they built themselves 1-2 years before are unable to reconstruct what they did. For this panel, we have selected a unique set of experts who have different experiences and perspectives on these important problems and how they should be dealt with in real environments. It is our hope that the panel discussion will not only produce recommendations on what to do about these painful impediments to successful project deployments, but also serve as an eye opener for the research community to the importance of paying close attention to issues of Data and Model Management in KDD, as well the need to think carefully about the lifecycle of models and how they can be managed, maintained, and deployed systematically. Without addressing these critical deployment and practice issues, our field will be challenged to grow in a healthy and sustainable way. The expert panelists for this panel along with the panel moderator: Usama Fayyad are listed below along with their biographical sketches. © 2017 Copyright held by the owner/author(s).}, document_type = {Conference Paper}, isbn = {978-1-4503-4887-4}, source = {Scopus}, note = {cited By 4} } @online{fazelniaLessonsUseNatural2024, title = {Lessons from the {{Use}} of {{Natural Language Inference}} ({{NLI}}) in {{Requirements Engineering Tasks}}}, author = {Fazelnia, Mohamad and Koscinski, Viktoria and Herzog, Spencer and Mirakhorli, Mehdi}, date = {2024-04-24}, eprint = {2405.05135}, eprinttype = {arXiv}, eprintclass = {cs}, url = {http://arxiv.org/abs/2405.05135}, urldate = {2024-07-01}, abstract = {We investigate the use of Natural Language Inference (NLI) in automating requirements engineering tasks. In particular, we focus on three tasks: requirements classification, identification of requirements specification defects, and detection of conflicts in stakeholders' requirements. While previous research has demonstrated significant benefit in using NLI as a universal method for a broad spectrum of natural language processing tasks, these advantages have not been investigated within the context of software requirements engineering. Therefore, we design experiments to evaluate the use of NLI in requirements analysis. We compare the performance of NLI with a spectrum of approaches, including prompt-based models, conventional transfer learning, Large Language Models (LLMs)-powered chatbot models, and probabilistic models. Through experiments conducted under various learning settings including conventional learning and zero-shot, we demonstrate conclusively that our NLI method surpasses classical NLP methods as well as other LLMs-based and chatbot models in the analysis of requirements specifications. Additionally, we share lessons learned characterizing the learning settings that make NLI a suitable approach for automating requirements engineering tasks.}, pubstate = {prepublished}, keywords = {Computer Science - Computation and Language,Computer Science - Machine Learning,Computer Science - Software Engineering} } @inproceedings{fearyMultipleViewsSafetyCritical2016, title = {Multiple {{Views}} on {{Safety-Critical Automation}}: {{Aircrafts}}, {{Autonomous Vehicles}}, {{Air Traffic Management}} and {{Satellite Ground Segments Perspectives}}}, shorttitle = {Multiple {{Views}} on {{Safety-Critical Automation}}}, author = {Feary, Michael and Martinie, Célia and Palanque, Philippe and Tscheligi, Manfred}, date = {2016}, pages = {1069--1072}, publisher = {ACM Press}, doi = {10.1145/2851581.2886430}, isbn = {978-1-4503-4082-3}, langid = {english}, note = {TL;DR \par This SIG targets at two problem areas: the engineering of the user interaction with (partly-) autonomous systems: how to design, build and assess autonomous behavior, especially in cases where there is a need to represent on the user interface both autonomous and interactive objects.} } @inproceedings{Febbo2016, title = {A Combined Plant/Controller Optimization Framework for Hybrid Vehicles with {{MPG}}, Emissions and Drivability Considerations}, author = {Febbo, H. and Ersal, T. and Stein, J.L.}, date = {2016}, series = {Proceedings of the {{ASME Design Engineering Technical Conference}}}, volume = {3}, publisher = {American Society of Mechanical Engineers (ASME)}, doi = {10.1115/DETC2016-60335}, abbrev_source_title = {Proc. ASME Des. Eng. Tech. Conf.}, affiliation = {Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, United States}, document_type = {Conference Paper}, isbn = {978-0-7918-5013-8}, langid = {english}, source = {Scopus}, note = {cited By 2} } @article{felfernigOverviewRecommenderSystems2019, title = {An Overview of Recommender Systems in the Internet of Things}, author = {Felfernig, Alexander and Polat-Erdeniz, Seda and Uran, Christoph and Reiterer, Stefan and Atas, Muesluem and Tran, Thi Ngoc Trang and Azzoni, Paolo and Kiraly, Csaba and Dolui, Koustabh}, date = {2019-04}, journaltitle = {J Intell Inf Syst}, volume = {52}, number = {2}, pages = {285--309}, issn = {0925-9902, 1573-7675}, doi = {10.1007/s10844-018-0530-7}, abstract = {The Internet Of Things (IoT) is an emerging paradigm that envisions a networked infrastructure enabling different types of devices to be interconnected. It creates different kinds of artifacts (e.g., services and applications) in various application domains such as health monitoring, sports monitoring, animal monitoring, enhanced retail services, and smart homes. Recommendation technologies can help to more easily identify relevant artifacts and thus will become one of the key technologies in future IoT solutions. In this article, we provide an overview of existing applications of recommendation technologies in the IoT context and present new recommendation techniques on the basis of real-world IoT scenarios.}, langid = {english}, keywords = {internet of things,recommendation systems}, note = {\textbf{Yellow Annotations (17/12/2020, 17:05:17)} \par "any system that guides a user in a personalized way to interesting or useful objects in a large space of possible options or that produces such objects as output" (\href{zotero://open-pdf/library/items/PZH6FFNA?page=1}{Felfernig et al 2019:285}) \par "collaborative filtering and content-based filtering. Collaborative Filtering (Konstan et al. 1997) is using the opinio" (\href{zotero://open-pdf/library/items/PZH6FFNA?page=2}{Felfernig et al 2019:286}) \par "IoT workflow development" (\href{zotero://open-pdf/library/items/PZH6FFNA?page=2}{Felfernig et al 2019:286}) \par "ecommendation of app" (\href{zotero://open-pdf/library/items/PZH6FFNA?page=2}{Felfernig et al 2019:286}) \par "domain-specific scenarios such as food recommendation (" (\href{zotero://open-pdf/library/items/PZH6FFNA?page=2}{Felfernig et al 2019:286}) \par "recommender engine can be included to assist users in the configuration of the gateway" (\href{zotero://open-pdf/library/items/PZH6FFNA?page=3}{Felfernig et al 2019:287}) \par "in recommending useful applications based on given gateway settings and user interaction protocols." (\href{zotero://open-pdf/library/items/PZH6FFNA?page=3}{Felfernig et al 2019:287}) \par "In the context of wildlife animal monitoring, measuring devices and data collection units (typically drones) have to be selected and parametrized in such a way that the observation area is completely covered" (\href{zotero://open-pdf/library/items/PZH6FFNA?page=3}{Felfernig et al 2019:287}) \par "In the context of smart homes, recommendation technologies improve the overall applicability of the installed equipment and can also help to optimize the usage of the available resources (e.g., minimizing power consumption)." (\href{zotero://open-pdf/library/items/PZH6FFNA?page=3}{Felfernig et al 2019:287}) \par "recommender systems can help the spectators to determine the current geographical location of certain athletes. This further results in recommended sites at which the athlete can be seen and cheered." (\href{zotero://open-pdf/library/items/PZH6FFNA?page=3}{Felfernig et al 2019:287}) \par "how recommenders can be applied in IoT scenarios and to propose new recommendation approaches for the IoT domain." (\href{zotero://open-pdf/library/items/PZH6FFNA?page=4}{Felfernig et al 2019:288}) \par "existing applications of recommendation technologies in the IoT." (\href{zotero://open-pdf/library/items/PZH6FFNA?page=4}{Felfernig et al 2019:288}) \par "IoT infrastructure, a recommender system does not have to only rely on the preferences of the user but can take into account further information sources." (\href{zotero://open-pdf/library/items/PZH6FFNA?page=4}{Felfernig et al 2019:288}) \par "Alex needs to receive some app, device or communication protocol (BLE, zigbee, etc.) recommendations according to the overall settings on the gateway." (\href{zotero://open-pdf/library/items/PZH6FFNA?page=5}{Felfernig et al 2019:289}) \par "support for choosing the sensors and configuring the system properly." (\href{zotero://open-pdf/library/items/PZH6FFNA?page=5}{Felfernig et al 2019:289}) \par "SEQREQ: Sequences based recommendation" (\href{zotero://open-pdf/library/items/PZH6FFNA?page=11}{Felfernig et al 2019:295}) \par "SEQREQ (Sequences based Recommendation) which recommends items based on sequential pattern mini" (\href{zotero://open-pdf/library/items/PZH6FFNA?page=11}{Felfernig et al 2019:295}) \par "CONFREQ: Recommendations for configurators" (\href{zotero://open-pdf/library/items/PZH6FFNA?page=13}{Felfernig et al 2019:297}) \par "DIAGREQ: recommending diagnoses" (\href{zotero://open-pdf/library/items/PZH6FFNA?page=16}{Felfernig et al 2019:300})} } @article{Feng2019368, title = {Dynamic Reliability Analysis Using the Extended Support Vector Regression ({{X-SVR}})}, author = {Feng, J. and Liu, L. and Wu, D. and Li, G. and Beer, M. and Gao, W.}, date = {2019}, journaltitle = {Mech. Syst. Signal Process.}, volume = {126}, pages = {368--391}, publisher = {Academic Press}, issn = {08883270}, doi = {10.1016/j.ymssp.2019.02.027}, abstract = {For engineering applications, the dynamic system responses can be significantly affected by uncertainties in the system parameters including material and geometric properties as well as by uncertainties in the excitations. The reliability of dynamic systems is widely evaluated based on the first-passage theory. To improve the computational efficiency, surrogate models are widely used to approximate the relationship between the system inputs and outputs. In this paper, a new machine learning based metamodel, namely the extended support vector regression (X-SVR), is proposed for the reliability analysis of dynamic systems via utilizing the first-passage theory. Furthermore, the capability of X-SVR is enhanced by a new kernel function developed from the vectorized Gegenbauer polynomial, especially for solving complex engineering problems. Through the proposed approach, the relationship between the extremum of the dynamic responses and the input uncertain parameters is approximated by training the X-SVR model such that the probability of failure can be efficiently predicted without using other computational tools for numerical analysis, such as the finite element analysis (FEM). The feasibility and performance of the proposed surrogate model in dynamic reliability analysis is investigated by comparing it with the conventional ε-insensitive support vector regression (ε-SVR) with Gaussian kernel and Monte Carlo simulation (MSC). Four numerical examples are adopted to evidently demonstrate the practicability and efficiency of the proposed X-SVR method. © 2019 Elsevier Ltd}, coden = {MSSPE}, document_type = {Article}, source = {Scopus}, note = {cited By 37} } @online{fengCodeBERTPreTrainedModel2020, ids = {feng-etal-2020-codebert}, title = {{{CodeBERT}}: {{A Pre-Trained Model}} for {{Programming}} and {{Natural Languages}}}, shorttitle = {{{CodeBERT}}}, author = {Feng, Zhangyin and Guo, Daya and Tang, Duyu and Duan, Nan and Feng, Xiaocheng and Gong, Ming and Shou, Linjun and Qin, Bing and Liu, Ting and Jiang, Daxin and Zhou, Ming}, date = {2020-09-18}, eprint = {2002.08155}, eprinttype = {arXiv}, eprintclass = {cs}, url = {http://arxiv.org/abs/2002.08155}, urldate = {2023-05-04}, abstract = {We present CodeBERT, a bimodal pre-trained model for programming language (PL) and nat-ural language (NL). CodeBERT learns general-purpose representations that support downstream NL-PL applications such as natural language codesearch, code documentation generation, etc. We develop CodeBERT with Transformer-based neural architecture, and train it with a hybrid objective function that incorporates the pre-training task of replaced token detection, which is to detect plausible alternatives sampled from generators. This enables us to utilize both bimodal data of NL-PL pairs and unimodal data, where the former provides input tokens for model training while the latter helps to learn better generators. We evaluate CodeBERT on two NL-PL applications by fine-tuning model parameters. Results show that CodeBERT achieves state-of-the-art performance on both natural language code search and code documentation generation tasks. Furthermore, to investigate what type of knowledge is learned in CodeBERT, we construct a dataset for NL-PL probing, and evaluate in a zero-shot setting where parameters of pre-trained models are fixed. Results show that CodeBERT performs better than previous pre-trained models on NL-PL probing.}, pubstate = {prepublished}, keywords = {Computer Science - Computation and Language,Computer Science - Programming Languages}, note = {Comment: Accepted to Findings of EMNLP 2020. 12 pages \par TL;DR \par This work develops CodeBERT with Transformer-based neural architecture, and trains it with a hybrid objective function that incorporates the pre-training task of replaced token detection, which is to detect plausible alternatives sampled from generators.} } @inproceedings{Ferdjoukh20131044, title = {A {{CSP}} Approach for Metamodel Instantiation}, author = {Ferdjoukh, A. and Baert, A.-E. and Chateau, A. and Coletta, R. and Nebut, C.}, date = {2013}, series = {Proceedings - {{International Conference}} on {{Tools}} with {{Artificial Intelligence}}, {{ICTAI}}}, pages = {1044--1051}, issn = {10823409}, doi = {10.1109/ICTAI.2013.156}, abbrev_source_title = {Proc. Int. Conf. Tools Artif. Intell. ICTAI}, affiliation = {LIRMM, Universit́e Montpellier 2, CNRS, Montpellier, France}, art_number = {6735367}, coden = {PCTIF}, correspondence_address1 = {LIRMM, , Montpellier, France}, document_type = {Conference Paper}, isbn = {978-1-4799-2971-9}, langid = {english}, source = {Scopus}, keywords = {GOAL\_Model-synthesis,notion,TECHNIQUE\_CSP}, note = {cited By 13 \par TL;DR \par The generation process is described, which appears to be quicker, more efficient and flexible than any other state-of-the-art approach to metamodel instantiation using CSP.} } @article{Fernández-Ceniceros2014151, title = {The Application of Metamodels Based on Soft Computing to Reproduce the Behaviour of Bolted Lap Joints in Steel Structures}, author = {Fernández-Ceniceros, J. and Urraca-Valle, R. and Antoñanzas-Torres, J. and Sanz-Garcia, A.}, editor = {Abraham A., Corchado E., Zelinka I., Bringas P.G., Herrero A., Baruque B., Quintian H., Corchado E., de Carvalho A.C.P.L.F., Klett F., Zelinka I., Snasel V.}, date = {2014}, journaltitle = {Adv. Intell. Syst. Comput.}, volume = {239}, pages = {151--160}, publisher = {Springer Verlag}, issn = {21945357}, doi = {10.1007/978-3-319-01854-6_16}, abbrev_source_title = {Adv. Intell. Sys. Comput.}, affiliation = {EDMANS Research Group, University of La Rioja, Logrono, Spain}, document_type = {Conference Paper}, isbn = {9783319018539}, langid = {english}, source = {Scopus}, note = {cited By 1 \par TL;DR \par The use of metamodels based on soft computing as an overall approximation system for structures analysis based on artificial neural network as global approximation technique and the parameters for more realistic and informative load-displacement curve including nonlinear effects are estimated for bolted steel lap joints.} } @inproceedings{ferraraReFAIRContextAwareRecommender2024, title = {{{ReFAIR}}: {{Toward}} a {{Context-Aware Recommender}} for {{Fairness Requirements Engineering}}}, shorttitle = {{{ReFAIR}}}, booktitle = {Proc. {{IEEEACM}} 46th {{Int}}. {{Conf}}. {{Softw}}. {{Eng}}.}, author = {Ferrara, Carmine and Casillo, Francesco and Gravino, Carmine and De Lucia, Andrea and Palomba, Fabio}, date = {2024-04-12}, pages = {1--12}, publisher = {ACM}, location = {Lisbon Portugal}, doi = {10.1145/3597503.3639185}, eventtitle = {{{ICSE}} '24: {{IEEE}}/{{ACM}} 46th {{International Conference}} on {{Software Engineering}}}, isbn = {979-8-4007-0217-4}, langid = {english} } @inproceedings{ferryCloudMFApplying2014, title = {Cloud {{MF}}: {{Applying MDE}} to Tame the Complexity of Managing Multi-Cloud Applications}, booktitle = {Proc. - 2014 {{IEEEACM}} 7th {{Int}}. {{Conf}}. {{Util}}. {{Cloud Comput}}. {{UCC}} 2014}, author = {Ferry, N. and Song, H. and Rossini, A. and Chauvel, F. and Solberg, A.}, date = {2014}, pages = {269--277}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/UCC.2014.36}, abstract = {The market of cloud computing encompasses an ever-growing number of cloud providers offering a multitude of infrastructure-as-a-service (IaaS) and platform-as-a-service (PaaS) solutions. The heterogeneity of these solutions hinders the proper exploitation of cloud computing since it prevents interoperability and promotes vendor lock-in, which increases the complexity of executing and managing multi-cloud applications (i.e., Applications that can be deployed across multiple cloud infrastructures and platforms). Providers of multi-cloud applications seek to exploit the peculiarities of each cloud solution and to combine the delivery models of IaaS and PaaS in order to optimise performance, availability, and cost. In this paper, we show how the Cloud Modelling Framework leverages upon model-driven engineering to tame this complexity by providing: (i) a tool-supported domain-specific language for specifying the provisioning and deployment of multi-cloud applications, and (ii) a models@run-time environment for enacting the provisioning, deployment, and adaptation of these applications. © 2014 IEEE.}, isbn = {978-1-4799-7881-6}, keywords = {Cloud computing,Cloud infrastructures,Cloud providers,Computer programming languages,Delivery models,Domain specific languages,Embedded systems,Infrastructure as a service (IaaS),Model-driven Engineering,Modeling languages,Modelling framework,Multi-clouds,Platform as a Service (PaaS),Problem oriented languages,Runtime environments}, note = {cited By 60 \par TL;DR \par This paper shows how the Cloud Modelling Framework leverages upon model-driven engineering to tame this complexity by providing a tool-supported domain-specific language for specifying the provisioning and deployment of multi-cloud applications and a models@run-time environment for enacting the provisioned, deployment, and adaptation of these applications.} } @article{ferryCloudMFModelDrivenManagement2018, title = {{{CloudMF}}: {{Model-Driven Management}} of {{Multi-Cloud Applications}}}, shorttitle = {{{CloudMF}}}, author = {Ferry, Nicolas and Chauvel, Franck and Song, Hui and Rossini, Alessandro and Lushpenko, Maksym and Solberg, Arnor}, date = {2018-01-20}, journaltitle = {ACM Trans. Internet Technol.}, volume = {18}, number = {2}, pages = {1--24}, issn = {15335399}, doi = {10.1145/3125621}, langid = {english} } @article{Feth2017135, title = {A Conceptual Safety Supervisor Definition and Evaluation Framework for Autonomous {{Systems}}}, author = {Feth, P. and Schneider, D. and Adler, R.}, editor = {Bitsch F., Tonetta S., Schoitsch E.}, date = {2017}, journaltitle = {Lect. Notes Comput. Sci. Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinforma.}, volume = {10488 LNCS}, pages = {135--148}, publisher = {Springer Verlag}, issn = {03029743}, doi = {10.1007/978-3-319-66266-4_9}, abbrev_source_title = {Lect. Notes Comput. Sci.}, affiliation = {Fraunhofer Institute for Experimental Software Engineering, Kaiserslautern, Germany}, correspondence_address1 = {Feth, P.; Fraunhofer Institute for Experimental Software EngineeringGermany; email: patrik.feth@iese.fraunhofer.de}, document_type = {Conference Paper}, isbn = {9783319662657}, langid = {english}, source = {Scopus}, note = {cited By 10 \par TL;DR \par This work presents a conceptual framework and a corresponding metamodel, which are motivated and justified by existing work in the field of runtime safety monitoring, and presents an SSV that is based on the ISO 22839 standard for forward collision mitigation.} } @software{fiegelPunkpeyeAwesomemcpservers2025, title = {Punkpeye/Awesome-Mcp-Servers}, author = {Fiegel, Frank}, date = {2025-04-11}, origdate = {2024-11-30T04:49:10Z}, url = {https://github.com/punkpeye/awesome-mcp-servers}, urldate = {2025-04-11}, abstract = {A collection of MCP servers.}, keywords = {/unread,ai,mcp} } @online{FightFireFire, title = {Fight {{Fire With Fire}}: {{How Much Can We Trust ChatGPT}} on {{Source Code-Related Tasks}}?}, url = {https://www.computer.org/csdl/journal/ts/2024/12/10745266/21CKnvpuwFi}, keywords = {source\_mobile} } @article{fillConceptualModelingLarge2023, title = {Conceptual {{Modeling}} and {{Large Language Models}}: {{Impressions From First Experiments With ChatGPT}}}, shorttitle = {Conceptual {{Modeling}} and {{Large Language Models}}}, author = {Fill, Hans-Georg and Fettke, Peter and Köpke, Julius}, date = {2023-04-06}, journaltitle = {Enterp. Model. Inf. Syst. Archit. EMISAJ}, pages = {3:1-15 Pages}, publisher = {{Enterprise Modelling and Information Systems Architectures (EMISAJ)}}, doi = {10.18417/EMISA.18.3}, langid = {english}, note = {\subsection{SeriesInformation} Enterprise Modelling and Information Systems Architectures (EMISAJ), Vol. 18 (2023)} } @article{fischerStackOverflowConsidered2017, title = {Stack {{Overflow Considered Harmful}}? {{The Impact}} of {{Copy}}\&{{Paste}} on {{Android Application Security}}}, shorttitle = {Stack {{Overflow Considered Harmful}}?}, author = {Fischer, Felix and Böttinger, Konstantin and Xiao, Huang and Stransky, Christian and Acar, Yasemin and Backes, Michael and Fahl, Sascha}, date = {2017-10-09}, journaltitle = {ArXiv171003135 Cs}, eprint = {1710.03135}, eprinttype = {arXiv}, eprintclass = {cs}, url = {http://arxiv.org/abs/1710.03135}, urldate = {2021-06-18}, abstract = {Online programming discussion platforms such as Stack Overflow serve as a rich source of information for software developers. Available information include vibrant discussions and oftentimes ready-to-use code snippets. Anecdotes report that software developers copy and paste code snippets from those information sources for convenience reasons. Such behavior results in a constant flow of community-provided code snippets into production software. To date, the impact of this behaviour on code security is unknown. We answer this highly important question by quantifying the proliferation of security-related code snippets from Stack Overflow in Android applications available on Google Play. Access to the rich source of information available on Stack Overflow including ready-to-use code snippets provides huge benefits for software developers. However, when it comes to code security there are some caveats to bear in mind: Due to the complex nature of code security, it is very difficult to provide ready-to-use and secure solutions for every problem. Hence, integrating a security-related code snippet from Stack Overflow into production software requires caution and expertise. Unsurprisingly, we observed insecure code snippets being copied into Android applications millions of users install from Google Play every day. To quantitatively evaluate the extent of this observation, we scanned Stack Overflow for code snippets and evaluated their security score using a stochastic gradient descent classifier. In order to identify code reuse in Android applications, we applied state-of-the-art static analysis. Our results are alarming: 15.4\% of the 1.3 million Android applications we analyzed, contained security-related code snippets from Stack Overflow. Out of these 97.9\% contain at least one insecure code snippet.}, keywords = {Computer Science - Cryptography and Security}, note = {TL;DR \par This work quantifies the proliferation of security-related code snippets from Stack Overflow in Android applications available on Google Play by quantifying the number of insecure code snippets and evaluated their security score using a stochastic gradient descent classifier.} } @online{FixingOurBroken, title = {Fixing Our Broken Knowledge Systems with the Unified Theory of Knowledge}, url = {https://d1ysz50cxb9zwl.cloudfront.net/LvGVmHlTJX36y0Eg7LL1gGLLg6r9ICvsKQMpCQLLd0arscdBg6OUCZyW0Ijw7Q4U/by/4646762/as/file.pdf?Expires=1744993766&Signature=E1rUIsVacwb1FzqfM1nXKpO1bqD5HeBcLbHEwxGfrL5rl-MfmDc0-KqIkFSJaha~c-mCNH~YQM1puxCMcWliGiCMLuWOEzJnToFhCpQw9zyTDnzCUTtIKdg0VpZ7dBVlyZKCI3GD~ad7sQGyONQWTL~x-khp4j4B4mNV06NJD50nxoRMmFo1mWwwvW5uBIZTCDpXKUL8pELLmSa9vixyW~~-IvyBxUDh5u6d8Q5Jqtkmw1j8igLRFVF4zO4ZizYx-tMT9VM5HUY6vhThW5my-WhHYl9GgHvBj2o44YoXBRMqcc7yEwN3ji6E6Zf4dwtt9QjjSSD36OjEqWRyXetkSg__&Key-Pair-Id=APKAJAERRT46LD6FN4NA}, urldate = {2025-04-18}, langid = {english}, keywords = {READ} } @article{fleckModelTransformationModularization2017, title = {Model {{Transformation Modularization}} as a {{Many-Objective Optimization Problem}}}, author = {Fleck, Martin and Troya, Javier and Kessentini, Marouane and Wimmer, Manuel and Alkhazi, Bader}, date = {2017}, journaltitle = {IEEE Trans. Softw. Eng.}, pages = {1--1}, issn = {0098-5589, 1939-3520}, doi = {10.1109/TSE.2017.2654255}, keywords = {Model transformation,modularization,multi-objective problem,optimization problem}, note = {TL;DR \par This study proposes an automated search-based approach to modularize model transformations based on higher-order transformations and shows that ATL transformations can be modularized automatically, efficiently, and effectively by this approach.} } @article{fleder2009blockbuster, title = {Blockbuster Culture's next Rise or Fall: {{The}} Impact of Recommender Systems on Sales Diversity}, author = {Fleder, Daniel and Hosanagar, Kartik}, date = {2009}, journaltitle = {Manag. Sci.}, volume = {55}, number = {5}, pages = {697--712}, publisher = {INFORMS}, note = {TL;DR \par An analytical model of recommender systems and their influence on consumer choice is developed to explore the strong, path-dependent interaction between recommendations and sales and is believed to be the first to attempt to isolate the impact ofRecommender systems on long-run sales diversity.} } @article{fleureyQualifyingInputTest2007, title = {Qualifying Input Test Data for Model Transformations}, author = {Fleurey, Franck and Baudry, Benoit and Muller, Pierre-Alain and Traon, Yves Le}, date = {2007}, journaltitle = {Softw. Syst. Model.}, volume = {8}, number = {2}, pages = {185--203}, doi = {10.1007/s10270-007-0074-8}, keywords = {software engineering} } @misc{FloresFranzinelliConflitto, title = {Flores- {{Franzinelli}} - {{Conflitto}} Tra Poteri} } @inproceedings{fogarasScalingLinkbasedSimilarity2005, title = {Scaling Link-Based Similarity Search}, booktitle = {Proc. 14th {{Int}}. {{Conf}}. {{World Wide Web}}}, author = {Fogaras, Dániel and Rácz, Balázs}, date = {2005}, series = {{{WWW}} '05}, pages = {641--650}, publisher = {ACM}, location = {New York, NY, USA}, url = {http://doi.acm.org/10.1145/1060745.1060839}, acmid = {1060839}, isbn = {1-59593-046-9}, nodoi = {10.1145/1060745.1060839}, numpages = {10}, keywords = {fingerprint,link-analysis,scalability,similarity search} } @book{Foreward2011, ids = {Foreward2011a}, title = {Foreward}, date = {2011}, journaltitle = {ACM International Conference Proceeding Series} } @misc{Form_valutazione_tesi_PhdAriannaFedeli, title = {Form\_valutazione\_tesi\_{{Phd}} - Arianna Fedeli.Pdf}, langid = {english}, keywords = {LOGSEQ} } @misc{forsyth_top_2021, title = {Top 5 {{Benefits}} of {{Low-Code}}}, author = {Forsyth, Alexander}, date = {2021-01}, abstract = {Today, more than 200 vendors claimed they offered low-code. But do they really? In this post, discover the benefits of low-code and who can deliver them.}, langid = {english} } @online{FOSDEM2016OSCAR, title = {{{FOSDEM}} 2016 - {{OSCAR}}: {{Address}} the New Challenges of Open-Source Software Quality}, url = {https://fosdem.org/2016/schedule/event/oscar/}, urldate = {2016-02-09} } @inproceedings{fowkesParameterfreeProbabilisticAPI2016, ids = {Fowkes:2016:PPA:2950290.2950319}, title = {Parameter-Free Probabilistic {{API}} Mining across {{GitHub}}}, booktitle = {Proc. 2016 24th {{ACM SIGSOFT Int}}. {{Symp}}. {{Found}}. {{Softw}}. {{Eng}}.}, author = {Fowkes, Jaroslav and Sutton, Charles}, date = {2016}, series = {{{FSE}} 2016}, pages = {254--265}, publisher = {ACM}, location = {New York, NY, USA}, url = {http://doi.acm.org/10.1145/2950290.2950319}, acmid = {2950319}, isbn = {978-1-4503-4218-6}, nodoi = {10.1145/2950290.2950319}, numpages = {12}, keywords = {API mining,sequential pattern mining}, note = {TL;DR \par PAM (Probabilistic API Miner), a near parameter-free probabilistic algorithm for mining the most interesting API call patterns, is presented and it is shown that PAM significantly outperforms both MAPO and UPMiner at retrieving relevant API call sequences from GitHub.} } @inproceedings{frakesTermConflationInformation1984, title = {Term Conflation for Information Retrieval}, booktitle = {Proc. 7th {{Annu}}. {{Int}}. {{ACM SIGIR Conf}}. {{Res}}. {{Dev}}. {{Inf}}. {{Retr}}.}, author = {Frakes, William B}, date = {1984}, pages = {383--389}, organization = {British Computer Society}, note = {TL;DR \par Based on results of two experiments concerned with term conflation for information retrieval, and the CATALOG retrieval system designed utilizing the results of the experiments,Term conflation can be automated in a retrieval system with no average loss of performance, thus allowing easier and user access to the system.} } @article{FrameworkVerificationModel, title = {A Framework for Verification of Model Transformations} } @book{franceModelDrivenEngineering2012, title = {Model Driven Engineering Languages and Systems: 15th {{International Conference}}, {{MODELS}} 2012, {{Innsbruck}}, {{Austria}}, {{September}} 30-{{October}} 5, 2012: Proceedings}, shorttitle = {Model Driven Engineering Languages and Systems}, editor = {France, Robert}, date = {2012}, series = {Lecture Notes in Computer Science}, number = {7590}, publisher = {Springer}, location = {Berlin ; New York}, eventtitle = {{{MODELS}} ({{Conference}})}, isbn = {978-3-642-33665-2}, pagetotal = {828}, keywords = {Computer software,Conference proceedings,Congresses,Development,Model-driven software architecture,Model-integrated computing}, note = {International conference proceedings \par Thomas A. Henzinger -- Jesús Sánchez-Cuadrado, Juan de Lara Esther Guerra -- Florian Noyrit, Sébastien Gérard Bran Selic -- Fazle Rabbi Wendy MacCaull -- Andres J. Ramirez, Betty H.C. Cheng, Nelly Bencomo Pete Sawyer -- Germán H. Alférez Vicente Pelechano -- François Fouquet, Grégory Nain, Brice Morin, Erwan Daubert Olivier Barais, Markus Scheidgen, Anatolij Zubow, Joachim Fischer Thomas H. Kolbe -- Benoit Combemale, Xavier Thirioux Benoit Baudry -- Ábel Hegedüs, Ákos Horváth, István Ráth Dániel Varró -- Jean-Marie Favre, Ralf Lämmel Andrei Varanovich Quantitative reactive models / Bottom-up meta-modelling: an interactive approach / FacadeMetamodel: masking UML / T-Square: a domain specific language for rapid workflow development / Relaxing claims: coping with uncertainty while evaluating assumptions at tun time / Dynamic evolution of context-aware systems with models at runtime / An Eclipse modelling framework alternative to meet the Models@Runtime requirements / Automated and transparent model fragmentation for persisting large models / Formally defining and iterating infinite models / Query-driven soft interconnection of EMF Models / Modeling the linguistic architecture of software products Rolf-Helge Pfeiffer Andrzej Wąsowski -- Carmine Gravino, Michele Risi, Giuseppe Scanniello Genoveffa Tortora -- Alexander Reder Alexander Egyed -- Kleinner Farias, Alessandro Garcia Carlos Lucena -- Lars Hamann, Oliver Hofrichter Martin Gogolla -- Julia Schroeter, Malte Lochau Tim Winkelmann -- Martin Fagereng Johansen, Øystein Haugen, Franck Fleurey, Anne Grete Eldegard Torbjørn Syversen -- Vinay Kulkarni, Souvik Barat Suman Roychoudhury -- Azzam Maraee Mira Balaban -- Chris Shaver Edward A. Lee Cross-language support mechanisms significantly aid software development / Do professional developers benefit from design pattern documentation?: a replication in the context of source code comprehension / Incremental consistency checking for complex design rules and larger model changes / Evaluating the impact of aspects on inconsistency detection effort: a controlled experiment / On integrating structure and behavior modeling with OCL / Multi-perspectives on feature models / Generating better partial covering arrays by modeling weights on sub-product lines / Towards business application product lines / Inter-association constraints in UML2: comparative analysis, usage recommendations, and modeling guidelines / The Coroutine model of computation Shahar Maoz Yaniv Sa'ar -- Adrian Kuhn, Gail C. Murphy C. Albert Thompson -- Yu Sun, Jeff Gray, Karlheinz Bulheller Nicolaus von Baillou -- Rick Salay, Shige Wang Vivien Suen -- Ethan K. Jackson, Wolfram Schulte Nikolaj Bjørner -- Mirco Kuhlmann Martin Gogolla -- Fabian Büttner, Marina Egea Jordi Cabot -- Carlos A. González Jordi Cabot -- Donghwan Shin, Eunkyoung Jee Doo-Hwan Bae -- El Arbi Aboussoror, Ileana Ober Iulian Ober -- Razieh Behjati, Tao Yue Lionel Briand Assume-guarantee scenarios: semantics and synthesis / An Exploratory study of forces and frictions affecting large-scale model-driven development / A Model-driven approach to support engineering changes in industrial robotics software / Managing related models in vehicle control software development / Detecting specification errors in declarative languages with constraints / From UML and OCL to relational logic and back / On verifying ATL transformations using 'off-the-shelf' SMT solvers / ATLTest: a white-box test generation approach for ATL transformations / Empirical evaluation on FBD model-based test coverage criteria using mutation analysis / Seeing errors: model driven simulation trace visualization / A Modeling approach to support the similarity-based reuse of configuration data Yihan Wu, Gang Huang, Hui Song Ying Zhang -- Michael Vierhauser, Paul Grünbacher, Wolfgang Heider, Gerald Holl Daniela Lettner -- Hajer Saada, Xavier Dolques, Marianne Huchard, Clémentine Nebut Houari Sahraoui -- Vincent Aranega, Anne Etien Sebastien Mosser -- Gerd Kainz, Christian Buckl Alois Knoll -- Gunnar Schulze, Joanna Chimiak-Opoka Birgit Grammel, Stefan Kastenholz Konrad Voigt -- Moisés Castelo Branco, Javier Troya, Krzysztof Czarnecki, Jochen Küster Hagen Völzer -- Muhammad Zohaib Iqbal, Shaukat Ali, Tao Yue Lionel Briand -- Sagar Sunkle Vinay Kulkarni Model driven configuration of fault tolerance solutions for component-based software system / Applying a consistency checking framework for heterogeneous models and artifacts in industrial product lines / Generation of operational transformation rules from examples of model transformations / Using feature model to build model transformation chains / A Generic approach simplifying model-to-model transformation chains / An Approach for synchronizing UML models and narrative text in literate modeling / Model matching for trace link generation in model-driven software development / Matching business process workflows across abstraction levels / Experiences of applying UML/MARTE on three industrial projects / Cost estimation for model-driven engineering Kleinner Farias, Alessandro Garcia, Jon Whittle, Christina Chavez Carlos Lucena -- Jorge Aranda, Daniela Damian Arber Borici -- Zille Huma, Christian Gerth, Gregor Engels Oliver Juwig -- Shaukat Ali, Tao Yue, Lionel Briand Suneth Walawege -- James R. Williams, Frank R. Burton, Richard F. Paige Fiona A.C. Polack -- Shiva Nejati, Stefano Di Alesio, Mehrdad Sabetzadeh Lionel Briand -- Galina Besova, Sven Walther, Heike Wehrheim Steffen Becker -- Lionel Briand, Davide Falessi, Shiva Nejati, Mehrdad Sabetzadeh Tao Yue -- Andrea Sindico, Marco Di Natale Alberto Sangiovanni-Vincentelli Evaluating the effort of composing design models: a controlled experiment / Transition to model-driven engineering: what Is revolutionary, what remains the same? / Towards an automatic service discovery for UML-based rich service descriptions / A Product line modeling and configuration methodology to support model-based testing: an industrial case study / Sensitivity analysis in model-driven engineering / Modeling and analysis of CPU usage in safety-critical embedded systems to support stress testing / Weaving-based configuration and modular transformation of multi-layer systems / Research-based innovation: a tale of three projects in model-driven engineering / An Industrial system engineering process integrating model driven architecture and model based design} } @inproceedings{franceProvidingSupportModel2007, title = {Providing Support for Model Composition in Metamodels}, booktitle = {Enterp. {{Distrib}}. {{Object Comput}}. {{Conf}}. 2007 {{EDOC}} 2007 11th {{IEEE Int}}.}, author = {France, Robert and Fleurey, Franck and Reddy, Raghu and Baudry, Benoit and Ghosh, Sudipto}, date = {2007}, pages = {253--253}, publisher = {IEEE}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4383998}, urldate = {2015-09-24}, note = {TL;DR \par This paper shows how this can be done by extending the UML metamodel with behavior describing symmetric, signature-based composition of UML model elements, and describes an implementation of the metAModel that supports systematic composition ofUML class models.} } @inproceedings{FranceR07, ids = {france2007model}, title = {Model-Driven Development of Complex Software: A Research Roadmap}, booktitle = {Int. {{Conf}}. {{Softw}}. {{Eng}}. {{ISCE}} 2007 {{Workshop Future Softw}}. {{Eng}}. {{FOSE}} 2007 {{May}} 23-25 2007 {{Minneap}}. {{MN USA}}}, author = {France, Robert B. and Rumpe, Bernhard}, editor = {Briand, Lionel C. and Wolf, Alexander L.}, date = {2007}, pages = {37--54}, publisher = {IEEE Computer Society}, doi = {10.1109/FOSE.2007.14}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {TL;DR \par It is argued that full realizations of the MDE vision may not be possible in the near to medium-term primarily because of the wicked problems involved, but attempting to realize the vision will provide insights that can be used to significantly reduce the gap between evolving software complexity and the technologies used to manage complexity.}, timestamp = {Thu, 23 Mar 2023 23:58:12 +0100} } @article{franceRepositoryModelDriven2007, title = {Repository for {{Model Driven Development}} ({{ReMoDD}})}, author = {France, Robert and Bieman, Jim and Cheng, Betty H. C.}, date = {2007}, journaltitle = {Models Softw. Eng.}, volume = {4364}, pages = {311--317}, doi = {10.1007/978-3-540-69489-2_38} } @article{franchUsingQualityModels2003, title = {Using Quality Models in Software Package Selection}, author = {Franch, Xavier and Carvallo, Juan Pablo}, date = {2003}, journaltitle = {IEEE Softw.}, volume = {20}, number = {1}, pages = {34--41}, url = {http://ieeexplore.ieee.org/abstract/document/1159027/}, urldate = {2017-02-25}, note = {TL;DR \par This work proposes a methodology for building structured quality models that helps solve the lack of structured and widespread descriptions of software packages and the precise statement of quality requirements, and consequently overall package selection and confidence in the result of the process.} } @incollection{Frank2013, title = {Domain-Specific Modeling Languages: {{Requirements}} Analysis and Design Guidelines}, booktitle = {Domain Engineering: {{Product}} Lines, Languages, and Conceptual Models}, author = {Frank, Ulrich}, editor = {Reinhartz-Berger, Iris and Sturm, Arnon and Clark, Tony and Cohen, Sholom and Bettin, Jorn}, date = {2013}, pages = {133--157}, publisher = {Springer Berlin Heidelberg}, location = {Berlin, Heidelberg}, doi = {10.1007/978-3-642-36654-3_6}, abstract = {In recent years, the development of domain-specific modeling languages has gained remarkable attention. This is for good reasons. A domain-specific modeling language incorporates concepts that represent domain-level knowledge. Hence, systems analysts are not forced to reconstruct these concepts from scratch. At the same time, domain-specific modeling languages contribute to model integrity, because they include already constraints that would otherwise have to be added manually. Even though there has been a considerable amount of research on developing and using domain-specific modeling languages, there is still lack of comprehensive methods to guide the design of these languages. With respect to the complexity and risk related to developing a domain-specific modeling language, this is a serious shortfall. This chapter is aimed at a contribution to filling the gap. At first, it presents guidelines for selecting a metamodeling language. Its main focus is on supporting the process from analyzing requirements to specifying and evaluating a domain-specific modeling language.}, isbn = {978-3-642-36654-3} } @article{fredericksPlanningOptimizationDynamically2019, title = {Planning as {{Optimization}}: {{Dynamically Discovering Optimal Configurations}} for {{Runtime Situations}}}, shorttitle = {Planning as {{Optimization}}}, author = {Fredericks, Erik M. and Gerostathopoulos, Ilias and Krupitzer, Christian and Vogel, Thomas}, date = {2019-06}, journaltitle = {2019 IEEE 13th Int. Conf. Self-Adapt. Self-Organ. Syst. SASO}, eprint = {1905.01071}, eprinttype = {arXiv}, pages = {1--10}, doi = {10.1109/SASO.2019.00010}, abstract = {The large number of possible configurations of modern software-based systems, combined with the large number of possible environmental situations of such systems, prohibits enumerating all adaptation options at design time and necessitates planning at run time to dynamically identify an appropriate configuration for a situation. While numerous planning techniques exist, they typically assume a detailed state-based model of the system and that the situations that warrant adaptations are known. Both of these assumptions can be violated in complex, real-world systems. As a result, adaptation planning must rely on simple models that capture what can be changed (input parameters) and observed in the system and environment (output and context parameters). We therefore propose planning as optimization: the use of optimization strategies to discover optimal system configurations at runtime for each distinct situation that is also dynamically identified at runtime. We apply our approach to CrowdNav, an open-source traffic routing system with the characteristics of a real-world system. We identify situations via clustering and conduct an empirical study that compares Bayesian optimization and two types of evolutionary optimization (NSGA-II and novelty search) in CrowdNav.}, note = {TL;DR \par CrowdNav is identified via clustering and an empirical study that compares Bayesian optimization and two types of evolutionary optimization (NSGA-II and novelty search) in CrowdNav.} } @book{fredKnowledgeDiscoveryKnowledge2015, title = {Knowledge {{Discovery}}, {{Knowledge Engineering}} and {{Knowledge Management}}}, editor = {Fred, Ana and Dietz, Jan L. G. and Aveiro, David and Liu, Kecheng and Filipe, Joaquim}, date = {2015}, series = {Communications in {{Computer}} and {{Information Science}}}, volume = {553}, publisher = {Springer International Publishing}, location = {Cham}, url = {http://link.springer.com/10.1007/978-3-319-25840-9}, urldate = {2015-11-10}, isbn = {978-3-319-25839-3 978-3-319-25840-9} } @inproceedings{freitasQueryingLinkedData2011, title = {Querying Linked Data Using Semantic Relatedness: {{A}} Vocabulary Independent Approach}, booktitle = {Proc. 16th {{Int}}. {{Conf}}. {{Nat}}. {{Lang}}. {{Process}}. {{Inf}}. {{Syst}}.}, author = {Freitas, André and Oliveira, João Gabriel and O'Riain, Seán and Curry, Edward and Da Silva, João Carlos Pereira}, date = {2011}, series = {{{NLDB}}'11}, pages = {40--51}, publisher = {Springer-Verlag}, location = {Berlin, Heidelberg}, url = {http://dl.acm.org/citation.cfm?id=2026011.2026017}, acmid = {2026017}, isbn = {978-3-642-22326-6}, numpages = {12}, keywords = {linked data,natural language queries}, note = {TL;DR \par This work focuses on the investigation of a vocabulary independent natural language query mechanism for Linked Data, using an approach based on the combination of entity search, a Wikipediabased semantic relatedness measure and spreading activation.} } @inproceedings{freitasTreoBesteffortNatural2011, title = {Treo: {{Best-effort}} Natural Language Queries over Linked Data}, booktitle = {Proc. 16th {{Int}}. {{Conf}}. {{Appl}}. {{Nat}}. {{Lang}}. {{Inf}}. {{Syst}}. {{NLDB}} 2011 {{Poster}}}, author = {Freitas, André and Oliveira, João and O'Riain, Seán and Curry, Edward and Pereira da Silva, João}, editor = {Muñoz, Rafael and Montoyo, Andrés and Métais, Elisabeth}, date = {2011}, series = {Lecture Notes in Computer Science}, volume = {6716}, pages = {286--289}, publisher = {Springer Berlin Heidelberg}, location = {Berlin, Heidelberg}, url = {http://www.edwardcurry.org/publications/Freitas_Treo_NLDB_2011.pdf}, abstract = {Linked Data promises an unprecedented availability of data on the Web. However, this vision comes together with the associated challenges of querying highly heterogeneous and distributed data. In order to query Linked Data on the Web today, end-users need to be aware of which datasets potentially contain the data and the data model behind these datasets. This query paradigm, deeply attached to the traditional perspective of structured queries over databases, does not suit the heterogeneity and scale of the Web, where it is impractical for data consumers to have an a priori understanding of the structure and location of available datasets. This work describes Treo, a best-effort natural language query mechanism for Linked Data, which focuses on the problem of bridging the semantic gap between end-user natural language queries and Linked Datasets.}, isbn = {978-3-642-22326-6}, mendeley-tags = {LEIdataspace,Linked Data,Natural Language Queries,Treo}, nodoi = {10.1007/978-3-642-22327-3}, keywords = {LEIdataspace,Linked Data,Natural Language Queries,Treo} } @article{fritsche2020avoiding, title = {Avoiding Unnecessary Information Loss: Correct and Efficient Model Synchronization Based on Triple Graph Grammars}, author = {Fritsche, Lars and Kosiol, Jens and Schürr, Andy and Taentzer, Gabriele}, date = {2020}, journaltitle = {Int. J. Softw. Tools Technol. Transf.}, pages = {1--34}, publisher = {Springer} } @article{Froger201932, title = {Generating Personalized and Certifiable Workflow Designs: {{A}} Prototype}, author = {Froger, M. and Bénaben, F. and Truptil, S. and Boissel-Dallier, N.}, editor = {Ferreira J.E., Musaev A., Zhang L.-J.}, date = {2019}, journaltitle = {Lect. Notes Comput. Sci. Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinforma.}, volume = {11515 LNCS}, pages = {32--47}, publisher = {Springer Verlag}, issn = {03029743}, doi = {10.1007/978-3-030-23554-3_3}, abbrev_source_title = {Lect. Notes Comput. Sci.}, affiliation = {Ecole des Mines d’Albi, Campus Jarlard, Albi Cedex 09, 81013, France; Iterop, 1B rue Antoine Lavoisier, Colomiers, 31770, France}, correspondence_address1 = {Froger, M.; Ecole des Mines d’Albi, Campus Jarlard, France; email: manon.froger@mines-albi.fr}, document_type = {Conference Paper}, isbn = {9783030235536}, langid = {english}, source = {Scopus}, note = {cited By 0 \par TL;DR \par A business-oriented prototype assisting users in getting certifiable specific business processes is presented, detailing the metamodel used to separately model norms and business’ existing procedures and then, the algorithm envisaged to deduce a corresponding cartography of processes.} } @inproceedings{frostChallengesOpportunitiesAutonomous2010, title = {Challenges and Opportunities for Autonomous Systems in Space}, booktitle = {Front. {{Eng}}. {{Rep}}. {{Lead}}.-{{Edge Eng}}. 2010 {{Symp}}.}, author = {Frost, C.}, date = {2010}, url = {http://books.google.com/books?hl=en&lr=&id=2lH3kI2g2yMC&oi=fnd&pg=PA89&dq=%22encounters+an+unplanned-for+situation,+it+stops+and+waits+for+human+help+(e.g.+it%22+%22of+the+implementation+details,+however,+intelligent+autonomous+systems+are%22+%22The+Roomba+user+provides+high-level+goals+(vacuum+the+floor,+but+don%E2%80%99t+vacuum%22+&ots=ErAPABO1Yh&sig=sSQZsesfdEbvr-v9TCuY3WAQkAk}, urldate = {2016-08-21}, note = {TL;DR \par The autonomous systems community anticipated that the advanced autonomy demonstrated on Deep Space 1 would soon be pervasive, enabling science missions, making spacecraft more resilient, and reducing operational costs.} } @article{Fu2021, title = {A Model-Driven Deep Unfolding Method for {{JPEG}} Artifacts Removal}, author = {Fu, X. and Wang, M. and Cao, X. and Ding, X. and Zha, Z.}, date = {2021}, journaltitle = {IEEE Trans. Neural Netw. Learn. Syst.}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {2162237X}, doi = {10.1109/TNNLS.2021.3083504}, abstract = {Deep learning-based methods have achieved notable progress in removing blocking artifacts caused by lossy JPEG compression on images. However, most deep learning-based methods handle this task by designing black-box network architectures to directly learn the relationships between the compressed images and their clean versions. These network architectures are always lack of sufficient interpretability, which limits their further improvements in deblocking performance. To address this issue, in this article, we propose a model-driven deep unfolding method for JPEG artifacts removal, with interpretable network structures. First, we build a maximum posterior (MAP) model for deblocking using convolutional dictionary learning and design an iterative optimization algorithm using proximal operators. Second, we unfold this iterative algorithm into a learnable deep network structure, where each module corresponds to a specific operation of the iterative algorithm. In this way, our network inherits the benefits of both the powerful model ability of data-driven deep learning method and the interpretability of traditional model-driven method. By training the proposed network in an end-to-end manner, all learnable modules can be automatically explored to well characterize the representations of both JPEG artifacts and image content. Experiments on synthetic and real-world datasets show that our method is able to generate competitive or even better deblocking results, compared with state-of-the-art methods both quantitatively and qualitatively. IEEE}, document_type = {Article}, source = {Scopus}, note = {cited By 5 \par TL;DR \par This article builds a maximum posterior (MAP) model for deblocking using convolutional dictionary learning and design an iterative optimization algorithm using proximal operators, which unfolds into a learnable deep network structure, where each module corresponds to a specific operation of the iterative algorithm.} } @inproceedings{Fukas202219, title = {The Management of Artificial Intelligence: {{Developing}} a Framework Based on the Artificial Intelligence Maturity Principle}, author = {Fukas, P.}, editor = {Looy A.V., Weber B., Rosemann M.}, date = {2022}, series = {{{CEUR Workshop Proceedings}}}, volume = {3139}, pages = {19--27}, publisher = {CEUR-WS}, issn = {16130073}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130763237&partnerID=40&md5=0470ee8a2332eeaf51d2d7d78d596809}, abbrev_source_title = {CEUR Workshop Proc.}, affiliation = {Osnabrück University, Lower Saxony, Osnabrück, Germany; German Research Center for Artificial Intelligence, Lower Saxony, Osnabrück, Germany; Strategion GmbH, Lower Saxony, Osnabrück, Germany}, correspondence_address1 = {Fukas, P.; Osnabrück University, Lower Saxony, Germany; email: philipp.fukas@uni-osnabrueck.de}, document_type = {Conference Paper}, langid = {english}, source = {Scopus}, keywords = {notion}, note = {cited By 0} } @article{fumarolaDataModelingRelationships, title = {Data {{Modeling}} for {{Relationships Handling}} and {{Data Distribution}}}, author = {Fumarola, Dr Fabio}, pages = {45}, langid = {english} } @online{FundingTenders, title = {Funding \& Tenders}, url = {https://ec.europa.eu/info/funding-tenders/opportunities/portal/screen/myarea/project/813884/program/31045243/organisation/999859511/roles/edit?name=Lowcomote}, urldate = {2019-10-30} } @inproceedings{FuTLNP22, title = {{{VulRepair}}: A {{T5-based}} Automated Software Vulnerability Repair}, booktitle = {Proc. 30th {{ACM Jt}}. {{Eur}}. {{Softw}}. {{Eng}}. {{Conf}}. {{Symp}}. {{Found}}. {{Softw}}. {{Eng}}. {{ESECFSE}} 2022 {{Singap}}. {{Singap}}. {{Novemb}}. 14-18 2022}, author = {Fu, Michael and Tantithamthavorn, Chakkrit and Le, Trung and Nguyen, Van and Phung, Dinh Q.}, date = {2022}, pages = {935--947}, publisher = {ACM}, note = {TL;DR \par VulRepair is proposed, a T5-based automated software vulnerability repair approach that leverages the pre-training and BPE components to address various technical limitations of prior work, and is considerably more accurate than two baseline approaches.} } @article{gadepallyBigDAWGManagingHeterogenous, title = {{{BigDAWG}}: {{Managing Heterogenous Data}} and {{Streaming}}}, author = {Gadepally, Dr Vijay}, pages = {48}, langid = {english} } @article{gainLowcodeAutoMLaugmentedData2021, title = {Low-Code {{AutoML-augmented Data Pipeline}} – {{A Review}} and {{Experiments}}}, author = {Gain, Ulla and Hotti, Virpi}, date = {2021-02-01}, journaltitle = {J. Phys.: Conf. Ser.}, volume = {1828}, number = {1}, pages = {012015}, issn = {1742-6588, 1742-6596}, doi = {10.1088/1742-6596/1828/1/012015}, abstract = {There is a lack of knowledge concerning the low-code autoML (automated machine learning) frameworks that can be used to enrich data for several purposes concerning either data engineering or software engineering. In this paper, 34 autoML frameworks have been reviewed based on the latest commits and augmentation properties of their GitHub content. The PyCaret framework was the result of the review due to requirements concerning adaptability by Google Colaboratory (Colab) and the BI (business intelligence) tool. Finally, the low-code autoMLaugmented data pipeline from raw data to dashboards and low-code apps has been drawn based on the experiments concerned classifications of the "Census Income" dataset. The constructed pipeline preferred the same data to be a ground for different reports, dashboards, and applications. However, the constructed low-code autoML-augmented data pipeline contains changeable building blocks such as libraries and visualisations.}, langid = {english}, note = {TL;DR \par 34 autoML frameworks have been reviewed based on the latest commits and augmentation properties of their GitHub content and the PyCaret framework was the result of the review due to requirements concerning adaptability by Google Colaboratory and the BI (business intelligence) tool.} } @article{galassoCodeSophisticationCode2022, ids = {galassoCodeSophisticationCode2022a}, title = {Code {{Sophistication}}: {{From Code Recommendation}} to {{Logic Recommendation}}}, shorttitle = {Code {{Sophistication}}}, author = {Galasso, Jessie and Famelis, Michalis and Sahraoui, Houari}, date = {2022-01-19}, journaltitle = {ArXiv220107674 Cs}, eprint = {2201.07674}, eprinttype = {arXiv}, eprintclass = {cs}, url = {http://arxiv.org/abs/2201.07674}, urldate = {2022-01-25}, abstract = {A typical approach to programming is to first code the main execution scenario, and then focus on filling out alternative behaviors and corner cases. But, almost always, there exist unusual conditions that trigger atypical behaviors, which are hard to predict in program specifications, and are thus often not coded. In this paper, we consider the problem of detecting and recommending such missing behaviors, a task that we call code sophistication. Previous research on coding assistants usually focuses on recommending code fragments based on specifications of the intended behavior. In contrast, code sophistication happens in the absence of a specification, aiming to help developers complete the logic of their programs with missing and unspecified behaviors. We outline the research challenges to this problem and present early results showing how program logic can be completed by leveraging code structure and information about the usage of input parameters.}, langid = {english}, keywords = {GOAL\_Model-Assistance,TECHNIQUE\_GraphConvolutionalNetworks} } @article{gallardoModelingCollaborationProtocols2013, title = {Modeling Collaboration Protocols for Collaborative Modeling Tools: {{Experiences}} and Applications}, author = {Gallardo, Jesús and Bravo, Crescencio and Redondo, Miguel A. and family=Lara, given=Juan, prefix=de, useprefix=true}, date = {2013}, journaltitle = {J. Vis. Lang. Comput.}, volume = {24}, number = {1}, pages = {10--23}, doi = {10.1016/j.jvlc.2012.10.006} } @article{GammaitoniK19, title = {F-Alloy: A Relational Model Transformation Language Based on Alloy}, author = {Gammaitoni, Loïc and Kelsen, Pierre}, date = {2019}, journaltitle = {Software Syst. Model.}, volume = {18}, number = {1}, pages = {213--247}, doi = {10.1007/S10270-017-0630-9}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Fri, 18 Sep 2020 11:19:23 +0200} } @article{ganserStagedModelEvolution2015, title = {Staged Model Evolution and Proactive Quality Guidance for Model Libraries}, author = {Ganser, Andreas and Lichter, Horst and Roth, Alexander and Rumpe, Bernhard}, date = {2015-11-25}, journaltitle = {Softw. Qual. J.}, issn = {0963-9314, 1573-1367}, doi = {10.1007/s11219-015-9298-y}, langid = {english}, note = {TL;DR \par This study proposes an approach for model evolution in UML model libraries that differs from general model evolution, since it is aimless and triggered by new external requirements.} } @article{Gao20182627, title = {{{ComNet}}: {{Combination}} of Deep Learning and Expert Knowledge in {{OFDM}} Receivers}, author = {Gao, X. and Jin, S. and Wen, C.-K. and Li, G.Y.}, date = {2018}, journaltitle = {IEEE Commun. Lett.}, volume = {22}, number = {12}, pages = {2627--2630}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {10897798}, doi = {10.1109/LCOMM.2018.2877965}, abstract = {In this letter, we propose a model-driven deep learning (DL) approach that combines DL with the expert knowledge to replace the existing orthogonal frequency-division multiplexing receiver in wireless communications. Different from the data-driven fully connected deep neural network (FC-DNN) method, we adopt the block-by-block signal processing method that divides the receiver into channel estimation subnet and signal detection subnet. Each subnet is constructed by a DNN and uses the existing simple and traditional solution as initialization. The proposed model-driven DL receiver offers more accurate channel estimation comparing with the linear minimum mean-squared error method and exhibits higher data recovery accuracy comparing with the existing methods and FC-DNN. Simulation results further demonstrate the robustness of the proposed approach in terms of signal-to-noise ratio and its superiority to the FC-DNN approach in the computational complexities or the memory usage. © 1997-2012 IEEE.}, art_number = {8509622}, coden = {ICLEF}, document_type = {Article}, source = {Scopus}, note = {cited By 113 \par TL;DR \par The proposed model-driven DL receiver offers more accurate channel estimation compared with the linear minimum mean-squared error method and exhibits higher data recovery accuracy comparing with the existing methods and FC-DNN.} } @article{GAO2021102556, title = {{{API}} Recommendation for the Development of {{Android App}} Features Based on the Knowledge Mined from {{App}} Stores}, author = {Gao, Shanquan and Liu, Lei and Liu, Yuzhou and Liu, Huaxiao and Wang, Yihui}, date = {2021}, journaltitle = {Sci. Comput. Program.}, volume = {202}, pages = {102556}, issn = {0167-6423}, doi = {10.1016/j.scico.2020.102556}, abstract = {To improve the efficiency, developers tend to use APIs to avoid reinventing wheels in the development of Apps. However, there are thousands of APIs for various purposes, so it is difficult for developers to identify suitable APIs according to the functionalities to be realized. App stores manage millions of products, which embody the experience and wisdom of developers, and they provide valuable data resource for solving this problem. By summarizing the API usage for the same or similar functionalities in Apps, reusable knowledge can be mined for the API recommendation. In this paper, we utilize the data resource in App stores and provide an API recommendation method for the development of Android Apps. Firstly, by using UI elements as the bridge, we establish mapping relationships between functionalities and APIs. Secondly, we build a framework to describe functionalities of Apps in the same category, and utilize relationships between functionalities and APIs to construct the API knowledge for each node in the framework. Thirdly, we identify nodes according to queried features and show the API knowledge to developers by giving recommendation lists. We conducted experiments based on Google Play, and the result shows that our method has a good recommendation performance.}, keywords = {API recommendation,App store mining,Feature extraction,Reusable knowledge,UI analysis} } @article{Gao2021767, title = {A Pairwise Attentive Adversarial Spatiotemporal Network for Cross-Domain Few-Shot Action Recognition-R2}, author = {Gao, Z. and Guo, L. and Guan, W. and Liu, A.-A. and Ren, T. and Chen, S.}, date = {2021}, journaltitle = {IEEE Trans. Image Process.}, volume = {30}, pages = {767--782}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {10577149}, doi = {10.1109/TIP.2020.3038372}, abstract = {Abstract - Action recognition is a popular research topic in the computer vision and machine learning domains. Although many action recognition methods have been proposed, only a few researchers have focused on cross-domain few-shot action recognition, which must often be performed in real security surveillance. Since the problems of action recognition, domain adaptation, and few-shot learning need to be simultaneously solved, the cross-domain few-shot action recognition task is a challenging problem. To solve these issues, in this work, we develop a novel end-to-end pairwise attentive adversarial spatiotemporal network (PASTN) to perform the cross-domain few-shot action recognition task, in which spatiotemporal information acquisition, few-shot learning, and video domain adaptation are realised in a unified framework. Specifically, the Resnet-50 network is selected as the backbone of the PASTN, and a 3D convolution block is embedded in the top layer of the 2D CNN (ResNet-50) to capture the spatiotemporal representations. Moreover, a novel attentive adversarial network architecture is designed to align the spatiotemporal dynamics actions with higher domain discrepancies. In addition, the pairwise margin discrimination loss is designed for the pairwise network architecture to improve the discrimination of the learned domain-invariant spatiotemporal feature. The results of extensive experiments performed on three public benchmarks of the cross-domain action recognition datasets, including SDAI Action I, SDAI Action II and UCF50-OlympicSport, demonstrate that the proposed PASTN can significantly outperform the state-of-the-art cross-domain action recognition methods in terms of both the accuracy and computational time. Even when only two labelled training samples per category are considered in the office1 scenario of the SDAI Action I dataset, the accuracy of the PASTN is improved by 6.1\%, 10.9\%, 16.8\%, and 14\% compared to that of the TA³N , TemporalPooling, I3D, and P3D methods, respectively. © 1992-2012 IEEE.}, art_number = {9268986}, author_keywords = {action recognition; attentive adversarial network; Cross-domain learning; few-shot; pairwise margin discrimination loss; TR3D}, coden = {IIPRE}, document_type = {Article}, source = {Scopus}, keywords = {article,artificial intelligence,Artificial intelligence,Australia,case report,China,clinical article,computer vision,Computer vision,convolutional neural network,education,Electrical and information engineerings,human,human experiment,information technology,learning,male,Ministry of Education,National natural science foundation of chinas,natural science,Network architecture,Security surveillance,software,Software engineering,Spatio temporal features,Spatio-temporal dynamics,Spatiotemporal information,Spatiotemporal networks,Supercomputers,videorecording}, note = {cited By 37 \par TL;DR \par The results of extensive experiments performed on three public benchmarks of the cross-domain action recognition datasets demonstrate that the proposed PASTN can significantly outperform the state-of-the-art cross- domain action recognition methods in terms of both the accuracy and computational time.} } @article{gaoCollaborativeFilteringRecommendation2019, title = {Collaborative {{Filtering Recommendation Algorithm}} for {{Heterogeneous Data Mining}} in the {{Internet}} of {{Things}}}, author = {Gao, Ying and Ran, Lingxi}, date = {2019}, journaltitle = {IEEE Access}, volume = {7}, pages = {123583--123591}, issn = {2169-3536}, doi = {10.1109/ACCESS.2019.2935224}, abstract = {With the popularization of Internet of Things (IOT) technology, a large number of multi-source heterogeneous data are constantly generated and collected by cloud platforms, which indicates that the problem of large data in IOT has become increasingly prominent, especially for massive tags and information in IOT which is urgent to use appropriate data mining algorithms to mine the value of these data. A collaborative filtering recommendation algorithm based on multi-information source fusion (CFR-MIF) is proposed where a feature vector and time weight function are introduced to improve the accuracy of top-N recommendation. It can conveniently and effectively process the IoT data, and furthermore integrate, manage and store the massive data collected from different industries and data formats. Besides, It also provides data mining services in the whole IoT realizing prediction and decision-making, which reverses control these sensor networks, so as to control the movement and development process of objective in the Internet of Things. The experimental results based on DeviceLens 1M data set show that the proposed algorithm greatly improves the accuracy of recommendation results, recall rate and F1 value compared with other advanced algorithms.}, langid = {english}, note = {TL;DR \par A collaborative filtering recommendation algorithm based on multi-information source fusion (CFR-MIF) is proposed where a feature vector and time weight function are introduced to improve the accuracy of top-N recommendation.} } @article{gaoCollaborativeLearningBasedIndustrial2020, title = {Collaborative {{Learning-Based Industrial IoT API Recommendation}} for {{Software-Defined Devices}}: {{The Implicit Knowledge Discovery Perspective}}}, shorttitle = {Collaborative {{Learning-Based Industrial IoT API Recommendation}} for {{Software-Defined Devices}}}, author = {Gao, Honghao and Qin, Xi and Barroso, Ramon J. Duran and Hussain, Walayat and Xu, Yueshen and Yin, Yuyu}, date = {2020}, journaltitle = {IEEE Trans. Emerg. Top. Comput. Intell.}, pages = {1--11}, issn = {2471-285X}, doi = {10.1109/TETCI.2020.3023155}, abstract = {The industrial Internet of things (IIoT), a new computing mode in Industry 4.0, is deployed to connect IoT devices and use communication technology to respond to control commands and handle industrial data. IIoT is typically employed to improve the efficiency of computing and sensing and can be used in many scenarios, such as intelligent manufacturing and video surveillance. To build an IIoT system, we need a collection of software to manage and monitor each system component when there are large-scale devices. Application programming interface (API) is an effective way to invoke public services provided by different platforms. Developers can invoke different APIs to operate IoT devices without knowing the implementation process. We can design a workflow to configure how and when to invoke target APIs. Thus, APIs are a powerful tool for rapidly developing industrial systems. However, the increasing number of APIs exacerbates the problem of finding suitable APIs. Current related recommendation methods have defects. For example, most existing methods focus on the relation between users and APIs but neglect the valuable relations among the users or APIs themselves. To address these problems, this article studies implicit knowledge in IIoT by using collaborative learning techniques. Considering the increased dimensions and dynamics of IoT devices, we explore the possible relationships between users and between APIs. We enhance the matrix factorization (MF) model with the mined implicit knowledge that are implicit relationships on both sides. We build an ensemble model by using all implicit knowledge. We conduct experiments on a collected real-world dataset and simulate industrial system scenarios. The experimental results verify the effectiveness and superiority of the proposed models.}, langid = {english} } @inproceedings{garavelPreface2022, ids = {garavelPreface2022a,garavelPreface2022b,garavelPreface2022c}, title = {Preface}, booktitle = {{{CEUR Workshop Proc}}.}, author = {Garavel, H. and family=Lara, given=J., prefix=de, useprefix=true and Molina, P.J. and Paige, R. and family=Ruscio, given=D., prefix=di, useprefix=true and Wimmer, M. and Barmpis, K. and Boronat, A. and Boubeta-Puig, J. and Bousse, E. and Le Calvar, T. and García-Domínguez, A. and Hinkel, G. and Horvath, A. and Rensink, A. and Cuadrado, J.S. and Varró, G. and Wei, R.}, editor = {Boronat A., Garcia-Dominguez A., Hinkel G.}, date = {2022}, volume = {3089}, publisher = {CEUR-WS}, issn = {16130073}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125368424&partnerID=40&md5=9c5143fc3bae825a245d87831a19c12d}, langid = {english}, note = {cited By 0 \par cited By 0 \par cited By 0 \par cited By 0} } @article{garcesEndtoendFinegrainedTraceability, title = {End-to-End {{Fine-grained Traceability Analysis}} in {{Model Transformations}} and {{Transformation Chains}}}, author = {Garces, Victor Guana}, pages = {155}, langid = {english} } @article{garcia_lightweight_2018, title = {Lightweight, {{Obfuscation-Resilient Detection}} and {{Family Identification}} of {{Android Malware}}}, author = {Garcia, Joshua and Hammad, Mahmoud and Malek, Sam}, date = {2018-01}, journaltitle = {ACM Trans. Softw. Eng. Methodol.}, volume = {26}, number = {3}, pages = {11:1--11:29}, issn = {1049-331X}, doi = {10.1145/3162625}, abstract = {The number of malicious Android apps is increasing rapidly. Android malware can damage or alter other files or settings, install additional applications, and so on. To determine such behaviors, a security analyst can significantly benefit from identifying the family to which an Android malware belongs rather than only detecting if an app is malicious. Techniques for detecting Android malware, and determining their families, lack the ability to handle certain obfuscations that aim to thwart detection. Moreover, some prior techniques face scalability issues, preventing them from detecting malware in a timely manner. To address these challenges, we present a novel machine-learning-based Android malware detection and family identification approach, RevealDroid, that operates without the need to perform complex program analyses or to extract large sets of features. Specifically, our selected features leverage categorized Android API usage, reflection-based features, and features from native binaries of apps. We assess RevealDroid for accuracy, efficiency, and obfuscation resilience using a large dataset consisting of more than 54,000 malicious and benign apps. Our experiments show that RevealDroid achieves an accuracy of 98\% in detection of malware and an accuracy of 95\% in determination of their families. We further demonstrate RevealDroid’s superiority against state-of-the-art approaches.}, keywords = {Android malware,lightweight,machine learning,native code,obfuscation,reflection} } @article{garcia-dominguezStresstestingRemoteModel2017, title = {Stress-Testing Remote Model Querying {{APIs}} for Relational and Graph-Based Stores}, author = {Garcia-Dominguez, Antonio and Barmpis, Konstantinos and Kolovos, Dimitrios S. and Wei, Ran and Paige, Richard F.}, date = {2017-06-30}, journaltitle = {Softw Syst Model}, pages = {1--29}, issn = {1619-1366, 1619-1374}, doi = {10.1007/s10270-017-0606-9}, abstract = {Recent research in scalable model-driven engineering now allows very large models to be stored and queried. Due to their size, rather than transferring such models over the network in their entirety, it is typically more efficient to access them remotely using networked services (e.g. model repositories, model indexes). Little attention has been paid so far to the nature of these services, and whether they remain responsive with an increasing number of concurrent clients. This paper extends a previous empirical study on the impact of certain key decisions on the scalability of concurrent model queries on two domains, using an Eclipse Connected Data Objects model repository, four configurations of the Hawk model index and a Neo4j-based configuration of the NeoEMF model store. The study evaluates the impact of the network protocol, the API design, the caching layer, the query language and the type of database and analyses the reasons for their varying levels of performance. The design of the API was shown to make a bigger difference compared to the network protocol (HTTP/TCP) used. Where available, the query-specific indexed and derived attributes in Hawk outperformed the comprehensive generic caching in CDO. Finally, the results illustrate the still ongoing evolution of graph databases: two tools using different versions of the same backend had very different performance, with one slower than CDO and the other faster than it.}, langid = {english} } @article{garciaModelDrivenAutomatedTesting, title = {Model-{{Driven Automated Testing}} of {{Ethical Requirements}} in {{Generative AI}}}, author = {García, Sergio Morales}, langid = {english}, keywords = {LOGSEQ}, note = {\section{Annotazioni\\ (14/6/2024, 18:27:49)} \par - “Ethical Requirements in Generative AI” (García, p. 1) \#ffd400\\ \mkbibemph{The research is very relevant and I encourage the student to engage in this very interesting and timely topic. One general comment that I have, I recommend to distinguish the concepts bias, fairness, and ethics distinguished. I'm recommending this because in the proposal sometimes such concepts are used interchangeably. Ethics is a very general concept and not all its constituting elements can be automated in my opinion. } \par - “Generative artificial intelligence (GenAI) systems, and specifically large language models (LLMs), are artificial intelligence components capable of generating new content, such as text, images, music, or other forms of data, based on patterns learned from observed data” (García, p. 4) \#5fb236\\ \mkbibemph{ } \par - “However, due to their nature, Generative AI systems and their underlying LLMs can face significant challenges related to fairness and balanced representation in the generated responses” (García, p. 4) \#5fb236\\ \mkbibemph{ } \par - “Generative AI models learn from large datasets, which may contain biases that the model may replicate or amplify in its output [4, 7, 21, 40, 41, 47].” (García, p. 4) \#a28ae5\\ \mkbibemph{ } \par - “Addressing biased responses in generative AI is crucial to prevent the generation of unwanted discriminatory and/or harmful content.” (García, p. 4) \#a28ae5\\ \mkbibemph{ } \par - “To this end, it is necessary to implement techniques to detect and mitigate biases in a model’s responses” (García, p. 4) \#a28ae5\\ \mkbibemph{ } \par - “generative AI models are probabilistic algorithms for identifying patterns and generating new similar data” (García, p. 4) \#5fb236\\ \mkbibemph{ } \par - “integrate a systematic ethical assessment within the development lifecycle of LLM-based applications,” (García, p. 5) \#a28ae5\\ \mkbibemph{ } \par - “from its creation to the continuous monitoring after deployment” (García, p. 5) \#a28ae5\\ \mkbibemph{ } \par - “judgements or allusions that are detrimental to a collective or an individual, selecting the best one for the most pressing ethical requirements in the project” (García, p. 5) \#a28ae5\\ \mkbibemph{ } \par - “understand the state of the art of the assessment of ethical requirements in the field of AI ethics, with a special focus on bias detection in generative AI text-to-text models.” (García, p. 5) \#a28ae5\\ \mkbibemph{ } \par - “establish their ethical requirements model and evaluate the corresponding lack of bias in candidate textto-text models” (García, p. 5) \#a28ae5\\ \mkbibemph{ } \par - “defining ethical requirements that automatically generates and executes a series of test cases in order to assess whether a target generative AI model satisfies such requirements, and provides informative and actionable feedback to stakeholder” (García, p. 5) \#a28ae5\\ \mkbibemph{ } \par - “Design and Creation [38].” (García, p. 5) \#a28ae5\\ \mkbibemph{ } \par - “model ethical principles aimed at integrating them into the software development proces” (García, p. 9) \#a28ae5\\ \mkbibemph{ } \par - “facilitate describing ethical requirements, but to provide a systematic method to automatically and efficiently assess them on concrete LLMs, driven by the user’s priorities and most pressing concerns.” (García, p. 9) \#a28ae5\\ \mkbibemph{ } \par - “Likewise, Dhamala et al. [14] and Alnegheimish et al. [1] use partial sentences to evaluate the inclusion of gendered pronouns and the generation of text, which may leads to undesired skewed representations.” (García, p. 9) \#5fb236\\ \mkbibemph{ } \par - “We will contribute broader coverage of biases and a more versatile detection method in addition to sentence completion.” (García, p. 9) \#ffd400\\ \mkbibemph{Which one? Can you list or prioritize some of them? The topic is very broad and it is necessary to start with some concrete case to generalize afterward. } \par - “we want to include further strategies that may reveal the existence of otherwise hidden bias, starting from a model-friven and platform-independent approach.” (García, p. 10) \#a28ae5\\ \mkbibemph{ } \par - “Our framework should be able to allow users to specify how an ethical requirement has to be assessed.” (García, p. 10) \#e56eee\\ \mkbibemph{ } \par - “continuous ethical assessment throughout the whole development process” (García, p. 10) \#e56eee\\ \mkbibemph{ } \par - “ethical requirements on a tailored set of particular ethical concerns at the modeling level” (García, p. 10) \#a28ae5\\ \mkbibemph{ } \par - “traceability mechanism to relate the testing results with the initial ethical requirements, in order to assess the fulfillment of the latter by the AI-based software system.” (García, p. 10) \#ffd400\\ \mkbibemph{How is it possible to that when using blackbox LLMs? } \par - “ethical requirements in the field of AI ethic” (García, p. 11) \#a28ae5\\ \mkbibemph{ } \par - “gaps between the ethical principles and contribute a mechanism for their operationalization within the software development industry.” (García, p. 11) \#a28ae5\\ \mkbibemph{ } \par - “Facilitate a systematic ethical assessment of AI-based software” (García, p. 11) \#a28ae5\\ \mkbibemph{ } \par - “elaborate an ethical requirements model that describes the individual ethical concerns that must be addressed for their specific context by themselves.” (García, p. 11) \#a28ae5\\ \mkbibemph{ } \par - “define the test scenarios that are the best fit for their needs” (García, p. 11) \#a28ae5\\ \mkbibemph{ } \par - “end-to-end traceability between the ethical requirements model and the testing results” (García, p. 12) \#ffd400\\ \mkbibemph{The feasability of this is not clear. } \par - “Ethical requirements model and test scenario definition via DSL” (García, p. 15) \#ffd400\\ \mkbibemph{ } \par - “Researching information systems and computing” (García, p. 19) \#a28ae5\\ \mkbibemph{ }} } @article{gargMUDABlueAutomaticCategorization2004, title = {{{MUDABlue}}: {{An}} Automatic Categorization System for Open Source Repositories}, author = {Garg, Pankaj K. and Kawaguchi, Shinji and Matsushita, Makoto and Inoue, Katsuro}, date = {2004}, journaltitle = {2013 20th Asia-Pac. Softw. Eng. Conf. APSEC}, pages = {184--193}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, issn = {1530-1362}, nodoi = {doi.ieeecomputersociety.org/10.1109/APSEC.2004.69} } @inproceedings{Garitselov2012316, title = {Fast-Accurate Non-Polynomial Metamodeling for Nano-{{CMOS PLL}} Design Optimization}, author = {Garitselov, O. and Mohanty, S.P. and Kougianos, E.}, date = {2012}, series = {Proceedings of the {{IEEE International Conference}} on {{VLSI Design}}}, pages = {316--321}, issn = {10639667}, doi = {10.1109/VLSID.2012.90}, abbrev_source_title = {Proc IEEE Int Conf VLSI Des}, affiliation = {Nano-Systems Design Laboratory, University of North Texas, Denton, TX, United States; Department of Computer Science and Engineering, University of North Texas, Denton, TX, United States; Department of Engineering Technology, University of North Texas, Denton, TX, United States}, art_number = {6167771}, coden = {PIVDE}, correspondence_address1 = {Garitselov, O.; Nano-Systems Design Laboratory, , Denton, TX, United States; email: omg0006@unt.edu}, document_type = {Conference Paper}, isbn = {978-0-7695-4638-4}, langid = {english}, source = {Scopus}, note = {cited By 12 \par TL;DR \par This paper presents non-polynomial metamodels (surrogate models) using neural networks to reduce the design optimization time of complex nano-CMOS circuit with no sacrifice on accuracy.} } @inproceedings{Garitselov2012580, title = {Metamodel-Assisted Ultra-Fast Memetic Optimization of a {{PLL}} for {{WiMax}} and {{MMDS}} Applications}, author = {Garitselov, O. and Mohanty, S.P. and Kougianos, E. and Okobiah, O.}, date = {2012}, series = {Proceedings - {{International Symposium}} on {{Quality Electronic Design}}, {{ISQED}}}, pages = {580--585}, issn = {19483287}, doi = {10.1109/ISQED.2012.6187552}, abbrev_source_title = {Proc. - Int. Symp. Qual. Electron. Des., ISQED}, affiliation = {NanoSystems Design Laboratory, University of North Texas, Denton, TX, United States; Department of Computer Science and Engineering, University of North Texas, Denton, TX, United States; Department of Engineering Technology, University of North Texas, Denton, TX, United States}, art_number = {6187552}, correspondence_address1 = {Garitselov, O.; NanoSystems Design Laboratory, , Denton, TX, United States; email: omg0006@unt.edu}, document_type = {Conference Paper}, isbn = {978-1-4673-1036-9}, langid = {english}, source = {Scopus}, note = {cited By 0 \par TL;DR \par A novel ultra-fast design flow that uses memetic-based optimization algorithms over neural-network based non-polynomial metamodels for design-space exploration and achieves optimal design to two different wireless specifications, WiMax and MMDS is proposed.} } @inproceedings{Garitselov2014221, title = {Metamodel-Assisted Disciplining Algorithm for Detecting Spoofed {{GNSS}} Time Signals}, author = {Garitselov, O. and Sohn, D.}, date = {2014}, series = {Proceedings of the {{Annual Precise Time}} and {{Time Interval Systems}} and {{Applications Meeting}}, {{PTTI}}}, volume = {2014-January}, pages = {221--227}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {23332085}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84943552646&partnerID=40&md5=25e8f3479adee4ec76849112cfa3446f}, abbrev_source_title = {Proc. Annu. Precis Time Time Interval Syst. Appl. Meet., PTTI}, affiliation = {Spectracom, Rochester, NY, United States}, document_type = {Conference Paper}, isbn = {978-1-63439-794-0}, langid = {english}, source = {Scopus}, note = {cited By 1 \par TL;DR \par A GNSS-receiver-independent solution that is added to the disciplining algorithm of a Global Positioning System Disciplined Oscillator (GPSDO) System in order to detect and filter abnormalities without introduction of any additional hardware to the timing system.} } @article{garousiGuidelinesIncludingGrey2019, ids = {garousiGuidelinesIncludingGrey2019a}, title = {Guidelines for Including Grey Literature and Conducting Multivocal Literature Reviews in Software Engineering}, author = {Garousi, Vahid and Felderer, Michael and Mäntylä, Mika V.}, date = {2019-02}, journaltitle = {Information and Software Technology}, volume = {106}, pages = {101--121}, issn = {09505849}, doi = {10.1016/j.infsof.2018.09.006}, langid = {english} } @article{GASPARIC2017236, title = {A Graphical User Interface for Presenting Integrated Development Environment Command Recommendations: {{Design}}, Evaluation, and Implementation}, author = {Gasparic, Marko and Janes, Andrea and Ricci, Francesco and Murphy, Gail C. and Gurbanov, Tural}, date = {2017}, journaltitle = {Inf. Softw. Technol.}, volume = {92}, pages = {236--255}, issn = {0950-5849}, doi = {10.1016/j.infsof.2017.08.006}, abstract = {Context A set of algorithms exist to generate integrated development environment (IDE) command recommendations. The recommendations are aimed at improving software developer’s interaction with an IDE. Even though the interface is a critical element of every recommender system, we are not aware of any existing graphical user interface to present such recommendations. Objective This paper describes and evaluates a novel design of a graphical user interface to recommend commands within an IDE. The interface contains a description of the suggested command, an explanation of why the command is recommended, and a command usage example. Method The proposed design is based on the analysis of guidelines identified in the literature. Its acceptance and usability were evaluated through a user study with 36 software developers and semi-structured interviews with 11 software developers. Results The results indicate that the suggested interface is well accepted, but it can be further improved. Through the interviews and the implementation of the interface, we identified a series of requirements important for the development of future IDE command recommender systems. Conclusions This paper shows that a convenient graphical user interface is critical to achieve high acceptance of IDE command recommendations. Our work also illustrates steps useful for undertaking user studies related to IDE command recommendations in a practical setting without human intervention. A future step is to evaluate the interface within the business environment, where recommendations are generated and presented in an IDE used by practicing software developers as part of their normal workday.}, keywords = {Command,Functionality,Integrated development environment,Recommender system,Software development,User interface} } @article{gasparicWhatRecommendationSystems2016, title = {What Recommendation Systems for Software Engineering Recommend}, author = {Gasparic, Marko and Janes, Andrea}, date = {2016-03}, journaltitle = {J. Syst. Softw.}, volume = {113}, number = {C}, pages = {101--113}, publisher = {Elsevier Science Inc.}, address = {New York, NY, USA}, issn = {0164-1212}, url = {http://dx.doi.org/10.1016/j.jss.2015.11.036}, acmid = {2896211}, issue_date = {March 2016}, nodoi = {10.1016/j.jss.2015.11.036}, numpages = {13}, keywords = {recommendation systems,Systematic literature review} } @article{gasparicWhatRecommendationSystems2016a, title = {What Recommendation Systems for Software Engineering Recommend: {{A}} Systematic Literature Review}, shorttitle = {What Recommendation Systems for Software Engineering Recommend}, author = {Gasparic, Marko and Janes, Andrea}, date = {2016-03}, journaltitle = {J. Syst. Softw.}, volume = {113}, pages = {101--113}, issn = {01641212}, url = {https://linkinghub.elsevier.com/retrieve/pii/S0164121215002605}, urldate = {2019-06-13}, abstract = {A recommendation system for software engineering (RSSE) is a software application that provides information items estimated to be valuable for a software engineering task in a given context. Present the results of a systematic literature review to reveal the typical functionality offered by existing RSSEs, research gaps, and possible research directions. We evaluated 46 papers studying the benefits, the data requirements, the information and recommendation types, and the effort requirements of RSSE systems. We include papers describing tools that support source code related development published between 2003 and 2013. The results show that RSSEs typically visualize source code artifacts. They aim to improve system quality, make the development process more efficient and less expensive, lower developer’s cognitive load, and help developers to make better decisions. They mainly support reuse actions and debugging, implementation, and maintenance phases. The majority of the systems are reactive. Unexploited opportunities lie in the development of recommender systems outside the source code domain. Furthermore, current RSSE systems use very limited context information and rely on simple models. Context-adapted and proactive behavior could improve the acceptance of RSSE systems in practice.}, langid = {english}, nodoi = {10.1016/j.jss.2015.11.036} } @inproceedings{Ge:2010_catalog_coverage, title = {Beyond Accuracy: {{Evaluating}} Recommender Systems by Coverage and Serendipity}, booktitle = {Proc. {{Fourth ACM Conf}}. {{Recomm}}. {{Syst}}.}, author = {Ge, Mouzhi and Delgado-Battenfeld, Carla and Jannach, Dietmar}, date = {2010}, series = {{{RecSys}} '10}, pages = {257--260}, publisher = {ACM}, location = {New York, NY, USA}, url = {http://doi.acm.org/10.1145/1864708.1864761}, acmid = {1864761}, isbn = {978-1-60558-906-0}, nodoi = {10.1145/1864708.1864761}, numpages = {4}, pagetotal = {4}, keywords = {coverage,evaluation metric,recommender system,serendipity}, note = {TL;DR \par It is argued that the new ways of measuring coverage and serendipity reflect the quality impression perceived by the user in a better way than previous metrics thus leading to enhanced user satisfaction.} } @article{geDataMiningAnalytics2017, title = {Data {{Mining}} and {{Analytics}} in the {{Process Industry}}: {{The Role}} of {{Machine Learning}}}, shorttitle = {Data {{Mining}} and {{Analytics}} in the {{Process Industry}}}, author = {Ge, Zhiqiang and Song, Zhihuan and Ding, Steven X. and Huang, Biao}, date = {2017}, journaltitle = {IEEE Access}, volume = {5}, pages = {20590--20616}, issn = {2169-3536}, doi = {10.1109/ACCESS.2017.2756872}, abstract = {Data mining and analytics have played an important role in knowledge discovery and decision making/supports in the process industry over the past several decades. As a computational engine to data mining and analytics, machine learning serves as basic tools for information extraction, data pattern recognition and predictions. From the perspective of machine learning, this paper provides a review on existing data mining and analytics applications in the process industry over the past several decades. The state-of-the-art of data mining and analytics are reviewed through eight unsupervised learning and ten supervised learning algorithms, as well as the application status of semi-supervised learning algorithms. Several perspectives are highlighted and discussed for future researches on data mining and analytics in the process industry.}, langid = {english}, note = {\textbf{Blue Annotations (11/2/2022, 09:06:52)} \par "Data mining and analytics have played an important role in knowledge discovery and decision making/supports in the process industry over the past several decades" (\href{zotero://open-pdf/library/items/8DHPJGXH?page=1}{Ge et al 2017:20590}) \par "information extraction, data pattern recognition and predictions" (\href{zotero://open-pdf/library/items/8DHPJGXH?page=1}{Ge et al 2017:20590}) \par "existing data mining and analytics applications" (\href{zotero://open-pdf/library/items/8DHPJGXH?page=1}{Ge et al 2017:20590}) \par "future researches on data mining and analytics in the process industry" (\href{zotero://open-pdf/library/items/8DHPJGXH?page=1}{Ge et al 2017:20590}) \par "European Union proposed 20-20-20 goals to achieve a sustainable future, which means 20\% increase in energy efciency, 20\% reduction of CO2 emissions, and 20\% renewables by 2020" (\href{zotero://open-pdf/library/items/8DHPJGXH?page=1}{Ge et al 2017:20590}) \par "intelligence into industrial manufacturing process" (\href{zotero://open-pdf/library/items/8DHPJGXH?page=1}{Ge et al 2017:20590}) \par "develop smart factories for producing smart products" (\href{zotero://open-pdf/library/items/8DHPJGXH?page=1}{Ge et al 2017:20590}) \par "data mining and analytics may serve as a basic tool to promote the process industry from machine automation to information automation and then to knowledge automation" (\href{zotero://open-pdf/library/items/8DHPJGXH?page=1}{Ge et al 2017:20590}) \par "data-driven process modeling, monitoring, prediction and control have received much attention in recent years" (\href{zotero://open-pdf/library/items/8DHPJGXH?page=1}{Ge et al 2017:20590}) \par "data have rarely been used for detailed analyses, which are instead only used for routinely technical checks and process log fulllments" (\href{zotero://open-pdf/library/items/8DHPJGXH?page=1}{Ge et al 2017:20590}) \par "analyzing the patterns of process data and relationships among variables" (\href{zotero://open-pdf/library/items/8DHPJGXH?page=1}{Ge et al 2017:20590}) \par "the main aim of data mining and data analyses is to extract useful information from process data, and transfer it to effective knowledge for improvement of understanding and decision supports of the process." (\href{zotero://open-pdf/library/items/8DHPJGXH?page=2}{Ge et al 2017:20591}) \par "four different types of machine learning algorithms" (\href{zotero://open-pdf/library/items/8DHPJGXH?page=2}{Ge et al 2017:20591}) \par "the study of pattern recognition and computational learning theory in arti- cial intelligence" (\href{zotero://open-pdf/library/items/8DHPJGXH?page=2}{Ge et al 2017:20591}) \par "theory of probability and statistics also plays a very important role in modern machine learning" (\href{zotero://open-pdf/library/items/8DHPJGXH?page=2}{Ge et al 2017:20591}) \par "Field of study that gives computers the ability to learn without being explicitly programmed" (\href{zotero://open-pdf/library/items/8DHPJGXH?page=2}{Ge et al 2017:20591}) \par "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E" (\href{zotero://open-pdf/library/items/8DHPJGXH?page=2}{Ge et al 2017:20591}) \par "there exists no such a report that details data mining and analytics from the viewpoint of machine learning, although the terminology has been mentioned many times in various papers and books." (\href{zotero://open-pdf/library/items/8DHPJGXH?page=2}{Ge et al 2017:20591}) \par "section IV, some new perspectives on the topic of machine learning applications in the process industry are presented" (\href{zotero://open-pdf/library/items/8DHPJGXH?page=2}{Ge et al 2017:20591})} } @online{GeneralizedAutomaticClustering, title = {A Generalized Automatic Clustering Algorithm in a Multiobjective Framework}, url = {http://www.sciencedirect.com/science/article/pii/S1568494612003493}, urldate = {2015-05-07} } @online{GenerativeaiFunctioncallingGeminiapi, title = {\#generativeai \#functioncalling \#geminiapi \#artificialintelligence | {{Generative AI}} | 39 Commenti}, url = {https://www.linkedin.com/posts/genai-works_generativeai-functioncalling-geminiapi-activity-7314899210942734337-U4G5}, urldate = {2025-04-07}, abstract = {🚀 Google Gemini’s Game-Changer: AI That Calls APIs \& Runs Code! Generative AI just got more powerful. With function calling, models don’t just generate text—they take real-world actions. What makes function calling a game changer? ➡️Augment Knowledge: Pull data from APIs, databases, and external sources. ➡️Extend Capabilities: Perform advanced tasks like calculations, chart creation, and automation. ➡️Take Action: Schedule meetings, send emails, control smart devices, and more. How it works in practice: ➡️You define function declarations with parameters (e.g., schedule\_meeting). ➡️The AI recognizes when to call a function and outputs structured arguments. ➡️Your application processes and executes the function, returning the result. ➡️The AI generates a final, user-friendly response incorporating real-world data. With parallel and compositional function calling, models can now handle multiple functions in one request, allowing for more sophisticated automation and multi-step workflows. How do you see function calling transforming AI-powered applications? Let’s discuss in the comments! ⬇️ \#generativeai \#functioncalling \#geminiapi \#artificialintelligence | 39 commenti su LinkedIn} } @article{generoBuildingMeasurebasedPrediction2007, title = {Building Measure-Based Prediction Models for {{UML}} Class Diagram Maintainability}, author = {Genero, Marcela and Manso, Esperanza and Visaggio, Aaron and Canfora, Gerardo and Piattini, Mario}, date = {2007}, journaltitle = {Empir. Softw. Eng.}, volume = {12}, number = {5}, pages = {517--549}, doi = {10.1007/s10664-007-9038-4}, note = {TL;DR \par Results obtained from a controlled experiment and a replica support the idea that useful prediction models for class diagrams understandability and modifiability can be built on the basis of early measures, in particular, measures that capture structural complexity through associations and generalizations.} } @article{generoSurveyMetricsUML2005, title = {A {{Survey}} of {{Metrics}} for {{UML Class Diagrams}}.}, author = {Genero, Marcela and Piattini, Mario and Calero, Coral}, date = {2005}, journaltitle = {J. Object Technol.}, volume = {4}, number = {9}, pages = {59}, doi = {10.5381/jot.2005.4.9.a1}, note = {TL;DR \par The primary aim of this work is to present a survey, as complete as possible, of the existing relevant works regarding class diagram metrics so that researchers and practitioners alike may gain broad and ready access to insights for measuring these quality characteristics.} } @online{gerasimouSoftwareEngineeringIntelligent2019, title = {Software {{Engineering}} for {{Intelligent}} and {{Autonomous Systems}}: {{Report}} from the {{GI Dagstuhl Seminar}} 18343}, shorttitle = {Software {{Engineering}} for {{Intelligent}} and {{Autonomous Systems}}}, author = {Gerasimou, Simos and Vogel, Thomas and Diaconescu, Ada}, date = {2019-04-02}, eprint = {1904.01518}, eprinttype = {arXiv}, eprintclass = {cs}, url = {http://arxiv.org/abs/1904.01518}, urldate = {2023-09-28}, abstract = {Software systems are increasingly used in application domains characterised by uncertain environments, evolving requirements and unexpected failures; sudden system malfunctioning raises serious issues of security, safety, loss of comfort or revenue. During operation, these systems will likely need to deal with several unpredictable situations including variations in system performance, sudden changes in system workload and component failures. These situations can cause deviation from the desired system behaviour and require dynamic adaptation of the system behaviour, parameters or architecture. Through using closed-loop control, typically realized with software, intelligent and autonomous software systems can dynamically adapt themselves, without any or with limited human involvement, by identifying abnormal situations, analysing alternative adaptation options, and finally, self-adapting to a suitable new configuration. This report summarises the research carried out during SEfIAS GI Dagstuhl seminar which provided a forum for strengthening interaction and collaboration for early-career researchers and practitioners from the research communities of SEAMS, ICAC/ICCAC, SASO, Self-Aware Computing and AAMAS.}, pubstate = {prepublished}, keywords = {Computer Science - Software Engineering}, note = {Comment: 28 pages \par TL;DR \par This report summarises the research carried out during SEfIAS GI Dagstuhl seminar which provided a forum for strengthening interaction and collaboration for early-career researchers and practitioners from the research communities of SEAMS, ICAC/ICCAC, SASO, Self-Aware Computing and AAMAS.} } @inproceedings{gerostathopoulosTRAPPedTrafficSelfAdaptive2019, title = {{{TRAPPed}} in {{Traffic}}? {{A Self-Adaptive Framework}} for {{Decentralized Traffic Optimization}}}, shorttitle = {{{TRAPPed}} in {{Traffic}}?}, booktitle = {2019 {{IEEEACM}} 14th {{Int}}. {{Symp}}. {{Softw}}. {{Eng}}. {{Adapt}}. {{Self-Manag}}. {{Syst}}. {{SEAMS}}}, author = {Gerostathopoulos, Ilias and Pournaras, Evangelos}, date = {2019-05}, pages = {32--38}, publisher = {IEEE}, location = {Montreal, QC, Canada}, doi = {10.1109/SEAMS.2019.00014}, abstract = {Optimizing the traffic flow in a city is a challenging problem, especially in a future traffic system of self-driving cars and sharing vehicles. This is due to the interactions between the individual traffic agents (vehicles) that compete for the use of the common infrastructure (streets) given traffic dynamics such as stop-and-go effects, changing lanes, and other. The goal of this paper is to provide a solution to the above problem that works in a fully decentralized and participatory way, i.e. autonomous agents collaborate without a centralized data collector and arbitrator. Such a solution should be scalable, privacypreserving, and flexible with respect to the degree of autonomy of agents. A self-adaptive framework to support this research is introduced: TRAPP – Traffic Reconfigurations via Adaptive Participatory Planning. The framework relies on a microscopic traffic simulator, SUMO, for simulating urban mobility scenarios, and on a decentralized multi-agent planning system, EPOS, for decentralized combinatorial optimization, applied here in traffic flows. A data-driven interoperation of the two tools in the proposed framework allows high modularity and customization for experimenting with different scenarios, optimization objectives and agents’ behavior and as such providing new perspectives for resilient future traffic infrastructures.}, eventtitle = {2019 {{IEEE}}/{{ACM}} 14th {{International Symposium}} on {{Software Engineering}} for {{Adaptive}} and {{Self-Managing Systems}} ({{SEAMS}})}, isbn = {978-1-7281-3368-3}, langid = {english}, note = {TL;DR \par A self-adaptive framework to support this research is introduced: TRAPP – Traffic Reconfigurations via Adaptive Participatory Planning, which relies on a microscopic traffic simulator for simulating urban mobility scenarios, and on a decentralized multi-agent planning system, EPOS, for decentralized combinatorial optimization, applied here in traffic flows.} } @article{gessertNoSQLDatabaseSystems2017, title = {{{NoSQL}} Database Systems: A Survey and Decision Guidance}, shorttitle = {{{NoSQL}} Database Systems}, author = {Gessert, Felix and Wingerath, Wolfram and Friedrich, Steffen and Ritter, Norbert}, date = {2017-07}, journaltitle = {Comput Sci Res Dev}, volume = {32}, number = {3--4}, pages = {353--365}, issn = {1865-2034, 1865-2042}, doi = {10.1007/s00450-016-0334-3}, abstract = {Today, data is generated and consumed at unprecedented scale. This has lead to novel approaches for scalable data management subsumed under the term “NoSQL” database systems to handle the ever-increasing data volume and request loads. However, the heterogeneity and diversity of the numerous existing systems impede the well-informed selection of a data store appropriate for a given application context. Therefore, this article gives a top-down overview of the field: instead of contrasting the implementation specifics of individual representatives, we propose a comparative classification model that relates functional and non-functional requirements to techniques and algorithms employed in NoSQL databases. This NoSQL Toolbox allows us to derive a simple decision tree to help practitioners and researchers filter potential system candidates based on central application requirements.}, langid = {english}, keywords = {TYPHONML} } @inproceedings{Ghamizi20201089, title = {Search-Based Adversarial Testing and Improvement of Constrained Credit Scoring Systems}, author = {Ghamizi, S. and Cordy, M. and Gubri, M. and Papadakis, M. and Boystov, A. and Le Traon, Y. and Goujon, A.}, editor = {Devanbu P., Cohen M., Zimmermann T.}, date = {2020}, series = {{{ESEC}}/{{FSE}} 2020 - {{Proceedings}} of the 28th {{ACM Joint Meeting European Software Engineering Conference}} and {{Symposium}} on the {{Foundations}} of {{Software Engineering}}}, pages = {1089--1100}, publisher = {Association for Computing Machinery, Inc}, doi = {10.1145/3368089.3409739}, abstract = {Credit scoring systems are critical FinTech applications that concern the analysis of the creditworthiness of a person or organization. While decisions were previously based on human expertise, they are now increasingly relying on data analysis and machine learning. In this paper, we assess the ability of state-of-the-art adversarial machine learning to craft attacks on a real-world credit scoring system. Interestingly, we find that, while these techniques can generate large numbers of adversarial data, these are practically useless as they all violate domain-specific constraints. In other words, the generated examples are all false positives as they cannot occur in practice. To circumvent this limitation, we propose CoEvA2, a search-based method that generates valid adversarial examples (satisfying the domain constraints). CoEvA2 utilizes multi-objective search in order to simultaneously handle constraints, perform the attack and maximize the overdraft amount requested. We evaluate CoEvA2 on a major bank's real-world system by checking its ability to craft valid attacks. CoEvA2 generates thousands of valid adversarial examples, revealing a high risk for the banking system. Fortunately, by improving the system through adversarial training (based on the produced examples), we increase its robustness and make our attack fail. © 2020 ACM.}, author_keywords = {Adversarial attacks; Credit Scoring; FinTech; Random Forest; Search-based}, document_type = {Conference Paper}, isbn = {978-1-4503-7043-1}, source = {Scopus}, keywords = {Banking systems,Domain constraint,Domain specific,False positive,Human expertise,Machine learning,Multi objective,Real-world system,Software engineering,State of the art}, note = {cited By 8 \par TL;DR \par CoEvA2 is proposed, a search-based method that generates valid adversarial examples (satisfying the domain constraints) and utilizes multi-objective search in order to simultaneously handle constraints, perform the attack and maximize the overdraft amount requested.} } @inproceedings{ghannem2013model, title = {Model Refactoring Using Interactive Genetic Algorithm}, booktitle = {Search {{Based Softw}}. {{Eng}}. 5th {{Int}}. {{Symp}}. {{SSBSE}} 2013 {{St Petersburg Russ}}. {{August}} 24-26 2013 {{Proc}}. 5}, author = {Ghannem, Adnane and El Boussaidi, Ghizlane and Kessentini, Marouane}, date = {2013}, pages = {96--110}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {TL;DR \par This paper proposes an approach by adapting the Interactive Genetic Algorithm IGA which enables to interact with users and integrate their feedbacks into a classic GA and uses a fitness function that combines the structural similarity between the analyzed design model and models from a base of examples.} } @article{GhannemKHE18, title = {Model Refactoring by Example: A Multi-Objective Search Based Software Engineering Approach}, author = {Ghannem, Adnane and Kessentini, Marouane and Hamdi, Mohammad Salah and El-Boussaidi, Ghizlane}, date = {2018}, journaltitle = {J. Softw. Evol. Process.}, volume = {30}, number = {4}, doi = {10.1002/SMR.1916}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {TL;DR \par The Non‐dominated Sorting Genetic Algorithm (NSGA‐II) is used to find a set of representative Pareto optimal solutions that present the best trade‐off between structural and textual similarities of models.}, timestamp = {Fri, 06 Mar 2020 21:54:32 +0100} } @inproceedings{Gharibi2019, title = {Automated Management of Deep Learning Experiments}, author = {Gharibi, G. and Walunj, V. and Alanazi, R. and Rella, S. and Lee, Y.}, date = {2019}, series = {Proceedings of the {{ACM SIGMOD International Conference}} on {{Management}} of {{Data}}}, publisher = {Association for Computing Machinery}, issn = {07308078}, doi = {10.1145/3329486.3329495}, abstract = {Developing a deep learning model is an iterative, experimental process that produces tens to hundreds of models before arriving at a satisfactory result. While there has been a surge in the number of software tools that aim to facilitate deep learning, the process of managing the models and their artifacts is still surprisingly challenging and time-consuming. Existing model-management solutions are either tailored for commercial platforms or require significant code changes. In this paper, we introduce a lightweight system, named ModelKB, that can automatically extract and manage the model's metadata and provenance information (e.g., the used datasets and hyperparameters). Our overarching goal is to automate the management of deep learning experiments with minimal user intervention. Moreover, ModelKB provides a stepping stone to facilitate model selection and reproducibility. © 2019 ACM.}, art_number = {3329495}, document_type = {Conference Paper}, isbn = {978-1-4503-6797-4}, source = {Scopus}, note = {cited By 5 \par TL;DR \par A lightweight system that can automatically extract and manage the model's metadata and provenance information (e.g., the used datasets and hyperparameters) and provides a stepping stone to facilitate model selection and reproducibility.} } @inproceedings{Gharibi201928, title = {{{ModelKB}}: {{Towards}} Automated Management of the Modeling Lifecycle in Deep Learning}, author = {Gharibi, G. and Walunj, V. and Rella, S. and Lee, Y.}, date = {2019}, series = {Proceedings - 2019 {{IEEE}}/{{ACM}} 7th {{International Workshop}} on {{Realizing Artificial Intelligence Synergies}} in {{Software Engineering}}, {{RAISE}} 2019}, pages = {28--34}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/RAISE.2019.00013}, abstract = {Deep Learning has improved the state-of-the-art results in an ever-growing number of domains. This success heavily relies on the development and training of deep learning models, also known as deep neural networks (DNN). Often, developing a DNN is an ad-hoc, iterative process that results in producing tens to hundreds of models before arriving at a satisfactory result. While there has been a surge in the number of tools and frameworks that aim at facilitating deep learning, the issues of model management have been largely ignored. In particular, deep learning practitioners have to manually track their experiments using text files, spreadsheets or folder hierarchies, which is expensive, time-consuming, and error-prone. In this paper, we present our ongoing work and vision towards automating end-to-end model management in deep learning. Specifically, we introduce a tool prototype, named ModelKB, that can automatically (1) extract and store the model's metadata-including its architecture, weights, and configuration; (2) visualize, query, and compare experiments; and (3) reproduce experiments. Our overarching goal is to automate the model management process with minimal user intervention using the user's favorite framework. We report the current status of ModelKB, a pilot user study, and the challenges of automating model management in deep learning. © 2019 IEEE.}, art_number = {8823655}, document_type = {Conference Paper}, isbn = {978-1-7281-2272-4}, source = {Scopus}, note = {cited By 5} } @article{Gharibi2021, title = {Automated End-to-End Management of the Modeling Lifecycle in Deep Learning}, author = {Gharibi, G. and Walunj, V. and Nekadi, R. and Marri, R. and Lee, Y.}, date = {2021}, journaltitle = {Empir. Softw. Eng.}, volume = {26}, number = {2}, publisher = {Springer}, issn = {13823256}, doi = {10.1007/s10664-020-09894-9}, abstract = {Deep learning has improved the state-of-the-art results in an ever-growing number of domains. This success heavily relies on the development and training of deep learning models–an experimental, iterative process that produces tens to hundreds of models before arriving at a satisfactory result. While there has been a surge in the number of tools and frameworks that aim at facilitating deep learning, the process of managing the models and their artifacts is still surprisingly challenging and time-consuming. Existing model-management solutions are either tailored for commercial platforms or require significant code changes. Moreover, most of the existing solutions address a single phase of the modeling lifecycle, such as experiment monitoring, while ignoring other essential tasks, such as model deployment. In this paper, we present a software system to facilitate and accelerate the deep learning lifecycle, named ModelKB. ModelKB can automatically manage the modeling lifecycle end-to-end, including (1) monitoring and tracking experiments; (2) visualizing, searching for, and comparing models and experiments; (3) deploying models locally and on the cloud; and (4) sharing and publishing trained models. Moreover, our system provides a stepping-stone for enhanced reproducibility. ModelKB currently supports TensorFlow 2.0, Keras, and PyTorch, and it can be extended to other deep learning frameworks easily. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature.}, art_number = {17}, coden = {ESENF}, document_type = {Article}, source = {Scopus}, note = {cited By 0 \par TL;DR \par ModelKB can automatically manage the modeling lifecycle end-to-end, including monitoring and tracking experiments; visualizing, searching for, and comparing models and experiments; deploying models locally and on the cloud; and sharing and publishing trained models.} } @article{Ghasemi2021, title = {Evolutionary Learning Based Simulation Optimization for Stochastic Job Shop Scheduling Problems}, author = {Ghasemi, A. and Ashoori, A. and Heavey, C.}, date = {2021}, journaltitle = {Appl. Soft Comput.}, volume = {106}, publisher = {Elsevier Ltd}, issn = {15684946}, doi = {10.1016/j.asoc.2021.107309}, abstract = {Simulation Optimization (SO) techniques refer to a set of methods that have been applied to stochastic optimization problems, structured so that the optimizer(s) are integrated with simulation experiments. Although SO techniques provide promising solutions for large and complex stochastic problems, the simulation model execution is potentially expensive in terms of computation time. Thus, the overall purpose of this research is to advance the evolutionary SO methods literature by researching the use of metamodeling within these techniques. Accordingly, we present a new Evolutionary Learning Based Simulation Optimization (ELBSO) method embedded within Ordinal Optimization. In ELBSO a Machine Learning (ML) based simulation metamodel is created using Genetic Programming (GP) to replace simulation experiments aimed at reducing computation. ELBSO is evaluated on a Stochastic Job Shop Scheduling Problem (SJSSP), which is a well known complex production planning problem in most industries such as semiconductor manufacturing. To build the metamodel from SJSSP instances that replace simulation replications, we employ a novel training vector to train GP. This then is integrated into an evolutionary two-phased Ordinal Optimization approach to optimize an SJSSP which forms the ELBSO method. Using a variety of experimental SJSSP instances, ELBSO is compared with evolutionary optimization methods from the literature and typical dispatching rules. Our findings include the superiority of ELBSO over all other algorithms in terms of the quality of solutions and computation time. Furthermore, the integrated procedures and results provided within this article establish a basis for future SO applications to large and complex stochastic problems. © 2021 Elsevier B.V.}, art_number = {107309}, document_type = {Article}, source = {Scopus}, note = {cited By 8} } @article{Ghiasi2018101, title = {Comparative Studies of Metamodeling and {{AI-Based}} Techniques in Damage Detection of Structures}, author = {Ghiasi, R. and Ghasemi, M.R. and Noori, M.}, date = {2018}, journaltitle = {Adv. Eng. Softw.}, volume = {125}, pages = {101--112}, publisher = {Elsevier Ltd}, issn = {09659978}, doi = {10.1016/j.advengsoft.2018.02.006}, abbrev_source_title = {Adv Eng Software}, affiliation = {Department of Civil Engineering, Faculty of Engineering, University of Sistan and Baluchestan, Zahedan, Iran; Mechanical Engineering Department, California Polytechnic State University, One Grand Avenue San Luis ObispoCA 93405, United States; Distinguished Visiting Professor, International Institute for Urban Systems Engineering, Southeast University, Nanjing, 210096, China}, coden = {AESOD}, correspondence_address1 = {Noori, M.; Mechanical Engineering Department, One Grand Avenue San Luis Obispo, United States; email: contact@mohammadnoori.com}, document_type = {Article}, langid = {english}, source = {Scopus}, keywords = {notion}, note = {cited By 47} } @article{Ghiasi2018561, title = {Optimization-Based Method for Structural Damage Detection with Consideration of Uncertainties- a Comparative Study}, author = {Ghiasi, R. and Ghasemi, M.R.}, date = {2018}, journaltitle = {Smart Struct. Syst.}, volume = {22}, number = {5}, pages = {561--574}, publisher = {Techno-Press}, issn = {17381584}, doi = {10.12989/sss.2018.22.5.561}, abbrev_source_title = {Smart Struct. Syst.}, affiliation = {Department of Civil Engineering, Faculty of Engineering, University of Sistan and Baluchestan, Zahedan, Iran}, correspondence_address1 = {Ghasemi, M.R.; Department of Civil Engineering, Iran; email: mrghasemi@eng.usb.ac.ir}, document_type = {Article}, langid = {english}, source = {Scopus}, note = {cited By 15} } @article{Ghose2001, title = {Taste Tests: {{Impacts}} of Consumer Perceptions and Preferences on Brand Positioning Strategies}, author = {Ghose, Sanjoy and Lowengart, Oded}, date = {2001-08-01}, journaltitle = {J. Target. Meas. Anal. Mark.}, volume = {10}, number = {1}, pages = {26--41}, issn = {1479-1862}, doi = {10.1057/palgrave.jt.5740031}, abstract = {Taste tests are being increasingly used by marketers to influence consumers to change their preferences toward their brands. This research indicates how perceptual and preferential taste tests can be used in conjunction with visual maps to provide support to marketing managers for making better brand positioning and targeting decisions on the basis of taste for different segments of consumers. An empirical blind taste-test study is used to illustrate the concepts. The preferential taste judgment part of the empirical study is designed to capture violations of the `Independence of Irrelevant Alternatives' (IIA) effect that is commonly observed in consumers' actual purchases. The present paper also uses a hypothetical example to indicate the importance of considering the location of consumer `ideal points' before making formulation changes in a brand as part of a targeting strategy. Various managerial implications of using the suggested perceptual preferential taste-mapping analyses are also discussed. Appropriate measurements of consumer tastes provide insights for identifying and targeting viable market segments.} } @article{giacobbeImplementationInfluxDBMonitoring2020, title = {An {{Implementation}} of {{InfluxDB}} for {{Monitoring}} and {{Analytics}} in {{Distributed IoT Environments}}}, author = {Giacobbe, Maurizio and Chaouch, Chakib and Scarpa, Marco and Puliafito, Antonio}, editor = {Bouhlel, Med Salim and Rovetta, Stefano}, date = {2020}, journaltitle = {Proc. 8th Int. Conf. Sci. Electron. Technol. Inf. Telecommun. SETIT’18 Vol1}, volume = {146}, pages = {155--162}, doi = {10.1007/978-3-030-21005-2_15}, langid = {english}, note = {TL;DR \par This work presents a case study based on the implementation of the InfluxDB time series database for monitoring and analytics in distributed IoT environments to enable IoT-as-a-Service in geo-distributed “smart” ecosystems thus creating new opportunities for heterogeneous stakeholders to meet and define new synergies.} } @article{giannottiEXplainableMachineLearning, title = {{{eXplainable}} Machine Learning for {{Trustworthy AI}}}, author = {Giannotti, Fosca}, pages = {44}, langid = {english} } @book{gibaldiMLAHandbookWriters2009, title = {{{MLA}} Handbook for Writers of Research Papers}, editor = {Gibaldi, Joseph and Modern Language Association of America}, date = {2009}, edition = {7th ed}, publisher = {Modern Language Association of America}, location = {New York}, isbn = {978-1-60329-024-1 978-1-60329-025-8}, langid = {english}, pagetotal = {292}, note = {TL;DR \par The fourth edition of the MLA Handbook presents a comprehensive guide to preparing research papers and contains detailed information on using computers for research and writing and on citing electronic publications.} } @inproceedings{Giese2011, title = {How Can Metamodels Be Used Flexibly}, booktitle = {{{ICSE}} 2011 {{Workshop Flex}}. {{Model}}. {{Tools}}}, author = {{Gabrysiak} and Gregor, Holger Giese, Alexander Lüders and Seibel, Andreas}, date = {2011}, volume = {22} } @article{gilWingsIntelligentWorkflowBased2011, title = {Wings: {{Intelligent Workflow-Based Design}} of {{Computational Experiments}}}, shorttitle = {Wings}, author = {Gil, Yolanda and Ratnakar, Varun and Kim, Jihie and Gonzalez-Calero, Pedro and Groth, Paul and Moody, Joshua and Deelman, Ewa}, date = {2011-01}, journaltitle = {IEEE Intell. Syst.}, volume = {26}, number = {1}, pages = {62--72}, issn = {1541-1672}, doi = {10.1109/MIS.2010.9}, note = {TL;DR \par Describes the Wings intelligent workflow system that assists scientists with designing computational experiments by automatically tracking constraints and ruling out invalid designs, letting scientists focus on their experiments and goals.} } @article{giraldoMethodEvaluateQuality2019, title = {A Method to Evaluate Quality of Modelling Languages Based on the {{Zachman}} Reference Taxonomy}, author = {Giraldo, Fáber D. and España, Sergio and Giraldo, William J. and Pastor, Óscar and Krogstie, John}, date = {2019-09-01}, journaltitle = {Software Qual J}, volume = {27}, number = {3}, pages = {1239--1269}, issn = {1573-1367}, doi = {10.1007/s11219-018-9434-6}, abstract = {The model-driven engineering (MDE) paradigm promotes the use of conceptual models in information systems (IS) engineering and research. As engineering products, conceptual models must be of high quality, which applies to both conceptual models and the modelling language used to build them. Quality is a growing concern in the MDE field; however, studies such as Giraldo, F.D. et al. Software Quality Journal, pp. 1–66 (2016b) and Goulão, M. et al. Software Quality Journal, pp. 1–33 (2016) demonstrate the divergence in several approaches that are proposed for addressing this topic. Due to the many challenges, divergences, and trends for quality assessment and assurance in the MDE context, one way to perform a quality evaluation process is to use an approach where the applicability and goals of modelling languages (and artifacts) can be compared with respect to the essential principles of the development of IS. We propose using principles from an IS architecture reference (i.e., the Zachman framework) as a taxonomy that is applied on the modelling languages used in information system development in order to perform analytic procedures. We also demonstrate that this taxonomy can be considered as a formal context for the application of the formal concept analysis (FCA) method. This paper derives formal, methodological, and technological requirements for a modelling language quality evaluation method (MMQEF) with the potential to tackle some of the open MDE quality challenges. In addition, a tool that operationalizes the taxonomic evaluation procedure and the FCA analytic method is also presented. In this work, we discuss how this taxonomy supports analytics that are in modelling languages for quality purposes through its management of the semantics.}, langid = {english}, keywords = {Information systems,Model-driven engineering,Modelling language evaluation,Quality,Reference taxonomy,The MMQEF method}, note = {TL;DR \par This work proposes using principles from an IS architecture reference as a taxonomy that is applied on the modelling languages used in information system development in order to perform analytic procedures and demonstrates that this taxonomy can be considered as a formal context for the application of the formal concept analysis (FCA) method.} } @article{girbaUsingMetaModelTransformation2005, title = {Using {{Meta-Model Transformation}} to {{Model Software Evolution}}}, author = {Gîrba, Tudor and Favre, Jean-Marie and Ducasse, Stéphane}, date = {2005-09}, journaltitle = {Electronic Notes in Theoretical Computer Science}, volume = {137}, number = {3}, pages = {57--64}, issn = {15710661}, doi = {10.1016/j.entcs.2005.07.005}, abstract = {Understanding how software systems evolve is useful from different perspectives: reverse engineering, empirical studies etc.. For an effective understanding we need an explicit meta-model. We introduce Hismo, a meta-model which is centered around the notion of history and we show how we can obtain it from a snapshot meta-model. Based on our experience in developing the Hismo reverse engineering system, we show how we can transform a snapshot meta-model in a history metamodel.}, langid = {english} } @article{Girum2019119, title = {Deep Generative Model-Driven Multimodal Prostate Segmentation in Radiotherapy}, author = {Girum, K.B. and Créhange, G. and Hussain, R. and Walker, P.M. and Lalande, A.}, editor = {Nguyen D., Jiang S., Xing L.}, date = {2019}, journaltitle = {Lect. Notes Comput. Sci. Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinforma.}, volume = {11850 LNCS}, pages = {119--127}, publisher = {Springer}, issn = {03029743}, doi = {10.1007/978-3-030-32486-5_15}, abstract = {Deep learning has shown unprecedented success in a variety of applications, such as computer vision and medical image analysis. However, there is still potential to improve segmentation in multimodal images by embedding prior knowledge via learning-based shape modeling and registration to learn the modality invariant anatomical structure of organs. For example, in radiotherapy automatic prostate segmentation is essential in prostate cancer diagnosis, therapy, and post-therapy assessment from T2-weighted MR or CT images. In this paper, we present a fully automatic deep generative model-driven multimodal prostate segmentation method using convolutional neural network (DGMNet). The novelty of our method comes with its embedded generative neural network for learning-based shape modeling and its ability to adapt for different imaging modalities via learning-based registration. The proposed method includes a multi-task learning framework that combines a convolutional feature extraction and an embedded regression and classification based shape modeling. This enables the network to predict the deformable shape of an organ. We show that generative neural network-based shape modeling trained on a reliable contrast imaging modality (such as MRI) can be directly applied to low contrast imaging modality (such as CT) to achieve accurate prostate segmentation. The method was evaluated on MRI and CT datasets acquired from different clinical centers with large variations in contrast and scanning protocols. Experimental results reveal that our method can be used to automatically and accurately segment the prostate gland in different imaging modalities. © Springer Nature Switzerland AG 2019.}, document_type = {Conference Paper}, isbn = {9783030324858}, source = {Scopus}, note = {cited By 7 \par TL;DR \par It is shown that generative neural network-based shape modeling trained on a reliable contrast Imaging modality can be directly applied to low contrast imaging modality (such as CT) to achieve accurate prostate segmentation.} } @online{GitHubFacebookresearchDlrm, title = {{{GitHub}} - Facebookresearch/Dlrm: {{An}} Implementation of a Deep Learning Recommendation Model ({{DLRM}})}, url = {https://github.com/facebookresearch/dlrm}, urldate = {2021-06-07} } @article{glauberCollaborativeFilteringVs2019, title = {Collaborative {{Filtering}} vs. {{Content-Based Filtering}}: Differences and Similarities}, shorttitle = {Collaborative {{Filtering}} vs. {{Content-Based Filtering}}}, author = {Glauber, Rafael and Loula, Angelo}, date = {2019-12-18}, journaltitle = {ArXiv191208932 Cs}, eprint = {1912.08932}, eprinttype = {arXiv}, eprintclass = {cs}, url = {http://arxiv.org/abs/1912.08932}, urldate = {2020-01-11}, abstract = {Recommendation Systems (SR) suggest items exploring user preferences, helping them with the information overload problem. Two approaches to SR have received more prominence, Collaborative Filtering, and Content-Based Filtering. Moreover, even though studies are indicating their advantages and disadvantages, few results empirically prove their characteristics, similarities, and differences. In this work, an experimental methodology is proposed to perform comparisons between recommendation algorithms for different approaches going beyond the "precision of the predictions". For the experiments, three algorithms of recommendation were tested: a baseline for Collaborative Filtration and two algorithms for Content-based Filtering that were developed for this evaluation. The experiments demonstrate the behavior of these systems in different data sets, its main characteristics and especially the complementary aspect of the two main approaches.}, note = {TL;DR \par An experimental methodology is proposed to perform comparisons between recommendation algorithms for different approaches going beyond the "precision of the predictions" and demonstrates the behavior of these systems in different data sets, including the complementary aspect of the two main approaches.} } @incollection{gleitzeFindingUniversalExecution2021, title = {Finding a {{Universal Execution Strategy}} for {{Model Transformation Networks}}}, booktitle = {Fundamental {{Approaches}} to {{Software Engineering}}}, author = {Gleitze, Joshua and Klare, Heiko and Burger, Erik}, editor = {Guerra, Esther and Stoelinga, Mariëlle}, date = {2021}, volume = {12649}, pages = {87--107}, publisher = {Springer International Publishing}, location = {Cham}, doi = {10.1007/978-3-030-71500-7_5}, abstract = {When using multiple models to describe a (software) system, one can use a network of model transformations to keep the models consistent after changes. No strategy exists, however, to orchestrate the execution of transformations if the network has an arbitrary topology. In this paper, we analyse how often and in which order transformations need to be executed. We argue why linear execution bounds are too restrictive to be useful in practice and prove that there is no upper bound for the number of necessary executions. To avoid non-termination, we propose a conservative strategy that makes execution failures easier to understand. These insights help developers and users of transformation networks to understand under which circumstances their networks can terminate. Additionally, the proposed strategy helps them to find the cause when a network cannot restore consistency.}, isbn = {978-3-030-71499-4 978-3-030-71500-7}, langid = {english}, note = {TL;DR \par It is argued why linear execution bounds are too restrictive to be useful in practice and proved that there is no upper bound for the number of necessary executions.} } @article{gloriaDesignImplementationIoT2017, title = {Design and Implementation of an {{IoT}} Gateway to Create Smart Environments}, author = {Glória, André and Cercas, Francisco and Souto, Nuno}, date = {2017}, journaltitle = {Procedia Computer Science}, volume = {109}, pages = {568--575}, issn = {18770509}, doi = {10.1016/j.procs.2017.05.343}, langid = {english} } @online{GmailConcettoDi, title = {Gmail - {{Concetto}} Di "Scenario Misto"}, url = {https://mail.google.com/mail/u/0/?ui=2&ik=c6f0013e0f&view=pt&search=inbox&type=14ce63765de5b01c&msg=14c64feb393c287b&siml=14c64feb393c287b}, urldate = {2015-04-24} } @article{gobertConceptualModelingManipulation, title = {Conceptual {{Modeling}} and {{Manipulation}} of {{Hybrid Polystores}}}, author = {Gobert, Maxime and Meurice, Loup and Cleve, Anthony}, pages = {14}, abstract = {An increasing number of organisations rely on NoSQL technologies to manage their mission-critical data. However, those technologies were not intended to replace relational database management systems, but rather to complement them. Hence the recent emergence of heterogeneous database architectures, commonly called hybrid polystores, that rely on a combination of several, possibly overlapping relational and NoSQL databases. Unfortunately, there is still a lack of models, methods and tools for data modeling and manipulation in such architectures. With the aim to fill this gap, we present HyDRa, a conceptual framework to design and manipulate hybrid polystores. HyDRa includes a textual modeling language to specify (1) the conceptual schema of the polystore, (2) the physical schemas of each of its databases, and (3) a set of mapping rules to express possibly complex correspondences between the conceptual schema elements and the physical databases. HyDRa provides the generation of a conceptual API, allowing developers to query hybrid polystores at a conceptual level, and to automatically enforce cross-database data integrity constraints. The use of HyDRa is supported by an Eclipse plugin, offering syntax highlighting, auto-completion and conceptual data access API generation.}, langid = {english} } @article{GogollaBR07, title = {{{USE}}: A {{UML-based}} Specification Environment for Validating {{UML}} and {{OCL}}}, author = {Gogolla, Martin and Büttner, Fabian and Richters, Mark}, date = {2007}, journaltitle = {Sci. Comput. Program.}, volume = {69}, number = {1--3}, pages = {27--34}, doi = {10.1016/J.SCICO.2007.01.013}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Wed, 17 Feb 2021 21:56:03 +0100} } @article{GogollaHD18, title = {Achieving Model Quality through Model Validation, Verification and Exploration}, author = {Gogolla, Martin and Hilken, Frank and Doan, Khanh-Hoang}, date = {2018}, journaltitle = {Comput. Lang. Syst. Struct.}, volume = {54}, pages = {474--511}, doi = {10.1016/J.CL.2017.10.001}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Tue, 11 Feb 2020 17:00:46 +0100} } @article{gomez-abajoSystematicEngineeringMutation2020, title = {Systematic {{Engineering}} of {{Mutation Operators}}.}, author = {Gómez-Abajo, Pablo and Guerra, Esther and family=Lara, given=Juan, prefix=de, useprefix=true and Merayo, Mercedes G.}, date = {2020}, journaltitle = {JOT}, volume = {19}, number = {3}, pages = {3:1}, issn = {1660-1769}, doi = {10.5381/jot.2020.19.3.a5}, langid = {english}, note = {TL;DR \par This work proposes a methodology and corresponding tool support for the proper engineering of mutation operators, representing the artefacts to be mutated as models, and shows automated support atop the W ODEL tool.} } @article{gomez-uribeNetflixRecommenderSystem2015, title = {The Netflix Recommender System: {{Algorithms}}, Business Value, and Innovation}, author = {Gomez-Uribe, Carlos A. and Hunt, Neil}, date = {2015-12}, journaltitle = {ACM Trans. Manage. Inf. Syst.}, volume = {6}, number = {4}, pages = {13:1-13:19}, publisher = {ACM}, address = {New York, NY, USA}, issn = {2158-656X}, url = {http://doi.acm.org/10.1145/2843948}, acmid = {2843948}, articleno = {13}, issue_date = {January 2016}, nodoi = {10.1145/2843948}, numpages = {19} } @inproceedings{gomez2012searching, title = {Searching the Boundaries of a Modeling Space to Test Metamodels}, booktitle = {2012 {{IEEE Fifth Int}}. {{Conf}}. {{Softw}}. {{Test}}. {{Verification Valid}}.}, author = {Gómez, Juan José Cadavid and Baudry, Benoit and Sahraoui, Houari}, date = {2012}, pages = {131--140}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {TL;DR \par This paper uses simulated annealing to select a set of models in the modeling space captured by a metamodel that satisfies those two objectives and reports on results using two meetamodels from two different domains.} } @incollection{gomezMapBasedTransparentPersistence2015, title = {Map-{{Based Transparent Persistence}} for {{Very Large Models}}}, booktitle = {Fundamental {{Approaches}} to {{Software Engineering}}}, author = {Gómez, Abel and Tisi, Massimo and Sunyé, Gerson and Cabot, Jordi}, editor = {Egyed, Alexander and Schaefer, Ina}, date = {2015-04-11}, series = {Lecture {{Notes}} in {{Computer Science}}}, number = {9033}, pages = {19--34}, publisher = {Springer Berlin Heidelberg}, url = {http://link.springer.com/chapter/10.1007/978-3-662-46675-9_2}, urldate = {2015-04-07}, abstract = {The progressive industrial adoption of Model-Driven Engineering (MDE) is fostering the development of large tool ecosystems like the Eclipse Modeling project. These tools are built on top of a set of base technologies that have been primarily designed for small-scale scenarios, where models are manually developed. In particular, efficient runtime manipulation for large-scale models is an under-studied problem and this is hampering the application of MDE to several industrial scenarios. In this paper we introduce and evaluate a map-based persistence model for MDE tools. We use this model to build a transparent persistence layer for modeling tools, on top of a map-based database engine. The layer can be plugged into the Eclipse Modeling Framework, lowering execution times and memory consumption levels of other existing approaches. Empirical tests are performed based on a typical industrial scenario, model-driven reverse engineering, where very large software models originate from the analysis of massive code bases. The layer is freely distributed and can be immediately used for enhancing the scalability of any existing Eclipse Modeling tool.}, isbn = {978-3-662-46674-2 978-3-662-46675-9}, langid = {english}, keywords = {software engineering}, note = {TL;DR \par This model is used to build a transparent persistence layer for modeling tools, on top of a map-based database engine, which can be plugged into the Eclipse Modeling Framework, lowering execution times and memory consumption levels of other existing approaches.} } @article{gonzalezATLTestWhiteBoxTest2012, title = {{{ATLTest}}: {{A White-Box Test Generation Approach}} for {{ATL Transformations}}}, author = {González, Carlos A. and Cabot, Jordi}, date = {2012}, journaltitle = {Model Driven Eng. Lang. Syst.}, volume = {7590}, pages = {449--464}, doi = {10.1007/978-3-642-33666-9_29} } @online{GoogleColab, title = {Google Colab}, url = {https://colab.research.google.com/drive/1OnIipRwuHOZbKHN0haHGD0OnckBGfzqx}, urldate = {2025-04-02}, langid = {english}, keywords = {/unread,MOSAICO} } @article{Gorodetsky2015765, title = {The Reference Ontology of Collective Behavior of Autonomous Agents and Its Extensions}, author = {Gorodetsky, V.I. and Samoylov, V.V. and Trotskii, D.V.}, date = {2015}, journaltitle = {J. Comput. Syst. Sci. Int.}, volume = {54}, number = {5}, pages = {765--782}, publisher = {Maik Nauka-Interperiodica Publishing}, issn = {10642307}, doi = {10.1134/S1064230715030089}, abbrev_source_title = {J. Comput. Syst. Sci. Int.}, affiliation = {St. Petersburg Institute for Informatics and Automation, Russian Academy of Sciences, 14 Liniya 39, St. Petersburg, Russian Federation; St. Petersburg State Polytechnic University, Polytechnicheskaya ul. 29, St. Petersburg, Russian Federation}, coden = {JSSIE}, correspondence_address1 = {Gorodetsky, V.I.; St. Petersburg Institute for Informatics and Automation, Russian Academy of Sciences, 14 Liniya 39, Russian Federation}, document_type = {Article}, langid = {english}, source = {Scopus}, note = {cited By 6 \par TL;DR \par An outline of the state of art in the field of behavioral models of artificial intelligence systems is given and a unified semantically interpreted behavioral metamodel in the form of a domain-independent reference ontology and its extensions for two particular practically important classes of applications are proposed.} } @article{gorrepotuSub1GHzMiniatureWireless2018, title = {Sub-{{1GHz}} Miniature Wireless Sensor Node for {{IoT}} Applications}, author = {Gorrepotu, Ramesh and Korivi, Narendra Swaroop and Chandu, Kavitha and Deb, Subimal}, date = {2018-09}, journaltitle = {Internet Things}, volume = {1--2}, pages = {27--39}, issn = {25426605}, doi = {10.1016/j.iot.2018.08.002}, abstract = {Considering the Sub-1 GHz frequency as a solution to address the key requirements in wireless networks as it supports multiple nodes and covers longer distances in contrast to other existing and widely used wireless technologies like GSM, BLE, Bluetooth and WiFi. Consequently the Sub-1 GHz spectrum requires lower power from the transceiver than the 2.4 GHz band making it a great choice for battery operated IoT sensor devices. For deploying nodes to cover large area and long range, sensing devices must be small, energy efficient and cost effective. IoT Sensor devices using the Sub-1 GHz spectrum can handle interference better. The lower frequency ISM bands enable the Sub-1 GHz transmissions to weave better between buildings in an urban environment. This paper deals with the design and development of hardware as well as software of a Sub-1 GHz gateway and miniature sensor node for IoT applications. CC1310 SoC, a Sub-1 GHz family microcontroller is used in the design of Sub-1 GHz, 868 MHz board.}, langid = {english} } @article{goscinskiSpecialIssueDistributed2023, title = {Special Issue on {{Distributed Intelligence}} at the {{Edge}} for the {{Future Internet}} of {{Things}}}, author = {Goscinski, Andrzej and Delicato, Flavia C. and Fortino, Giancarlo and Kobusińska, Anna and Srivastava, Gautam}, date = {2023-01}, journaltitle = {Journal of Parallel and Distributed Computing}, volume = {171}, pages = {157--162}, issn = {07437315}, doi = {10.1016/j.jpdc.2022.09.014}, langid = {english} } @article{gouesBridgingGapResearch2018, title = {Bridging the {{Gap}}: {{From Research}} to {{Practical Advice}}}, shorttitle = {Bridging the {{Gap}}}, author = {Goues, C. L. and Jaspan, C. and Ozkaya, I. and Shaw, M. and Stolee, K. T.}, date = {2018-09}, journaltitle = {IEEE Softw.}, volume = {35}, number = {5}, pages = {50--57}, issn = {0740-7459}, doi = {10.1109/MS.2018.3571235}, abstract = {Software engineers must solve practical problems under deadline pressure. They rely on the best-codified knowledge available, turning to weaker results and their expert judgment when sound science is unavailable. Meanwhile, software engineering researchers seek fully validated results, resulting in a lag to practical guidance. To bridge this gap, research results should be systematically distilled into actionable guidance in a way that respects differences in strength and scope among the results. Starting with the practitioners’ need for actionable guidance, this article reviews the evolution of software engineering research expectations, identifies types of results and their strengths, and draws on evidence-based medicine for a concrete example of deriving pragmatic guidance from mixed-strength research results. It advances a levels-of-evidence framework to allow researchers to clearly identify the strengths of their claims and the supporting evidence for their results and to work with practitioners to synthesize actionable recommendations from diverse types of evidence. This article is part of a special issue on software engineering’s 50th anniversary.}, keywords = {software engineering} } @online{GrahamjensonList_of_recommender_systemsList, title = {Grahamjenson/List\_of\_recommender\_systems: {{A List}} of {{Recommender Systems}} and {{Resources}}}, url = {https://github.com/grahamjenson/list_of_recommender_systems}, urldate = {2017-03-10} } @online{GrandePasticcioContratti, title = {Il Grande Pasticcio Dei Contratti Di Ricerca: Perché La Riforma Non Funziona e Come Rimediare - {{La}} Stampa}, url = {https://www.lastampa.it/tuttoscienze/2025/04/16/news/il_grande_pasticcio_dei_contratti_di_ricerca_perche_la_riforma_non_funziona_e_come_rimediare-15103474/}, urldate = {2025-04-24}, langid = {english} } @article{grayExplicitImplicitModels2022, title = {Explicit versus Implicit Models: {{What}} Are Good Languages for Modeling?}, shorttitle = {Explicit versus Implicit Models}, author = {Gray, Jeff and Rumpe, Bernhard}, date = {2022-04-07}, journaltitle = {Softw Syst Model}, pages = {s10270-022-01001-4}, issn = {1619-1366, 1619-1374}, doi = {10.1007/s10270-022-01001-4}, langid = {english} } @video{gregisenbergModelContextProtocol2025, entrysubtype = {video}, title = {Model Context Protocol ({{MCP}}), Clearly Explained (Why It Matters)}, editor = {{Greg Isenberg}}, editortype = {director}, date = {2025-03-14}, url = {https://www.youtube.com/watch?v=7j_NE6Pjv-E}, urldate = {2025-04-24}, langid = {english} } @article{greifenbergEngineeringTaggingLanguages, title = {Engineering {{Tagging Languages}} for {{DSLs}}}, author = {Greifenberg, Timo and Look, Markus and Roidl, Sebastian and Rumpe, Bernhard}, url = {http://www.se-rwth.de/publications/Engineering-Tagging-Languages-for-DSLs.pdf}, urldate = {2015-09-24}, note = {TL;DR \par This paper presents a systematic approach to define a DSL-specific tag language and a corresponding schema language, combining the advantages of both worlds.} } @online{griffithsWhyJavaEndures2025, title = {Why Java Endures: The Foundation of Modern Enterprise Development}, shorttitle = {Why Java Endures}, author = {Griffiths, Andrea}, date = {2025-03-11}, url = {https://github.blog/developer-skills/why-java-endures-the-foundation-of-modern-enterprise-development/}, urldate = {2025-03-23}, abstract = {For 30 years, Java has been a cornerstone of enterprise software development. Here’s why—and how to learn Java.}, langid = {english}, organization = {The Github Blog}, keywords = {/unread} } @article{grigorevMLOps10Minutes_, title = {{{MLOps}} in 10 {{Minutes}}\_ by {{Alexey Grigorev}} \_ {{Towards Data Science}}}, author = {Grigorev, Alexey}, pages = {13}, langid = {english} } @misc{gronback_eclipse_2021, title = {Eclipse {{Modeling Project}} | {{The Eclipse Foundation}}}, author = {Gronback, Richard}, date = {2021-03}, abstract = {The Eclipse Foundation - home to a global community, the Eclipse IDE, Jakarta EE and over 375 open source projects, including runtimes, tools and frameworks.}, langid = {english} } @article{Grossberg:2013:ART:2405841.2405958, title = {Adaptive Resonance Theory: {{How}} a Brain Learns to Consciously Attend, Learn, and Recognize a Changing World}, author = {Grossberg, Stephen}, date = {2013-01}, journaltitle = {Neural Netw.}, volume = {37}, pages = {1--47}, publisher = {Elsevier Science Ltd.}, address = {Oxford, UK, UK}, issn = {0893-6080}, url = {http://dx.doi.org/10.1016/j.neunet.2012.09.017}, acmid = {2405958}, issue_date = {January, 2013}, nodoi = {10.1016/j.neunet.2012.09.017}, numpages = {47}, keywords = {Adaptive Resonance Theory,Adaptive timing,Amygdala,Attention,Basal ganglia,Consciousness,Entorhinal cortex,Expectation,Gamma and beta oscillations,Hippocampal cortex,Inferotemporal cortex,Learning,Parietal cortex,Prefrontal cortex,Recognition,reinforcement learning,Speech perception,Synchrony,Working memory} } @misc{GRuMFlexibleModelDriven, title = {{{GRuM}} – {{A Flexible Model-Driven Runtime Monitoring Framework}} and Its {{Application}} to {{Automated Aerial}} and {{Ground Vehicls}}}, keywords = {LOGSEQ} } @inproceedings{gu_assemble_2022, title = {Assemble {{Foundation Models}} for {{Automatic Code Summarization}}}, booktitle = {2022 {{IEEE Int}}. {{Conf}}. {{Softw}}. {{Anal}}. {{Evol}}. {{Reengineering SANER}}}, author = {Gu, Jian and Salza, Pasquale and Gall, Harald C.}, date = {2022-03}, pages = {935--946}, doi = {10.1109/SANER53432.2022.00112}, abstract = {Automatic code summarization is beneficial to software development and maintenance since it reduces the burden of manual tasks. Currently, artificial intelligence is undergoing a paradigm shift. The foundation models pretrained on massive data and finetuned to downstream tasks surpass specially customized models. This trend inspired us to consider reusing foundation models instead of learning from scratch. Based on this, we propose a flexible and robust approach for automatic code summarization based on neural networks. We assemble available foundation models, such as CodeBERT and GPT-2, into a single model named AdaMo. Moreover, we utilize Gaussian noise as the simulation of contextual information to optimize the latent representation. Furthermore, we introduce two adaptive schemes from the perspective of knowledge transfer, namely continuous pretraining and intermediate finetuning, and design intermediate stage tasks for general sequence-to-sequence learning. Finally, we evaluate AdaMo against a benchmark dataset for code summarization, by comparing it with state-of-the-art models.}, keywords = {Adaptation models,adaptive scheme,code summarization,Codes,Gaussian noise,Manuals,Market research,Neural networks,transfer learning,Transfer learning,Transformer}, note = {ISSN: 1534-5351} } @inproceedings{Gu2016DeepAPI, title = {Deep {{API}} Learning}, booktitle = {24th {{ACM SIGSOFT Int}}. {{Symp}}. {{Found}}. {{Softw}}. {{Eng}}.}, author = {Gu, Xiaodong and Zhang, Hongyu and Zhang, Dongmei and Kim, Sunghun}, date = {2016}, pages = {631--642}, publisher = {ACM}, location = {New York}, doi = {10.1145/2950290.2950334}, isbn = {978-1-4503-4218-6}, nodoi = {10.1145/2950290.2950334}, note = {TL;DR \par DeepAPI is proposed, a deep learning based approach to generate API usage sequences for a given natural language query that adapts a neural language model named RNN Encoder-Decoder, and generates an API sequence based on the context vector.} } @inproceedings{Gu2018DeepCode, title = {Deep Code Search}, booktitle = {40th {{Int}}. {{Conf}}. {{Softw}}. {{Eng}}.}, author = {Gu, Xiaodong and Zhang, Hongyu and Kim, Sunghun}, date = {2018}, pages = {933--944}, publisher = {ACM}, location = {New York}, isbn = {978-1-4503-5638-1}, nodoi = {10.1145/3180155.3180167} } @article{guanaChainTrackerModelTransformationTrace2014, title = {{{ChainTracker}}, a {{Model-Transformation Trace Analysis Tool}} for {{Code-Generation Environments}}}, author = {Guana, Victor and Stroulia, Eleni}, date = {2014}, journaltitle = {Theory Pract. Model Transform.}, volume = {8568}, pages = {146--153}, doi = {10.1007/978-3-319-08789-4_11}, note = {TL;DR \par This paper presents ChainTracker, a general conceptual framework, and model-transformation composition analysis tool, that supports developers when maintaining and synchronizing evolving code-generation environments.} } @article{guerraAutomatedVerificationModel2012, title = {Automated Verification of Model Transformations Based on Visual Contracts}, author = {Guerra, Esther and Lara, Juan and Wimmer, Manuel and Kappel, Gerti and Kusel, Angelika and Retschitzegger, Werner and Schönböck, Johannes and Schwinger, Wieland}, date = {2012}, journaltitle = {Autom. Softw. Eng.}, volume = {20}, number = {1}, pages = {5--46}, doi = {10.1007/s10515-012-0102-y}, note = {TL;DR \par A declarative language for the specification of visual contracts is proposed, enabling the verification of transformations defined with any transformation language, i.e., irrespective of the actual transformation language used.} } @online{GuestEditorialSpecial, title = {Guest {{Editorial}}: {{Special}} Issue on Constrained Decision-Making in Robotics - {{Online First}} - {{Springer}}}, url = {http://link.springer.com/article/10.1007/s10514-015-9489-1}, urldate = {2015-08-19} } @article{Guha:1998:CEC:276305.276312, title = {{{CURE}}: {{An}} Efficient Clustering Algorithm for Large Databases}, author = {Guha, Sudipto and Rastogi, Rajeev and Shim, Kyuseok}, date = {1998-06}, journaltitle = {SIGMOD Rec.}, volume = {27}, number = {2}, pages = {73--84}, publisher = {ACM}, address = {New York, NY, USA}, issn = {0163-5808}, url = {http://doi.acm.org/10.1145/276305.276312}, acmid = {276312}, issue_date = {June 1998}, nodoi = {10.1145/276305.276312}, numpages = {12} } @misc{Guidanuovocontrattoricercasett22, title = {Guida-Nuovo-Contratto-Ricerca-Sett22}, langid = {english} } @online{GuideIntelligentCode, title = {A {{Guide}} to {{Intelligent Code Completion Using Eclipse Code Recommenders}}}, url = {https://medium.com/codetrails/insert-knowledge-here-a2f71c2862d2} } @online{GuideLowcodePlatforms, title = {A {{Guide}} to {{Low-code Platforms}} - {{Federico Tomassetti}} - {{Software Architect}}}, url = {https://tomassetti.me/a-guide-to-low-code-platforms/}, urldate = {2020-04-08}, keywords = {lowcode} } @inproceedings{Gunawardana:2009:UAB:1639714.1639735, title = {A Unified Approach to Building Hybrid Recommender Systems}, booktitle = {Proc. {{Third ACM Conf}}. {{Recomm}}. {{Syst}}.}, author = {Gunawardana, Asela and Meek, Christopher}, date = {2009}, series = {{{RecSys}} '09}, pages = {117--124}, publisher = {ACM}, location = {New York, NY, USA}, url = {http://doi.acm.org/10.1145/1639714.1639735}, acmid = {1639735}, isbn = {978-1-60558-435-5}, nodoi = {10.1145/1639714.1639735}, numpages = {8}, keywords = {boltzmann machines,cold start,collaborative filtering,content-based filtering,recommender systems} } @inproceedings{Guo:2013:NBS:2540128.2540506, title = {A Novel Bayesian Similarity Measure for Recommender Systems}, booktitle = {Proc. {{Twenty-Third Int}}. {{Jt}}. {{Conf}}. {{Artif}}. {{Intell}}.}, author = {Guo, Guibing and Zhang, Jie and Yorke-Smith, Neil}, date = {2013}, series = {{{IJCAI}} '13}, pages = {2619--2625}, publisher = {AAAI Press}, location = {Beijing, China}, url = {http://dl.acm.org/citation.cfm?id=2540128.2540506}, acmid = {2540506}, isbn = {978-1-57735-633-2}, numpages = {7} } @article{Guo2015, title = {Robust Design Space Modeling}, author = {Guo, Q. and Chen, T. and Zhou, Z.-H. and Temam, O. and Li, L. and Qian, D. and Chen, Y.}, date = {2015}, journaltitle = {ACM Trans. Des. Autom. Electron. Syst.}, volume = {20}, number = {2}, publisher = {Association for Computing Machinery}, issn = {10844309}, doi = {10.1145/2668118}, abbrev_source_title = {ACM Trans. Design Autom. Electron. Syst.}, affiliation = {Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, United States; State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China; National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, 210023, China; Inria, France; Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China; Beihang University, Beijing, 100191, China}, art_number = {18}, correspondence_address1 = {Chen, Y.; State Key Laboratory of Computer Architecture, China}, document_type = {Article}, langid = {english}, source = {Scopus}, note = {cited By 6} } @article{Guo2022547, title = {{{CSI}} Feedback with Model-Driven Deep Learning of Massive {{MIMO}} Systems}, author = {Guo, J. and Wang, L. and Li, F. and Xue, J.}, date = {2022}, journaltitle = {IEEE Commun. Lett.}, volume = {26}, number = {3}, pages = {547--551}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {10897798}, doi = {10.1109/LCOMM.2021.3138927}, abstract = {In order to achieve reliable communication with a high data rate of massive multiple-input multiple-output (MIMO) systems in frequency division duplex (FDD) mode, the estimated channel state information (CSI) at the receiver needs to be fed back to the transmitter. However, the feedback overhead becomes exorbitant with the increasing number of antennas. In this letter, a two stages low rank (TSLR) CSI feedback scheme for millimeter wave (mmWave) massive MIMO systems is proposed to reduce the feedback overhead based on model-driven deep learning. Besides, we design a deep iterative neural network, named FISTA-Net, by unfolding the fast iterative shrinkage thresholding algorithm (FISTA) to achieve more efficient CSI feedback. Moreover, a shrinkage thresholding network (ST-Net) is designed in FISTA-Net based on the attention mechanism, which can choose the threshold adaptively. Simulation results show that the proposed TSLR CSI feedback scheme and FISTA-Net outperform the existing algorithms in various scenarios. © 1997-2012 IEEE.}, coden = {ICLEF}, document_type = {Article}, source = {Scopus}, note = {cited By 0} } @online{guoGraphCodeBERTPretrainingCode2021, ids = {GraphCodeBERT_2021}, title = {{{GraphCodeBERT}}: {{Pre-training Code Representations}} with {{Data Flow}}}, shorttitle = {{{GraphCodeBERT}}}, author = {Guo, Daya and Ren, Shuo and Lu, Shuai and Feng, Zhangyin and Tang, Duyu and Liu, Shujie and Zhou, Long and Duan, Nan and Svyatkovskiy, Alexey and Fu, Shengyu and Tufano, Michele and Deng, Shao Kun and Clement, Colin and Drain, Dawn and Sundaresan, Neel and Yin, Jian and Jiang, Daxin and Zhou, Ming}, date = {2021-09-13}, eprint = {2009.08366}, eprinttype = {arXiv}, eprintclass = {cs}, url = {http://arxiv.org/abs/2009.08366}, urldate = {2023-05-04}, abstract = {Pre-trained models for programming language have achieved dramatic empirical improvements on a variety of code-related tasks such as code search, code completion, code summarization, etc. However, existing pre-trained models regard a code snippet as a sequence of tokens, while ignoring the inherent structure of code, which provides crucial code semantics and would enhance the code understanding process. We present GraphCodeBERT, a pre-trained model for programming language that considers the inherent structure of code. Instead of taking syntactic-level structure of code like abstract syntax tree (AST), we use data flow in the pre-training stage, which is a semantic-level structure of code that encodes the relation of "where-the-value-comes-from" between variables. Such a semantic-level structure is neat and does not bring an unnecessarily deep hierarchy of AST, the property of which makes the model more efficient. We develop GraphCodeBERT based on Transformer. In addition to using the task of masked language modeling, we introduce two structure-aware pre-training tasks. One is to predict code structure edges, and the other is to align representations between source code and code structure. We implement the model in an efficient way with a graph-guided masked attention function to incorporate the code structure. We evaluate our model on four tasks, including code search, clone detection, code translation, and code refinement. Results show that code structure and newly introduced pre-training tasks can improve GraphCodeBERT and achieves state-of-the-art performance on the four downstream tasks. We further show that the model prefers structure-level attentions over token-level attentions in the task of code search.}, issue = {arXiv:2009.08366}, pubstate = {prepublished}, keywords = {Computer Science - Computation and Language,Computer Science - Software Engineering}, note = {arXiv:2009.08366 [cs] \par Comment: Accepted by ICLR2021} } @online{guoLargeLanguageModel2024, title = {Large {{Language Model}} Based {{Multi-Agents}}: {{A Survey}} of {{Progress}} and {{Challenges}}}, shorttitle = {Large {{Language Model}} Based {{Multi-Agents}}}, author = {Guo, Taicheng and Chen, Xiuying and Wang, Yaqi and Chang, Ruidi and Pei, Shichao and Chawla, Nitesh V. and Wiest, Olaf and Zhang, Xiangliang}, date = {2024-04-19}, eprint = {2402.01680}, eprinttype = {arXiv}, eprintclass = {cs}, doi = {10.48550/arXiv.2402.01680}, abstract = {Large Language Models (LLMs) have achieved remarkable success across a wide array of tasks. Due to the impressive planning and reasoning abilities of LLMs, they have been used as autonomous agents to do many tasks automatically. Recently, based on the development of using one LLM as a single planning or decision-making agent, LLM-based multi-agent systems have achieved considerable progress in complex problem-solving and world simulation. To provide the community with an overview of this dynamic field, we present this survey to offer an in-depth discussion on the essential aspects of multi-agent systems based on LLMs, as well as the challenges. Our goal is for readers to gain substantial insights on the following questions: What domains and environments do LLM-based multi-agents simulate? How are these agents profiled and how do they communicate? What mechanisms contribute to the growth of agents' capacities? For those interested in delving into this field of study, we also summarize the commonly used datasets or benchmarks for them to have convenient access. To keep researchers updated on the latest studies, we maintain an open-source GitHub repository, dedicated to outlining the research on LLM-based multi-agent systems.}, pubstate = {prepublished}, keywords = {Computer Science - Artificial Intelligence,Computer Science - Computation and Language,Computer Science - Multiagent Systems}, note = {Comment: This work is ongoing an(“Limes. La Russia cambia il mondo.pdf”)d we welcome your contribution! \par \href{zotero://note/u/LM2HXBBI/}{Annotazioni (16/12/2024, 22:55:42)} \par \href{zotero://note/u/XNLG5RLZ/}{Welcome to Better }Notes} } @article{gurioliThisYouLLM, title = {Is This You, {{LLM}}? {{Recognizing AI-written}} Programs with Multilingual Code Stylometry}, author = {Gurioli, Andrea and Gabbrielli, Maurizio and Zacchiroli, Stefano}, abstract = {With the increasing popularity of LLM-based code completers, like GitHub Copilot, the interest in automatically detecting AI-generated code is also increasing—in particular in contexts where the use of LLMs to program is forbidden by policy due to security, intellectual property, or ethical concerns. We introduce a novel technique for AI code stylometry, i.e., the ability to distinguish code generated by LLMs from code written by humans, based on a transformer-based encoder classifier. Differently from previous work, our classifier is capable of detecting AI-written code across 10 different programming languages with a single machine learning model, maintaining high average accuracy across all languages (84.1\% ± 3.8\%).}, langid = {english}, keywords = {⛔ No DOI found} } @incollection{guthDetailedAnalysisIoT2018, title = {A {{Detailed Analysis}} of {{IoT Platform Architectures}}: {{Concepts}}, {{Similarities}}, and {{Differences}}}, shorttitle = {A {{Detailed Analysis}} of {{IoT Platform Architectures}}}, booktitle = {Internet of {{Everything}}}, author = {Guth, Jasmin and Breitenbücher, Uwe and Falkenthal, Michael and Fremantle, Paul and Kopp, Oliver and Leymann, Frank and Reinfurt, Lukas}, editor = {Di Martino, Beniamino and Li, Kuan-Ching and Yang, Laurence T. and Esposito, Antonio}, date = {2018}, pages = {81--101}, publisher = {Springer Singapore}, location = {Singapore}, doi = {10.1007/978-981-10-5861-5_4}, isbn = {978-981-10-5860-8 978-981-10-5861-5}, langid = {english}, note = {TL;DR \par It is shown that the various components of the different platforms can be mapped to an abstract reference architecture, and the effectiveness of this mapping is analyzed.} } @inproceedings{Gutierrez20171565, title = {Adapting with Honeypot Configurations to Detect Evolving Exploits}, author = {Gutierrez, M. and Kiekintveld, C.}, editor = {Durfee E., Winikoff M., Das S., Larson K.}, date = {2017}, series = {Proceedings of the {{International Joint Conference}} on {{Autonomous Agents}} and {{Multiagent Systems}}, {{AAMAS}}}, volume = {3}, pages = {1565--1567}, publisher = {{International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)}}, issn = {15488403}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85046433349&partnerID=40&md5=5c01c53b2c7284ad6257e977525865f4}, abstract = {Honeypots axe decoy cyberdefense systems placed in a network to entice malicious entities into attacking in order to waste attacker resources and learn information about attack behavior or previously unknown exploits. We focus on the strategic selection of various honeypot configurations in order to adapt to an intelligent attacker amidst a dynamic environment. In order to infiltrate networks, attackers leverage various exploits on the system. However, these exploits and the value they provide dynamically change over time as more information is gathered about them. We intro-duce a model that addresses the combinatorial complexity of the honeypot selection problem and allow for these dynamic exploits. To solve this new problem, we map this model to a Multi-Armed Bandit (MAB) problem, which is a class of machine learning problems that maintain balance between exploration and exploitation. We show empirically that both stochastic and adversarial MAB solutions improve over static defense strategies. © Copyright 2017, International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.}, author_keywords = {Adversarial learning; Exploit; Honeynet; Honeypot; Machine learning; Modelling and simulation}, document_type = {Conference Paper}, isbn = {978-1-5108-5507-6}, source = {Scopus}, keywords = {Adversarial learning,Autonomous agents,Computer aided software engineering,Exploit,Honeynet,Honeypots,Learning systems,Modelling and simulations,Multi agent systems,Network security,Problem solving,Stochastic systems}, note = {cited By 3 \par TL;DR \par This work introduces a model that addresses the combinatorial complexity of the honeypot selection problem and allows for these dynamic exploits on the system and shows empirically that both stochastic and adversarial MAB solutions improve over static defense strategies.} } @book{haddadProceedings2005ACM2005, title = {Proceedings of the 2005 {{ACM Symposium}} on {{Applied Computing}} ({{SAC}}), {{Santa Fe}}, {{New Mexico}}, {{USA}}, {{March}} 13-17, 2005}, editor = {Haddad, Hisham and Liebrock, Lorie M. and Omicini, Andrea and Wainwright, Roger L.}, date = {2005}, publisher = {ACM}, doi = {10.1145/1066677}, isbn = {1-58113-964-0}, note = {TL;DR \par The symposium provides an avenue for discussion and exchange of new ideas addressing computational algorithms and complex applications, reflected in the spectrum of application areas and tutorials designed to provide a wide range of discussion topics during this event.} } @article{hadipourAutomaticWashingSystem2018, title = {Automatic Washing System of {{LED}} Street Lighting via {{Internet}} of {{Things}}}, author = {Hadipour, Morteza and Derakhshandeh, Javad Farrokhi and Shiran, Mohsen Aghazadeh and Rezaei, Reza}, date = {2018-09}, journaltitle = {Internet Things}, volume = {1--2}, pages = {74--80}, issn = {25426605}, doi = {10.1016/j.iot.2018.08.006}, abstract = {The illumination of the streets and public area in metropolitan cities is a vital service, which is not only related to the type of the light but also the dirtiness of the surface of the light. In this paper, both subjects are considered to increase the productivity of the light. To achieve this goal, a novel Automatic washing system (AWS) of LED street/public light surface was designed, manufactured and installed practically. The proposed mechanism consists of two main parts comprising mechanical and electrical systems. AWS operates based on internet interconnection technique known as Internet of Things (IoT) with a high productivity. The system has the potential to be designed and employed by four types of control system; (i) using a timer switch, (ii) using a GSM 900, (iii) using a push button manually by an operator, and (iv) using a remote-control module such as GSM, SIM 808 or GPRS/GPS/SMS through the Ethernet network. A practical system has been manufactured and installed in Kermanshah city in Iran, due to its low cost, low maintenance, upgradability, and feasibility of installing different recognition sensors such as rain and dust sensors. © 2018 Elsevier B.V. All rights reserved.}, langid = {english} } @misc{hadoop, title = {Apache {{Hadoop}}}, url = {https://hadoop.apache.org/} } @article{HaertelHHLV17, title = {Interconnected Linguistic Architecture}, author = {Härtel, Johannes and Härtel, Lukas and Heinz, Marcel and Lämmel, Ralf and Varanovich, Andrei}, date = {2017}, journaltitle = {Art Sci. Eng. Program. J.}, volume = {1}, number = {1}, note = {27 pages.} } @article{Halkidi01onclustering, title = {On Clustering Validation Techniques}, author = {Halkidi, Maria and Batistakis, Yannis and Vazirgiannis, Michalis}, date = {2001}, journaltitle = {J. Intell. Inf. Syst.}, volume = {17}, pages = {107--145} } @article{hallWEKADataMining2009, title = {The {{WEKA}} Data Mining Software: {{An}} Update}, author = {Hall, Mark and Frank, Eibe and Holmes, Geoffrey and Pfahringer, Bernhard and Reutemann, Peter and Witten, Ian H.}, date = {2009-11}, journaltitle = {SIGKDD Explor. Newsl.}, volume = {11}, number = {1}, pages = {10--18}, publisher = {ACM}, address = {New York, NY, USA}, issn = {1931-0145}, url = {http://doi.acm.org/10.1145/1656274.1656278}, acmid = {1656278}, issue_date = {June 2009}, nodoi = {10.1145/1656274.1656278}, numpages = {9} } @incollection{hamidModelDrivenMethodologyApproach2014, title = {A {{Model-Driven Methodology Approach}} for {{Developing}} a {{Repository}} of {{Models}}}, booktitle = {Model and {{Data Engineering}}}, author = {Hamid, Brahim}, date = {2014}, pages = {29--44}, publisher = {Springer}, url = {http://link.springer.com/chapter/10.1007/978-3-319-11587-0_5}, urldate = {2015-10-29} } @article{hamiltonWhatErrorsTell2018, title = {What the {{Errors Tell Us}}}, author = {Hamilton, M. H.}, date = {2018-09}, journaltitle = {IEEE Softw.}, volume = {35}, number = {5}, pages = {32--37}, issn = {0740-7459}, doi = {10.1109/MS.2018.290110447}, abstract = {Margaret Hamilton talks about her experiences over the last 60 years and how a “theory of errors” was derived from the errors made along the way. Its axioms of control led to the Universal Systems Language (USL) together with its automation and preventative paradigm, development-before-the-fact. The pressing issues haven’t gone away, largely because the traditional paradigm continues in force. With a preventative paradigm, most errors aren’t allowed into a system in the first place, just by the way the system is defined. Unlike a traditional approach, with a preventative approach the more reliable the system, the higher the productivity in its lifecycle. This article is part of a theme issue on software engineering’s 50th anniversary.}, keywords = {software engineering}, note = {TL;DR \par Margaret Hamilton talks about her experiences over the last 60 years and how a “theory of errors” was derived from the errors made along the way, which led to the Universal Systems Language (USL) together with its automation and preventative paradigm, development-before-the-fact.} } @article{hammadDeepCloneModelingClones2020, title = {{{DeepClone}}: {{Modeling Clones}} to {{Generate Code Predictions}}}, shorttitle = {{{DeepClone}}}, author = {Hammad, Muhammad and Babur, Önder and Basit, Hamid Abdul and family=Brand, given=Mark, prefix=van den, useprefix=false}, date = {2020-12-05}, journaltitle = {ArXiv200711671 Cs}, eprint = {2007.11671}, eprinttype = {arXiv}, eprintclass = {cs}, url = {http://arxiv.org/abs/2007.11671}, urldate = {2021-02-02}, abstract = {Programmers often reuse code from source code repositories to reduce the development effort. Code clones are candidates for reuse in exploratory or rapid development, as they represent often repeated functionality in software systems. To facilitate code clone reuse, we propose DeepClone, a novel approach utilizing a deep learning algorithm for modeling code clones to predict the next set of tokens (possibly a complete clone method body) based on the code written so far. The predicted tokens require minimal customization to fit the context. DeepClone applies natural language processing techniques to learn from a large code corpus, and generates code tokens using the model learned. We have quantitatively evaluated our solution to assess (1) our model's quality and its accuracy in token prediction, and (2) its performance and effectiveness in clone method prediction. We also discuss various application scenarios for our approach.}, keywords = {Computer Science - Software Engineering}, note = {Comment: 16 pages} } @inproceedings{HammoudehGarcia2019329, title = {Bootstrapping {{MDE}} Development from {{ROS}} Manual Code - Part 1: {{Metamodeling}}}, author = {Hammoudeh Garcia, N. and Ludtke, M. and Kortik, S. and Kahl, B. and Bordignon, M.}, date = {2019}, series = {Proceedings - 3rd {{IEEE International Conference}} on {{Robotic Computing}}, {{IRC}} 2019}, pages = {329--336}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/IRC.2019.00060}, abbrev_source_title = {Proc. - IEEE Int. Conf. Robot. Comput., IRC}, affiliation = {Fraunhofer Institute for Manufacturing Engineering and Automation IPA, Nobelstr. 12, Stuttgart, 70569, Germany}, art_number = {8675668}, document_type = {Conference Paper}, isbn = {978-1-5386-9245-5}, langid = {english}, source = {Scopus}, note = {cited By 11 \par TL;DR \par A family of three metamodels to respectively model ROS nodes, communication interfaces, and systems composed from subsystems are presented, used to model ROS systems of arbitrary complexity and generate with correctness guarantees the software artifacts for their composition and deployment.} } @article{Hamrani2021, title = {Machine Learning Surrogate Modeling for Meshless Methods: {{Leveraging}} Universal Approximation}, author = {Hamrani, A. and Akbarzadeh, A. and Madramootoo, C.A. and Bouarab, F.Z.}, date = {2021}, journaltitle = {Int. J. Comput. Methods}, publisher = {World Scientific}, issn = {02198762}, doi = {10.1142/S021987622141022X}, abbrev_source_title = {Int. J. Comput. Methods}, abstract = {This paper presents a machine learning (ML) surrogate modeling for fast processing in meshless/meshfree methods. The main idea is to leverage the universal approximation (UA) propriety of supervised ML models (shallow/deep learning and other regression models) to surrogate the heavy shape function construction in meshless methods. The resulting ML metamodel preserves the same accuracy of the meshless interpolation while avoiding costly matrix inversion operations. The total computation time for solving 3D test simulation problems (using more than 20k nodes) is reduced by a factor of 1k in the case of the Gaussian process (GP) metamodel. © 2021 World Scientific Publishing Company.}, affiliation = {Department of Mechanical and Materials Engineering, Florida International University, Miami, FL, United States; Department of Bioresource Engineering, McGill University, Montreal, QC H9X3V9, Canada; Department of Mechanical Engineering, McGill University, Montreal, QC H3A 0C3, Canada; UR-MPE Université m'Hamed Bougara, rue de la liberté, Boumerdès, 35000, Algeria}, art_number = {2141022}, correspondence_address1 = {Hamrani, A.; Department of Mechanical and Materials Engineering, United States; email: hamrani.abderrachid@gmail.com}, document_type = {Article}, source = {Scopus}, keywords = {notion}, note = {cited By 0 \par cited By 0 \par TL;DR \par The main idea is to leverage the universal approximation (UA) propriety of supervised ML models (shallow/deep learning and other regression models) to surrogate the heavy shape function construction in meshless methods.} } @article{Han2019108, title = {Adversary Resistant Deep Neural Networks via Advanced Feature Nullification}, author = {Han, K. and Li, Y. and Hang, J.}, date = {2019}, journaltitle = {Knowl.-Based Syst.}, volume = {179}, pages = {108--116}, publisher = {Elsevier B.V.}, issn = {09507051}, doi = {10.1016/j.knosys.2019.05.007}, abstract = {Deep neural networks (DNNs) have been achieving excellent performance in many learning tasks. However, recent studies reveal that DNNs are vulnerable to adversarial examples. Fortunately, a random feature nullification (RFN) algorithm is proposed to improve the robustness of DNNs against gradient-based adversarial examples. However, experimental results demonstrate that RFN ruins the availability of DNNs in some cases. To explore more efficient feature nullification (FN) algorithms, we theoretically prove that FN can improve the robustness of DNNs. Moreover, sliding window feature nullification (SWFN) and fixed stride feature nullification (FSFN) algorithms are proposed to improve the robustness of DNNs. The experimental results demonstrate that compared to RFN, the proposed algorithms can maintain the availability of DNNs without decreasing its robustness against gradient-based attacks. © 2019 Elsevier B.V.}, author_keywords = {Adversarial machine learning; Deep learning; Feature nullification; Hadamard product}, coden = {KNSYE}, document_type = {Article}, source = {Scopus}, keywords = {Artificial intelligence,Deep learning,Deep neural networks,Feature nullification,Gradient based,Hadamard products,Knowledge based systems,Learning tasks,Random features,Sliding Window,Software engineering}, note = {cited By 4} } @article{Han20201980, title = {Deep Learning-Based {{FDD}} Non-Stationary Massive {{MIMO}} Downlink Channel Reconstruction}, author = {Han, Y. and Li, M. and Jin, S. and Wen, C.-K. and Ma, X.}, date = {2020}, journaltitle = {IEEE J. Sel. Areas Commun.}, volume = {38}, number = {9}, pages = {1980--1993}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {07338716}, doi = {10.1109/JSAC.2020.3000836}, abstract = {This paper proposes a model-driven deep learning-based downlink channel reconstruction scheme for frequency division duplexing (FDD) massive multi-input multi-output (MIMO) systems. The spatial non-stationarity, which is the key feature of the future extremely large aperture massive MIMO system, is considered. Instead of the channel matrix, the channel model parameters are learned by neural networks to save the overhead and improve the accuracy of channel reconstruction. By viewing the channel as an image, we introduce You Only Look Once (YOLO), a powerful neural network for object detection, to enable a rapid estimation process of the model parameters, including the detection of angles and delays of the paths and the identification of visibility regions of the scatterers. The deep learning-based scheme avoids the complicated iterative process introduced by the algorithm-based parameter extraction methods. A low-complexity algorithm-based refiner further refines the YOLO estimates toward high accuracy. Given the efficiency of model-driven deep learning and the combination of neural network and algorithm, the proposed scheme can rapidly and accurately reconstruct the non-stationary downlink channel. Moreover, the proposed scheme is also applicable to widely concerned stationary systems and achieves comparable reconstruction accuracy as an algorithm-based method with greatly reduced time consumption. © 1983-2012 IEEE.}, art_number = {9110882}, coden = {ISACE}, document_type = {Conference Paper}, source = {Scopus}, note = {cited By 10 \par TL;DR \par Seeing the channel as an image, You Only Look Once (YOLO), a powerful neural network for object detection, is introduced to enable a rapid estimation process of the model parameters, including the detection of angles and delays of the paths and the identification of visibility regions of the scatterers.} } @online{HandsonManageYour, title = {Hands-on: {{Manage}} Your Devices with {{Lightweight M2M}} and Connect Them to Your Cloud | {{EclipseCon Europe}} 2016}, url = {https://www.eclipsecon.org/europe2016/session/hands-manage-your-devices-lightweight-m2m-and-connect-them-your-cloud}, urldate = {2016-09-27} } @inproceedings{happelPotentialsChallengesRecommendation2008, title = {Potentials and Challenges of Recommendation Systems for Software Development}, booktitle = {Proc. 2008 {{Int}}. {{Workshop Recomm}}. {{Syst}}. {{Softw}}. {{Eng}}. - {{RSSE}} 08}, author = {Happel, Hans-Jörg and Maalej, Walid}, date = {2008}, pages = {11}, publisher = {ACM Press}, location = {Atlanta, Georgia}, url = {http://portal.acm.org/citation.cfm?doid=1454247.1454251}, urldate = {2019-06-13}, abstract = {By surveying recommendation systems in software development, we found that existing approaches have been focusing on “you might like what similar developers like” scenarios. However structured artifacts and semantically well-defined development activities bear large potentials for further recommendation scenarios. We introduce a novel “landscape” of software development recommendation systems and line out several scenarios for knowledge sharing and collaboration. Basic challenges are improving context-awareness and particularly addressing information providers.}, isbn = {978-1-60558-228-3}, langid = {english}, nodoi = {10.1145/1454247.1454251}, note = {TL;DR \par A novel "landscape" of software development recommendation systems is introduced and several scenarios for knowledge sharing and collaboration are line out.} } @inproceedings{Harel-Canada2020851, title = {Is Neuron Coverage a Meaningful Measure for Testing Deep Neural Networks?}, author = {Harel-Canada, F. and Wang, L. and Gulzar, M.A. and Gu, Q. and Kim, M.}, editor = {Devanbu P., Cohen M., Zimmermann T.}, date = {2020}, series = {{{ESEC}}/{{FSE}} 2020 - {{Proceedings}} of the 28th {{ACM Joint Meeting European Software Engineering Conference}} and {{Symposium}} on the {{Foundations}} of {{Software Engineering}}}, pages = {851--862}, publisher = {Association for Computing Machinery, Inc}, doi = {10.1145/3368089.3409754}, abstract = {Recent effort to test deep learning systems has produced an intuitive and compelling test criterion called neuron coverage (NC), which resembles the notion of traditional code coverage. NC measures the proportion of neurons activated in a neural network and it is implicitly assumed that increasing NC improves the quality of a test suite. In an attempt to automatically generate a test suite that increases NC, we design a novel diversity promoting regularizer that can be plugged into existing adversarial attack algorithms. We then assess whether such attempts to increase NC could generate a test suite that (1) detects adversarial attacks successfully, (2) produces natural inputs, and (3) is unbiased to particular class predictions. Contrary to expectation, our extensive evaluation finds that increasing NC actually makes it harder to generate an effective test suite: higher neuron coverage leads to fewer defects detected, less natural inputs, and more biased prediction preferences. Our results invoke skepticism that increasing neuron coverage may not be a meaningful objective for generating tests for deep neural networks and call for a new test generation technique that considers defect detection, naturalness, and output impartiality in tandem. © 2020 Owner/Author.}, author_keywords = {Adversarial Attack; Machine Learning; Neuron Coverage; Software Engineering; Testing}, document_type = {Conference Paper}, isbn = {978-1-4503-7043-1}, source = {Scopus}, keywords = {Class prediction,Code coverage,Deep learning,Deep neural networks,Defect detection,Defects,Learning systems,Neural networks,Neurons,Regularizer,Software engineering,Test criteria,Test generations,Testing}, note = {cited By 47 \par TL;DR \par The results invoke skepticism that increasing neuron coverage may not be a meaningful objective for generating tests for deep neural networks and call for a new test generation technique that considers defect detection, naturalness, and output impartiality in tandem.} } @article{harelAutonomicsSearchFoundation2020, title = {Autonomics: {{In}} Search of a Foundation for next-Generation Autonomous Systems}, shorttitle = {Autonomics}, author = {Harel, David and Marron, Assaf and Sifakis, Joseph}, date = {2020-07-28}, journaltitle = {Proc. Natl. Acad. Sci. U.S.A.}, volume = {117}, number = {30}, pages = {17491--17498}, issn = {0027-8424, 1091-6490}, doi = {10.1073/pnas.2003162117}, abstract = {Significance Autonomous systems are replacing humans in a variety of tasks, and in the years to come, such systems will become central and crucial to human life. They will include vehicles of all kinds, medical and industrial robots, agricultural and manufacturing facilities, traffic management systems, and much more. While many organizations strive to develop the next generation of trustworthy, cost-effective autonomous systems, a major gap exists between the challenges in developing these and the state of the art. There is a crucial need for a common scientific and engineering foundation for developing these systems, which we term “autonomics.” We believe that such a foundation will dramatically accelerate the deployment and acceptance of high-quality autonomous systems, for the benefit of human society. , The potential benefits of autonomous systems are obvious. However, there are still major issues to be dealt with before developing such systems becomes a commonplace engineering practice, with accepted and trustworthy deliverables. We argue that a solid, evolving, publicly available, community-controlled foundation for developing next-generation autonomous systems is a must, and term the desired foundation “autonomics.” We focus on three main challenges: 1) how to specify autonomous system behavior in the face of unpredictability; 2) how to carry out faithful analysis of system behavior with respect to rich environments that include humans, physical artifacts, and other systems; and 3) how to build such systems by combining executable modeling techniques from software engineering with artificial intelligence and machine learning.}, langid = {english}, note = {TL;DR \par It is argued that a solid, evolving, publicly available, community-controlled foundation for developing next-generation autonomous systems is a must, and term the desired foundation “autonomics.”} } @article{harelCreatingFoundationNextGeneration2022, title = {Creating a {{Foundation}} for {{Next-Generation Autonomous Systems}}}, author = {Harel, David and Marron, Assaf and Sifakis, Joseph}, date = {2022-02}, journaltitle = {IEEE Des. Test}, volume = {39}, number = {1}, pages = {49--56}, issn = {2168-2356, 2168-2364}, doi = {10.1109/MDAT.2021.3069959}, note = {TL;DR \par The need for a new autonomics foundation with a focus on decision-making logic and its processes for building trustworthy autonomous systems is advocated.} } @incollection{harelDevelopmentReactiveSystems1985, title = {On the {{Development}} of {{Reactive Systems}}}, booktitle = {Logics and {{Models}} of {{Concurrent Systems}}}, author = {Harel, D. and Pnueli, A.}, editor = {Apt, Krzysztof R.}, date = {1985}, pages = {477--498}, publisher = {Springer Berlin Heidelberg}, location = {Berlin, Heidelberg}, doi = {10.1007/978-3-642-82453-1_17}, isbn = {978-3-642-82455-5 978-3-642-82453-1}, langid = {english}, note = {TL;DR \par The recently proposed statechart method is recommended for finding satisfactory methods for behavioral description in reactive systems, observing that most reactive systems cannot be developed in a linear stepwise fashion, but, rather, give rise to a two-dimensional development process, featuring behavioral aspects in the one dimension and implementational ones in the other.} } @article{harelLaborDivisionMovable2019, title = {Labor {{Division}} with {{Movable Walls}}: {{Composing Executable Specifications}} with {{Machine Learning}} and {{Search}} ({{Blue Sky Idea}})}, shorttitle = {Labor {{Division}} with {{Movable Walls}}}, author = {Harel, David and Marron, Assaf and Rosenfeld, Ariel and Vardi, Moshe and Weiss, Gera}, date = {2019-07-17}, journaltitle = {AAAI}, volume = {33}, number = {01}, pages = {9770--9774}, issn = {2374-3468, 2159-5399}, doi = {10.1609/aaai.v33i01.33019770}, abstract = {Artificial intelligence (AI) techniques, including, e.g., machine learning, multi-agent collaboration, planning, and heuristic search, are emerging as ever-stronger tools for solving hard problems in real-world applications. Executable specification techniques (ES), including, e.g., Statecharts and scenario-based programming, is a promising development approach, offering intuitiveness, ease of enhancement, compositionality, and amenability to formal analysis. We propose an approach for integrating AI and ES techniques in developing complex intelligent systems, which can greatly simplify agile/spiral development and maintenance processes. The approach calls for automated detection of whether certain goals and sub-goals are met; a clear division between sub-goals solved with AI and those solved with ES; compositional and incremental addition of AI-based or ES-based components, each focusing on a particular gap between a current capability and a well-stated goal; and, iterative refinement of sub-goals solved with AI into smaller sub-sub-goals where some are solved with ES, and some with AI. We describe the principles of the approach and its advantages, as well as key challenges and suggestions for how to tackle them.} } @article{Hart201228, title = {An Advanced Cost Estimation Methodology for Engineering Systems}, author = {Hart, C.G. and He, Z. and Sbragio, R. and Vlahopoulos, N.}, date = {2012}, journaltitle = {Syst. Eng.}, volume = {15}, number = {1}, pages = {28--40}, issn = {10981241}, doi = {10.1002/sys.20192}, abbrev_source_title = {Syst. Eng.}, affiliation = {Naval Architecture and Marine Engineering Department, College of Engineering, University of Michigan, Ann Arbor, MI 48105, United States; Michigan Engineering Services, LLC, Ann Arbor, MI 48105, United States}, correspondence_address1 = {Hart, C.G.3293 Taney Lane, Falls Church, VA 22042, United States; email: hartcg@umich.edu}, document_type = {Article}, langid = {english}, source = {Scopus}, note = {cited By 11} } @article{hartelClassificationAPIsHierarchical2018, title = {Classification of {{APIs}} by {{Hierarchical Clustering}}}, author = {Härtel, Johannes}, date = {2018}, pages = {11}, abstract = {APIs can be classified according to the programming domains (e.g., GUIs, databases, collections, or security) that they address. Such classification is vital in searching repositories (e.g., the Maven Central Repository for Java) and for understanding the technology stack used in software projects. We apply hierarchical clustering to a curated suite of Java APIs to compare the computed API clusters with preexisting API classifications. Clustering entails various parameters (e.g., the choice of IDF versus LSI versus LDA). We describe the corresponding variability in terms of a feature model. We exercise all possible configurations to determine the maximum correlation with respect to two baselines: i) a smaller suite of APIs manually classified in previous research; ii) a larger suite of APIs from the Maven Central Repository, thereby taking advantage of crowd-sourced classification while relying on a threshold-based approach for identifying important APIs and versions thereof, subject to an API dependency analysis on GitHub. We discuss the configurations found in this way and we examine the influence of particular features on the correlation between computed clusters and baselines. To this end, we also leverage interactive exploration of the parameter space and the resulting dendrograms. In this manner, we can also identify issues with the use of classifiers (e.g., missing classifiers) in the baselines and limitations of the clustering approach.}, langid = {english}, note = {TL;DR \par This work applies hierarchical clustering to a curated suite of Java APIs to compare the computed API clusters with preexisting API classifications and examines the influence of particular features on the correlation between computed clusters and baselines.} } @inproceedings{Hartmann2019300, title = {Meta-Modelling Meta-Learning}, author = {Hartmann, T. and Moawad, A. and Schockaert, C. and Fouquet, F. and Le Traon, Y.}, editor = {Kessentini M., Yue T., Pretschner A., Voss S., Burgueno L., Burgueno L., Yue T.}, date = {2019}, series = {Proceedings - 2019 {{ACM}}/{{IEEE}} 22nd {{International Conference}} on {{Model Driven Engineering Languages}} and {{Systems}}, {{MODELS}} 2019}, pages = {300--305}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/MODELS.2019.00014}, abbrev_source_title = {Proc. - ACM/IEEE Int. Conf. Model Driven Eng. Lang. Syst., MODELS}, affiliation = {DataThings, University of Luxembourg, Luxembourg, Luxembourg; Paul Wurth, Luxembourg, Luxembourg; University of Luxembourg, Luxembourg, Luxembourg}, art_number = {8906948}, document_type = {Conference Paper}, isbn = {978-1-7281-2535-0}, langid = {english}, source = {Scopus}, note = {cited By 6} } @article{hartmannNextEvolutionMDE2017, title = {The next Evolution of {{MDE}}: A Seamless Integration of Machine Learning into Domain Modeling}, shorttitle = {The next Evolution of {{MDE}}}, author = {Hartmann, Thomas and Moawad, Assaad and Fouquet, Francois and Traon, Yves Le}, date = {2017-05-29}, journaltitle = {Software Syst. Model.}, issn = {1619-1366, 1619-1374}, doi = {10.1007/s10270-017-0600-2}, abstract = {Machine learning algorithms are designed to resolve unknown behaviours by extracting commonalities over massive datasets. Unfortunately, learning such global behaviours can be inaccurate and slow for systems composed of heterogeneous elements, which behave very differently, for instance as it is the case for cyber-physical systems and Internet of Things applications. Instead, to make smart decisions, such systems have to continuously refine the behaviour on a per-element basis and compose these small learning units together. However, combining and composing learned behaviours from different elements is challenging and requires domain knowledge. Therefore, there is a need to structure and combine the learned behaviours and domain knowledge together in a flexible way. In this paper we propose to weave machine learning into domain modeling. More specifically, we suggest to decompose machine learning into reusable, chainable, and independently computable small learning units, which we refer to as micro learning units. These micro learning units are modeled together with and at the same level as the domain data. We show, based on a smart grid case study, that our approach can be significantly more accurate than learning a global behaviour while the performance is fast enough to be used for live learning.}, langid = {english}, keywords = {/unread} } @article{hassamAssistanceSystemOCL2011, title = {Assistance {{System}} for {{OCL Constraints Adaptation}} during {{Metamodel Evolution}}}, author = {Hassam, Kahina and Sadou, Salah and Gloahec, Vincent Le and Fleurquin, Regis}, date = {2011}, journaltitle = {2011 15th Eur. Conf. Softw. Maint. Reengineering}, pages = {151--160}, doi = {10.1109/CSMR.2011.21} } @article{hassanAINativeSoftwareEngineering, title = {Towards {{AI-Native Software Engineering}} ({{SE}} 3.0): {{A Vision}} and a {{Challenge Roadmap}}}, author = {Hassan, Ahmed E and Oliva, Gustavo A and Lin, Dayi and Chen, Boyuan}, journaltitle = {J. ACM}, volume = {37}, number = {4}, langid = {english}, keywords = {LOGSEQ,MOSAICO} } @article{hassanRethinkingSoftwareEngineering, title = {Rethinking {{Software Engineering}} in the {{Foundation Model Era}}: {{From Task-Driven AI Copilots}} to {{Goal-Driven AI Pair Programmers}}}, author = {Hassan, Ahmed E and Oliva, Gustavo A and Lin, Dayi and Chen, Boyuan and Ming, Zhen}, abstract = {The advent of Foundation Models (FMs) and AI-powered copilots has transformed the landscape of software development, offering unprecedented code completion capabilities and enhancing developer productivity. However, the current task-driven nature of these copilots falls short in addressing the broader goals and complexities inherent in software engineering (SE). In this paper, we propose a paradigm shift towards goal-driven AI-powered pair programmers that collaborate with human developers in a more holistic and context-aware manner. We envision AI pair programmers that are goal-driven, human partners, SE-aware, and self-learning. These AI partners engage in iterative, conversation-driven development processes, aligning closely with human goals and facilitating informed decision-making. We discuss the desired attributes of such AI pair programmers and outline key challenges that must be addressed to realize this vision. Ultimately, our work represents a shift from AI-augmented SE to AI-transformed SE by replacing code completion with a collaborative partnership between humans and AI that enhances both productivity and software quality.}, langid = {english}, keywords = {LOGSEQ} } @online{hassanRethinkingSoftwareEngineering2024, title = {Rethinking {{Software Engineering}} in the {{Foundation Model Era}}: {{A Curated Catalogue}} of {{Challenges}} in the {{Development}} of {{Trustworthy FMware}}}, shorttitle = {Rethinking {{Software Engineering}} in the {{Foundation Model Era}}}, author = {Hassan, Ahmed E. and Lin, Dayi and Rajbahadur, Gopi Krishnan and Gallaba, Keheliya and Cogo, Filipe R. and Chen, Boyuan and Zhang, Haoxiang and Thangarajah, Kishanthan and Oliva, Gustavo Ansaldi and Lin, Jiahuei and Abdullah, Wali Mohammad and Jiang, Zhen Ming}, date = {2024-03-03}, eprint = {2402.15943}, eprinttype = {arXiv}, eprintclass = {cs}, doi = {10.48550/arXiv.2402.15943}, abstract = {Foundation models (FMs), such as Large Language Models (LLMs), have revolutionized software development by enabling new use cases and business models. We refer to software built using FMs as FMware. The unique properties of FMware (e.g., prompts, agents, and the need for orchestration), coupled with the intrinsic limitations of FMs (e.g., hallucination) lead to a completely new set of software engineering challenges. Based on our industrial experience, we identified 10 key SE4FMware challenges that have caused enterprise FMware development to be unproductive, costly, and risky. In this paper, we discuss these challenges in detail and state the path for innovation that we envision. Next, we present FMArts, which is our long-term effort towards creating a cradle-to-grave platform for the engineering of trustworthy FMware. Finally, we (i) show how the unique properties of FMArts enabled us to design and develop a complex FMware for a large customer in a timely manner and (ii) discuss the lessons that we learned in doing so. We hope that the disclosure of the aforementioned challenges and our associated efforts to tackle them will not only raise awareness but also promote deeper and further discussions, knowledge sharing, and innovative solutions across the software engineering discipline.}, pubstate = {prepublished}, keywords = {Computer Science - Artificial Intelligence,Computer Science - Software Engineering}, note = {TL;DR \par This paper identifies 10 key SE4FMware challenges that have caused enterprise FMware development to be unproductive, costly, and risky and presents FMArts, which is the long-term effort towards creating a cradle-to-grave platform for the engineering of trustworthy FMware.} } @article{haugeAdoptionOpenSource2010, title = {Adoption of Open Source Software in Software-Intensive Organizations – {{A}} Systematic Literature Review}, author = {Hauge, Øyvind and Ayala, Claudia and Conradi, Reidar}, date = {2010-11}, journaltitle = {Inf. Softw. Technol.}, volume = {52}, number = {11}, pages = {1133--1154}, issn = {09505849}, doi = {10.1016/j.infsof.2010.05.008}, langid = {english} } @inproceedings{haugeEmpiricalStudySelection2009, title = {An Empirical Study on Selection of {{Open Source Software-Preliminary}} Results}, booktitle = {Emerg. {{Trends Free}}. {{Source Softw}}. {{Res}}. {{Dev}}. 2009 {{FLOSS09 ICSE Workshop On}}}, author = {Hauge, Oyvind and Osterlie, Thomas and Sorensen, Carl-Fredrik and Gerea, Marinela}, date = {2009}, pages = {42--47}, publisher = {IEEE}, url = {http://ieeexplore.ieee.org/abstract/document/5071359/}, urldate = {2017-02-25} } @article{haugheyNOSQLDataLake2017, title = {{{NOSQL}} and {{Data Lake Architecture}}}, author = {Haughey, Tom}, date = {2017}, pages = {28}, langid = {english} } @article{hauslerEstimationImpactVisualization2024, title = {Estimation, {{Impact}} and {{Visualization}} of {{Schema Evolution}} in {{Graph Databases}}}, author = {Hausler, Dominique}, date = {2024}, abstract = {Graph databases offer a flexible storage of interconnected data. Due to NoSQL databases being schemaless, heterogeneous data can occur when performing data changes. Evolution is conducted through so called evolution operations like add, rename, delete, merge, copy, move or split. As a user can not foresee the results of the evolution operation neither the amount of data changes nor the possible schema violations or a relaxed schema, a system to show the impact of evolution is essential. To ensure schema conformity, we present an approach to close the gap of a schema management tool for graph databases in order to estimate and illustrate the impact of evolution on the schema level. To illustrate, explore, evolve and change the schema all required information is handled through a schema management layer. Besides extracting the schema, so called structure profiles are designed for an initial data exploration. The preview of the schema and structure profiles shows the impact of evolution on the data by comparing versions. Moreover, the system uses an intuitive syntax to also enable the usage of graph databases for non-experts.}, langid = {english}, keywords = {LOGSEQ}, note = {\section{Annotazioni\\ (22/7/2024, 16:05:35)} \par - “Schema Evolution in Graph Databases” (Hausler, 2024, p. 1) \#5fb236 \par - “Due to NoSQL databases being schemaless, heterogeneous data can occur when performing data changes.” (Hausler, 2024, p. 1) \#5fb236 \par - “As a user can not foresee the results of the evolution operation neither the amount of data changes nor the possible schema violations or a relaxed schema, a system to show the impact of evolution is essential.” (Hausler, 2024, p. 1) \#a28ae5 \par - “estimate and illustrate the impact of evolution on the schema level” (Hausler, 2024, p. 1) \#5fb236 \par - “structure profiles are designed for an initial data exploration” (Hausler, 2024, p. 1) \#5fb236 \par - “Due to graph database offering schemalessness, heterogeneity can occur through optional elements or structural error.” (Hausler, 2024, p. 1) \#5fb236 \par - “our goal is to involve users by informing them about such occurrences and giving the option to make changes if needed.” (Hausler, 2024, p. 1) \#5fb236 \par - “Schema Management Layer (SML)” (Hausler, 2024, p. 1) \#5fb236 \par - “schema and statistical information can be extracted, compared and constraints detected.” (Hausler, 2024, p. 1) \#ffd400\\ \mkbibemph{This seems not new. What's the novelty with respect to existing work? I'm missing relevant work from Atzeni et al. like: Paolo Atzeni,~Francesca Bugiotti,~Luca Cabibbo,~Riccardo Torlone:\\ Data modeling in the NoSQL world.~Comput. Stand. Interfaces~67~(2020) Paolo Atzeni,~Francesca Bugiotti,~Luca Rossi:\\ Uniform access to NoSQL systems.~Inf. Syst.~43:~117-133~(2014) Francesca Bugiotti,~Luca Cabibbo,~Paolo Atzeni,~Riccardo Torlone:\\ Database Design for NoSQL Systems.~ER~2014:~223-231 Concerning the evolution management, the author should also consider: Maxime Gobert,~Loup Meurice,~Anthony Cleve:\\ Modeling, manipulating and evolving hybrid polystores with HyDRa.~Sci. Comput. Program.~230:~102972~(2023) Maxime Gobert,~Csaba Nagy,~Henrique Rocha,~Serge Demeyer,~Anthony Cleve:\\ Best practices of testing database manipulation code.~Inf. Syst.~111:~102105~(2023) Anthony Cleve,~Maxime Gobert,~Loup Meurice,~Jerome Maes,~Jens H. Weber:\\ Understanding database schema evolution: A case study.~Sci. Comput. Program.~97:~113-121~(2015) Compared to such related work, I see potential novelty mainly concerning RQ3 and RQ4.} \par - “the schema extraction paired with the information extraction for structure profiles is new” (Hausler, 2024, p. 1) \#ffd400\\ \mkbibemph{Why? What's the limitation of existing work?} \par - “RQ3: How to estimate the effort and illustrate the impact of evolution operations as well as the affected elements? • RQ4: What kind of optimizations are needed?” (Hausler, 2024, p. 2) \#ffd400\\ \mkbibemph{Maybe this part is new. Concerning RQ1 and RQ2, they are related to existing work that is not mentioned.} \par - “his limitation is due to schema evolution affecting only the property names np .” (Hausler, 2024, p. 3) \#5fb236 \par - “estimate and display the affected elements through the Preview Module.” (Hausler, 2024, p. 4) \#ffd400\\ \mkbibemph{What do you mean with estimate? What kinds of estimation are you supporting?} \par - “Schema evolution is conducted through the evolution operations add, rename, delete, copy, move, split and merge” (Hausler, 2024, p. 5) \#5fb236 \par - “estimation of the impact of evolution on the database” (Hausler, 2024, p. 5) \#5fb236 \par - “easy-to-understand language called GEO.” (Hausler, 2024, p. 5) \#a28ae5} } @inproceedings{haveliwalaTopicsensitivePageRank2002, title = {Topic-Sensitive {{PageRank}}}, booktitle = {Proc. 11th {{Int}}. {{Conf}}. {{World Wide Web}}}, author = {Haveliwala, Taher H.}, date = {2002}, series = {{{WWW}} '02}, pages = {517--526}, publisher = {ACM}, location = {New York, NY, USA}, url = {http://doi.acm.org/10.1145/511446.511513}, acmid = {511513}, isbn = {1-58113-449-5}, nodoi = {10.1145/511446.511513}, numpages = {10}, keywords = {link structure,PageRank,personalized search,search,search in context,web graph} } @article{HCI-009, title = {Collaborative Filtering Recommender Systems}, author = {Ekstrand, Michael D. and Riedl, John T. and Konstan, Joseph A.}, date = {2011}, journaltitle = {Found. Trends® Human–Computer Interact.}, volume = {4}, number = {2}, pages = {81--173}, issn = {1551-3955}, url = {http://dx.doi.org/10.1561/1100000009}, nodoi = {10.1561/1100000009}, note = {TL;DR \par This study presents an overview of the field of recommender systems with current generation of recommendation methods and examines comprehensively CF systems with its algorithms.} } @article{he0216JSSOFTWARED23011822024, title = {02-16-{{JSSOFTWARE-D-23-01182}}}, shorttitle = {Bit}, author = {He, Xiao and Zan, Tao}, date = {2024}, doi = {10.2139/ssrn.4686181}, abstract = {Model-driven development is a model-centric software development paradigm that automates the development process by converting high-level models into low-level code and documents. To maintain synchronization between models and code/documents—which can evolve independently—this paper introduces BIT, a bidirectional language that can serve as a conventional template language for model-to-text transformations. However, a BIT program can function as both a printer, generating text by filling template holes with values from the input model, and a parser, putting parsed values back into the model. BIT comprises a surface language for better usability and a core language for formal definition. We define the semantics of the core language based on the theory of bidirectional transformation, and provide the translation from the surface to the core. We present the proof sketch of the well behavedness of BIT as a formal evidence of soundness. We also conduct two preliminary case studies to empirically demonstrate the expressiveness of BIT. Based on the proof and the case studies, BIT covers the major features of existing template languages, and offers sufficient expressiveness to define real-world model-to-text transformations that can be executed bidirectionally and incrementally.}, langid = {english}, keywords = {LOGSEQ}, note = {\section{Annotazioni\\ (5/3/2024, 12:21:10)} \par - “To maintain synchronization between models and code/documents—which can evolve independently—this paper introduces BIT, a bidirectional language that can serve as a conventional template language for modelto-text transformations.” (He e Zan, 2024, p. 0) \#f0ff00\\ \mkbibemph{Does it solve the problem with target protected areas? } \par - “However, a BIT program can function as both a printer, generating text by filling template holes with values from the input model, and a parser, putting parsed values back into the model.” (He e Zan, 2024, p. 1) \#f0ff00\\ \mkbibemph{Is TCS in the related work? } \par - “very few solutions for synchronizing models and text.” (He e Zan, 2024, p. 2) \#f0ff00\\ \mkbibemph{what about boomerang? And TCS? I would have introduced a motivating example to show when and how existing works fall short. } \par - “Bidirectional transformation (BX) [22, 23, 24, 25, 26, 27, 28] can serve as the foundation of data synchronization. A BX program is a single specification that can be consistently evaluated in” (He e Zan, 2024, p. 3) \#f0ff00\\ \mkbibemph{have you considered JTL, QVT relation, etc.? } \par - “their approach is limited code generation from Ecore models and is not generally applicable.” (He e Zan, 2024, p. 3) \#f0ff00\\ \mkbibemph{do you plan to propose an approach that works on any ecosystem further than Ecore? } \par - “two challenges” (He e Zan, 2024, p. 3) \#f0ff00\\ \mkbibemph{it is nessary to give evidence of such issues by showing an explanatory examples that cannot be managed by existing appoaches. } \par - “As for control directives, Xtend templates support loops (e.g., lines 6–14), conditions (e.g., lines 7–10), and assignments (e.g., lines 5 and 8). Within an Xtend template, other templates may be invoked.” (He e Zan, 2024, p. 5) \#00b036\\ \mkbibemph{ } \par - “Figure 3: Demonstration of BIT template (colored background shows the changed text layout)” (He e Zan, 2024, p. 6) \#f0ff00\\ \mkbibemph{what about the attribute no? I think it should be in the parsed output, isn't it? } \par - “with a lexical rule that guides our approach in the parsing mode. For example, «no|INT» in line 10 indicates that this hole will be filled with a string that is produced by the expression no and conforms to lexical rule INT, where the rule is defined by regular expression -?[0-9]+” (He e Zan, 2024, p. 6) \#00b036\\ \mkbibemph{ } \par - “infer a lexical rule.” (He e Zan, 2024, p. 6) \#f0ff00\\ \mkbibemph{how? Is there any default rule that is applied? } \par - “of the third paragraph will be "Appreciation", rather than "appreciation", because the first character in the old head is capitalized as "S"” (He e Zan, 2024, p. 7) \#f0ff00\\ \mkbibemph{that's not clear. What about the "Some text" that is ignored and "Thanks" is instead in the output. Moreover the management of capital letters is not clear. } \par - “The BIT approach” (He e Zan, 2024, p. 7) \#f0ff00\\ \mkbibemph{very much similar to QVT relation and QVT Core. It is necessary to discuss and compare BIT with respect to QVT technologies. } \par - “Figure 5: Essential grammar of the surface language” (He e Zan, 2024, p. 8) \#f0ff00\\ \mkbibemph{the full grammar is not needed here. You can move it as an appendix and instead you can discuss in this section the peculiar aspects of the language by means of representative and illustrative cases. } \par - “LexRule is a regular expression or a rule name bound to a regular expression, e.g., ID refers to [\_a-zA-Z][\_a-zA-Z0-9]*” (He e Zan, 2024, p. 9) \#f0ff00\\ \mkbibemph{it is necessary to make explicit what's the relation of what you specify with rules and the corresponding metaclasses, which are supposed to type the instances that can be created by parsing elements with respect to such rules. } \par - “Case studies” (He e Zan, 2024, p. 26) \#f0ff00\\ \mkbibemph{This section needs to be improved by presenting a proper evaluation section, which starts by describing the research questions that the authors want to answer by mens of the performed evaluation. The way related works have been identified is also important. For instance, I don't see among them existing works like QVT, JTL, TCS. } \par - “General information of the benchmark examples” (He e Zan, 2024, p. 29) \#f0ff00\\ \mkbibemph{Instead of presenting the table in terms of examples, I suggest to reorganise the table and thus, the corresponding text, by making explicit the peculiar cases that existing approach have issues in supporting them. Talking about examples is not a proper way to sustain a strong and organized discussion. Thus the examples column needs to be properly refined and decomposed with respect to their peculiar characteristcs. Moreover, I would define a conceptual framework to compare existing approach by identifying peculiar features and discussing their support by the analysed approaches. Such an analysis should be presented earlier in the paper, when motivating the needs for a novel BX template language. I don't see in the table Boomerang neither. If it is because it is not Ecore based, then "Platform" is one of the different dimensions that should be considered for the comparison. } \par - “major features of” (He e Zan, 2024, p. 30) \#f0ff00\\ \mkbibemph{what are they? see my previous comments about the conceptual framework for comparison. } \par - “Left Recursions. Parsers derived from BIT templates cannot handle left recursions. Figure 14 shows a concrete example template containing left recursion. Currently, the tool support of BIT cannot check left recursion statically. It will be our future work to investigate how to detect left recursions in templates by adopting existing techniques in the field of compilers. \#[[ \#foreach (\$woogie in \$boogie) nothing will happen to \$woogie” (He e Zan, 2024, p. 31) \#f0ff00\\ \mkbibemph{left recursion is another dimension. } \par - “Efficiency” (He e Zan, 2024, p. 31) \#f0ff00\\ \mkbibemph{this is another possible dimension but needs to be elaborated more in order to present the problems related to efficiency, and talk existing works also with respect to such a dimension. } \par - “Specifically” (He e Zan, 2024, p. 32) \#f0ff00\\ \mkbibemph{what about Epsilon ECL? } \par - “Our previous work [20] on bidirectional model transformation proposed a putback-based language which enabled us to define a backward transformation from which a well-behaved BX can be derived.” (He e Zan, 2024, p. 32) \#f0ff00\\ \mkbibemph{have you compared with your previous work? } \par - “a template language for code matching and rewriting. In matching process, Comby interprets a code template and” (He e Zan, 2024, p. 33) \#f0ff00\\ \mkbibemph{ } \par - “verbose text and template extension.” (He e Zan, 2024, p. 34) \#f0ff00\\ \mkbibemph{see the comparison comment above. }} } @article{He201977, title = {Model-Driven Deep Learning for Physical Layer Communications}, author = {He, H. and Jin, S. and Wen, C.-K. and Gao, F. and Li, G.Y. and Xu, Z.}, date = {2019}, journaltitle = {IEEE Wirel. Commun.}, volume = {26}, number = {5}, pages = {77--83}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {15361284}, doi = {10.1109/MWC.2019.1800447}, abstract = {Intelligent communication is gradually becoming a mainstream direction. As a major branch of machine learning, deep learning (DL) has been applied in physical layer communications and has demonstrated an impressive performance improvement in recent years. However, most existing works related to DL focus on data-driven approaches, which consider the communication system as a black box and train it by using a huge volume of data. Training a network requires sufficient computing resources and extensive time, both of which are rarely found in communication devices. By contrast, model-driven DL approaches combine communication domain knowledge with DL to reduce the demand for computing resources and training time. This article discusses the recent advancements in model-driven DL approaches in physical layer communications, including transmission schemes, receiver design, and channel information recovery. Several open issues for future research are also highlighted. © 2002-2012 IEEE.}, art_number = {8715338}, coden = {IWCEA}, document_type = {Article}, source = {Scopus}, note = {cited By 150} } @article{He20201702, title = {Model-Driven Deep Learning for Mimo Detection}, author = {He, H. and Wen, C.-K. and Jin, S. and Li, G.Y.}, date = {2020}, journaltitle = {IEEE Trans. Signal Process.}, volume = {68}, pages = {1702--1715}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {1053587X}, doi = {10.1109/TSP.2020.2976585}, abstract = {In this paper, we investigate the model-driven deep learning (DL) for MIMO detection. In particular, the MIMO detector is specially designed by unfolding an iterative algorithm and adding some trainable parameters. Since the number of trainable parameters is much fewer than the data-driven DL based signal detector, the model-driven DL based MIMO detector can be rapidly trained with a much smaller data set. The proposed MIMO detector can be extended to soft-input soft-output detection easily. Furthermore, we investigate joint MIMO channel estimation and signal detection (JCESD), where the detector takes channel estimation error and channel statistics into consideration while channel estimation is refined by detected data and considers the detection error. Based on numerical results, the model-driven DL based MIMO detector significantly improves the performance of corresponding traditional iterative detector, outperforms other DL-based MIMO detectors and exhibits superior robustness to various mismatches. © 1991-2012 IEEE.}, art_number = {9018199}, coden = {ITPRE}, document_type = {Article}, source = {Scopus}, note = {cited By 80 \par TL;DR \par The model-driven DL based MIMO detector significantly improves the performance of corresponding traditional iterative detector, outperforms other DL-based M IMO detectors and exhibits superior robustness to various mismatches.} } @article{He20202216, title = {Model-Driven Deep Learning for Massive {{MU-MIMO}} with Finite-Alphabet Precoding}, author = {He, H. and Zhang, M. and Jin, S. and Wen, C.-K. and Li, G.Y.}, date = {2020}, journaltitle = {IEEE Commun. Lett.}, volume = {24}, number = {10}, pages = {2216--2220}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {10897798}, doi = {10.1109/LCOMM.2020.3002082}, abstract = {Massive multiuser multiple-input multiple-output (MU-MIMO) has been the mainstream technology in fifth-generation wireless systems. To reduce high hardware costs and power consumption in massive MU-MIMO, low-resolution digital-to-analog converters (DAC) for each antenna and radio frequency (RF) chain in downlink transmission is used, which brings challenges for precoding design. To circumvent these obstacles, we develop a model-driven deep learning (DL) network for massive MU-MIMO with finite-alphabet precoding in this article. The architecture of the network is specially designed by unfolding an iterative algorithm. Compared with the traditional state-of-the-art techniques, the proposed DL-based precoder shows significant advantages in performance, complexity, and robustness to channel estimation error under Rayleigh fading channel. © 1997-2012 IEEE.}, art_number = {9115718}, coden = {ICLEF}, document_type = {Article}, source = {Scopus}, note = {cited By 4} } @article{He2021, title = {Spectral Response Function-Guided Deep Optimization-Driven Network for Spectral Super-Resolution}, author = {He, J. and Li, J. and Yuan, Q. and Shen, H. and Zhang, L.}, date = {2021}, journaltitle = {IEEE Trans. Neural Netw. Learn. Syst.}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {2162237X}, doi = {10.1109/TNNLS.2021.3056181}, abstract = {Hyperspectral images (HSIs) are crucial for many research works. Spectral super-resolution (SSR) is a method used to obtain high-spatial-resolution (HR) HSIs from HR multispectral images. Traditional SSR methods include model-driven algorithms and deep learning. By unfolding a variational method, this article proposes an optimization-driven convolutional neural network (CNN) with a deep spatial-spectral prior, resulting in physically interpretable networks. Unlike the fully data-driven CNN, auxiliary spectral response function (SRF) is utilized to guide CNNs to group the bands with spectral relevance. In addition, the channel attention module (CAM) and the reformulated spectral angle mapper loss function are applied to achieve an effective reconstruction model. Finally, experiments on two types of data sets, including natural and remote sensing images, demonstrate the spectral enhancement effect of the proposed method, and also, the classification results on the remote sensing data set verified the validity of the information enhanced by the proposed method. IEEE}, document_type = {Article}, source = {Scopus}, note = {cited By 9 \par TL;DR \par This article proposes an optimization-driven convolutional neural network with a deep spatial–spectral prior, resulting in physically interpretable networks, and experiments on two types of data sets demonstrate the spectral enhancement effect of the proposed method.} } @misc{he2021pyart, ids = {he_pyart_2021}, title = {{{PyART}}: {{Python API}} Recommendation in Real-Time}, shorttitle = {{{PyART}}}, author = {He, Xincheng and Xu, Lei and Zhang, Xiangyu and Hao, Rui and Feng, Yang and Xu, Baowen}, date = {2021}, eprint = {2102.04706}, eprinttype = {arXiv}, eprintclass = {cs}, url = {http://arxiv.org/abs/2102.04706}, urldate = {2021-02-12}, abstract = {API recommendation in real-time is challenging for dynamic languages like Python. Many existing API recommendation techniques are highly effective, but they mainly support static languages. A few Python IDEs provide API recommendation functionalities based on type inference and training on a large corpus of Python libraries and third-party libraries. As such, they may fail to recommend or make poor recommendations when type information is missing or target APIs are projectspecific. In this paper, we propose a novel approach, PyART, to recommending APIs for Python programs in real-time. It features a light-weight analysis to derive so-called optimistic data-flow, which is neither sound nor complete, but simulates the local data-flow information humans can derive. It extracts three kinds of features: data-flow, token similarity, and token co-occurrence, in the context of the program point where a recommendation is solicited. A predictive model is trained on these features using the Random Forest algorithm. Evaluation on 8 popular Python projects demonstrates that PyART can provide effective API recommendations. When historic commits can be leveraged, which is the target scenario of a state-of-theart tool ARIREC, our average top-1 accuracy is over 50\% and average top-10 accuracy over 70\%, outperforming APIREC and Intellicode (i.e., the recommendation component in Visual Studio) by 28.48\%-39.05\% for top-1 accuracy and 24.41\%-30.49\% for top-10 accuracy. In other applications such as when historic comments are not available and cross-project recommendation, PyART also shows better overall performance. The time to make a recommendation is less than a second on average, satisfying the real-time requirement.}, langid = {english}, keywords = {Computer Science - Software Engineering}, note = {arXiv: 2102.04706 \par arXiv: 2102.04706 \par Comment: 12 pages \par Comment: 12 pages \par TL;DR \par This paper proposes a novel approach, PyART, to recommend APIs for Python programs in real-time, which features a light-weight analysis to derives so-called optimistic data-flow, which is neither sound nor complete, but simulates the local data- flow information humans can derive.} } @article{hearstSupportVectorMachines1998, title = {Support Vector Machines}, author = {Hearst, Marti A.}, date = {1998-07}, journaltitle = {IEEE Intell. Syst.}, volume = {13}, number = {4}, pages = {18--28}, publisher = {IEEE Educational Activities Department}, address = {Piscataway, NJ, USA}, issn = {1541-1672}, url = {http://dx.doi.org/10.1109/5254.708428}, acmid = {630387}, issue_date = {July 1998}, nodoi = {10.1109/5254.708428}, numpages = {11} } @article{heAutoMLSurveyStateoftheart2021, ids = {HE2021106622}, title = {{{AutoML}}: {{A}} Survey of the State-of-the-Art}, shorttitle = {{{AutoML}}}, author = {He, Xin and Zhao, Kaiyong and Chu, Xiaowen}, date = {2021-01}, journaltitle = {Knowledge-Based Systems}, volume = {212}, pages = {106622}, issn = {09507051}, doi = {10.1016/j.knosys.2020.106622}, langid = {english}, keywords = {Automated machine learning (autoML),Deep learning,Hyperparameter optimization (HPO),Neural architecture search (NAS)} } @incollection{heidenreichIntegratingOCLTextual2011, title = {Integrating {{OCL}} and Textual Modelling Languages}, booktitle = {Models in {{Software Engineering}}}, author = {Heidenreich, Florian and Johannes, Jendrik and Karol, Sven and Seifert, Mirko and Thiele, Michael and Wende, Christian and Wilke, Claas}, editor = {Dingel, Juergen and Solberg, Arnor}, date = {2011}, volume = {6627}, pages = {349--363}, publisher = {Springer Berlin Heidelberg}, location = {Berlin, Heidelberg}, doi = {10.1007/978-3-642-21210-9_34}, abstract = {In the past years, many OCL tools achieved a transition of OCL from a language meant to constrain UML models to a universal constraint language applied to various modelling and metamodelling languages. However, OCL users still experience a discrepancy between the now highly extensible parsing and evaluation backend of OCL tools and the lack of appropriate frontend tooling like advanced OCL editors that adapt to the different application scenarios.}, isbn = {978-3-642-21209-3 978-3-642-21210-9}, langid = {english} } @inproceedings{hein2009model, title = {Model-Driven Tool Integration with Modelbus}, booktitle = {Workshop {{Future Trends Model-Driven Dev}}.}, author = {Hein, Christian and Ritter, Tom and Wagner, Michael}, date = {2009}, pages = {50--52} } @article{heitmannUsingLinkedData2010, title = {Using Linked Data to Build Open, Collaborative Recommender Systems}, author = {Heitmann, Benjamin and Hayes, Conor}, date = {2010}, journaltitle = {Artif. Intell.}, pages = {76--81}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.174.2755 http://www.aaai.org/ocs/index.php/SSS/SSS10/paper/viewPDFInterstitial/1067/1452}, abstract = {While recommender systems can greatly enhance the user experience, the entry barriers in terms of data acquisition are very high, making it hard for new service providers to compete with existing recommendation services. This paper proposes to build open recommender systems which can utilise Linked Data to mitigate the new-user, new-item and sparsity problems of collaborative recommender systems. We describe how to aggregate data about object centred sociality from different sources and how to process it for collaborative recommendation. To demonstrate the validity of our approach, we augment the data from a closed collaborative music recommender system with Linked Data, and significantly improve its precision and recall.}, mendeley-tags = {SML-LIB-BIBLIO,lang:ENG}, keywords = {lang:ENG,SML-LIB-BIBLIO,technical report ss 10 07}, note = {TL;DR \par This paper proposes to build open recommender systems which can utilise Linked Data to mitigate the new-user, new-item and sparsity problems of collaborativeRecommender systems.} } @online{heJSSOFTWARED2301182R12024, title = {{{JSSOFTWARE-D-23-01182R1}}}, shorttitle = {Bit}, author = {He, Xiao and Zan, Tao}, date = {2024}, doi = {10.2139/ssrn.4686181}, pubstate = {prepublished}, keywords = {⛔ No INSPIRE recid found,LOGSEQ} } @online{heLLMbasedMultiagentSystems2024a, title = {{{LLM-based}} Multi-Agent Systems for Software Engineering: Literature Review, Vision and the Road Ahead}, shorttitle = {{{LLM-based}} Multi-Agent Systems for Software Engineering}, author = {He, Junda and Treude, Christoph and Lo, David}, date = {2024-12-20}, eprint = {2404.04834}, eprinttype = {arXiv}, eprintclass = {cs}, doi = {10.48550/arXiv.2404.04834}, abstract = {Integrating Large Language Models (LLMs) into autonomous agents marks a significant shift in the research landscape by offering cognitive abilities that are competitive with human planning and reasoning. This paper explores the transformative potential of integrating Large Language Models into Multi-Agent (LMA) systems for addressing complex challenges in software engineering (SE). By leveraging the collaborative and specialized abilities of multiple agents, LMA systems enable autonomous problem-solving, improve robustness, and provide scalable solutions for managing the complexity of real-world software projects. In this paper, we conduct a systematic review of recent primary studies to map the current landscape of LMA applications across various stages of the software development lifecycle (SDLC). To illustrate current capabilities and limitations, we perform two case studies to demonstrate the effectiveness of state-of-the-art LMA frameworks. Additionally, we identify critical research gaps and propose a comprehensive research agenda focused on enhancing individual agent capabilities and optimizing agent synergy. Our work outlines a forward-looking vision for developing fully autonomous, scalable, and trustworthy LMA systems, laying the foundation for the evolution of Software Engineering 2.0.}, langid = {english}, pubstate = {prepublished}, keywords = {Computer Science - Software Engineering}, note = {\textbf{NOTES and RELEVANT REFERENCES} \par ALERT! 90\% of the references here are from arXiv. This is symptomatic and should be taken into account by the community. The review process should be revised to deal with the problem related to the proliferation of papers that are not validated/reviewed by community experts. Then it’s up to the reader judge the quality of what she/he is reading even though the reader does not always have the expertise to do that. \par [90] David Lo. 2023. Trustworthy and Synergistic Artificial Intelligence for Software Engineering: Vision and Roadmaps. arXiv preprint arXiv:2309.04142 (2023) \par [24] Yuheng Cheng, Ceyao Zhang, Zhengwen Zhang, Xiangrui Meng, Sirui Hong, Wenhao Li, Zihao Wang, Zekai Wang, Feng Yin, Junhua Zhao, et al. 2024. Exploring Large Language Model based Intelligent Agents: Definitions, Methods, and Prospects. arXiv preprint arXiv:2401.03428 (2024). \par [144] Michael Wooldridge. 2009. An introduction to multiagent systems. John wiley \& sons. \par --{$>$} This is interesting to introduce the concept of multi-agent in the slides. \par [168] Xin Zhou, Sicong Cao, Xiaobing Sun, and David Lo. 2024. Large Language Model for Vulnerability Detection and Repair: Literature Review and the Road Ahead. arXiv preprint arXiv:2404.02525 (2024) \par --{$>$} This is important for Umar \par [115] Malik Abdul Sami, Muhammad Waseem, Zheying Zhang, Zeeshan Rasheed, Kari Systä, and Pekka Abrahamsson. 2024. AI based Multiagent Approach for Requirements Elicitation and Analysis. arXiv preprint arXiv:2409.00038 (2024). \par --{$>$} Maybe this can be used to motivate our work on paraphrasing user stories? \par [17] Yuzhe Cai, Shaoguang Mao, Wenshan Wu, Zehua Wang, Yaobo Liang, Tao Ge, Chenfei Wu, Wang You, Ting Song, Yan Xia, et al. 2023. \textbf{Low-code llm}: Visual programming over llms. arXiv preprint arXiv:2304.08103 2 (2023). \par [132] Hanbin Wang, Zhenghao Liu, Shuo Wang, Ganqu Cui, Ning Ding, Zhiyuan Liu, and Ge Yu. 2024. INTERVENOR: Prompting the Coding Ability of Large Language Models with the Interactive Chain of Repair. In Findings of the Association for Computational Linguistics ACL 2024. 2081–2107. \par --{$>$} This is interesting for the mediation / discussion part of MOSAICO \par [132] Hanbin Wang, Zhenghao Liu, Shuo Wang, Ganqu Cui, Ning Ding, Zhiyuan Liu, and Ge Yu. 2024. INTERVENOR: Prompting the Coding Ability of Large Language Models with the Interactive Chain of Repair. In Findings of the Association for Computational Linguistics ACL 2024. 2081–2107. \par --{$>$} Self-repair is another interesting concept to look at.} } @article{henderson-sellersMultiLevelMetaModellingUnderpin2013, title = {Multi-{{Level Meta-Modelling}} to {{Underpin}} the {{Abstract}} and {{Concrete Syntax}} for {{Domain-Specific Modelling Languages}}}, author = {Henderson-Sellers, Brian and Gonzalez-Perez, Cesar}, date = {2013}, journaltitle = {Domain Eng.}, pages = {291--316}, doi = {10.1007/978-3-642-36654-3_12}, note = {TL;DR \par This chapter provides a solid theoretical foundation for the construction of domain-specific modelling languages that can help define both the abstract and concrete syntax aspects of a meta-modelling paradigm.} } @inproceedings{Henkel:2005:CCR:1062455.1062512, title = {{{CatchUp}}!: {{Capturing}} and Replaying Refactorings to Support {{API}} Evolution}, booktitle = {Proc. 27th {{Int}}. {{Conf}}. {{Softw}}. {{Eng}}.}, author = {Henkel, Johannes and Diwan, Amer}, date = {2005}, series = {{{ICSE}} '05}, pages = {274--283}, publisher = {ACM}, location = {New York, NY, USA}, url = {http://doi.acm.org/10.1145/1062455.1062512}, acmid = {1062512}, isbn = {1-58113-963-2}, nodoi = {10.1145/1062455.1062512}, numpages = {10}, keywords = {application programming interfaces,refactoring,software evolution} } @inproceedings{henningenRetrievingSoftwareObjects1991, title = {Retrieving Software Objects in an Example-Based Programming Environment}, booktitle = {Proc. 14th {{Annu}}. {{Int}}. {{ACM SIGIR Conf}}. {{Res}}. {{Dev}}. {{Inf}}. {{Retr}}. {{Chic}}. {{Ill}}. {{USA Oct}}. 13-16 1991 {{Spec}}. {{Issue SIGIR Forum}}}, author = {Henningen, Scott}, date = {1991}, pages = {251--260}, doi = {10.1145/122860.122886}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/bib/conf/sigir/Henningen91}, note = {TL;DR \par A prototype system named CODEFINDER, which explores issues of retrieving software objects relevant to the design task, is presented and supports human-computer dialogue by providing the means to incrementally construct a query and by providing associative cues that are compatible with human memory retrieval principles.}, timestamp = {Tue, 06 Nov 2018 11:07:25 +0100} } @inproceedings{HenshingArendtBJKT10, title = {Henshin: Advanced Concepts and Tools for in-Place {{EMF}} Model Transformations}, booktitle = {Proc 13th {{Int}}. {{Conf}}. {{Model Driven Eng}}. {{Lang}}. {{Syst}}. {{Model}}.}, author = {Arendt, Thorsten and Biermann, Enrico and Jurack, Stefan and Krause, Christian and Taentzer, Gabriele}, date = {2010}, series = {Lecture Notes in Computer Science}, volume = {6394}, pages = {121--135}, publisher = {Springer}, doi = {10.1007/978-3-642-16145-2\_9}, langid = {english}, x-editor = {Dorina C. Petriu and Nicolas Rouquette and Øystein Haugen}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {TL;DR \par Henshin is a new language and associated tool set for in-place transformations of EMF models using pattern-based rules on the lowest level, which can be structured into nested transformation units with well-defined operational semantics.} } @online{HeresWhatVoicecontrolled, title = {Here’s What Voice-Controlled {{Android}} Home Automation Looks like [Video]}, url = {http://www.androidauthority.com/voice-controlled-android-home-automation-video-205316/}, urldate = {2015-04-16} } @article{hermannIntegratingLowCodeViewbased2024, title = {Towards {{Integrating Low-Code}} in {{View-based Development}}}, author = {Hermann, Anne-Kathrin and König, Lars and Burger, Erik and Reussner, Ralf}, date = {2024}, abstract = {In recent years, low-code development has been established as an innovative method for software development. It enables the development of a wide range of applications using graphical tools, with little or no knowledge of text-based programming languages. Closely related is model-driven development, where models play a primary role in specifying software systems and generating code partially automatically. While model-driven development supports development processes where developers from different domains work on different models that are kept consistent, in practice, classical model-driven tools are often difficult to use for domain experts with a less technical background. To bridge this gap, we propose a concept for integrating low-code platforms through projective views into model-driven development environments. We provide an initial evaluation of the feasibility of our concept using a development platform for smart home systems as a case study.}, langid = {english}, keywords = {LOGSEQ} } @inproceedings{Herrmann201979, title = {Motion Data and Model Management for Applied Statistical Motion Synthesis}, author = {Herrmann, E. and Du, H. and Antakli, A. and Rubinstein, D. and Schubotz, R. and Sprenger, J. and Hosseini, S. and Cheema, N. and Zinnikus, I. and Manns, M. and Fischer, K. and Slusallek, P.}, editor = {Agus M., Corsini M., Pintus R.}, date = {2019}, series = {Italian {{Chapter Conference}} 2019 - {{Smart Tools}} and {{Apps}} in Computer {{Graphics}}, {{STAG}} 2019}, pages = {79--88}, publisher = {Eurographics Association}, doi = {10.2312/stag.20191366}, abstract = {Machine learning based motion modelling methods such as statistical modelling require a large amount of input data. In practice, the management of the data can become a problem in itself for artists who want to control the quality of the motion models. As a solution to this problem, we present a motion data and model management system and integrate it with a statistical motion modelling pipeline. The system is based on a data storage server with a REST interface that enables the efficient storage of different versions of motion data and models. The database system is combined with a motion preprocessing tool that provides functions for batch editing, retargeting and annotation of the data. For the application of the motion models in a game engine, the framework provides a stateful motion synthesis server that can load the models directly from the data storage server. Additionally, the framework makes use of a Kubernetes compute cluster to execute time consuming processes such as the preprocessing and modelling of the data. The system is evaluated in a use case for the simulation of manual assembly workers. © 2019 The Author(s) Eurographics Proceedings © 2019 The Eurographics Association.}, document_type = {Conference Paper}, isbn = {978-3-03868-100-7}, source = {Scopus}, note = {cited By 1 \par TL;DR \par This work presents a motion data and model management system and integrate it with a statistical motion modelling pipeline that makes use of a Kubernetes compute cluster to execute time consuming processes such as the preprocessing and modelling of the data.} } @incollection{Herrmannsdoerfer_2011, title = {{{COPE}} – a Workbench for the Coupled Evolution of Metamodels and Models}, booktitle = {Software {{Language Engineering}}}, author = {Herrmannsdoerfer, Markus}, editor = {Malloy, Brian and Staab, Steffen and Van Den Brand, Mark}, date = {2011}, series = {Lecture Notes in Computer Science}, volume = {6563}, pages = {286--295}, publisher = {Springer Berlin Heidelberg}, location = {Berlin, Heidelberg}, doi = {10.1007/978-3-642-19440-5_18}, isbn = {978-3-642-19439-9}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found} } @inproceedings{HerrmannsdoerferR09, title = {Limitations of Automating Model Migration in Response to Metamodel Adaptation}, booktitle = {Models {{Softw}}. {{Eng}}. {{Workshop Symp}}. {{MODELS}} 2009 {{Denver CO USA Oct}}. 4-9 2009 {{Rep}}. {{Revis}}. {{Sel}}. {{Pap}}.}, author = {Herrmannsdoerfer, Markus and Ratiu, Daniel}, editor = {Ghosh, Sudipto}, date = {2009}, series = {Lecture Notes in Computer Science}, volume = {6002}, pages = {205--219}, publisher = {Springer}, doi = {10.1007/978-3-642-12261-3\_20}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {TL;DR \par This paper formally characterize metamodel adaptations in terms of the effort needed for model migration, and outlines different possibilities to systematically cope with these kinds of metAModel changes.}, timestamp = {Wed, 23 Feb 2022 12:58:06 +0100} } @article{hicksChatGPTBullshit2024, title = {{{ChatGPT}} Is Bullshit}, author = {Hicks, Michael Townsen and Humphries, James and Slater, Joe}, date = {2024-06-08}, journaltitle = {Ethics Inf Technol}, volume = {26}, number = {2}, pages = {38}, issn = {1572-8439}, doi = {10.1007/s10676-024-09775-5}, abstract = {Recently, there has been considerable interest in large language models: machine learning systems which produce human-like text and dialogue. Applications of these systems have been plagued by persistent inaccuracies in their output; these are often called “AI hallucinations”. We argue that these falsehoods, and the overall activity of large language models, is better understood as bullshit in the sense explored by Frankfurt (On Bullshit, Princeton, 2005): the models are in an important way indifferent to the truth of their outputs. We distinguish two ways in which the models can be said to be bullshitters, and argue that they clearly meet at least one of these definitions. We further argue that describing AI misrepresentations as bullshit is both a more useful and more accurate way of predicting and discussing the behaviour of these systems.}, langid = {english}, keywords = {Artificial intelligence,Assertion,Bullshit,ChatGPT,Content,Frankfurt,Large language models,LLMs} } @article{hidasiSessionbasedRecommendationsRecurrent2015, title = {Session-Based Recommendations with Recurrent Neural Networks}, author = {Hidasi, Balázs and Karatzoglou, Alexandros and Baltrunas, Linas and Tikk, Domonkos}, date = {2015}, journaltitle = {CoRR}, volume = {abs/1511.06939}, eprint = {1511.06939}, eprinttype = {arXiv}, url = {http://arxiv.org/abs/1511.06939}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/bib/journals/corr/HidasiKBT15}, note = {TL;DR \par It is argued that by modeling the whole session, more accurate recommendations can be provided by an RNN-based approach for session-based recommendations, and introduced several modifications to classic RNNs such as a ranking loss function that make it more viable for this specific problem.}, timestamp = {Wed, 07 Jun 2017 14:42:57 +0200} } @inproceedings{Hildebrandt2017128, title = {Metamodeling Lightweight Data Compression Algorithms and Its Application Scenarios}, author = {Hildebrandt, J. and Habich, D. and Kuhn, T. and Damme, P. and Lehner, W.}, editor = {Cabanillas C., Farshidi S., Espana S.}, date = {2017}, series = {{{CEUR Workshop Proceedings}}}, volume = {1979}, pages = {128--141}, publisher = {CEUR-WS}, issn = {16130073}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85034960838&partnerID=40&md5=33294c448edfceb4dead3c958f0f06de}, abbrev_source_title = {CEUR Workshop Proc.}, affiliation = {Technische Universitat Dresden, Database Systems Group, Dresden, Germany; Technische Universitat Dresden, Software Technology Group, Dresden, Germany}, document_type = {Conference Paper}, langid = {english}, source = {Scopus}, note = {cited By 5} } @article{HilkenGBV18, title = {Testing Models and Model Transformations Using Classifying Terms}, author = {Hilken, Frank and Gogolla, Martin and Burgueño, Loli and Vallecillo, Antonio}, date = {2018}, journaltitle = {Software Syst. Model.}, volume = {17}, number = {3}, pages = {885--912}, doi = {10.1007/S10270-016-0568-3}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {TL;DR \par It is shown how the concept of classifying terms, which are general OCL terms on a class model enriched with OCL constraints can be effectively used in combination with Tracts for testing both directional and bidirectional model transformations and for analyzing their behavior.}, timestamp = {Fri, 18 Sep 2020 11:19:16 +0200} } @article{hintzeViolinPlotsBox1998, title = {Violin Plots: {{A}} Box Plot-Density Trace Synergism}, author = {Hintze, Jerry L. and {Ray D. Nelson}}, date = {1998}, journaltitle = {Am. Stat.}, volume = {52}, number = {2}, eprint = {https://amstat.tandfonline.com/doi/pdf/10.1080/00031305.1998.10480559}, pages = {181--184}, publisher = {Taylor \& Francis}, url = {https://amstat.tandfonline.com/doi/abs/10.1080/00031305.1998.10480559}, nodoi = {10.1080/00031305.1998.10480559} } @article{Hirschberg:1977:ALC:322033.322044, title = {Algorithms for the Longest Common Subsequence Problem}, author = {Hirschberg, Daniel S.}, date = {1977-10}, journaltitle = {J. ACM}, volume = {24}, number = {4}, pages = {664--675}, publisher = {ACM}, address = {New York, NY, USA}, issn = {0004-5411}, url = {http://doi.acm.org/10.1145/322033.322044}, acmid = {322044}, issue_date = {Oct. 1977}, nodoi = {10.1145/322033.322044}, numpages = {12}, note = {TL;DR \par A lgor i thm is appl icable in the genera l case and requi res O ( p n + n log n) t ime for any input strings o f lengths m and n even though the lower bound on T ime of O ( m n ) need not apply to all inputs.} } @online{hirzelLowCodeProgrammingModels2022, title = {Low-{{Code Programming Models}}}, author = {Hirzel, Martin}, date = {2022-05-04}, eprint = {2205.02282}, eprinttype = {arXiv}, eprintclass = {cs}, url = {http://arxiv.org/abs/2205.02282}, urldate = {2023-05-17}, abstract = {Traditionally, computer programming has been the prerogative of professional developers using textual programming languages such as C, Java, or Python. Low-code programming promises an alternative: letting citizen developers create programs using visual abstractions, demonstrations, or natural language. While low-code programming is currently getting a lot of attention in industry, the relevant research literature is scattered, and in fact, rarely uses the term "low-code". This article brings together low-code literature from various research fields, explaining how techniques work while providing a unified point of view. Low-code has the potential to empower more people to automate tasks by creating computer programs, making them more productive and less dependent on scarce professional software developers.}, pubstate = {prepublished}, keywords = {Computer Science - Programming Languages,LOGSEQ} } @online{HitchhikersGuideIoT, title = {Hitchhiker's {{Guide}} to {{IoT Standards}} and {{Protocols}} - {{DZone IoT}}}, url = {https://dzone.com/articles/hitchhikers-guide-to-iot-standards-and-protocols?edition=216186&utm_source=Spotlight&utm_medium=email&utm_campaign=iot%202016-09-27}, urldate = {2016-09-27} } @inproceedings{hnetynkaUsingComponentEnsembles2020, title = {Using Component Ensembles for Modeling Autonomic Component Collaboration in Smart Farming}, booktitle = {Proc. {{IEEEACM}} 15th {{Int}}. {{Symp}}. {{Softw}}. {{Eng}}. {{Adapt}}. {{Self-Manag}}. {{Syst}}.}, author = {Hnetynka, Petr and Bures, Tomas and Gerostathopoulos, Ilias and Pacovsky, Jan}, date = {2020-06-29}, pages = {156--162}, publisher = {ACM}, location = {Seoul Republic of Korea}, doi = {10.1145/3387939.3391599}, eventtitle = {{{SEAMS}} '20: {{IEEE}}/{{ACM}} 15th {{International Symposium}} on {{Software Engineering}} for {{Adaptive}} and {{Self-Managing Systems}}}, isbn = {978-1-4503-7962-5}, langid = {english}, note = {TL;DR \par The paper shows how the autonomic component ensembles can easily capture complex collaboration rules and how they can include both controllable autonomic components (i.e. drones) and non-controllable environment agents (flocks of birds in this case).} } @inproceedings{hoareRoleFormalTechniques1996, title = {The Role of Formal Techniques: Past, Current and Future or How Did Software Get so Reliable without Proof?}, shorttitle = {The Role of Formal Techniques}, booktitle = {Proc. 18th {{Int}}. {{Conf}}. {{Softw}}. {{Eng}}.}, author = {Hoare, C. A. R.}, date = {1996}, pages = {233--234}, publisher = {IEEE Computer Society}, url = {http://dl.acm.org/citation.cfm?id=227765}, urldate = {2016-11-20} } @article{hodaRiseEvolutionAgile2018, title = {The {{Rise}} and {{Evolution}} of {{Agile Software Development}}}, author = {Hoda, R. and Salleh, N. and Grundy, J.}, date = {2018-09}, journaltitle = {IEEE Softw.}, volume = {35}, number = {5}, pages = {58--63}, issn = {0740-7459}, doi = {10.1109/MS.2018.290111318}, abstract = {Agile software development has dominated the second half of the past 50 years of software engineering. Retrospectives, one of the most common agile practices, enables reflection on past performance, discussion of current progress, and charting forth directions for future improvement. Because of agile’s burgeoning popularity as the software development model of choice and a significant research subdomain of software engineering, it demands a retrospective of its own. This article provides a historical overview of agile’s main focus areas and a holistic synthesis of its trends, their evolution over the past two decades, agile’s current status, and, forecast from these, agile’s likely future. This article is part of a theme issue on software engineering’s 50th anniversary.}, keywords = {software engineering}, note = {TL;DR \par A historical overview of agile’s main focus areas and a holistic synthesis of its trends, their evolution over the past two decades, agile‘s current status, and, forecast from these, agile's likely future are provided.} } @article{hoisl2016modeling, title = {Modeling and Enforcing Secure Object Flows in Process-Driven {{SOAs}}: {{A}} Pattern-Based Security Modeling Language}, author = {Hoisl, Bernhard and Sobernig, Stefan and Strembeck, Mark}, journaltitle = {J. Syst. Softw.}, volume = {112} } @article{hoislCatalogReusableDesign, title = {A {{Catalog}} of {{Reusable Design Decisions}} for {{Developing UML-}} and {{MOF-based Domain-Specific Modeling Languages}}}, author = {Hoisl, Bernhard and Sobernig, Stefan and Schefer-Wenzl, Sigrid and Strembeck, Mark and Baumgrass, Anne}, pages = {24}, abstract = {In the process of model-driven development (MDD) of software artifacts, domain-specific modeling languages (DSMLs) are an integral part. They act as the communication vehicle for aligning the requirements of the domain expert with the needs of the software engineer. With the rise of the UML as de facto standard for modeling software systems, MOF/UMLbased DSMLs are now widely used for MDD. This paper documents design decisions from ten DSML projects which are based on the MOF/UML and which we conducted over the last years. We present our experiences in the form of reusable decision templates for all decision points detected in each phase of the DSML development process. Furthermore, we report also on identified decision dependencies which may occur within a single decision or between two decisions.}, langid = {english}, note = {TL;DR \par This collection of decision-record documents targets decision makers in DSML development (e.g., DSML engineers, software architects, domain experts) and presents design decisions collected from 90 UML/MOF-based DSML projects.} } @book{hollerMachinetomachineInternetThings2014, title = {From Machine-to-Machine to the {{Internet}} of Things: Introduction to a New Age of Intelligence}, shorttitle = {From Machine-to-Machine to the {{Internet}} of Things}, editor = {Höller, Jan}, date = {2014}, publisher = {Elsevier Academic Press}, location = {Amsterdam}, abstract = {This book outlines the background and overall vision for the Internet of Things (IoT) and M2M communications and services, including major standards. Key technologies are described: Everything from physical instrumentation devices to the cloud infrastructures used to collect data, derive information and map it to current processes, as well as system architectures and regulatory requirements. Real world service use case studies provide the hands-on knowledge needed to successfully develop and implement M2M and IoT technologies sustainably and profitably}, isbn = {978-0-12-407684-6 978-0-08-099401-7}, langid = {english}, pagetotal = {331}, keywords = {internet of things} } @article{Holm1979a, title = {A Simple Sequentially Rejective Multiple Test Procedure}, author = {Holm, Sture}, date = {1979}, journaltitle = {Scand. J. Stat.}, pages = {65--70}, publisher = {JSTOR} } @article{holmes_strathcona_nodate, title = {Strathcona {{Example Recommendation Tool}}}, author = {Holmes, Reid and Walker, Robert J and Murphy, Gail C}, date = {2015}, pages = {4}, abstract = {Using the application programming interfaces (API) of large software systems requires developers to understand details about the interfaces that are often not explicitly defined. However, documentation about the API is often incomplete or out of date. Existing systems that make use of the API provide a form of implicit information on how to use that code. Manually searching through existing projects to find relevant source code is tedious and time consuming. We have created the Strathcona Example Recommendation Tool to assist developers in finding relevant fragments of code, or examples, of an API’s use. These examples can be used by developers to provide insight on how they are supposed to interact with the API.}, langid = {english} } @inproceedings{holmesStrathconaExampleRecommendation2005, ids = {DBLP:conf/sigsoft/HolmesWM05}, title = {Strathcona Example Recommendation Tool}, booktitle = {Proc. 10th {{Eur}}. {{Softw}}. {{Eng}}. {{Conf}}. {{Held Jointly}} 13th {{ACM SIGSOFT Int}}. {{Symp}}. {{Found}}. {{Softw}}. {{Eng}}. 2005 {{Lisbon Port}}. {{Sept}}. 5-9 2005}, author = {Holmes, Reid and Walker, Robert J. and Murphy, Gail C.}, date = {2005}, pages = {237--240}, doi = {10.1145/1081706.1081744}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/bib/conf/sigsoft/HolmesWM05}, note = {TL;DR \par The Strathcona Example is created to assist developers in finding relevant fragments of code, or examples, of an API's use and can be used by developers to provide insight on how they are supposed to interact with the API.}, timestamp = {Tue, 06 Nov 2018 16:59:23 +0100} } @article{holzmannCodeVault2018, title = {Code {{Vault}}}, author = {Holzmann, G. J.}, date = {2018-09}, journaltitle = {IEEE Softw.}, volume = {35}, number = {5}, pages = {85--87}, issn = {0740-7459}, doi = {10.1109/MS.2018.3571225}, abstract = {So, what has changed since that first NATO software engineering conference in 1968? Depending on your point of view, nothing much has changed, or everything has changed. The part that didn’t change much is that we still struggle with writing code that’s robust enough to trust. The part that has changed dramatically is the performance of the hardware that runs our code. This article is part of a theme issue on software engineering’s 50th anniversary.} } @online{HomeServerNoob, title = {Home Server Noob. {{Can}}'t Get {{CouchPotato}} to Communicate with {{Deluge}}. : {{HomeServer}}}, url = {http://www.reddit.com/r/HomeServer/comments/2r15vh/home_server_noob_cant_get_couchpotato_to/}, urldate = {2015-04-22} } @online{HomeSystemsConsulting, title = {Home {{Systems Consulting}}}, url = {http://www.hsyco.com/}, urldate = {2015-04-08} } @article{hong2023metagpt, title = {Metagpt: Meta Programming for Multi-Agent Collaborative Framework}, author = {Hong, Sirui and Zheng, Xiawu and Chen, Jonathan and Cheng, Yuheng and Wang, Jinlin and Zhang, Ceyao and Wang, Zili and Yau, Steven Ka Shing and Lin, Zijuan and Zhou, Liyang and {Others}}, date = {2023}, journaltitle = {arXiv prepr. arXiv:2308,00352}, eprint = {2308.00352}, eprinttype = {arXiv}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found} } @inproceedings{hora_apiwave_2015, title = {Apiwave: {{Keeping}} Track of {{API}} ,Ty and Migration}, shorttitle = {Apiwave}, booktitle = {2015 {{IEEE Int Conf Softw}}. {{Maint}}. {{Evol}}. {{ICSME}}}, author = {Hora, André and Valente, Marco Tulio}, date = {2015-09}, pages = {321--323}, doi = {10.1109/ICSM.2015.7332478}, abstract = {Every day new frameworks and libraries are created and existing ones evolve. To benefit from such newer or improved APIs, client developers should update their applications. In practice, this process presents some challenges: APIs are commonly backward-incompatible (causing client applications to fail when updating) and multiple APIs are available (making it difficult to decide which one to use). To address these challenges, we propose apiwave, a tool that keeps track of API popularity and migration of major frameworks/libraries. The current version includes data about the evolution of top 650 GitHub Java projects, from which 320K APIs were extracted. We also report an experience using apiwave on real-world scenarios.}, keywords = {API migration,API popularity,Apiwave,application program interfaces,Data mining,Databases,GitHub Java project,Java,Libraries,Market research,Software maintenance} } @online{HORIZON2020, title = {{{HORIZON}} 2020}, url = {http://een.unioncamerepuglia.it/Italiano/News/HORIZON-2020/}, urldate = {2015-04-08} } @article{hortBiasMitigationMachine2023, title = {Bias {{Mitigation}} for {{Machine Learning Classifiers}}: {{A Comprehensive Survey}}}, shorttitle = {Bias {{Mitigation}} for {{Machine Learning Classifiers}}}, author = {Hort, Max and Chen, Zhenpeng and Zhang, Jie M. and Harman, Mark and Sarro, Federica}, date = {2023-11}, journaltitle = {ACM J. Responsib. Comput.}, pages = {3631326}, issn = {2832-0565}, doi = {10.1145/3631326}, abstract = {This paper provides a comprehensive survey of bias mitigation methods for achieving fairness in Machine Learning (ML) models. We collect a total of 341 publications concerning bias mitigation for ML classifiers. These methods can be distinguished based on their intervention procedure (i.e., pre-processing, in-processing, post-processing) and the technique they apply. We investigate how existing bias mitigation methods are evaluated in the literature. In particular, we consider datasets, metrics and benchmarking. Based on the gathered insights (e.g., What is the most popular fairness metric? How many datasets are used for evaluating bias mitigation methods?), we hope to support practitioners in making informed choices when developing and evaluating new bias mitigation methods.}, langid = {english} } @article{hossainIEEEPressEditorial, title = {{{IEEE Press Editorial Board}}}, author = {Hossain, Ekram and Fortino, Giancarlo and Grier, David Alan and Heirman, Donald and Li, Xiaoou and Molisch, Andreas and Nahavandi, Saeid and Perez, Ray and Reed, Jeffrey and Shafer, Linda and Shahidehpour, Mohammad and Spurgeon, Sarah and Tekalp, Ahmet Murat}, pages = {693}, langid = {english} } @inproceedings{Hou:2013:CCA:2550526.2550556, title = {Content Categorization of {{API}} Discussions}, booktitle = {Proc. 2013 {{IEEE Int}}. {{Conf}}. {{Softw}}. {{Maint}}.}, author = {Hou, Daqing and Mo, Lingfeng}, date = {2013}, series = {{{ICSM}} '13}, pages = {60--69}, publisher = {IEEE Computer Society}, location = {Washington, DC, USA}, url = {http://dx.doi.org/10.1109/ICSM.2013.17}, acmid = {2550556}, isbn = {978-0-7695-4981-1}, nodoi = {10.1109/ICSM.2013.17}, numpages = {10}, keywords = {APIs,AWT/Swing,MALLET,Naive Bayes,Online Forums,Text Categorization}, note = {TL;DR \par A study to explore the question as to how well machine learning algorithms can be applied to categorize API discussions based on their content, and investigates factors that impact classification accuracy, including size of the training set and multi-label documents.} } @online{houLargeLanguageModels2023, title = {Large {{Language Models}} for {{Software Engineering}}: {{A Systematic Literature Review}}}, shorttitle = {Large {{Language Models}} for {{Software Engineering}}}, author = {Hou, Xinyi and Zhao, Yanjie and Liu, Yue and Yang, Zhou and Wang, Kailong and Li, Li and Luo, Xiapu and Lo, David and Grundy, John and Wang, Haoyu}, date = {2023-09-12}, eprint = {2308.10620}, eprinttype = {arXiv}, eprintclass = {cs}, doi = {10.48550/arXiv.2308.10620}, abstract = {Large Language Models (LLMs) have significantly impacted numerous domains, including Software Engineering (SE). Many recent publications have explored LLMs applied to various SE tasks. Nevertheless, a comprehensive understanding of the application, effects, and possible limitations of LLMs on SE is still in its early stages. To bridge this gap, we conducted a systematic literature review on LLM4SE, with a particular focus on understanding how LLMs can be exploited to optimize processes and outcomes. We collect and analyze 229 research papers from 2017 to 2023 to answer four key research questions (RQs). In RQ1, we categorize different LLMs that have been employed in SE tasks, characterizing their distinctive features and uses. In RQ2, we analyze the methods used in data collection, preprocessing, and application highlighting the role of well-curated datasets for successful LLM for SE implementation. RQ3 investigates the strategies employed to optimize and evaluate the performance of LLMs in SE. Finally, RQ4 examines the specific SE tasks where LLMs have shown success to date, illustrating their practical contributions to the field. From the answers to these RQs, we discuss the current state-of-the-art and trends, identifying gaps in existing research, and flagging promising areas for future study.}, pubstate = {prepublished}, keywords = {Computer Science - Artificial Intelligence,Computer Science - Software Engineering}, note = {TL;DR \par A systematic literature review on LLM4SE is conducted, with a particular focus on understanding how LLMs can be exploited to optimize processes and outcomes.} } @article{houLargeLanguageModels2024, title = {Large {{Language Models}} for {{Software Engineering}}: {{A Systematic Literature Review}}}, shorttitle = {Large {{Language Models}} for {{Software Engineering}}}, author = {Hou, Xinyi and Zhao, Yanjie and Liu, Yue and Yang, Zhou and Wang, Kailong and Li, Li and Luo, Xiapu and Lo, David and Grundy, John and Wang, Haoyu}, date = {2024-11-30}, journaltitle = {ACM Trans. Software Eng. Methodol.}, volume = {33}, number = {8}, pages = {1--79}, issn = {1049-331X, 1557-7392}, doi = {10.1145/3695988}, abstract = {Large Language Models (LLMs) have significantly impacted numerous domains, including Software Engineering (SE). Many recent publications have explored LLMs applied to various SE tasks. Nevertheless, a comprehensive understanding of the application, effects, and possible limitations of LLMs on SE is still in its early stages. To bridge this gap, we conducted a Systematic Literature Review (SLR) on LLM4SE, with a particular focus on understanding how LLMs can be exploited to optimize processes and outcomes. We selected and analyzed 395 research articles from January 2017 to January 2024 to answer four key Research Questions (RQs). In RQ1, we categorize different LLMs that have been employed in SE tasks, characterizing their distinctive features and uses. In RQ2, we analyze the methods used in data collection, pre-processing, and application, highlighting the role of well-curated datasets for successful LLM for SE implementation. RQ3 investigates the strategies employed to optimize and evaluate the performance of LLMs in SE. Finally, RQ4 examines the specific SE tasks where LLMs have shown success to date, illustrating their practical contributions to the field. From the answers to these RQs, we discuss the current state-of-the-art and trends, identifying gaps in existing research, and highlighting promising areas for future study. Our artifacts are publicly available at https://github.com/security-pride/LLM4SE\_SLR .}, langid = {english}, keywords = {/unread,LLMs} } @article{howard_fastai_2020, title = {Fastai: {{A Layered API}} for {{Deep Learning}}}, shorttitle = {Fastai}, author = {Howard, Jeremy and Gugger, Sylvain}, date = {2020-02}, journaltitle = {Information}, volume = {11}, number = {2}, pages = {108}, issn = {2078-2489}, doi = {10.3390/info11020108}, abstract = {fastai is a deep learning library which provides practitioners with high-level components that can quickly and easily provide state-of-the-art results in standard deep learning domains, and provides researchers with low-level components that can be mixed and matched to build new approaches. It aims to do both things without substantial compromises in ease of use, flexibility, or performance. This is possible thanks to a carefully layered architecture, which expresses common underlying patterns of many deep learning and data processing techniques in terms of decoupled abstractions. These abstractions can be expressed concisely and clearly by leveraging the dynamism of the underlying Python language and the flexibility of the PyTorch library. fastai includes: a new type dispatch system for Python along with a semantic type hierarchy for tensors; a GPU-optimized computer vision library which can be extended in pure Python; an optimizer which refactors out the common functionality of modern optimizers into two basic pieces, allowing optimization algorithms to be implemented in 4-5 lines of code; a novel 2-way callback system that can access any part of the data, model, or optimizer and change it at any point during training; a new data block API; and much more. We have used this library to successfully create a complete deep learning course, which we were able to write more quickly than using previous approaches, and the code was more clear. The library is already in wide use in research, industry, and teaching. NB: This paper covers fastai v2, which is currently in pre-release at http://dev.fast.ai/}, keywords = {Computer Science - Computer Vision and Pattern Recognition,Computer Science - Machine Learning,Computer Science - Neural and Evolutionary Computing,Statistics - Machine Learning}, note = {arXiv: 2002.04688 \par TL;DR \par Fastai is a deep learning library which provides practitioners with high-level components that can quickly and easily provide state-of-the-art results in standard deep learning domains, and provides researchers with low-level component that can be mixed and matched to build new approaches.} } @online{HowCanUse, title = {How {{Can I Use This Method}}? - {{IEEE Conference Publication}}}, url = {https://ieeexplore.ieee.org/document/7194634/}, urldate = {2018-07-27} } @online{HowDevelopersAdapt, title = {How {{Do Developers Adapt Code Snippets}} to {{Their Contexts}}? {{An Empirical Study}} of {{Context-Based Code Snippet Adaptations}}}, url = {https://www.computer.org/csdl/journal/ts/2024/11/10510659/1WyZkBHeDGo}, keywords = {source\_mobile} } @online{HowDidWe, title = {How Did We Get to {{vLLM}}, and What Was Its Genius? | by {{Purusharth Malik}} | {{Feb}}, 2025 | {{AI Advances}}}, url = {https://ai.gopubby.com/how-did-we-get-to-vllm-and-what-was-its-genius-ca4c918e58f2}, keywords = {source\_mobile} } @online{HowExactlyMachine, title = {How Exactly Is Machine Learning Used in Recommendation Engines? - {{Quora}}}, url = {https://www.quora.com/How-exactly-is-machine-learning-used-in-recommendation-engines}, urldate = {2017-03-10} } @misc{HttpPdmaidsdibrisunigeitQuestionnairephp, title = {{{http://pdm-aids.dibris.unige.it/questionnaire.php}}} } @article{Huang:2012:LCD:2343876.2343884, title = {Learning a Concept-Based Document Similarity Measure}, author = {Huang, Lan and Milne, David and Frank, Eibe and Witten, Ian H.}, date = {2012-08}, journaltitle = {J. Am. Soc. Inf. Sci. Technol.}, volume = {63}, number = {8}, pages = {1593--1608}, publisher = {John Wiley \& Sons, Inc.}, address = {New York, NY, USA}, issn = {1532-2882}, url = {http://dx.doi.org/10.1002/asi.22689}, acmid = {2343884}, issue_date = {August 2012}, nodoi = {10.1002/asi.22689}, numpages = {16}, keywords = {content analysis,semantic analysis,text mining}, note = {TL;DR \par A new measure is proposed that assesses similarity at both the lexical and semantic levels, and learns from human judgments how to combine them by using machine-learning techniques, and shows that it improves both classification and clustering performance.} } @article{Huang20221457, title = {Noncoherent Massive Random Access for Inhomogeneous Networks: {{From}} Message Passing to Deep Learning}, author = {Huang, J. and Zhang, H. and Huang, C. and Yang, L. and Zhang, W.}, date = {2022}, journaltitle = {IEEE J. Sel. Areas Commun.}, volume = {40}, number = {5}, pages = {1457--1472}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {07338716}, doi = {10.1109/JSAC.2022.3143260}, abstract = {Massive machine-type communications (mMTC) are expected to support a large amount of randomly deployed users for short package transmissions. Noncoherent random access provides an efficient and practical multi-access protocol for mMTC, and also poses new challenges for the receiver design. In this paper, we leverage two well-known methods, i.e., message passing and deep learning, to jointly detect the user activity and the desired data for the noncoherent mMTC. First, by exploiting the exact distribution information of the received signal, a generalized approximate message passing (GAMP)-based algorithm is proposed, which is shown to jointly detect the user activity and the desired data by two modules: inter-user interference elimination and data detection for each user. Inspired by the two-module GAMP-based algorithm, we then propose a model-driven deep learning method, which utilizes the deep neural networks (DNNs) to approximate both the two modules. The loss function for training the DNNs is derived by formulating the two-module detection as an unconstrained optimization problem. Simulation results reveal that the proposed GAMP-based algorithm outperforms the proposed deep learning method when the channel distribution is perfectly known, while it suffers from a significant performance degradation for the case with imperfect channel distribution information. © 1983-2012 IEEE.}, coden = {ISACE}, document_type = {Article}, source = {Scopus}, note = {cited By 1} } @article{huangAIAgents, title = {{{AI Agents}}}, author = {Huang, Evan and Xue, Emily and Sercinoglu, Olcan and Riedel, Sebastian and Baveja, Satinder and Gulli, Antonio and Nawalgaria, Anant}, langid = {english}, keywords = {P1} } @inproceedings{huangSimilarityMeasuresText2008, title = {Similarity Measures for Text Document Clustering}, booktitle = {Proc. {{Sixth N}}. {{Z}}. {{Comput}}. {{Sci}}. {{Res}}. {{Stud}}. {{Conf}}. {{NZCSRSC2008 Christch}}. {{N}}. {{Z}}.}, author = {Huang, A.}, date = {2008}, pages = {49--56}, url = {http://scholar.google.com.au/scholar.bib?q=info:enBKVjSSXjQJ:scholar.google.com/&output=citation&hl=en&as_sdt=2000&ct=citation&cd=0}, added-at = {2010-01-10T13:17:50.000+0100}, biburl = {http://www.bibsonomy.org/bibtex/22731fc9ee66915a56f9ee14f1436aabf/cdevries}, interhash = {b05294a51336b00c449ecaeb25940212}, intrahash = {2731fc9ee66915a56f9ee14f1436aabf}, keywords = {clustering measures}, note = {TL;DR \par A wide variety of distance functions and similarity measures have been used for clustering, such as squared Euclidean distance, cosine similarity, and relative entropy, and a comparison of these measures in partitional clustering for text document datasets is compared and analyzed.}, timestamp = {2010-01-10T13:17:50.000+0100} } @online{hudsonSoftwareEngineeringPerspective2024, title = {A {{Software Engineering Perspective}} on {{Testing Large Language Models}}: {{Research}}, {{Practice}}, {{Tools}} and {{Benchmarks}}}, shorttitle = {A {{Software Engineering Perspective}} on {{Testing Large Language Models}}}, author = {Hudson, Sinclair and Jit, Sophia and Hu, Boyue Caroline and Chechik, Marsha}, date = {2024-06-12}, eprint = {2406.08216}, eprinttype = {arXiv}, eprintclass = {cs}, doi = {10.48550/arXiv.2406.08216}, abstract = {Large Language Models (LLMs) are rapidly becoming ubiquitous both as stand-alone tools and as components of current and future software systems. To enable usage of LLMs in the high-stake or safety-critical systems of 2030, they need to undergo rigorous testing. Software Engineering (SE) research on testing Machine Learning (ML) components and ML-based systems has systematically explored many topics such as test input generation and robustness. We believe knowledge about tools, benchmarks, research and practitioner views related to LLM testing needs to be similarly organized. To this end, we present a taxonomy of LLM testing topics and conduct preliminary studies of state of the art and practice approaches to research, open-source tools and benchmarks for LLM testing, mapping results onto this taxonomy. Our goal is to identify gaps requiring more research and engineering effort and inspire a clearer communication between LLM practitioners and the SE research community.}, pubstate = {prepublished}, keywords = {Computer Science - Software Engineering} } @article{huebscherSurveyAutonomicComputing2008, title = {A Survey of Autonomic Computing—Degrees, Models, and Applications}, author = {Huebscher, Markus C. and McCann, Julie A.}, date = {2008}, journaltitle = {ACM Comput. Surv. CSUR}, volume = {40}, number = {3}, pages = {7}, url = {http://dl.acm.org/citation.cfm?id=1380585}, urldate = {2016-08-29} } @article{hug_surprise_2020, title = {Surprise: {{A Python}} Library for Recommender Systems}, shorttitle = {Surprise}, author = {Hug, Nicolas}, date = {2020-08}, journaltitle = {J. Open Source Softw.}, volume = {5}, number = {52}, pages = {2174}, issn = {2475-9066}, doi = {10.21105/joss.02174}, abstract = {Recommender systems aim at providing users with a list of recommendations of items that a service offers. For example, a video streaming service will typically rely on a recommender system to propose a personalized list of movies or series to each of its users. A typical problem in recommendation is that of rating prediction: given an incomplete dataset of useritem interations which take the form of numerical ratings (e.g. on a scale from 1 to 5), the goal is to predict the missing ratings for all remaining user-item pairs.}, langid = {english} } @misc{HUGOAFONSOInvestigation_Plan___Model_driven_DevOps75Hugoman, title = {{{HUGO-AFONSO-Investigation}}\_{{Plan}}\_\_\_{{Model}}\_driven\_{{DevOps}} (75) - Hugoman 98}, langid = {english}, note = {\section{Annotazioni\\ (17/3/2025, 14:56:20)} \par - “intersection of DevOps and Model-driven development,” (“HUGO-AFONSO-Investigation\_Plan\_\_\_Model\_driven\_DevOps (75) - hugoman 98”, p. i) \#5fb236 \par - “most efforts are focused on specific domains, tasks, or tools” (“HUGO-AFONSO-Investigation\_Plan\_\_\_Model\_driven\_DevOps (75) - hugoman 98”, p. i) \#5fb236 \par - “holistic approach that goes beyond particular technologies.” (“HUGO-AFONSO-Investigation\_Plan\_\_\_Model\_driven\_DevOps (75) - hugoman 98”, p. i) \#ffd400\\ \mkbibemph{This is necessary to stress to show novelty!} \par - “Based on this analysis, we created a meta-model capable of representing CI/CD pipelines across widely used technologies. This approach leverages model-driven techniques, enabling users to define pipelines without dealing with the intricate implementation details, such as configuration files, of different tools.” (“HUGO-AFONSO-Investigation\_Plan\_\_\_Model\_driven\_DevOps (75) - hugoman 98”, p. i) \#a28ae5 \par - “block-based language derived from the meta-model, allowing users to visually design CI/CD pipelines and automatically generate configuration files for multiple CI/CD platforms.” (“HUGO-AFONSO-Investigation\_Plan\_\_\_Model\_driven\_DevOps (75) - hugoman 98”, p. i) \#ffd400\\ \mkbibemph{What are the critical research issues? Are they related to the specification, execution, validation/analysis, ????} \par - “migrating” (“HUGO-AFONSO-Investigation\_Plan\_\_\_Model\_driven\_DevOps (75) - hugoman 98”, p. i) \#ffd400\\ \mkbibemph{What are the critical migration challenges that you encountered and you decided to focus on?} \par - “RQ1 - What is the current state of the art for MDE and DevOps?” (“HUGO-AFONSO-Investigation\_Plan\_\_\_Model\_driven\_DevOps (75) - hugoman 98”, p. 2) \#ffd400 \par - “shortage of tools that enable users to perform various DevOps tasks while abstracting away the underlying technologies.” (“HUGO-AFONSO-Investigation\_Plan\_\_\_Model\_driven\_DevOps (75) - hugoman 98”, p. 2) \#a28ae5 \par - “RQ2 - What is the current state of the art for CI/CD practice?” (“HUGO-AFONSO-Investigation\_Plan\_\_\_Model\_driven\_DevOps (75) - hugoman 98”, p. 3) \#ffd400 \par - “reengineering CI/CD?” (“HUGO-AFONSO-Investigation\_Plan\_\_\_Model\_driven\_DevOps (75) - hugoman 98”, p. 3) \#ffd400\\ \mkbibemph{why stratgin from scratch?} \par - “• A meta-model capable of representing CI/CD pipelines and capable of helping during the migration process between different CI/CD technologies.” (“HUGO-AFONSO-Investigation\_Plan\_\_\_Model\_driven\_DevOps (75) - hugoman 98”, p. 4) \#2ea8e5 \par - “• A visual framework capable of defining whole CI/CD pipelines without resorting to concrete technologies and helping users during the creation and migration processes of CI/CD pipelines.” (“HUGO-AFONSO-Investigation\_Plan\_\_\_Model\_driven\_DevOps (75) - hugoman 98”, p. 4) \#2ea8e5 \par - “applying MDE techniques in the field of DevOp” (“HUGO-AFONSO-Investigation\_Plan\_\_\_Model\_driven\_DevOps (75) - hugoman 98”, p. 5) \#5fb236 \par - “open-source software repositories to gather insights on the use of CI/CD technologies” (“HUGO-AFONSO-Investigation\_Plan\_\_\_Model\_driven\_DevOps (75) - hugoman 98”, p. 5) \#5fb236 \par - “migration trends between these technologies” (“HUGO-AFONSO-Investigation\_Plan\_\_\_Model\_driven\_DevOps (75) - hugoman 98”, p. 5) \#5fb236 \par - “meta-model in Chapter 5,” (“HUGO-AFONSO-Investigation\_Plan\_\_\_Model\_driven\_DevOps (75) - hugoman 98”, p. 6) \#5fb236 \par - “the different tasks of our work plan as well as a detailed timeline” (“HUGO-AFONSO-Investigation\_Plan\_\_\_Model\_driven\_DevOps (75) - hugoman 98”, p. 6) \#5fb236 \par - “GitLab” (“HUGO-AFONSO-Investigation\_Plan\_\_\_Model\_driven\_DevOps (75) - hugoman 98”, p. 8) \#5fb236 \par - “applies MDE to the field of DevOps.” (“HUGO-AFONSO-Investigation\_Plan\_\_\_Model\_driven\_DevOps (75) - hugoman 98”, p. 14) \#5fb236 \par - “requirements for using MDE in DevOps” (“HUGO-AFONSO-Investigation\_Plan\_\_\_Model\_driven\_DevOps (75) - hugoman 98”, p. 15) \#5fb236 \par - “CI/CD technologies across open-source software repositories hosted on GitHub” (“HUGO-AFONSO-Investigation\_Plan\_\_\_Model\_driven\_DevOps (75) - hugoman 98”, p. 33) \#5fb236 \par - “100 million developers used GitHub” (“HUGO-AFONSO-Investigation\_Plan\_\_\_Model\_driven\_DevOps (75) - hugoman 98”, p. 33) \#a28ae5 \par - “284 million public repositories” (“HUGO-AFONSO-Investigation\_Plan\_\_\_Model\_driven\_DevOps (75) - hugoman 98”, p. 33) \#a28ae5 \par - “comprehensive understanding of the prevailing utilization of diverse CI/CD technologies.” (“HUGO-AFONSO-Investigation\_Plan\_\_\_Model\_driven\_DevOps (75) - hugoman 98”, p. 34) \#5fb236 \par - “to which extent the technologies can serve the projects’ needs.” (“HUGO-AFONSO-Investigation\_Plan\_\_\_Model\_driven\_DevOps (75) - hugoman 98”, p. 34) \#5fb236 \par - “two technologies and that some (few cases) use up to 13 different technologies at the same time” (“HUGO-AFONSO-Investigation\_Plan\_\_\_Model\_driven\_DevOps (75) - hugoman 98”, p. 34) \#a28ae5 \par - “This motivates further investigation on the interoperability of the different technologies and the support developers have to work with multiple CI/CD technologies at the same time.” (“HUGO-AFONSO-Investigation\_Plan\_\_\_Model\_driven\_DevOps (75) - hugoman 98”, p. 34) \#ffd400\\ \mkbibemph{I agree this is a critical point. However, it is necessary to give more evidend on why it is necessary to support such an interoperability aspects. Can you say elaborate more on what happens with and without such interoperability support?} \par - “Table 4.1: CI/CD technologies we can identify and analyze.” (“HUGO-AFONSO-Investigation\_Plan\_\_\_Model\_driven\_DevOps (75) - hugoman 98”, p. 36) \#a28ae5 \par - “GitLab” (“HUGO-AFONSO-Investigation\_Plan\_\_\_Model\_driven\_DevOps (75) - hugoman 98”, p. 36) \#5fb236 \par - “Jenkins” (“HUGO-AFONSO-Investigation\_Plan\_\_\_Model\_driven\_DevOps (75) - hugoman 98”, p. 36) \#5fb236 \par - “GitHubActions” (“HUGO-AFONSO-Investigation\_Plan\_\_\_Model\_driven\_DevOps (75) - hugoman 98”, p. 36) \#5fb236 \par - “Kubernetes” (“HUGO-AFONSO-Investigation\_Plan\_\_\_Model\_driven\_DevOps (75) - hugoman 98”, p. 36) \#5fb236 \par - “Support the Migration and Evolution” (“HUGO-AFONSO-Investigation\_Plan\_\_\_Model\_driven\_DevOps (75) - hugoman 98”, p. 53) \#5fb236 \par - “GitHub Actions, Jenkins, GitLab CI/CD, CircleCI, and Travis CI.” (“HUGO-AFONSO-Investigation\_Plan\_\_\_Model\_driven\_DevOps (75) - hugoman 98”, p. 54) \#a28ae5 \par - “these four technologies account for 96.3\% of all found pipelines” (“HUGO-AFONSO-Investigation\_Plan\_\_\_Model\_driven\_DevOps (75) - hugoman 98”, p. 54) \#a28ae5 \par - “1. A Model-driven approach to create DevOps CI/CD pipelines” (“HUGO-AFONSO-Investigation\_Plan\_\_\_Model\_driven\_DevOps (75) - hugoman 98”, p. 59) \#2ea8e5 \par - “This implies DevOps teams need to learn quite some tools, languages, and notations to be able to define concrete DevOps processes” (“HUGO-AFONSO-Investigation\_Plan\_\_\_Model\_driven\_DevOps (75) - hugoman 98”, p. 59) \#ffd400\\ \mkbibemph{How technology independent can you actually be?} \par Dear members of the Advisory Committee of the PhD student~\textbf{Hugo~Gião}, \par On behalf of the Scientific Committee of the Doctoral Programme in Informatics Engineering (ProDEI) of the Faculty of Engineering of the University of Porto (FEUP), we would like to inform you that the Advisory Committee indicated below has been nominated and will be chaired by Professor~\textbf{Rui~}Filipe Lima\textbf{~Maranhão~}de Abreu\textbf{~}(Full Professor at DEI/ FEUP), as member of the Scientific Committee of ProDEI. \par \textbf{~} \par We would like to thank you all for the collaboration, which is very important for the success of this project. \par Apart from the President, the Advisory Committee is composed of the following members*: \par \begin{itemize} \item External Member:~\textbf{Davide Di Ruscio}~(\href{https://www.disim.univaq.it/DavideDiRuscio}{https://www.disim.univaq.it/DavideDiRuscio}), Full Professor, Department of Information Engineering Computer Science and Mathematics, University of L’Aquila; \end{itemize} \begin{itemize} \item Internal Member:~\textbf{Filipe Alexandre Pais de Figueiredo Correia},~Assistant Professor, Department of Informatics Engineering, Faculdade de Engenharia - Universidade do Porto; \item Supervisor:~\textbf{Jácome Miguel Costa da Cunha}, Associate Professor, Department of Informatics Engineering, Faculdade de Engenharia - Universidade do Porto; \item Co-supervisor:~\textbf{Rui Alexandre Pereira}, OutSystems. \end{itemize} \par \mkbibemph{*We kindly ask you to check your data above and inform us of any discrepancy.} \par The main task of the Advisory Committee is to meet and discuss the~\textbf{research plan}:~\href{https://drive.google.com/drive/folders/1Db2V6Ruuw-GUtXHbmXwQ3MNaaTfAcCPt}{https://drive.google.com/drive/folders/1Db2V6Ruuw-GUtXHbmXwQ3MNaaTfAcCPt}. \par The meeting starts with a 30-minute presentation by the student, followed by a discussion, usually about one hour long, that is mainly driven by the two non-supervisor members.~Other members of the committee may also ask questions or make comments. \par Any necessary additional information about the role of the Advisory Committee will be provided by the President during the meeting. \par \textbf{Evaluation of the Workplan} \par The outcome of the meeting is one of the following:~ \par \begin{itemize} \item The thesis proposal is~accepted: the student can continue the PhD work as stated in the thesis proposal. However, the student may still be required to make some (typically minor) changes to the workplan, according to the recommendations of the Advisory Committee. The student will be informed of the required changes, which will later be checked and validated by the supervisor(s). \item The thesis proposal is~rejected: in this case, the jury must complement the meeting minutes with a text explaining what the student is expected to do in order to improve the thesis proposal. The student will be allowed to submit another proposal within a period of time deemed appropriate. Further information about the process will be provided at that time. \end{itemize} \par Therefore,~the next step is to schedule the meeting, so we kindly ask Prof.~@Jácome Cunha~to lead this scheduling. \par Please do not hesitate to contact us for any clarification. \par Best Regards, \par \textbf{~} \par Catarina Castro} } @incollection{hulsbuschShowingFullSemantics2010, title = {Showing {{Full Semantics Preservation}} in {{Model Transformation}} - {{A Comparison}} of {{Techniques}}}, booktitle = {Integrated {{Formal Methods}}}, author = {Hülsbusch, Mathias and König, Barbara and Rensink, Arend and Semenyak, Maria and Soltenborn, Christian and Wehrheim, Heike}, editor = {Méry, Dominique and Merz, Stephan}, date = {2010}, series = {Lecture {{Notes}} in {{Computer Science}}}, number = {6396}, pages = {183--198}, publisher = {Springer Berlin Heidelberg}, url = {http://link.springer.com/chapter/10.1007/978-3-642-16265-7_14}, urldate = {2015-03-24}, abstract = {Model transformation is a prime technique in modern, model-driven software design. One of the most challenging issues is to show that the semantics of the models is not affected by the transformation. So far, there is hardly any research into this issue, in particular in those cases where the source and target languages are different. In this paper, we are using two different state-of-the-art proof techniques (explicit bisimulation construction versus borrowed contexts) to show bisimilarity preservation of a given model transformation between two simple (self-defined) languages, both of which are equipped with a graph transformation-based operational semantics. The contrast between these proof techniques is interesting because they are based on different model transformation strategies: triple graph grammars versus in situ transformation. We proceed to compare the proofs and discuss scalability to a more realistic setting.}, isbn = {978-3-642-16264-0 978-3-642-16265-7}, langid = {english}, keywords = {software engineering} } @inproceedings{Hundt20191797, title = {The {{CoSTAR}} Block Stacking Dataset: {{Learning}} with Workspace Constraints}, author = {Hundt, A. and Jain, V. and Lin, C.-H. and Paxton, C. and Hager, G.D.}, date = {2019}, series = {{{IEEE International Conference}} on {{Intelligent Robots}} and {{Systems}}}, pages = {1797--1804}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {21530858}, doi = {10.1109/IROS40897.2019.8967784}, abbrev_source_title = {IEEE Int Conf Intell Rob Syst}, affiliation = {Johns Hopkins University, Department of Computer Science, United States; NVIDIA, United States}, art_number = {8967784}, coden = {85RBA}, document_type = {Conference Paper}, isbn = {978-1-7281-4004-9}, langid = {english}, source = {Scopus}, note = {cited By 2} } @online{hurleySimplifyMachineLearning2020, title = {Simplify {{Machine Learning Workflows}}}, author = {Hurley, David}, date = {2020-07-02T20:48:29}, url = {https://towardsdatascience.com/simplify-machine-learning-workflows-e9d4f404aaeb}, urldate = {2021-03-18}, abstract = {How to use Pipelines to standardize data preprocessing, data transformation, and modeling steps of a machine learning workflow}, langid = {english}, organization = {Medium} } @online{huRouterBenchBenchmarkMultiLLM2024, title = {{{RouterBench}}: A Benchmark for Multi-{{LLM}} Routing System}, shorttitle = {{{RouterBench}}}, author = {Hu, Qitian Jason and Bieker, Jacob and Li, Xiuyu and Jiang, Nan and Keigwin, Benjamin and Ranganath, Gaurav and Keutzer, Kurt and Upadhyay, Shriyash Kaustubh}, date = {2024-03-28}, eprint = {2403.12031}, eprinttype = {arXiv}, doi = {10.48550/arXiv.2403.12031}, abstract = {As the range of applications for Large Language Models (LLMs) continues to grow, the demand for effective serving solutions becomes increasingly critical. Despite the versatility of LLMs, no single model can optimally address all tasks and applications, particularly when balancing performance with cost. This limitation has led to the development of LLM routing systems, which combine the strengths of various models to overcome the constraints of individual LLMs. Yet, the absence of a standardized benchmark for evaluating the performance of LLM routers hinders progress in this area. To bridge this gap, we present RouterBench, a novel evaluation framework designed to systematically assess the efficacy of LLM routing systems, along with a comprehensive dataset comprising over 405k inference outcomes from representative LLMs to support the development of routing strategies. We further propose a theoretical framework for LLM routing, and deliver a comparative analysis of various routing approaches through RouterBench, highlighting their potentials and limitations within our evaluation framework. This work not only formalizes and advances the development of LLM routing systems but also sets a standard for their assessment, paving the way for more accessible and economically viable LLM deployments. The code and data are available at https://github.com/withmartian/routerbench.}, langid = {english}, pubstate = {prepublished}, keywords = {Computer Science - Artificial Intelligence,Computer Science - Machine Learning} } @article{husarAutonomousSystemsModeling2013, title = {Autonomous {{Systems Modeling During Early Architecture Development}}}, author = {Husar, Rosteslaw M. and Stracener, Jerrell}, date = {2013}, journaltitle = {Procedia Comput. Sci.}, volume = {20}, pages = {242--247}, issn = {18770509}, doi = {10.1016/j.procs.2013.09.268}, langid = {english} } @inproceedings{hutchinsonEmpiricalAssessmentMDE2011, title = {Empirical Assessment of {{MDE}} in Industry}, booktitle = {Proc. 33rd {{Int}}. {{Conf}}. {{Softw}}. {{Eng}}.}, author = {Hutchinson, John and Whittle, Jon and Rouncefield, Mark and Kristoffersen, Steinar}, date = {2011}, pages = {471--480}, publisher = {ACM}, url = {http://dl.acm.org/citation.cfm?id=1985858}, urldate = {2015-10-29}, note = {TL;DR \par Using largely qualitative questionnaire and interview methods, a range of technical, organizational and social factors that apparently influence organizational responses to MDE are investigated and its perception as a successful or unsuccessful organizational intervention is investigated.} } @article{hutchinsonModeldrivenEngineeringPractices2013, title = {Model-Driven Engineering Practices in Industry: {{Social}}, Organizational and Managerial Factors That Lead to Success or Failure}, author = {Hutchinson, John and Whittle, Jon and Rouncefield, Mark}, date = {2013}, journaltitle = {Sci. Comput. Program.}, doi = {10.1016/j.scico.2013.03.017} } @online{huyenAgents2025, title = {Agents}, author = {Huyen, Chip}, date = {2025-01-07}, url = {https://huyenchip.com/2025/01/07/agents.html}, urldate = {2025-04-30}, abstract = {Intelligent agents are considered by many to be the ultimate goal of AI. The classic book by Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach (Prentice Hall, 1995), defines the field of AI research as “the study and design of rational agents.”}, langid = {english}, organization = {Chip Huyen} } @online{HybridApproachMetamodel, title = {Hybrid {{Approach}} for {{Metamodel}} and {{Model Co-evolution}} - {{Springer}}}, url = {http://link.springer.com/chapter/10.1007%2F978-3-319-19578-0_46}, urldate = {2015-07-19} } @inproceedings{Ickin202072, title = {Ensemble-Based Synthetic Data Synthesis for Federated {{QoE}} Modeling}, author = {Ickin, S. and Vandikas, K. and Moradi, F. and Taghia, J. and Hu, W.}, editor = {De Turck F., Chemouil P., Zhani M.F., Cerroni W., Pasquini R., Zhu Z., Wauters T.}, date = {2020}, series = {Proceedings of the 2020 {{IEEE Conference}} on {{Network Softwarization}}: {{Bridging}} the {{Gap Between AI}} and {{Network Softwarization}}, {{NetSoft}} 2020}, pages = {72--76}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/NetSoft48620.2020.9165379}, abstract = {Quality of Experience (QoE) models need good generalization that necessitates sufficient amount of user-labeled datasets associated with measurements related to underlying QoE factors. However, obtaining QoE datasets is often costly, since they are preferably collected from many subjects with diverse background, and eventually dataset sizes and representations are limited. Models can be improved by sharing and merging those collected local datasets, however regulations such as GDPR make data sharing difficult, as those local user datasets might contain sensitive information about the subjects. A privacy-preserving machine learning approach such as Federated Learning (FL) is a potential candidate that enables sharing of QoE data models between collaborators without exposing ground truth, but only by means of sharing the securely aggregated form of extracted model parameters. While FL can enable a seamless QoE model management, if collaborators do not have the same level of data quality, more iterations of information sharing over a communication channel might be necessary for models to reach an acceptable accuracy. In this paper, we present an ensemble based Bayesian synthetic data generation method for FL, LOO (Leave-One-Out), which reduces the training time by 30\% and the network footprint in the communication channel by 60\%. © 2020 IEEE.}, art_number = {9165379}, document_type = {Conference Paper}, isbn = {978-1-7281-5684-2}, source = {Scopus}, note = {cited By 2 \par TL;DR \par An ensemble based Bayesian synthetic data generation method for FL, LOO (Leave-One-Out), which reduces the training time by 30\% and the network footprint in the communication channel by 60\%.} } @misc{Icse2025paper1115, title = {Icse2025-Paper1115}, keywords = {LOGSEQ}, note = {\section{Annotazioni\\ (30/9/2024, 22:59:45)} \par - “generate accurate and 11 comprehensive UML models described through the PlantUML 12 syntax.” (“icse2025-paper1115”, p. 1) \#5fb236 \par - “accuracy, completeness, and ease 18 of understanding” (“icse2025-paper1115”, p. 1) \#5fb236 \par - “31 for capturing system architectures. However, manual creation 32 of these models can be time-consuming and error-prone, 33” (“icse2025-paper1115”, p. 1) \#00b036 \par - “LLMs have witnessed a meteoric rise in 40 popularity and utility across various domains” (“icse2025-paper1115”, p. 1) \#5fb236 \par - “41 characterized by their ability to comprehend and generate 42 human-like contents, have demonstrated remarkable efficacy 43” (“icse2025-paper1115”, p. 1) \#00b036 \par - “in creating artifacts from scratch, ranging from articles and 44 stories” (“icse2025-paper1115”, p. 1) \#00b036 \par - “The goal of is paper is to perform a comprehensive 49 investigation into the ability of LLMs to automatically 50 generate UML class diagrams that capture informal,” (“icse2025-paper1115”, p. 1) \#00b036 \par - “descriptions” (“icse2025-paper1115”, p. 1) \#f0ff00\\ \mkbibemph{If we have more at different levels of abstraction, how to link them?} \par - “developers to harness the power of LLMs 69 for automating UML diagram generation tasks.” (“icse2025-paper1115”, p. 1) \#00b036 \par - “The idea of automatically generating models and other 93 software engineering artefacts from descriptions expressed in 94 natural language has been around for years before the advent 95 of LLMs.” (“icse2025-paper1115”, p. 1) \#5fb236 \par - “ChatGPT struggled with accurately representing complex 138 diagrams involving classes and advanced UML concepts such 139 as association classes and multiple inheritance.” (“icse2025-paper1115”, p. 2) \#00b036 \par - “into an educational context where they demonstrated that 144 utilizing ChatGPT in software modeling courses achieves 145 improved outcomes in formative assessments compared to 146 those using traditional methods.” (“icse2025-paper1115”, p. 2) \#00b036 \par - “Hence, we decided to focus our 178 attention on the presence or absence of the most important 179 elements in a class diagram: classes, attributes, associations, 180 cardinalities, operations.” (“icse2025-paper1115”, p. 2) \#5fb236 \par - “The ultimate goal of this research is to evaluate the ability 187 of LLMss to automatically generate UML models described 188 through class diagrams and to compare their performance 189 based on the quality of the obtained results.” (“icse2025-paper1115”, p. 2) \#00b036 \par - “The ultimate goal of this research is to evaluate the ability 187 of LLMss to automatically generate UML models described 188 through class diagrams and to compare their performance 189 based on the quality of the obtained results.” (“icse2025-paper1115”, p. 2) \#a28ae5 \par - “The textual descriptions were 193 originally created as exercises to be developed by students of 194 a Software Engineering class to evaluate their proficiency in 195 UML modeling.” (“icse2025-paper1115”, p. 2) \#5fb236 \par - “We used the textual descriptions 200 as basis to prompt LLMs and the corresponding UML models 201 as ground truth against which to compare the models generated 202 by the LLMs.1” (“icse2025-paper1115”, p. 2) \#a28ae5 \par - “(i) Experiment Preparation” (“icse2025-paper1115”, p. 3) \#5fb236 \par - “(ii) Experiment 208 Execution” (“icse2025-paper1115”, p. 3) \#5fb236 \par - “(iii) Data Analysis” (“icse2025-paper1115”, p. 3) \#5fb236 \par - “1) LLM selection:” (“icse2025-paper1115”, p. 3) \#2ea8e5 \par - “ChatGPT-4 and ChatGPT-3.5 have demonstrated excep232 tional performance across a wide range of NLP tasks, in233 cluding text generation, translation, and summarization” (“icse2025-paper1115”, p. 3) \#5fb236 \par - “excep232” (“icse2025-paper1115”, p. 3) \#00b036 \par - “a deep understanding of” (“icse2025-paper1115”, p. 3) \#f0ff00\\ \mkbibemph{I have some doults.} \par - “semantic nuances,” (“icse2025-paper1115”, p. 3) \#f0ff00\\ \mkbibemph{Are we sure?} \par - “LLama 3 has been fine-tuned on 250 diverse datasets, making it adept at handling a wide range 251 of inputs and producing coherent and contextually relevant 252 outputs [21]” (“icse2025-paper1115”, p. 3) \#5fb236 \par - “Its effi- 255 ciency and capability in capturing structural details make it an 256 important LLM to evaluate in our study” (“icse2025-paper1115”, p. 3) \#5fb236 \par - “Through this comparative study, we sought to determine 258 whether each LLM shows specific characteristics in the gen- 259 eration of UML class diagrams and whether it is possible to 260 identify one that outperforms the others.” (“icse2025-paper1115”, p. 3) \#a28ae5 \par - “2) Preparation of descriptions and prompt engineering” (“icse2025-paper1115”, p. 3) \#2ea8e5 \par - “he dataset we used in this study consists of UML class 263 diagrams generated from informal descriptions for which an 264 expert-provided corresponding UML model was available” (“icse2025-paper1115”, p. 3) \#5fb236 \par - “identifying and 266 collecting the informal descriptions” (“icse2025-paper1115”, p. 3) \#e56eee \par - “generating the dataset 267 by feeding the informal descriptions, though a suitable prompt, 268 to the selected LLMs.” (“icse2025-paper1115”, p. 3) \#e56eee \par - “a suitable” (“icse2025-paper1115”, p. 3) \#f0ff00\\ \mkbibemph{Check if suitability has been defined in the context of this work.} \par - “they described realistic—if not real—applications” (“icse2025-paper1115”, p. 3) \#ffd400\\ \mkbibemph{What does it mean in practice?} \par - “realistic—if not real—” (“icse2025-paper1115”, p. 3) \#f0ff00\\ \mkbibemph{What's the difference in the context of this work?} \par - “Each exercise starts with a textual description of an 279 application, and asks students to model it through a UML class 280 diagram.” (“icse2025-paper1115”, p. 4) \#a28ae5 \par - “The exercises were inspired by different sources, 285 including real-life applications available to the general public 286 and systems being developed at INSTITUTION.” (“icse2025-paper1115”, p. 4) \#5fb236 \par - “the course instructor,” (“icse2025-paper1115”, p. 4) \#f0ff00\\ \mkbibemph{Only one? In my opinion this can be a bias related to the construction of the ground truth} \par - “prompt 292 engineering, wherein tailored prompts were crafted to elicit 293 the most effective responses from each LLM for UML class 294 diagram generation from informal textual descriptions” (“icse2025-paper1115”, p. 4) \#5fb236 \par - “trial-and-error process,” (“icse2025-paper1115”, p. 4) \#2ea8e5 \par - “each LLM 296 was tested with different prompts to determine the optimal 297 phrasing that produced the best results.” (“icse2025-paper1115”, p. 4) \#ffd400\\ \mkbibemph{A notion of optimality is needed here.} \par - “One key resource was 300 the work done by White et al. [22] that outlined 16 distinct 301 prompt patterns, offering valuable insights into various strate302 gies for crafting effective prompts.” (“icse2025-paper1115”, p. 4) \#5fb236 \par - “White et al. [22] that outlined 16 distinct 301 prompt patterns,” (“icse2025-paper1115”, p. 4) \#f0ff00\\ \mkbibemph{To be downloaded.} \par - “We tested prospective prompts 306 on 2-3 examples of textual descriptions, until we settled on a 307 final formulation.” (“icse2025-paper1115”, p. 4) \#5fb236 \par - “We tested prospective prompts 306 on 2-3 examples of textual descriptions, until we settled on a 307 final formulation.” (“icse2025-paper1115”, p. 4) \#f0ff00\\ \mkbibemph{Which LLM ha been used in this process of creating the "best" prompt formulation? Seem next comment, this phase an represent a potential bias.} \par - “We initially used prompts that were more 308 abstract and general, and that did not include any specific 309 keywords.” (“icse2025-paper1115”, p. 4) \#5fb236 \par - “these initial versions did not yield sat310 isfactory results, hence, we refined the prompt by introducing 311 specific keywords, until we arrived at our desired version.” (“icse2025-paper1115”, p. 4) \#5fb236 \par - “Specifying 317 “PlantUML” directed the LLMs to produce output in a specific, 318 recognized format, which enhanced the clarity and usability 319 of the generated diagrams” (“icse2025-paper1115”, p. 4) \#5fb236 \par - “question goes here” (“icse2025-paper1115”, p. 4) \#ffd400\\ \mkbibemph{i guess also the question should be properly crafted, beyond the previous fragment of the prompt about the supposed role of the LLM.} \par - “Fig. 2.” (“icse2025-paper1115”, p. 4) \#f0ff00\\ \mkbibemph{This is another bias because it is not sure that one prompt formulation that works best for a given LLM cn achieve the same accuracy for other LLMs. Potentially., the authors should have defined different best prompts, one for each LLM.} \par - “different LLMs, the same 326 prompt” (“icse2025-paper1115”, p. 4) \#f0ff00 \par - “the same 326 prompt formulation was used for each LLM” (“icse2025-paper1115”, p. 4) \#ffd400\\ \mkbibemph{So, all the LLMs have been used with the same prompt formulation? This can represent a bias, isn't it?} \par - “3) Selecting the evaluation criteria:” (“icse2025-paper1115”, p. 4) \#2ea8e5 \par - “there is no agreement in 332 the literature regarding the best approach to assess the quality 333 of UML models” (“icse2025-paper1115”, p. 4) \#e56eee \par - “we decided to base the evaluation of 334 LLMs’ results on the same criteria used to asses the solutions 335 of the students of the Software Engineering class from which 336 the textual descriptions originate” (“icse2025-paper1115”, p. 4) \#5fb236 \par - “presence of all needed classe” (“icse2025-paper1115”, p. 4) \#a28ae5 \par - “correct identification of 339 attributes and their assignment to classe” (“icse2025-paper1115”, p. 4) \#a28ae5 \par - “orrect identification 340 of associations” (“icse2025-paper1115”, p. 4) \#a28ae5 \par - “correct- 341 ness of association cardinalities” (“icse2025-paper1115”, p. 4) \#2ea8e5 \par - “correct identification of 342 operations and their association with classes.” (“icse2025-paper1115”, p. 4) \#5fb236 \par - “readability and 347 clarity” (“icse2025-paper1115”, p. 4) \#ffd400\\ \mkbibemph{how to measure them in an objective manner? Moreover, a given problem can be rightly modeled in different manners. How have you addressed such critical point for the evaluation?} \par - “clarity of the PlantUML” (“icse2025-paper1115”, p. 4) \#f0ff00\\ \mkbibemph{How?} \par - “deciphered” (“icse2025-paper1115”, p. 4) \#f0ff00\\ \mkbibemph{I'm not sure this is the correct item. Textual descriptions were no cyphred I guess.} \par - “Model generation was performed for each 358 textual input, with the generated PlantUML code stored for 359 subsequent analysis.” (“icse2025-paper1115”, p. 4) \#5fb236 \par - “360 Finally, the PlantUML code generated by the LLMs” (“icse2025-paper1115”, p. 4) \#f0ff00\\ \mkbibemph{Was this done straightaway or some post process of generated code was needed?} \par - “Its functionality 365 facilitated the seamless conversion of textual representations 366 into concrete visual artifacts, thereby bridging the gap between 367 abstract textual descriptions and tangible UML diagrams” (“icse2025-paper1115”, p. 4) \#5fb236 \par - “a dataset of 188 UML class dia- 369 grams” (“icse2025-paper1115”, p. 4) \#5fb236 \par - “was produced across all LLMs. 371” (“icse2025-paper1115”, p. 4) \#5fb236 \par - “assigning a score to each of the characteristics iden- 374 tified in the experiment preparation phase” (“icse2025-paper1115”, p. 4) \#2ea8e5 \par - “were generated from 47 textual descriptions. The generated 379 graphical representations were manually evaluated taking into 380 account the expert-provided solutions, which served as our 381 “ground truth”. The comparison was done by expert software” (“icse2025-paper1115”, p. 4) \#f0ff00\\ \mkbibemph{Also in this phase, we have the same potential bias related to the ground truth creation. An agreement phase is needed both for the creation of diagrams in the ground truth and for evaluating/judging/score the diagrams generated by the considered LLMs.} \par - “we considered and ranked also higher-level aspects, that is, the 389 ability of LLMs to show a good understanding of the domain, 390 the clarity and readability of the generated diagrams, and the 391 length of the generation process.” (“icse2025-paper1115”, p. 5) \#ffd400\\ \mkbibemph{TO CHECK THE METRICS THAT HAVE BEEN USED TO MEASURE AND RANK WRT SUCH ASPECTS.} \par - “three. Cohen’s Kappa Score [23] was computed to evaluate 414 the level of inter-rater agreement and ensure the reliability of 415 the assessment process. The results of the whole evaluation 416 process are presented in the next section.” (“icse2025-paper1115”, p. 5) \#00b036\\ \mkbibemph{Good!} \par - “We took into account the possibility of 442 having synonyms and, in cases of missing classes, we assigned 443 a penalty based on the importance of these classes in the 444 original textual description and the extent to which they were 445 strictly needed or could have been omitted.” (“icse2025-paper1115”, p. 5) \#5fb236 \par - “we took into account synonyms and the importance of 469 specific attributes in the textual description.” (“icse2025-paper1115”, p. 5) \#5fb236 \par - “main operations for managing events and notifications 487 are present in the LLM-generated solution and are quite 488 aligned with the ground truth.” (“icse2025-paper1115”, p. 5) \#e56eee \par - “host” (“icse2025-paper1115”, p. 6) \#5fb236 \par - “some participants.” (“icse2025-paper1115”, p. 6) \#5fb236 \par - “configuration of events. When a new participant is added to an event, the Event Manager uses another component, the Notification Handler,” (“icse2025-paper1115”, p. 6) \#00b036 \par - “The evaluation of UML models generated by various LLMs 621 across multiple criteria reveals distinct strengths and weak622 nesses.” (“icse2025-paper1115”, p. 8) \#5fb236 \par - “Table II provides a comprehensive summary 625 of the performance metrics” (“icse2025-paper1115”, p. 8) \#a28ae5 \par - “In contrast, LLaMA 3 struggles with class identification and 640 association definition, showing lower accuracy in cardinalities 641 and clarity.” (“icse2025-paper1115”, p. 8) \#5fb236 \par - “188 generated UML models were evaluated by three of the 699 co-authors.” (“icse2025-paper1115”, p. 9) \#5fb236 \par - “These 14 models were then also evaluated by the other 702 two coauthors, both of whom have over 20 years of experience 703 in UML modeling.” (“icse2025-paper1115”, p. 9) \#5fb236 \par - “the process of evaluating UML models is in- 726 herently subjective.” (“icse2025-paper1115”, p. 9) \#ffd400\\ \mkbibemph{Exactly, I agree with this!} \par - “they were all produced 739 by a single instructor, so to mitigate this issue future studies 740 could incorporate descriptions from diverse authors” (“icse2025-paper1115”, p. 9) \#ffd400\\ \mkbibemph{This is a problem in my opinion. Even the solutions for the considered cases, should have been discussed among the different authors for creating the ground truth, that currently is defined by a single instructor.} \par - “By scoring and comparing the 770 generated diagrams to expert-defined solutions, this systematic 771 evaluation framework provides a robust basis for assessing 772 the quality of UML models generated by LLMs.” (“icse2025-paper1115”, p. 10) \#5fb236 \par - “By evaluating a broader range of LLMs, 803 we can better understand their strengths and limitations, ulti- 804 mately identifying those that excel at generating high-quality 805 UML diagrams. This will not only help in advancing the field, 806 but also provide valuable insights for practitioners seeking the 807 best tools for their software modeling needs.” (“icse2025-paper1115”, p. 10) \#5fb236 \par - “18” (“icse2025-paper1115”, p. 11) \#f0ff00\\ \mkbibemph{To be checked.}} } @misc{Icse2025paper149pdf, title = {Icse2025-Paper149.Pdf}, langid = {english}, keywords = {LOGSEQ}, note = {\section{Annotazioni\\ (3/6/2024, 14:17:58)} \par - “Resolving issues of various kinds is one of thecentral activities of open source software (OSS) developers.” (“icse2025-paper149.pdf”, p. 1) \#5fb236\\ \mkbibemph{ } \par - “ac” (“icse2025-paper149.pdf”, p. 1) \#ff6666\\ \mkbibemph{ } \par - “organization of issues,” (“icse2025-paper149.pdf”, p. 1) \#5fb236\\ \mkbibemph{ } \par - “operationalized by the uncertainties involved in their processes” (“icse2025-paper149.pdf”, p. 1) \#ffd400\\ \mkbibemph{ } \par - “predictor of the issues’ processing efficiency in terms of their overall lifetime and transition time between consecutive steps” (“icse2025-paper149.pdf”, p. 1) \#5fb236\\ \mkbibemph{ } \par - “dis-organization, inside the issues’ processes.” (“icse2025-paper149.pdf”, p. 1) \#ffd400\\ \mkbibemph{TO BE UNDERSTOOD BETTER! What does it mean? } \par - “entropy of the DFGs’ transitions” (“icse2025-paper149.pdf”, p. 1) \#a28ae5\\ \mkbibemph{ } \par - “rocess models” (“icse2025-paper149.pdf”, p. 1) \#ffd400\\ \mkbibemph{How to cluster processes? } \par - “higher level of process uncertainty is significantly associated with an increased issue lifetime.” (“icse2025-paper149.pdf”, p. 1) \#5fb236\\ \mkbibemph{ } \par - “The results suggest a potential way to improve issue resolution efficiency is to better organize (i.e. be more certain about) their processes, starting locally with each step, and eventually the overall processes” (“icse2025-paper149.pdf”, p. 1) \#ffd400\\ \mkbibemph{What does it mean? } \par - “processing issues introduces a heavy workload to project maintainers, which calls for better understanding and management of issues and their workflows” (“icse2025-paper149.pdf”, p. 1) \#a28ae5\\ \mkbibemph{ } \par - “There are also evidences showing that having an organized, coordinated task resolution process is positively associated with OSS projects’ survivability and team performance” (“icse2025-paper149.pdf”, p. 1) \#5fb236\\ \mkbibemph{ } \par - “open collaborations around issues are often carried out in a highly ad hoc manner without a predefined process, making it difficult to gain insights about the internal processes of complex issues, or making assessments about how well the issues are organized.” (“icse2025-paper149.pdf”, p. 1) \#a28ae5\\ \mkbibemph{ } \par - “entropy” (“icse2025-paper149.pdf”, p. 1) \#5fb236\\ \mkbibemph{ } \par - “a process with higher entropy is more complicated and involve more uncertainties, which indicates a lower level of organization in our context” (“icse2025-paper149.pdf”, p. 1) \#a28ae5\\ \mkbibemph{ } \par - “direct follow graphs (DFGs)” (“icse2025-paper149.pdf”, p. 1) \#5fb236\\ \mkbibemph{ } \par - “we adopt DFGs to provide a unified representation of issues during process mining as introduced next.” (“icse2025-paper149.pdf”, p. 1) \#5fb236\\ \mkbibemph{ } \par - “entropy-based measure, we aim at quantifying the overall organization of issue resolution processes in an OSS project.” (“icse2025-paper149.pdf”, p. 1) \#e56eee\\ \mkbibemph{ } \par - “‘representative’, we aim at discovering concise models with each model representing a group of issues with similar processes.” (“icse2025-paper149.pdf”, p. 1) \#a28ae5\\ \mkbibemph{ } \par - “to group issues with similar processes to find the representative models” (“icse2025-paper149.pdf”, p. 2) \#ffd400\\ \mkbibemph{I'm curious to see how processes are clustered! } \par - “augment” (“icse2025-paper149.pdf”, p. 2) \#a28ae5\\ \mkbibemph{ } \par - “effectiveness” (“icse2025-paper149.pdf”, p. 2) \#5fb236\\ \mkbibemph{ } \par - “usefulness” (“icse2025-paper149.pdf”, p. 2) \#5fb236\\ \mkbibemph{ } \par - “representative yet simple processes from issues” (“icse2025-paper149.pdf”, p. 2) \#ffd400\\ \mkbibemph{What kind of processes and representative with respect to what? } \par - “correlation” (“icse2025-paper149.pdf”, p. 2) \#5fb236\\ \mkbibemph{ } \par - “process entropy” (“icse2025-paper149.pdf”, p. 2) \#5fb236\\ \mkbibemph{ } \par - “issue lifetime” (“icse2025-paper149.pdf”, p. 2) \#5fb236\\ \mkbibemph{ } \par - “issue lifetime,” (“icse2025-paper149.pdf”, p. 2) \#a28ae5\\ \mkbibemph{ } \par - “extract” (“icse2025-paper149.pdf”, p. 2) \#ff6666\\ \mkbibemph{extracted? } \par - “its following up transition time” (“icse2025-paper149.pdf”, p. 2) \#ffd400\\ \mkbibemph{Similar to the previous research question, isn't it? } \par - “follow transition time” (“icse2025-paper149.pdf”, p. 2) \#a28ae5\\ \mkbibemph{ } \par - “entropy-based metric to quantify the organization (uncertainty) of issue resolution processes in OSS projects.” (“icse2025-paper149.pdf”, p. 2) \#a28ae5\\ \mkbibemph{ } \par - “mine representative yet simple models for issue resolution processes from event logs” (“icse2025-paper149.pdf”, p. 2) \#a28ae5\\ \mkbibemph{ } \par - “level” (“icse2025-paper149.pdf”, p. 2) \#ff6666\\ \mkbibemph{level of? } \par - “Markov models are shown to be useful in generating probabilistic representations of the event sequences” (“icse2025-paper149.pdf”, p. 2) \#5fb236\\ \mkbibemph{ } \par - “DFGs as simple yet informative representations for both issue event sequences, and corresponding mined processes.” (“icse2025-paper149.pdf”, p. 2) \#a28ae5\\ \mkbibemph{ } \par - “A recent survey” (“icse2025-paper149.pdf”, p. 3) \#ffd400\\ \mkbibemph{It's not a so recent work! } \par - “In this work, we augment the KMeans++ algorithm for trace clustering when mining processes from the complex issue event logs.” (“icse2025-paper149.pdf”, p. 3) \#a28ae5\\ \mkbibemph{ } \par - “there is a large body of literature that performs process mining in software repositories” (“icse2025-paper149.pdf”, p. 3) \#a28ae5\\ \mkbibemph{ } \par - “DFG-based issue resolution processes from event logs” (“icse2025-paper149.pdf”, p. 3) \#5fb236\\ \mkbibemph{ } \par - “correlation between entropy and OSS issue resolution process, demonstrating the usefulness of the proposed process mining and entropy-based metric in understanding real-world collective behaviour in OSS repositories.” (“icse2025-paper149.pdf”, p. 3) \#5fb236\\ \mkbibemph{ } \par - “There is also study aiming at issue characterization and classification for related issue recommendation” (“icse2025-paper149.pdf”, p. 3) \#5fb236\\ \mkbibemph{ } \par - “With the mined processes for resolving issues in OSS repositories, in this work, we propose to measure the level of organization of the issue processes with an information entropy based metric, and study its correlation with issues’ lifetime.” (“icse2025-paper149.pdf”, p. 3) \#e56eee\\ \mkbibemph{ } \par - “This section presents the proposed approach to mine issue resolution processes.” (“icse2025-paper149.pdf”, p. 3) \#5fb236\\ \mkbibemph{ } \par - “fetch issue timeline” (“icse2025-paper149.pdf”, p. 3) \#5fb236\\ \mkbibemph{ } \par - “records users’ activities in issues” (“icse2025-paper149.pdf”, p. 3) \#5fb236\\ \mkbibemph{ } \par - “key contents, including the actors’ roles, event types, action time and actor name from the raw data, and encode the issues into event sequences.” (“icse2025-paper149.pdf”, p. 3) \#ffd400\\ \mkbibemph{What are event types? And what about actors' roles? } \par - “During the clustering and mining phase, we calculate the overall entropy of the mind process models as shown on the right half of Figure 1(c)” (“icse2025-paper149.pdf”, p. 3) \#ff6666\\ \mkbibemph{An explanatory example would be great here! Moreover, what is the mind process model? Ah ok, it's mined not mind! } \par - “following Sec. IV” (“icse2025-paper149.pdf”, p. 3) \#ff6666\\ \mkbibemph{ } \par - “We perform process clustering and mining, and process model measuring for different clustering parameters, and then choose the optimal parameters.” (“icse2025-paper149.pdf”, p. 3) \#ffd400\\ \mkbibemph{It's not clear what you want to do. It's necessary an explanatory/motivating example. } \par - “count of occurrences of the corresponding direct follow relationship” (“icse2025-paper149.pdf”, p. 3) \#5fb236\\ \mkbibemph{ } \par - “computational efficient way to represent the processes” (“icse2025-paper149.pdf”, p. 3) \#5fb236\\ \mkbibemph{ } \par - “A lower (higher) source entropy corresponds to less (more) representative sequences which means a higher model certainty (uncertainty)” (“icse2025-paper149.pdf”, p. 4) \#a28ae5\\ \mkbibemph{ } \par - “we temporally segment a project’s issues into a series of snapshots with a non-overlapping sliding window of one month.” (“icse2025-paper149.pdf”, p. 4) \#a28ae5\\ \mkbibemph{ } \par - “Extract Issue Event Sequence from Raw Data” (“icse2025-paper149.pdf”, p. 4) \#5fb236\\ \mkbibemph{ } \par - “we process raw data and turn it into event sequence.” (“icse2025-paper149.pdf”, p. 4) \#5fb236\\ \mkbibemph{ } \par - “SubscribedEvent and MentionedEvent always show together in consecutive order, which is by ‘@’ someone in the comment” (“icse2025-paper149.pdf”, p. 4) \#ffd400\\ \mkbibemph{??? } \par - “For the above case, both Label and Unlabel are marked as the Label event.” (“icse2025-paper149.pdf”, p. 4) \#ffd400\\ \mkbibemph{??? } \par - “We define parallel events as events that take place at the same time, and are carried out by the same person” (“icse2025-paper149.pdf”, p. 4) \#ffd400\\ \mkbibemph{Examples? The same person that does different actions at the same time? This is not clear. } \par - “We use direct follow probability matrices instead of more complex models like Petri-Nets [17] because we can directly use them as feature matrices for clustering, and calculate the entropy-based metrics” (“icse2025-paper149.pdf”, p. 5) \#a28ae5\\ \mkbibemph{ } \par - “Build a DFG-Based Model for a Single Issue:” (“icse2025-paper149.pdf”, p. 5) \#ffd400\\ \mkbibemph{It's not clear how this works in practice. An issue consists of a sequence of events, and it is not clear how the probability concept comes to the place here. } \par - “issues that involve the same sets of events, but different in event orders including cases of parallel events.” (“icse2025-paper149.pdf”, p. 5) \#a28ae5\\ \mkbibemph{ } \par - “It is straightforward to extend our approach to build a model for a group of issues by extending the count()” (“icse2025-paper149.pdf”, p. 5) \#5fb236\\ \mkbibemph{ } \par - “groups of issues” (“icse2025-paper149.pdf”, p. 5) \#ffd400\\ \mkbibemph{ } \par - “Sec III-C” (“icse2025-paper149.pdf”, p. 5) \#ffd400\\ \mkbibemph{We are in Sec III-C. This reference is not needed. } \par - “Manhattan distance is used to calculate distance between data points in order to measure the difference between process models” (“icse2025-paper149.pdf”, p. 5) \#5fb236\\ \mkbibemph{ } \par - “d” (“icse2025-paper149.pdf”, p. 5) \#5fb236\\ \mkbibemph{ } \par - “We adopt the “trace clustering and mining” paradigm [22] to discover the underlying process models of issue event sequences extracted from raw issue timeline data in OSS repositories.” (“icse2025-paper149.pdf”, p. 5) \#5fb236\\ \mkbibemph{ } \par - “have” (“icse2025-paper149.pdf”, p. 5) \#ff6666\\ \mkbibemph{has? } \par - “Given the set of data points D to be clustered” (“icse2025-paper149.pdf”, p. 5) \#ffd400\\ \mkbibemph{It is necessary to explain what these datapoints represent in the case at hand (i.e., management of issue process models). In other words it is necessary to describe clearly the process that comes from the original issues, and encoding them in a way, which is amenable for clusterization purposes. } \par - “finding representative and simple process models.” (“icse2025-paper149.pdf”, p. 5) \#5fb236\\ \mkbibemph{ } \par - “In our proposed algorithm, we combine the Silhouette score and information gain score as the measurement for optimal cluster number evaluation.” (“icse2025-paper149.pdf”, p. 5) \#a28ae5\\ \mkbibemph{ } \par - “goodness of clustering by taking both within and between cluster distances into account.” (“icse2025-paper149.pdf”, p. 5) \#a28ae5\\ \mkbibemph{ } \par - “N is the total number of issues.” (“icse2025-paper149.pdf”, p. 6) \#a28ae5\\ \mkbibemph{ } \par - “When Sil = 1, it means the clusters are dense and well separated.” (“icse2025-paper149.pdf”, p. 6) \#a28ae5\\ \mkbibemph{ } \par - “Sil = 0, it means the clusters are not clearly separated [28].” (“icse2025-paper149.pdf”, p. 6) \#a28ae5\\ \mkbibemph{ } \par - “selecting the one with the highest Sil value.” (“icse2025-paper149.pdf”, p. 6) \#a28ae5\\ \mkbibemph{ } \par - “representative yet simple processes” (“icse2025-paper149.pdf”, p. 6) \#ffd400\\ \mkbibemph{Apart from the definition and usage of different metrics, which underpin the definition of issue clusters, as a reader I'm missing a presentation and discussion of explanatory example showing real issues and corresponding clusters and what's the benefit of having issues that are clustered as proposed by the authors. } \par - “the local entropy measures the degree of uncertainty with respect to each event’s transition in the mined model.” (“icse2025-paper149.pdf”, p. 6) \#5fb236\\ \mkbibemph{ } \par - “The overall entropy also increases with the increasing frequencies of events whose entropy is higher than the overall entropy” (“icse2025-paper149.pdf”, p. 7) \#5fb236\\ \mkbibemph{ } \par - “A higher overall entropy indicates a more complicated process model, meaning that there are more uncertainties present in the model.” (“icse2025-paper149.pdf”, p. 7) \#ffd400\\ \mkbibemph{Authors assume a correlation of model complexity vs model uncertainties. I can agree with this but the paper does not provide any evidence in terms of explanatory examples. } \par - “The measured overall process entropy is linked to the organization of an OSS community in issue processing.” (“icse2025-paper149.pdf”, p. 7) \#ffd400\\ \mkbibemph{How can we support statements like this one? } \par - “organization of a project is associated with its sustainability” (“icse2025-paper149.pdf”, p. 7) \#5fb236\\ \mkbibemph{ } \par - “information entropy to measure the process of issue resolution by taking into account not only the creators and solvers [14], but also the actors’ roles and events, for every step.” (“icse2025-paper149.pdf”, p. 7) \#a28ae5\\ \mkbibemph{ } \par - “we argue that the proposed overall process entropy also quantifies the level of organization for issue resolution,” (“icse2025-paper149.pdf”, p. 7) \#ffd400\\ \mkbibemph{This is what you argued. Let's see if it is evaluated. } \par - “does higher local uncertainty links to longer time consumed for events in a process?” (“icse2025-paper149.pdf”, p. 7) \#a28ae5\\ \mkbibemph{ } \par - “2022 APRIL” (“icse2025-paper149.pdf”, p. 7) \#ffd400\\ \mkbibemph{As I said, this is a resubmission of a paper submitted long time ago somewhere else. } \par - “Table V, Sec. V-D” (“icse2025-paper149.pdf”, p. 7) \#ff6666\\ \mkbibemph{I would avoid references to sections/elements which are not presented yet.- } \par - “reduced” (“icse2025-paper149.pdf”, p. 7) \#ff6666\\ \mkbibemph{reduce } \par - “proposed entropy metrics about mined issue processes and the overall issue lifetime, as well as the events’ local transition time, respectively, with respect to the three research questions in the Introduction.” (“icse2025-paper149.pdf”, p. 7) \#5fb236\\ \mkbibemph{ } \par - “abnormal issue recor” (“icse2025-paper149.pdf”, p. 7) \#ffd400\\ \mkbibemph{what do you mean? Is there any thresholds used and how/why you defined it? } \par - “17 projects are selected.” (“icse2025-paper149.pdf”, p. 7) \#5fb236\\ \mkbibemph{ } \par - “exclude extreme outliers, issues with issue length or issue lifetime beyond the upper outer fences are removed” (“icse2025-paper149.pdf”, p. 7) \#ff6666\\ \mkbibemph{Please rewrite. } \par - “dependent variables” (“icse2025-paper149.pdf”, p. 8) \#5fb236\\ \mkbibemph{ } \par - “control variables” (“icse2025-paper149.pdf”, p. 8) \#5fb236\\ \mkbibemph{ } \par - “representative yet simple processes” (“icse2025-paper149.pdf”, p. 8) \#ffd400\\ \mkbibemph{Why are you targeting them? Simple and representative with respect to what? From which perspective? } \par - “KMeans++ and the proposed clustering algorithm, do not show significant differences (marked as =).” (“icse2025-paper149.pdf”, p. 8) \#5fb236\\ \mkbibemph{ } \par - “The results suggest that, under all settings of α, the proposed clustering algorithm always finds issue clusters that correspond to significantly less entropy of the mined processes than the original KMeans++ algorithm with more clusters” (“icse2025-paper149.pdf”, p. 8) \#5fb236\\ \mkbibemph{ } \par - “is effective in helping the mining algorithm to find representative and simple process models that better describe the issues than the traditional, KMeans++ algorithm” (“icse2025-paper149.pdf”, p. 8) \#ffd400\\ \mkbibemph{This is not evident at all! } \par - “RQ2 as: Higher level of process uncertainty measured by Hprocess is associated with an increased issue lifetime.” (“icse2025-paper149.pdf”, p. 9) \#a28ae5\\ \mkbibemph{ } \par - “Hprocess through better organized processes with less uncertainty” (“icse2025-paper149.pdf”, p. 9) \#a28ae5\\ \mkbibemph{ } \par - “we breakdown the overall process entropy into local entropy that measures the uncertainty associated with each event, and studies the correlation between such local entropy and the time spent on each event’s transitions to the direct following, next events.” (“icse2025-paper149.pdf”, p. 9) \#ffd400\\ \mkbibemph{I'm surprised that the whole study is done by considering the process model, without considering at all issue descriptions, comments, etc. Moreover, it is not clear why and how it is possible to rule out the complexity behind an issue that can be related to some requested functionalities or difficulties related to the resolution of some bug in the source code. } \par - “the local uncertainty measured by the proposed local event transition entropy (Hevent) is also positively associated with the events’ transition time during issue processing.” (“icse2025-paper149.pdf”, p. 10) \#5fb236\\ \mkbibemph{ } \par - “The findings of this paper may not generalise well to other projects.” (“icse2025-paper149.pdf”, p. 10) \#ffd400\\ \mkbibemph{That's the point! }} } @unpublished{Icse2025paper1504, title = {Icse2025-Paper1504}, url = {https://icse2025.hotcrp.com/doc/icse2025-paper1504-revised_manuscript_tracking.pdf}, urldate = {2024-09-23}, keywords = {LOGSEQ} } @misc{Icse2025paper1545, title = {Icse2025-Paper1545}, keywords = {LOGSEQ}, note = {\section{Annotazioni\\ (2/10/2024, 16:56:44)} \par “Traditionally, software engineering focuses on manually created artifacts such as source code and the process of creating them, as well as best practices for integrating them, i.e., software architectures.” (“icse2025-paper1545”, p. 1) \par “ML models must be embedded in traditional software, often forming complex topologies” (“icse2025-paper1545”, p. 1) \par “little is known about the characteristics of real-world ML-enabled systems.” (“icse2025-paper1545”, p. 1) What do you mean? \par “Our findings provide practitioners and researchers with insight into practices for embedding and integrating ML models, bringing data science and software engineering closer together.” (“icse2025-paper1545”, p. 1) \par “In fact, industrial surveys report that 78 \% of ML projects stall [6],” (“icse2025-paper1545”, p. 1) Such reference is of 2019... things might have changed. \par “as many as 85 \% fail [7].” (“icse2025-paper1545”, p. 1) Same comment like before. \par “Many factors can cause such failures [8], [9] [10], including data quality, waterfall-like development processes, limited experience and education, but also lack of proper tools [11], [8], [12], [13].” (“icse2025-paper1545”, p. 1) \par “probabilistic models” (“icse2025-paper1545”, p. 1) \par “data quality, integration of ML models with traditional software components, tool support for managing MLrelated artifacts, and quality assurance, particularly concerning dependability and safety” (“icse2025-paper1545”, p. 1) \par “Current research in software engineering seeks to address these deficiencies by proposing novel workflow patterns and providing improved tool support supporting ML experiment” (“icse2025-paper1545”, p. 1) \par “we still know very little about how ML models are actually embedded and integrated into ML-enabled systems.” (“icse2025-paper1545”, p. 1) \par “We are especially interested in the reuse of ML models across software systems, as well as the integration of ML models.” (“icse2025-paper1545”, p. 1) \par “model reuse and sharing ML models across systems is essential in practice.” (“icse2025-paper1545”, p. 1) \par “identifying patterns of how (multiple) ML-models are integrated, as well as the relationship between ML models and source code” (“icse2025-paper1545”, p. 2) \par “predictions, preprocessing data or, since recently, creating content via generative AI” (“icse2025-paper1545”, p. 2) \par “These arise from the fundamentally different nature of ML models compared to traditional software” (“icse2025-paper1545”, p. 2) \par “different toolchains for ML models and software.” (“icse2025-paper1545”, p. 2) \par “There also is a lack of tools for managing (e.g., versioning) the many different artifacts (called ML assets in the remainder) for ML” (“icse2025-paper1545”, p. 2) \par “Traditional software engineering relies on wellestablished version-control and collaborative development systems, such as Git.” (“icse2025-paper1545”, p. 2) \par “TensorFlow and PyTorch are frameworks allowing developers to implement their ML models modularly.” (“icse2025-paper1545”, p. 2) \par ““Module,” which represents either complete models or their building blocks (e.g., layers).” (“icse2025-paper1545”, p. 2) \par “(i) in transfer learning [38] the model is trained on a generic dataset and is then fine-tuned to solve a more specific problem;” (“icse2025-paper1545”, p. 2) \par “(ii) pretrained models (PTMs) are fully trained and usually reused without any fine-tuning.” (“icse2025-paper1545”, p. 2) \par “there is still a lack of hard empirical data on reuse in real ML-enabled systems.” (“icse2025-paper1545”, p. 2) \par “Optimizing non-functional properties via retraining can then easily lead to a combinatorial explosion due to the different training parameters for models or the interactions of multiple models.” (“icse2025-paper1545”, p. 3) \par “A study suggests a microservice architecture for ML models [42], where ML models are modularized and interact with the rest of the system via REST.” (“icse2025-paper1545”, p. 3) \par “high quality set of ML-enabled software systems of a certain size and maturity. For instance, we excluded tutorials and small toy projects.” (“icse2025-paper1545”, p. 3) \par “we excluded projects that were forked directly from GitHub, which can be accessed through GitHub’s REST API.” (“icse2025-paper1545”, p. 3) \par “2,928 repositories, containing systems with medium to high maturity.” (“icse2025-paper1545”, p. 3) \par “classification of the relation of ML to the software system.” (“icse2025-paper1545”, p. 4) What do you mean with "relation of ML to the software system"? \par “We searched the code for class definitions that extend these two classes or any of their subclasses provided in the libraries.” (“icse2025-paper1545”, p. 4) \par “Stored model files: Typically, trained models can be saved to a file for later reuse. TensorFlow and PyTorch each provide their own file types for saving trained ML models as binary files.” (“icse2025-paper1545”, p. 4) \par “Metrics. Based on the identified ML-related assets, we calculate metric characterizing the ML-enabled systems in respect to their ML-related parts.” (“icse2025-paper1545”, p. 4) \par “(i) Use of pre-trained models involves obtaining an already trained ML model from an external source;” (“icse2025-paper1545”, p. 4) \par “(ii) reuse of only the architecture of an ML model, by copying the related implementation in source code.” (“icse2025-paper1545”, p. 4) \par “a sample of our subject systems” (“icse2025-paper1545”, p. 4) Can you say something more about this sample? \par “ML Functionalities and Tasks: What are the ML models used for? What functionality is implemented using ML? Which ML task realizes the functionality?” (“icse2025-paper1545”, p. 5) How to answer such qualitative questions? \par “Integration of Models: How do multiple models interact? (Parallel, alternative, sequential, fusion/forking)” (“icse2025-paper1545”, p. 5) How have you assessed this? \par “There are slightly more applications than frameworks,” (“icse2025-paper1545”, p. 6) \par “ML-related and non-MLrelated code.” (“icse2025-paper1545”, p. 6) \par “The large majority of ML-enabled systems on GitHub are libraries and frameworks. Interestingly, end-user-oriented applications are still a minority. These systems use ML in different ways, either developing new ML technology or applying ML on concrete problems. A smaller, but still substantial, number of tools that support ML engineering exists. Our systems vary in size and contain many different ML assets, but also large amounts of non-ML code. Quality assurance, unfortunately, is largely neglected.” (“icse2025-paper1545”, p. 7) \par “Reuse of ML models is prevalent among the systems. 1,209 systems load pre-trained models and 1,690 systems copy ML implementations directly. The origins of the copied ML implementations are a few repositories that contain ML technology provided by well-known companies.” (“icse2025-paper1545”, p. 8) \par “Number and Type of ML Models.” (“icse2025-paper1545”, p. 8) The different paragraphs of this section (e.g., "Number and Type of ML Models", "Interaction between ML models", etc.) need to be motivated and introduced to simplify the reading of the whole section. Moreover, the title of the section "ML Integration Architectures" does not reflect the content of the section, which instead discuss different characteristsics of ML models, like their origin, the types of the input and output data, required pre and post processing stepts, etc.). In this respect, it is not about architecture, but insted about ML characteristics and usages? \par “models is the most common pattern (15 systems), where multiple models are offered for the same task, either automatically by the program or based on user input.” (“icse2025-paper1545”, p. 9) \par “While more than half of our subject systems were libraries that provide ML functionalities, only 18 \% of systems are applications that use ML to provide end-user-oriented functionality” (“icse2025-paper1545”, p. 10) \par “ML model implementations are often copied between repositories” (“icse2025-paper1545”, p. 10) \par “many pre-trained models from model hubs are used” (“icse2025-paper1545”, p. 10) \par “poor dependency management” (“icse2025-paper1545”, p. 10) \par “how ML models are integrated often depends on the domain of the system. However, despite the lack of well-researched integration patterns [15], we observed different kinds of interations of ML models.” (“icse2025-paper1545”, p. 10)} } @misc{Icse2025paper1580, title = {Icse2025-Paper1580}, keywords = {LOGSEQ}, note = {\section{Annotazioni\\ (29/9/2024, 19:18:59)} \par - “data cleaning prior to analysis and evaluation” (“icse2025-paper1580”, p. 1) \#5fb236 \par - “whether an LLM can find its way around a spreadsheet and how to support end-users in taking their free-form data processing requests to fruition.” (“icse2025-paper1580”, p. 1) \#a28ae5 \par - “we can retrieve elements from a code library to compose data preprocessing pipelines” (“icse2025-paper1580”, p. 1) \#a28ae5 \par - “LLMs can function as coding assistants, enabling users with limited proficiency to produce accurate and executable code to accomplish their tasks” (“icse2025-paper1580”, p. 1) \#5fb236 \par - “processing tabular data, e.g., in the form of spreadsheets” (“icse2025-paper1580”, p. 1) \#a28ae5 \par - “lack of standardization, necessitating the creation of highly specialized pipeline” (“icse2025-paper1580”, p. 1) \#5fb236 \par - “correcting typographical errors in a dataset may inadvertently lead to over-clustering or under-clustering of the relevant column, while inaccurately resolving entities can distort the true distribution of data points” (“icse2025-paper1580”, p. 1) \#a28ae5 \par - ““human-in-the-loop”” (“icse2025-paper1580”, p. 1) \#5fb236 \par - “LLM can effectively find its way around a spreadsheet and how it can support end-users in fulfilling their free-form data preprocessing requests.” (“icse2025-paper1580”, p. 1) \#a28ae5 \par - “TradeSweep” (“icse2025-paper1580”, p. 1) \#5fb236 \par - “generates code proposals that can be composed and applied to targeted datasets, achieving high performance.” (“icse2025-paper1580”, p. 1) \#ffd400\\ \mkbibemph{How to deal with the specificity of the spreadsheet environment in use?} \par - “Similar to how Retrieval-Augmented Generation (RAG) retrieves context to answer users’ queries, we demonstrate how elements from a code library can be stored and retrieved to compose complex pipelines.” (“icse2025-paper1580”, p. 1) \#5fb236 \par - “English conversations to understand and respond to users’ requests with Python code encompassing preprocessing functions, supporting three main capabilities:” (“icse2025-paper1580”, p. 1) \#ffd400\\ \mkbibemph{Python? So I assume you are supporting CSV files... details of the environment in use should be given.} \par - “The proposed code is automatically tailored to the target data, including accurate column names and suitable algorithms.” (“icse2025-paper1580”, p. 2) \#ffd400\\ \mkbibemph{see my previous comment.} \par - “efficient” (“icse2025-paper1580”, p. 2) \#ffd400\\ \mkbibemph{how to support this?} \par - “This line of work has facilitated the convergence between information retrieval and information generation” (“icse2025-paper1580”, p. 2) \#5fb236 \par - “support developers rather than end-users” (“icse2025-paper1580”, p. 2) \#ffd400\\ \mkbibemph{TO what extent users are different from developers in the context of this work?} \par - “SKCoder” (“icse2025-paper1580”, p. 2) \#5fb236 \par - “they still encounter difficulties in handling algorithmic complexities and often require human verification [42, 43] to ensure correct and/or efficient code” (“icse2025-paper1580”, p. 2) \#a28ae5 \par - “either CSV or Excel format and submit a preprocessing request” (“icse2025-paper1580”, p. 2) \#ffd400\\ \mkbibemph{This detail should be also given in the introduction just to make clear such a technical constraint of the proposed approach.} \par - “column names c1, c2, ..., cm” (“icse2025-paper1580”, p. 2) \#5fb236 \par - “FEATURE COMPARISON” (“icse2025-paper1580”, p. 3) \#ffd400\\ \mkbibemph{How those features have been elicited/selected among those of the analyzed systems?} \par - “In practical applications, it is often necessary to perform data cleaning (e.g., handling inconsistent formats, outliers, missing values) and preprocessing (e.g., normalization, label encoding) on the dataset D before training a machine learning model.” (“icse2025-paper1580”, p. 3) \#5fb236 \par - “generate Python code fˆ and a processed dataset D” (“icse2025-paper1580”, p. 3) \#a28ae5 \par - “prompting curriculum” (“icse2025-paper1580”, p. 3) \#ffd400\\ \mkbibemph{What's that?} \par - “TradeSweep operates without requiring programming expertise from the user, as the specific columns A necessitating modification are automatically determined by the model, and the entire process from input description to execution outcome is managed by the system.” (“icse2025-paper1580”, p. 3) \#ffd400\\ \mkbibemph{I'm not sure about that.... Python code is generated and I doubt that users without programming expertise can work with it.} \par - “vulnerability to hallucinations.” (“icse2025-paper1580”, p. 3) \#a28ae5 \par - “library L” (“icse2025-paper1580”, p. 3) \#ffd400\\ \mkbibemph{What are the functions that are part of this library as default? I guess it is not empty, isn't it?} \par - “This allows the library L to continually learn and provide more accurate and advanced reference functions in the future.” (“icse2025-paper1580”, p. 3) \#5fb236\\ \mkbibemph{That's important.} \par - “This makes the tool accessible for nonexpert programmers” (“icse2025-paper1580”, p. 4) \#ffd400\\ \mkbibemph{AS previously mentioned, I'm not fully convinced about this aspect.} \par - “The library comprises code functions commonly applied in various preprocessing tasks. Along with these functions, data information is also provided to the LLM to aid its understanding of the dataset structure” (“icse2025-paper1580”, p. 4) \#ffd400\\ \mkbibemph{Have you checked the difference in accuracy with and without the prompt augmentation?} \par - “Instead of presenting all code functions in the prompt, we utilize information retrieval (IR) techniques to perform a more precise selection on code functions, providing the LLM with the most relevant and useful codes.” (“icse2025-paper1580”, p. 4) \#5fb236 \par - “This process not only effectively shortens the prompt length and reduces the LLM’s response time but also helps the LLM focus on functions most pertinent to fulfilling the user’s request” (“icse2025-paper1580”, p. 4) \#ffd400\\ \mkbibemph{It is important to show that such a step is really needed. What happens if top-k relevant codes are not retrieved and given to the next step (prompt construction)?} \par - “candidate codes” (“icse2025-paper1580”, p. 4) \#ffd400\\ \mkbibemph{Can this represent a source of bias, or in general bring the LLM to the wrong path during the inference phase? It is necessary to show what I asked in my previous point!} \par - “If the LLM does not find any candidate code that aligns with the user’s request, it generates a novel code function” (“icse2025-paper1580”, p. 4) \#a28ae5 \par - “Incorrect Code Proposal Format:” (“icse2025-paper1580”, p. 4) \#5fb236 \par - “Execution Error:” (“icse2025-paper1580”, p. 4) \#5fb236 \par - “User Feedback:” (“icse2025-paper1580”, p. 4) \#5fb236 \par - “they can request code revisions by providing feedback describing necessary fixes to the LLM” (“icse2025-paper1580”, p. 4) \#5fb236 \par - “Fig. 4.” (“icse2025-paper1580”, p. 4) \#ffd400\\ \mkbibemph{I agree that the Python code shown in Fig. 4 is easy to understand. However, it is not possible to sustain that users without any programming experience can understand what that source is supposed to do. I suggest to smooth statements supporting such claims. Moreover, I don't see in the code any reference to the "Arrival Date" column as asked by the user.} \par - “it generates a novel code proposal. Since this newly generated code is not initially included in the library, we incorporate it back into the library to improve efficiency and accuracy for future code generations.” (“icse2025-paper1580”, p. 5) \#ffd400 \par - “IV. EXPERIMENTS” (“icse2025-paper1580”, p. 5) \#ffd400\\ \mkbibemph{The section is missing an explicit description of the research questions that you wanted to answer by means of the performed experiments.} \par - “effectiveness of identifying shipments potentially circumventing economic sanctions, high tariffs, or engaging in suspicious activities largely depends on the quality of data initially cleaned and preprocessed.” (“icse2025-paper1580”, p. 5) \#a28ae5 \par - “import prohibitions,” (“icse2025-paper1580”, p. 5) \#5fb236 \par - “high tariff rates” (“icse2025-paper1580”, p. 5) \#5fb236 \par - “sanctions” (“icse2025-paper1580”, p. 5) \#5fb236 \par - “Teak” (“icse2025-paper1580”, p. 5) \#5fb236 \par - “Panjiva1” (“icse2025-paper1580”, p. 5) \#5fb236 \par - “Grain” (“icse2025-paper1580”, p. 5) \#5fb236 \par - “ExportGenius2” (“icse2025-paper1580”, p. 5) \#5fb236 \par - “Timber” (“icse2025-paper1580”, p. 5) \#5fb236 \par - “ExportGenius and ImportGenius.” (“icse2025-paper1580”, p. 5) \#5fb236 \par - “Consequently, data analysis becomes a laborious and time-consuming task for analysts, highlighting the urgency and importance of effective data preprocessing.” (“icse2025-paper1580”, p. 5) \#5fb236 \par - “Qdrant is employed to efficiently store and retrieve code functions based on its vector embeddings.” (“icse2025-paper1580”, p. 5) \#5fb236 \par - “CodeLlama-13b-Instruct” (“icse2025-paper1580”, p. 5) \#5fb236 \par - “12 widely recognized and commonly used data preprocessing functions.” (“icse2025-paper1580”, p. 5) \#5fb236 \par - “we developed three baselines for comparison” (“icse2025-paper1580”, p. 5) \#5fb236 \par - “Baseline 1 (B1): State-of-the-Art (SOTA) Simulation Code Generation Using Only LLM.” (“icse2025-paper1580”, p. 5) \#ffd400\\ \mkbibemph{User request should be added in the title} \par - “This setup evaluates the LLM’s capability to produce relevant code purely from the textual description provided by the user, which may involve significant effort and may result in less effective data cleaning outcomes.” (“icse2025-paper1580”, p. 6) \#ffd400\\ \mkbibemph{Without giving as input the spreadsheet to clean?} \par - “we provided the LLM with a set of top-k candidate codes retrieved from the code library, along with the user’s request.” (“icse2025-paper1580”, p. 6) \#a28ae5 \par - “excludes specific data details” (“icse2025-paper1580”, p. 6) \#ffd400\\ \mkbibemph{What do you mean? What kind of data details are excluded?} \par - “Providing the Entire Code Library Without Descriptions.” (“icse2025-paper1580”, p. 6) \#ffd400\\ \mkbibemph{Add also the fact that user request is given (if this is~ the case, I think so).} \par - “DATA PREPROCESSING TASKS USED IN OUR EXPERIMENTS” (“icse2025-paper1580”, p. 6) \#ffd400\\ \mkbibemph{It's not clear if the tasks to be executed to each dataset has been decided by the authors, or it was TradeSweep that decided them by looking at the datasets.} \par - “Only show records where trading country is US” (“icse2025-paper1580”, p. 6) \#5fb236 \par - “preprocessed and cleaned” (“icse2025-paper1580”, p. 6) \#ffd400\\ \mkbibemph{The proposed approach does not seem to distinguish preprocess vs clean operations. If they are used interchangeably, this must be clarified.} \par - “During the data preprocessing operation, if TradeSweep is unable to identify a relevant function in the code library, it generates a novel code. This limitation causes TradeSweep to behave similarly to SOTA tools, relying solely on the LLM to generate code.” (“icse2025-paper1580”, p. 10) \#5fb236 \par - “Our results demonstrate TradeSweep ’s effectiveness in practical data transformation scenarios.” (“icse2025-paper1580”, p. 10) \#5fb236 \par - “The ablation study highlights that utilizing a code library and a vector database for information retrieval accelerates the code generation process.” (“icse2025-paper1580”, p. 10) \#a28ae5} } @misc{Icse2025paper1709, title = {Icse2025-Paper1709}, keywords = {LOGSEQ}, note = {\section{Annotazioni\\ (29/9/2024, 14:38:38)} \par - “semantic matchers and alternatives to baseline methods for metamodel matching. However, metamodels can be large, which can overwhelm LLMs” (“icse2025-paper1709”, p. 1) \#00b036 \par - “initial mapping derived from their elements’ definitions.” (“icse2025-paper1709”, p. 1) \#f0ff00\\ \mkbibemph{How is this obtained?} \par - “Industry standards also drive migration for better integration and tool use, such as legacy proprietary models to AUTOSTAR [5], [6]” (“icse2025-paper1709”, p. 1) \#00b036 \par - “similarities with metamodels” (“icse2025-paper1709”, p. 1) \#00b036 \par - “KM3 and DOT” (“icse2025-paper1709”, p. 2) \#f0ff00\\ \mkbibemph{I'm not sure this is the right motivating example.....} \par - “When we prompt GPT-4 using the providing only specific parts of the metamodels, Node is correctly identified as a correspondence for DataType.” (“icse2025-paper1709”, p. 2) \#f0ff00\\ \mkbibemph{That table does not show the matched elements. For instance oncernig the third row, Instead of showing only Node, I would also shows the Data Type of the Dot metamodel.} \par - “large” (“icse2025-paper1709”, p. 2) \#f0ff00\\ \mkbibemph{Large? They are very small.} \par - “III. THE SMATH-M-LLM APPROACH” (“icse2025-paper1709”, p. 2) \#ffd400\\ \mkbibemph{In general this section needs reworking because it is not clear. A running and explanatory example is needed to give an overview of the approach before giving the details of the single components shown in Fig. 3.} \par - “prompting them with relevant fragments of the source and target metamodels.” (“icse2025-paper1709”, p. 2) \#5fb236 \par - “Fig. 3. SMATH-M-LLM” (“icse2025-paper1709”, p. 3) \#ffd400\\ \mkbibemph{It's not clear why the process includes the calculation of initial correspondences and then finalize these after creating source and target fragments.} \par - “components SMATH-M” (“icse2025-paper1709”, p. 3) \#ff4400\\ \mkbibemph{Of} \par - “definitions’ similarity.” (“icse2025-paper1709”, p. 3) \#f0ff00\\ \mkbibemph{What tool is exploited in his phase?} \par - “Fragment Constructor partitions the source metamodel into fragments using a community detection algorithm, it creates an initial target fragment for each source fragment by identifying the target concepts that correspond to the source concepts within the relevant fragment.” (“icse2025-paper1709”, p. 3) \#f0ff00\\ \mkbibemph{The needs for this component and how it works should be elaborated.} \par - “In our work, the stopping criteria are satisfied if the correspondences are non-abstract concepts and all their features are presented in the target fragment. In the following sections, we describe each component in detail and demonstrate its application to the motivational example.” (“icse2025-paper1709”, p. 3) \#f0ff00\\ \mkbibemph{The process is not clear. Maybe an explanatory example is needed.} \par - “Definitions,” (“icse2025-paper1709”, p. 3) \#f0ff00\\ \mkbibemph{Which one?} \par - “include more general roles, namely super-type and container concepts.” (“icse2025-paper1709”, p. 3) \#f0ff00\\ \mkbibemph{Explain.} \par - “represented by their direct super-type.” (“icse2025-paper1709”, p. 3) \#00b036 \par - “This is represented by lines 1-2 in Algorithm 1.” (“icse2025-paper1709”, p. 3) \#f0ff00\\ \mkbibemph{We are still missing the description of listing 1 and you already talk about algorithm 1 which has not been yet introduced?} \par - “Applying this step to the metamodels KM3 and DOT (see Section II) results in selecting five representative concepts out of 16 concepts for KM3 (LocatedElement, ModelElement, Classifier, TypedElement, StructuralFeature) and eight out of 26 concepts for DOT (Label, Compartment, GraphElement, Arc, Shape, Nodelike, NodeShape, ComplexNodeShape). Notably, DataType and Node are excluded from the representative sets because they do not meet the specified criteria of being a super-type or a container. Additionally, although Package and Class are container concepts in KM3, they are not included in the representative set because they are already represented by Classifier and ModelElement, respectively. Furthermore, the contained element StructuralFeature is part of the representative set, which negates the need to include Class separately.” (“icse2025-paper1709”, p. 3) \#f0ff00\\ \mkbibemph{By looking at these concepts I don't see sets of concepts that should be matched. Dot is related to graphical representations, km3 about metanodels why this should be matched without specifying the need of doing so? In that specific case, dot can be used to specify the graphical representation of metanodels (ie km3). Such a goal of the match is missing and I do not see how this is inferred (at least so far).} \par - “generate definitions for the selected concepts to enrich the matching process.” (“icse2025-paper1709”, p. 3) \#f0ff00\\ \mkbibemph{I see where you want to go, and it can make sense, let's see} \par - “These definitions offer richer semantic information about the concepts.” (“icse2025-paper1709”, p. 3) \#ffd400\\ \mkbibemph{This can be very domain specific and potentially not part of the training data (I'm thinking for instance to custom domain specific languages that are not part of the training data.} \par - “Algorithm 1.” (“icse2025-paper1709”, p. 3) \#f0ff00\\ \mkbibemph{I suggest to introduce this properly.} \par - “TABLE II CONCEPTS AND GENERATED DEFINITIONS” (“icse2025-paper1709”, p. 4) \#f0ff00\\ \mkbibemph{This means that the concepts should have appeared in the training data. What about new dsl?} \par - “Table II shows examples of definitions generated by GPT-4 for some concepts in KM3 and DOT metamodels.” (“icse2025-paper1709”, p. 4) \#ffd400\\ \mkbibemph{From the shown definition it is probable that the considered metamodels are part of the training data. See my previous comment.} \par - “we represent them as continuous dense vectors,” (“icse2025-paper1709”, p. 4) \#f0ff00\\ \mkbibemph{Can you show an example?} \par - “we select the most similar representative target concept based on the highest cosine similarity score (lines 8-10). This curated list of concept correspondences (concept correspondences in Algorithm 1) constitutes the final output of this component.” (“icse2025-paper1709”, p. 4) \#f0ff00\\ \mkbibemph{How many? What's the acceptance threshold?} \par - “The task of refining the initial correspondences to achieve exact matches is done by the subsequent components.” (“icse2025-paper1709”, p. 4) \#f0ff00\\ \mkbibemph{No humans in the loop?} \par - “measure cosine similarity” (“icse2025-paper1709”, p. 4) \#ffd400\\ \mkbibemph{Between what? It is not clear how couples of concepts are selected for doing such a measurement.} \par - “The Fragment Constructor component is responsible for preparing pairs of source and target fragments, with the pairing based on the list of correspondences identified by the Concept Matcher.” (“icse2025-paper1709”, p. 4) \#ffd400\\ \mkbibemph{Also in this cases it is needed to present an illustrative example to explain the steps of the process.} \par - “Source Fragmentation” (“icse2025-paper1709”, p. 4) \#ffd400\\ \mkbibemph{You mix activities (e.g., pre-processing, source fragmentation) with artifacts (e.g. target fragment)} \par - “after constructing the fragments using Louvain, we ensure that relevant representative concepts are included in each fragment.” (“icse2025-paper1709”, p. 4) \#5fb236 \par - “Before providing the fragments to the LLM, we convert them into PlantUML format [26]. This format is chosen because it is concise, and it is widely recognized, thereby increasing the likelihood that LLMs will interpret it accurately” (“icse2025-paper1709”, p. 6) \#ffd400\\ \mkbibemph{Even this part of representing fragments to LLM after having converted them into PlantUML is not clear. User might want to match any potential couple of DSLs that might not have nothing todo with UML. Moreover, in which part of Listing 2, PlantUML specifications are given? It is not clear from Listing 2. n} \par - “baseline matching techniques” (“icse2025-paper1709”, p. 6) \#a28ae5 \par - “seven metamodels” (“icse2025-paper1709”, p. 6) \#a28ae5 \par - “three established baseline methods” (“icse2025-paper1709”, p. 7) \#a28ae5 \par - “Ontology Matching Methods” (“icse2025-paper1709”, p. 7) \#5fb236 \par - “Lexical Database Methods.” (“icse2025-paper1709”, p. 7) \#5fb236 \par - “LLM-based Methods.” (“icse2025-paper1709”, p. 7) \#5fb236 \par - “semantically equivalent from its respective transformation rules provided” (“icse2025-paper1709”, p. 7) \#ffd400\\ \mkbibemph{This does not mean that concepts are semantically equivalent!!!!} \par - “The proposed LLM-based approach significantly outperforms baseline semantic matchers using ontology and lexical databases, indicating its potential to replace these methods with superior accuracy in identifying correspondences, addressing the known challenges” (“icse2025-paper1709”, p. 8) \#5fb236 \par - “Impact of context” (“icse2025-paper1709”, p. 8) \#ffd400\\ \mkbibemph{The notion of context is not clear. It is not evident how the concepts to be matched get automatically distinguished from the rest of the concepts belonging to the metamodels of interest.} \par - “use of fragments” (“icse2025-paper1709”, p. 8) \#ffd400 \par - “Prompting for each concept individually improves accuracy significantly but increases cost.” (“icse2025-paper1709”, p. 8) \#ffd400\\ \mkbibemph{The paper is not effective in explaining how such a prompting approach is done.} \par - “Concrete super-classes. We assume that concrete superclasses are not permitted. We designed our approach accordingly and refined the metamodels that have super-classes (e.g., UML2.0) by abstracting concrete super-classes. In fact, our method mandates mapping to a non-superclass by removing the super-class from the fragment if it is selected by the LLM.” (“icse2025-paper1709”, p. 10) \#ffd400\\ \mkbibemph{Even though the authors recognize this as a potential threat to validity, they should discuss how frequent this occurs at least for the cases they have analyzed for the performed experiments.}} } @misc{Icse2025paper215pdf, title = {Icse2025-Paper215.Pdf}, langid = {english}, keywords = {LOGSEQ}, note = {\section{Annotazioni\\ (4/6/2024, 14:15:07)} \par - “Application Programming Interface (API) recommendation aims to recommend APIs for developers that meet their functional requirements, which can compensate for developers’ lack of API knowledge” (“icse2025-paper215.pdf”, p. 1) \#a28ae5\\ \mkbibemph{ } \par - “Instances of this include recommending fictitious APIs, APIs whose calling conditions cannot be satisfied, or API sequences failure to conform to the interface parameter types.” (“icse2025-paper215.pdf”, p. 1) \#a28ae5\\ \mkbibemph{ } \par - “Knowledge-guided framework for LLM-based API Recommendation (KG4LLM)” (“icse2025-paper215.pdf”, p. 1) \#f0ff00\\ \mkbibemph{is it similarto RAG? } \par - “Java Development Kit (JDK) documentation to” (“icse2025-paper215.pdf”, p. 1) \#f0ff00\\ \mkbibemph{maybe the jdk doumentaion is already in the training set of the used LLM? } \par - “even the most skilled developer can hardly master using all the APIs. Therefore, this paper focuses on the API recommendation task to compensate for developers’ lack of API knowledge.” (“icse2025-paper215.pdf”, p. 1) \#a28ae5\\ \mkbibemph{ } \par - “specific interface parameter types predefined by the software architect” (“icse2025-paper215.pdf”, p. 1) \#e56eee\\ \mkbibemph{ } \par - “API Recommendation under specific Interface Parameter Types (APIRIP)” (“icse2025-paper215.pdf”, p. 1) \#a28ae5\\ \mkbibemph{ } \par - “During APIRIP, the APIrecommender system takes functional description and interface parameter types (if no interface parameter types, set them to “void”) as the query and recommends API sequences that conform to the interface parameter types to fulfill the functional requirement.” (“icse2025-paper215.pdf”, p. 1) \#00b036\\ \mkbibemph{ } \par - “The parameter types “Desktop” and “URL”, on which the recommended API sequence relies, conform to the interface parameter types.” (“icse2025-paper215.pdf”, p. 1) \#a28ae5\\ \mkbibemph{ } \par - “APIRID requires API recommender systems to be sensitive enough to the parameter types defined in the interface.” (“icse2025-paper215.pdf”, p. 1) \#e56eee\\ \mkbibemph{ } \par - “In this condition, to “open URL by Desktop object”, the API recommender system should recommend an additional API named “Desktop.getDesktop()” to generate a “Desktop” object. As a result, the recommended API sequence only relies on the parameter “URL”, which conforms to the interface parameter types. Similarly, if the interface parameter type becomes “void”, an additional API “URL.URL(String)” will be added to the recommended API sequence to create an “URL” object” (“icse2025-paper215.pdf”, p. 1) \#a28ae5\\ \mkbibemph{ } \par - “Fig. 1. Examples of APIRID.” (“icse2025-paper215.pdf”, p. 1) \#f0ff00\\ \mkbibemph{it's not completely cleary. Let's see if later gets better. } \par - “previous” (“icse2025-paper215.pdf”, p. 1) \#f0ff00\\ \mkbibemph{previous and then you put a reference to the next section? } \par - “UTIs” (“icse2025-paper215.pdf”, p. 1) \#ffd400\\ \mkbibemph{what about hallucinations that you previously mentioned? } \par - “fictitious APIs” (“icse2025-paper215.pdf”, p. 1) \#a28ae5\\ \mkbibemph{ } \par - “unsatisfied calling conditions” (“icse2025-paper215.pdf”, p. 1) \#a28ae5\\ \mkbibemph{ } \par - “interface parameter types.” (“icse2025-paper215.pdf”, p. 1) \#a28ae5\\ \mkbibemph{ } \par - “beam search” (“icse2025-paper215.pdf”, p. 2) \#a28ae5\\ \mkbibemph{ } \par - “fine-tuning” (“icse2025-paper215.pdf”, p. 2) \#f0ff00\\ \mkbibemph{ } \par - “UTI 3” (“icse2025-paper215.pdf”, p. 2) \#e56eee\\ \mkbibemph{ } \par - “data augmentation,” (“icse2025-paper215.pdf”, p. 2) \#f0ff00\\ \mkbibemph{check this how it works later. } \par - “generate more training instances with more matched combinations of interface parameter types and API sequences to fine-tune LLM” (“icse2025-paper215.pdf”, p. 2) \#a28ae5\\ \mkbibemph{ } \par - “UTI 1, UTI 2 and further alleviate UTI 3,” (“icse2025-paper215.pdf”, p. 2) \#e56eee\\ \mkbibemph{ } \par - “Finally, we compile a Java dataset tailored for the APIRIP task and introduce a set of evaluation metrics designed to assess the trustworthiness of LLM recommendations.” (“icse2025-paper215.pdf”, p. 2) \#a28ae5\\ \mkbibemph{ } \par - “benchmark LLMs’ T R@1” (“icse2025-paper215.pdf”, p. 2) \#a28ae5\\ \mkbibemph{ } \par - “new variant of the API recommendation task named APIRIP for scenarios where developers program under specific interface parameter types.” (“icse2025-paper215.pdf”, p. 2) \#a28ae5\\ \mkbibemph{ } \par - “first study on the trustworthiness of LLM for API recommendation task.” (“icse2025-paper215.pdf”, p. 2) \#a28ae5\\ \mkbibemph{ } \par - “KG4LLM can effectively improve the trustworthiness of LLM” (“icse2025-paper215.pdf”, p. 2) \#a28ae5\\ \mkbibemph{ } \par - “CodeBERT” (“icse2025-paper215.pdf”, p. 2) \#ffd400\\ \mkbibemph{ } \par - “When UTI 3 occurs, it often means that the recommended APIs do not meet the developer’s functional requirements.” (“icse2025-paper215.pdf”, p. 2) \#5fb236\\ \mkbibemph{ } \par - “Since the generation process of existing LLMs is mainly based on probabilistic sampling of tokens without any constraints for trustworthiness, the untrustworthy issue is common in almost all varieties of LLM.” (“icse2025-paper215.pdf”, p. 2) \#5fb236\\ \mkbibemph{ } \par - “Fig. 3. Overall framework of KG4LLM.” (“icse2025-paper215.pdf”, p. 3) \#ffd400\\ \mkbibemph{It's not clear how the proposed approach has been implemented. Fig. 3 is fairly clear, even though later in the paper it is not clear how the different components of Fig. 3 have been implemented. It seems that CodeT5 underpins the proposed approach, even though it is not clear how Algorithm 1 and Algorithm 2 have been developed in synergy with CodetT5. } \par - “generate more training instances containing more combinations of matched interface parameter types and API sequences for fine-tuning LLMs” (“icse2025-paper215.pdf”, p. 3) \#5fb236\\ \mkbibemph{ } \par - “interface parameter types after the functional description and adding a separator between them as the input.” (“icse2025-paper215.pdf”, p. 3) \#5fb236\\ \mkbibemph{ } \par - “vocabulary constraints to guarantee that LLMs can only generate real-world APIs whose calling conditions can be met.” (“icse2025-paper215.pdf”, p. 3) \#ffd400\\ \mkbibemph{What does it mean. How can you ensure that calling conditions are met? } \par - “ontology of API as shown in Figure 4.” (“icse2025-paper215.pdf”, p. 3) \#5fb236\\ \mkbibemph{ } \par - “dimension of the API parameter, API return type, or member type.” (“icse2025-paper215.pdf”, p. 3) \#ffd400\\ \mkbibemph{Why dim is relevant? } \par - “method annotations as the functional descriptions” (“icse2025-paper215.pdf”, p. 3) \#ffd400\\ \mkbibemph{This is a potential threat to validity } \par - “Fig. 6” (“icse2025-paper215.pdf”, p. 4) \#ffd400\\ \mkbibemph{byte should be byte[] in the "interface parameter types" section, isn't it? } \par - “Knowledge-Guided Data Augmentation” (“icse2025-paper215.pdf”, p. 4) \#5fb236\\ \mkbibemph{ } \par - “knowledge-guided data augmentation” (“icse2025-paper215.pdf”, p. 4) \#5fb236\\ \mkbibemph{ } \par - “we treat the unsatisfied parameter types of the augmented API sequence as interface parameter types to ensure that enhanced API sequences can conform to these interface parameter types.” (“icse2025-paper215.pdf”, p. 4) \#ffd400\\ \mkbibemph{While such generation can be beneficial to avoid calling conditions that are not satisfied, this my contribute to the generation of fictitious APIs, isn't it? } \par - “Fig” (“icse2025-paper215.pdf”, p. 4) \#ffd400\\ \mkbibemph{Why FileFilter has been renamed as FilenameFilter in Fig.7? } \par - “Each triple consists of a interface parameter types-API sequence pair with a score that represents the quality of the pair” (“icse2025-paper215.pdf”, p. 4) \#5fb236\\ \mkbibemph{ } \par - “number of APIs whose return value is not used by subsequent APIs, which is designed based on the following intuition: After calling an API, if it returns a object, it is likely to be used by the subsequent APIs. The smaller the score, the higher the quality. Next, we sort all the triples according to the score in the ascending order, and output top 5 of them. Finally, we combine each generated interface parameter typesAPI sequence pair with the original functional description text and add them to the training set.” (“icse2025-paper215.pdf”, p. 4) \#ffd400\\ \mkbibemph{"The smaller the score, the higher the quality" What's the concept of quality that is used here? } \par - “Knowledge-Guided Beam Search” (“icse2025-paper215.pdf”, p. 4) \#5fb236\\ \mkbibemph{ } \par - “vocabulary constraints and beam reordering.” (“icse2025-paper215.pdf”, p. 4) \#a28ae5\\ \mkbibemph{ } \par - “we propose vocabulary constraints to guide LLMs only to generate real-world APIs with satisfied calling conditions by constraining the output vocabulary based on the API knowledge base.” (“icse2025-paper215.pdf”, p. 4) \#5fb236\\ \mkbibemph{ } \par - “generate” (“icse2025-paper215.pdf”, p. 5) \#ff6666\\ \mkbibemph{ } \par - “If yes, we multiply a manually set weight r on the score of the API sequence given by beam search and reorder all the API sequences in the beam list” (“icse2025-paper215.pdf”, p. 5) \#ffd400\\ \mkbibemph{This is not clear. What's the score r? } \par - “We set the beam size to 2 and r to 2 (because we change the log softmax to softmax, r must be greater than 1 to increase a positive score in this illustrative example). We use the average of the probabilities of generating each token (marked in purple in Figure 8 ) to denote the score of each API sequence. For example, the score of “file.read{$<$}e{$>$}” is p(f ile.read {$<$} e {$>$}) = 1 4 (p(f ile| {$<$} s {$>$}) + p(.| {$<$} s {$>$} f ile) + p(read| {$<$} s {$>$} f ile.) + p({$<$} e {$>$} | {$<$} s {$>$} f ile.read)) = 1 4 (0.3 + 0.6 + 0.4 + 0.5) = 0.45.” (“icse2025-paper215.pdf”, p. 5) \#ffd400\\ \mkbibemph{This is not very clear. The description of Fig. 8 should be improved. } \par - “knowledge-guided data augmentation” (“icse2025-paper215.pdf”, p. 7) \#5fb236\\ \mkbibemph{ } \par - “knowledgeguided beam search” (“icse2025-paper215.pdf”, p. 7) \#5fb236\\ \mkbibemph{ } \par - “SantaCoder” (“icse2025-paper215.pdf”, p. 7) \#5fb236\\ \mkbibemph{ } \par - “This may be because a larger model has a stronger fitting ability, which may be more likely to lead to overfitting after increasing the number of samples through data augmentation” (“icse2025-paper215.pdf”, p. 7) \#5fb236\\ \mkbibemph{ } \par - “Therefore, the knowledge-guided data augmentation may be more suitable for smaller LLMs.” (“icse2025-paper215.pdf”, p. 7) \#e56eee\\ \mkbibemph{ } \par - “number of interface parameter types varies” (“icse2025-paper215.pdf”, p. 7) \#5fb236\\ \mkbibemph{ } \par - “reducing different types of untrustworthy issues” (“icse2025-paper215.pdf”, p. 8) \#5fb236\\ \mkbibemph{ } \par - “This is because LLMs are guided to generate only realworld APIs whose calling conditions can be met through the vocabulary constraints.” (“icse2025-paper215.pdf”, p. 8) \#5fb236\\ \mkbibemph{ } \par - “[11]” (“icse2025-paper215.pdf”, p. 11) \#00b036\\ \mkbibemph{ }} } @misc{Icse2025paper239pdf, title = {Icse2025-Paper239.Pdf}, langid = {english}, keywords = {LOGSEQ}, note = {\section{Annotazioni\\ (5/5/2024, 18:38:10)} \par - “programming language when a user creates a new file without specifying a file extension [7]. While the original guesslang is currently unmaintained (last commit in September 2021) and relies on deprecated TensorFlow abstractions, VS Code developers maintain a Node.js client that facilitates the testing of the underlying model [26].” (“icse2025-paper239.pdf”, p. 2) \#f0ff00\\ \mkbibemph{Interesting. I have to check it } \par - “a stratified” (“icse2025-paper239.pdf”, p. 3) \#f0ff00\\ \mkbibemph{What do you mean? } \par - “100 most prominent file extensions” (“icse2025-paper239.pdf”, p. 3) \#f0ff00\\ \mkbibemph{What's the coverage of this with respect those in references [29]... Below? } \par - “detection errors (in the case of trusting one tool above others). We use four heuristics for validation: file size, magic bytes (for binary files), character encoding (for text files), and file trustworthiness. For file size, we require any sample in our” (“icse2025-paper239.pdf”, p. 3) \#f0ff00\\ \mkbibemph{Hope the four heuristics are described later. Here they are only mentioned without explanation } \par - “set of necessary but not sufficient” (“icse2025-paper239.pdf”, p. 3) \#f0ff00\\ \mkbibemph{It can be source of bias right? } \par - “obtain 10K samples per content type for” (“icse2025-paper239.pdf”, p. 3) \#f0ff00\\ \mkbibemph{Check the math } \par - “validation dataset consists” (“icse2025-paper239.pdf”, p. 3) \#f0ff00\\ \mkbibemph{Check the usage of this. } \par - “multi-class classification problem, we estimate per-type precision as:” (“icse2025-paper239.pdf”, p. 4) \#f0ff00\\ \mkbibemph{multi-class because binary, text, exe?..... } \par - “[11].” (“icse2025-paper239.pdf”, p. 5) \#f0ff00\\ \mkbibemph{Da vedere. } \par - “content types, with an average F1 score of 96\% when using the MIME flag,” (“icse2025-paper239.pdf”, p. 5) \#f0ff00\\ \mkbibemph{This mime management needs to be better understood. } \par - “signatures created by experts.” (“icse2025-paper239.pdf”, p. 6) \#f0ff00\\ \mkbibemph{Check why signatures need to be created by users. } \par - “Speed.” (“icse2025-paper239.pdf”, p. 6) \#f0ff00\\ \mkbibemph{How can you affect this aspect? What are the intervention dimensions? } \par - “Reshape size, number and size of Dense layers, normalization type, and amount of dropout applied before converging on this specific design.” (“icse2025-paper239.pdf”, p. 6) \#ff4400\\ \mkbibemph{Does not parse. } \par - (“icse2025-paper239.pdf”, p. 7) \#f0ff00\\ \mkbibemph{Dropout? } \par - “not enough bytes” (“icse2025-paper239.pdf”, p. 7) \#f0ff00\\ \mkbibemph{Hat does it mean? When you get not enough types? } \par - “a Dense layer.” (“icse2025-paper239.pdf”, p. 7) \#f0ff00\\ \mkbibemph{To be checked. } \par - “gelu” (“icse2025-paper239.pdf”, p. 7) \#f0ff00\\ \mkbibemph{? } \par - “softmax activation” (“icse2025-paper239.pdf”, p. 7) \#f0ff00\\ \mkbibemph{? } \par - “an argmax” (“icse2025-paper239.pdf”, p. 7) \#f0ff00\\ \mkbibemph{? } \par - “C. Training” (“icse2025-paper239.pdf”, p. 7) \#f0ff00\\ \mkbibemph{Interesting because it is a typical setting. } \par - “for data augmentation,” (“icse2025-paper239.pdf”, p. 7) \#f0ff00\\ \mkbibemph{Why data augmentation? When is it performed in the process? } \par - “30 epochs” (“icse2025-paper239.pdf”, p. 7) \#f0ff00\\ \mkbibemph{Check } \par - “Figure 3 shows how the validation loss and validation accuracy progress with the number of epochs. Note how the” (“icse2025-paper239.pdf”, p. 7) \#f0ff00\\ \mkbibemph{Validation loss and accuracy } \par - “deep learning model either through a command line or API. As part of this, we use OnnxRuntime [39] (instead of Tensorflow and Keras, which we use for training), because it is roughly 15x faster in loading the model (while having a similar model” (“icse2025-paper239.pdf”, p. 8) \#f0ff00\\ \mkbibemph{How is this possible? } \par - “[11].” (“icse2025-paper239.pdf”, p. 8) \#f0ff00\\ \mkbibemph{To check } \par - “-1\%” (“icse2025-paper239.pdf”, p. 9) \#f0ff00\\ \mkbibemph{Negative? } \par - “MIME flag), followed by MAGIKA, exiftool, trid, and guesslang.” (“icse2025-paper239.pdf”, p. 9) \#f0ff00\\ \mkbibemph{ } \par - “OnnxRuntime.” (“icse2025-paper239.pdf”, p. 9) \#f0ff00\\ \mkbibemph{But this is for execution time. } \par - “a command line wrapper” (“icse2025-paper239.pdf”, p. 9) \#f0ff00\\ \mkbibemph{? } \par - “, on a daily basis, MAGIKA routes to MS Officespecific malware scanners” (“icse2025-paper239.pdf”, p. 10) \#f0ff00\\ \mkbibemph{This is a different problem, isn't it? } \par - “alternatives. We subsequently engaged in a dialogue regarding current feature gaps, primarily focusing on the necessity for more fine-grained detection of specific content types (e.g., C vs. C++, JAVASCRIPT vs. TYPESCRIPT, INI vs. TOML), which we are currently exploring.VIII. DISCUSSION” (“icse2025-paper239.pdf”, p. 10) \#f0ff00\\ \mkbibemph{How difficult is this process? } \par - “Trained on 24M samples” (“icse2025-paper239.pdf”, p. 10) \#f0ff00\\ \mkbibemph{ } \par - “for malware analysis,” (“icse2025-paper239.pdf”, p. 10) \#f0ff00\\ \mkbibemph{How content type detection is linked to this? }} } @misc{Icse2025paper548pdf, title = {Icse2025-Paper548.Pdf}, langid = {english}, keywords = {LOGSEQ}, note = {\section{Annotazioni\\ (4/5/2024, 12:00:49)} \par - “Software Model Evolution with Large Language Models: Experiments on Simulated, Public, and Industrial Datasets” (“icse2025-paper548.pdf”, p. 1) \#5fb236\\ \mkbibemph{ 假设} \par - “model evolution” (“icse2025-paper548.pdf”, p. 1) \#f0ff00\\ \mkbibemph{The term model evolution seems to be used not appropriately.} \par - “model repositories and software modelhistories interchangeably,” (“icse2025-paper548.pdf”, p. 2) \#f0ff00\\ \mkbibemph{These have different meanings.} \par - “Abstract Syntax Graph” (“icse2025-paper548.pdf”, p. 3) \#a28ae5 \par - “Labeled Directed Graph” (“icse2025-paper548.pdf”, p. 3) \#a28ae5 \par - “Structural Model Difference” (“icse2025-paper548.pdf”, p. 3) \#a28ae5 \par - “Simple Change Graph” (“icse2025-paper548.pdf”, p. 3) \#a28ae5 \par - “adjacent” (“icse2025-paper548.pdf”, p. 3) \#f0ff00\\ \mkbibemph{I I understand what you mean but meybe you should say better. I guess you are referring to model elements that are related to changes.} \par - “Endogenous model transformation)” (“icse2025-paper548.pdf”, p. 3) \#f0ff00\\ \mkbibemph{It's endogenous because the source and target metanodels are the same, right?} \par - “edit operation” (“icse2025-paper548.pdf”, p. 3) \#a28ae5 \par - “a set of model transformations that have the same simple change graph” (“icse2025-paper548.pdf”, p. 3) \#e56eee \par - “Given an edit operation ε and a concrete model m, one can perform the removal of “Remove” nodes and the gluing of “Add” nodes as defined by the simple change graph corresponding to ε, and then set concrete attributes.” (“icse2025-paper548.pdf”, p. 3) \#ffd400\\ \mkbibemph{Why are you specifically focusing on the "Remove" then "Add" change? Edit operations can be arbitrary.} \par - “We are interested in completing software models. That is, for an observed evolution m ε→ n, we want to find a completion γ ∈ E, such that m ε→ n γ→ c is an observable completion.” (“icse2025-paper548.pdf”, p. 3) \#ffd400\\ \mkbibemph{So far it is not clear what are the benefits of the given formalization. Let's see what's next.} \par - “(Software) model completion is the task of further evolving a software model based on a given (partial) model.” (“icse2025-paper548.pdf”, p. 3) \#a28ae5 \par - “Model Completion” (“icse2025-paper548.pdf”, p. 4) \#a28ae5 \par - “Definition III.7 (Model Completion). Given a set of model transformations T , model completion is a computable function C : T → T that, given a model transformation m ε→ n from a source model m to a (partial) target model n, computes a model transformation C(m ε→ n) = n γ→ c. We call the edit operation γ a software model completion.” (“icse2025-paper548.pdf”, p. 4) \#ffd400\\ \mkbibemph{The given formalization does not manage the case when you start from a model n without knowing what are the transformations that have previously applied to obtain the model n from some unknown source model m.} \par - “Language Model)” (“icse2025-paper548.pdf”, p. 4) \#a28ae5 \par - “A language model is a conditional probability distribution P(ω|c) for a (sequence of) token(s) ω, given a sequence of context tokens c.” (“icse2025-paper548.pdf”, p. 4) \#a28ae5 \par - “Retrieval-augmented generation includes additional knowledge in the context (or prompt).” (“icse2025-paper548.pdf”, p. 4) \#5fb236 \par - “Fine-tuning adjusts the LLM’s weights based on additional training data.” (“icse2025-paper548.pdf”, p. 4) \#5fb236 \par - “As said in Section I,” (“icse2025-paper548.pdf”, p. 4) \#ff6666 \par - “our approach of how” (“icse2025-paper548.pdf”, p. 4) \#ff6666 \par - “)” (“icse2025-paper548.pdf”, p. 4) \#ff6666 \par - “LLMs” (“icse2025-paper548.pdf”, p. 4) \#5fb236\\ \mkbibemph{ [[LLMs]]} \par - “retrieval-augmented generation” (“icse2025-paper548.pdf”, p. 4) \#5fb236\\ \mkbibemph{ [[RAG]]} \par - “straight forwardly” (“icse2025-paper548.pdf”, p. 4) \#ff6666 \par - “First, we do not have to work with the entire software model representation, but we can focus on slices of the models around recently changed elements” (“icse2025-paper548.pdf”, p. 4) \#ffd400\\ \mkbibemph{That's ok, I'm just wondering if considering the context, ie. the nodes that are in the model but not in the simple change graph might improve the performance of the recommender.} \par - “This is one tactic of dealing with the common problem of the limited context of a LLM (see Section III-B).” (“icse2025-paper548.pdf”, p. 4) \#ffd400\\ \mkbibemph{This limitation is not discussed in Sec. III-B, thus I would remove the reference to that section.} \par - “is explained in Section III.” (“icse2025-paper548.pdf”, p. 4) \#ffd400\\ \mkbibemph{I would also remove such a back reference.} \par - “, w” (“icse2025-paper548.pdf”, p. 4) \#ff6666 \par - “s” (“icse2025-paper548.pdf”, p. 4) \#ff6666 \par - “Figure 3: Overview of the RAMC approach.” (“icse2025-paper548.pdf”, p. 5) \#ffd400\\ \mkbibemph{I would anticipate this section earlier in section IV.} \par - “Regarding our running example in a⃝ of Figure 1, we also highlighted these model differences by color, that is, “added” model elements are depicted in green.” (“icse2025-paper548.pdf”, p. 5) \#ffd400\\ \mkbibemph{In general, what is the object of the recommendation? While editing, it might be possible to get as recommendation the name of an attribute to be added to the previously specified class, or even its type. How do you see this should work in practice?} \par - “weakly connected components” (“icse2025-paper548.pdf”, p. 5) \#ffd400\\ \mkbibemph{Weakly connected components are not previously defined. Maybe you can define them in the context of the simple change graph definition.} \par - “EdgeList” (“icse2025-paper548.pdf”, p. 5) \#5fb236 \par - “weakly connected simple change graph components” (“icse2025-paper548.pdf”, p. 5) \#ffd400\\ \mkbibemph{"weakly connected ... " they are "simple change graph", isn't it? If this is the case, I would avoid naming differently concepts already introduced. This makes the text confusing.} \par - “EdgeList” (“icse2025-paper548.pdf”, p. 5) \#5fb236 \par - “Sampling new edges. We can sample multiple model completion candidates from the LLM by using a beam search, or, instructing the model to generate several new edges. The edge sampling algorithms are given in detail on the supplementary Website.” (“icse2025-paper548.pdf”, p. 5) \#ffd400\\ \mkbibemph{As previously said in one of my comments, it is necessary to clearly define what are the elements that build recommendations.} \par - “We evaluate to what extent our approach is able to derive structurally and semantically correct completion operations from the software model history.” (“icse2025-paper548.pdf”, p. 5) \#a28ae5 \par - “applicability in industrial scenarios.” (“icse2025-paper548.pdf”, p. 5) \#ffd400\\ \mkbibemph{Check if industrial scenarios have been properly evaluated in the paper.} \par - “we need to manually check for semantic correctness of serialized simple change graph” (“icse2025-paper548.pdf”, p. 5) \#a28ae5 \par - “The reason is that our data include a significant amount of natural language text (e.g., comments that will rarely match), the order of the serialized edges and node ids is ambiguous (and there are up to n! possibilities), and application- specific identifiers in models (e.g., user-chosen attribute names) can rarely be exactly completed.” (“icse2025-paper548.pdf”, p. 5) \#ffd400\\ \mkbibemph{Please revise. It is not clear.} \par - “On the supplementary Website a table summarizing related work on model completion is given,” (“icse2025-paper548.pdf”, p. 6) \#ffd400\\ \mkbibemph{Where? I've not found it.} \par - “In this table we list related work together with the proposed method, the evaluation task, evaluation metrics, dataset used, if the approach works on histories or not, and whether any other limitations are present. Among other limitations, this table highlights that most of related work is not applied to histories of models but static snapshots.” (“icse2025-paper548.pdf”, p. 6) \#ffd400\\ \mkbibemph{In any case, this constitutes a significant portion of the paper and should be included within the main body of the document. While the appendix can offer additional details, the evidence demonstrating the novelty of the paper compared to existing research must be presented within the main text.} \par - “semantic retrieval” (“icse2025-paper548.pdf”, p. 6) \#ffd400\\ \mkbibemph{The semantic retrieval aspect of RQ2 seems to overlap with the "retrieval-augmented generation" of RQ1, isn't it?} \par - “modification” (“icse2025-paper548.pdf”, p. 6) \#ff6666 \par - “Revisions” (“icse2025-paper548.pdf”, p. 6) \#ffd400\\ \mkbibemph{What does "Number ore revisions" refer to? Are you covering the case of having different subsequent versions of a given model?} \par - “Tinnes et al. [63]: We used a metamodel that resembles a simple component model So” (“icse2025-paper548.pdf”, p. 7) \#ff6666\\ \mkbibemph{Punctuation.} \par - “samples” (“icse2025-paper548.pdf”, p. 7) \#ffd400\\ \mkbibemph{Samples of what? In the datasets you have models, revisions, changes. What have you considered as samples?} \par - “First, we analyze the correctness of the generated completions:” (“icse2025-paper548.pdf”, p. 7) \#ffd400\\ \mkbibemph{The section needs to be revised with the aim of improving the presentation. It is necessary to clearly describe the evaluation approach and supporting tools. Currently, it is not even clear what are the inputs and with respect to what the correctness of the output is assessed.} \par - “we ensure that every project is included, at least, once, even if it is very small.” (“icse2025-paper548.pdf”, p. 7) \#ffd400\\ \mkbibemph{Included where? What does it mean?} \par - “This leaves us with 210 samples for the SYNTHETIC dataset, 221 samples for the REPAIRVISION dataset, and 200 samples for the INDUSTRY dataset.” (“icse2025-paper548.pdf”, p. 7) \#ffd400\\ \mkbibemph{Such figures need explanation!} \par - “graph isomorphism test” (“icse2025-paper548.pdf”, p. 7) \#5fb236 \par - “isomorphism check of the completed edge(s)” (“icse2025-paper548.pdf”, p. 7) \#ffd400\\ \mkbibemph{Please clarify what you mean with isomorphism check here.} \par - “semantic correctness” (“icse2025-paper548.pdf”, p. 7) \#5fb236 \par - “we sample from the INDUSTRY dataset” (“icse2025-paper548.pdf”, p. 7) \#ffd400\\ \mkbibemph{It seems you have operated different sampling steps without discussing what are the corresponding criteria that have been applied to perform them.} \par - “semantic clusters that we defined.” (“icse2025-paper548.pdf”, p. 7) \#ffd400\\ \mkbibemph{The performed clustering step has not been presented.} \par - “similar change” (“icse2025-paper548.pdf”, p. 7) \#ffd400\\ \mkbibemph{How change similarity is done?} \par - “This leaves 122 samples” (“icse2025-paper548.pdf”, p. 7) \#ffd400\\ \mkbibemph{How you got such number is not clear. Please explain.} \par - “We also investigate how this affects correctness, that is, whether the similarity-based retrieval in RAMC affects the correctness of completions. To this end, we compare semantic sampling with few-shot samples that have been randomly retrieved from the training data. We evaluate this for semantic correctness” (“icse2025-paper548.pdf”, p. 7) \#ffd400\\ \mkbibemph{The paper contains several paragraphs/sentences like this one that I would consider too dense since they try to convey too many things without clearly providing the required details to get the message.} \par - “For this reason, and also to reduce the LLMs usage costs, we perform this analysis only for the INDUSTRY dataset.” (“icse2025-paper548.pdf”, p. 7) \#ffd400\\ \mkbibemph{I understand the concern of reducing the costs due to LLMs usage. However, this represents a threat to validity, since you have focused only on one dataset, and this can represent a relevant bias.} \par - “outlining hypotheses for potential future research directions,” (“icse2025-paper548.pdf”, p. 9) \#5fb236 \par - “Not only are more than 90\% of completions correct w.r.t. the serialization format, but we also find more than 62\% of semantically correct completions for a real-world industrial setting. This indicates that LLMs with retrieval-augmented generation seem to be a promising solution for model completion.” (“icse2025-paper548.pdf”, p. 9) \#a28ae5 \par - “For example, we found cases of functional evolution where the language model is missing (domain) knowledge or requirements, or cases of a refactoring without any relevant few-shot sample.” (“icse2025-paper548.pdf”, p. 10) \#ffd400\\ \mkbibemph{RAG should overcame this issue. I found this a bit strange.} \par - “fine-tuning during our evaluation” (“icse2025-paper548.pdf”, p. 10) \#ffd400\\ \mkbibemph{How the fine-tuning phases compare with the process shown in Fig. 3? This is not clear in the paper.}} } @misc{Icse2025paper797, title = {Icse2025-Paper797}, keywords = {LOGSEQ}, note = {\section{Annotazioni\\ (1/10/2024, 23:07:48)} \par - “Detecting vulnerabilities and curating corresponding datasets is important for cybersecurity” (“icse2025-paper797”, p. 1) \#5fb236 \par - “vulnerabilities are very rare.” (“icse2025-paper797”, p. 1) \#5fb236 \par - “classifier iteratively searches which observations are labeled next, and then repeatedly learns from such labeling feedback” (“icse2025-paper797”, p. 1) \#a28ae5 \par - “We contribute novel simulations that evaluate the properties of active learning under well-defined and repeatable assumptions, concluding on performance, threats, and limitations. We contribute a novel empirical study where we evaluate active learning in the setting of curating pull request reviews on vulnerable code. The method behaves as expected from the simulation. We present a corresponding dataset of 234 vulnerability related reviews, while we only need to label 1247 reviews. This is a percentage of 18.7\% vulnerabilities during manual labeling.” (“icse2025-paper797”, p. 1) \#ffd400\\ \mkbibemph{The abstract is not effective in presenting the contribution of the paper.} \par - “simulations” (“icse2025-paper797”, p. 1) \#2ea8e5 \par - “fails in producing datasets of high quality” (“icse2025-paper797”, p. 1) \#a28ae5 \par - “This affects the classifiers for detection and causes ineffective cybersecurity whenever classifiers are used” (“icse2025-paper797”, p. 1) \#a28ae5 \par - “A dataset is created (e.g., [5]) on which then a classifier is trained (e.g., [6])” (“icse2025-paper797”, p. 1) \#5fb236 \par - “one million commits Liu et al. find that 1462 are vulnerable” (“icse2025-paper797”, p. 1) \#a28ae5 \par - “0.1\% chance is frustrating” (“icse2025-paper797”, p. 1) \#a28ae5 \par - “selectively label instances for a dataset,” (“icse2025-paper797”, p. 1) \#5fb236 \par - “active learning” (“icse2025-paper797”, p. 1) \#a28ae5 \par - “We study this active learning loop in the context of curating pull request reviews on vulnerable code” (“icse2025-paper797”, p. 1) \#a28ae5 \par - “GitHub and collecting 4,191,892 unlabeled pull request reviews.” (“icse2025-paper797”, p. 1) \#5fb236 \par - “We reach a fraction of 18.7\% vulnerabilities labels, which is far beyond 0.1\%, and acceptable for manual labeling” (“icse2025-paper797”, p. 1) \#ffd400\\ \mkbibemph{It depends if 18.7\% is correctly labeled.} \par - “a fine-tuned language model for sequence classification.” (“icse2025-paper797”, p. 1) \#a28ae5 \par - “transfers active learning to vulnerability detection.” (“icse2025-paper797”, p. 1) \#5fb236 \par - “evaluating the method in a simulation and empirical study” (“icse2025-paper797”, p. 2) \#5fb236 \par - “Sec. III starts with the active learning method transferred to curate a dataset of pull request reviews on vulnerable code.” (“icse2025-paper797”, p. 2) \#ffd400\\ \mkbibemph{Why you start section II by referring Section III?} \par - “We then evaluate the method in a simulation study (in Sec. IV) and in an empirical study (in Sec. V). For a methodological underpinning of such hybrid design, we refer to [17] and [18]. We expect both studies to strongly correspond, but they are orthogonal in their conclusions:” (“icse2025-paper797”, p. 2) \#ffd400\\ \mkbibemph{I don't understand why you describe the structure of the next sections at the beginning of the study design section.} \par - “The simulation study stresses active learning under transparent and repeatable assumptions on a simulated scenario in which the method is applied. We simulate different scenarios, varying the predictability of the labels, or the correlation in the data. We repeat the simulation until confidence intervals sufficiently converged to rule out randomness in our conclusions entirely. The simulation is highly parametric. We do not examine all parameters, only those we consider most important. We stress the method to its limit, to detect where it does not work, and what might be a potential threat. We also use it to test our code. We avoid an additional threats and limitation sections in this paper because the simulation study is basically this. Every piece of the simulation is operational and deployed online. It is fully reproducible” (“icse2025-paper797”, p. 2) \#ffd400\\ \mkbibemph{This is vaguous and generic. Any context is given to help the reader understand.} \par - “We expect the method to behave as in the simulation. This is the case. We interpret minor differences in this section and show that the method can curate a real dataset successfully. We also show a more realistic classifier by fine-tuning an existing language model. The empirical study is only reproducible to a certain degree because GitHub is a moving target. For readers that search for confidence intervals or p-values in the empirical study: We don’t have independent measurements of properties of the method because the method is not repeatable in the same way as the simulation. Reporting on this would be fraud.” (“icse2025-paper797”, p. 2) \#ffd400\\ \mkbibemph{See my previous point.} \par - “Active learning is an umbrella term for methods that learn a model (or classifier) iteratively by a (human) feedback mechanism” (“icse2025-paper797”, p. 2) \#a28ae5 \par - “pull request reviews discuss vulnerable code” (“icse2025-paper797”, p. 2) \#a28ae5 \par - “The label if a review is about vulnerable code is the expensive feature that we miss” (“icse2025-paper797”, p. 2) \#a28ae5 \par - “used to train the classifier.” (“icse2025-paper797”, p. 2) \#a28ae5 \par - “The classifier selects the candidates RC,i as a subset of those with unknown labels from the previous iteration RU,i−1.” (“icse2025-paper797”, p. 2) \#a28ae5 \par - “The particular model type used depends on the problem. The upcoming simulations will use a basic feed-forward neural network for binary classification.” (“icse2025-paper797”, p. 2) \#5fb236 \par - “We assume an imbalance in label types” (“icse2025-paper797”, p. 2) \#5fb236 \par - “This will later be vulnerability related (rare-label) vs. non-vulnerability related (majority-label).” (“icse2025-paper797”, p. 2) \#2ea8e5 \par - “elect those with the highest entropy” (“icse2025-paper797”, p. 3) \#ffd400\\ \mkbibemph{I would remove section 2 or revise it with the aim of gain some space and for instance add explanatory examples to discuss the different steps of the process and give some evidence about relevant concepts, like entropy in the context of this work.} \par - “classifier is most certain” (“icse2025-paper797”, p. 3) \#ffd400\\ \mkbibemph{How do you assess that? Do you assign a probability to the outputs of the classifier? This is not clear.} \par - “We borrow a classifier to set the candidates of the first iteration. We use this in our empirical study.” (“icse2025-paper797”, p. 3) \#ffd400\\ \mkbibemph{This is not clear. What are the characteristics of the considered classifier? Can you be more precise on this?} \par - “The simulation study examines properties of our method under well-defined and repeatable assumptions. The section answers RQ3.” (“icse2025-paper797”, p. 3) \#ffd400\\ \mkbibemph{This is a presentation issue. Do you start with answering RQ3?} \par - “We now have synthetic data for our input xs and the corresponding labels ys. Since we simulate, we know both. We will now stress active learning methods that try to recover many rare lables in ys without having exhaustive access to ys. We repeat running the simulation to rule out randomness entirely.” (“icse2025-paper797”, p. 4) \#ffd400\\ \mkbibemph{The section is not properly connected with the problem under investigation. It is not clear how the presented code and discussion is linked to the problem of detecting pull request reviews that discuss vulnerable code.} \par - “Additional Simulation Assumptions There are more assumptions where we omit a discussion based on code. The code can be found in the online material. We present the big picture of such assumptions, itemized here.” (“icse2025-paper797”, p. 4) \#ffd400\\ \mkbibemph{Same problem, see my previous comment. It is not easy to understand the context of the given esentences. COncepts are introduced without any context. For instance "To produce imbalance, we modify the last bias of the network.", which network are the authors referring to here? Many sentences are like this one.} \par - “However, the simulation just makes a statement that overfitting matters. Finding a good configuration may differ from case to case. Cross-validation may help. In our empirical study, we used cross-validation. We assume that also other methods to prevent overfitting will work, like regularization, dropout, or reduction of the model size.” (“icse2025-paper797”, p. 6) \#ffd400\\ \mkbibemph{Again this just another example of paragraphs that do not convey any specific content.} \par - “4,191,892 pull request reviews, from 4,937 distinct repositories, and 492,266 distinct pull requests.” (“icse2025-paper797”, p. 6) \#5fb236 \par - “We also label if bots are writing a review by Bot.” (“icse2025-paper797”, p. 7) \#ffd400\\ \mkbibemph{WHat does it mean?} \par - “while we consider the Vuldev classification as an interesting but premature artifact. We plan to follow up on the Vuldev dataset in future work” (“icse2025-paper797”, p. 7) \#ffd400\\ \mkbibemph{THis is a limitation of the paper, whereas it is supposed to be one of the novel aspects of the proposed approach.} \par - “We are not interested in an exhaustive enumeration of keywords here, since this is the task for the following active learning loop.” (“icse2025-paper797”, p. 7) \#5fb236 \par - “2) Active Learning Classifier: We rely on transfer learning by fine-tuning an existing language model for sequence classification (Roberta-base [24]). The input of the model is the tokenized text of the pull request (up to 64 tokens). The output of the model is the category that we manually assigned during labeling.” (“icse2025-paper797”, p. 7) \#ffd400\\ \mkbibemph{By looking at this paragraph it is not clear if Roberta is used as it is for the classification or how it is used for the active learning process.}} } @misc{Icse2025paper818, title = {Icse2025-Paper818}, keywords = {LOGSEQ}, note = {\section{Annotazioni\\ (2/10/2024, 13:55:52)} \par “selecting appropriate models for new local datasets has become a time-consuming and resourceintensive task.” (“icse2025-paper818”, p. 1) \par “AutoDGR, a novel dual-graph-based model recommendation approach, designed to retrieve the most suitable models by incorporating features across datasets and models.” (“icse2025-paper818”, p. 1) \par “AutoDGR enhances task and model feature representation by employing semantic task representations and model architectures, effectively capturing relations among datasets and models in separate graphs.” (“icse2025-paper818”, p. 1) \par “DNNs excel in tackling complex challenges such as visual comprehension, speech recognition, natural language processing, and more.” (“icse2025-paper818”, p. 1) \par “As a consequence, utilizing pre-trained models (PTMs) has emerged as a more favorable option.” (“icse2025-paper818”, p. 1) \par “difficult for non-experts to select a model that aligns with their specific requirements.” (“icse2025-paper818”, p. 1) \par “The issue of model recommendation has thus become urgent for the rapid and widespread deployment of DNN models, aiming to streamline the process for users and conserve resources.” (“icse2025-paper818”, p. 1) \par “43 types of tasks and hundreds of dataset names available for model selection” (“icse2025-paper818”, p. 1) \par “the task names are often general and domain-agnostic, lacking the ability to reflect personalized user requirements.” (“icse2025-paper818”, p. 1) \par “encodes user task descriptions and multimodal dataset samples and then decodes them to embed model features and provide recommendations.” (“icse2025-paper818”, p. 1) \par “similarities of datasets themselves and ignored the influence of the base architecture of models.” (“icse2025-paper818”, p. 1) \par “based on the semantics of the user task, we would likely choose the second model as the best fit.” (“icse2025-paper818”, p. 1) \par “meta-features to enhance model recommendation” (“icse2025-paper818”, p. 2) \par “First, we propose a semantic graph construction methodology for both datasets and models, utilizing semantic label representation and model architecture information.” (“icse2025-paper818”, p. 2) \par “This involves establishing two types of relations: semantic relations and structural relations.” (“icse2025-paper818”, p. 2) \par “Second” (“icse2025-paper818”, p. 2) \par “we employ separate graph convolutional neural networks with residual connections to embed datasets and models into vector representations according to these two types of relations” (“icse2025-paper818”, p. 2) \par “Third” (“icse2025-paper818”, p. 2) \par “297 image classification tasks with datasets from CIFAR-10” (“icse2025-paper818”, p. 2) \par “two state-of-the-art model recommendation methods.” (“icse2025-paper818”, p. 2) \par “Moreover, the regression version of AutoDGR offers better generality when model hubs are frequently updated.” (“icse2025-paper818”, p. 2) \par “task-based and architecturebased meta-features.” (“icse2025-paper818”, p. 2) \par “predict well-performing models for unseen tasks on frequently changing model hubs.” (“icse2025-paper818”, p. 2) \par “benchmark dataset” (“icse2025-paper818”, p. 2) \par “By collecting metadata from various model hubs, the dataset PTMTorrent [10] is proposed to support model reuse [11].” (“icse2025-paper818”, p. 2) These references are very important for our [[PROJECTS/MOSAICO]] \par “AutoDGR builds a model graph using information from the model hub and does not require extensive computational resource for architecture search, making it a more efficient way to utilize pre-trained models.” (“icse2025-paper818”, p. 2) That's very important. \par “Model recommendation aims to identify the most suitable pre-trained models for a given task from large model hubs” (“icse2025-paper818”, p. 2) \par “predicting the performance of hyperparameters on the target dataset” (“icse2025-paper818”, p. 2) \par “maximize the performance of the model m, denoted as: max m∈M p(m|D). (1)” (“icse2025-paper818”, p. 3) \par “Directly calculating the performance p(m|D) by running all models on the dataset is time-consuming and resourceintensive.” (“icse2025-paper818”, p. 3) \par “estimate the performance using the known historical performance of similar models on similar datasets.” (“icse2025-paper818”, p. 3) \par “Assumption 1: Models with similar parameters, neural architectures, and scales tend to have similar performance on the same dataset.” (“icse2025-paper818”, p. 3) Strong Assumption!!!!! \par “∝” (“icse2025-paper818”, p. 3) Something wrong with horizontal space \par “Assumption 2:” (“icse2025-paper818”, p. 3) This is a strong assumption that limits the applicability of the proposed approach. It is important to see some supporting reference for such an assumption. \par “Models on similar datasets tend to have similar performance.” (“icse2025-paper818”, p. 3) \par “mi on a new dataset D′ using the historical performance of models on other datasets.” (“icse2025-paper818”, p. 3) \par “Di and existing datasets Dj,” (“icse2025-paper818”, p. 3) \par “minimize the difference between the target model mi and the existing models m ̃ on Dj.” (“icse2025-paper818”, p. 3) \par “By formulating the problem as minimizing the differences between datasets and models, we can apply unified representations to user tasks and models to solve the problem effectively.” (“icse2025-paper818”, p. 3) \par “By extracting features from datasets and models effectively, it can construct representation graphs for both datasets and models” (“icse2025-paper818”, p. 3) \par “Then classification and regression methods can be employed on these high-dimensional features to generate the model recommendation results.” (“icse2025-paper818”, p. 3) \par “To obtain richer semantics from task labels and similar datasets, we construct a graph to share task information.” (“icse2025-paper818”, p. 3) \par “hsmp(ti) is the representation of samples with label ti” (“icse2025-paper818”, p. 3) \par “standard sample size” (“icse2025-paper818”, p. 3) \par “number of samples hcount” (“icse2025-paper818”, p. 3) \par “of task labels hcat.” (“icse2025-paper818”, p. 4) \par “hnum(D) = [hsize(D), hcount(D), hcat(D)].” (“icse2025-paper818”, p. 4) \par “Therefore, we use these output feature vectors as the architectural features during feature extraction.” (“icse2025-paper818”, p. 4) \par “averaging along the second dimension to obtain a mean feature vector vm” (“icse2025-paper818”, p. 4) Can this represent a bias? Similarly to the possible unbalance of the dataset that you have addressed for the data encoding. \par “umber of floating-point operations (FLOPs)” (“icse2025-paper818”, p. 5) \par “ground-truth optimal model” (“icse2025-paper818”, p. 6) How has the ground-truth created? How to ensure that the ground-truth refers the optima model of a given task? The description of the process for the ground-truth creation is necessary. \par “Considering the scale of data in different scopes, we choose image classification as the evaluation scope for its mature community and large scale of datasets and models” (“icse2025-paper818”, p. 6) This is an important threat to external validity of the approach. It is not clear if the approach works for tasks that are different from that of image classification.} } @unpublished{Icse2025paper882, title = {Icse2025-Paper882}, url = {https://icse2025.hotcrp.com/doc/icse2025-paper882-revised_manuscript_tracking.pdf}, urldate = {2024-09-23}, keywords = {LOGSEQ} } @online{Icse2025paper882revised_manuscript_tracking, title = {Icse2025-Paper882-Revised\_manuscript\_tracking}, url = {https://icse2025.hotcrp.com/doc/icse2025-paper882-revised_manuscript_tracking.pdf}, urldate = {2024-09-23} } @misc{ICSOFT_2025_73, title = {{{ICSOFT}}\_2025\_73}, keywords = {LOGSEQ}, note = {\section{Annotazioni\\ (1/4/2025, 11:33:27)} \par - “IT Project Documentation Elements” (“ICSOFT\_2025\_73”, p. 1) \#5fb236\\ \mkbibemph{ } \par - “Model Transformation Chai” (“ICSOFT\_2025\_73”, p. 1) \#5fb236\\ \mkbibemph{ } \par - “define requirements, establish goals, and mitigate risks.” (“ICSOFT\_2025\_73”, p. 1) \#a28ae5\\ \mkbibemph{ } \par - “we proposed a model transformation chain to facilitate the generation of IT project artefacts, which are offered as a solution in last 5 years scientific papers” (“ICSOFT\_2025\_73”, p. 1) \#a28ae5\\ \mkbibemph{ } \par - “structured from the transformation chain more detailed representation.” (“ICSOFT\_2025\_73”, p. 1) \#ff6666\\ \mkbibemph{ } \par - “In the early stages of an IT project, well-structured documentation is particularly essential, as it guides stakeholders in defining requirements, establishing goals, and mitigating risks before implementation begins.” (“ICSOFT\_2025\_73”, p. 1) \#5fb236\\ \mkbibemph{ } \par - “Furthermore, IT project documentation must adhere to established standards and best practices to ensure consistency, completeness, and usability across different stakeholders and project phases.” (“ICSOFT\_2025\_73”, p. 1) \#5fb236\\ \mkbibemph{ } \par - “Compliance with standar” (“ICSOFT\_2025\_73”, p. 1) \#ff6666\\ \mkbibemph{ } \par - “By aligning documentation with recognized frameworks, organizations can improve collaboration, ensure regulatory compliance, and streamline project execution.” (“ICSOFT\_2025\_73”, p. 1) \#5fb236\\ \mkbibemph{ } \par - “we introduced a model transformation chain designed to facilitate the generation of IT project artefacts” (“ICSOFT\_2025\_73”, p. 1) \#5fb236\\ \mkbibemph{ } \par - “Solutions published in 133 studies offering IT project elements obtaining with principles of model-driven elements obtaining with principles of model-driven engineering, machine learning and generative engineering, machine learning and generative artificial intelligence, learning and generative artificial intelligence, as well as and generative artificial intelligence, as well as manual practices, artificial intelligence, as well as manual practices, were mapped into artefacts used in the initial stages were mapped into artefacts used in the initial stages of IT project identified in (Nikiforova et al., 2025a). of IT project identified in (Nikiforova et al., 2025a). This transformation chain provides a” (“ICSOFT\_2025\_73”, p. 1) \#ffd400\\ \mkbibemph{what kinds of solutions are you talking about? } \par - “ensuring alignme” (“ICSOFT\_2025\_73”, p. 1) \#ffd400\\ \mkbibemph{how? what does it mean/imply from a technical point of view? } \par - “This paper follows our previous work by focusing on the extraction and structuring of IT project on the extraction and structuring of IT project documentation elements as mapping of them into the documentation elements as mapping of them into the created model transformation chain. We explore the created model transformation chain. We explore the elements of documentation that can be derived, the elements of documentation that can be derived, the methodologies employed to extract relevant methodologies employed to extract relevant information, employed to extract relevant information, and the to extract relevant information, and the benefits extract relevant information, and the benefits of relevant information, and the benefits of integrating this information, and the benefits of integrating this approach into IT project management practices. approach into IT project management practices. Through this study, we aim to provide a systematic” (“ICSOFT\_2025\_73”, p. 2) \#ffd400\\ \mkbibemph{At this stage the goal of the paper is vaguous and general. What are the limitations and challenges that you want to address? } \par - “Agile IT project life cycle and IT Project Management Plan project life cycle and IT Project Management Plan structure, which is used for mapping in the next structure, which is used for mapping in the next section. Section 4 specify the fragments of the sectio” (“ICSOFT\_2025\_73”, p. 2) \#ffd400\\ \mkbibemph{These are suddenly mentioned without any introduction! } \par - “However, challenges such as contextual ambiguity challenges such as contextual ambiguity (AI and such as contextual ambiguity (AI and NLP-based as contextual ambiguity (AI and NLP-based techniques struggle to ambiguity (AI and NLP-based techniques struggle to fully understand context-dependent requirements), fully understand context-dependent requirements), domain-specific variations, and the need for domain-specific variations, and the need for large, labelled datasets make it difficult to achieve labelled datasets make it difficult to achieve fully automated and error-free documentation extraction. automated and error-free documentation extraction. Model-driven development (MDD) offers a” (“ICSOFT\_2025\_73”, p. 2) \#ffd400\\ \mkbibemph{I think there is some recent work employing LLMs for code generation. It is necessary to compare the proposed approach with some baseline (not necessarily based on LLMs) to highlight the novelty of what has been presented here. } \par - “Automated tools such as Papyrus, MagicDraw, and Enterprise Architect facilitate the extraction of textual Enterprise Architect facilitate the extraction of textual documentation from models, reducing manual effort documentation from models, reducing manual effort and improving accuracy. However, different and improving accuracy. However, different documentation accuracy. However, different documentation tools and methodologies different documentation tools and methodologies often lack documentation tools and methodologies often lack seamless seamless integration. Ensuring” (“ICSOFT\_2025\_73”, p. 2) \#ffd400\\ \mkbibemph{These are old statements / tools. } \par - “The primary goal is to capture the essence of the product and its goal is to capture the essence of the product and its intended value, rather than producing exhaustive intended value, rather than producing exhaustive documentation (Pasuksmit et al., 2021). This documentation (Pasuksmit et al., 2021). This approach” (“ICSOFT\_2025\_73”, p. 3) \#5fb236\\ \mkbibemph{ } \par - “Key agile artefacts produced during these early stages include the product vision and roadmap” (“ICSOFT\_2025\_73”, p. 3) \#5fb236\\ \mkbibemph{ } \par - “These artefacts are typically maintained in collaborative tools like Jira or Trello, allowing for continuous refinement and adaptation.” (“ICSOFT\_2025\_73”, p. 3) \#ffd400\\ \mkbibemph{???? } \par - “MODEL TRANSFORMATION CHAIN FOR IT PROJECT INITIATION ARTEFACTS” (“ICSOFT\_2025\_73”, p. 4) \#5fb236\\ \mkbibemph{ } \par - “In this article, we take and examine in more detail a fragment of this large chain that related to project documentation artefacts according to the IT Project Management Plan.” (“ICSOFT\_2025\_73”, p. 4) \#5fb236\\ \mkbibemph{ } \par - “chain fragment for Scope Management” (“ICSOFT\_2025\_73”, p. 4) \#5fb236\\ \mkbibemph{ } \par - “The rest of subsections demonstrates only transformation chain types” (“ICSOFT\_2025\_73”, p. 4) \#5fb236\\ \mkbibemph{ } \par - “This transformation is performed manually” (“ICSOFT\_2025\_73”, p. 6) \#5fb236\\ \mkbibemph{ } \par - “automation should be combined with manual work of stakeholders, where they select the corresponding use cases to be the user stories for implementation and prioritize them” (“ICSOFT\_2025\_73”, p. 6) \#5fb236\\ \mkbibemph{ } \par - “Obtaining consistent and well-structured IT project documentation is a continuous challenge for project managers and developers’ team due to the complexity and evolving nature of project artefacts.” (“ICSOFT\_2025\_73”, p. 7) \#ffd400\\ \mkbibemph{Indeed this is a relevant and difficult problem. However, the paper touches the problem in a generic an vaguous manner. Further than exploring some stepts of the required model refinements and transformation process, it's not clear e.g. how the mentioned transformations are actually implemented, orchestrated, chained. Chaining model transformations is critical problem and there are existing work that authors have not mentioned at all. }} } @article{IEEECOMPUTINGEDGE2022, title = {{{IEEE COMPUTING EDGE}}}, date = {2022-11}, journaltitle = {IEEE Softw.}, volume = {39}, number = {6}, pages = {C2-C2}, issn = {0740-7459, 1937-4194}, doi = {10.1109/MS.2022.3210313}, langid = {english}, keywords = {LOGSEQ} } @online{IEEESoftwareBlog, title = {{{IEEE Software Blog}}: {{Autonomous Computing Systems}}: {{The Convergence}} of {{Control Theory}} and {{Computing Systems}}}, url = {http://blog.ieeesoftware.org/2019/07/autonomous-computing-systems.html}, urldate = {2020-10-05} } @article{Ige20237, title = {Adversarial Sampling for Fairness Testing in Deep Neural Network}, author = {Ige, T. and Marfo, W. and Tonkinson, J. and Adewale, S. and Matti, B.H.}, date = {2023}, journaltitle = {Int. J. Adv. Comput. Sci. Appl.}, volume = {14}, number = {2}, pages = {7--13}, publisher = {{Science and Information Organization}}, issn = {2158107X}, doi = {10.14569/IJACSA.2023.0140202}, abstract = {In this research, we focus on the usage of adversarial sampling to test for the fairness in the prediction of deep neural network model across different classes of image in a given dataset. While several framework had been proposed to ensure robustness of machine learning model against adversarial attack, some of which includes adversarial training algorithm. There is still the pitfall that adversarial training algorithm tends to cause disparity in accuracy and robustness among different group. Our research is aimed at using adversarial sampling to test for fairness in the prediction of deep neural network model across different classes or categories of image in a given dataset. We successfully demonstrated a new method of ensuring fairness across various group of input in deep neural network classifier. We trained our neural network model on the original image, and without training our model on the perturbed or attacked image. When we feed the adversarial samplings to our model, it was able to predict the original category/ class of the image the adversarial sample belongs to. We also introduced and used the separation of concern concept from software engineering whereby there is an additional standalone filter layer that filters perturbed image by heavily removing the noise or attack before automatically passing it to the network for classification, we were able to have accuracy of 93.3\%. Cifar-10 dataset have ten categories of dataset, and so, in order to account for fairness, we applied our hypothesis across each categories of dataset and were able to get a consistent result and accuracy © 2023, International Journal of Advanced Computer Science and Applications.All Rights Reserved.}, author_keywords = {adversarial defense; Adversarial machine learning, adversarial attack; adversarial sampling; deep neural network; fairness testing; machine learning fairness}, document_type = {Article}, source = {Scopus}, keywords = {adversarial attack,Adversarial defense,Adversarial machine learning,Adversarial sampling,Deep neural networks,Different class,Fairness testing,Forecasting,Learning algorithms,Learning systems,Machine learning fairness,Machine-learning,Neural network model,Neural network models,Software engineering,Statistical tests,Training algorithms}, note = {cited By 0 \par TL;DR \par This research focuses on the usage of adversarial sampling to test for the fairness in the prediction of deep neural network model across different classes of image in a given dataset and successfully demonstrated a new method of ensuring fairness across various group of input inDeep neural network classifier.} } @article{iglesiaMAPEKFormalTemplates2015, title = {{{MAPE-K}} Formal Templates to Rigorously Design Behaviors for Self-Adaptive Systems}, author = {Iglesia, Didac Gil De La and Weyns, Danny}, date = {2015}, journaltitle = {ACM Trans. Auton. Adapt. Syst. TAAS}, volume = {10}, number = {3}, pages = {15}, url = {http://dl.acm.org/citation.cfm?id=2724719}, urldate = {2016-09-19}, note = {TL;DR \par A set of formally specified MAPE-K templates that encode design expertise for a family of self-adaptive systems and demonstrate the reusability of the formal templates are performed in which final-year Masters students used the templates to design different self- Adaptive systems.} } @article{ilahiChallengesCountermeasuresAdversarial2020, title = {Challenges and {{Countermeasures}} for {{Adversarial Attacks}} on {{Deep Reinforcement Learning}}}, author = {Ilahi, Inaam and Usama, Muhammad and Qadir, Junaid and Janjua, Muhammad Umar and Al-Fuqaha, Ala and Hoang, Dinh Thai and Niyato, Dusit}, date = {2020-01-27}, journaltitle = {ArXiv200109684 Cs}, eprint = {2001.09684}, eprinttype = {arXiv}, eprintclass = {cs}, url = {http://arxiv.org/abs/2001.09684}, urldate = {2021-04-02}, abstract = {Deep Reinforcement Learning (DRL) has numerous applications in the real world thanks to its outstanding ability in quickly adapting to the surrounding environments. Despite its great advantages, DRL is susceptible to adversarial attacks, which precludes its use in real-life critical systems and applications (e.g., smart grids, traffic controls, and autonomous vehicles) unless its vulnerabilities are addressed and mitigated. Thus, this paper provides a comprehensive survey that discusses emerging attacks in DRL-based systems and the potential countermeasures to defend against these attacks. We first cover some fundamental backgrounds about DRL and present emerging adversarial attacks on machine learning techniques. We then investigate more details of the vulnerabilities that the adversary can exploit to attack DRL along with the state-of-the-art countermeasures to prevent such attacks. Finally, we highlight open issues and research challenges for developing solutions to deal with attacks for DRL-based intelligent systems.}, keywords = {Computer Science - Artificial Intelligence,Computer Science - Cryptography and Security,Computer Science - Machine Learning}, note = {TL;DR \par This work investigates the vulnerabilities that an adversary can exploit to attack DRL along with state-of-the-art countermeasures to prevent such attacks and highlights open issues and research challenges for developing solutions to deal with attacks on DRL-based intelligent systems.} } @article{imamDataModelingGuidelines2018, title = {Data {{Modeling Guidelines}} for {{NoSQL Document-Store Databases}}}, author = {Imam, Abdullahi Abubakar and Basri, Shuib and Ahmad, Rohiza and Watada, Junzo and T., Maria and Ahmad, Malek}, date = {2018}, journaltitle = {ijacsa}, volume = {9}, number = {10}, issn = {21565570, 2158107X}, doi = {10.14569/IJACSA.2018.091066}, abstract = {Good database design is key to high data availability and consistency in traditional databases, and numerous techniques exist to abet designers in modeling schemas appropriately. These schemas are strictly enforced by traditional database engines. However, with the emergence of schema-free databases (NoSQL) coupled with voluminous and highly diversified datasets (big data), such aid becomes even more important as schemas in NoSQL are enforced by application developers, which requires a high level of competence. Precisely, existing modeling techniques and guides used in traditional databases are insufficient for bigdata storage settings. As a synthesis, new modeling guidelines for NoSQL document-store databases are posed. These guidelines cut across both logical and physical stages of database designs. Each is developed based on solid empirical insights, yet they are prepared to be intuitive to developers and practitioners. To realize this goal, we employ an exploratory approach to the investigation of techniques, empirical methods and expert consultations. We analyze how industry experts prioritize requirements and analyze the relationships between datasets on the one hand and error prospects and awareness on the other hand. Few proprietary guidelines were extracted from a heuristic evaluation of 5 NoSQL databases. In this regard, the proposed guidelines have great potential to function as an imperative instrument of knowledge transfer from academia to NoSQL database modeling practices.}, langid = {english}, note = {TL;DR \par New modeling guidelines for NoSQL document-store databases are posed and have great potential to function as an imperative instrument of knowledge transfer from academia to NoSQL database modeling practices.} } @online{Implementation, title = {𝗢𝗽𝗲𝗻 𝗦𝗼𝘂𝗿𝗰𝗲 Implementation of 𝗗𝗲𝗲𝗽 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗔𝗴𝗲𝗻𝘁 from Scratch}, url = {https://www.linkedin.com/posts/aurimas-griciunas_llm-ai-machinelearning-activity-7316788862557552641-7CCh/?utm_source=share&utm_medium=member_desktop&rcm=ACoAAACaoXoBMvdj060syLix4vC3rwOgjAE9MpU}, urldate = {2025-04-14}, langid = {english}, keywords = {/unread,P1} } @online{incLowCodePlatformRapidly2020, title = {Low-{{Code Platform}}: {{Rapidly Build Enterprise-Grade Analytics Apps}}}, shorttitle = {Low-{{Code Platform}}}, author = {Inc, Gramener}, date = {2020-10-13T15:41:09+00:00}, url = {https://blog.gramener.com/low-code-platform-for-enterprise-analytics-applications/}, urldate = {2021-03-18}, abstract = {This article talks about the importance of low-code platform in the analytics world and Low code vs traditional application development.}, langid = {american}, organization = {Gramener Blog} } @article{indamutsaLowCodeDevelopmentEnvironment2021, ids = {indamutsaLowCodeDevelopmentEnvironment2021a,indamutsaLowCodeDevelopmentEnvironment2021b,indamutsaLowCodeDevelopmentEnvironment2021c}, title = {A {{Low-Code Development Environment}} to {{Orchestrate Model Management Services}}}, author = {Indamutsa, A. and Di Ruscio, D. and Pierantonio, A.}, editor = {Dolgui A., Bernard A., Lemoine D., von Cieminski G., Romero D.}, date = {2021}, journaltitle = {Adv. Prod. Manag. Syst. Artif. Intell. Sustain. Resilient Prod. Syst. - IFIP WG 57 Int. Conf. APMS 2021 Nantes Fr. Sept. 5-9 2021 Proc. Part I}, series = {{{IFIP Advances}} in {{Information}} and {{Communication Technology}}}, volume = {630 IFIP}, pages = {342--350}, publisher = {{Springer Science and Business Media Deutschland GmbH}}, issn = {18684238}, doi = {10.1007/978-3-030-85874-2_36}, abstract = {The current digital transformation in production systems has positioned model-driven engineering (MDE) as a promising development solution to leverage models as first-class entities and support complex systems’ development through dedicated abstractions. Models are specified through domain-specific languages and consumed by dedicated model management services, which implement automation and analysis services. Achieving complex model-driven tasks that involve several model management services and multiple model repositories can be a difficult and error-prone task. For instance, modelers have to identify the proper atomic operations among available services, connect to remote model repositories, and figure out their composition to satisfy the final goal. Different composition proposals have been introduced in MDE even though a satisfactory solution is still missing. In this paper, we propose a low-code development environment to support citizen developers to plan, organize, specify and execute model-management workflows underpinning the development of complex systems. Thus, developers are relieved from managing low-level details, e.g., related to the discovery, orchestration, and integration of the needed model management services. © 2021, IFIP International Federation for Information Processing.}, isbn = {9783030858735}, langid = {english}, keywords = {Artificial intelligence,Development solutions,Digital transformation,Domain specific languages,Environmental management,Industrial management,Model repositories,Model-driven Engineering,Multiple-modeling,Problem oriented languages,Production system,Satisfactory solutions}, note = {cited By 1 \par cited By 1 \par cited By 1 \par cited By 4} } @online{IndustrialCyberPhysicalSystems, title = {Industrial {{Cyber-Physical Systems Center}} ({{iCyPhy}})}, url = {http://www.icyphy.org/}, urldate = {2016-01-26} } @online{Industry40Eclipse, title = {Industry 4.0 with {{Eclipse Kura}} | {{EclipseCon Europe}} 2016}, url = {https://www.eclipsecon.org/europe2016/session/industry-40-eclipse-kura}, urldate = {2016-09-27} } @article{INFSOFD2300931_R1_reviewerpdf, title = {{{INFSOF-D-23-00931}}\_{{R1}}\_reviewer.Pdf} } @misc{INFSOFD2300931_R2_reviewer, title = {{{INFSOF-D-23-00931}}\_{{R2}}\_reviewer}, langid = {english} } @online{IntelligenzaArtificialeSegnali, title = {Intelligenza Artificiale, i Segnali Di Un Suo Secondo Inverno}, url = {https://lanuovabq.it/it/intelligenza-artificiale-i-segnali-di-un-suo-secondo-inverno}, urldate = {2025-04-13}, abstract = {Oggi ci sono segnali secondo cui l’intelligenza artificiale potrebbe entrare in un suo secondo “inverno” di sviluppo. Un problema cruciale è la scarsità di dati di alta qualità. E prima ancora l’IA è incapace di astrarre, a differenza dell’intelletto umano.}, langid = {english}, keywords = {/unread} } @online{InternetThingsCS, title = {Internet of {{Things}} [{{CS Open CourseWare}}]}, url = {http://ocw.cs.pub.ro/courses/iot}, urldate = {2016-09-11} } @online{InternetThingsRoad, title = {The {{Internet}} of {{Things}} Is on the {{Road}} to {{Autonomous Driving}}}, url = {http://www.intel.com/content/www/us/en/internet-of-things/infographics/iot-autonomous-driving-infographic.html}, urldate = {2016-09-03} } @online{Intocpsaudk, title = {Into-Cps.Au.Dk}, url = {http://into-cps.au.dk/}, urldate = {2016-02-09} } @online{IntroductionControlSystems, title = {An {{Introduction To Control Systems}}}, url = {https://www.facstaff.bucknell.edu/mastascu/eControlHTML/Intro/Intro1.html}, urldate = {2016-11-01}, note = {TL;DR \par This significantly revised edition presents a broad introduction to Control Systems and balances new, modern methods with the more classical, and includes an introductory guide to some more recent developments, namely fuzzy logic control and neural networks.} } @online{IntroductionParallelComputing, title = {Introduction to {{Parallel Computing}}}, url = {https://computing.llnl.gov/tutorials/parallel_comp/#Whatis}, urldate = {2017-02-23}, note = {TL;DR \par Performance and Scalability of Parallel Systems, General Issues in Mapping Systolic Systems Onto Parallel Computers, and Speedup Anomalies in Parallel Search Algorithms.} } @online{IntroduzioneScuolaCultura, title = {Introduzione - {{Scuola}} e Cultura}, url = {https://www.scuola-e-cultura.it/manuale-della-cultura/introduzione.htm}, urldate = {2022-07-16} } @online{IoTSecurityAction, title = {{{IoT Security}} in Action! | {{EclipseCon Europe}} 2016}, url = {https://www.eclipsecon.org/europe2016/session/iot-security-action}, urldate = {2016-09-27} } @online{IoTVsM2M, title = {{{IoT}} vs. {{M2M}}, {{CPS}}, {{WoT}}....: {{Are}} These Terms Synonyms? | {{John Soldatos}} | {{Pulse}} | {{LinkedIn}}}, url = {https://www.linkedin.com/pulse/iot-vs-m2m-cps-wot-terms-synonyms-john-soldatos}, urldate = {2016-08-21} } @article{Iovino2012OnTI, title = {On the Impact Significance of Metamodel Evolution in {{MDE}}}, author = {Iovino, Ludovico and Pierantonio, Alfonso and Malavolta, Ivano}, date = {2012}, journaltitle = {J. Object Technol.}, volume = {11}, pages = {3: 1-33}, note = {TL;DR \par The problem of identifying, predicting and evaluating the significance of the metamodel change impact over the existing artifacts is discussed and the approach can be considered as preparatory to any systematic adaptation process.} } @article{IovinoBRH20, title = {Model {{Repair}} with {{Quality-Based Reinforcement Learning}}.}, author = {Ludovico, Iovino and Barriga, Angela and Rutle, Adrian and Heldal, Rogardt}, date = {2020}, journaltitle = {JOT}, volume = {19}, number = {2}, pages = {17:1}, issn = {1660-1769}, doi = {10.5381/jot.2020.19.2.a17}, abstract = {Domain modeling is a core activity in Model-Driven Engineering, and these models must be correct. A large number of artifacts may be constructed on top of these domain models, such as instance models, transformations, and editors. Similar to any other software artifact, domain models are subject to the introduction of errors during the modeling process. There are a number of existing tools that reduce the burden of manually dealing with correctness issues in models. Although various approaches have been proposed to support the quality assessment of modeling artifacts in the past decade, the quality of the automatically repaired models has not been the focus of repairing processes. In this paper, we propose the integration of an automatic evaluation of domain models based on a quality model with a framework for personalized and automatic model repair. The framework uses reinforcement learning to find the best sequence of actions for repairing a broken model.}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found,GOAL-Model\_Repair,notion,TECHNIQUE\_ReinforcementLearning}, note = {TL;DR \par This paper proposes the integration of an automatic evaluation of domain models based on a quality model with a framework for personalized and automatic model repair, which uses reinforcement learning to find the best sequence of actions for repairing a broken model.}, timestamp = {Sat, 09 Apr 2022 12:28:30 +0200} } @online{IscrivitiLinkedIn, title = {Iscriviti | {{LinkedIn}}}, url = {https://www.linkedin.com/}, urldate = {2025-04-21}, abstract = {Oltre 500 milioni di utenti | Gestisci la tua identità professionale. Crea la tua rete professionale e interagisci con essa. Accedi al sapere, a informazioni importanti e a tante opportunità.} } @article{ISINKAYE2015261, title = {Recommendation Systems: {{Principles}}, Methods and Evaluation}, author = {Isinkaye, F.O. and Folajimi, Y.O. and Ojokoh, B.A.}, date = {2015}, journaltitle = {Egypt. Inform. J.}, volume = {16}, number = {3}, pages = {261--273}, issn = {1110-8665}, url = {http://www.sciencedirect.com/science/article/pii/S1110866515000341}, nodoi = {https://doi.org/10.1016/j.eij.2015.06.005}, nonote = {tex.nodoi: https://doi.org/10.1016/j.eij.2015.06.005}, keywords = {Collaborative filtering,Content-based filtering,Evaluation,Hybrid filtering technique,recommendation systems,Recommendation systems} } @inproceedings{islamLeveragingAutomatedSentiment2017, title = {Leveraging {{Automated Sentiment Analysis}} in {{Software Engineering}}}, author = {Islam, Md Rakibul and Zibran, Minhaz F.}, date = {2017-05}, pages = {203--214}, publisher = {IEEE}, doi = {10.1109/MSR.2017.9}, isbn = {978-1-5386-1544-7}, note = {TL;DR \par SentiStrength-SE, a tool for improved sentiment analysis especially designed for application in the software engineering domain, achieves 73.85\% precision and 85\% recall, which are significantly higher than a state-of-the-art sentiment analysis tool the authors compare with.} } @article{islamSemanticTextSimilarity2008, title = {Semantic Text Similarity Using Corpus-Based Word Similarity and String Similarity}, author = {Islam, Aminul and Inkpen, Diana}, date = {2008-07}, journaltitle = {ACM Trans. Knowl. Discov. Data}, volume = {2}, number = {2}, pages = {10:1-10:25}, publisher = {ACM}, address = {New York, NY, USA}, issn = {1556-4681}, url = {http://doi.acm.org/10.1145/1376815.1376819}, acmid = {1376819}, articleno = {10}, issue_date = {July 2008}, nodoi = {10.1145/1376815.1376819}, numpages = {25}, keywords = {corpus-based measures,Semantic similarity of words,similarity of short texts} } @article{izasaInconsistencyManagementSchema, title = {Inconsistency Management in Schema Evolution and Data Base Migration}, author = {Izasa, Fernando Arango}, abstract = {Automation of conceptual schema evolution process should allow database management systems to smoothly perform schema transformation and data migration. However, current approaches lack the ability to perform well in all circumstances. They can’t manage, for example, schema restrictions strengthening without lost of information or the aid of an external migration tool, which, besides of being costly, have to assume database correctness responsibility. In this paper we present a novelty approach to schema evolution automation that performs well in all circumstances. It allows the schema transformation and data migration to be performed in the realm of day by day system use without lost of information or the use of an external migration tool. The approach key idea is to make database management system, not only capable of performing schema changes and data migration, but capable of managing schema evolution/data migration inconsistencies. Instead of pretending an abrupt elimination of evolution related inconsistencies, our approach opts for predict, permit, detect and gradually eliminate them. The approach elements are stated in the framework of the formal approach to the object oriented software development, OASIS. OASIS allows us to rigorously specify and to automatically obtain a software ambient prototype oriented to the creation, animation, evolution, versioning and versions interoperation, of OASIS conceptual schemas, that has, in addition, inconsistencies management capabilities.}, langid = {english} } @article{jaccardDistributionFloraAlpine1912, title = {The Distribution of the Flora in the Alpine Zone}, author = {Jaccard, Paul}, date = {1912}, journaltitle = {New Phytol.}, volume = {11}, number = {2}, pages = {37--50}, nodoi = {10.1111/j.1469-8137.1912.tb05611.x} } @article{Jackson202090, title = {Neuroevolutionary Approach to Metamodeling of Production-Inventory Systems with Lost-Sales and Markovian Demand}, author = {Jackson, I.}, date = {2020}, journaltitle = {Lect. Notes Netw. Syst.}, volume = {117}, pages = {90--99}, publisher = {Springer}, issn = {23673370}, doi = {10.1007/978-3-030-44610-9_10}, abbrev_source_title = {Lect. Notes Networks Syst.}, affiliation = {Transport and Telecommunication Institute (TTI), Lomonosova iela 1, Riga, Latvia}, correspondence_address1 = {Jackson, I.; Transport and Telecommunication Institute (TTI), Lomonosova iela 1, Latvia; email: jackson.i@tsi.lv}, document_type = {Book Chapter}, langid = {english}, source = {Scopus}, note = {cited By 0 \par TL;DR \par The proposed framework incorporates multilayer perceptron and genetic algorithm and demonstrates the application of this framework to metamodeling of multiproduct production-inventory system with lost-sales and Markov-modulated demand.} } @article{Jackson202184, title = {Neuroevolutionary Approach to Metamodel-Based Optimization in Production and Logistics}, author = {Jackson, I.}, editor = {Kabashkin I., Yatskiv I., Prentkovskis O.}, date = {2021}, journaltitle = {Lect. Notes Netw. Syst.}, volume = {195}, pages = {84--93}, publisher = {{Springer Science and Business Media Deutschland GmbH}}, issn = {23673370}, doi = {10.1007/978-3-030-68476-1_8}, abbrev_source_title = {Lect. Notes Networks Syst.}, affiliation = {Transport and Telecommunication Institute (TTI), Lomonosova Iela 1, Riga, Latvia}, correspondence_address1 = {Jackson, I.; Transport and Telecommunication Institute (TTI), Lomonosova Iela 1, Latvia; email: jackson.i@tsi.lv}, document_type = {Conference Paper}, isbn = {9783030684754}, langid = {english}, source = {Scopus}, note = {cited By 1 \par TL;DR \par Whether it is feasible and efficient to combine artificial neural network with genetic algorithm for metamodeling automation of logistic and production systems and the possibility of using the proposed approach to derive optimal control parameters is discussed.} } @article{jacksonAutomaticallyReasoningMetamodeling2015, title = {Automatically Reasoning about Metamodeling}, author = {Jackson, Ethan K. and Levendovszky, Tihamer and Balasubramanian, Daniel}, date = {2015-02}, journaltitle = {Softw. Syst. Model.}, volume = {14}, number = {1}, pages = {271--285}, issn = {1619-1366, 1619-1374}, doi = {10.1007/s10270-013-0315-y}, langid = {english}, note = {TL;DR \par This paper presents one approach to this problem: metamodeling frameworks are specified modularly using algebraic data types and constraint logic programming (CLP).} } @article{jainDataClusteringReview1999, ids = {jain1999data}, title = {Data Clustering: A Review}, shorttitle = {Data Clustering}, author = {Jain, Anil K. and Murty, M. Narasimha and Flynn, Patrick J.}, date = {1999}, journaltitle = {ACM Comput. Surv. CSUR}, volume = {31}, number = {3}, pages = {264--323}, publisher = {Acm}, url = {http://dl.acm.org/citation.cfm?id=331504}, urldate = {2015-04-24}, note = {TL;DR \par An overview of pattern clustering methods from a statistical pattern recognition perspective is presented, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners.} } @article{Jamei2015133, title = {Security Breach Possibility with {{RSS-Based}} Localization of Smart Meters Incorporating Maximum Likelihood Estimator}, author = {Jamei, M. and Sarwat, A.I. and Iyengar, S.S. and Kaleem, F.}, date = {2015}, journaltitle = {Adv. Intell. Syst. Comput.}, volume = {1089}, pages = {133--139}, publisher = {Springer Verlag}, issn = {21945357}, doi = {10.1007/978-3-319-08422-0_20}, abstract = {Deployment of smart meters has been greatly increased over the recent years. Most of the installed smart meters have been equipped with Advanced Metering Infrastructure (AMI) which enables a bidirectional wireless communication to gather the usage data from gas, electricity and water meters. The insecure wireless channel used by AMI meters jeopardizes the privacy of costumers and brings up cybersecurity issues since it allows hackers to monitor the energy usage data from different houses. To show the penetrability of the system, Received Signal Strength (RSS) - based localization of smart meters incorporating Maximum Likelihood (ML) estimator has been proposed in this paper. By decoding the received signal from a smart meter, one can localize the unoccupied houses or track the people's daily routines. The effectiveness of the proposed ML location estimator has been examined through MATLAB simulation, under the assumption of a log-normal path loss model and Frequency Shift Keying (FSK) modulation and demodulation. Particle Swarm Optimization (PSO) has been used to find the ML estimation. Finally, the effect of the variance, the number of the sensors and the path loss exponent has been studied on the average Miss Distance Error (MDE). © Springer International Publishing Switzerland 2015.}, document_type = {Conference Paper}, isbn = {9783319084213}, source = {Scopus}, note = {cited By 2 \par TL;DR \par To show the penetrability of the system, Received Signal Strength (RSS) - based localization of smart meters incorporating Maximum Likelihood (ML) estimator has been proposed in this paper.} } @inproceedings{Jamshidi201939, title = {Machine Learning Meets Quantitative Planning: {{Enabling}} Self-Adaptation in Autonomous Robots}, author = {Jamshidi, P. and Camara, J. and Schmerl, B. and Kaestner, C. and Garlan, D.}, date = {2019}, series = {{{ICSE Workshop}} on {{Software Engineering}} for {{Adaptive}} and {{Self-Managing Systems}}}, volume = {2019-May}, pages = {39--50}, publisher = {IEEE Computer Society}, issn = {21572305}, doi = {10.1109/SEAMS.2019.00015}, abstract = {Modern cyber-physical systems (e.g., robotics systems) are typically composed of physical and software components, the characteristics of which are likely to change over time. Assumptions about parts of the system made at design time may not hold at run time, especially when a system is deployed for long periods (e.g., over decades). Self-adaptation is designed to find reconfigurations of systems to handle such run-time inconsistencies. Planners can be used to find and enact optimal reconfigurations in such an evolving context. However, for systems that are highly configurable, such planning becomes intractable due to the size of the adaptation space. To overcome this challenge, in this paper we explore an approach that (a) uses machine learning to find Pareto-optimal configurations without needing to explore every configuration and (b) restricts the search space to such configurations to make planning tractable. We explore this in the context of robot missions that need to consider task timeliness and energy consumption. An independent evaluation shows that our approach results in high-quality adaptation plans in uncertain and adversarial environments. © 2019 IEEE.}, art_number = {8787014}, author_keywords = {artificial intelligence; Machine learning; quantitative planning; robotics systems; self-adaptive systems}, document_type = {Conference Paper}, isbn = {978-1-7281-3368-3}, source = {Scopus}, keywords = {Adaptive systems,Adversarial environments,Artificial intelligence,Embedded systems,Energy utilization,Learning systems,Machine learning,Optimal reconfigurations,Pareto principle,Pareto-optimal configurations,Robot programming,Robotics,Robotics systems,Search spaces,Self adaptation,Self-adaptive system,Software component,Software engineering}, note = {cited By 37} } @article{Javidan2019602, title = {Variance-Based Global Sensitivity Analysis for Fuzzy Random Structural Systems}, author = {Javidan, M.M. and Kim, J.}, date = {2019}, journaltitle = {Comput.-Aided Civ. Infrastruct. Eng.}, volume = {34}, number = {7}, pages = {602--615}, publisher = {Blackwell Publishing Inc.}, issn = {10939687}, doi = {10.1111/mice.12436}, abbrev_source_title = {Comput.-Aided Civ. Infrastruct. Eng.}, affiliation = {Department of Civil \& Architectural Engineering, Sungkyunkwan University, Suwon, South Korea}, coden = {CCIEF}, correspondence_address1 = {Kim, J.; Department of Civil \& Architectural Engineering, South Korea; email: jkim12@skku.edu}, document_type = {Article}, langid = {english}, source = {Scopus}, note = {cited By 20} } @article{jeanjeanIDECodeReifying2021, title = {{{IDE}} as {{Code}}: {{Reifying Language Protocols}} as {{First-Class Citizens}}}, author = {Jeanjean, Pierre and Combemale, Benoit and Barais, Olivier}, date = {2021}, pages = {6}, abstract = {To cope with the ever-growing number of programming languages, manufacturers of Integrated Development Environments (IDE) have recently defined protocols as a way to use and share multiple language services (e.g., auto-completion, type checker, language runtime) in language-agnostic environments (i.e., the user interface provided by the IDE): the most notable are the Language Server Protocol (LSP) for textual editors, and the Debug Adapter Protocol (DAP) for debugging facilities. These protocols rely on a proper specification of the services that are commonly found in the tool support of general-purpose languages, and define a fixed set of capabilities to offer in the IDE. However, new languages appear regularly offering unique constructs (e.g., Domain-Specific Languages), and supported by dedicated services to be offered as new capabilities in IDEs. This trend leads to the multiplication of new protocols, hard to combine and possibly incompatible (e.g., overlap, different technological stacks). Beyond the proposition of specific protocols, the goal of this paper is to stress out the importance of being able to specify language protocols and to offer IDEs to be configured with such protocol specifications. We present our vision by discussing the main concepts for the specification of language protocols, and an approach that can make use of these specifications in order to deploy an IDE as a set of coordinated, individually deployed, language capabilities (e.g., microservice choreography). IDEs went from directly supporting languages to protocols, and we envision in this paper the next step: IDE as Code, where language protocols are created or inferred on demand and serve as support of an adaptation loop taking in charge of the (re)configuration of the IDE.}, langid = {english} } @article{jeffreyHybridMethodologyAnomaly2024, title = {A Hybrid Methodology for Anomaly Detection in {{Cyber}}–{{Physical Systems}}}, author = {Jeffrey, Nicholas and Tan, Qing and Villar, José R.}, date = {2024-02-01}, journaltitle = {Neurocomputing}, volume = {568}, pages = {127068}, issn = {0925-2312}, doi = {10.1016/j.neucom.2023.127068}, abstract = {The rapid adoption of Industry 4.0 has seen Information Technology (IT) networks increasingly merged with Operational Technology (OT) networks, which have traditionally been isolated on air-gapped and fully trusted networks. This increased attack surface has resulted in compromises of Cyber–Physical Systems (CPS) with significant economic and life safety consequences. This paper proposes a hybrid model of anomaly detection of security threats to CPS by blending the signature-based and threshold-based Intrusion Detection Systems (IDS) commonly used in IT networks, with a Machine Learning (ML) model designed to detect behaviour-based anomalies in OT networks. This hybrid model achieves more rapid detection of known threats through signature-based and threshold-based detection strategies, and more accurate detection of unknown threats via behaviour-based anomaly detection using ML algorithms.}, keywords = {Cyber–Physical Systems,Machine learning,Security threats} } @inproceedings{jehSimRankMeasureStructuralcontext2002, title = {{{SimRank}}: {{A}} Measure of Structural-Context Similarity}, booktitle = {Proc. {{Eighth ACM SIGKDD Int}}. {{Conf}}. {{Knowl}}. {{Discov}}. {{Data Min}}.}, author = {Jeh, Glen and Widom, Jennifer}, date = {2002}, series = {{{KDD}} '02}, pages = {538--543}, publisher = {ACM}, location = {New York, NY, USA}, url = {http://doi.acm.org/10.1145/775047.775126}, acmid = {775126}, isbn = {1-58113-567-X}, nodoi = {10.1145/775047.775126}, numpages = {6}, note = {TL;DR \par A complementary approach, applicable in any domain with object-to-object relationships, that measures similarity of the structural context in which objects occur, based on their relationships with other objects is proposed.} } @article{Jeon2022, title = {Artificial Intelligence for Physical-Layer Design of {{MIMO}} Communications with One-Bit {{ADCs}}}, author = {Jeon, Y. and Kim, D. and Hong, S. and Lee, N. and Heath, R.W.}, date = {2022}, journaltitle = {IEEE Commun. Mag.}, pages = {1--7}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {01636804}, doi = {10.1109/MCOM.007.2200002}, abstract = {Wireless systems continue to go towards higher carrier frequencies, including terahertz bands, to take advantage of higher bandwidth channels. At the same time, antenna arrays remain important with continued increases in array elements. Yet, the power consumption of RF and digital circuits can increase proportionally to both the amount of signal bandwidth and the number of antennas. The use of one-bit analog-to-digital converters (ADCs) at the receiver is a cost-and power-efficient solution for wideband and/or massive antenna wireless systems. The nonlinearity of one-bit received signals brings challenges in physical-layer design at the receiver. At the same time, the binary nature of these signals opens new opportunities for artificial intelligence (AI) based physical-layer (PHY) design. This article covers recent progress in incorporating AI into the design of classical PHY techniques and emerging studies on establishing AIinspired frameworks that fundamentally replace classical model-driven techniques with data-driven AI techniques. It concludes with a discussion, including practical challenges and future research directions. IEEE}, coden = {ICOMD}, document_type = {Article}, source = {Scopus}, note = {cited By 0 \par TL;DR \par Recent progress in incorporating AI into the design of classical PHY techniques and emerging studies on establishing AI-inspired frameworks that fundamentally replace classical model-driven techniques with data-driven AI techniques are covered.} } @article{Jha20212374, title = {Online Downlink Multi-User Channel Estimation for {{mmWave}} Systems Using Bayesian Neural Network}, author = {Jha, N.K. and Lau, V.K.N.}, date = {2021}, journaltitle = {IEEE J. Sel. Areas Commun.}, volume = {39}, number = {8}, pages = {2374--2387}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {07338716}, doi = {10.1109/JSAC.2021.3087249}, abstract = {We propose a Bayesian deep learning framework for model driven online sparse channel estimation task in Multi-user MIMO systems. Tools from Bayesian neural network and stochastic variational Bayesian Inference are utilized to capture aleatoric and epistemic uncertainty estimates. We treat the network prediction as an auxiliary variable to allow inference performance to be unaffected by the stage of training of the network. In addition to providing uncertainty estimates, being Bayesian, the framework enables us the possibility to marginalize over penalty parameters and is well suited for online scenario with changing environments. Our simulations show that the framework is robust to model mismatch, and efficiently captures uncertainty in the predictions. © 1983-2012 IEEE.}, art_number = {9448139}, coden = {ISACE}, document_type = {Article}, source = {Scopus}, note = {cited By 1 \par TL;DR \par A Bayesian deep learning framework for model driven online sparse channel estimation task in Multi-user MIMO systems that enables the possibility to marginalize over penalty parameters and is well suited for online scenario with changing environments.} } @article{jhaAdversarialMachineLearning, title = {Adversarial {{Machine Learning}} ({{AML}})}, author = {Jha, Somesh}, pages = {71}, langid = {english}, keywords = {adversarial machine learning} } @inproceedings{Jia2021, title = {Estimation of Room-Level Cooling Energy in Hot/Arid Climate by Machine Learning-Based Approaches}, author = {Jia, B. and Hou, D. and Wang, L.L. and Hassan, I.G.}, date = {2021}, series = {Proceedings of the 2021 {{ASME Verification}} and {{Validation Symposium}}, {{VVS}} 2021}, publisher = {American Society of Mechanical Engineers (ASME)}, doi = {10.1115/VVS2021-65272}, abbrev_source_title = {Proc. ASME Verif. Valid. Symp., VVS}, affiliation = {Concordia University, Montreal, Canada; Texas AandM University at Qatar, Doha, Qatar}, art_number = {VVS2021-65272}, document_type = {Conference Paper}, isbn = {978-0-7918-8478-2}, langid = {english}, source = {Scopus}, note = {cited By 0} } @online{jiaAgentStoreScalableIntegration2024, title = {{{AgentStore}}: Scalable Integration of Heterogeneous Agents {{As}} Specialized Generalist Computer Assistant}, shorttitle = {{{AgentStore}}}, author = {Jia, Chengyou and Luo, Minnan and Dang, Zhuohang and Sun, Qiushi and Xu, Fangzhi and Hu, Junlin and Xie, Tianbao and Wu, Zhiyong}, date = {2024-10-24}, eprint = {2410.18603}, eprinttype = {arXiv}, eprintclass = {cs}, doi = {10.48550/arXiv.2410.18603}, abstract = {Digital agents capable of automating complex computer tasks have attracted considerable attention due to their immense potential to enhance human-computer interaction. However, existing agent methods exhibit deficiencies in their generalization and specialization capabilities, especially in handling open-ended computer tasks in real-world environments. Inspired by the rich functionality of the App store, we present AgentStore, a scalable platform designed to dynamically integrate heterogeneous agents for automating computer tasks. AgentStore empowers users to integrate third-party agents, allowing the system to continuously enrich its capabilities and adapt to rapidly evolving operating systems. Additionally, we propose a novel core \textbackslash textbf\{MetaAgent\} with the \textbackslash textbf\{AgentToken\} strategy to efficiently manage diverse agents and utilize their specialized and generalist abilities for both domain-specific and system-wide tasks. Extensive experiments on three challenging benchmarks demonstrate that AgentStore surpasses the limitations of previous systems with narrow capabilities, particularly achieving a significant improvement from 11.21\textbackslash\% to 23.85\textbackslash\% on the OSWorld benchmark, more than doubling the previous results. Comprehensive quantitative and qualitative results further demonstrate AgentStore's ability to enhance agent systems in both generalization and specialization, underscoring its potential for developing the specialized generalist computer assistant. All our codes will be made publicly available in https://chengyou-jia.github.io/AgentStore-Home.}, langid = {english}, pubstate = {prepublished}, keywords = {Computer Science - Artificial Intelligence,Computer Science - Robotics} } @article{Jiang20217655, title = {{{AI-Aided}} Online Adaptive {{OFDM}} Receiver: {{Design}} and Experimental Results}, author = {Jiang, P. and Wang, T. and Han, B. and Gao, X. and Zhang, J. and Wen, C.-K. and Jin, S. and Li, G.Y.}, date = {2021}, journaltitle = {IEEE Trans. Wirel. Commun.}, volume = {20}, number = {11}, pages = {7655--7668}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {15361276}, doi = {10.1109/TWC.2021.3087191}, abstract = {Orthogonal frequency division multiplexing (OFDM) has been widely applied in many wireless communi- cation systems. The artificial intelligence (AI)-aided OFDM receivers are currently brought to the forefront to replace and improve the traditional OFDM receivers. In this paper, we first compare two AI-aided OFDM receivers, namely, data-driven fully connected deep neural network and model-driven ComNet, through extensive simulation and real-time video transmission using a 5G rapid prototyping system for an over-the-air (OTA) test. We find a performance gap between the simulation and the OTA test caused by the discrepancy between the channel model for offline training and the real environment. We develop a novel online training system, which is called SwitchNet receiver, to address this issue. This receiver has a flexible and extendable architecture and can adapt to real channels by training only several parameters online. From the OTA test, the AI-aided OFDM receivers, especially the SwitchNet receiver, are robust to OTA environments and promising for future communication systems. At the end of this paper, we discuss potential challenges and future research inspired by our initial study in this paper. © 2002-2012 IEEE.}, document_type = {Article}, source = {Scopus}, note = {cited By 1 \par TL;DR \par From the OTA test, the AI-aided OFDM receivers, especially the SwitchNet receiver, are robust to OTA environments and promising for future communication systems.} } @online{jiangArtPromptASCIIArtbased2024, title = {{{ArtPrompt}}: {{ASCII Art-based Jailbreak Attacks}} against {{Aligned LLMs}}}, shorttitle = {{{ArtPrompt}}}, author = {Jiang, Fengqing and Xu, Zhangchen and Niu, Luyao and Xiang, Zhen and Ramasubramanian, Bhaskar and Li, Bo and Poovendran, Radha}, date = {2024-02-22}, eprint = {2402.11753}, eprinttype = {arXiv}, eprintclass = {cs}, doi = {10.48550/arXiv.2402.11753}, abstract = {Safety is critical to the usage of large language models (LLMs). Multiple techniques such as data filtering and supervised fine-tuning have been developed to strengthen LLM safety. However, currently known techniques presume that corpora used for safety alignment of LLMs are solely interpreted by semantics. This assumption, however, does not hold in real-world applications, which leads to severe vulnerabilities in LLMs. For example, users of forums often use ASCII art, a form of text-based art, to convey image information. In this paper, we propose a novel ASCII art-based jailbreak attack and introduce a comprehensive benchmark Vision-in-Text Challenge (ViTC) to evaluate the capabilities of LLMs in recognizing prompts that cannot be solely interpreted by semantics. We show that five SOTA LLMs (GPT-3.5, GPT-4, Gemini, Claude, and Llama2) struggle to recognize prompts provided in the form of ASCII art. Based on this observation, we develop the jailbreak attack ArtPrompt, which leverages the poor performance of LLMs in recognizing ASCII art to bypass safety measures and elicit undesired behaviors from LLMs. ArtPrompt only requires black-box access to the victim LLMs, making it a practical attack. We evaluate ArtPrompt on five SOTA LLMs, and show that ArtPrompt can effectively and efficiently induce undesired behaviors from all five LLMs.}, pubstate = {prepublished}, keywords = {Computer Science - Artificial Intelligence,Computer Science - Computation and Language}, note = {TL;DR \par The jailbreak attack ArtPrompt is developed, which leverages the poor performance of LLMs in recognizing ASCII art to bypass safety measures and elicit undesired behaviors from LLMs.} } @online{jiangEmpiricalStudyPreTrained2023, title = {An {{Empirical Study}} of {{Pre-Trained Model Reuse}} in the {{Hugging Face Deep Learning Model Registry}}}, author = {Jiang, Wenxin and Synovic, Nicholas and Hyatt, Matt and Schorlemmer, Taylor R. and Sethi, Rohan and Lu, Yung-Hsiang and Thiruvathukal, George K. and Davis, James C.}, date = {2023-03-04}, eprint = {2303.02552}, eprinttype = {arXiv}, eprintclass = {cs}, doi = {10.48550/arXiv.2303.02552}, abstract = {Deep Neural Networks (DNNs) are being adopted as components in software systems. Creating and specializing DNNs from scratch has grown increasingly difficult as state-of-the-art architectures grow more complex. Following the path of traditional software engineering, machine learning engineers have begun to reuse large-scale pre-trained models (PTMs) and fine-tune these models for downstream tasks. Prior works have studied reuse practices for traditional software packages to guide software engineers towards better package maintenance and dependency management. We lack a similar foundation of knowledge to guide behaviors in pre-trained model ecosystems. In this work, we present the first empirical investigation of PTM reuse. We interviewed 12 practitioners from the most popular PTM ecosystem, Hugging Face, to learn the practices and challenges of PTM reuse. From this data, we model the decision-making process for PTM reuse. Based on the identified practices, we describe useful attributes for model reuse, including provenance, reproducibility, and portability. Three challenges for PTM reuse are missing attributes, discrepancies between claimed and actual performance, and model risks. We substantiate these identified challenges with systematic measurements in the Hugging Face ecosystem. Our work informs future directions on optimizing deep learning ecosystems by automated measuring useful attributes and potential attacks, and envision future research on infrastructure and standardization for model registries.}, pubstate = {prepublished}, keywords = {Computer Science - Artificial Intelligence,Computer Science - Machine Learning,Computer Science - Software Engineering,LOGSEQ}, note = {Comment: Proceedings of the ACM/IEEE 45th International Conference on Software Engineering (ICSE) 2023} } @inproceedings{jiangSemanticSimilarityBased1997, title = {Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy}, booktitle = {Proc {{Intl Conf Res}}. {{Comput}}. {{Linguist}}.}, author = {Jiang, J.J. and Conrath, D.W.}, date = {1997}, pages = {19--33}, url = {http://www.cse.iitb.ac.in/~cs626-449/Papers/WordSimilarity/4.pdf}, abstract = {This paper presents a new approach for measuring semantic similarity/distance between words and concepts. It combines a lexical taxonomy structure with corpus statistical information so that the semantic distance between nodes in the semantic space constructed by the taxonomy can be better quantified with the computational evidence derived from a distributional analysis of corpus data. Specifically, the proposed measure is a combined approach that inherits the edge-based approach of the edge counting scheme, which is then enhanced by the node-based approach of the information content calculation. When tested on a common data set of word pair similarity ratings, the proposed approach outperforms other computational models. It gives the highest correlation value (r = 0.828) with a benchmark based on human similarity judgements, whereas an upper bound (r = 0.885) is observed when human subjects replicate the same task.}, added-at = {2010-03-12T16:18:27.000+0100}, biburl = {http://www.bibsonomy.org/bibtex/2c4ffc507dafc908eab62fde53f7e4f7a/sdo}, description = {Jiang Conrath Maß}, interhash = {175ec03ee8c47d4b2d0a083609a78e05}, intrahash = {c4ffc507dafc908eab62fde53f7e4f7a}, keywords = {1997 Conrath Jiang JiangConrath folksonomy lexical measure semantic similarity taxonomy}, note = {TL;DR \par This paper presents a new approach for measuring semantic similarity/distance between words and concepts that combines a lexical taxonomy structure with corpus statistical information so that the semantic distance between nodes in the semantic space constructed by the taxonomy can be better quantified with the computational evidence derived from a distributional analysis of corpus data.}, timestamp = {2010-03-12T16:18:27.000+0100} } @article{jiangWhyHowDevelopers2017, title = {Why and How Developers Fork What from Whom in {{GitHub}}}, author = {Jiang, Jing and Lo, David and He, Jiahuan and Xia, Xin and Kochhar, Pavneet Singh and Zhang, Li}, date = {2017-02}, journaltitle = {Empir. Softw Engg}, volume = {22}, number = {1}, pages = {547--578}, publisher = {Kluwer Academic Publishers}, address = {Hingham, MA, USA}, issn = {1382-3256}, url = {https://doi.org/10.1007/s10664-016-9436-6}, acmid = {3042043}, issue_date = {February 2017}, nodoi = {10.1007/s10664-016-9436-6}, numpages = {32}, keywords = {Fork,GitHub,Open source software}, note = {TL;DR \par The results show that forking is mainly used for making contributions of original repositories, and it is beneficial for OSS community, and the value of recommendation is shown and provides important insights for GitHub to recommend repositories.} } @inproceedings{jilani2014search, title = {A Search Based Test Data Generation Approach for Model Transformations}, booktitle = {Theory {{Pract}}. {{Model Transform}}. 7th {{Int}}. {{Conf}}. {{ICMT}} 2014 {{Held Part STAF}} 2014 {{York UK July}} 21-22 2014 {{Proc}}. 7}, author = {Jilani, Atif Aftab and Iqbal, Muhammad Zohaib and Khan, Muhammad Uzair}, date = {2014}, pages = {17--24}, publisher = {Springer}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found} } @article{Jindal20213202, title = {Machine Learning for Cloud Data Systems: {{The}} Progress so Far and the Path Forward}, author = {Jindal, A. and Interlandi, M.}, editor = {Dong X.L., Naumann F.}, date = {2021}, journaltitle = {Proc. VLDB Endow.}, volume = {14}, number = {12}, pages = {3202--3205}, publisher = {VLDB Endowment}, issn = {21508097}, doi = {10.14778/3476311.3476408}, abstract = {The goal of this tutorial is to educate the audience about the state of the art in ML for cloud data systems, both in research and in practice. The tutorial is divided in two parts: the progress, and the path forward. Part I covers the recent successes in deploying machine learning solutions for cloud data systems. We will discuss the practical considerations taken into account and the progress made at various levels. The goal is to compare and contrast the promise of ML for systems with the ground actually covered in industry. Finally, Part II discusses practical issues of machine learning in the enterprise covering the generation of explanations, model debugging, model deployment, model management, constraints on eyes-on data usage and anonymization, and a discussion of the technical debt that can accrue through machine learning and models in the enterprise. © The authors.}, document_type = {Conference Paper}, source = {Scopus}, note = {cited By 0 \par TL;DR \par The goal of this tutorial is to educate the audience about the state of the art in ML for cloud data systems, both in research and in practice, and compare and contrast the promise of ML for systems with the ground actually covered in industry.} } @inproceedings{jinghanSurveyNoSQLDatabase2011, title = {Survey on {{NoSQL}} Database}, booktitle = {2011 6th {{Int}}. {{Conf}}. {{Pervasive Comput}}. {{Appl}}.}, author = {{Jing Han} and {Haihong E} and {Guan Le} and {Jian Du}}, date = {2011-10}, pages = {363--366}, doi = {10.1109/ICPCA.2011.6106531}, abstract = {With the development of the Internet and cloud computing, there need databases to be able to store and process big data effectively, demand for high-performance when reading and writing, so the traditional relational database is facing many new challenges. Especially in large scale and high-concurrency applications, such as search engines and SNS, using the relational database to store and query dynamic user data has appeared to be inadequate. In this case, NoSQL database created. This paper describes the background, basic characteristics, data model of NoSQL. In addition, this paper classifies NoSQL databases according to the CAP theorem. Finally, the mainstream NoSQL databases are separately described in detail, and extract some properties to help enterprises to choose NoSQL.}, eventtitle = {2011 6th {{International Conference}} on {{Pervasive Computing}} and {{Applications}}}, keywords = {Big Data,Blogs,CAP theorem,cloud computing,column-oriented,Computational modeling,data model,database,Databases,document,Facebook,high-concurrency applications,Internet,key-value,NoSQL,NoSQL database,query dynamic user data,query processing,relational database,relational databases,Reliability,search engines,SNS,SQL} } @article{jinSurveyFairnessawareRecommender2023, title = {A Survey on Fairness-Aware Recommender Systems}, author = {Jin, Di and Wang, Luzhi and Zhang, He and Zheng, Yizhen and Ding, Weiping and Xia, Feng and Pan, Shirui}, date = {2023-12}, journaltitle = {Information Fusion}, volume = {100}, pages = {101906}, issn = {15662535}, doi = {10.1016/j.inffus.2023.101906}, abstract = {As information filtering services, recommender systems have extremely enriched our daily life by providing personalized suggestions and facilitating people in decision-making, which makes them vital and indispensable to human society in the information era. However, as people become more dependent on them, recent studies show that recommender systems potentially own unintentional impacts on society and individuals because of their unfairness (e.g., gender discrimination in job recommendations). To develop trustworthy services, it is crucial to devise fairness-aware recommender systems that can mitigate these bias issues. In this survey, we summarize existing methodologies and practices of fairness in recommender systems. Firstly, we present concepts of fairness in different recommendation scenarios, comprehensively categorize current advances, and introduce typical methods to promote fairness in different stages of recommender systems. Next, after introducing datasets and evaluation metrics applied to assess the fairness of recommender systems, we will delve into the significant influence that fairness-aware recommender systems exert on real-world industrial applications. Subsequently, we highlight the connection between fairness and other principles of trustworthy recommender systems, aiming to consider trustworthiness principles holistically while advocating for fairness. Finally, we summarize this review, spotlighting promising opportunities in comprehending concepts, frameworks, the balance between accuracy and fairness, and the ties with trustworthiness, with the ultimate goal of fostering the development of fairness-aware recommender systems.}, langid = {english}, note = {TL;DR \par A review of existing methodologies and practices of fairness in recommender systems, spotlighting promising opportunities in comprehending concepts, frameworks, the balance between accuracy and fairness, and the ties with trustworthiness with the ultimate goal of fostering the development of fairness-awareRecommender systems.} } @article{johannKiefMorrisInfrastructure2017, entrysubtype = {magazine}, title = {Kief {{Morris}} on {{Infrastructure}} as {{Code}}}, author = {Johann, Sven}, date = {2017}, journaltitle = {IEEE Software}, volume = {34}, number = {1}, pages = {117--120}, issn = {0740-7459}, abstract = {Cloud specialist Kief Morris joins Software Engineering Radio host Sven Johann to discuss the benefits of infrastructure as code, including security, auditability, testing, documentation, and traceability.}, keywords = {software engineering} } @article{JohnKLT23, title = {A Graph-Based Framework for Model-Driven Optimization Facilitating Impact Analysis of Mutation Operator Properties}, author = {John, Stefan and Kosiol, Jens and Lambers, Leen and Taentzer, Gabriele}, date = {2023}, journaltitle = {Software Syst. Model.}, volume = {22}, number = {4}, pages = {1281--1318}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found} } @online{JointICTPIAEASchool, title = {Joint {{ICTP-IAEA School}} on {{LoRa Enabled Radiation}} and {{Environmental Monitoring Sensors}}}, url = {http://wireless.ictp.it/school_2018/}, urldate = {2021-01-05}, keywords = {grafana,influxdb,internet of things} } @inproceedings{JongelingCC19, title = {Impediments to Introducing Continuous Integration for Model-Based Development in Industry}, booktitle = {45th {{Euromicro Conf}}. {{Softw}}. {{Eng}}. {{Adv}}. {{Appl}}. {{SEAA}} 2019 {{Kallithea-Chalkidiki Greece August}} 28-30 2019}, author = {Jongeling, Robbert and Carlson, Jan and Cicchetti, Antonio}, editor = {Staron, Miroslaw and Capilla, Rafael and Skavhaug, Amund}, date = {2019}, pages = {434--441}, publisher = {IEEE}, doi = {10.1109/SEAA.2019.00071}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {TL;DR \par Model synchronization and tool interoperability are found the most challenging to overcome and the ways in which they are circumvented in practice are detrimental for introducing continuous integration.}, timestamp = {Mon, 25 Nov 2019 13:49:21 +0100} } @article{jorro-aragoneses_recolibry_2020, title = {{{RecoLibry Suite}}: A Set of Intelligent Tools for the Development of Recommender Systems}, shorttitle = {{{RecoLibry Suite}}}, author = {Jorro-Aragoneses, Jose Luis and Díaz-Agudo, Belén and Recio-García, Juan A. and Jimenez-Díaz, Guillermo}, date = {2020-06}, journaltitle = {Autom. Softw. Eng.}, volume = {27}, number = {1--2}, pages = {63--89}, issn = {0928-8910, 1573-7535}, doi = {10.1007/s10515-020-00269-4}, abstract = {Recommendation systems are a key part of almost every modern consumer website. Recommender systems include techniques to filter, explore and rank a huge amount of information and items according to the user’s current interests, and the similarity among users and items. Designing and implementing a recommender system usually requires high programming and machine learning skills. To alleviate these processes we present RecoLibry Suite: a set of intelligent tools to assist different types of users on the development of recommender systems. RecoLibry Suite supports not only the design and development of recommender systems but also its deployment as software as a service. We have evaluated the usability of the proposed tools with real users.}, langid = {english}, note = {TL;DR \par RecoLibry Suite is presented: a set of intelligent tools to assist different types of users on the development of recommender systems, and the usability of the proposed tools is evaluated with real users.} } @article{josebaAutomaticImpactAnalysis, title = {Automatic Impact Analysis of Software Architecture Migration on {{Model Driven Software Development}}}, author = {Joseba, Agirre and Leire, Etxeberria and Goiuria, Sagardui}, journaltitle = {AMT MoDELS 2013} } @article{journals/bmcbi/SchlickerDRL06, title = {A New Measure for Functional Similarity of Gene Products Based on {{Gene Ontology}}.}, author = {Schlicker, Andreas and Domingues, Francisco S. and Rahnenführer, Jörg and Lengauer, Thomas}, year = {2009-11-10, 2006}, journaltitle = {BMC Bioinformatics}, volume = {7}, pages = {302}, url = {http://dblp.uni-trier.de/db/journals/bmcbi/bmcbi7.html#SchlickerDRL06}, added-at = {2009-11-10T00:00:00.000+0100}, biburl = {http://www.bibsonomy.org/bibtex/209c4c56514b6a72f7b855ebea6cdacd0/dblp}, description = {dblp}, ee = {http://dx.doi.org/10.1186/1471-2105-7-302}, interhash = {799547cf798d57975c427f4f389a5e0b}, intrahash = {09c4c56514b6a72f7b855ebea6cdacd0}, keywords = {dblp}, timestamp = {2009-11-10T00:00:00.000+0100} } @misc{JSSOFTWARED2400320_reviewerpdf, title = {{{JSSOFTWARE-D-24-00320}}\_reviewer.Pdf}, langid = {english}, keywords = {⛔ No INSPIRE recid found,LOGSEQ}, note = {\section{Annotazioni\\ (24/6/2024, 15:54:07)} \par - “software developers struggle to manage the dependencies between third-party libraries for many reasons,such as unknown update efforts and the lack of awareness about related security issues.” (“JSSOFTWARE-D-24-00320\_reviewer.pdf”, p. 8) \#5fb236\\ \mkbibemph{ } \par - “TPL update prioritization approach, which providesvaluable insights for mobile app developers to help improve the decision-making process” (“JSSOFTWARE-D-24-00320\_reviewer.pdf”, p. 8) \#5fb236\\ \mkbibemph{ } \par - “a case study” (“JSSOFTWARE-D-24-00320\_reviewer.pdf”, p. 8) \#ffd400\\ \mkbibemph{Case study or questionnaire? } \par - “bot’s resultsuseful” (“JSSOFTWARE-D-24-00320\_reviewer.pdf”, p. 8) \#ff6666\\ \mkbibemph{ } \par - “quick and easy-to-implement features” (“JSSOFTWARE-D-24-00320\_reviewer.pdf”, p. 8) \#a28ae5\\ \mkbibemph{ } \par - “security vulnerabilities” (“JSSOFTWARE-D-24-00320\_reviewer.pdf”, p. 8) \#5fb236\\ \mkbibemph{ } \par - “high maintenance efforts” (“JSSOFTWARE-D-24-00320\_reviewer.pdf”, p. 8) \#5fb236\\ \mkbibemph{ } \par - “Recently” (“JSSOFTWARE-D-24-00320\_reviewer.pdf”, p. 8) \#ffd400\\ \mkbibemph{You use recently referring a paper 0f 3-4 years ago. Overall, the authors should consider most recent works. They have missing relevant papers coming from the editions 2022 and 2022 of top SE venues. } \par - “developers' responsiveness” (“JSSOFTWARE-D-24-00320\_reviewer.pdf”, p. 8) \#5fb236\\ \mkbibemph{ } \par - “duration of the response when developers receive notifications” (“JSSOFTWARE-D-24-00320\_reviewer.pdf”, p. 8) \#a28ae5\\ \mkbibemph{ } \par - “recent empirical studies” (“JSSOFTWARE-D-24-00320\_reviewer.pdf”, p. 8) \#ffd400\\ \mkbibemph{2020? } \par - “dependencies migration a practice requiring dedicated time and effort” (“JSSOFTWARE-D-24-00320\_reviewer.pdf”, p. 8) \#a28ae5\\ \mkbibemph{ } \par - “Therefore, we aim to help software developers prioritize and make betterdecisions regarding TPLs update process. Accordingly, the approach may boost software quality and helpdevelopers dedicate maintenance efforts based on their needs” (“JSSOFTWARE-D-24-00320\_reviewer.pdf”, p. 9) \#5fb236\\ \mkbibemph{ } \par - “This research aims to propose an approach that can help mobile app developers prioritize third-partysoftware libraries in their software for the update process.” (“JSSOFTWARE-D-24-00320\_reviewer.pdf”, p. 9) \#ffd400\\ \mkbibemph{So, is a tool what you are proposing? } \par - “Propose metrics” (“JSSOFTWARE-D-24-00320\_reviewer.pdf”, p. 9) \#a28ae5\\ \mkbibemph{ } \par - “Build a chatbot” (“JSSOFTWARE-D-24-00320\_reviewer.pdf”, p. 9) \#a28ae5\\ \mkbibemph{ } \par - “set of metrics” (“JSSOFTWARE-D-24-00320\_reviewer.pdf”, p. 9) \#a28ae5\\ \mkbibemph{ } \par - “prioritization approach” (“JSSOFTWARE-D-24-00320\_reviewer.pdf”, p. 9) \#a28ae5\\ \mkbibemph{ } \par - “Developing A chatbot” (“JSSOFTWARE-D-24-00320\_reviewer.pdf”, p. 9) \#ff6666\\ \mkbibemph{ } \par - “Several studies investigated software third-party library updates in mobile applications from different perspectives, such as developers' responsiveness to the update, types of software updates, motivations behind updating TPLs, and reasons behind update delay/ignorance” (“JSSOFTWARE-D-24-00320\_reviewer.pdf”, p. 9) \#5fb236\\ \mkbibemph{ } \par - “2752 apps and investigated the technical lag between library releases.” (“JSSOFTWARE-D-24-00320\_reviewer.pdf”, p. 9) \#5fb236\\ \mkbibemph{ } \par - “developers seldom update TPLs” (“JSSOFTWARE-D-24-00320\_reviewer.pdf”, p. 9) \#5fb236\\ \mkbibemph{ } \par - “developers may downgrade to an older version due to incompatibility issues” (“JSSOFTWARE-D-24-00320\_reviewer.pdf”, p. 9) \#5fb236\\ \mkbibemph{ } \par - “DeepLib can recommend the appropriate library next version for either a single library or a set of libraries.” (“JSSOFTWARE-D-24-00320\_reviewer.pdf”, p. 10) \#5fb236\\ \mkbibemph{ } \par - “those tools do not cover the update from one version to another for the same library.” (“JSSOFTWARE-D-24-00320\_reviewer.pdf”, p. 11) \#5fb236\\ \mkbibemph{ } \par - “TPL update prioritization” (“JSSOFTWARE-D-24-00320\_reviewer.pdf”, p. 11) \#ffd400\\ \mkbibemph{What's the issue that authors want to address with TPL update prioritization? } \par - “app developers prioritize TPLs' updates” (“JSSOFTWARE-D-24-00320\_reviewer.pdf”, p. 11) \#5fb236\\ \mkbibemph{ } \par - “first” (“JSSOFTWARE-D-24-00320\_reviewer.pdf”, p. 12) \#ffd400\\ \mkbibemph{updated first with respect to what?\\ what's the criteria that authors wanted to meet? } \par - “factors” (“JSSOFTWARE-D-24-00320\_reviewer.pdf”, p. 12) \#e56eee\\ \mkbibemph{ } \par - “Compatibility, Accessibility, Update Effort, Business Value, and Security.” (“JSSOFTWARE-D-24-00320\_reviewer.pdf”, p. 12) \#e56eee\\ \mkbibemph{ } \par - “TPL updates process and conducting a validation survey,” (“JSSOFTWARE-D-24-00320\_reviewer.pdf”, p. 13) \#a28ae5\\ \mkbibemph{ } \par - “we focused more on the metrics that can be automatically measured” (“JSSOFTWARE-D-24-00320\_reviewer.pdf”, p. 13) \#a28ae5\\ \mkbibemph{ } \par - “Analytical Hierarchy Process (AHP)” (“JSSOFTWARE-D-24-00320\_reviewer.pdf”, p. 13) \#5fb236\\ \mkbibemph{ } \par - “Simple Additive Weighting” (“JSSOFTWARE-D-24-00320\_reviewer.pdf”, p. 13) \#5fb236\\ \mkbibemph{ } \par - “However, for security, since it is a combination of real numbers (vulnerabilities and unnecessary permissions) instead of percentages as other factors, we handle it separately; if a library has security threats, we give it the highest priority over the other libraries” (“JSSOFTWARE-D-24-00320\_reviewer.pdf”, p. 14) \#ffd400\\ \mkbibemph{To check later how this security aspect is managed later. } \par - “value” (“JSSOFTWARE-D-24-00320\_reviewer.pdf”, p. 14) \#ff6666\\ \mkbibemph{ } \par - “AHP method to obtain weight for each category” (“JSSOFTWARE-D-24-00320\_reviewer.pdf”, p. 14) \#a28ae5\\ \mkbibemph{ } \par - “SAW method to multiply each category value by its weight (obtained from AHP) and sum those values to generate the ranking value for each library.” (“JSSOFTWARE-D-24-00320\_reviewer.pdf”, p. 14) \#5fb236\\ \mkbibemph{ } \par - “chatbot” (“JSSOFTWARE-D-24-00320\_reviewer.pdf”, p. 14) \#ffd400\\ \mkbibemph{to check if details about its implementation have been given. } \par - “AHP” (“JSSOFTWARE-D-24-00320\_reviewer.pdf”, p. 14) \#5fb236\\ \mkbibemph{AHP to obtain the wight for each category. } \par - “SAW” (“JSSOFTWARE-D-24-00320\_reviewer.pdf”, p. 14) \#a28ae5\\ \mkbibemph{THis is for calculating rangking values for each category. } \par - “We performed evaluation sessions in two phases to ensure that the proposed metrics and the prioritization approach were helpful and suitable.” (“JSSOFTWARE-D-24-00320\_reviewer.pdf”, p. 14) \#ffd400\\ \mkbibemph{what does it mean? } \par - “MCQ” (“JSSOFTWARE-D-24-00320\_reviewer.pdf”, p. 14) \#ffd400\\ \mkbibemph{CHeck what is MCQ } \par - “first section of the survey discusses the developers' industry background” (“JSSOFTWARE-D-24-00320\_reviewer.pdf”, p. 14) \#5fb236\\ \mkbibemph{ } \par - “second section targets the behavior of third-party libraries” (“JSSOFTWARE-D-24-00320\_reviewer.pdf”, p. 14) \#5fb236\\ \mkbibemph{ } \par - “Always, Often, Sometimes, Rarely, and Never.” (“JSSOFTWARE-D-24-00320\_reviewer.pdf”, p. 14) \#a28ae5\\ \mkbibemph{ } \par - “users who have worked” (“JSSOFTWARE-D-24-00320\_reviewer.pdf”, p. 14) \#5fb236\\ \mkbibemph{ } \par - “individuals who identified themselves as developers on GitHub” (“JSSOFTWARE-D-24-00320\_reviewer.pdf”, p. 14) \#5fb236\\ \mkbibemph{ } \par - “280 and 232 emails to contributors and individuals, respectively.” (“JSSOFTWARE-D-24-00320\_reviewer.pdf”, p. 14) \#5fb236\\ \mkbibemph{ } \par - “60\% of the developers have five years of experience or below, 35\% are between six and ten years, and 5.5\%” (“JSSOFTWARE-D-24-00320\_reviewer.pdf”, p. 15) \#ffd400\\ \mkbibemph{Numbers are not consistent with the content of Table 3. } \par - “57\% and 53\% at the individuals' level, respectively” (“JSSOFTWARE-D-24-00320\_reviewer.pdf”, p. 16) \#ffd400\\ \mkbibemph{why two percentages? } \par - “compatibility, conflicts between TPLs, update's complexity, the library's GitHub status, and others” (“JSSOFTWARE-D-24-00320\_reviewer.pdf”, p. 19) \#a28ae5\\ \mkbibemph{ } \par - “67\% of our participants consider TPL updates' prioritization challenging” (“JSSOFTWARE-D-24-00320\_reviewer.pdf”, p. 19) \#ffd400\\ \mkbibemph{Such a conclusion is not evident. } \par - “details of the TPLs update prioritization.” (“JSSOFTWARE-D-24-00320\_reviewer.pdf”, p. 19) \#a28ae5\\ \mkbibemph{ } \par - “overview of each category and what factors fall under it, whereas Table 6 contains the high-level definition of each category.” (“JSSOFTWARE-D-24-00320\_reviewer.pdf”, p. 19) \#a28ae5\\ \mkbibemph{ } \par - “5.1.1 Compatibility” (“JSSOFTWARE-D-24-00320\_reviewer.pdf”, p. 20) \#a28ae5\\ \mkbibemph{ } \par - “Compatibility” (“JSSOFTWARE-D-24-00320\_reviewer.pdf”, p. 20) \#a28ae5\\ \mkbibemph{ } \par - “API stability, which indicates compatibility if a client can compile to the new API change.” (“JSSOFTWARE-D-24-00320\_reviewer.pdf”, p. 20) \#a28ae5\\ \mkbibemph{ } \par - “gap between the installed release and the target release” (“JSSOFTWARE-D-24-00320\_reviewer.pdf”, p. 20) \#a28ae5\\ \mkbibemph{ } \par - “ratio of recently reported issues” (“JSSOFTWARE-D-24-00320\_reviewer.pdf”, p. 20) \#a28ae5\\ \mkbibemph{ } \par - “Accessibility” (“JSSOFTWARE-D-24-00320\_reviewer.pdf”, p. 21) \#a28ae5\\ \mkbibemph{ } \par - “migration effort is one of the developers' main concerns while managing dependencies in their projects and making related decisions” (“JSSOFTWARE-D-24-00320\_reviewer.pdf”, p. 22) \#a28ae5\\ \mkbibemph{ } \par - “After proposing a set of metrics and classifying them into five categories, we used AHP and SAW methods to build our prioritization model [30][31].” (“JSSOFTWARE-D-24-00320\_reviewer.pdf”, p. 23) \#a28ae5\\ \mkbibemph{ } \par - “last two related to library extraction and rankin” (“JSSOFTWARE-D-24-00320\_reviewer.pdf”, p. 24) \#5fb236\\ \mkbibemph{ } \par - “each library's official releases from the Maven website [45] and then check if the installed release number from the Gradle file is available in the official releases;” (“JSSOFTWARE-D-24-00320\_reviewer.pdf”, p. 25) \#5fb236\\ \mkbibemph{ } \par - “After we extract and confirm TPLs, we collect the required resources to calculate the metrics. First, we download the library's files (jar and aar) and the release metadata from the Maven website [45]; this is required to calculate SFS, MSS, UD, and UPC metrics as they depend on the source code of the library, except for UD metric, which requires only releases' dates. We use the RevAPI website tool [46] to check the difference between the two Maven library releases. RevAPI tool [46] shows the difference between two APIs in terms of changes and their severities; we consider "BREAKING" and "POTENTIALLY\_BREAKING" as breaking changes and others as non-breaking changes to calculate the BCR metric. Unfortunately, the RevAPI tool [46] sometimes fails to get the API difference for a library; in that case, we ignore the BCR metric for that library and calculate compatibility as of DD and UD metrics only. Figure 8 shows the process of calculating compatibility metrics.” (“JSSOFTWARE-D-24-00320\_reviewer.pdf”, p. 26) \#ffd400\\ \mkbibemph{This phase needs more detail especially because it seems there is a disassembly operation, which is performed to get the source code of the libraries under analysis. } \par - “three case studies” (“JSSOFTWARE-D-24-00320\_reviewer.pdf”, p. 30) \#ffd400\\ \mkbibemph{Can you comment on the representativeness of these thee repositories? } \par - “for each metric; they were given only each factor's (metric) definition and its value, and each evaluator evaluated at least two projects” (“JSSOFTWARE-D-24-00320\_reviewer.pdf”, p. 30) \#ffd400\\ \mkbibemph{What's the goal of this? Just to double check the values of the metrics? } \par - “After that, we asked seven evaluators to solve those tasks based on the given library without using the Chatbot, which was considered the baseline in our work” (“JSSOFTWARE-D-24-00320\_reviewer.pdf”, p. 31) \#ffd400\\ \mkbibemph{We might have some threats to validity here related to the expertise level of each evaluator. } \par - “e asked the participants to measure the time needed for each task” (“JSSOFTWARE-D-24-00320\_reviewer.pdf”, p. 32) \#ffd400\\ \mkbibemph{Can you say something more on the tasks you asked to be performed? } \par - “The results clearly show that the bot is significantly faster than the baseline,” (“JSSOFTWARE-D-24-00320\_reviewer.pdf”, p. 32) \#ffd400\\ \mkbibemph{So the implemented tool calculate metrics faster than humans. Is this the kind of feedback auhtors wanted to get? } \par - “participants were unable to solve it” (“JSSOFTWARE-D-24-00320\_reviewer.pdf”, p. 33) \#5fb236\\ \mkbibemph{ } \par - “Based on reported issues to the library, can you determine the percentage of closed issues in the last quarter?” (“JSSOFTWARE-D-24-00320\_reviewer.pdf”, p. 35) \#ffd400\\ \mkbibemph{What is the goal of asking evaluators to calculate the percentage of closed issues in the last quarter? Is this to test the correctenes of the developed metrics? } \par - “2018” (“JSSOFTWARE-D-24-00320\_reviewer.pdf”, p. 38) \#ffd400\\ \mkbibemph{ } \par - “May 04, 2022” (“JSSOFTWARE-D-24-00320\_reviewer.pdf”, p. 41) \#ffd400\\ \mkbibemph{ } \par - “2023” (“JSSOFTWARE-D-24-00320\_reviewer.pdf”, p. 41) \#ffd400\\ \mkbibemph{ } \par - “[58] “Renovatebot.”” (“JSSOFTWARE-D-24-00320\_reviewer.pdf”, p. 42) \#ffd400\\ \mkbibemph{ } \par - “[59] “OWASP Dependency-Check .”” (“JSSOFTWARE-D-24-00320\_reviewer.pdf”, p. 42) \#ffd400\\ \mkbibemph{ }} } @misc{JSSOFTWARED2400573, title = {{{JSSOFTWARE-D-24-00573}}}, keywords = {LOGSEQ}, note = {\section{Annotazioni\\ (16/9/2024, 11:18:00)} \par - “In our previous publications [3, 4], we assessed the quality of student-developed (ecommerce) web apps in order to understand whether Computer Science (CS ) undergraduate students are equipped to address three quality characteristics: security, reliability, and maintainability. Our results suggested that the students enrolled in the academic year (a.y.) 2021-22 of a” (“JSSOFTWARE-D-24-00573”, p. 2) \#00b036 \par - “training intervention” (“JSSOFTWARE-D-24-00573”, p. 2) \#f0ff00\\ \mkbibemph{To be checked what they have done.} \par - “self-determination theory,” (“JSSOFTWARE-D-24-00573”, p. 2) \#f0ff00\\ \mkbibemph{What's that?} \par - “reliability and maintainability” (“JSSOFTWARE-D-24-00573”, p. 2) \#f0ff00\\ \mkbibemph{Why these two? How have you decided such characteristics?} \par - “processed and made available in a dashboard. These” (“JSSOFTWARE-D-24-00573”, p. 3) \#00b036 \par - “The results suggested that the SATs were useful to help junior programmers detect and fix bugs.” (“JSSOFTWARE-D-24-00573”, p. 4) \#f0ff00\\ \mkbibemph{So, we had already such a study. What's new here?} \par - “Goal-Question-Metric (GQM )” (“JSSOFTWARE-D-24-00573”, p. 5) \#00b036 \par - “It is worth mentioning that the research presented in this paper, although focused on software engineering education, also has possible implications for researchers and practitioners. This is to say that we will not limit ourselves to presenting results of interest for educators” (“JSSOFTWARE-D-24-00573”, p. 6) \#00b036 \par - “Also, they were attending courses on computer networks and algorithm design while taking the STW course” (“JSSOFTWARE-D-24-00573”, p. 6) \#5fb236 \par - “The a.y. 2022-23 students were different from a.y. 2021-22 ones.” (“JSSOFTWARE-D-24-00573”, p. 6) \#f0ff00\\ \mkbibemph{In what respect?} \par - “e-commerce web app” (“JSSOFTWARE-D-24-00573”, p. 6) \#5fb236 \par - “(i) customers and administrators to log in and log out” (“JSSOFTWARE-D-24-00573”, p. 6) \#5fb236 \par - “(ii) customers to sign up, navigate the catalogs of items, add items to the cart, buy items, and check their own orders” (“JSSOFTWARE-D-24-00573”, p. 6) \#5fb236 \par - “(iii) administrators to manage the catalogs of items and customers’ orders” (“JSSOFTWARE-D-24-00573”, p. 6) \#5fb236 \par - “each web app had to use the MVC pattern, interact with a relational database, and use the technologies presented in the course” (“JSSOFTWARE-D-24-00573”, p. 6) \#5fb236 \par - “pallet” (“JSSOFTWARE-D-24-00573”, p. 6) \#ff4400\\ \mkbibemph{Pallet?} \par - “76” (“JSSOFTWARE-D-24-00573”, p. 6) \#ff4400\\ \mkbibemph{Where does this number come from?} \par - “five hours” (“JSSOFTWARE-D-24-00573”, p. 7) \#5fb236 \par - “SonarCloud within GitHub Action workflows and on how to use SonarCloud to detect and resolve security issues.” (“JSSOFTWARE-D-24-00573”, p. 7) \#5fb236 \par - “and bugs and code smells to quantify, respectively, the reliability and maintainability of software applications.” (“JSSOFTWARE-D-24-00573”, p. 7) \#a28ae5 \par - “This is because autonomous motivation is fed by the satisfaction of three basic psychological needs: competence, which concerns the experience of effectiveness and mastery; autonomy, which refers to the experience of volition and willingness; and relatedness, which refers to one’s interpersonal connections with others.” (“JSSOFTWARE-D-24-00573”, p. 7) \#a28ae5 \par - “Moreover, if they resolve bugs and code smells, the underlying reason is that they recognize the usefulness of SonarCloud in developing reliable and maintainable web apps.” (“JSSOFTWARE-D-24-00573”, p. 7) \#ffd400\\ \mkbibemph{I'm not sure about such underlying reason.} \par - “reliability and maintainability of the a.y. 202122 and 2022-23 web apps by using SonarQube” (“JSSOFTWARE-D-24-00573”, p. 8) \#5fb236 \par - “dependent variables” (“JSSOFTWARE-D-24-00573”, p. 8) \#a28ae5 \par - “The higher the value of this variable, the lower the reliability of the web app. On the other hand, the number of maintainability issues indicates the number of code smells SonarQube detected in the last commit of a given project. The higher the value of this variable, the lower the maintainability of the web app.” (“JSSOFTWARE-D-24-00573”, p. 8) \#5fb236 \par - “results of both RQ1 and RQ2,” (“JSSOFTWARE-D-24-00573”, p. 8) \#5fb236 \par - “post-questionnaire administered to the a.y. 2022-23 STW students.” (“JSSOFTWARE-D-24-00573”, p. 8) \#5fb236 \par - “2021-22 (45 projects by 120 students without using any SAT) and 2022-23 (65 projects by 151 students using SonarCloud as a SAT).” (“JSSOFTWARE-D-24-00573”, p. 8) \#ffd400\\ \mkbibemph{Teams in 2022-23 were consisting of 2 members (on average) in contrast of more 3 members for the previous year. Was this by accident? or somehow intentional?} \par - “we postulate that this result is partially due to the refactoring operations performed to resolve the code smells detected by SonarCloud.” (“JSSOFTWARE-D-24-00573”, p. 8) \#ffd400\\ \mkbibemph{Or because of smaller groups?} \par - “Yes 5.89” (“JSSOFTWARE-D-24-00573”, p. 9) \#ffd400 \par - “Yes 10.54” (“JSSOFTWARE-D-24-00573”, p. 9) \#ffd400\\ \mkbibemph{Are these due to some restructuring?} \par - “To verify whether and to what extent the reduction in the number of reliability and maintainability issues is statistically significant between the a.y. 2021-22 and a.y. 2022-23, we tested the following null hypotheses:” (“JSSOFTWARE-D-24-00573”, p. 10) \#a28ae5 \par - “The former is to study RQ1 while the latter is to study RQ2.” (“JSSOFTWARE-D-24-00573”, p. 10) \#5fb236 \par - “indicating the presence of a statistically significant difference between the two groups being compared.” (“JSSOFTWARE-D-24-00573”, p. 11) \#5fb236 \par - “Cliff ’s δ effect size [” (“JSSOFTWARE-D-24-00573”, p. 11) \#5fb236 \par - “The top three most violated bugs relate to accessibility” (“JSSOFTWARE-D-24-00573”, p. 12) \#ffd400\\ \mkbibemph{How these are related to JSP, Java files as presented in Table 1.} \par - “inadequate use of HTML tags and attributes,” (“JSSOFTWARE-D-24-00573”, p. 12) \#5fb236 \par - “non-empty statements should change control flow or have at least one side-effect” (“JSSOFTWARE-D-24-00573”, p. 12) \#5fb236 \par - “happens when a variable declared in a specific scope has the same name as a variable in an outer scope.” (“JSSOFTWARE-D-24-00573”, p. 13) \#5fb236 \par - “Below, we briefly describe the meaning of each resolution status reported in that table according to SonarSource’s documentation [28].” (“JSSOFTWARE-D-24-00573”, p. 13) \#5fb236 \par - “This is to say that the analysis with SonarCloud could show a different number of violations from those returned by the analysis with SonarQube.” (“JSSOFTWARE-D-24-00573”, p. 14) \#5fb236 \par - “A training intervention based on SonarCloud (but not specifically focused on reliability and maintainability) significantly improves the reliability and maintainability of (e-commerce) web apps developed by bachelor students in CS. The magnitude of this improvement is large for both reliability (RQ1) and maintainability (RQ2) issues. Moreover, the improvement, in terms of reliability and maintainability, is statistically significant, regardless of the severity of the issue, although the magnitude of this improvement ranges from small to large.” (“JSSOFTWARE-D-24-00573”, p. 15) \#5fb236\\ \mkbibemph{INTERESTING} \par - “the reason why the students did not use SonarCloud)” (“JSSOFTWARE-D-24-00573”, p. 15) \#f0ff00\\ \mkbibemph{The first and second part of the questionnaire do not make sense for these students, isn't it?} \par - “The third part of the questionnaire concerned the following open question: “Describe your experience (both positive and negative) related to using SonarCloud”.” (“JSSOFTWARE-D-24-00573”, p. 17) \#f0ff00\\ \mkbibemph{Even this part does not make any sense with students of the previous academic year.} \par - “Finding (\#1). A training intervention based on a SAT like SonarCloud, even though not specifically focused on the reliability and maintainability quality characteristics, leads CS graduate students to improve the reliability and maintainability of the web apps they develop.” (“JSSOFTWARE-D-24-00573”, p. 17) \#5fb236\\ \mkbibemph{INTERESTING} \par - “as security, reliability, and maintainability.” (“JSSOFTWARE-D-24-00573”, p. 18) \#5fb236 \par - “This is likely due to autonomous motivations (specifically, integrated extrinsic motivation), which pushed students to resolve reliability and maintainability issues although we neither asked nor forced them to do so.” (“JSSOFTWARE-D-24-00573”, p. 18) \#5fb236 \par - “Finding (\#2). Reliability issues are resolved to a greater extent and perceived as more relevant than maintainability issues.” (“JSSOFTWARE-D-24-00573”, p. 18) \#5fb236\\ \mkbibemph{INTERESTING} \par - “reliability and maintainability” (“JSSOFTWARE-D-24-00573”, p. 21) \#5fb236 \par - “our training intervention, based on the use of SonarCloud, might also positively affect the reliability and maintainability quality characteristics due to integrated extrinsic motivation. According to the self-determination theory, integrated extrinsic motivation is a kind of autonomous motivation that occurs when people are motivated because the value and regulation of the activity have been integrated within themselves” (“JSSOFTWARE-D-24-00573”, p. 21) \#5fb236} } @inproceedings{jungBuildingAutomationSmart2013, title = {Building {{Automation}} and {{Smart Cities}}: {{An Integration Approach Based}} on a {{Service-Oriented Architecture}}}, shorttitle = {Building {{Automation}} and {{Smart Cities}}}, author = {Jung, Markus and Weidinger, J. and Kastner, W. and Olivieri, A.}, date = {2013-03}, pages = {1361--1367}, publisher = {IEEE}, doi = {10.1109/WAINA.2013.200}, isbn = {978-1-4673-6239-9 978-0-7695-4952-1}, note = {TL;DR \par The integration approach aims at providing a homogeneous integration layer that can be used to build advanced control scenarios that might arise in the context of smart cities.} } @article{Jurgelaitis202163, title = {Smart Contract Code Generation from Platform Specific Model for Hyperledger Go}, author = {Jurgelaitis, M. and Drungilas, V. and Čeponienė, L. and Vaičiukynas, E. and Butkienė, R. and Čeponis, J.}, editor = {Rocha A., Adeli H., Moreira F., Correia A.M.R., Dzemyda G.}, date = {2021}, journaltitle = {Adv. Intell. Syst. Comput.}, volume = {1368 AISC}, pages = {63--73}, publisher = {{Springer Science and Business Media Deutschland GmbH}}, issn = {21945357}, doi = {10.1007/978-3-030-72654-6_7}, abbrev_source_title = {Adv. Intell. Sys. Comput.}, affiliation = {Informatics Faculty, Kaunas University of Technology, Kaunas, Lithuania}, correspondence_address1 = {Jurgelaitis, M.; Informatics Faculty, Lithuania; email: mantas.jurgelaitis@ktu.lt}, document_type = {Conference Paper}, isbn = {9783030726539}, langid = {english}, source = {Scopus}, note = {cited By 2} } @inproceedings{KalliamvakouGBSGD14, title = {The Promises and Perils of Mining {{GitHub}}}, booktitle = {11th {{Work}}. {{Conf}}. {{Min}}. {{Softw}}. {{Repos}}. {{MSR}} 2014 {{Proc}}. {{May}} 31 - {{June}} 1 2014 {{Hyderabad India}}}, author = {Kalliamvakou, Eirini and Gousios, Georgios and Blincoe, Kelly and Singer, Leif and Germán, Daniel M. and Damian, Daniela E.}, date = {2014}, pages = {92--101}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/msr/KalliamvakouGBSGD14.bib}, note = {TL;DR \par It is shown, for example, that the majority of the projects are personal and inactive; that GitHub is also being used for free storage and as a Web hosting service; and that almost 40\% of all pull requests do not appear as merged, even though they were.}, timestamp = {Wed, 14 Nov 2018 10:57:56 +0100} } @article{Kanetaki2021V, title = {Creating a Metamodel for Predicting Learners Satisfaction by Utilizing an Educational Information System during {{COVID-19}} Pandemic}, author = {Kanetaki, Z. and Stergiou, C. and Bekas, G. and Troussas, C. and Sgouropoulou, C.}, editor = {Frasson C., Kabassi K., Voulodimos A.}, date = {2021}, journaltitle = {Front. Artif. Intell. Appl.}, volume = {338}, pages = {V-VI}, publisher = {IOS Press BV}, issn = {09226389}, doi = {10.3233/FAIA210085}, abbrev_source_title = {Front. Artif. Intell. Appl.}, affiliation = {University of West Attica, Athens, Greece}, art_number = {127-136}, document_type = {Conference Paper}, isbn = {9781643682044}, langid = {english}, source = {Scopus}, note = {cited By 4 \par TL;DR \par This study investigates students’ satisfaction prediction in a first-semester Mechanical Engineering CAD module combined with the evaluation and the effectiveness of specific curriculum reforms.} } @inproceedings{KappelKKKRRSW06, title = {Lifting Metamodels to Ontologies: A Step to the Semantic Integration of Modeling Languages}, booktitle = {Model {{Driven Eng}}. {{Lang}}. {{Syst}}. 9th {{Int}}. {{Conf}}. {{MoDELS}} 2006 {{Genova Italy Oct}}. 1-6 2006 {{Proc}}.}, author = {Kappel, Gerti and Kapsammer, Elisabeth and Kargl, Horst and Kramler, Gerhard and Reiter, Thomas and Retschitzegger, Werner and Schwinger, Wieland and Wimmer, Manuel}, editor = {Nierstrasz, Oscar and Whittle, Jon and Harel, David and Reggio, Gianna}, date = {2006}, series = {Lecture Notes in Computer Science}, volume = {4199}, pages = {528--542}, publisher = {Springer}, doi = {10.1007/11880240\_37}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Tue, 24 May 2022 15:28:49 +0200} } @misc{kapua, title = {Eclipse {{Kapua}}}, url = {https://projects.eclipse.org/projects/iot.kapua} } @inproceedings{karasneh2013online, title = {Online {{Img2UML}} Repository: {{An}} Online Repository for {{UML}} Models.}, booktitle = {{{EESSMOD MoDELS}}}, author = {Karasneh, Bilal and Chaudron, Michel RV}, date = {2013}, pages = {61--66} } @inproceedings{Karatzoglou:2017:DLR:3109859.3109933, title = {Deep Learning for Recommender Systems}, booktitle = {Proc. {{Elev}}. {{ACM Conf}}. {{Recomm}}. {{Syst}}.}, author = {Karatzoglou, Alexandros and Hidasi, Balázs}, date = {2017}, series = {{{RecSys}} '17}, pages = {396--397}, publisher = {ACM}, location = {New York, NY, USA}, url = {http://doi.acm.org/10.1145/3109859.3109933}, acmid = {3109933}, isbn = {978-1-4503-4652-8}, nodoi = {10.1145/3109859.3109933}, numpages = {2}, keywords = {deep learning,recommender systems} } @article{karinHighperformanceComputingContinuum1998, title = {The High-Performance Computing Continuum}, author = {Karin, Sidney and Graham, Susan}, date = {1998-11}, journaltitle = {Commun. ACM}, volume = {41}, number = {11}, pages = {32--35}, issn = {0001-0782, 1557-7317}, doi = {10.1145/287831.287837}, langid = {english}, note = {TL;DR \par Just as in the worldwide PC environment, high-end systems are increasingly ubiquitous, continuous, and pervasive, while the emergence of the WorldWide Web has produced another trend—the dynamic use of multiple physical computer systems viewed by the end user as a single system.} } @inproceedings{karmakar_what_2022, title = {What Do Pre-Trained Code Models Know about Code?}, booktitle = {Proc. 36th {{IEEEACM Int}}. {{Conf}}. {{Autom}}. {{Softw}}. {{Eng}}.}, author = {Karmakar, Anjan and Robbes, Romain}, date = {2022-06}, series = {{{ASE}} '21}, pages = {1332--1336}, publisher = {IEEE Press}, location = {Melbourne, Australia}, doi = {10.1109/ASE51524.2021.9678927}, abstract = {Pre-trained models of code built on the transformer architecture have performed well on software engineering (SE) tasks such as predictive code generation, code summarization, among others. However, whether the vector representations from these pre-trained models comprehensively encode characteristics of source code well enough to be applicable to a broad spectrum of downstream tasks remains an open question. One way to investigate this is with diagnostic tasks called probes. In this paper, we construct four probing tasks (probing for surface-level, syntactic, structural, and semantic information) for pre-trained code models. We show how probes can be used to identify whether models are deficient in (understanding) certain code properties, characterize different model layers, and get insight into the model sample-efficiency. We probe four models that vary in their expected knowledge of code properties: BERT (pre-trained on English), CodeBERT and CodeBERTa (pre-trained on source code, and natural language documentation), and GraphCodeBERT (pre-trained on source code with dataflow). While GraphCodeBERT performs more consistently overall, we find that BERT performs surprisingly well on some code tasks, which calls for further investigation.}, isbn = {978-1-6654-0337-5}, keywords = {probing,software engineering tasks,source code models,transformers}, note = {TL;DR \par Four probing tasks are constructed (probing for surface-level, syntactic, structural, and semantic information) for pre-trained code models to identify whether models are deficient in (understanding) certain code properties, characterize different model layers, and get insight into the model sample-efficiency.} } @inproceedings{karsai2009modeling, title = {Modeling Languages for Model-Based Development of Cyber-Physical Systems}, booktitle = {Workshop {{Des}}. {{Model}}. {{Eval}}. {{Cyber-Phys}}. {{Syst}}.}, author = {Karsai, Gabor and Neema, Sandeep and Shi, Feng}, date = {2009} } @inproceedings{karsaiDistributedManagedResearch2014, title = {Distributed and {{Managed}}: {{Research Challenges}} and {{Opportunities}} of the {{Next Generation Cyber-Physical Systems}}}, shorttitle = {Distributed and {{Managed}}}, author = {Karsai, Gabor and Balasubramanian, Daniel and Dubey, Abhishek and Otte, William R.}, date = {2014-06}, pages = {1--8}, publisher = {IEEE}, doi = {10.1109/ISORC.2014.36}, isbn = {978-1-4799-4430-9} } @incollection{karsaiModelintegratedDevelopmentCyberphysical2008, title = {Model-Integrated Development of Cyber-Physical Systems}, booktitle = {Software {{Technologies}} for {{Embedded}} and {{Ubiquitous Systems}}}, author = {Karsai, Gabor and Sztipanovits, Janos}, date = {2008}, pages = {46--54}, publisher = {Springer}, url = {http://link.springer.com/chapter/10.1007/978-3-540-87785-1_5}, urldate = {2016-03-10}, note = {TL;DR \par A model-integrated development approach is introduced that addresses the development needs of cyber-physical systems through the pervasive use of models and a complete model-based view is proposed that covers all aspects of the hardware and software components, as well as their interactions.} } @article{Karypis:1999:CHC:619043.621303, title = {Chameleon: {{Hierarchical}} Clustering Using Dynamic Modeling}, author = {Karypis, George and Han, Eui-Hong (Sam) and Kumar, Vipin}, date = {1999-08}, journaltitle = {Computer}, volume = {32}, number = {8}, pages = {68--75}, publisher = {IEEE Computer Society Press}, address = {Los Alamitos, CA, USA}, issn = {0018-9162}, url = {http://dx.doi.org/10.1109/2.781637}, acmid = {621303}, issue_date = {August 1999}, nodoi = {10.1109/2.781637}, numpages = {8}, note = {TL;DR \par Chameleon's key feature is that it accounts for both interconnectivity and closeness in identifying the most similar pair of clusters, which is important for dealing with highly variable clusters.} } @inproceedings{Karypis:2001:EIT:502585.502627, title = {Evaluation of Item-Based Top-n Recommendation Algorithms}, booktitle = {Procs {{Tenth Int}}. {{Conf Inf}}. {{Knowl}}. {{Manag}}.}, author = {Karypis, George}, date = {2001}, series = {{{CIKM}} '01}, pages = {247--254}, publisher = {ACM}, location = {New York, NY, USA}, acmid = {502627}, isbn = {1-58113-436-3}, numpages = {8}, keywords = {collaborative filtering,recommender system} } @article{KASHFI201937, title = {Integrating {{UX}} Principles and Practices into Software Development Organizations: A Case Study of Influencing Events}, author = {Kashfi, Pariya and Feldt, Robert and Nilsson, Agneta}, date = {2019}, journaltitle = {J. Syst. Software}, volume = {154}, pages = {37--58}, issn = {0164-1212}, doi = {10.1016/j.jss.2019.03.066}, abstract = {Current studies on User eXperience (UX) integration often do not investigate or reflect on the transition companies go through from only developing Graphical User Interfaces (GUI) to also considering usability and more recently UX. Understanding this transition provides a more holistic and realistic picture of integration and can be a rich source of knowledge for improving UX integration in the software industry. Applying case study and grounded theory research we show that UX integration, like other organizational changes, can include a mixture of planned and emergent initiatives, and is influenced by various intertwined events; not only those that reside inside an organization but also those external to it. We also show that different decisions that are made outside the authority of UX practitioners have an inevitable impact on enabling or prohibiting UX integration. In addition, we found that for a successful integration, practitioners need to explicitly consider and address the characteristics of UX, otherwise, the integration efforts may have a lopsided focus on the pragmatic aspect of UX, consequently, leave the hedonic aspect unaddressed. Based on our findings, we present four lessons learned and five pitfalls companies should consider to go beyond GUI design and usability to also address UX.}, langid = {english}, keywords = {⛔ No INSPIRE recid found,Case study,Grounded theory,Organizational change,Software quality,Usability,User eXperience (UX)} } @article{Kasrin202176, title = {Data-Sharing Markets for Integrating {{IoT}} Data Processing Functionalities}, author = {Kasrin, N. and Benabbas, A. and Elmamooz, G. and Nicklas, D. and Steuer, S. and Sünkel, M.}, date = {2021}, journaltitle = {CCF Trans. Pervasive Comput. Interact.}, volume = {3}, number = {1}, pages = {76--93}, publisher = {Springer}, issn = {2524521X}, doi = {10.1007/s42486-020-00054-y}, abstract = {The recent evolution of the Internet of Things into a cyber-physical reality has spawned various challenges from a data management perspective. In addition, IoT platform designers are faced with another set of questions. How can platforms be extended to smoothly integrate new data management functionalities? Currently, data processing related tasks are typically realized by manually developed code and functions which creates difficulties in maintenance and growth. Hence we need to explore other approaches to integration for IoT platforms. In this paper we cover both these aspects: (1) we explore several emerging data management challenges, and (2) we propose an IoT platform integration model that can combine disparate functionalities under one roof. For the first, we focus on the following challenges: sensor data quality, privacy in data streams, machine learning model management, and resource-aware data management. For the second, we propose an information-integration model for IoT platforms. The model revolves around the concept of a Data-Sharing Market where data management functionalities can share and exchange information about their data with other functionalities. In addition, data-sharing markets themselves can be combined into networks of markets where information flows from one market to another, which creates a web of information exchange about data resources. To motivate this work we present a use-case application in smart cities. © 2021, The Author(s).}, document_type = {Article}, source = {Scopus}, note = {cited By 2} } @article{katirtzisSummarizingSoftwareAPI, title = {Summarizing {{Software API Usage Examples}} Using {{Clustering Techniques}}}, author = {Katirtzis, Nikolaos and Diamantopoulos, Themistoklis and Sutton, Charles}, pages = {17}, abstract = {As developers often use third-party libraries to facilitate software development, the lack of proper API documentation for these libraries undermines their reuse potential. And although several approaches extract usage examples for libraries, they are usually tied to specific language implementations, while their produced examples are often redundant and are not presented as concise and readable snippets. In this work, we propose a novel approach that extracts API call sequences from client source code and clusters them to produce a diverse set of source code snippets that effectively covers the target API. We further construct a summarization algorithm to present concise and readable snippets to the users. Upon evaluating our system on software libraries, we indicate that it achieves high coverage in API methods, while the produced snippets are of high quality and closely match handwritten examples.}, langid = {english} } @inproceedings{katsamakasWhyMostOpen2007, title = {Why Most Open Source Development Projects Do Not Succeed?}, booktitle = {Emerg. {{Trends FLOSS Res}}. {{Dev}}. 2007 {{FLOSS07 First Int}}. {{Workshop On}}}, author = {Katsamakas, Evangelos and Georgantzas, Nicholas}, date = {2007}, pages = {3--3}, publisher = {IEEE}, url = {http://ieeexplore.ieee.org/abstract/document/4273074/}, urldate = {2017-06-23} } @incollection{katzReluplexEfficientSMT2017, title = {Reluplex: {{An Efficient SMT Solver}} for {{Verifying Deep Neural Networks}}}, shorttitle = {Reluplex}, booktitle = {Computer {{Aided Verification}}}, author = {Katz, Guy and Barrett, Clark and Dill, David L. and Julian, Kyle and Kochenderfer, Mykel J.}, editor = {Majumdar, Rupak and Kunčak, Viktor}, date = {2017}, volume = {10426}, pages = {97--117}, publisher = {Springer International Publishing}, location = {Cham}, doi = {10.1007/978-3-319-63387-9_5}, isbn = {978-3-319-63386-2 978-3-319-63387-9}, langid = {english}, note = {TL;DR \par Results show that the novel, scalable, and efficient technique presented can successfully prove properties of networks that are an order of magnitude larger than the largest networks verified using existing methods.} } @book{kaufman:clustering1990, ids = {opac-b1087461}, title = {Finding {{Groups}} in {{Data}}: An Introduction to Cluster Analysis}, author = {Kaufman, L. and Rousseeuw, P.J.}, date = {1990}, publisher = {Wiley}, added-at = {2017-11-14T13:30:05.000+0100}, biburl = {https://www.bibsonomy.org/bibtex/254cc9fb0fc88d6057dc9b1ce3feb1293/tomhanika}, description = {Dissertation}, foo = {bar}, interhash = {119bf8c432712ad3bbc1612759e0b7b4}, intrahash = {54cc9fb0fc88d6057dc9b1ce3feb1293}, keywords = {clustering kdd17}, note = {TL;DR \par An electrical signal transmission system, applicable to the transmission of signals from trackside hot box detector equipment for railroad locomotives and rolling stock, wherein a basic pulse train is transmitted whereof the pulses are of a selected first amplitude and represent a train axle count.}, timestamp = {2017-11-24T16:26:04.000+0100} } @book{kaufman2009finding, title = {Finding Groups in Data: An Introduction to Cluster Analysis}, author = {Kaufman, Leonard and Rousseeuw, Peter J}, date = {2009}, volume = {344}, publisher = {John Wiley \& Sons} } @incollection{KaufmanL1987Cbmo, title = {Clustering by Means of Medoids}, booktitle = {Statistical Data Analysis Based on the {{L1}} Norm and Related Methods}, author = {Kaufman, L and Rousseeuw, Peter}, date = {1987}, pages = {405--416}, publisher = {North-Holland; Amsterdam}, url = {$$Uhttps://lirias.kuleuven.be/retrieve/377090$$DKaufmanRousseeuw_ClusteringByMedoids_L1Norm_1987.pdf [Available for KU Leuven users]}, isbn = {0-444-70273-3}, langid = {english}, organization = {Dodge, Y} } @inproceedings{Kaur2021671, title = {Improved Skin Cancer Detection Classification Residual Network Feature Engineering}, author = {Kaur, R. and Kaur, N.}, editor = {Paul S., Verma J.K.}, date = {2021}, series = {2021 {{International Conference}} on {{Computational Performance Evaluation}}, {{ComPE}} 2021}, pages = {671--675}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/ComPE53109.2021.9751930}, abstract = {Skin cancer is a prevalent kind of cancer, and early diagnosis significantly improves the chance of survival. The purpose of this article is to develop a deep learning feature engineering model with an optimised xg-boost classifier for the purpose of classifying dermal cell pictures and detecting skin cancer. Utilization of Methodology Classification method based on features mapped on nonlinear space using Resnet 50 basis feature engineering, followed by learning via Xg-boost structure optimization. Structure optimization is accomplished by grey wolf optimization. Within the Results The deep learning with xgboost model developed here was evaluated on standard datasets and combined datasets, and the metric accuracy and precision were found to be 98.34 percent and 97.35 percent, respectively. Conclude that a practitioner may use model-driven architecture to rapidly develop deep learning models for skin cancer prediction. © 2021 IEEE.}, document_type = {Conference Paper}, isbn = {978-1-6654-3656-4}, source = {Scopus}, note = {cited By 0 \par TL;DR \par A deep learning feature engineering model with an optimised xg-boost classifier for the purpose of classifying dermal cell pictures and detecting skin cancer and it is concluded that a practitioner may use model-driven architecture to rapidly develop deep learning models for skin cancer prediction.} } @article{kazmanManagingEnergyConsumption2018, title = {Managing {{Energy Consumption}} as an {{Architectural Quality Attribute}}}, author = {Kazman, R. and Haziyev, S. and Yakuba, A. and Tamburri, D. A.}, date = {2018-09}, journaltitle = {IEEE Softw.}, volume = {35}, number = {5}, pages = {102--107}, issn = {0740-7459}, doi = {10.1109/MS.2018.3571227}, abstract = {A look at the software for an automated weather station shows that energy can be treated like any other architectural quality attribute. It’s no different, from the perspective of architectural design, than modifiability, performance, or availability. It can be modeled and prototyped, and we can reason about the design tradeoffs required to achieve better energy use.}, keywords = {internet of things} } @inproceedings{Kazmi2017449, title = {Generic Framework to Predict Repeat Behavior of Customers Using Their Transaction History}, author = {Kazmi, A.H. and Shroff, G. and Agarwal, P.}, date = {2017}, series = {Proceedings - 2016 {{IEEE}}/{{WIC}}/{{ACM International Conference}} on {{Web Intelligence}}, {{WI}} 2016}, pages = {449--452}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/WI.2016.0072}, abbrev_source_title = {Proc. - IEEE/WIC/ACM Int. Conf. Web Intell., WI}, affiliation = {TCS Research, India}, art_number = {7817089}, document_type = {Conference Paper}, isbn = {978-1-5090-4470-2}, langid = {english}, source = {Scopus}, note = {cited By 8} } @online{KDMWelcome, title = {{{KDM}} - {{Welcome}}}, url = {http://kdm.dataview.org/}, urldate = {2018-04-30} } @online{KeepAllYour, title = {Keep All Your Packages up to Date with {{Dependabot}} - {{The GitHub Blog}}}, url = {https://github.blog/2020-06-01-keep-all-your-packages-up-to-date-with-dependabot/}, urldate = {2021-01-11} } @article{kehrerUnderstandComplexChanges, title = {Understand Complex Changes and Improve the Quality of Your {{UML}} and Domain-Specific Models}, author = {Kehrer, Timo}, pages = {79}, langid = {english} } @article{kephartVisionAutonomicComputing2003, title = {The Vision of Autonomic Computing}, author = {Kephart, Jeffrey O. and Chess, David M.}, date = {2003}, journaltitle = {Computer}, volume = {36}, number = {1}, pages = {41--50}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1160055}, urldate = {2016-08-26}, note = {TL;DR \par A 2001 IBM manifesto noted the almost impossible difficulty of managing current and planned computing systems, which require integrating several heterogeneous environments into corporate-wide computing systems that extend into the Internet.} } @article{kerstenFivePredictionsComing2018, title = {Five {{Predictions}} for the {{Coming Decades}} of {{Software}}}, author = {Kersten, M.}, date = {2018-09}, journaltitle = {IEEE Softw.}, volume = {35}, number = {5}, pages = {7--9}, issn = {0740-7459}, doi = {10.1109/MS.2018.3571232}, abstract = {To help celebrate software engineering’s 50th anniversary, department editor Mik Kersten considers how software engineering will evolve over the coming 50 years. His five predictions aren’t intended to be precise; they aim to provide discussion topics for the shape of software engineering trends to come. This article is part of a theme issue on software engineering’s 50th anniversary.}, keywords = {artificial intelligence,software engineering}, note = {TL;DR \par To help celebrate software engineering’s 50th anniversary, department editor Mik Kersten considers how software engineering will evolve over the coming 50 years and makes five predictions.} } @inproceedings{kessentini2008model, title = {Model Transformation as an Optimization Problem}, booktitle = {Int. {{Conf}}. {{Model Driven Eng}}. {{Lang}}. {{Syst}}.}, author = {Kessentini, Marouane and Sahraoui, Houari and Boukadoum, Mounir}, date = {2008}, pages = {159--173}, publisher = {Springer}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {TL;DR \par This approach, named model transformation as optimization by examples (MOTOE), combines transformation blocks extracted from examples to generate a target model and can operate independently from the source and target metamodels.} } @article{KESSENTINI201949, title = {Automated Metamodel/Model Co-Evolution: {{A}} Search-Based Approach}, author = {Kessentini, Wael and Sahraoui, Houari and Wimmer, Manuel}, date = {2019}, journaltitle = {Inf. Softw. Technol.}, volume = {106}, pages = {49--67}, issn = {0950-5849}, doi = {10.1016/j.infsof.2018.09.003}, keywords = {Coupled evolution,Metamodel/model co-evolution,Model migration,Search based software engineering} } @article{kessentini2022semi, title = {Semi-Automated Metamodel/Model Co-Evolution: A Multi-Level Interactive Approach}, author = {Kessentini, Wael and Alizadeh, Vahid}, date = {2022}, journaltitle = {Software Syst. Model.}, volume = {21}, number = {5}, pages = {1853--1876}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {TL;DR \par This paper proposes an interactive approach that enables designers to select their preference simultaneously in the objective and decision spaces and compared it to existing fully automated and interactive co-evolution techniques.} } @incollection{kessentiniAutomatedCoevolutionMetamodels2018, ids = {kessentini2018automated}, title = {Automated {{Co-evolution}} of {{Metamodels}} and {{Transformation Rules}}: {{A Search-Based Approach}}}, shorttitle = {Automated {{Co-evolution}} of {{Metamodels}} and {{Transformation Rules}}}, booktitle = {Search-{{Based Software Engineering}}}, author = {Kessentini, Wael and Sahraoui, Houari and Wimmer, Manuel}, editor = {Colanzi, Thelma Elita and McMinn, Phil}, date = {2018}, volume = {11036}, pages = {229--245}, publisher = {Springer International Publishing}, location = {Cham}, doi = {10.1007/978-3-319-99241-9_12}, isbn = {978-3-319-99240-2 978-3-319-99241-9}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {TL;DR \par This paper proposes a novel search-based approach to recommend transformation rule changes to make transformations coherent with the new metamodel versions by finding a trade-off between maximizing the coverage of metAModel changes and minimizing the number of static errors in the transformation and thenumber of applied changes to the transformation.} } @inproceedings{kessentiniIntegratingDesignerIntheloop2018, ids = {10.1145/3239372.3239375}, title = {Integrating the {{Designer}} In-the-Loop for {{Metamodel}}/{{Model Co-Evolution}} via {{Interactive Computational Search}}}, booktitle = {Proc. 21th {{ACMIEEE Int}}. {{Conf}}. {{Model Driven Eng}}. {{Lang}}. {{Syst}}.}, author = {Kessentini, Wael and Wimmer, Manuel and Sahraoui, Houari}, date = {2018-10-14}, series = {{{MODELS}} '18}, pages = {101--111}, publisher = {ACM}, location = {Copenhagen Denmark}, doi = {10.1145/3239372.3239375}, abstract = {Metamodels evolve even more frequently than programming languages. This evolution process may result in a large number of instance models that are no longer conforming to the revised meta-model. On the one hand, the manual adaptation of models after the metamodels' evolution can be tedious, error-prone, and time-consuming. On the other hand, the automated co-evolution of metamodels/models is challenging especially when new semantics is introduced to the metamodels. In this paper, we propose an interactive multi-objective approach that dynamically adapts and interactively suggests edit operations to developers and takes their feedback into consideration. Our approach uses NSGA-II to find a set of good edit operation sequences that minimizes the number of conformance errors, maximizes the similarity with the initial model (reduce the loss of information) and minimizes the number of proposed edit operations. The designer can approve, modify, or reject each of the recommended edit operations, and this feedback is then used to update the proposed rankings of recommended edit operations. We evaluated our approach on a set of metamodel/model coevolution case studies and compared it to fully automated coevolution techniques.}, eventtitle = {{{MODELS}} '18: {{ACM}}/{{IEEE}} 21th {{International Conference}} on {{Model Driven Engineering Languages}} and {{Systems}}}, isbn = {978-1-4503-4949-9}, langid = {english}, pagetotal = {11}, keywords = {/unread,⛔ No INSPIRE recid found,Coupled Evolution,Interactive Optimization,Metamodel/Model Co-Evolution,Search Based Software Engineering}, note = {TL;DR \par This paper proposes an interactive multi-objective approach that dynamically adapts and interactively suggests edit operations to developers and takes their feedback into consideration, and compared it to fully automated coevolution techniques.} } @article{KessentiniMWOD17, title = {Search-Based Detection of Model Level Changes}, author = {Kessentini, Marouane and Mansoor, Usman and Wimmer, Manuel and Ouni, Ali and Deb, Kalyanmoy}, date = {2017}, journaltitle = {Empirical Software Eng.}, volume = {22}, number = {2}, pages = {670--715}, doi = {10.1007/S10664-016-9442-8}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Tue, 25 Aug 2020 16:58:56 +0200} } @article{kessentiniSearchbasedMetamodelMatching2014, title = {Search-Based Metamodel Matching with Structural and Syntactic Measures}, author = {Kessentini, Marouane and Ouni, Ali and Langer, Philip and Wimmer, Manuel and Bechikh, Slim}, date = {2014-11}, journaltitle = {J. Syst. Softw.}, volume = {97}, pages = {1--14}, issn = {01641212}, doi = {10.1016/j.jss.2014.06.040}, abstract = {The use of different domain-specific modeling languages and diverse versions of the same modeling language often entails the need to translate models between the different languages and language versions. The first step in establishing a transformation between two languages is to find their corresponding concepts, i.e., finding correspondences between their metamodel elements. Although, metamodels use heterogeneous terminologies and structures, they often still describe similar language concepts. In this paper, we propose to combine structural metrics (e.g., number of properties per concept) and syntactic metrics to generate correspondences between metamodels. Because metamodel matching requires to cope with a huge search space of possible element combinations, we adapted a local and a global metaheuristic search algorithm to find the best set of correspondences between metamodels. The efficiency and effectiveness of our proposal is evaluated on different matching scenarios based on existing benchmarks. In addition, we compared our technique to state-of-the-art ontology matching and model matching approaches.}, langid = {english}, note = {TL;DR \par This paper adapted a local and a global metaheuristic search algorithm to find the best set of correspondences between metamodels and compared the technique to state-of-the-art ontology matching and model matching approaches.} } @article{khakpourFormalModelingEvolving2012, title = {Formal Modeling of Evolving Self-Adaptive Systems}, author = {Khakpour, Narges and Jalili, Saeed and Talcott, Carolyn and Sirjani, Marjan and Mousavi, MohammadReza}, date = {2012-11}, journaltitle = {Sci. Comput. Program.}, volume = {78}, number = {1}, pages = {3--26}, issn = {01676423}, doi = {10.1016/j.scico.2011.09.004}, langid = {english} } @report{khalilSupportingEvolutionUML2013, title = {Supporting the Evolution of {{UML}} Models in Model Driven Software Developmeny: {{A Survey}}}, shorttitle = {Supporting the Evolution of {{UML}} Models in Model Driven Software Developmeny}, author = {Khalil, Amal and Dingel, Juergen}, date = {2013}, institution = {Technical Report, School of Computing. Queens University, Canada}, url = {http://research.cs.queensu.ca/TechReports/Reports/2013-602.pdf}, urldate = {2015-04-02} } @article{Khan:2016:STS:3004996.3005218, title = {A Survey on Test Suite Reduction Frameworks and Tools}, author = {Khan, Saif Ur Rehman and Lee, Sai Peck and Ahmad, Raja Wasim and Akhunzada, Adnan and Chang, Victor}, date = {2016-12}, journaltitle = {Int. J. Inf. Manag.}, volume = {36}, number = {6}, pages = {963--975}, publisher = {Elsevier Science Publishers B. V.}, address = {Amsterdam, The Netherlands, The Netherlands}, issn = {0268-4012}, url = {https://doi.org/10.1016/j.ijinfomgt.2016.05.025}, acmid = {3005218}, issue_date = {December 2016}, nodoi = {10.1016/j.ijinfomgt.2016.05.025}, numpages = {13}, keywords = {Fault localization,Frameworks,Regression testing,Test suite optimization,Test Suite Reduction} } @article{Khan2021130, title = {Robustness of {{AI-based}} Prognostic and Systems Health Management}, author = {Khan, S. and Tsutsumi, S. and Yairi, T. and Nakasuka, S.}, date = {2021}, journaltitle = {Annu. Rev. Control}, volume = {51}, pages = {130--152}, publisher = {Elsevier Ltd}, issn = {13675788}, doi = {10.1016/j.arcontrol.2021.04.001}, abbrev_source_title = {Annu Rev Control}, affiliation = {University of Tokyo, Department of Aeronautics and Astronautics, Tokyo, 113-8654, Japan; Japan Aerospace Exploration Agency, Research and Development Directorate, Kanagawa, 252-5210, Japan}, coden = {ARCOF}, correspondence_address1 = {Khan, S.; University of Tokyo, Department of Aeronautics and Astronautics, Japan; email: khan@ailab.t.u-tokyo.ac.jp}, document_type = {Article}, langid = {english}, source = {Scopus}, note = {cited By 1} } @article{khanFederatedLearningInternet2020, title = {Federated {{Learning}} for {{Internet}} of {{Things}}: {{Recent Advances}}, {{Taxonomy}}, and {{Open Challenges}}}, shorttitle = {Federated {{Learning}} for {{Internet}} of {{Things}}}, author = {Khan, Latif U. and Saad, Walid and Han, Zhu and Hossain, Ekram and Hong, Choong Seon}, date = {2020-09-27}, journaltitle = {ArXiv200913012 Cs}, eprint = {2009.13012}, eprinttype = {arXiv}, eprintclass = {cs}, url = {http://arxiv.org/abs/2009.13012}, urldate = {2020-12-22}, abstract = {The Internet of Things (IoT) will be ripe for the deployment of novel machine learning algorithms for both network and application management. However, given the presence of massively distributed and private datasets, it is challenging to use classical centralized learning algorithms in the IoT. To overcome this challenge, federated learning can be a promising solution that enables on-device machine learning without the need to migrate the private end-user data to a central cloud. In federated learning, only learning model updates are transferred between end-devices and the aggregation server. Although federated learning can offer better privacy preservation than centralized machine learning, it has still privacy concerns. In this paper, first, we present the recent advances of federated learning towards enabling federated learning-powered IoT applications. A set of metrics such as sparsification, robustness, quantization, scalability, security, and privacy, is delineated in order to rigorously evaluate the recent advances. Second, we devise a taxonomy for federated learning over IoT networks. Third, we propose two IoT use cases of dispersed federated learning that can offer better privacy preservation than federated learning. Finally, we present several open research challenges with their possible solutions.}, langid = {english}, keywords = {federated learning,internet of things}, note = {Comment: This paper has been submitted to IEEE Communications Surveys and Tutorials} } @inproceedings{kharlamovSemanticApproachPolystores2016, title = {A Semantic Approach to Polystores}, author = {Kharlamov, E. and Mailis, T. and Bereta, K. and Bilidas, D. and Brandt, S. and Jimenez-Ruiz, E. and Lamparter, S. and Neuenstadt, C. and Ozcep, O. and Soylu, A. and Svingos, C. and Xiao, G. and Zheleznyakov, D. and Calvanese, D. and Horrocks, I. and Giese, M. and Ioannidis, Y. and Kotidis, Y. and Moller, R. and Waaler, A.}, date = {2016-12}, pages = {2565--2573}, publisher = {IEEE}, doi = {10.1109/BigData.2016.7840898}, isbn = {978-1-4673-9005-7}, langid = {english} } @article{Khaytbaev2020115, title = {Decision Routing Problems in a Wireless Sensor Network Based on a Neural Mechanism}, author = {Khaytbaev, A.F.}, date = {2020}, journaltitle = {J. ICT Res. Appl.}, volume = {14}, number = {2}, pages = {115--133}, publisher = {{Institute for Research and Community Services, Institut Teknologi Bandung}}, issn = {23375787}, doi = {10.5614/itbj.ict.res.appl.2020.14.2.2}, abbrev_source_title = {J. ICT Res. Appl.}, affiliation = {Department of Telecommunication Engineering, Tashkent University of Information Technologies named after Muhammad Al-Khwarizmi, A. Temur St., 108, Tashkent, 100084, Uzbekistan}, correspondence_address1 = {Khaytbaev, A.F.; Department of Telecommunication Engineering, A. Temur St., 108, Uzbekistan; email: a.xaytbayev1981@inbox.ru}, document_type = {Article}, langid = {english}, source = {Scopus}, note = {cited By 0} } @article{khelladiDetectingComplexChanges2016, ids = {KHELLADI2016220}, title = {Detecting Complex Changes and Refactorings during ({{Meta}})Model Evolution}, author = {Khelladi, Djamel Eddine and Hebig, Regina and Bendraou, Reda and Robin, Jacques and Gervais, Marie-Pierre}, date = {2016-12}, journaltitle = {Information Systems}, volume = {62}, pages = {220--241}, issn = {03064379}, doi = {10.1016/j.is.2016.05.002}, langid = {english} } @article{khomhSoftwareEngineeringMachineLearning2018, title = {Software {{Engineering}} for {{Machine-Learning Applications}}: {{The Road Ahead}}}, shorttitle = {Software {{Engineering}} for {{Machine-Learning Applications}}}, author = {Khomh, F. and Adams, B. and Cheng, J. and Fokaefs, M. and Antoniol, G.}, date = {2018-09}, journaltitle = {IEEE Softw.}, volume = {35}, number = {5}, pages = {81--84}, issn = {0740-7459}, doi = {10.1109/MS.2018.3571224}, abstract = {The First Symposium on Software Engineering for Machine Learning Applications (SEMLA) aimed to create a space in which machine learning (ML) and software engineering (SE) experts could come together to discuss challenges, new insights, and practical ideas regarding the engineering of ML and AI-based systems. Key challenges discussed included the accuracy of systems built using ML and AI models, the testing of those systems, industrial applications of AI, and the rift between the ML and SE communities. This article is part of a theme issue on software engineering’s 50th anniversary.}, keywords = {artificial intelligence,DONE,machine learning,software engineering}, note = {\textbf{Gray Annotations (18/12/2020, 00:14:17)} \par "difficulty of testing ML and AI systems." (\href{zotero://open-pdf/library/items/5486JT7B?page=2}{Khomh et al 2018:82}) \par \textbf{Green Annotations (18/12/2020, 00:14:17)} \par "we still experience failures and shortcomings in the resulting soft­ ware systems. The main reason is the shift in the development paradigm in­ duced by ML and AI." (\href{zotero://open-pdf/library/items/5486JT7B?page=1}{Khomh et al 2018:81}) \par "with ML techniques, these rules are inferred from training data (from which the requirements are gener­ ated inductively)." (\href{zotero://open-pdf/library/items/5486JT7B?page=1}{Khomh et al 2018:81}) \par "This paradigm shift makes reasoning about the be­ havior of software systems with ML components difficult, resulting in software systems that are intrinsi­ cally challenging to test and verify." (\href{zotero://open-pdf/library/items/5486JT7B?page=1}{Khomh et al 2018:81}) \par "the learned behavior of an ML­based system might be incorrect, even if the learning algorithm is imple­ mented correctly, a situation in which traditional testing techniques are ineffective." (\href{zotero://open-pdf/library/items/5486JT7B?page=1}{Khomh et al 2018:81}) \par "critical problem is how to effectively develop, test, and evolve such systems, given that they don't have (complete) specifications or even source code corresponding to some of their critical behaviors." (\href{zotero://open-pdf/library/items/5486JT7B?page=1}{Khomh et al 2018:81}) \par \mkbibemph{TESTING (ML) SYSTEMS THAT LACK SPECIFICATIONS OR EVEN SOURCE CODE (\href{zotero://open-pdf/library/items/5486JT7B?page=1}{note on p.81})} \par "AI technology's strength comes from the ability to abstract up from different factors of varia­ tion between environments, to obtain models that can general­ ize and transfer to situations that weren't encountered before" (\href{zotero://open-pdf/library/items/5486JT7B?page=2}{Khomh et al 2018:82}) \par "AI tech­ nologies' main challenge is" (\href{zotero://open-pdf/library/items/5486JT7B?page=2}{Khomh et al 2018:82}) \par "he need for sufficient, labeled data to cover all important factors (features) of a given problem." (\href{zotero://open-pdf/library/items/5486JT7B?page=2}{Khomh et al 2018:82}) \par "AI, in fact, needs more training data than humans do!" (\href{zotero://open-pdf/library/items/5486JT7B?page=2}{Khomh et al 2018:82}) \par "appli­ cations of AI still risk being limited to domains in which labeled data is cheap." (\href{zotero://open-pdf/library/items/5486JT7B?page=2}{Khomh et al 2018:82}) \par "instead of touting a "100 percent self­driving car," auto­ motive companies should advertise their products as "AI­assisted cars," with a clear list of the ways in which AI is assisting." (\href{zotero://open-pdf/library/items/5486JT7B?page=2}{Khomh et al 2018:82}) \par "If a traditional computer science algorithm can solve a problem, we should just use that." (\href{zotero://open-pdf/library/items/5486JT7B?page=2}{Khomh et al 2018:82}) \par "how can we perform adequate quality assurance (QA) of AI models, given that the number of environments in which the mod­ els will be deployed is unlimited and that the human operator will re­ quire a detailed explanation of any failures?" (\href{zotero://open-pdf/library/items/5486JT7B?page=2}{Khomh et al 2018:82}) \par "use AI tech­ nology to reduce the search space of the environments to be tested" (\href{zotero://open-pdf/library/items/5486JT7B?page=2}{Khomh et al 2018:82}) \par "AI impacts the hu­ mans' recommendations, those rec­ ommendations are also a human filter for AI failures." (\href{zotero://open-pdf/library/items/5486JT7B?page=3}{Khomh et al 2018:83}) \par "Creating an efficient syntax for automatic differentiation that can deliver ease of implementation, per­ formance, usability, and flexibility is important but difficult." (\href{zotero://open-pdf/library/items/5486JT7B?page=3}{Khomh et al 2018:83}) \par \mkbibemph{CHALLENGES (\href{zotero://open-pdf/library/items/5486JT7B?page=3}{note on p.83})} \par "esting and debugging these implementations are also salient challenges." (\href{zotero://open-pdf/library/items/5486JT7B?page=3}{Khomh et al 2018:83}) \par "How should software develop­ ment teams integrate the AI model lifecycle (training, testing, deploying, evolving, and so on) into their software process?" (\href{zotero://open-pdf/library/items/5486JT7B?page=4}{Khomh et al 2018:84}) \par "What new roles, artifacts, and activities come into play, and how do they tie into existing agile or DevOps processes?" (\href{zotero://open-pdf/library/items/5486JT7B?page=4}{Khomh et al 2018:84}) \par "testing" (\href{zotero://open-pdf/library/items/5486JT7B?page=4}{Khomh et al 2018:84}) \par "intersections" (\href{zotero://open-pdf/library/items/5486JT7B?page=4}{Khomh et al 2018:84}) \par "critical challenges of as­ suring the quality of AI and software systems in general." (\href{zotero://open-pdf/library/items/5486JT7B?page=4}{Khomh et al 2018:84})} } @inproceedings{Khrouf:2013:HER:2507157.2507171, title = {Hybrid Event Recommendation Using Linked Data and User Diversity}, booktitle = {Proc. 7th {{ACM Conf}}. {{Recomm}}. {{Syst}}.}, author = {Khrouf, Houda and Troncy, Raphaël}, date = {2013}, series = {{{RecSys}} '13}, pages = {185--192}, publisher = {ACM}, location = {New York, NY, USA}, url = {http://doi.acm.org/10.1145/2507157.2507171}, acmid = {2507171}, isbn = {978-1-4503-2409-0}, nodoi = {10.1145/2507157.2507171}, numpages = {8}, keywords = {event recommendation,linked data,lode ontology,user diversity}, note = {TL;DR \par This paper uses a content-based system enriched with Linked Data to overcome the data sparsity, a problem induced by the transiency of events, and incorporates a collaborative filtering to involve the social aspect, an influential feature in decision making.} } @incollection{khusroRecommenderSystemsIssues2016, title = {Recommender Systems: {{Issues}}, Challenges, and Research Opportunities}, booktitle = {Information Science and Applications ({{ICISA}}) 2016}, author = {Khusro, Shah and Ali, Zafar and Ullah, Irfan}, editor = {Kim, Kuinam J. and Joukov, Nikolai}, date = {2016}, pages = {1179--1189}, publisher = {Springer Singapore}, location = {Singapore}, doi = {10.1007/978-981-10-0557-2₁12}, abstract = {A recommender system is an Information Retrieval technology that improves access and proactively recommends relevant items to users by considering the users' explicitly mentioned preferences and objective behaviors. A recommender system is one of the major techniques that handle information overload problem of Information Retrieval by suggesting users with appropriate and relevant items. Today, several recommender systems have been developed for different domains however, these are not precise enough to fulfil the information needs of users. Therefore, it is necessary to build high quality recommender systems. In designing such recommenders, designers face several issues and challenges that need proper attention. This paper investigates and reports the current trends, issues, challenges, and research opportunities in developing high-quality recommender systems. If properly followed, these issues and challenges will introduce new research avenues and the goal towards fine-tuned and high-quality recommender systems can be achieved.}, isbn = {978-981-10-0557-2}, note = {TL;DR \par The current trends, issues, challenges, and research opportunities in developing high-quality recommender systems are investigated and the goal towards fine-tuned and high- quality recommender system can be achieved is achieved.} } @article{kienzleModeldrivenSustainabilityEvaluation2020, title = {Toward Model-Driven Sustainability Evaluation}, author = {Kienzle, Jörg and Mussbacher, Gunter and Combemale, Benoit and Bastin, Lucy and Bencomo, Nelly and Bruel, Jean-Michel and Becker, Christoph and Betz, Stefanie and Chitchyan, Ruzanna and Cheng, Betty H. C. and Klingert, Sonja and Paige, Richard F. and Penzenstadler, Birgit and Seyff, Norbert and Syriani, Eugene and Venters, Colin C.}, date = {2020-02-24}, journaltitle = {Commun. ACM}, volume = {63}, number = {3}, pages = {80--91}, issn = {0001-0782, 1557-7317}, doi = {10.1145/3371906}, langid = {english} } @article{kienzleSecondWorkingSession, title = {Second {{Working Session}} on a {{Common Architecture}}/{{Infrastructure}} for {{Modelling Tools}} for {{Teaching}}}, author = {Kienzle, Jorg and Zschaler, Steffen}, abstract = {Many academic modelling tools have accumulated significant technical debt and lack of the nowadays quality standards. Following on from the success of the 1st working session on a common architecture/infrastructure for modelling tools for teaching, held at MODELS’23, which produced a comprehensive catalogue of requirements for modelling tools for teaching to be published in SoSyM, we propose a 2nd working session to focus on designs and implementations delivering on these requirements. The session will have a short programme of presentations, but will prioritise discussion and consensus-forming to work towards community-owned assets as the foundations for modelling tools for teaching.}, langid = {english}, note = {\section{Annotazioni\\ (18/3/2024, 21:55:23)} \par - “Second Working Session” (Kienzle e Zschaler, p. 1) \#2ea8e5\\ \mkbibemph{test} \par - “Infrastructure for Modelling Tools for Teac” (Kienzle e Zschaler, p. 1) \#5fb236\\ \mkbibemph{test2}} } @inproceedings{kim_f_2018, ids = {DBLP:conf/icse/KimKBC0KT18}, title = {{{FaCoY}}: A Code-to-Code Search Engine}, shorttitle = {F}, booktitle = {Proc. 40th {{Int}}. {{Conf}}. {{Softw}}. {{Eng}}. - {{ICSE}} 18}, author = {Kim, Kisub and Kim, Dongsun and Bissyandé, Tegawendé F. and Choi, Eunjong and Li, Li and Klein, Jacques and Traon, Yves Le}, date = {2018}, pages = {946--957}, publisher = {ACM Press}, location = {Gothenburg, Sweden}, url = {http://dl.acm.org/citation.cfm?doid=3180155.3180187}, urldate = {2019-09-04}, abstract = {Code search is an unavoidable activity in software development. Various approaches and techniques have been explored in the literature to support code search tasks. Most of these approaches focus on serving user queries provided as natural language free-form input. However, there exists a wide range of use-case scenarios where a code-to-code approach would be most beneficial. For example, research directions in code transplantation, code diversity, patch recommendation can leverage a code-to-code search engine to find essential ingredients for their techniques. In this paper, we propose FaCoY, a novel approach for statically finding code fragments which may be semantically similar to user input code. FaCoY implements a query alternation strategy: instead of directly matching code query tokens with code in the search space, FaCoY first attempts to identify other tokens which may also be relevant in implementing the functional behavior of the input code. With various experiments, we show that (1) FaCoY is more effective than online code-to-code search engines; (2) FaCoY can detect more semantic code clones (i.e., Type-4) in BigCloneBench than the state-of-theart; (3) FaCoY, while static, can detect code fragments which are indeed similar with respect to runtime execution behavior; and (4) FaCoY can be useful in code/patch recommendation.}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/bib/conf/icse/KimKBC0KT18}, isbn = {978-1-4503-5638-1}, langid = {english}, nodoi = {10.1145/3180155.3180187}, note = {TL;DR \par FaCoY is proposed, a novel approach for statically finding code fragments which may be semantically similar to user input code which is more effective than online code-to-code search engines and can be useful in code/patch recommendation.}, timestamp = {Wed, 21 Nov 2018 12:43:59 +0100} } @article{KIM20081200, title = {A Recommender System Using {{GA K-means}} Clustering in an Online Shopping Market}, author = {Kim, Kyoung-jae and Ahn, Hyunchul}, date = {2008}, journaltitle = {Expert Syst. Appl.}, volume = {34}, number = {2}, pages = {1200--1209}, issn = {0957-4174}, doi = {10.1016/j.eswa.2006.12.025}, abstract = {The Internet is emerging as a new marketing channel, so understanding the characteristics of online customers’ needs and expectations is considered a prerequisite for activating the consumer-oriented electronic commerce market. In this study, we propose a novel clustering algorithm based on genetic algorithms (GAs) to effectively segment the online shopping market. In general, GAs are believed to be effective on NP-complete global optimization problems, and they can provide good near-optimal solutions in reasonable time. Thus, we believe that a clustering technique with GA can provide a way of finding the relevant clusters more effectively. The research in this paper applied K-means clustering whose initial seeds are optimized by GA, which is called GA K-means, to a real-world online shopping market segmentation case. In this study, we compared the results of GA K-means to those of a simple K-means algorithm and self-organizing maps (SOM). The results showed that GA K-means clustering may improve segmentation performance in comparison to other typical clustering algorithms. In addition, our study validated the usefulness of the proposed model as a preprocessing tool for recommendation systems.}, keywords = {Case-based reasoning,Genetic algorithms,Market segmentation,Recommender system,Self-organizing maps} } @inproceedings{kim2014convolutional, title = {Convolutional Neural Networks for Sentence Classification}, booktitle = {Proc. 2014 {{Conf Empir}}. {{Methods NLP EMNLP}} 2014 {{Oct}}. 25-29 2014 {{Doha Qatar}}}, author = {Kim, Yoon}, date = {2014}, pages = {1746--1751}, added-at = {2017-01-25T12:34:20.000+0100}, bibsource = {dblp computer science bibliography, http://dblp.org}, biburl = {https://www.bibsonomy.org/bibtex/2ca3690ad4dc124a0e0f30afaa475adb9/albinzehe}, description = {dblp: BibTeX record conf/emnlp/Kim14}, interhash = {5a18dcdef0fe1455c8d7d96cee67e2b6}, intrahash = {ca3690ad4dc124a0e0f30afaa475adb9}, keywords = {cnn gpu kallimachos ma-zehe mlnlp neuralnet sentimentanalysis}, note = {TL;DR \par The CNN models discussed herein improve upon the state of the art on 4 out of 7 tasks, which include sentiment analysis and question classification, and are proposed to allow for the use of both task-specific and static vectors.}, timestamp = {2018-06-08T05:08:41.000+0200} } @article{Kim2022, title = {Instance-Based Transfer Learning Method via Modified Domain-Adversarial Neural Network with Influence Function: {{Applications}} to Design Metamodeling and Fault Diagnosis}, author = {Kim, J. and Lee, J.}, date = {2022}, journaltitle = {Appl. Soft Comput.}, volume = {123}, publisher = {Elsevier Ltd}, issn = {15684946}, doi = {10.1016/j.asoc.2022.108934}, abbrev_source_title = {Appl. Soft Comput.}, affiliation = {School of Mechanical Engineering, Yonsei University, Seoul, 03722, South Korea}, art_number = {108934}, correspondence_address1 = {Lee, J.; School of Mechanical Engineering, South Korea; email: jleej@yonsei.ac.kr}, document_type = {Article}, langid = {english}, source = {Scopus}, note = {cited By 0} } @online{kimLanguageModelsCan2023, title = {Language {{Models}} Can {{Solve Computer Tasks}}}, author = {Kim, Geunwoo and Baldi, Pierre and McAleer, Stephen}, date = {2023-11-16}, eprint = {2303.17491}, eprinttype = {arXiv}, eprintclass = {cs}, url = {http://arxiv.org/abs/2303.17491}, urldate = {2024-01-18}, abstract = {Agents capable of carrying out general tasks on a computer can improve efficiency and productivity by automating repetitive tasks and assisting in complex problem-solving. Ideally, such agents should be able to solve new computer tasks presented to them through natural language commands. However, previous approaches to this problem require large amounts of expert demonstrations and task-specific reward functions, both of which are impractical for new tasks. In this work, we show that a pre-trained large language model (LLM) agent can execute computer tasks guided by natural language using a simple prompting scheme where the agent Recursively Criticizes and Improves its output (RCI). The RCI approach significantly outperforms existing LLM methods for automating computer tasks and surpasses supervised learning (SL) and reinforcement learning (RL) approaches on the MiniWoB++ benchmark. We compare multiple LLMs and find that RCI with the InstructGPT-3+RLHF LLM is state-of-the-art on MiniWoB++, using only a handful of demonstrations per task rather than tens of thousands, and without a task-specific reward function. Furthermore, we demonstrate RCI prompting's effectiveness in enhancing LLMs' reasoning abilities on a suite of natural language reasoning tasks, outperforming chain of thought (CoT) prompting with external feedback. We find that RCI combined with CoT performs better than either separately. Our code can be found here: https://github.com/posgnu/rci-agent.}, pubstate = {prepublished}, keywords = {Computer Science - Artificial Intelligence,Computer Science - Computation and Language,Computer Science - Human-Computer Interaction,Computer Science - Machine Learning} } @article{kirchhofMontiThingsModelDrivenDevelopment2022, title = {{{MontiThings}}: {{Model-Driven Development}} and {{Deployment}} of {{Reliable IoT Applications}}}, shorttitle = {{{MontiThings}}}, author = {Kirchhof, Jörg Christian and Rumpe, Bernhard and Schmalzing, David and Wortmann, Andreas}, date = {2022-01}, journaltitle = {Journal of Systems and Software}, volume = {183}, pages = {111087}, issn = {01641212}, doi = {10.1016/j.jss.2021.111087}, abstract = {Internet of Things (IoT) applications are exposed to harsh conditions due to factors such as device failure, network problems, or implausible sensor values. We investigate how the inherent encapsulation of component and connector (C\&C) architectures can be used to develop and deploy reliable IoT applications. Existing C\&C languages for the development of IoT applications mainly focus on the description of architectures and the distribution of components to IoT devices. Furthermore, related approaches often pollute the models with low-level implementation details, tying the models to a particular platform and making them harder to understand. In this paper, we introduce MontiThings, a C\&C language offering automatic error handling capabilities and a clear separation between business logic and implementation details. The error-handling methods presented in this paper can make C\&Cbased IoT applications more reliable without cluttering the business logic with error-handling code that is time-consuming to develop and makes the models hard to understand, especially for non-experts.}, langid = {english}, keywords = {LOGSEQ} } @inproceedings{KitchenhamBLBB11, title = {Repeatability of Systematic Literature Reviews}, booktitle = {15th {{Int}}. {{Conf}}. {{Eval}}. {{Assess}}. {{Softw}}. {{Eng}}. {{EASE}} 2011 {{Durh}}. {{UK}} 11-12 {{April}} 2011 {{Proc}}.}, author = {Kitchenham, Barbara A. and Brereton, Pearl and Li, Zhi and Budgen, David and Burn, Andrew James}, date = {2011}, pages = {46--55}, doi = {10.1049/ic.2011.0006}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/ease/KitchenhamBLBB11.bib}, timestamp = {Fri, 09 Apr 2021 18:54:10 +0200} } @article{KizhakkeKodakkattu2020553, title = {Design Optimization of Helicopter Rotor with Trailing-Edge Flaps Using Genetic Algorithm}, author = {Kizhakke Kodakkattu, S.}, editor = {Venkata Rao R., Taler J.}, date = {2020}, journaltitle = {Adv. Intell. Syst. Comput.}, volume = {949}, pages = {553--562}, publisher = {Springer Verlag}, issn = {21945357}, doi = {10.1007/978-981-13-8196-6_48}, abbrev_source_title = {Adv. Intell. Sys. Comput.}, affiliation = {Government Engineering College Kozhikode, West Hill P.O., Kozhikode, Kerala 673008, India}, correspondence_address1 = {Kizhakke Kodakkattu, S.; Government Engineering College Kozhikode, West Hill P.O., India; email: saijalkk@gmail.com}, document_type = {Conference Paper}, isbn = {9789811381959}, langid = {english}, source = {Scopus}, note = {cited By 0} } @misc{klein_2015, title = {Model Driven Engineering: Automatic Code Generation and Beyond}, author = {Klein, John}, date = {2015-05}, url = {https://insights.sei.cmu.edu/blog/model-driven-engineering-automatic-code-generation-and-beyond/}, howpublished = {Carnegie Mellon University, Software Engineering Institute's Insights (blog)}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {Accessed: 2024-Mar-5} } @book{kleppe2003mda, title = {{{MDA}} Explained: The Model Driven Architecture : Practice and Promise}, author = {Kleppe, A.G. and Warmer, J.B. and Bast, W.}, date = {2003}, series = {Addison-{{Wesley}} Object Technology Series}, publisher = {Addison-Wesley}, url = {https://books.google.es/books?id=nC6oS5xQGukC}, isbn = {978-0-321-19442-8}, langid = {english}, lccn = {2003043725}, keywords = {/unread,⛔ No INSPIRE recid found} } @article{klimekREdrivenModelDevelopment2018, title = {{{RE-driven Model Development}} with {{Deduction-based Verification}}}, author = {Klimek, Radoslaw}, date = {2018}, abstract = {The requirements engineering (RE) phase is critical for obtaining high-quality software. A visualisation and pervasive approach to modelling key development artefacts, along with formal verification, may be essential in this context. We proposed the conception of a framework aimed at specific Unified Modeling Language (UML) diagrams and preliminary system modelling. The tool provides visualisations of various modelling stages. The obtained behavioural models are automatically translated into logical specifications. Deduction-based formal verification addresses logical requirements and logical relations between software artefacts. It also enables the maintenance of both logical and visual traceability of key artefacts. Finally, program skeletons are automatically generated.}, langid = {english}, keywords = {LOGSEQ}, note = {\section{Annotazioni\\ (1/8/2024, 16:30:30)} \par - “visualisation and pervasive approach to modelling key development artefacts, along with formal verification, may be essential in this context” (Klimek, 2018, p. 1) \#5fb236 \par - “models are automatically translated into logical specifications.” (Klimek, 2018, p. 1) \#ffd400\\ \mkbibemph{Check the novelty of this!!!} \par - “s [8]” (Klimek, 2018, p. 1) \#ffd400\\ \mkbibemph{It's a more than 10 years reference.} \par - “valid [11]” (Klimek, 2018, p. 1) \#ffd400\\ \mkbibemph{Even worste} \par - “Logical specifications for behavioural models are difficult to specify manually by inexperienced users, which constitutes a hindrance in the building of logic-based frameworks.” (Klimek, 2018, p. 1) \#5fb236 \par - “F-IDE” (Klimek, 2018, p. 1) \#5fb236 \par - “class” (Klimek, 2018, p. 1) \#ff6666 \par - “Our future intention is to implement a F-IDE system that will keep the analyst informed, constantly, about the inconsistency of the behaviour model regarding the planned logical assertions.” (Klimek, 2018, p. 1) \#ffd400\\ \mkbibemph{I'm not an expert but in the context of critical systems, there are already techniques and tools supporting the logical specification of systems. I remember TopCased did something similar!} \par - “mature enough and correct” (Klimek, 2018, p. 1) \#ffd400\\ \mkbibemph{What does it mean? Mature with respect to what?} \par - “classic logical assertions” (Klimek, 2018, p. 1) \#5fb236 \par - “deductive reasoning about behavioural models developed during the (in-depth) RE, and then generating program skeletons, which are equivalent to designed behavioural models” (Klimek, 2018, p. 1) \#5fb236 \par - “This approach can be used in a variety of software projects to obtain formally verified and reliable skeletons for further developments” (Klimek, 2018, p. 1) \#5fb236 \par - “creating graphical representations of systems and their component interactions” (Klimek, 2018, p. 1) \#5fb236 \par - “Consequently, visual models enhance the comprehension of complex systems and improve project management efficiency” (Klimek, 2018, p. 1) \#a28ae5 \par - “Correctness-by-Construction” (Klimek, 2018, p. 1) \#5fb236 \par - “compositional approach,” (Klimek, 2018, p. 2) \#5fb236 \par - “Requirements eLicitation (RL)” (Klimek, 2018, p. 2) \#a28ae5 \par - “Requirements Analysis (RA)” (Klimek, 2018, p. 2) \#a28ae5 \par - “Requirements Specification (RS)” (Klimek, 2018, p. 2) \#a28ae5 \par - “Requirements Verification (RV)” (Klimek, 2018, p. 2) \#a28ae5 \par - “A more precise description of the RV phase and its steps is given below” (Klimek, 2018, p. 2) \#5fb236 \par - “deduction theorem” (Klimek, 2018, p. 2) \#5fb236 \par - “Remark 1. The presented structuring demonstrates a natural progression from general artifacts to increasingly formal behavioural models, which undergo rigorous logical verification.” (Klimek, 2018, p. 2) \#a28ae5 \par - “A set of patterns” (Klimek, 2018, p. 3) \#5fb236 \par - “Π = \{Seq, SeqSeq, Cond, Alt, Para, Loop\}” (Klimek, 2018, p. 3) \#5fb236 \par - “Compositionality in workflows” (Klimek, 2018, p. 3) \#5fb236 \par - “Propositional linear-time temporal logic PLTL” (Klimek, 2018, p. 3) \#5fb236 \par - “They are used in a process of translating the natural language of scenarios into formal activity diagrams AD j=1,... as workflows.” (Klimek, 2018, p. 3) \#ffd400 \par - “Figure 3: The basic system architecture of LoRE+, and its data flows. (Modellers, builder and creator provide the possibility of interactive work when developing a software system, while generator and reasoner guarantee fully automatic verification operations. The Gen generator implements two algorithms, see Formulas (2) and (3). Π and Σ are defined once and used widely)” (Klimek, 2018, p. 4) \#ffd400\\ \mkbibemph{It's difficult to see how the process can work in practice. Starting from textual specifications, logical formulas are generated. Such a critical process is vaguosly presented and the conditions under which this can work are not discussed.} \par - “available upon request” (Klimek, 2018, p. 4) \#ffd400\\ \mkbibemph{Why not making this available directly?} \par - “This problem involved investigating how the size of a formula affects testing time, assuming the formula comprises groups of clauses with fixed lengths (number of used variables/atoms/literals). We generated formulas consisting of 50, 100, 200, 500, 1000, and 2000 clauses separately.” (Klimek, 2018, p. 4) \#ffd400\\ \mkbibemph{All of this are vaguos and are not conveying any message. Instead, the authors are invited to revise the paper by discussing an running example to show how the different components of the proposed approach work in practice. By discussing them in this manner, it is possible to highlight the strenghts and the limitations of the proposed approach with respect to the state-of-art techniques.} \par - “Figure 4: Problem \#5 for Prover9 (top), SPASS (middle) and InKreSAT (bottom), clauses against time” (Klimek, 2018, p. 4) \#ffd400\\ \mkbibemph{It's not possible to appreciate the strenghts of the approach and the data shown in Fig. 4 without properly presenting the context where they have been produced, etc.}} } @inproceedings{KlingJWBC12, title = {{{MoScript}}: {{A DSL}} for Querying and Manipulating Model Repositories}, booktitle = {Proc. {{SLE}} 2011}, author = {Kling, Wolfgang and Jouault, Frédéric and Wagelaar, Dennis and Brambilla, Marco and Cabot, Jordi}, date = {2012}, series = {{{LNCS}}}, volume = {6940}, pages = {180--200}, publisher = {Springer} } @inproceedings{koch_semi-automated_2017, title = {Semi-Automated Discovery of Server-Based Information Oversharing Vulnerabilities in {{Android}} Applications}, booktitle = {Proc. 26th {{ACM SIGSOFT Int}}. {{Symp}}. {{Softw}}. {{Test}}. {{Anal}}.}, author = {Koch, William and Chaabane, Abdelberi and Egele, Manuel and Robertson, William and Kirda, Engin}, date = {2017-07}, series = {{{ISSTA}} 2017}, pages = {147--157}, publisher = {Association for Computing Machinery}, location = {New York, NY, USA}, doi = {10.1145/3092703.3092708}, abstract = {Modern applications are often split into separate client and server tiers that communicate via message passing over the network. One well-understood threat to privacy for such applications is the leakage of sensitive user information either in transit or at the server. In response, an array of defensive techniques have been developed to identify or block unintended or malicious information leakage. However, prior work has primarily considered privacy leaks originating at the client directed at the server, while leakage in the reverse direction – from the server to the client – is comparatively under-studied. The question of whether and to what degree this leakage constitutes a threat remains an open question. We answer this question in the affirmative with Hush, a technique for semi-automatically identifying Server-based InFormation OvershariNg (SIFON) vulnerabilities in multi-tier applications. In particular, the technique detects SIFON vulnerabilities using a heuristic that overshared sensitive information from server-side APIs will not be displayed by the application's user interface. The technique first performs a scalable static program analysis to screen applications for potential vulnerabilities, and then attempts to confirm these candidates as true vulnerabilities with a partially-automated dynamic analysis. Our evaluation over a large corpus of Android applications demonstrates the effectiveness of the technique by discovering several previously-unknown SIFON vulnerabilities in eight applications.}, isbn = {978-1-4503-5076-1}, keywords = {Android testing,information leakage,software analysis}, note = {TL;DR \par Hush is a technique for semi-automatically identifying Server-based InFormation OvershariNg (SIFON) vulnerabilities in multi-tier applications using a heuristic that overshared sensitive information from server-side APIs will not be displayed by the application's user interface.} } @article{Kochovski2021215, title = {Smart Contract for Cross-Border {{AI}} Model Management}, author = {Kochovski, P. and Kum, S. and Moon, J. and Vujić, A. and Stankovski, V.}, editor = {Tserpes K., Banares J.A., Tuffin B., Altmann J., Ben-Yehuda O.A., Stankovski V., Djemame K.}, date = {2021}, journaltitle = {Lect. Notes Comput. Sci. Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinforma.}, volume = {13072 LNCS}, pages = {215--222}, publisher = {{Springer Science and Business Media Deutschland GmbH}}, issn = {03029743}, doi = {10.1007/978-3-030-92916-9_20}, abstract = {The new wave of Artificial Intelligence (AI) implementation has made it possible to deploy and (re)use AI models seamlessly. Modern software engineering techniques make it possible to containerize and orchestrate AI services globally, and across the whole computing continuum from the Cloud to the Edge. However, the data processed by AI services may be subject to various privacy and governance constraints, and thus subject to governmental regulations. In this work we present an advanced Smart Contract that is built to achieve regulatory compliance in cross-border AI model sharing between the European Union and the Republic of Korea. Key feature of the Smart Contract are specially developed oracle adapters that are used to achieve fine-grained control on AI model management. © 2021, Springer Nature Switzerland AG.}, document_type = {Conference Paper}, isbn = {9783030929152}, source = {Scopus}, note = {cited By 0} } @inproceedings{koegel2010emfstore, ids = {KoegelH10}, title = {{{EMFStore}}: A Model Repository for {{EMF}} Models}, booktitle = {2010 {{ACMIEEE}} 32nd {{Int}}. {{Conf}}. {{Softw}}. {{Eng}}.}, author = {Koegel, Maximilian and Helming, Jonas}, date = {2010}, volume = {2}, pages = {307--308}, publisher = {ACM}, doi = {10.1145/1810295.1810364}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, organization = {IEEE}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {TL;DR \par EMFStore is a Software Configuration Management system tailored to the specific requirements for versioning models that employs operation-based change tracking, conflict detection and merging.}, timestamp = {Tue, 06 Nov 2018 11:06:56 +0100} } @inproceedings{KoegelHLHD10, title = {Comparing State- and Operation-Based Change Tracking on Models}, booktitle = {Proc. 14th {{IEEE Int}}. {{Enterp}}. {{Distrib}}. {{Object Comput}}. {{Conf}}. {{EDOC}} 2010 {{Vitória Braz}}. 25-29 {{Oct}}. 2010}, author = {Koegel, Maximilian and Herrmannsdoerfer, Markus and Li, Yang and Helming, Jonas and David, Jörn}, date = {2010}, pages = {163--172}, publisher = {IEEE Computer Society}, doi = {10.1109/EDOC.2010.15}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Fri, 24 Mar 2023 00:04:50 +0100} } @inproceedings{koegelOperationbasedConflictDetection2010, title = {Operation-Based Conflict Detection}, booktitle = {Proc. 1st {{Int}}. {{Workshop Model Comp}}. {{Pract}}.}, author = {Koegel, Maximilian and Herrmannsdoerfer, Markus and Von Wesendonk, Otto and Helming, Jonas}, date = {2010-07}, pages = {21--30}, publisher = {ACM}, location = {Malaga Spain}, doi = {10.1145/1826147.1826154}, abstract = {In recent years, models are increasingly used throughout the entire lifecycle in software engineering projects. In effect, the need for collaboration and for management of change on these models emerged. Traditionally, Software Configuration Management (SCM) systems are employed to facilitate collaboration on software engineering artifacts and to control change to these artifacts. For scalability and to support offline operation, most of these systems employ optimistic concurrency control and therefore require methods to detect concurrent change—also known as conflict detection. However, many researchers have shown that existing approaches for SCM systems do not work well on graph-like models, since they are geared towards textual artifacts and do not take the graph structure of models into account. The approaches for conflict detection in these systems show many false positives, since they require a merge every time the same configuration item — in this case the same file — is changed. In this paper, we propose operation-based conflict detection, which detects conflicts directly on the operations that change the model. We compare operation-based conflict detection to file-based conflict detection in a multi-case study and show that operation-based conflict detection results in less conflicts and therefore requires fewer merges.}, eventtitle = {{{IWMCP}} '10: {{International Workshop}} on {{Model Comparison}} in {{Practice}}}, isbn = {978-1-60558-960-2}, langid = {english} } @inproceedings{Kohavi:1995:SCB:1643031.1643047, title = {A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection}, booktitle = {Proc. 14th {{Int}}. {{Jt}}. {{Conf}}. {{Artif}}. {{Intell}}. - {{Vol}}. 2}, author = {Kohavi, Ron}, date = {1995}, series = {{{IJCAI}}'95}, pages = {1137--1143}, publisher = {Morgan Kaufmann Publishers Inc.}, location = {San Francisco, CA, USA}, url = {http://dl.acm.org/citation.cfm?id=1643031.1643047}, acmid = {1643047}, isbn = {1-55860-363-8}, numpages = {7} } @article{kolahdouz-rahimiEvaluationModelTransformation2014, title = {Evaluation of Model Transformation Approaches for Model Refactoring}, author = {Kolahdouz-Rahimi, S. and Lano, K. and Pillay, S. and Troya, J. and Van Gorp, P.}, date = {2014-06}, journaltitle = {Sci. Comput. Program.}, volume = {85}, pages = {5--40}, issn = {01676423}, doi = {10.1016/j.scico.2013.07.013}, langid = {english} } @article{kolovosCEURWorkshopProceedings2015, ids = {kolovosCEURWorkshopProceedings2015a}, title = {{{CEUR Workshop Proceedings}}}, author = {Kolovos, D. and Di Ruscio, D. and Matragkas, N. and Cuadrado, J.S. and Rath, I. and Tisi, M.}, date = {2015}, journaltitle = {CEUR Workshop Proc.}, volume = {1406}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84938634077&partnerID=40&md5=4fdd9c5a6a15ac65fad0a31a6aac77a9}, note = {cited By 1 \par cited By 1} } @inproceedings{kolovosDetectingRepairingInconsistencies2008, title = {Detecting and {{Repairing Inconsistencies}} across {{Heterogeneous Models}}}, booktitle = {2008 {{Int}}. {{Conf}}. {{Softw}}. {{Test}}. {{Verification Valid}}.}, author = {Kolovos, Dimitrios and Paige, Richard and Polack, Fiona}, date = {2008-04}, pages = {356--364}, publisher = {IEEE}, location = {Lillehammer, Norway}, doi = {10.1109/ICST.2008.23}, abstract = {With the advent of Domain Specific Languages for Model Engineering, detecting inconsistencies between models is becoming increasingly challenging. Nowadays, it is not uncommon for models participating in the same development process to be captured using different modelling languages and even different modelling technologies. We present a classification of the types of relationships that can arise between models participating in a software development process and outline the types of inconsistencies each relationship can suffer from. From this classification we identify a set of requirements for a generic inconsistency detection and reconciliation mechanism and use a case study to demonstrate how those requirements are implemented in the Epsilon Validation Language (EVL), a task-specific language developed in the context of the Epsilon GMT component.}, eventtitle = {2008 {{International Conference}} on {{Software Testing}}, {{Verification}}, and {{Validation}}}, isbn = {978-0-7695-3127-4}, langid = {english} } @article{kolovosEugeniaDisciplinedAutomated2015, title = {Eugenia: Towards Disciplined and Automated Development of {{GMF-based}} Graphical Model Editors}, shorttitle = {Eugenia}, author = {Kolovos, Dimitrios S. and García-Domínguez, Antonio and Rose, Louis M. and Paige, Richard F.}, date = {2015-02-26}, journaltitle = {Softw. Syst. Model.}, issn = {1619-1366, 1619-1374}, doi = {10.1007/s10270-015-0455-3}, langid = {english}, note = {TL;DR \par Eugenia, an open-source tool that implements the proposed approach to metamodel annotation and model transformation techniques can help to manage the complexity of GMF and EMF and deliver significant productivity, quality, and maintainability benefits.} } @inproceedings{KolovosG22, title = {The Epsilon Playground}, booktitle = {Proc. 25th {{Int}}. {{Conf}}. {{Model Driven Eng}}. {{Lang}}. {{Syst}}. {{Companion Proc}}. {{MODELS}} 2022 {{Montr}}. {{Quebec Can}}. {{Oct}}. 23-28 2022}, author = {Kolovos, Dimitris S. and García-Domínguez, Antonio}, editor = {Kühn, Thomas and Sousa, Vasco}, date = {2022}, pages = {131--137}, publisher = {ACM}, doi = {10.1145/3550356.3556507}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {TL;DR \par An overview of the capabilities and the architecture of the Epsilon Playground, an open-source and publicly-available web application that enables users to experiment with metamodelling, modelling and common model management activities from the convenience of their web browser.}, timestamp = {Mon, 26 Jun 2023 20:42:07 +0200} } @article{koModelTransformationVerification2013, title = {Model Transformation Verification Using Similarity and Graph Comparison Algorithm}, author = {Ko, Jong-Won and Chung, Kyung-Yong and Han, Jung-Soo}, date = {2013}, journaltitle = {Multimed. Tools Appl.}, doi = {10.1007/s11042-013-1581-y}, note = {TL;DR \par This study suggests a new verification method by defining the meta-model which has additional structural attributes and property information and the transformation profile, and using graph comparison algorithm which checks whether the information acquired from transformation is correct.} } @article{Kontolatis2013313, title = {Image-Based Part Programming with Process Parameter Selection Guidance for Laser Milling}, author = {Kontolatis, N. and Vosniakos, G.-C. and Gogouvitis, X.V.}, date = {2013}, journaltitle = {Int. J. Manuf. Res.}, volume = {8}, number = {3}, pages = {313--335}, publisher = {Inderscience Publishers}, issn = {17500591}, doi = {10.1504/IJMR.2013.055246}, abbrev_source_title = {Int. J. Manuf. Res.}, affiliation = {School of Mechanical Engineering, National Technical University of Athens, Zografou Campus, HeroonPolytechniou 9, 15780 Zografou, Greece}, correspondence_address1 = {Vosniakos, G.-C.; School of Mechanical Engineering, HeroonPolytechniou 9, 15780 Zografou, Greece; email: vosniak@central.ntua.gr}, document_type = {Article}, langid = {english}, source = {Scopus}, note = {cited By 1} } @article{kosar2016comparative, title = {Comparative Study of {{DSL}} Design Processes}, author = {Kosar, Tomaz and Oliveira, Nuno and Mernik, Marjan and Pereira, Maria Joao Varanda and Saraiva, João and Črepinšek, Matej}, date = {2016}, journaltitle = {Softw. Qual. J.}, volume = {24}, number = {3}, pages = {755--795}, publisher = {Springer} } @inproceedings{Koseler201915, title = {Realization of a Machine Learning Domain Specific Modeling Language: {{A}} Baseball Analytics Case Study}, author = {Koseler, K. and McGraw, K. and Stephan, M.}, editor = {Hammoudi S., Pires L.F., Selic B.}, date = {2019}, series = {{{MODELSWARD}} 2019 - {{Proceedings}} of the 7th {{International Conference}} on {{Model-Driven Engineering}} and {{Software Development}}}, pages = {15--26}, publisher = {SciTePress}, doi = {10.5220/0007245800150026}, abbrev_source_title = {MODELSWARD - Proc. Int. Conf. Model-Driven Eng. Softw. Dev.}, affiliation = {Dept. of Computer Science and Software Engineering, Miami University, 510 East High Street, Oxford Ohio, United States}, document_type = {Conference Paper}, isbn = {978-989-758-358-2}, langid = {english}, source = {Scopus}, note = {cited By 2} } @article{koshimaReconciliationFrameworkSupport2013, title = {A {{Reconciliation Framework}} to {{Support Cooperative Work}} with {{DSM}}}, author = {Koshima, Amanuel Alemayehu and Englebert, Vincent and Thiran, Philippe}, date = {2013}, journaltitle = {Domain Eng.}, pages = {239--259}, doi = {10.1007/978-3-642-36654-3_10} } @article{kotsiantis2007supervised, title = {Supervised Machine Learning: {{A}} Review of Classification Techniques}, author = {Kotsiantis, Sotiris B and Zaharakis, I and Pintelas, P}, date = {2007}, journaltitle = {Emerg. Artif. Intell. Appl. Comput. Eng.}, volume = {160}, pages = {3--24}, note = {TL;DR \par This paper describes various supervised machine learning classification techniques, and suggests possible bias combinations that have yet to be explored.} } @book{kounevSelfAwareComputingSystems2017, title = {Self-{{Aware Computing Systems}}}, editor = {Kounev, Samuel and Kephart, Jeffrey O. and Milenkoski, Aleksandar and Zhu, Xiaoyun}, date = {2017}, publisher = {Springer International Publishing}, location = {Cham}, doi = {10.1007/978-3-319-47474-8}, isbn = {978-3-319-47472-4 978-3-319-47474-8}, langid = {english}, note = {TL;DR \par This book addresses the new notion of computational self-awareness as a fundamental concept for designing and operating computing systems from an engineering perspective, aiming at developing primitives for building systems and applications.} } @online{kouraniProcessModelingLarge2024, title = {Process {{Modeling With Large Language Models}}}, author = {Kourani, Humam and Berti, Alessandro and Schuster, Daniel and family=Aalst, given=Wil M. P., prefix=van der, useprefix=true}, date = {2024-04-08}, eprint = {2403.07541}, eprinttype = {arXiv}, eprintclass = {cs}, url = {http://arxiv.org/abs/2403.07541}, urldate = {2024-05-02}, abstract = {In the realm of Business Process Management (BPM), process modeling plays a crucial role in translating complex process dynamics into comprehensible visual representations, facilitating the understanding, analysis, improvement, and automation of organizational processes. Traditional process modeling methods often require extensive expertise and can be time-consuming. This paper explores the integration of Large Language Models (LLMs) into process modeling to enhance flexibility, efficiency, and accessibility of process modeling for both expert and non-expert users. We propose a framework that leverages LLMs for the automated generation and iterative refinement of process models starting from textual descriptions. Our framework involves innovative prompting strategies for effective LLM utilization, along with a secure model generation protocol and an error-handling mechanism. Moreover, we instantiate a concrete system extending our framework. This system provides robust quality guarantees on the models generated and supports exporting them in standard modeling notations, such as the Business Process Modeling Notation (BPMN) and Petri nets. Preliminary results demonstrate the framework’s ability to streamline process modeling tasks, underscoring the transformative potential of generative AI in the BPM field.}, langid = {english}, pubstate = {prepublished}, keywords = {Computer Science - Databases,Computer Science - Software Engineering} } @inproceedings{Kourouklidis2021160, title = {A Model-Driven Engineering Approach for Monitoring Machine Learning Models}, author = {Kourouklidis, P. and Kolovos, D. and Noppen, J. and Matragkas, N.}, date = {2021}, series = {Companion {{Proceedings}} - 24th {{International Conference}} on {{Model-Driven Engineering Languages}} and {{Systems}}, {{MODELS-C}} 2021}, pages = {160--164}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/MODELS-C53483.2021.00028}, abbrev_source_title = {Companion Proc. - Int. Conf. Model-Driven Eng. Lang. Syst., MODELS-C}, affiliation = {University of York, British Telecom, Ipswich, United Kingdom; University of York, York, United Kingdom}, document_type = {Conference Paper}, isbn = {978-1-6654-2484-4}, langid = {english}, source = {Scopus}, note = {cited By 0} } @inproceedings{kowald_popularity_2022, title = {Popularity {{Bias}} in {{Collaborative Filtering-Based Multimedia Recommender Systems}}}, booktitle = {Adv. {{Bias Fairness Inf}}. {{Retr}}.}, author = {Kowald, Dominik and Lacic, Emanuel}, editor = {Boratto, Ludovico and Faralli, Stefano and Marras, Mirko and Stilo, Giovanni}, date = {2022}, series = {Communications in {{Computer}} and {{Information Science}}}, pages = {1--11}, publisher = {Springer International Publishing}, location = {Cham}, doi = {10.1007/978-3-031-09316-6_1}, abstract = {Multimedia recommender systems suggest media items, e.g., songs, (digital) books and movies, to users by utilizing concepts of traditional recommender systems such as collaborative filtering. In this paper, we investigate a potential issue of such collaborative-filtering based multimedia recommender systems, namely popularity bias that leads to the underrepresentation of unpopular items in the recommendation lists. Therefore, we study four multimedia datasets, i.e., Last.fm, MovieLens, BookCrossing and MyAnimeList, that we each split into three user groups differing in their inclination to popularity, i.e., LowPop, MedPop and HighPop. Using these user groups, we evaluate four collaborative filtering-based algorithms with respect to popularity bias on the item and the user level. Our findings are three-fold: firstly, we show that users with little interest into popular items tend to have large user profiles and thus, are important data sources for multimedia recommender systems. Secondly, we find that popular items are recommended more frequently than unpopular ones. Thirdly, we find that users with little interest into popular items receive significantly worse recommendations than users with medium or high interest into popularity.}, isbn = {978-3-031-09316-6}, langid = {english}, keywords = {algorithmic fairness,collaborative filtering,multimedia recommender systems,popularity bias}, note = {TL;DR \par It is shown that users with little interest into popular items tend to have large user profiles and thus, are important data sources for multimedia recommender systems, and it is found that popular items are recommended more frequently than unpopular ones.} } @article{koyanagiExploringEffectMultiple2024, title = {Exploring the {{Effect}} of {{Multiple Natural Languages}} on {{Code Suggestion Using GitHub Copilot}}}, author = {Koyanagi, Kei and Wang, Dong and Noguchi, Kotaro and Kondo, Masanari and Serebrenik, Alexander and Kamei, Yasutaka and Ubayashi, Naoyasu}, date = {2024-01-13}, journaltitle = {21st Int. Conf. Min. Softw. Repos.}, abstract = {GitHub Copilot is an AI-enabled tool that automates program synthesis. It has gained significant attention since its launch in 2021. Recent studies have extensively examined Copilot's capabilities in various programming tasks, as well as its security issues. However, little is known about the effect of different natural languages on code suggestion. Natural language is considered a social bias in the field of NLP, and this bias could impact the diversity of software engineering. To address this gap, we conducted an empirical study to investigate the effect of three popular natural languages (English, Japanese, and Chinese) on Copilot. We used 756 questions of varying difficulty levels from AtCoder contests for evaluation purposes. The results highlight that the capability varies across natural languages, with Chinese achieving the worst performance. Furthermore, regardless of the type of natural language, the performance decreases significantly as the difficulty of questions increases. Our work represents the initial step in comprehending the significance of natural languages in Copilot's capability and introduces promising opportunities for future endeavors.}, note = {TL;DR \par An empirical study is conducted to investigate the effect of three popular natural languages (English, Japanese, and Chinese) on Copilot and highlights that the capability varies across natural languages, with Chinese achieving the worst performance.} } @inproceedings{kramerSelfManagedSystemsArchitectural2007, title = {Self-{{Managed Systems}}: An {{Architectural Challenge}}}, shorttitle = {Self-{{Managed Systems}}}, author = {Kramer, Jeff and Magee, Jeff}, date = {2007-05}, pages = {259--268}, publisher = {IEEE}, doi = {10.1109/FOSE.2007.19}, isbn = {978-0-7695-2829-8}, note = {TL;DR \par Some of the current promising work in self-management is discussed and an outline three-layer reference model is presented as a context in which to articulate some of the main outstanding research challenges.} } @article{krauseMetamodelSpecificCoupledEvolution2013, title = {Metamodel-{{Specific Coupled Evolution Based}} on {{Dynamically Typed Graph Transformations}}}, author = {Krause, Christian and Dyck, Johannes and Giese, Holger}, date = {2013}, journaltitle = {Theory Pract. Model Transform.}, volume = {7909}, pages = {76--91}, doi = {10.1007/978-3-642-38883-5_10} } @incollection{Krenker11, title = {Introduction to the Artificial Neural Networks}, booktitle = {Artificial Neural Networks}, author = {Krenker, Andrej and Bester, Janez and Kos, Andrej}, editor = {Suzuki, Kenji}, date = {2011}, publisher = {IntechOpen}, location = {Rijeka}, chapter = {1}, nodoi = {10.5772/15751} } @inproceedings{Krishnan2017, title = {{{PALM}}: {{Machine}} Learning Explanations for Iterative Debugging}, author = {Krishnan, S. and Wu, E.}, date = {2017}, series = {Proceedings of the 2nd {{Workshop}} on {{Human-In-the-Loop Data Analytics}}, {{HILDA}} 2017}, publisher = {Association for Computing Machinery, Inc}, doi = {10.1145/3077257.3077271}, abbrev_source_title = {Proc. Workshop Hum.-Loop Data Anal.}, affiliation = {UC Berkeley, United States; Columbia University, United States}, art_number = {3077271}, document_type = {Conference Paper}, isbn = {978-1-4503-5029-7}, langid = {english}, source = {Scopus}, keywords = {GOAL\_Debugging,notion}, note = {cited By 34 \par TL;DR \par PALM is a tool that learns and summarizes this responsibility structure to aide machine learning debugging, which approximates a complex model using a meta-model that partitions the training data, and a set of sub-models that approximate the patterns within each partition.} } @article{Kroetz2017394, title = {Performance of Global Metamodeling Techniques in Solution of Structural Reliability Problems}, author = {Kroetz, H.M. and Tessari, R.K. and Beck, A.T.}, date = {2017}, journaltitle = {Adv. Eng. Softw.}, volume = {114}, pages = {394--404}, publisher = {Elsevier Ltd}, issn = {09659978}, doi = {10.1016/j.advengsoft.2017.08.001}, abbrev_source_title = {Adv Eng Software}, affiliation = {Department of Structural Engineering, University of São Paulo, São Carlos, Brazil}, coden = {AESOD}, correspondence_address1 = {Kroetz, H.M.; Department of Structural Engineering, Brazil; email: henrique.kroetz@usp.br}, document_type = {Article}, langid = {english}, source = {Scopus}, note = {cited By 25} } @article{krupitzerSurveyEngineeringApproaches2015, title = {A Survey on Engineering Approaches for Self-Adaptive Systems}, author = {Krupitzer, Christian and Roth, Felix Maximilian and VanSyckel, Sebastian and Schiele, Gregor and Becker, Christian}, date = {2015-02}, journaltitle = {Pervasive Mob. Comput.}, volume = {17}, pages = {184--206}, issn = {15741192}, doi = {10.1016/j.pmcj.2014.09.009}, langid = {english} } @article{kuangNewSearchEngine, title = {A {{New Search Engine Integrating Hierarchical Browsing}} and {{Keyword Search}}}, author = {Kuang, Da and Li, Xiao and Ling, Charles X}, pages = {6}, abstract = {The original Yahoo! search engine consists of manually organized topic hierarchy of webpages for easy browsing. Modern search engines (such as Google and Bing), on the other hand, return a flat list of webpages based on keywords. It would be ideal if hierarchical browsing and keyword search can be seamlessly combined. The main difficulty in doing so is to automatically (i.e., not manually) classify and rank a massive number of webpages into various hierarchies (such as topics, media types, regions of the world). In this paper we report our attempt towards building this integrated search engine, called SEE (Search Engine with hiErarchy). We implement a hierarchical classification system based on Support Vector Machines, and embed it in SEE. We also design a novel user interface that allows users to dynamically adjust their desire for a higher accuracy vs. more results in any (sub)category of the hierarchy. Though our current search engine is still small (indexing about 1.2 million webpages), the results, including a small user study, have shown a great promise for integrating such techniques in the next-generation search engine.}, langid = {english} } @inproceedings{kuhn2005enriching, title = {Enriching Reverse Engineering with Semantic Clustering}, booktitle = {12th {{Work}}. {{Conf}}. {{Reverse Eng}}. {{WCRE05}}}, author = {Kuhn, Adrian and Ducasse, Stéphane and Girba, Tudor}, date = {2005}, pages = {10-pp}, organization = {IEEE}, note = {TL;DR \par This paper analyzes how semantics of the source code are spread over the source artifacts using latent semantic indexing, an information retrieval technique that cluster artifacts that use similar terms, and reveals the most relevant terms for the computed clusters.} } @misc{kula_rauximpact--security-advisories--library-migrations_2018, title = {Raux/{{Impact-of-Security-Advisories-on-Library-Migrations}}}, author = {Kula, R. G.}, date = {2018-06}, abstract = {Contribute to raux/Impact-of-Security-Advisories-on-Library-Migrations development by creating an account on GitHub.}, nourl = {https://github.com/raux/Impact-of-Security-Advisories-on-Library-Migrations}, note = {original-date: 2017-02-15T08:36:47Z} } @article{kulaDevelopersUpdateTheir2018, ids = {kula_developers_2018}, title = {Do Developers Update Their Library Dependencies?: {{An}} Empirical Study on the Impact of Security Advisories on Library Migration}, shorttitle = {Do Developers Update Their Library Dependencies?}, author = {Kula, Raula Gaikovina and German, Daniel M. and Ouni, Ali and Ishio, Takashi and Inoue, Katsuro}, date = {2018-02}, journaltitle = {Empir Software Eng}, volume = {23}, number = {1}, pages = {384--417}, issn = {1382-3256, 1573-7616}, doi = {10.1007/s10664-017-9521-5}, langid = {english}, nourl = {http://link.springer.com/10.1007/s10664-017-9521-5} } @article{kullbackInformationSufficiency1951, title = {On Information and Sufficiency}, author = {Kullback, S. and Leibler, R. A.}, date = {1951}, journaltitle = {Ann Math Stat.}, volume = {22}, number = {1}, pages = {79--86}, added-at = {2010-10-31T19:59:47.000+0100}, biburl = {https://www.bibsonomy.org/bibtex/2560a5719c537c5c4a496bfebd4a21603/leeₚeck}, description = {Kullback , Leibler : On Information and Sufficiency}, interhash = {f9d41d76a07383cca4c3a1a94c24d533}, intrahash = {560a5719c537c5c4a496bfebd4a21603}, keywords = {51 Kullback Leibler divergence kl}, timestamp = {2010-10-31T19:59:47.000+0100} } @article{kumarToolRecommenderSystem2021, title = {Tool Recommender System in {{Galaxy}} Using Deep Learning}, author = {Kumar, Anup and Rasche, Helena and Grüning, Björn and Backofen, Rolf}, date = {2021-01-06}, journaltitle = {GigaScience}, volume = {10}, number = {1}, pages = {giaa152}, issn = {2047-217X}, doi = {10.1093/gigascience/giaa152}, abstract = {Abstract Background Galaxy is a web-based and open-source scientific data-processing platform. Researchers compose pipelines in Galaxy to analyse scientific data. These pipelines, also known as workflows, can be complex and difficult to create from thousands of tools, especially for researchers new to Galaxy. To help researchers with creating workflows, a system is developed to recommend tools that can facilitate further data analysis. Findings A model is developed to recommend tools using a deep learning approach by analysing workflows composed by researchers on the European Galaxy server. The higher-order dependencies in workflows, represented as directed acyclic graphs, are learned by training a gated recurrent units neural network, a variant of a recurrent neural network. In the neural network training, the weights of tools used are derived from their usage frequencies over time and the sequences of tools are uniformly sampled from training data. Hyperparameters of the neural network are optimized using Bayesian optimization. Mean accuracy of 98\% in recommending tools is achieved for the top-1 metric. Conclusions The model is accessed by a Galaxy API to provide researchers with recommended tools in an interactive manner using multiple user interface integrations on the European Galaxy server. High-quality and highly used tools are shown at the top of the recommendations. The scripts and data to create the recommendation system are available under MIT license at https://github.com/anuprulez/galaxy\_tool\_recommendation.}, langid = {english}, note = {TL;DR \par A model is created to recommend tools by analysing workflows, composed by researchers on the European Galaxy server, using a deep learning approach to make creating workflows easier, faster and less error-prone.} } @article{kuselRealityCheckModel, title = {Reality {{Check}} for {{Model Transformation Reuse}}: {{The ATL Transformation Zoo Case Study}}}, author = {Kusel, A and Schonbock, J and Wimmer, M and Retschitzegger, W and Schwinger, W and Kappel, G}, note = {TL;DR \par This paper developed a semi-automated process for extracting transformation projects from the ATL Transformation Zoo, which are classified and analyzed with respect to the application frequency of reuse mechanisms.} } @article{kuselReuseModeltomodelTransformation2015, title = {Reuse in Model-to-Model Transformation Languages: Are We There Yet?}, shorttitle = {Reuse in Model-to-Model Transformation Languages}, author = {Kusel, A. and Schönböck, J. and Wimmer, M. and Kappel, G. and Retschitzegger, W. and Schwinger, W.}, date = {2015-05}, journaltitle = {Softw. Syst. Model.}, volume = {14}, number = {2}, pages = {537--572}, issn = {1619-1366, 1619-1374}, doi = {10.1007/s10270-013-0343-7}, langid = {english}, note = {TL;DR \par An in-depth comparison of reuse mechanisms in model-to-model transformation languages and categorizes them along their intended scope of application is provided.} } @article{Kusiak201590, title = {Effective Strategies of Metamodelling of Industrial Metallurgical Processes}, author = {Kusiak, J. and Sztangret, Ł. and Pietrzyk, M.}, date = {2015}, journaltitle = {Adv. Eng. Softw.}, volume = {89}, pages = {90--97}, publisher = {Elsevier Ltd}, issn = {09659978}, doi = {10.1016/j.advengsoft.2015.02.002}, abbrev_source_title = {Adv Eng Software}, affiliation = {Department of Applied Computer Science and Modelling, AGH University of Science and Technology, Kraków, Poland}, coden = {AESOD}, correspondence_address1 = {Kusiak, J.; Department of Applied Computer Science and Modelling, Poland; email: kusiak@agh.edu.pl}, document_type = {Article}, langid = {english}, source = {Scopus}, note = {cited By 17} } @incollection{kusterDependentConflictingChange2009, title = {Dependent and {{Conflicting Change Operations}} of {{Process Models}}}, booktitle = {Model {{Driven Architecture}} - {{Foundations}} and {{Applications}}}, author = {Küster, Jochen M. and Gerth, Christian and Engels, Gregor}, editor = {Paige, Richard F. and Hartman, Alan and Rensink, Arend}, date = {2009}, volume = {5562}, pages = {158--173}, publisher = {Springer Berlin Heidelberg}, location = {Berlin, Heidelberg}, doi = {10.1007/978-3-642-02674-4_12}, abstract = {Version management of models is common for structural diagrams such as class diagrams but still challenging for behavioral models such as process models. For process models, conflicts of change operations are difficult to resolve because often dependencies to other change operations exist. As a consequence, conflicts and dependencies between change operations must be computed and shown to the user who can then take them into account while creating a consolidated version. In this paper, we introduce the concepts of dependencies and conflicts of change operations for process models and provide a method how to compute them. We then discuss different possibilities for resolving conflicts. Using our approach it is possible to enable version management of process models with minimal manual intervention of the user.}, isbn = {978-3-642-02673-7 978-3-642-02674-4}, langid = {english} } @article{kutsche2008bizycle, title = {Bizycle: {{Model-based}} Interoperability Platform for Software and Data Integration}, author = {Kutsche, Ralf and Milanovic, Nikola and Bauhoff, Gregor and Baum, Timo and Cartsburg, Mario and Kumpe, Daniel and Widiker, Jürgen}, date = {2008}, journaltitle = {Proc. MDTPI ECMDA}, volume = {430} } @inproceedings{kuwaharaAutomatedPlanningSystem2019, title = {Automated Planning of System Rollback in Declarative {{IT}} System Update}, booktitle = {2019 {{IFIPIEEE Symp}}. {{Integr}}. {{Netw}}. {{Serv}}. {{Manag}}. {{IM}} 2019}, author = {Kuwahara, T. and Kuroda, T. and Nakanoya, M. and Yakuwa, Y. and Sato, Y. and Matsunaga, Y.}, date = {2019}, pages = {428--434}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85067009661&partnerID=40&md5=e12df5fd076b85398777e6937d090a82}, abstract = {The automation of system management has been expanding, and there has been interest lately in an automated workflow generation that automatically generates the workflows of system updates. However, because these automation technologies operate under the assumption that systems work in accordance with their underlying system model, they are not good at handling unexpected behaviors of target systems.In this paper, we propose a way to incorporate unexpected handling into our declarative system update mechanism by automatically generating a "recovery workflow" to roll back a target system in the event of abnormal system stops. We evaluate our tool through a practical three-tier architecture system operating a simple Web service, and found that our method can complete generation of a recovery workflow in one second, and roll back the system from all system states. © 2019 IFIP.}, isbn = {978-3-903176-15-7}, keywords = {AI planning,Automation,Change management,Client server computer systems,Computer system recovery,Declarative,Fault management,Model-driven,System rollback,Web services}, note = {cited By 1 \par TL;DR \par This paper proposes a way to incorporate unexpected handling into the authors' declarative system update mechanism by automatically generating a "recovery workflow" to roll back a target system in the event of abnormal system stops.} } @inproceedings{Kwon202053, title = {{{FriendNet}} Backdoor: {{Indentifying}} Backdoor Attack That Is Safe for Friendly Deep Neural Network}, author = {Kwon, H. and Yoon, H. and Park, K.-W.}, date = {2020}, series = {{{ACM International Conference Proceeding Series}}}, pages = {53--57}, publisher = {Association for Computing Machinery}, doi = {10.1145/3378936.3378938}, abstract = {Deep neural networks (DNNs) provide good performance in image recognition, speech recognition and pattern analysis. However, DNNs are vulnerable to backdoor attacks. Backdoor attacks allow attackers to proactively access training data of DNNs to train additional malicious data, including the specific trigger. In normal times, DNNs correctly classify the normal data, but the malicious data with the specific trigger trained by attackers can cause misclassification of DNNs. For example, if an attacker sets up a road sign that includes a specific trigger, an autonomous vehicle equipped with a DNN may misidentify the road sign and cause an accident. Thus, an attacker can use a backdoor attack to threaten the DNN at any time. However, this backdoor attack can be useful in certain situations, such as in military situations. Since there is a mixture of enemy and friendly force in the military situations, it is necessary to cause misclassification of the enemy equipment and classification of the friendly equipment. Therefore, it is necessary to make backdoor attacks that are correctly recognized by friendly equipment and misrecognized by the enemy equipment. In this paper, we propose a friendnet backdoor that is correctly recognized by friendly classifier and misclassified by the enemy classifier. This method additionally trains the friendly and enemy classifier with the proposed data, including the specific trigger that is correctly recognized by friendly classifier and misclassified by enemy classifier. We used MNIST and Fashion-MNIST as experimental datasets and Tensorflow as a machine learning library. Experimental results show that the proposed method in MNIST and Fashion-MNIST has 100\% attack success rate of the enemy classifier and the 99.21\% and 92.3\% accuracy of the friendly classifier, respectively. © 2020 Association for Computing Machinery.}, author_keywords = {Adversarial example; Backdoor attack; Deep neural network; Machine learning; Poisoning attack}, document_type = {Conference Paper}, isbn = {978-1-4503-7690-7}, source = {Scopus}, keywords = {Accidents,Adversarial example,Backdoor attack,Big data,Classification (of information),Deep learning,Deep neural networks,Highway administration,Image recognition,Indentifying,Information management,Learning systems,Malware,Misclassifications,Neural networks,Pattern analysis,Poisoning attacks,Road signs,Roads and streets,Software engineering,Speech recognition,Training data}, note = {cited By 2 \par TL;DR \par A friendnet backdoor that is correctly recognized by friendly classifier and misclassified by the enemy classifier is proposed, which additionally trains the friendly and enemy classifiers with the proposed data, including the specific trigger that is incorrectly recognized byfriendly classifiers and mis classified by enemyclassifier.} } @article{Kyriacou2014895, title = {Efficient {{PCA-driven EAs}} and Metamodel-Assisted {{EAs}}, with Applications in Turbomachinery}, author = {Kyriacou, S.A. and Asouti, V.G. and Giannakoglou, K.C.}, date = {2014}, journaltitle = {Eng. Optim.}, volume = {46}, number = {7}, pages = {895--911}, publisher = {{Taylor and Francis Ltd.}}, issn = {0305215X}, doi = {10.1080/0305215X.2013.812726}, abbrev_source_title = {Eng Optim}, affiliation = {Parallel CFD and Optimization Unit, National Technical University of Athens, Heroon Polytechniou 9, Athens 15780, Greece; Andritz HYDRO GmbH, Lunzerstrasse 78, 4031 Linz, Austria}, coden = {EGOPA}, correspondence_address1 = {Giannakoglou, K.C.; Parallel CFD and Optimization Unit, National Technical University of Athens, Heroon Polytechniou 9, Athens 15780, Greece; email: kgianna@central.ntua.gr}, document_type = {Article}, langid = {english}, source = {Scopus}, note = {cited By 21} } @article{kyriazisSmartAutonomousReliable2013, title = {Smart, {{Autonomous}} and {{Reliable Internet}} of {{Things}}}, author = {Kyriazis, Dimosthenis and Varvarigou, Theodora}, date = {2013}, journaltitle = {Procedia Comput. Sci.}, volume = {21}, pages = {442--448}, issn = {18770509}, doi = {10.1016/j.procs.2013.09.059}, langid = {english} } @article{L04, title = {Detection of Redundant Code Using {{R2D2}}}, author = {Leitão, António Menezes}, date = {2004-12-01}, journaltitle = {Softw. Qual. J.}, volume = {12}, number = {4}, pages = {361--382}, issn = {1573-1367}, doi = {10.1023/B:SQJO.0000039793.31052.72}, abstract = {We present the R2D2 redundancy detector. R2D2 identifies redundant code fragments in large software systems written in Lisp. For each pair of code fragments, R2D2 uses a combination of techniques ranging from syntax-based analysis to semantics-based analysis, that detects positive and negative evidences regarding the redundancy of the analyzed code fragments. These evidences are combined according to a well-defined model and sufficiently redundant fragments are reported to the user. R2D2 explores several techniques and heuristics to operate within reasonable time and space bounds and is designed to be extensible.}, note = {TL;DR \par R2D2 identifies redundant code fragments in large software systems written in Lisp using a combination of techniques ranging from syntax-based analysis to semantics- based analysis, that detects positive and negative evidences regarding the redundancy of the analyzed code fragments.} } @online{labonneLargeLanguageModel2025, title = {The Large Language Model Course}, author = {Labonne, Maxime}, date = {2025-01-16}, url = {https://towardsdatascience.com/the-large-language-model-course-b6663cd57ceb/}, urldate = {2025-02-17}, abstract = {How to become an LLM Scientist or Engineer from scratch}, langid = {english}, organization = {Towards Data Science}, keywords = {/unread,P1} } @article{lacavaEvaluatingRecommenderSystems2021, title = {Evaluating Recommender Systems for {{AI-driven}} Biomedical Informatics}, author = {La Cava, William and Williams, Heather and Fu, Weixuan and Vitale, Steve and Srivatsan, Durga and Moore, Jason H}, editor = {Wren, Jonathan}, date = {2021-04-19}, journaltitle = {Bioinformatics}, volume = {37}, number = {2}, pages = {250--256}, issn = {1367-4803, 1460-2059}, doi = {10.1093/bioinformatics/btaa698}, abstract = {Abstract Motivation Many researchers with domain expertise are unable to easily apply machine learning (ML) to their bioinformatics data due to a lack of ML and/or coding expertise. Methods that have been proposed thus far to automate ML mostly require programming experience as well as expert knowledge to tune and apply the algorithms correctly.~Here, we study a method of automating biomedical data science using a web-based AI platform to recommend model choices and conduct experiments. We have two goals in mind: first, to make it easy to construct sophisticated models of biomedical processes; and second, to provide a fully automated AI agent that can choose and conduct promising experiments for the user, based on the user’s experiments as well as prior knowledge.~To validate this framework, we conduct an experiment on 165 classification problems, comparing to state-of-the-art, automated approaches. Finally, we use this tool to develop predictive models of septic shock in critical care patients. Results We find that matrix factorization-based recommendation systems outperform metalearning methods for automating ML. This result mirrors the results of earlier recommender systems research in other domains. The proposed AI is competitive with state-of-the-art automated ML methods in terms of choosing optimal algorithm configurations for datasets. In our application to prediction of septic shock, the AI-driven analysis produces a competent ML model (AUROC 0.85±0.02) that performs on par with state-of-the-art deep learning results for this task, with much less computational effort. Availability and implementation PennAI is available free of charge and open-source. It is distributed under the GNU public license (GPL) version 3. Supplementary information Supplementary data are available at Bioinformatics online.}, langid = {english} } @online{laffraImposterSyndromeStress2022, title = {The {{Imposter Syndrome}}, {{Stress}}, and {{Burnout}}}, author = {Laffra, Chris}, date = {2022-01-25T21:42:34}, url = {https://laffra.medium.com/the-imposter-syndrome-stress-and-burnout-d51ea3eccf55}, urldate = {2025-05-09}, abstract = {Strategies for tumultuous times.}, langid = {english}, organization = {Medium} } @article{laiRobustOnlinePath2016, title = {A Robust Online Path Planning Approach in Cluttered Environments for Micro Rotorcraft Drones}, author = {Lai, Shupeng and Wang, Kangli and Qin, Hailong and Cui, Jin Q. and Chen, Ben M.}, date = {2016-02}, journaltitle = {Control Theory Technol.}, volume = {14}, number = {1}, pages = {83--96}, issn = {2095-6983, 2198-0942}, doi = {10.1007/s11768-016-6007-8}, langid = {english}, note = {TL;DR \par A robust online path planning method, which allows a micro rotorcraft drone to fly safely in GPS-denied and obstacle-strewn environments with limited onboard computational power, and has been realized on actual drones platforms and successfully demonstrated in real flight tests.} } @inproceedings{Lakshminarayan20192043, title = {Enterprise-Wide Machine Learning Using Teradata Vantage: {{An}} Integrated Analytics Platform}, author = {Lakshminarayan, C. and Ramakrishnan, T. and Al-Omari, A. and Bouaziz, K. and Ahmad, F. and Raghavan, S. and Agarwal, P.}, editor = {Baru C., Huan J., Hu X.T., Ak R., Tian Y., Barga R., Zaniolo C., Lee K., Ye Y.F., Khan L.}, date = {2019}, series = {Proceedings - 2019 {{IEEE International Conference}} on {{Big Data}}, {{Big Data}} 2019}, pages = {2043--2046}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/BigData47090.2019.9006321}, abstract = {Big data characterized by variety can be divided into 3 principal categories: numeric structured data, semi-structured data, and unstructured multimedia data involving audio, video, and text. Decision making requires multiple analytical engines suitable for each type of data, programming languages, algorithms, visualization tools, and user interfaces. More often than not, industrial analytics is conducted ad hoc by lashing together analytics components such as distributed data sources, analytics engines, and algorithms. This kind of piecemeal approach ignores scale, security, governance, reliability, model management and fault tolerance that are paramount for industrial strength analytics. A unified, versatile, and robust architecture that combines various components in a single integrated platform is the need of the hour. Teradata Vantage (TD Vantage) is such a platform for delivering production quality enterprise analytics at scale. In this paper, we outline the proposed TD Vantage (available in the market and under continuous development) that unifies data, engines, and algorithms operating in a seamless symphony. We will demonstrate its capabilities through three proofs of concept biz: image data using TensorFlow, text data using Spark, and transaction data using Aster (now renamed Machine Learning Engine or MLE), with Teradata orchestrating interactions among the various components. © 2019 IEEE.}, art_number = {9006321}, document_type = {Conference Paper}, isbn = {978-1-7281-0858-2}, source = {Scopus}, note = {cited By 2 \par TL;DR \par The proposed TD Vantage is outlined and its capabilities are demonstrated through three proofs of concept biz: image data using TensorFlow, text data using Spark, and transaction data using Aster, with Teradata orchestrating interactions among the various components.} } @inproceedings{Lakshminarayan20196110, title = {Model Management and Handwritten Character Recognition: {{An}} Enterprise Solution}, author = {Lakshminarayan, C. and Ramakrishnan, T. and Al-Omari, A. and Bouaziz, K. and Ahmad, F. and Raghavan, S. and Agarwal, P.}, editor = {Baru C., Huan J., Hu X.T., Ak R., Tian Y., Barga R., Zaniolo C., Lee K., Ye Y.F., Khan L.}, date = {2019}, series = {Proceedings - 2019 {{IEEE International Conference}} on {{Big Data}}, {{Big Data}} 2019}, pages = {6110--6112}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/BigData47090.2019.9005445}, abstract = {Ease-of-use analytics at scale is the holy grail of industrial strength machine learning. In order to reap benefits from the mother-lode of business related data; tools, technologies, and analytical functions should operate in perpetual symphony to derive insightful business outcomes. While there have been advances in APIs, algorithms, and user interfaces, building an end to end workflow spanning data ingestion, data preparation, model training, model scoring, visualization and finally continuous improvement and model management received limited investment. In this paper we demonstrate an analytical workflow that integrates multiple analytical tools and techniques for image recognition wrapped in the model management framework. As analytics in industry is maturing, analytics implementations are no longer one-off, but are components of Analytics Operations known as AnalyticsOps. We introduce the notion of Model Quality Index (MQI) to track model performance. The MQI is similar to Process Capability Index (PCI) common in 6 σprograms in manufacturing. Our solution combines relational databases (Teradata DB), Machine Learning (Teradata/Aster), Deep Learning (TensorFlow), Hadoop Distributed File System (HDFS), and user interface tools over a communication fabric (Teradata QueryGrid). In particular, we demonstrate a hand written word recognition use-case for an enterprise customer cast in a model management workflow for repeatable deployments across a range of businesses. © 2019 IEEE.}, art_number = {9005445}, document_type = {Conference Paper}, isbn = {978-1-7281-0858-2}, source = {Scopus}, note = {cited By 1 \par TL;DR \par This paper demonstrates an analytical workflow that integrates multiple analytical tools and techniques for image recognition wrapped in the model management framework and introduces the notion of Model Quality Index (MQI) to track model performance.} } @book{lalandaAutonomicComputing2013, title = {Autonomic {{Computing}}}, author = {Lalanda, Philippe and McCann, Julie A. and Diaconescu, Ada}, date = {2013}, series = {Undergraduate {{Topics}} in {{Computer Science}}}, publisher = {Springer London}, location = {London}, url = {http://link.springer.com/10.1007/978-1-4471-5007-7}, urldate = {2016-09-29}, isbn = {978-1-4471-5006-0 978-1-4471-5007-7} } @inproceedings{lam_shilling_2004, title = {Shilling Recommender Systems for Fun and Profit}, booktitle = {Proc. 13th {{Conf}}. {{World Wide Web}} - {{WWW}} 04}, author = {Lam, Shyong K. and Riedl, John}, date = {2004}, pages = {393}, publisher = {ACM Press}, location = {New York, NY, USA}, doi = {10.1145/988672.988726}, abstract = {Recommender systems have emerged in the past several years as an effective way to help people cope with the problem of information overload. One application in which they have become particularly common is in e-commerce, where recommendation of items can often help a customer find what she is interested in and, therefore can help drive sales. Unscrupulous producers in the never-ending quest for market penetration may find it profitable to shill recommender systems by lying to the systems in order to have their products recommended more often than those of their competitors. This paper explores four open questions that may affect the effectiveness of such shilling attacks: which recommender algorithm is being used, whether the application is producing recommendations or predictions, how detectable the attacks are by the operator of the system, and what the properties are of the items being attacked. The questions are explored experimentally on a large data set of movie ratings. Taken together, the results of the paper suggest that new ways must be used to evaluate and detect shilling attacks on recommender systems.}, isbn = {978-1-58113-844-3}, langid = {english}, note = {TL;DR \par Four open questions are explored that may affect the effectiveness of shilling attacks on recommender systems: which recommender algorithm is being used, whether the application is producing recommendations or predictions, how detectable the attacks are by the operator of the system, and what the properties are of the items being attacked.} } @article{Landauer1998, title = {An Introduction to Latent Semantic Analysis}, author = {Landauer, T.K. and Foltz, P.W. and Laham, D.}, date = {1998}, journaltitle = {Discourse Process.}, volume = {25}, pages = {259--284}, publisher = {ABLEX PUBLISHING CO}, added-at = {2009-11-19T19:28:27.000+0100}, biburl = {https://www.bibsonomy.org/bibtex/2d07817c8e498b282f56f8abc53c156d9/georg.oettl}, interhash = {60c2cae5093c82d65be9f2e516da9b29}, intrahash = {d07817c8e498b282f56f8abc53c156d9}, keywords = {Psycho NLP semantic}, note = {TL;DR \par The adequacy of LSA's reflection of human knowledge has been established in a variety of ways, for example, its scores overlap those of humans on standard vocabulary and subject matter tests; it mimics human word sorting and category judgments; it simulates word‐word and passage‐word lexical priming data.}, timestamp = {2009-11-19T19:28:27.000+0100} } @book{landauer2006latent, title = {Latent Semantic Analysis}, author = {Landauer, Thomas K}, date = {2006}, publisher = {Wiley Online Library} } @video{langchainLLManagerAutomateApprovals2025, entrysubtype = {video}, title = {{{LLManager}} - Automate Approvals through a Memory Agent}, editor = {{LangChain}}, editortype = {director}, date = {2025-04-16}, url = {https://www.youtube.com/watch?v=uqRK_aJBR2w}, urldate = {2025-04-24}, langid = {english} } @article{langerEMFProfilesLightweight2012, title = {{{EMF Profiles}}: {{A Lightweight Extension Approach}} for {{EMF Models}}.}, shorttitle = {{{EMF Profiles}}}, author = {Langer, Philip and Wieland, Konrad and Wimmer, Manuel and Cabot, Jordi}, date = {2012}, journaltitle = {J. Object Technol.}, volume = {11}, number = {1}, pages = {8:1}, issn = {1660-1769}, doi = {10.5381/jot.2012.11.1.a8}, langid = {english}, note = {TL;DR \par This paper advocates for the use of EMF Profiles, an adaptation of the UML Profile concept to DSMLs, and proposes reusable profile definition mechanisms whereby profiles are defined independently of any DSML and, later on, coupled with all DS MLs that can benefit from these profiles.} } @article{langerPosterioriOperationDetection2013, title = {A Posteriori Operation Detection in Evolving Software Models}, author = {Langer, Philip and Wimmer, Manuel and Brosch, Petra and Herrmannsdörfer, Markus and Seidl, Martina and Wieland, Konrad and Kappel, Gerti}, date = {2013-02}, journaltitle = {J. Syst. Softw.}, volume = {86}, number = {2}, pages = {551--566}, issn = {01641212}, doi = {10.1016/j.jss.2012.09.037}, langid = {english} } @inproceedings{Lano2020277, title = {Enhancing Model Transformation Synthesis Using Natural Language Processing}, author = {Lano, K. and Fang, S. and Umar, M.A. and Yassipour-Tehrani, S.}, date = {2020}, series = {Proceedings - 23rd {{ACM}}/{{IEEE International Conference}} on {{Model Driven Engineering Languages}} and {{Systems}}, {{MODELS-C}} 2020 - {{Companion Proceedings}}}, pages = {277--286}, publisher = {Association for Computing Machinery, Inc}, doi = {10.1145/3417990.3421386}, abbrev_source_title = {Proc. - ACM/IEEE Int. Conf. Model Driven Eng. Lang. Syst., MODELS-C - Companion Proc.}, affiliation = {Dept. of Informatics, King's College London, London, United Kingdom; National Automative Innovation Centre, University of Warwick, United Kingdom}, document_type = {Conference Paper}, isbn = {978-1-4503-8135-2}, langid = {english}, source = {Scopus}, keywords = {GOAL\_Model-Transformation-Development,notion,TECHNIQUE\_ILP}, note = {cited By 3} } @inproceedings{Lano2021173, title = {Automated Requirements Formalisation for Agile {{MDE}}}, author = {Lano, K. and Yassipour-Tehrani, S. and Umar, M.A.}, date = {2021}, series = {Companion {{Proceedings}} - 24th {{International Conference}} on {{Model-Driven Engineering Languages}} and {{Systems}}, {{MODELS-C}} 2021}, pages = {173--180}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/MODELS-C53483.2021.00030}, abbrev_source_title = {Companion Proc. - Int. Conf. Model-Driven Eng. Lang. Syst., MODELS-C}, affiliation = {Dept. of Informatics, King's College London, London, United Kingdom; Roehampton University, Dept. of Computer Science, London, United Kingdom}, document_type = {Conference Paper}, isbn = {978-1-6654-2484-4}, langid = {english}, source = {Scopus}, keywords = {GOAL\_Model-Requirements,notion,TECHNIQUE\_DECISION-TREE}, note = {cited By 0 \par TL;DR \par Techniques for automating the derivation of software specifications from requirements statements are described, in order to reduce the effort required in creating MDE specifications, and hence to improve the usability and agility of MDE.} } @article{Lano2022, title = {Model Transformation Development Using Automated Requirements Analysis, Metamodel Matching, and Transformation by Example}, author = {Lano, K. and Kolahdouz-Rahimi, S. and Fang, S.}, date = {2022}, journaltitle = {ACM Trans. Softw. Eng. Methodol.}, volume = {31}, number = {2}, publisher = {Association for Computing Machinery}, issn = {1049331X}, doi = {10.1145/3471907}, abbrev_source_title = {ACM Trans. Software Eng. Methodol.}, affiliation = {Dept. of Informatics, King's College London, Strand, London, WC2R 2LS, United Kingdom; Dept. of Software Engineering, University of Isfahan, Isfahan, Iran}, art_number = {18}, coden = {ATSME}, document_type = {Article}, langid = {english}, source = {Scopus}, keywords = {GOAL\_Model-Transformation-Development,notion}, note = {cited By 0} } @inproceedings{Lano2022362, title = {Program Translation Using Model-Driven Engineering}, author = {Lano, K.}, date = {2022}, series = {Proceedings - {{International Conference}} on {{Software Engineering}}}, pages = {362--363}, publisher = {IEEE Computer Society}, issn = {02705257}, doi = {10.1109/ICSE-Companion55297.2022.9793785}, abbrev_source_title = {Proc Int Conf Software Eng}, affiliation = {King's College London, Dept. of Informatics, London, United Kingdom}, coden = {PCSED}, correspondence_address1 = {Lano, K.; King's College London, United Kingdom; email: kevin.lano@kcl.ac.uk}, document_type = {Conference Paper}, isbn = {978-1-6654-9598-1}, langid = {english}, source = {Scopus}, note = {cited By 0} } @article{lanoConstraintbasedSpecificationModel2013, title = {Constraint-Based Specification of Model Transformations}, author = {Lano, K. and Kolahdouz-Rahimi, S.}, date = {2013}, journaltitle = {J. Syst. Softw.}, volume = {86}, number = {2}, pages = {412--436}, doi = {10.1016/j.jss.2012.09.006} } @article{LanoX23, title = {Code Generation by Example Using Symbolic Machine Learning}, author = {Lano, Kevin and Xue, Qiaomu}, date = {2023}, journaltitle = {SN Comput, Sci,}, volume = {4}, number = {2}, pages = {170}, doi = {10.1007/S42979-022-01573-4}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Sat, 11 Mar 2023 00:13:04 +0100} } @article{lanzaPolymetricViewsLightweight2003, title = {Polymetric Views - {{A}} Lightweight Visual Approach to Reverse Engineering}, author = {Lanza, M. and Ducasse, S.}, date = {2003}, journaltitle = {IEEE Trans. Softw. Eng.}, volume = {29}, number = {9}, pages = {782--795}, doi = {10.1109/TSE.2003.1232284}, note = {TL;DR \par The concept of a polymetric view is presented, a lightweight software visualization technique enriched with software metrics information that helps to understand the structure and detect problems of a software system in the initial phases of a reverse engineering process.} } @article{laraAbstractingModellingLanguages2012, title = {Abstracting {{Modelling Languages}}: {{A Reutilization Approach}}}, author = {Lara, Juan and Guerra, Esther and Sánchez-Cuadrado, Jesús}, date = {2012}, journaltitle = {Adv. Inf. Syst. Eng.}, volume = {7328}, pages = {127--143}, doi = {10.1007/978-3-642-31095-9_9}, note = {TL;DR \par A catalogue of reusable abstractions that are defined once and can be reused over families of modelling languages sharing certain requirements is presented, together with an implementation in the MetaDepth multi-level meta-modelling tool.} } @article{laraModeldrivenEngineeringDomainspecific2013, title = {Model-Driven Engineering with Domain-Specific Meta-Modelling Languages}, author = {Lara, Juan and Guerra, Esther and Cuadrado, Jesús Sánchez}, date = {2013}, journaltitle = {Softw. Syst. Model.}, doi = {10.1007/s10270-013-0367-z}, note = {TL;DR \par This paper applies multi-level meta-modelling for the systematic engineering of DSMM architectures, and provides a flexible approach to define textual concrete syntaxes for DSMM languages, and extends existing model management languages to work in a multi- level setting, thus enabling the practical use ofDSMM in MDE.} } @article{laraTypesTypeRequirements2011, title = {From Types to Type Requirements: Genericity for Model-Driven Engineering}, author = {Lara, Juan and Guerra, Esther}, date = {2011}, journaltitle = {Softw. Syst. Model.}, volume = {12}, number = {3}, pages = {453--474}, doi = {10.1007/s10270-011-0221-0}, keywords = {software engineering} } @article{larruceaSoftwareEngineeringInternet2017, title = {Software {{Engineering}} for the {{Internet}} of {{Things}}}, author = {Larrucea, Xabier and Combelles, Annie and Favaro, John and Taneja, Kunal}, date = {2017-01}, journaltitle = {IEEE Softw.}, volume = {34}, number = {1}, pages = {24--28}, issn = {0740-7459}, doi = {10.1109/MS.2017.28}, langid = {english} } @online{LatexSource, type = {Latex source}, title = {Latex Source}, url = {https://github.com/MDEGroup/ESEM2020}, organization = {Latex source} } @online{LatexSourcePaper, title = {Latex Source of the Paper}, url = {https://www.overleaf.com/project/5e5fdaa1a7e16e00013cf31f}, urldate = {2020-03-10} } @report{Lawrence981, type = {Technical report}, title = {The {{PageRank}} Citation Ranking: {{Bringing}} Order to the Web}, author = {Lawrence, Page and Sergey, Brin and Motwani, Rajeev and Winograd, Terry}, date = {1998}, institution = {Stanford University}, note = {TL;DR \par This paper describes PageRank, a mathod for rating Web pages objectively and mechanically, effectively measuring the human interest and attention devoted to them, and shows how to efficiently compute PageRank for large numbers of pages.} } @article{lebensRiseCitizenDeveloper2021, title = {Rise of the {{Citizen Developer}}}, author = {Lebens, Mary and J Finnegan, Roger and C Sorsen, Steven and Shah, Jinal}, date = {2021}, journaltitle = {MBR}, volume = {5}, pages = {101--111}, issn = {2640-6373}, doi = {10.28945/4885}, abstract = {A worldwide shortage of developers has made low- and no-code platforms important and necessary. This paper investigates the use of these platforms in organizations, along with the role of workforce automation tools. A survey was conducted to find out how prevalent low- and no-code platforms and workforce automation tools are within companies. These platforms are used by citizen developers, employees who are working outside of the Information Technology (IT) department and are not professional programmers. With low- and no-code platforms citizen developers can create the applications that are needed by their work units or even their entire organizations. These platforms are seen as key to the demands of digital transformation. The results of this study are that companies both large and small are making use of low- and no-code platforms, as well as workforce automation tools. In addition, the majority of organizations have employees outside of the IT department who are creating technology solutions. The broad implication of this research is that citizen developers using low- and no-code platforms to create technology solutions may be the solution to the current shortage of developers. By using low- and no-code platforms, the citizen developer can create the applications that the manager needs for their team. This increases the technology available to the organization while at the same time reducing the pressure on the IT department.}, langid = {english}, keywords = {LOGSEQ}, note = {TL;DR \par The broad implication of this research is that citizen developers using low- and no-code platforms to create technology solutions may be the solution to the current shortage of developers.} } @article{LeClair2018AdaptingNT, title = {Adapting Neural Text Classification for Improved Software Categorization}, author = {LeClair, Alexander and Eberhart, Zachary and McMillan, Collin}, date = {2018}, journaltitle = {CoRR}, volume = {abs/1806.01742}, note = {TL;DR \par This paper proposes a set of adaptations to a state-of-the-art neural classification algorithm and performs two evaluations: one with reference data from Debian end-user programs, and one with aSet of C/C++ libraries that the authors hired professional programmers to annotate.} } @online{Lecture6IoT, title = {Lecture 6: {{IoT Data Processing}}}, url = {https://www2.slideshare.net/PayamBarnaghi/lecture-6-iot-data-processing?qid=8711baae-0a4e-45df-ba7a-9eb987306850&v=&b=&from_search=14}, urldate = {2021-01-05}, keywords = {data processing,internet of things} } @online{LecturesSENG371, title = {Lectures {{SENG}} 371 {{Software Evolution}}}, url = {http://www.engr.uvic.ca/~seng371/lectures.html}, urldate = {2016-09-19} } @inproceedings{lee_adlib_2019, title = {Adlib: Analyzer for Mobile Ad Platform Libraries}, shorttitle = {Adlib}, booktitle = {Proc. 28th {{ACM SIGSOFT Int}}. {{Symp}}. {{Softw}}. {{Test}}. {{Anal}}.}, author = {Lee, Sungho and Ryu, Sukyoung}, date = {2019-07}, series = {{{ISSTA}} 2019}, pages = {262--272}, publisher = {Association for Computing Machinery}, location = {New York, NY, USA}, doi = {10.1145/3293882.3330562}, abstract = {Mobile advertising has become a popular advertising approach by taking advantage of various information from mobile devices and rich interaction with users. Mobile advertising platforms show advertisements of nearby restaurants to users using the geographic locations of their mobile devices, and also allow users to make reservations easily using their phone numbers. However, at the same time, they may open the doors for advertisements to steal device information or to perform malicious behaviors. When application developers integrate mobile advertising platform SDKs (AdSDKs) to their applications, they are informed of only the permissions required by the AdSDKs, and they may not be aware of the rich functionalities of the SDKs that are available to advertisements. In this paper, we first report that various AdSDKs provide powerful functionalities to advertisements, which are seriously vulnerable to security threats. We present representative malicious behaviors by advertisements using APIs provided by AdSDKs. To mitigate the security vulnerability, we develop a static analyzer, Adlib, which analyzes Android Java libraries that use hybrid features to enable communication with JavaScript code and detects possible flows from the APIs that are accessible from third-party advertisements to device-specific features like geographic locations. Our evaluation shows that Adlib found genuine security vulnerabilities from real-world AdSDKs.}, isbn = {978-1-4503-6224-5}, keywords = {Advertisement Attacks,Advertising Libraries,Android Hybrid Apps,Malicious Advertisements}, note = {TL;DR \par A static analyzer, Adlib, is developed, which analyzes Android Java libraries that use hybrid features to enable communication with JavaScript code and detects possible flows from the APIs that are accessible from third-party advertisements to device-specific features like geographic locations.} } @article{Lee202035, title = {An Approximate Optimization Strategy Using Refined Hybrid Metamodel}, author = {Lee, K.-H. and Jeong, G.-I. and Lee, S.-H.}, date = {2020}, journaltitle = {Comput. Intell.}, volume = {36}, number = {1}, pages = {35--54}, publisher = {Blackwell Publishing Inc.}, issn = {08247935}, doi = {10.1111/coin.12237}, abbrev_source_title = {Comput Intell}, affiliation = {Department of Mechanical Engineering, Dong-A University, Busan, South Korea}, coden = {COMIE}, correspondence_address1 = {Lee, K.-H.; Department of Mechanical Engineering, South Korea; email: leekh@dau.ac.kr}, document_type = {Article}, langid = {english}, source = {Scopus}, note = {cited By 3 \par TL;DR \par Five mathematical test problems, two‐bar design, spring design, and propeller shaft design problems are solved with the suggested method, verifying its usefulness.} } @inproceedings{leeCyberPhysicalSystems2008, title = {Cyber {{Physical Systems}}: {{Design Challenges}}}, shorttitle = {Cyber {{Physical Systems}}}, author = {Lee, Edward A.}, date = {2008-05}, pages = {363--369}, publisher = {IEEE}, doi = {10.1109/ISORC.2008.25}, isbn = {978-0-7695-3132-8}, note = {TL;DR \par It is concluded that it will not be sufficient to improve design processes, raise the level of abstraction, or verify designs that are built on today's abstractions to realize the full potential of cyber-Physical Systems.} } @article{leeInternetThingsIoT2015, title = {The {{Internet}} of {{Things}} ({{IoT}}): {{Applications}}, Investments, and Challenges for Enterprises}, shorttitle = {The {{Internet}} of {{Things}} ({{IoT}})}, author = {Lee, In and Lee, Kyoochun}, date = {2015-07}, journaltitle = {Bus. Horiz.}, volume = {58}, number = {4}, pages = {431--440}, issn = {00076813}, doi = {10.1016/j.bushor.2015.03.008}, langid = {english} } @article{leePresentFutureCyberPhysical2015, title = {The {{Past}}, {{Present}} and {{Future}} of {{Cyber-Physical Systems}}: {{A Focus}} on {{Models}}}, shorttitle = {The {{Past}}, {{Present}} and {{Future}} of {{Cyber-Physical Systems}}}, author = {Lee, Edward}, date = {2015-02-26}, journaltitle = {Sensors}, volume = {15}, number = {3}, pages = {4837--4869}, issn = {1424-8220}, doi = {10.3390/s150304837}, langid = {english} } @article{leeRetiNeuraliGrado, title = {Reti Neurali in Grado Di Apprendere}, author = {Lee, Bruce}, langid = {english}, keywords = {/unread,⛔ No DOI found} } @article{leeSelfAdaptiveFrameworkBased2019, title = {Self-{{Adaptive Framework Based}} on {{MAPE Loop}} for {{Internet}} of {{Things}}}, author = {Lee, Euijong and Seo, Young-Duk and Kim, Young-Gab}, date = {2019-07-07}, journaltitle = {Sensors}, volume = {19}, number = {13}, pages = {2996}, issn = {1424-8220}, doi = {10.3390/s19132996}, abstract = {The Internet of Things (IoT) connects a wide range of objects and the types of environments in which IoT can be deployed dynamically change. Therefore, these environments can be modified dynamically at runtime considering the emergence of other requirements. Self-adaptive software alters its behavior to satisfy the requirements in a dynamic environment. In this context, the concept of self-adaptive software is suitable for some dynamic IoT environments (e.g., smart greenhouses, smart homes, and reality applications). In this study, we propose a self-adaptive framework for decision-making in an IoT environment at runtime. The framework comprises a finite-state machine model design and a game theoretic decision-making method for extracting efficient strategies. The framework was implemented as a prototype and experiments were conducted to evaluate its runtime performance. The results demonstrate that the proposed framework can be applied to IoT environments at runtime. In addition, a smart greenhouse-based use case is included to illustrate the usability of the proposed framework.}, langid = {english}, note = {TL;DR \par This study proposes a self-adaptive framework for decision-making in an IoT environment at runtime that comprises a finite-state machine model design and a game theoretic decision- making method for extracting efficient strategies.} } @article{Lejeune2021, title = {Geometric Stability Classification: {{Datasets}}, Metamodels, and Adversarial Attacks}, author = {Lejeune, E.}, date = {2021}, journaltitle = {CAD Comput. Aided Des.}, volume = {131}, publisher = {Elsevier Ltd}, issn = {00104485}, doi = {10.1016/j.cad.2020.102948}, abbrev_source_title = {CAD Comput Aided Des}, affiliation = {Department of Mechanical Engineering, Boston University, Boston, MA 02215, United States}, art_number = {102948}, coden = {CAIDA}, document_type = {Article}, langid = {english}, source = {Scopus}, keywords = {GOAL\_Model-Classification,notion}, note = {cited By 4} } @article{leopoldTextCategorizationSupport2002, title = {Text Categorization with Support Vector Machines. {{How}} to Represent Texts in Input Space?}, author = {Leopold, Edda and Kindermann, Jörg}, date = {2002}, journaltitle = {Mach. Learn.}, volume = {46}, number = {1--3}, pages = {423--444}, publisher = {Springer}, note = {TL;DR \par It is shown that in the case of text classification, term-frequency transformations have a larger impact on the performance of SVM than the kernel itself.} } @article{lepallecSupportQualityMetrics2013, title = {Support for Quality Metrics in Metamodelling}, author = {Le Pallec, Xavier and Dupuy-Chessa, Sophie}, date = {2013}, journaltitle = {Proc. Second Workshop Graph. Model. Lang. Dev. - GMLD 13}, pages = {23--31}, doi = {10.1145/2489820.2489825}, note = {TL;DR \par This paper presents functions that are necesary to calculate metrics in a metamodeling environment, and introduces how metrics are integrated in a modeling environment named ModX.} } @article{Leppänen2020308, title = {Service Modeling for Opportunistic Edge Computing Systems with Feature Engineering}, author = {Leppänen, T. and Savaglio, C. and Fortino, G.}, date = {2020}, journaltitle = {Comput. Commun.}, volume = {157}, pages = {308--319}, publisher = {Elsevier B.V.}, issn = {01403664}, doi = {10.1016/j.comcom.2020.04.011}, abbrev_source_title = {Comput Commun}, affiliation = {Center for Ubiquitous Computing, University of Oulu, Finland; Department of Informatics, Modeling, Electronics and Systems, University of Calabria, Italy}, coden = {COCOD}, correspondence_address1 = {Leppänen, T.P.O.Box 4500, FI-90014 University of Oulu, Finland; email: teemu.leppanen@oulu.fi}, document_type = {Article}, langid = {english}, source = {Scopus}, note = {cited By 10} } @incollection{lethbridgeLowCodeOftenHighCode2021, title = {Low-{{Code Is Often High-Code}}, {{So We Must Design Low-Code Platforms}} to {{Enable Proper Software Engineering}}}, booktitle = {Leveraging {{Applications}} of {{Formal Methods}}, {{Verification}} and {{Validation}}}, author = {Lethbridge, Timothy C.}, editor = {Margaria, Tiziana and Steffen, Bernhard}, date = {2021}, volume = {13036}, pages = {202--212}, publisher = {Springer International Publishing}, location = {Cham}, doi = {10.1007/978-3-030-89159-6_14}, abstract = {The concept of low-code (and no-code) platforms has been around for decades, even before the term was used. The idea is that applications on these platforms can be built by people with less technical expertise than a professional programmer, yet can leverage powerful technology such as, for example, for databases, financial analysis, web development and machine learning. However, in practice, software written on such platforms often accumulates large volumes of complex code, which can be worse to maintain than in traditional languages because the low-code platforms tend not to properly support good engineering practices such as version control, separation of concerns, automated testing and literate programming. In this paper we discuss experiences with several low-code platforms and provide suggestions for directions forward towards an era where the benefits of low-code can be obtained without accumulation of technical debt. Our recommendations focus on ensuring low-code platforms enable scaling, understandability, documentability, testability, vendor-independence, and the overall user experience for developers those end-users who do some development.}, isbn = {978-3-030-89158-9 978-3-030-89159-6}, langid = {english}, keywords = {LOGSEQ} } @misc{LEV4REC-deployment, title = {{{LEV4REC-deployment}}: {{A}} Deployment Module for the {{LEV4REC}} Recommendation System}, author = {{MDEGroup}}, date = {2023}, url = {https://github.com/MDEGroup/LEV4REC-deployment}, urldate = {2023-11-06}, commit = {8868ea67a8f719ce9f9b8c2470793df57fb3d418}, organization = {GitHub} } @incollection{levendovszkyNovelApproachSemiautomated2010, title = {A {{Novel Approach}} to {{Semi-automated Evolution}} of {{DSML Model Transformation}}}, booktitle = {Software {{Language Engineering}}}, author = {Levendovszky, Tihamer and Balasubramanian, Daniel and Narayanan, Anantha and Karsai, Gabor}, editor = {Van Den Brand, Mark and Gašević, Dragan and Gray, Jeff}, editora = {Hutchison, David and Kanade, Takeo and Kittler, Josef and Kleinberg, Jon M. and Mattern, Friedemann and Mitchell, John C. and Naor, Moni and Nierstrasz, Oscar and Pandu Rangan, C. and Steffen, Bernhard and Sudan, Madhu and Terzopoulos, Demetri and Tygar, Doug and Vardi, Moshe Y. and Weikum, Gerhard}, editoratype = {redactor}, date = {2010}, volume = {5969}, pages = {23--41}, publisher = {Springer Berlin Heidelberg}, location = {Berlin, Heidelberg}, doi = {10.1007/978-3-642-12107-4_4}, abstract = {In the industrial applications of Model-Based Development, the evolution of modeling languages is an inevitable issue. The migration to the new language involves the reuse of the existing artifacts created for the original language, such as models and model transformations. This paper is devoted to an evolution method for model transformations as well as the related algorithms. The change description is assumed to be available in a modeling language specific to the evolution. Based on the change description, our method is able to automate certain parts of the evolution. When automation is not possible, our algorithms automatically alert the user about the missing semantic information, which can then be provided manually after the automatic part of the interpreter evolution. The algorithms have been implemented and tested in an industrial environment. The results indicate that the semi-automated evolution of model transformations decreases the time and effort required with a manual approach.}, isbn = {978-3-642-12106-7 978-3-642-12107-4}, langid = {english} } @article{levenshtein1966bcc, ids = {Levenshtein_SPD66}, title = {Binary Codes Capable of Correcting Deletions, Insertions and Reversals}, author = {family=Levenshtein, given=VI, given-i=VI}, date = {1966}, journaltitle = {Sov. Phys. Dokl.}, volume = {10}, pages = {707}, added-at = {2008-03-15T10:37:17.000+0100}, biburl = {https://www.bibsonomy.org/bibtex/21a29b294552edb63828d57f3495e2eb2/brightbyte}, interhash = {55f7ad93fcb9ae3ed999afaa6e24937d}, intrahash = {1a29b294552edb63828d57f3495e2eb2}, keywords = {edit-distance,lexicography similarity}, note = {Doklady Akademii Nauk SSSR, V163 No4 845-848 1965 \par Doklady Akademii Nauk SSSR, V163 No4 845-848 1965 \par Doklady Akademii Nauk SSSR, V163 No4 845-848 1965 \par Doklady Akademii Nauk SSSR, V163 No4 845-848 1965 \par Doklady Akademii Nauk SSSR, V163 No4 845-848 1965}, timestamp = {2009-01-23T09:58:50.000+0100} } @online{lewisRetrievalAugmentedGenerationKnowledgeIntensive2021, title = {Retrieval-{{Augmented Generation}} for {{Knowledge-Intensive NLP Tasks}}}, author = {Lewis, Patrick and Perez, Ethan and Piktus, Aleksandra and Petroni, Fabio and Karpukhin, Vladimir and Goyal, Naman and Küttler, Heinrich and Lewis, Mike and Yih, Wen-tau and Rocktäschel, Tim and Riedel, Sebastian and Kiela, Douwe}, date = {2021-04-12}, eprint = {2005.11401}, eprinttype = {arXiv}, eprintclass = {cs}, url = {http://arxiv.org/abs/2005.11401}, urldate = {2024-01-18}, abstract = {Large pre-trained language models have been shown to store factual knowledge in their parameters, and achieve state-of-the-art results when fine-tuned on downstream NLP tasks. However, their ability to access and precisely manipulate knowledge is still limited, and hence on knowledge-intensive tasks, their performance lags behind task-specific architectures. Additionally, providing provenance for their decisions and updating their world knowledge remain open research problems. Pretrained models with a differentiable access mechanism to explicit non-parametric memory have so far been only investigated for extractive downstream tasks. We explore a general-purpose fine-tuning recipe for retrieval-augmented generation (RAG) — models which combine pre-trained parametric and non-parametric memory for language generation. We introduce RAG models where the parametric memory is a pre-trained seq2seq model and the non-parametric memory is a dense vector index of Wikipedia, accessed with a pre-trained neural retriever. We compare two RAG formulations, one which conditions on the same retrieved passages across the whole generated sequence, and another which can use different passages per token. We fine-tune and evaluate our models on a wide range of knowledgeintensive NLP tasks and set the state of the art on three open domain QA tasks, outperforming parametric seq2seq models and task-specific retrieve-and-extract architectures. For language generation tasks, we find that RAG models generate more specific, diverse and factual language than a state-of-the-art parametric-only seq2seq baseline.}, langid = {english}, pubstate = {prepublished}, keywords = {Computer Science - Computation and Language,Computer Science - Machine Learning,LOGSEQ}, note = {Comment: Accepted at NeurIPS 2020 \par TL;DR \par A general-purpose fine-tuning recipe for retrieval-augmented generation (RAG) -- models which combine pre-trained parametric and non-parametric memory for language generation, and finds that RAG models generate more specific, diverse and factual language than a state-of-the-art parametric-only seq2seq baseline.} } @article{lewisWhatItTrust2022, title = {What Is It like to Trust a Rock? {{A}} Functionalist Perspective on Trust and Trustworthiness in Artificial Intelligence}, shorttitle = {What Is It like to Trust a Rock?}, author = {Lewis, Peter R. and Marsh, Stephen}, date = {2022-03-01}, journaltitle = {Cognitive Systems Research}, volume = {72}, pages = {33--49}, issn = {1389-0417}, doi = {10.1016/j.cogsys.2021.11.001}, abstract = {The trustworthiness (or otherwise) of AI has been much in discussion of late, not least because of the recent publication of the EU Guidelines for Trustworthy AI. Discussions range from how we might make people trust AI to AI being not possible to trust, with many points inbetween. In this article, we question whether or not these discussions somewhat miss the point, which is that people are going ahead and basically doing their own thing anyway, and that we should probably help them. Acknowledging that trust is a heuristic that is widely used by humans in a range of situations, we lean on the literature concerning how humans make trust decisions, to arrive at a general model of how people might consider trust in AI (and other artefacts) for specific purposes in a human world. We then use a series of thought experiments and observations of trust and trustworthiness, to illustrate the use of the model in taking a functionalist perspective on trust decisions, including with machines. Our hope is that this forms a useful basis upon which to develop intelligent systems in a way that considers how and when people may trust them, and in doing so empowers people to make better trust decisions about AI.}, keywords = {Artificial intelligence,Trust,Trustworthiness} } @inproceedings{li_fairgan_2022, title = {{{FairGAN}}: {{GANs-based Fairness-aware Learning}} for {{Recommendations}} with {{Implicit Feedback}}}, shorttitle = {{{FairGAN}}}, booktitle = {Proc. {{ACM Web Conf}}. 2022}, author = {Li, Jie and Ren, Yongli and Deng, Ke}, date = {2022-04}, series = {{{WWW}} '22}, pages = {297--307}, publisher = {Association for Computing Machinery}, location = {New York, NY, USA}, doi = {10.1145/3485447.3511958}, abstract = {Ranking algorithms in recommender systems influence people to make decisions. Conventional ranking algorithms based on implicit feedback data aim to maximize the utility to users by capturing users’ preferences over items. However, these utility-focused algorithms tend to cause fairness issues that require careful consideration in online platforms. Existing fairness-focused studies does not explicitly consider the problem of lacking negative feedback in implicit feedback data, while previous utility-focused methods ignore the importance of fairness in recommendations. To fill this gap, we propose a Generative Adversarial Networks (GANs) based learning algorithm FairGAN mapping the exposure fairness issue to the problem of negative preferences in implicit feedback data. FairGAN does not explicitly treat unobserved interactions as negative, but instead, adopts a novel fairness-aware learning strategy to dynamically generate fairness signals. This optimizes the search direction to make FairGAN capable of searching the space of the optimal ranking that can fairly allocate exposure to individual items while preserving users’ utilities as high as possible.}, isbn = {978-1-4503-9096-5}, keywords = {Exposure,Fairness,GANs,Ranking,Recommendation} } @article{li_fairsr_2022, title = {{{FairSR}}: {{Fairness-aware Sequential Recommendation}} through {{Multi-Task Learning}} with {{Preference Graph Embeddings}}}, shorttitle = {{{FairSR}}}, author = {Li, Cheng-Te and Hsu, Cheng and Zhang, Yang}, date = {2022-02}, journaltitle = {ACM Trans. Intell. Syst. Technol.}, volume = {13}, number = {1}, pages = {16:1--16:21}, issn = {2157-6904}, doi = {10.1145/3495163}, abstract = {Sequential recommendation (SR) learns from the temporal dynamics of user-item interactions to predict the next ones. Fairness-aware recommendation mitigates a variety of algorithmic biases in the learning of user preferences. This article aims at bringing a marriage between SR and algorithmic fairness. We propose a novel fairness-aware sequential recommendation task, in which a new metric, interaction fairness, is defined to estimate how recommended items are fairly interacted by users with different protected attribute groups. We propose a multi-task learning-based deep end-to-end model, FairSR, which consists of two parts. One is to learn and distill personalized sequential features from the given user and her item sequence for SR. The other is fairness-aware preference graph embedding (FPGE). The aim of FPGE is two-fold: incorporating the knowledge of users’ and items’ attributes and their correlation into entity representations, and alleviating the unfair distributions of user attributes on items. Extensive experiments conducted on three datasets show FairSR can outperform state-of-the-art SR models in recommendation performance. In addition, the recommended items by FairSR also exhibit promising interaction fairness.}, keywords = {Fairness-aware models,knowledge graph embedding,multi-task learning,sequential recommendation}, note = {TL;DR \par A novel fairness-aware sequential recommendation task, in which a new metric, interaction fairness, is defined to estimate how recommended items are fairly interacted by users with different protected attribute groups, is proposed.} } @article{Li2015122, title = {An Improved Support Vector Regression and Its Modelling of Manoeuvring Performance in Multidisciplinary Ship Design Optimization}, author = {Li, D. and Wilson, P.A. and Jiang, Z.}, date = {2015}, journaltitle = {Int. J. Model. Simul.}, volume = {35}, number = {3--4}, pages = {122--128}, publisher = {{Taylor and Francis Ltd.}}, issn = {02286203}, doi = {10.1080/02286203.2015.1111055}, abbrev_source_title = {Int J Modell Simul}, affiliation = {School of Naval Architecture \& Ocean Engineering, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu 212003, China; Faculty of Engineering and the Environment, University of Southampton, Southampton, SO17 1BJ, United Kingdom}, coden = {IMSIE}, correspondence_address1 = {Li, D.; School of Naval Architecture \& Ocean Engineering, China; email: lidq@just.edu.cn}, document_type = {Article}, langid = {english}, source = {Scopus}, note = {cited By 7} } @article{LI2019157, title = {Revisiting the Impact of Common Libraries for Android-Related Investigations}, author = {Li, Li and Riom, Timothée and Bissyandé, Tegawendé F. and Wang, Haoyu and Klein, Jacques and Yves, Le Traon}, date = {2019}, journaltitle = {J. Syst. Softw.}, volume = {154}, pages = {157--175}, issn = {0164-1212}, doi = {10.1016/j.jss.2019.04.065}, abstract = {The packaging model of Android apps requires the entire code to be shipped into a single APK file in order to be installed and executed on a device. This model introduces noises to Android app analyses, e.g., detection of repackaged applications, malware classification, as not only the core developer code but also the other assistant code will be visited. Such assistant code is often contributed by common libraries that are used pervasively by all apps. Despite much effort has been put in our community to investigate Android libraries, the momentum of Android research has not yet produced a complete and reliable set of common libraries for supporting thorough analyses of Android apps. In this work, we hence leverage a dataset of about 1.5 million apps from Google Play to identify potential common libraries, including advertisement libraries, and their abstract representations. With several steps of refinements, we finally collect 1113 libraries supporting common functions and 240 libraries for advertisement. For each library, we also collected its various abstract representations that could be leveraged to find new usages, including obfuscated cases. Based on these datasets, we further empirically revisit three popular Android app analyses, namely (1) repackaged app detection, (2) machine learning-based malware detection, and (3) static code analysis, aiming at measuring the impact of common libraries on their analysing performance. Our experimental results demonstrate that common library can indeed impact the performance of Android app analysis approaches. Indeed, common libraries can introduce both false positive and false negative results to repackaged app detection approaches. The existence of common libraries in Android apps may also impact the performance of machine learning-based classifications as well as that of static code analysers. All in all, the aforementioned results suggest that it is essential to harvest a reliable list of common libraries and also important to pay special attention to them when conducting Android-related investigations.} } @article{Li201928737, title = {A Sequential {{Kriging}} Method Assisted by Trust Region Strategy for Proxy Cache Size Optimization of the Streaming Media Video Data Due to Fragment Popularity Distribution}, author = {Li, Y. and Zhang, Q. and Wu, Y. and Wang, S.}, date = {2019}, journaltitle = {Multimed. Tools Appl.}, volume = {78}, number = {20}, pages = {28737--28756}, publisher = {Springer New York LLC}, issn = {13807501}, doi = {10.1007/s11042-018-6563-7}, abbrev_source_title = {Multimedia Tools Appl}, affiliation = {School of Mechanical and Electrical Engineering, Xuchang University, Xuchang, 461000, China; National CAD Centre, Huazhong University of Science and Technology, Wuhan, China}, coden = {MTAPF}, correspondence_address1 = {Li, Y.; School of Mechanical and Electrical Engineering, China; email: liyaohui@hust.edu.cn}, document_type = {Article}, langid = {english}, source = {Scopus}, note = {cited By 4 \par TL;DR \par This work proposes a sequential Kriging method assisted by trust region strategy (SKM-TRS) to solve unconstrained black-box problems and demonstrates the efficiency and robustness of the SKM- TRS in contrast with Efficient Global Optimization (EGO), Trust Region Implementation in Kriged-based optimization with Expected improvement (TRIKE) and an Adaptive Metamodel based Globaloptimization algorithm (AMGO).} } @inproceedings{Li2021172, title = {Data-Driven Receiver for {{OTFS}} System with Deep Learning}, author = {Li, Q. and Gong, Y. and Meng, F. and Xu, Z.}, date = {2021}, series = {Proceedings of 2021 7th {{IEEE International Conference}} on {{Network Intelligence}} and {{Digital Content}}, {{IC-NIDC}} 2021}, pages = {172--176}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/IC-NIDC54101.2021.9660432}, abstract = {Recently researches about receiver structures for orthogonal time-frequency space (OTFS) have been received widespread attention. Previous OTFS receiver algorithms are based on model-driven, which would lead to complex structures. Motivated by recent advances in data-driven receivers, this paper proposes a data-driven OTFS receiver with a deep neural network (DNN). We demonstrate that the proposed data-driven receiver for OTFS can be generalized to different high mobility scenarios. Specifically, this scheme combines the power of deep learning (DL), which is widely used in various fields. With DL, the proposed algorithm can achieve excellent robustness and strong generalization ability for channel parameters, which are ubiquitous challenges in the design of receiver algorithms. Through a good deal of numerical experiments, simulation results show that the proposed data-driven receiver based on DNN for OTFS can achieve superior performance than comparison methods. © 2021 IEEE.}, document_type = {Conference Paper}, isbn = {978-1-6654-0582-9}, source = {Scopus}, note = {cited By 0 \par TL;DR \par It is demonstrated that the proposed data-driven receiver for OTFS can be generalized to different high mobility scenarios and can achieve superior performance than comparison methods.} } @book{liang2003introduction, title = {Introduction to Java Programming}, author = {Liang, Y Daniel}, date = {2003}, publisher = {Pearson Education India}, isbn = {978-0-13-376131-3} } @article{liangModeldrivenClusterResource2022, title = {Model-Driven {{Cluster Resource Management}} for {{AI Workloads}} in {{Edge Clouds}}}, author = {Liang, Qianlin and Hanafy, Walid A. and Ali-Eldin, Ahmed and Shenoy, Prashant}, date = {2022-01-18}, journaltitle = {ArXiv220107312 Cs Eess}, eprint = {2201.07312}, eprinttype = {arXiv}, eprintclass = {cs, eess}, url = {http://arxiv.org/abs/2201.07312}, urldate = {2022-01-25}, abstract = {Since emerging edge applications such as Internet of Things (IoT) analytics and augmented reality have tight latency constraints, hardware AI accelerators have been recently proposed to speed up deep neural network (DNN) inference run by these applications. Resource-constrained edge servers and accelerators tend to be multiplexed across multiple IoT applications, introducing the potential for performance interference between latency-sensitive workloads. In this paper, we design analytic models to capture the performance of DNN inference workloads on shared edge accelerators, such as GPU and edgeTPU, under different multiplexing and concurrency behaviors. After validating our models using extensive experiments, we use them to design various cluster resource management algorithms to intelligently manage multiple applications on edge accelerators while respecting their latency constraints. We implement a prototype of our system in Kubernetes and show that our system can host 2.3X more DNN applications in heterogeneous multi-tenant edge clusters with no latency violations when compared to traditional knapsack hosting algorithms.}, langid = {english}, keywords = {{Computer Science - Distributed, Parallel, and Cluster Computing},Electrical Engineering and Systems Science - Systems and Control} } @article{Liao20201724, title = {A Model-Driven Deep Learning Method for Massive {{MIMO}} Detection}, author = {Liao, J. and Zhao, J. and Gao, F. and Li, G.Y.}, date = {2020}, journaltitle = {IEEE Commun. Lett.}, volume = {24}, number = {8}, pages = {1724--1728}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {10897798}, doi = {10.1109/LCOMM.2020.2989672}, abstract = {In this letter, an efficient massive multiple-input multiple-output (MIMO) detector is proposed by employing a deep neural network (DNN). Specifically, we first unfold an existing iterative detection algorithm into the DNN structure, such that the detection task can be implemented by deep learning (DL) approach. We then introduce two auxiliary parameters at each layer to better cancel multiuser interference (MUI). The first parameter is to generate the residual error vector while the second one is to adjust the relationship among previous layers. We further design the training procedure to optimize the auxiliary parameters with pre-processed inputs. The so derived MIMO detector falls into the category of model-driven DL. The simulation results show that the proposed MIMO detector can achieve preferable detection performance compared to the existing detectors for massive MIMO systems. © 1997-2012 IEEE.}, art_number = {9075976}, coden = {ICLEF}, document_type = {Article}, source = {Scopus}, note = {cited By 16} } @article{liaoDataAdapterQuerying2016, title = {Data Adapter for Querying and Transformation between {{SQL}} and {{NoSQL}} Database}, author = {Liao, Ying-Ti and Zhou, Jiazheng and Lu, Chia-Hung and Chen, Shih-Chang and Hsu, Ching-Hsien and Chen, Wenguang and Jiang, Mon-Fong and Chung, Yeh-Ching}, date = {2016-12}, journaltitle = {Future Gener. Comput. Syst.}, volume = {65}, pages = {111--121}, issn = {0167739X}, doi = {10.1016/j.future.2016.02.002}, abstract = {As the growing of applications with big data in cloud computing become popular, many existing systems expect to expand their service to support the explosive increase of data. We propose a data adapter system to support hybrid database architecture including a relational database (RDB) and NoSQL database. It can support query from application and deal with database transformation at the same time. We provide three modes of query approach in data adapter system: blocking transformation mode (BT mode), blocking dump mode (BD mode), and direct access mode (DA mode). We provide a data synchronization mechanism and describe the design and implementation in detail. This paper focuses on velocity with proposed three modes and partly variety with data stored in RDB, NoSQL database and temporary files. With the proposed data adapter system, we can provide a seamless mechanism to use RDB and NoSQL database at the same time.}, langid = {english} } @online{liLLMassistedStaticAnalysis2024, title = {{{LLM-assisted}} Static Analysis for Detecting Security Vulnerabilities}, author = {Li, Ziyang and Dutta, Saikat and Naik, Mayur}, date = {2024-11-11}, eprint = {2405.17238}, eprinttype = {arXiv}, url = {http://arxiv.org/abs/2405.17238}, urldate = {2024-11-15}, abstract = {Software is prone to security vulnerabilities. Program analysis tools to detect them have limited effectiveness in practice due to their reliance on human labeled specifications. Large language models (or LLMs) have shown impressive code generation capabilities but they cannot do complex reasoning over code to detect such vulnerabilities especially since this task requires whole-repository analysis. We propose IRIS, a neuro-symbolic approach that systematically combines LLMs with static analysis to perform whole-repository reasoning for security vulnerability detection. Specifically, IRIS leverages LLMs to infer taint specifications and perform contextual analysis, alleviating needs for human specifications and inspection. For evaluation, we curate a new dataset, CWE-Bench-Java, comprising 120 manually validated security vulnerabilities in real-world Java projects. A state-of-the-art static analysis tool CodeQL detects only 27 of these vulnerabilities whereas IRIS with GPT-4 detects 55 (+28) and improves upon CodeQL's average false discovery rate by 5\% points. Furthermore, IRIS identifies 6 previously unknown vulnerabilities which cannot be found by existing tools.}, langid = {english}, pubstate = {prepublished}, keywords = {Computer Science - Cryptography and Security,Computer Science - Programming Languages,Computer Science - Software Engineering} } @inproceedings{Lim2019219, title = {Reliability-Based {{MOGA}} Design Optimization Using Probabilistic Response Surface Method and {{Bayesian}} Neural Network}, author = {Lim, J. and Lee, J.}, date = {2019}, series = {{{GECCO}} 2019 {{Companion}} - {{Proceedings}} of the 2019 {{Genetic}} and {{Evolutionary Computation Conference Companion}}}, pages = {219--220}, publisher = {Association for Computing Machinery, Inc}, doi = {10.1145/3319619.3321901}, abbrev_source_title = {GECCO Companion - Proc. Genet. Evolut. Comput. Conf. Companion}, affiliation = {Yonsei University, Seoul, South Korea}, document_type = {Conference Paper}, isbn = {978-1-4503-6748-6}, langid = {english}, source = {Scopus}, note = {cited By 0 \par TL;DR \par An effective optimization approach, which integrated the probabilistic surrogate model, non-dominated sorting genetic algorithm, and reliability index method, is proposed to multi-objective reliability-based design optimization.} } @thesis{liManagementSelfAdmittedTechnical2024, title = {Management of {{Self-Admitted Technical Debt}} Using {{Machine Learning}}:}, shorttitle = {Management of {{Self-Admitted Technical Debt}} Using {{Machine Learning}}}, author = {Li, Yikun}, date = {2024-04-23}, institution = {University of Groningen}, doi = {10.33612/diss.972242960}, langid = {english} } @online{liMoreAgentsAll2024, title = {More {{Agents Is All You Need}}}, author = {Li, Junyou and Zhang, Qin and Yu, Yangbin and Fu, Qiang and Ye, Deheng}, date = {2024-02-03}, eprint = {2402.05120}, eprinttype = {arXiv}, eprintclass = {cs}, url = {http://arxiv.org/abs/2402.05120}, urldate = {2024-02-09}, abstract = {We find that, simply via a sampling-and-voting method, the performance of large language models (LLMs) scales with the number of agents instantiated. Also, this method is orthogonal to existing complicated methods to further enhance LLMs, while the degree of enhancement is correlated to the task difficulty. We conduct comprehensive experiments on a wide range of LLM benchmarks to verify the presence of our finding, and to study the properties that can facilitate its occurrence. Our code is publicly available at: \textbackslash url\{https://anonymous.4open.science/r/more\_agent\_is\_all\_you\_need\}.}, pubstate = {prepublished}, keywords = {Computer Science - Artificial Intelligence,Computer Science - Computation and Language,Computer Science - Machine Learning} } @inproceedings{Lin:1998:IDS:645527.657297, title = {An Information-Theoretic Definition of Similarity}, booktitle = {Proc. {{Fifteenth Int}}. {{Conf}}. {{Mach}}. {{Learn}}.}, author = {Lin, Dekang}, date = {1998}, series = {{{ICML}} '98}, pages = {296--304}, publisher = {Morgan Kaufmann Publishers Inc.}, location = {San Francisco, CA, USA}, url = {http://dl.acm.org/citation.cfm?id=645527.657297}, acmid = {657297}, isbn = {1-55860-556-8}, numpages = {9}, note = {TL;DR \par This work presents an informationtheoretic definition of similarity that is applicable as long as there is a probabilistic model and demonstrates how this definition can be used to measure the similarity in a number of different domains.} } @inproceedings{Linares-Vasquez:2014:ACT:2597008.2597155, title = {How Do {{API}} Changes Trigger Stack Overflow Discussions? {{A}} Study on the Android {{SDK}}}, booktitle = {Proc. {{22Nd Int}}. {{Conf}}. {{Program Comprehension}}}, author = {Linares-Vásquez, Mario and Bavota, Gabriele and Di Penta, Massimiliano and Oliveto, Rocco and Poshyvanyk, Denys}, date = {2014}, series = {{{ICPC}} 2014}, pages = {83--94}, publisher = {ACM}, location = {New York, NY, USA}, url = {http://doi.acm.org/10.1145/2597008.2597155}, acmid = {2597155}, isbn = {978-1-4503-2879-1}, nodoi = {10.1145/2597008.2597155}, numpages = {12}, keywords = {Android,API changes,Social media,StackOverflow}, note = {TL;DR \par It is suggested that Android developers usually have more questions when the behavior of APIs is modified, and deleting public methods from APIs is a trigger for questions that are more discussed and of major interest for the community, and posted by more experienced developers.} } @article{Linares-Vasquez:2014:UML:2617668.2617703, title = {On Using Machine Learning to Automatically Classify Software Applications into Domain Categories}, author = {Linares-Vásquez, Mario and Mcmillan, Collin and Poshyvanyk, Denys and Grechanik, Mark}, date = {2014-06}, journaltitle = {Empir. Softw Engg}, volume = {19}, number = {3}, pages = {582--618}, publisher = {Kluwer Academic Publishers}, address = {Hingham, MA, USA}, issn = {1382-3256}, url = {http://dx.doi.org/10.1007/s10664-012-9230-z}, acmid = {2617703}, issue_date = {June 2014}, nodoi = {10.1007/s10664-012-9230-z}, numpages = {37}, keywords = {Closed-source,machine learning,Open-source,Software categorization}, note = {TL;DR \par A new approach is proposed that makes it possible to categorize software projects without any source code using a small number of API calls as attributes, and furthermore a comprehensive empirical evaluation of automatic categorization approaches is carried out.} } @inproceedings{linares-vasquezAPIChangeFault2013, title = {{{API}} Change and Fault Proneness: A Threat to the Success of {{Android}} Apps}, shorttitle = {{{API}} Change and Fault Proneness}, booktitle = {Proc. 2013 9th {{Jt}}. {{Meet}}. {{Found}}. {{Softw}}. {{Eng}}. - {{ESECFSE}} 2013}, author = {Linares-Vásquez, Mario and Bavota, Gabriele and Bernal-Cárdenas, Carlos and Di Penta, Massimiliano and Oliveto, Rocco and Poshyvanyk, Denys}, date = {2013}, pages = {477}, publisher = {ACM Press}, location = {Saint Petersburg, Russia}, doi = {10.1145/2491411.2491428}, abstract = {During the recent years, the market of mobile software applications (apps) has maintained an impressive upward trajectory. Many small and large software development companies invest considerable resources to target available opportunities. As of today, the markets for such devices feature over 850K+ apps for Android and 900K+ for iOS. Availability, cost, functionality, and usability are just some factors that determine the success or lack of success for a given app. Among the other factors, reliability is an important criteria: users easily get frustrated by repeated failures, crashes, and other bugs; hence, abandoning some apps in favor of others. This paper reports a study analyzing how the fault- and change-proneness of APIs used by 7,097 (free) Android apps relates to applications’ lack of success, estimated from user ratings. Results of this study provide important insights into a crucial issue: making heavy use of fault- and change-prone APIs can negatively impact the success of these apps.}, eventtitle = {The 2013 9th {{Joint Meeting}}}, isbn = {978-1-4503-2237-9}, langid = {english}, note = {TL;DR \par A study analyzing how the fault- and change-proneness of APIs used by 7,097 (free) Android apps relates to applications' lack of success, estimated from user ratings.} } @inproceedings{linares-vasquezAutomaticallyDetectingSimilar2016, title = {On Automatically Detecting Similar Android Apps}, booktitle = {Program {{Comprehension ICPC}} 2016 {{IEEE}} 24th {{Int}}. {{Conf}}. {{On}}}, author = {Linares-Vásquez, Mario and Holtzhauer, Andrew and Poshyvanyk, Denys}, date = {2016}, pages = {1--10}, publisher = {IEEE}, url = {http://ieeexplore.ieee.org/abstract/document/7503721/}, urldate = {2017-09-25}, note = {TL;DR \par The results show that using Android-specific semantic anchors are useful for detecting similar Android apps across different categories, and the results suggest that there is significant difference in the accuracy when third-party libraries are excluded.} } @article{Linden:2003:ARI:642462.642471, title = {Amazon.{{Com}} Recommendations: {{Item-to-item}} Collaborative Filtering}, author = {Linden, Greg and Smith, Brent and York, Jeremy}, date = {2003-01}, journaltitle = {IEEE Internet Comput.}, volume = {7}, number = {1}, pages = {76--80}, publisher = {IEEE Educational Activities Department}, address = {Piscataway, NJ, USA}, issn = {1089-7801}, url = {http://dx.doi.org/10.1109/MIC.2003.1167344}, acmid = {642471}, issue_date = {January 2003}, nodoi = {10.1109/MIC.2003.1167344}, numpages = {5}, note = {TL;DR \par This work compares three common approaches to solving the recommendation problem: traditional collaborative filtering, cluster models, and search-based methods, and their algorithm, which is called item-to-item collaborative filtering.} } @article{linModelingUsersMobile, title = {Modeling {{Users}}’ {{Mobile App Privacy Preferences}}: {{Restoring Usability}} in a {{Sea}} of {{Permission Settings}}}, author = {Lin, Jialiu and Liu, Bin and Sadeh, Norman and Hong, Jason I}, pages = {14}, abstract = {In this paper, we investigate the feasibility of identifying a small set of privacy profiles as a way of helping users manage their mobile app privacy preferences. Our analysis does not limit itself to looking at permissions people feel comfortable granting to an app. Instead it relies on static code analysis to determine the purpose for which an app requests each of its permissions, distinguishing for instance between apps relying on particular permissions to deliver their core functionality and apps requesting these permissions to share information with advertising networks or social networks. Using privacy preferences that reflect people’s comfort with the purpose for which different apps request their permissions, we use clustering techniques to identify privacy profiles. A major contribution of this work is to show that, while people’s mobile app privacy preferences are diverse, it is possible to identify a small number of privacy profiles that collectively do a good job at capturing these diverse preferences.}, langid = {english} } @article{linSentimentAnalysisSoftware2018, title = {Sentiment {{Analysis}} for {{Software Engineering}}: {{How Far Can We Go}}?}, author = {Lin, Bin and Zampetti, Fiorella and Bavota, Gabriele and Penta, Massimiliano Di and Lanza, Michele and Oliveto, Rocco}, date = {2018}, pages = {11}, abstract = {Sentiment analysis has been applied to various software engineering (SE) tasks, such as evaluating app reviews or analyzing developers’ emotions in commit messages. Studies indicate that sentiment analysis tools provide unreliable results when used out-of-the-box, since they are not designed to process SE datasets. The silver bullet for a successful application of sentiment analysis tools to SE datasets might be their customization to the specific usage context. We describe our experience in building a software library recommender exploiting developers’ opinions mined from Stack Overflow. To reach our goal, we retrained—on a set of 40k manually labeled sentences/words extracted from Stack Overflow—a state-of-the-art sentiment analysis tool exploiting deep learning. Despite such an effort- and time-consuming training process, the results were negative. We changed our focus and performed a thorough investigation of the accuracy of commonly used tools to identify the sentiment of SE related texts. Meanwhile, we also studied the impact of different datasets on tool performance. Our results should warn the research community about the strong limitations of current sentiment analysis tools.}, langid = {english}, note = {TL;DR \par This work retrained—on a set of 40k manually labeled sentences/words extracted from Stack Overflow—a state-of-the-art sentiment analysis tool exploiting deep learning, and found the results were negative.} } @article{linsteadSourcererMiningSearching2009, ids = {DBLP:journals/datamine/LinsteadBNRLB09}, title = {Sourcerer: Mining and Searching Internet-Scale Software Repositories}, shorttitle = {Sourcerer}, author = {Linstead, Erik and Bajracharya, Sushil and Ngo, Trung and Rigor, Paul and Lopes, Cristina and Baldi, Pierre}, date = {2009-04}, journaltitle = {Data Min. Knowl. Discov.}, volume = {18}, number = {2}, pages = {300--336}, issn = {1384-5810, 1573-756X}, url = {http://link.springer.com/10.1007/s10618-008-0118-x}, urldate = {2019-09-04}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/bib/journals/datamine/LinsteadBNRLB09}, langid = {english}, nodoi = {10.1007/s10618-008-0118-x}, timestamp = {Sat, 20 May 2017 00:25:07 +0200} } @article{liPreprocessingMethodsPipelines, title = {Preprocessing {{Methods}} and {{Pipelines}} of {{Data Mining}}}, shorttitle = {0}, author = {Li, Canchen}, journaltitle = {0}, url = {0}, urldate = {2021-03-18}, abstract = {0}, keywords = {Computer Science - Databases,Computer Science - Machine Learning,DONE,STAR,Statistics - Machine Learning}, note = {Comment: 7 pages, 3 figures, IEEE conference format \par Comment: 7 pages, 3 figures, IEEE conference format} } @article{liProposedTitleIoTnative, title = {Proposed Title: {{IoT-native}} Software Engineering}, author = {Li, Zheng and O’Brien, Liam and Yang, Ye and Gorton, Ian}, langid = {english}, note = {{$<$}!-- ~== For your convenience, this form can be processed by EasyChair ~== automatically. You can fill out this form offline and then upload it ~== to EasyChair. Several review forms can be uploaded simultaneously. ~== You can modify your reviews as many times as you want. ~== ~== When filling out the review form please mind the following rules: ~== ~== (1) Blocks such as this are comments. EasyChair will ignore them. ~== ~ ~ Do not write any text into these blocks as it will be ignored. ~== ~ ~ You can add comments to the review form or remove them. ~== (2) Write only into the tags where instructed. Do not modify any ~== ~ ~ tags and attributes, or the review will become unusable and will ~== ~ ~ be rejected by EasyChair. ~--{$><$}review id=":7075322::3424433" ~ ~ ~ ~submission="35" ~ ~ ~ ~title="IoT-native Software Engineering" ~ ~ ~ ~authors="Zheng Li, ~ ~ ~ ~ ~ ~ ~ ~ Liam O'Brien, ~ ~ ~ ~ ~ ~ ~ ~ Ye Yang, ~ ~ ~ ~ ~ ~ ~ ~ Ian Gorton" ~ ~ ~ ~pc\_member="Davide Di Ruscio"{$><$}field id="611399" name="SCORE: Overall evaluation of the submitted document"{$>$} ~{$<$}!-- ~ ~== This field defines the "proposal overall quality". ~ ~--{$>$} ~{$<$}score{$>$} ~{$<$}!-- ~ ~== Select your choice from the options below and write its number below, ~ ~== before the {$<$}/score{$>$} tag. ~ ~== ~ ~== 3 Accept ~ ~== 2 Borderline ~ ~== 1 Reject ~ ~--{$>$} ~ ~ ~{$<$}/score{$><$}/field{$><$}!-- ~ ~ Additional Scores ~ ~ --{$><$}field id="611401" name="TOPIC Rating (1-100)"{$>$} ~{$<$}!-- ~ ~== This field represents the importance of the TOPIC presented ~ ~== (independently from the proposal quality) ~ ~--{$>$} ~{$<$}score{$>$} ~{$<$}!-- ~ ~== Select your choice from the options below and write its number below, ~ ~== before the {$<$}/score{$>$} tag. ~ ~== ~ ~== 5 (81-100) Strong TOPIC that definitively should be published. ~ ~== 4 (61-80) Interesting TOPIC that most likely will generate interest; ~ ~== 3 (41-60) Potentially a good TOPIC; ~ ~== 2 (21-40) Weak TOPIC; ~ ~== 1 (1-20) Very weak TOPIC that should not be published; ~ ~--{$>$} ~ ~ ~{$<$}/score{$><$}/field{$><$}field id="611402" name="Is the Topic relevant and timely?"{$>$} ~{$<$}!-- ~ ~== Please describe if the topic is of importance for the IEEE SW ~ ~--{$>$} ~{$<$}score{$>$} ~{$<$}!-- ~ ~== Select your choice from the options below and write its number below, ~ ~== before the {$<$}/score{$>$} tag. ~ ~== ~ ~== 2 Yes ~ ~== 1 Not ~ ~--{$>$} ~ ~ ~{$<$}/score{$><$}/field{$><$}field id="611403" name="Do you expect this theme issue to attract an adequate number of submissions?"{$>$} ~{$<$}!-- ~ ~== Please describe if the guest editors are supposed to be able to ~ ~== attract an adequate ({$>>$}10) number of submissions. ~ ~--{$>$} ~{$<$}score{$>$} ~{$<$}!-- ~ ~== Select your choice from the options below and write its number below, ~ ~== before the {$<$}/score{$>$} tag. ~ ~== ~ ~== 3 I expect a high number of submissions. ~ ~== 2 I expect a proper number of submissions; ~ ~== 1 I expect a limited number of submissions; ~ ~--{$>$} ~ ~ ~{$<$}/score{$><$}/field{$><$}!-- ~ ~ Review ~ ~ --{$><$}field id="611405" name="Review"{$>$} ~{$<$}!-- ~ ~== Please provide a detailed review, including justification for your ~ ~== scores. This field is required. ~ ~--{$>$} ~{$<$}text required="required"{$>$} ~{$<$}!-- ~ ~== Enter the text for the field ~ ~== ~ Review ~ ~== below. This field is required. ~ ~--{$>$}The proposal is too broad. It aims at covering "any aspect of IoT-native Software Engineering", which is risky. For instance, proponents should decide if they want to elicit empirical studies or experience reports. In any case, I don't think pure empirical studies can be appropriate for this venue especially considering the length of typical IEEE Software papers. Overall, it is not clear the need that the proponents aim to fill. ~{$<$}/text{$><$}/field{$><$}field id="611406" name="Confidential remarks for the board members"{$>$} ~{$<$}!-- ~ ~== If you wish to add any remarks intended only for board members ~ ~== please write them below. These remarks will only be seen by the ~ ~== board members having access to reviews for this submission. They ~ ~== will not be sent to the authors. This field is optional. ~ ~--{$>$} ~{$<$}text optional="optional"{$>$} ~{$<$}!-- ~ ~== Enter the text for the field ~ ~== ~ Confidential remarks for the board members ~ ~== below. This field is optional. ~ ~--{$>$} ~{$<$}/text{$><$}/field{$><$}reviewer{$>$} ~{$<$}!-- ~ ~== If the review was written by (or with the help from) a subreviewer ~ ~== different from the PC member in charge, add information about ~ ~== the subreviewer below. Write subreviewer's first name, last name ~ ~== and email between the tags below. ~ ~--{$>$} ~{$<$}first\_name{$>$} ~{$<$}/first\_name{$>$} ~{$<$}last\_name{$>$} ~ {$<$}/last\_name{$>$} ~{$<$}email{$>$} ~{$<$}/email{$><$}/reviewer{$><$}/review{$>$}} } @article{liSystematicMappingStudy2015, title = {A Systematic Mapping Study on Technical Debt and Its Management}, author = {Li, Zengyang and Avgeriou, Paris and Liang, Peng}, date = {2015-03}, journaltitle = {Journal of Systems and Software}, volume = {101}, pages = {193--220}, issn = {01641212}, doi = {10.1016/j.jss.2014.12.027}, langid = {english} } @inproceedings{liu_searching_2018, title = {Searching {{StackOverflow Questions}} with {{Multi-Faceted Categorization}}}, booktitle = {Proc. {{Tenth Asia-Pac}}. {{Symp}}. {{Internetware}} - {{Internetware}} 18}, author = {Liu, Mingwei and Peng, Xin and Jiang, Qingtao and Marcus, Andrian and Yang, Junwen and Zhao, Wenyun}, date = {2018}, pages = {1--10}, publisher = {ACM Press}, location = {Beijing, China}, doi = {10.1145/3275219.3275227}, abstract = {StackOverflow provides answers for a huge number of software development questions that are frequently encountered by developers. However, searching relevant questions in StackOverflow is not always easy using the keyword based search engine provided by StackOverflow. A software development question can be characterized by multiple attributes, such as, its concern (e.g.,configuration problem, error handling, sample code, etc.), programming language, operating system, and involved middleware, framework, library and software technology. We propose a multi-faceted and interactive approach for searching StackOverflow questions (called MFISSO), which leverages these attributes of the questions. Our approach starts with an initial keyword-based query and extracts a multifaceted categorization from all the candidate questions using natural language processing and data mining. It then allows developers to iteratively refine the search results through an interactive process. We evaluated an implementation of MFISSO in a controlled experiments with 20 computing students, solving ten software development tasks using StackOverflow. The experiment shows that MFISSO can help developers find relevant questions faster and with higher accuracy.}, isbn = {978-1-4503-6590-1}, langid = {english}, note = {TL;DR \par This work proposes a multi-faceted and interactive approach for searching StackOverflow questions (called MFISSO), which extracts a multifaceted categorization from all the candidate questions using natural language processing and data mining, and allows developers to iteratively refine the search results through an interactive process.} } @inproceedings{Liu:2006:GDS:1150402.1150522, title = {{{GPLAG}}: {{Detection}} of Software Plagiarism by Program Dependence Graph Analysis}, booktitle = {Proc. 12th {{ACM SIGKDD Int}}. {{Conf}}. {{Knowl}}. {{Discov}}. {{Data Min}}.}, author = {Liu, Chao and Chen, Chen and Han, Jiawei and Yu, Philip S.}, date = {2006}, series = {{{KDD}} '06}, pages = {872--881}, publisher = {ACM}, location = {New York, NY, USA}, url = {http://doi.acm.org/10.1145/1150402.1150522}, acmid = {1150522}, isbn = {1-59593-339-5}, nodoi = {10.1145/1150402.1150522}, numpages = {10}, keywords = {graph mining,program dependence graph,software plagiarism detection}, note = {TL;DR \par A new plagiarism detection tool, called GPLAG, is developed, which detects plagiarism by mining program dependence graphs (PDGs) and is more effective than state-of-the-art tools for plagiarism Detection.} } @inproceedings{Liu2015, title = {Towards Nonlinear Multimaterial Topology Optimization Using Unsupervised Machine Learning and Metamodel-Based Optimization}, author = {Liu, K. and Tovar, A. and Nutwell, E. and Detwiler, D.}, date = {2015}, series = {Proceedings of the {{ASME Design Engineering Technical Conference}}}, volume = {2B-2015}, publisher = {American Society of Mechanical Engineers (ASME)}, doi = {10.1115/DETC201546534}, abbrev_source_title = {Proc. ASME Des. Eng. Tech. Conf.}, affiliation = {School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, United States; Dept. of Mechanical Engineering, Indiana Univ.-Purdue Univ. Indpls, Indianapolis, IN 46202, United States; Honda R and D Americas, Raymond, OH 43067, United States}, correspondence_address1 = {Tovar, A.; Dept. of Mechanical Engineering, United States; email: tovara@iupui.edu}, document_type = {Conference Paper}, isbn = {978-0-7918-5708-3}, langid = {english}, source = {Scopus}, note = {cited By 7 \par TL;DR \par This work introduces a multimaterial density-based topology optimization method suitable for nonlinear structural problems and is demonstrated with the design of multimaterial stiff structures, compliant mechanisms, and a thin-walled S-rail structure for crashworthiness.} } @inproceedings{Liu2016, title = {Machine Learning and Metamodel-Based Design Optimization of Nonlinear Multimaterial Structures}, author = {Liu, K. and Detwiler, D. and Tovar, A.}, date = {2016}, series = {Proceedings of the {{ASME Design Engineering Technical Conference}}}, volume = {2B-2016}, publisher = {American Society of Mechanical Engineers (ASME)}, doi = {10.1115/DETC2016-60471}, abbrev_source_title = {Proc. ASME Des. Eng. Tech. Conf.}, affiliation = {School of Mechanical Engr., Purdue University, West Lafayette, IN, United States; Honda R and D Americas, Inc., Raymond, OH, United States; Dept. of Mechanical Engr., Indiana U - Purdue U Indianapolis, Indianapolis, IN, United States}, correspondence_address1 = {Tovar, A.; Dept. of Mechanical Engr., United States; email: tovara@iupui.edu}, document_type = {Conference Paper}, isbn = {978-0-7918-5011-4}, langid = {english}, source = {Scopus}, note = {cited By 0} } @article{Liu2017, title = {Optimal Design of Nonlinear Multimaterial Structures for Crashworthiness Using Cluster Analysis}, author = {Liu, K. and Detwiler, D. and Tovar, A.}, date = {2017}, journaltitle = {J. Mech. Des. Trans. ASME}, volume = {139}, number = {10}, publisher = {American Society of Mechanical Engineers (ASME)}, issn = {10500472}, doi = {10.1115/1.4037620}, abbrev_source_title = {J Mech Des, Trans ASME}, affiliation = {School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, United States; Honda RandD Americas, Inc., Raymond, OH 43067, United States; Department of Mechanical Engineering, Indiana University-Purdue, University Indianapolis, Indianapolis, IN 46202, United States}, art_number = {101401}, coden = {JMDEE}, correspondence_address1 = {Tovar, A.; Department of Mechanical Engineering, United States; email: tovara@iupui.edu}, document_type = {Article}, langid = {english}, source = {Scopus}, note = {cited By 10} } @inproceedings{Liu2019, title = {Deep {{CNN}} for Spectrum Sensing in Cognitive Radio}, author = {Liu, C. and Liu, X. and Liang, Y.-C.}, date = {2019}, series = {{{IEEE International Conference}} on {{Communications}}}, volume = {2019-May}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {15503607}, doi = {10.1109/ICC.2019.8761360}, abstract = {The existing spectrum sensing methods mostly make decisions using model-driven test statistics, such as energy and eigenvalues. A weakness of these model-driven methods is the difficulty in accurately modeling for practical environment. In contrast to the model-driven approach, in this paper, we use a deep neural network to automatically learn features from data itself, and develop a data-driven detection approach. Inspired by the powerful capability of convolutional neural network (CNN) in extracting features of matrix-shaped data, we use the sample covariance matrix as the input of CNN, proposing a novel covariance matrix-aware CNN-based detection scheme, which consists of offline training and online detection. Different from the existing deep learning-based detection methods which replace the whole detection system by an end-to-end neural network, in this work, we use CNN for offline test statistic design and develop a practical threshold-based online detection mechanism. Specially, according to the maximum a posteriori probability (MAP) criterion, we derive the cost function for offline training in the spectrum sensing model, which guarantees the optimality of the designed test statistic. Simulation results have shown that whether the PU signals are independent or correlated, the detection performance of the proposed method is close to the optimal bound of estimator-correlator detector. Particularly, when the PU signals are correlated with a correlation coefficient 0.7, the probability of detection of the proposed method outperforms the conventional maximum eigenvalue detection method by nearly 7.5 times at SNR = -14dB. © 2019 IEEE.}, art_number = {8761360}, document_type = {Conference Paper}, isbn = {978-1-5386-8088-9}, source = {Scopus}, keywords = {EXCLUDED}, note = {cited By 13 \par TL;DR \par This paper uses a deep neural network to automatically learn features from data itself, and develops a data-driven detection approach, which outperforms the conventional maximum eigenvalue detection method and derives the cost function for offline training in the spectrum sensing model, which guarantees the optimality of the designed test statistic.} } @article{Liu20223, title = {Efficient Online Service Based on Go-Tensorflow in the Middle-Station Scenario of Grid Service}, author = {Liu, P. and Lu, Y. and Wang, G. and Zhou, W.}, editor = {Qiu M., Gai K., Qiu H.}, date = {2022}, journaltitle = {Lect. Notes Comput. Sci. Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinforma.}, volume = {13202 LNCS}, pages = {3--13}, publisher = {{Springer Science and Business Media Deutschland GmbH}}, issn = {03029743}, doi = {10.1007/978-3-030-97774-0_1}, abstract = {The application of machine learning and deep learning is widely used in the business of the power grid. However, the business of the power grid is complicated, and the online service of deep learning faces greater performance challenges. In order to solve this problem, this paper proposes an online service EOSP based on go-tensorflow. EOSP service is divided into 3 modules, namely model configuration module, execution engine module and model management module. The model configuration module mainly includes functions such as online model configuration and model configuration information synchronization. The execution engine can execute graphical model calls, and has optimized performance based on the characteristics of golang language coroutines. The model management module is responsible for model registration, update, uninstallation and version management. Experiments show that the EOSP service is highly stable, which greatly reduces the time consumption of online services. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.}, document_type = {Conference Paper}, isbn = {9783030977733}, source = {Scopus}, note = {cited By 0} } @article{liuFollowMyRecommendations, title = {Follow {{My Recommendations}}: {{A Personalized Privacy Assistant}} for {{Mobile App Permissions}}}, author = {Liu, Bin and Andersen, Mads Schaarup and Schaub, Florian and Almuhimedi, Hazim and Zhang, Shikun and Sadeh, Norman and Acquisti, Alessandro and Agarwal, Yuvraj}, pages = {16}, abstract = {Modern smartphone platforms have millions of apps, many of which request permissions to access private data and resources, like user accounts or location. While these smartphone platforms provide varying degrees of control over these permissions, the sheer number of decisions that users are expected to manage has been shown to be unrealistically high. Prior research has shown that users are often unaware of, if not uncomfortable with, many of their permission settings. Prior work also suggests that it is theoretically possible to predict many of the privacy settings a user would want by asking the user a small number of questions. However, this approach has neither been operationalized nor evaluated with actual users before. We report on a field study (n=72) in which we implemented and evaluated a Personalized Privacy Assistant (PPA) with participants using their own Android devices. The results of our study are encouraging. We find that 78.7\% of the recommendations made by the PPA were adopted by users. Following initial recommendations on permission settings, participants were motivated to further review and modify their settings with daily “privacy nudges.” Despite showing substantial engagement with these nudges, participants only changed 5.1\% of the settings previously adopted based on the PPA’s recommendations. The PPA and its recommendations were perceived as useful and usable. We discuss the implications of our results for mobile permission management and the design of personalized privacy assistant solutions.}, langid = {english}, note = {TL;DR \par A field study in which a Personalized Privacy Assistant (PPA) was implemented and evaluated with participants using their own Android devices, and it is found that 78.7\% of the recommendations made by the PPA were adopted by users.} } @online{liuLargeLanguageModelBased2024, ids = {liuLargeLanguageModelbased2024}, title = {Large {{Language Model-Based Agents}} for {{Software Engineering}}: {{A Survey}}}, shorttitle = {Large {{Language Model-Based Agents}} for {{Software Engineering}}}, author = {Liu, Junwei and Wang, Kaixin and Chen, Yixuan and Peng, Xin and Chen, Zhenpeng and Zhang, Lingming and Lou, Yiling}, date = {2024-09-04}, eprint = {2409.02977}, eprinttype = {arXiv}, eprintclass = {cs}, doi = {10.48550/arXiv.2409.02977}, abstract = {The recent advance in Large Language Models (LLMs) has shaped a new paradigm of AI agents, i.e., LLM-based agents. Compared to standalone LLMs, LLM-based agents substantially extend the versatility and expertise of LLMs by enhancing LLMs with the capabilities of perceiving and utilizing external resources and tools. To date, LLM-based agents have been applied and shown remarkable effectiveness in Software Engineering (SE). The synergy between multiple agents and human interaction brings further promise in tackling complex real-world SE problems. In this work, we present a comprehensive and systematic survey on LLM-based agents for SE. We collect 106 papers and categorize them from two perspectives, i.e., the SE and agent perspectives. In addition, we discuss open challenges and future directions in this critical domain. The repository of this survey is at https://github.com/FudanSELab/Agent4SE-Paper-List.}, langid = {english}, pubstate = {prepublished}, keywords = {Computer Science - Artificial Intelligence,Computer Science - Software Engineering} } @online{liuPretrainPromptPredict2021, title = {Pre-Train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing}, shorttitle = {Pre-Train, Prompt, and Predict}, author = {Liu, Pengfei and Yuan, Weizhe and Fu, Jinlan and Jiang, Zhengbao and Hayashi, Hiroaki and Neubig, Graham}, date = {2021-07-28}, eprint = {2107.13586}, eprinttype = {arXiv}, doi = {10.48550/arXiv.2107.13586}, abstract = {This paper surveys and organizes research works in a new paradigm in natural language processing, which we dub "prompt-based learning". Unlike traditional supervised learning, which trains a model to take in an input x and predict an output y as P(y|x), prompt-based learning is based on language models that model the probability of text directly. To use these models to perform prediction tasks, the original input x is modified using a template into a textual string prompt x' that has some unfilled slots, and then the language model is used to probabilistically fill the unfilled information to obtain a final string x, from which the final output y can be derived. This framework is powerful and attractive for a number of reasons: it allows the language model to be pre-trained on massive amounts of raw text, and by defining a new prompting function the model is able to perform few-shot or even zero-shot learning, adapting to new scenarios with few or no labeled data. In this paper we introduce the basics of this promising paradigm, describe a unified set of mathematical notations that can cover a wide variety of existing work, and organize existing work along several dimensions, e.g.the choice of pre-trained models, prompts, and tuning strategies. To make the field more accessible to interested beginners, we not only make a systematic review of existing works and a highly structured typology of prompt-based concepts, but also release other resources, e.g., a website http://pretrain.nlpedia.ai/ including constantly-updated survey, and paperlist.}, langid = {english}, pubstate = {prepublished}, keywords = {Computer Science - Artificial Intelligence,Computer Science - Computation and Language,Computer Science - Machine Learning} } @online{liuRefiningChatGPTGeneratedCode2023, title = {Refining {{ChatGPT-Generated Code}}: {{Characterizing}} and {{Mitigating Code Quality Issues}}}, shorttitle = {Refining {{ChatGPT-Generated Code}}}, author = {Liu, Yue and Le-Cong, Thanh and Widyasari, Ratnadira and Tantithamthavorn, Chakkrit and Li, Li and Le, Xuan-Bach D. and Lo, David}, date = {2023-07-24}, eprint = {2307.12596}, eprinttype = {arXiv}, eprintclass = {cs}, url = {http://arxiv.org/abs/2307.12596}, urldate = {2023-07-28}, abstract = {In this paper, we systematically study the quality of 4,066 ChatGPT-generated code implemented in two popular programming languages, i.e., Java and Python, for 2,033 programming tasks. The goal of this work is three folds. First, we analyze the correctness of ChatGPT on code generation tasks and uncover the factors that influence its effectiveness, including task difficulty, programming language, time that tasks are introduced, and program size. Second, we identify and characterize potential issues with the quality of ChatGPT-generated code. Last, we provide insights into how these issues can be mitigated. Experiments highlight that out of 4,066 programs generated by ChatGPT, 2,757 programs are deemed correct, 1,081 programs provide wrong outputs, and 177 programs contain compilation or runtime errors. Additionally, we further analyze other characteristics of the generated code through static analysis tools, such as code style and maintainability, and find that 1,933 ChatGPT-generated code snippets suffer from maintainability issues. Subsequently, we investigate ChatGPT's self-debugging ability and its interaction with static analysis tools to fix the errors uncovered in the previous step. Experiments suggest that ChatGPT can partially address these challenges, improving code quality by more than 20\%, but there are still limitations and opportunities for improvement. Overall, our study provides valuable insights into the current limitations of ChatGPT and offers a roadmap for future research and development efforts to enhance the code generation capabilities of AI models like ChatGPT.}, pubstate = {prepublished}, keywords = {Computer Science - Software Engineering} } @online{liuSyntaxDomainAware2023, title = {Syntax and {{Domain Aware Model}} for {{Unsupervised Program Translation}}}, author = {Liu, Fang and Li, Jia and Zhang, Li}, date = {2023-03-09}, eprint = {2302.03908}, eprinttype = {arXiv}, eprintclass = {cs}, url = {http://arxiv.org/abs/2302.03908}, urldate = {2023-04-25}, abstract = {There is growing interest in software migration as the development of software and society. Manually migrating projects between languages is error-prone and expensive. In recent years, researchers have begun to explore automatic program translation using supervised deep learning techniques by learning from large-scale parallel code corpus. However, parallel resources are scarce in the programming language domain, and it is costly to collect bilingual data manually. To address this issue, several unsupervised programming translation systems are proposed. However, these systems still rely on huge monolingual source code to train, which is very expensive. Besides, these models cannot perform well for translating the languages that are not seen during the pre-training procedure. In this paper, we propose SDA-Trans, a syntax and domain-aware model for program translation, which leverages the syntax structure and domain knowledge to enhance the cross-lingual transfer ability. SDA-Trans adopts unsupervised training on a smaller-scale corpus, including Python and Java monolingual programs. The experimental results on function translation tasks between Python, Java, and C++ show that SDA-Trans outperforms many large-scale pre-trained models, especially for unseen language translation.}, pubstate = {prepublished}, keywords = {Computer Science - Artificial Intelligence,Computer Science - Software Engineering}, note = {Comment: Accepted by International Conference on Software Engineering (ICSE-2023) \par TL;DR \par SDA-Trans is proposed, a syntax and domain-aware model for program translation, which leverages the syntax structure and domain knowledge to enhance the cross-lingual transfer ability and outperforms many large-scale pre-trained models, especially for unseen language translation.} } @misc{LLM4SEESEMv23, title = {{{LLM4SE-ESEM-v2}}.3}, langid = {english}, keywords = {LLMs} } @online{LLMs4EUProject, title = {{{LLMs4EU}} Project}, url = {https://www.linkedin.com/posts/francescoferrero_leaflet-of-the-llms4eu-project-ugcPost-7270810187081420800-TKp8?utm_source=share&utm_medium=member_android}, keywords = {source\_mobile} } @online{LLMVisualization, title = {{{LLM Visualization}}}, url = {https://www.linkedin.com/posts/kalyanksnlp_llms-visualization-nlproc-activity-7279056097745772544-e7hG?utm_source=share&utm_medium=member_android}, keywords = {source\_mobile} } @article{Lo2022, title = {Architectural Patterns for the Design of Federated Learning Systems}, author = {Lo, S.K. and Lu, Q. and Zhu, L. and Paik, H.-Y. and Xu, X. and Wang, C.}, date = {2022}, journaltitle = {J. Syst. Softw.}, volume = {191}, publisher = {Elsevier Inc.}, issn = {01641212}, doi = {10.1016/j.jss.2022.111357}, abstract = {Federated learning has received fast-growing interests from academia and industry to tackle the challenges of data hungriness and privacy in machine learning. A federated learning system can be viewed as a large-scale distributed system with different components and stakeholders as numerous client devices participate in federated learning. Designing a federated learning system requires software system design thinking apart from the machine learning knowledge. Although much effort has been put into federated learning from the machine learning technique aspects, the software architecture design concerns in building federated learning systems have been largely ignored. Therefore, in this paper, we present a collection of architectural patterns to deal with the design challenges of federated learning systems. Architectural patterns present reusable solutions to a commonly occurring problem within a given context during software architecture design. The presented patterns are based on the results of a systematic literature review and include three client management patterns, four model management patterns, three model training patterns, four model aggregation patterns, and one configuration pattern. The patterns are associated to the particular state transitions in a federated learning model lifecycle, serving as a guidance for effective use of the patterns in the design of federated learning systems. © 2022 Elsevier Inc.}, art_number = {111357}, coden = {JSSOD}, document_type = {Article}, source = {Scopus}, note = {cited By 0} } @online{LondonbasedGyanaRaises, title = {London-Based {{Gyana}} Raises \$3.{{9M}} for a No-Code Approach to Data Science – {{TechCrunch}}}, url = {https://techcrunch.com/2020/02/27/london-based-gyana-raises-3-9m-for-a-no-code-approach-to-data-science/amp/?guce_referrer=aHR0cHM6Ly90LmNvL0p4U1pmVFJ4dms_YW1wPTE&guce_referrer_sig=AQAAAK7PsQ7LRtmCbJPzeDGcZKBNQWYD7Kx1bOzyc7RPk9m25HkGQKbBfxKc&guccounter=2}, urldate = {2020-03-02} } @article{lonettiDesigningTestingSystems2022, ids = {lonetti_designing_2022}, title = {Designing and Testing Systems of Systems: {{From}} Variability Models to Test Cases Passing through Desirability Assessment}, shorttitle = {Designing and Testing Systems of Systems}, author = {Lonetti, Francesca and Oliveira Neves, Vânia and Bertolino, Antonia}, date = {2022-01-17}, journaltitle = {J Software Evolu Process}, issn = {2047-7473, 2047-7481}, doi = {10.1002/smr.2427}, abstract = {In the early stages of a system of systems (SoS) conception, several constituent systems could be available that provide similar functionalities. An SoS design methodology should provide adequate means to model variability in order to support the opportunistic selection of the most desirable SoS configuration. We propose the VANTESS approach that (i) supports SoS modeling taking into account the variation points implied by the considered constituent systems; (ii) includes a heuristics to weight benefits and costs of potential architectural choices (called as SoS variants) for the selection of the constituent systems; and finally (iii) also helps test planning for the selected SoS variant by deriving a simulation model on which test objectives and scenarios can be devised. We illustrate an application example of VANTESS to the “educational” SoS and discuss its pros and cons within a focus group.}, langid = {english}, keywords = {LOGSEQ}, note = {TL;DR \par The VANTESS approach is proposed that supports SoS modeling taking into account the variation points implied by the considered constituent systems, and includes a heuristics to weight benefits and costs of potential architectural choices for the selection of the constituent systems.} } @inproceedings{lopez-fernandezAssessingQualityMetamodels2014, title = {Assessing the {{Quality}} of {{Meta-models}}}, booktitle = {11th {{Workshop Model Driven Eng}}. {{Verification Valid}}. {{MoDeVVa}} 2014}, author = {López-Fernández, Jesús J. and Guerra, Esther and family=Lara, given=Juan, prefix=de, useprefix=true}, date = {2014}, pages = {3}, url = {http://ceur-ws.org/Vol-1235/MoDeVVa2014-complete.pdf#page=9}, urldate = {2015-09-15}, note = {TL;DR \par A language and tool are presented to specify and check properties on meta-models and visualise the problematic elements and draw recommendations for both MDE practitioners and meta-model tool builders.} } @article{lopez-fernandezExampledrivenMetamodelDevelopment2015, title = {Example-Driven Meta-Model Development}, author = {López-Fernández, Jesús J. and Cuadrado, Jesús Sánchez and Guerra, Esther and family=Lara, given=Juan, prefix=de, useprefix=true}, date = {2015-10}, journaltitle = {Softw. Syst. Model.}, volume = {14}, number = {4}, pages = {1323--1347}, issn = {1619-1366, 1619-1374}, doi = {10.1007/s10270-013-0392-y}, langid = {english} } @article{López-Quintero20181845, title = {A Personal Knowledge Management Metamodel Based on Semantic Analysis and Social Information}, author = {López-Quintero, J.F. and Cueva Lovelle, J.M. and González Crespo, R. and García-Díaz, V.}, date = {2018}, journaltitle = {Soft Comput.}, volume = {22}, number = {6}, pages = {1845--1854}, publisher = {Springer Verlag}, issn = {14327643}, doi = {10.1007/s00500-016-2437-y}, abbrev_source_title = {Soft Comput.}, affiliation = {Universidad de Oviedo, Oviedo, Spain; Departamento de Informática, Universidad de Oviedo, Oviedo, Spain; Escuela Superior de Ingeniería y Tecnología, Universidad Internacional de La Rioja (UNIR), Logroño, Spain}, correspondence_address1 = {González Crespo, R.; Escuela Superior de Ingeniería y Tecnología, Spain; email: ruben.gonzalez@unir.net}, document_type = {Article}, langid = {english}, source = {Scopus}, keywords = {notion}, note = {cited By 12 \par TL;DR \par The result is the design of a functional architecture that permits integrating the Web 2.0 application and a semantic analysis algorithm from unstructured information by applying machine learning techniques.} } @article{López2022967, title = {{{ModelSet}}: A Dataset for Machine Learning in Model-Driven Engineering}, author = {López, J.A.H. and Cánovas Izquierdo, J.L. and Cuadrado, J.S.}, date = {2022}, journaltitle = {Softw. Syst. Model.}, volume = {21}, number = {3}, pages = {967--986}, publisher = {{Springer Science and Business Media Deutschland GmbH}}, issn = {16191366}, doi = {10.1007/s10270-021-00929-3}, abbrev_source_title = {Softw. Syst. Model.}, affiliation = {Facultad de Informática, Universidad de Murcia, Murcia, Spain; UOC - IN3, Castelldefels, Spain}, correspondence_address1 = {López, J.A.H.; Facultad de Informática, Spain; email: joseantonio.hernandez6@um.es}, document_type = {Article}, langid = {english}, source = {Scopus}, keywords = {EXCLUDED\_NO-AI-ML}, note = {cited By 0 \par TL;DR \par To create ModelSet, a labelled dataset of software models intended to enable the application of ML to address software modelling problems, a method designed to facilitate the exploration and labelling of model datasets by interactively grouping similar models using off-the-shelf technologies like a search engine is devised.} } @article{lopez2022efficient, title = {An Efficient and Scalable Search Engine for Models}, author = {López, José Antonio Hernández and Cuadrado, Jesús Sánchez}, date = {2022}, journaltitle = {Software Syst. Model.}, volume = {21}, number = {5}, pages = {1715--1737}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found} } @article{LOPEZ2024103009, ids = {Izquierdo_Cuadrado_2024}, title = {{{ModelSet}}: {{A}} Labelled Dataset of Software Models for Machine Learning}, author = {López, José Antonio Hernández and Cánovas Izquierdo, Javier Luis and Cuadrado, Jesús Sánchez}, date = {2024}, journaltitle = {Sci. Comput. Program.}, volume = {231}, pages = {103009}, issn = {0167-6423}, doi = {10.1016/j.scico.2023.103009}, abstract = {Curated collections of models are essential for the success of Machine Learning (ML) and Data Analytics in Model-Driven Engineering (MDE). However, current datasets are either too small or not properly curated. In this paper, we present ModelSet, a dataset composed of 5,466 Ecore models and 5,120 UML models which have been manually labelled to support ML tasks. We describe the structure of the dataset and explain how to use the associated library to develop ML applications in Python. Finally, we present some applications which can be addressed using ModelSet. Tool Website: https://github.com/modelset}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found,Dataset,Machine learning,Model-driven engineering,Software models} } @article{LopezC23, ids = {lopez2022generating}, title = {Generating Structurally Realistic Models with Deep Autoregressive Networks}, author = {López, José Antonio Hernández and Cuadrado, Jesús Sánchez}, date = {2023}, journaltitle = {IEEE Trans, Softw. Eng,}, volume = {49}, number = {4}, pages = {2661--2676}, doi = {10.1109/TSE.2022.3228630}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Sat, 29 Apr 2023 19:26:32 +0200} } @online{lopezInterdatasetCodeDuplication2024, title = {On {{Inter-dataset Code Duplication}} and {{Data Leakage}} in {{Large Language Models}}}, author = {López, José Antonio Hernández and Chen, Boqi and Sharma, Tushar and Varró, Dániel}, date = {2024-01-15}, eprint = {2401.07930}, eprinttype = {arXiv}, eprintclass = {cs}, url = {http://arxiv.org/abs/2401.07930}, urldate = {2024-01-20}, abstract = {Motivation. Large language models (LLMs) have exhibited remarkable proficiency in diverse software engineering (SE) tasks. Handling such tasks typically involves acquiring foundational coding knowledge on large, general-purpose datasets during a pre-training phase, and subsequently refining on smaller, task-specific datasets as part of a fine-tuning phase. Problem statement. Data leakage is a well-known issue in training of machine learning models. A manifestation of this issue is the intersection of the training and testing splits. While intra-dataset code duplication examines this intersection within a given dataset and has been addressed in prior research, inter-dataset code duplication, which gauges the overlap between different datasets, remains largely unexplored. If this phenomenon exists, it could compromise the integrity of LLM evaluations because of the inclusion of fine-tuning test samples that were already encountered during pre-training, resulting in inflated performance metrics. Contribution. This paper explores the phenomenon of inter-dataset code duplication and its impact on evaluating LLMs across diverse SE tasks. Study design. We conduct an empirical study using the CSN dataset, a widely adopted pre-training dataset, and five fine-tuning datasets used for various SE tasks. We first identify the intersection between the pre-training and fine-tuning datasets using a deduplication process. Then, we fine-tune four models pre-trained on CSN to evaluate their performance on samples encountered during pre-training and those unseen during that phase. Results. Our findings reveal a potential threat to the evaluation of various LLMs across multiple SE tasks, stemming from the inter-dataset code duplication phenomenon. Moreover, we demonstrate that this threat is accentuated by factors like the LLM's size and the chosen fine-tuning technique.}, pubstate = {prepublished}, keywords = {Computer Science - Software Engineering} } @article{lopezModelXGlueBenchmarkingFramework2024, title = {{{ModelXGlue}}: A Benchmarking Framework for {{ML}} Tools in {{MDE}}}, shorttitle = {{{ModelXGlue}}}, author = {López, José Antonio Hernández and Cuadrado, Jesús Sánchez and Rubei, Riccardo and Di Ruscio, Davide}, date = {2024-06-10}, journaltitle = {Softw Syst Model}, issn = {1619-1374}, doi = {10.1007/s10270-024-01183-z}, abstract = {The integration of machine learning (ML) into model-driven engineering (MDE) holds the potential to enhance the efficiency of modelers and elevate the quality of modeling tools. However, a consensus is yet to be reached on which MDE tasks can derive substantial benefits from ML and how progress in these tasks should be measured. This paper introduces ModelXGlue , a dedicated benchmarking framework to empower researchers when constructing benchmarks for evaluating the application of ML to address MDE tasks. A benchmark is built by referencing datasets and ML models provided by other researchers, and by selecting an evaluation strategy and a set of metrics. ModelXGlue is designed with automation in mind and each component operates in an isolated execution environment (via Docker containers or Python environments), which allows the execution of approaches implemented with diverse technologies like Java, Python, R, etc. We used ModelXGlue to build reference benchmarks for three distinct MDE tasks: model classification, clustering, and feature name recommendation. To build the benchmarks we integrated existing third-party approaches in ModelXGlue . This shows that ModelXGlue is able to accommodate heterogeneous ML models, MDE tasks and different technological requirements. Moreover, we have obtained, for the first time, comparable results for these tasks. Altogether, it emerges that ModelXGlue is a valuable tool for advancing the understanding and evaluation of ML tools within the context of MDE.}, langid = {english}, keywords = {Benchmarking,Machine Learning,Model-Driven Engineering} } @article{lopezWordEmbeddingsModelDriven, title = {Word {{Embeddings}} for {{Model-Driven Engineering}}}, author = {Lopez, Jose Antonio Hernandez and Dura, Carlos and Cuadrado, Jesus Sanchez}, abstract = {Model-Driven Engineering practitioners have to deal with the construction of modelling evironments by devising metamodels, grammars, editors, etc. One of the goals of the application of Machine Learning to MDE is to use ML algorithms to assist the MDE expert in these tasks. These algorithms cannot directly receive raw models or meta-models as input, but they typically have to be transformed into a numeric representation, i.e., a vector. In this context, a common approach is to use pre-trained Word Embeddings, which define mapping functions that associate words to semantic vectors. However, current word embeddings are trained with general texts and lack the technical words which typically arise in the modelling domain. To tackle this issue, we have collected a corpus of modelling texts from well-known modelling venues, and we have trained two types of word embedding models. The resulting embeddings (named WordE4MDE) are specialised to address ML tasks in the MDE domain. We have performed an extensive evaluation using the Ecore models of the ModelSet dataset and two state-of-the-art word embeddings (GloVe and Word2Vec) as baselines. We show that WordE4MDE outperforms these two baselines in three meta-modelling tasks, namely meta-model classification, meta-model clustering, and meta-model concept recommendation. WordE4MDE embeddings are available at https://github.com/models-lab/worde4mde and can be loaded using standard Python libraries for their use in ML pipelines.}, langid = {english} } @incollection{LopsCB, title = {Content-Based Recommender Systems: {{State}} of the Art and Trends.}, booktitle = {Recommender Systems Handbook}, author = {Lops, Pasquale and family=Gemmis, given=Marco, prefix=de, useprefix=true and Semeraro, Giovanni}, editor = {Ricci, Francesco and Rokach, Lior and Shapira, Bracha and Kantor, Paul B.}, date = {2011}, pages = {73--105}, publisher = {Springer}, url = {http://dblp.uni-trier.de/db/reference/rsh/rsh2011.html#LopsGS11}, isbn = {978-0-387-85819-7}, keywords = {dblp}, note = {TL;DR \par The role of User Generated Content is described as a way for taking into account evolving vocabularies, and the challenge of feeding users with serendipitous recommendations, that is to say surprisingly interesting items that they might not have otherwise discovered.} } @article{lorenzoniMachineLearningModel2021, title = {Machine {{Learning Model Development}} from a {{Software Engineering Perspective}}: {{A Systematic Literature Review}}}, shorttitle = {Machine {{Learning Model Development}} from a {{Software Engineering Perspective}}}, author = {Lorenzoni, Giuliano and Alencar, Paulo and Nascimento, Nathalia and Cowan, Donald}, date = {2021-02-15}, journaltitle = {ArXiv210207574 Cs}, eprint = {2102.07574}, eprinttype = {arXiv}, eprintclass = {cs}, url = {http://arxiv.org/abs/2102.07574}, urldate = {2021-03-25}, abstract = {Data scientists often develop machine learning models to solve a variety of problems in the industry and academy but not without facing several challenges in terms of Model Development. The problems regarding Machine Learning Development involves the fact that such professionals do not realize that they usually perform ad-hoc practices that could be improved by the adoption of activities presented in the Software Engineering Development Lifecycle. Of course, since machine learning systems are different from traditional Software systems, some differences in their respective development processes are to be expected. In this context, this paper is an effort to investigate the challenges and practices that emerge during the development of ML models from the software engineering perspective by focusing on understanding how software developers could benefit from applying or adapting the traditional software engineering process to the Machine Learning workflow.}, keywords = {Computer Science - Artificial Intelligence,Computer Science - Machine Learning,Computer Science - Software Engineering}, note = {Comment: 9 pages, 2 columns. Under review \par TL;DR \par This paper is an effort to investigate the challenges and practices that emerge during the development of ML models from the software engineering perspective by focusing on understanding how software developers could benefit from applying or adapting the traditional software engineering process to the Machine Learning workflow.} } @online{LostArtFocus, title = {The (Lost) Art of Focus: Why We Lost It — and How to Get It Back | by {{Silvia Ersetti}} | {{Dec}}, 2024 | {{Medium}}}, url = {https://medium.com/@silviaersetti/the-lost-art-of-focus-why-we-lost-it-and-how-to-get-it-back-5fe9e9fa8f75}, keywords = {source\_mobile} } @article{lourencoChoosingRightNoSQL2015, title = {Choosing the Right {{NoSQL}} Database for the Job: A Quality Attribute Evaluation}, shorttitle = {Choosing the Right {{NoSQL}} Database for the Job}, author = {Lourenço, João Ricardo and Cabral, Bruno and Carreiro, Paulo and Vieira, Marco and Bernardino, Jorge}, date = {2015-12}, journaltitle = {Journal of Big Data}, volume = {2}, number = {1}, pages = {18}, issn = {2196-1115}, doi = {10.1186/s40537-015-0025-0}, langid = {english}, keywords = {LOGSEQ,TYPHONML} } @misc{lowcode, title = {The Forrester Wave: {{Low-code}} Development Platforms, Q2 2016}, author = {Richardson, C. and Rymer, J. R.}, date = {2016-04}, note = {Technical report, Forrester Research} } @online{LowcodeAbstractionLevels, title = {Low-Code and Abstraction Levels - {{Stefan Dreverman}} - {{Medium}}}, url = {https://medium.com/@stefan.dreverman/low-code-and-abstraction-levels-e9412e9e5329}, urldate = {2020-04-08}, keywords = {lowcode} } @online{LowCodeDevelopment, title = {Low {{Code Development Platforms}}: {{A Complete Guide}} | {{QuickBase}}}, url = {https://www.quickbase.com/resources/articles/low-code-development-platforms}, urldate = {2020-04-08}, keywords = {lowcode} } @online{LowcodeDevelopmentModeldriven, title = {Low-Code Development and Model-Driven Engineering: {{Two}} Sides of the Same Coin? | {{SpringerLink}}}, url = {https://link.springer.com/article/10.1007/s10270-021-00970-2}, urldate = {2023-05-17} } @online{LowCodeDevelopmentPlatform, title = {Low-{{Code Development Platform Economic}} ({{Free}}) {{Survey}}}, url = {https://docs.google.com/document/d/1F1pLpNudMnth3bxYd1RyfjkxxUYeTbi-qa3BTJmat_8/edit?ts=5e3d9277&usp=embed_facebook}, urldate = {2020-02-11}, abstract = {Low-Code Development Platform Economic (Free) Survey Unlimited free version Free Trial Period OutSystems 60 days 30 days 15 days Visual LANSA Appian Kissflow (14 days) Mendix FileMaker (45 days) Microsoft PowerApps Zoho Creator (15 days) Kony Heroku (verifies accoun...}, langid = {british}, organization = {Google Docs}, keywords = {lowcode} } @online{LowcodeNocodeDevelopment, ids = {LowcodeNocodeDevelopmenta}, title = {Low-Code and No-Code Development Platforms}, url = {https://www.computerweekly.com/feature/Low-code-and-no-code-development-platforms}, urldate = {2020-03-29}, keywords = {lowcode} } @online{LowcodePlatformsFuture2020, title = {Low-Code Platforms: {{The Future}} of {{Data Analytics}}}, shorttitle = {Low-Code Platforms}, date = {2020-07-15T12:53:47+00:00}, url = {https://bigdataanalyticsnews.com/low-code-platforms-future-of-data-analytics/}, urldate = {2021-03-18}, abstract = {The future of low-code platforms is improving which eliminates the progression of the hard side of coding. The trend of low coding is now evolving towards data sciences and analytics.}, langid = {american}, organization = {Big Data Analytics News} } @online{LowCodePlatformsSurvey_MoDELS, title = {Low-{{Code Platforms Survey}}\_{{MoDELS}} Conference\_v2}, url = {https://www.overleaf.com/4361461464kxwpjrzcvszz}, urldate = {2020-02-11}, abstract = {An online LaTeX editor that's easy to use. No installation, real-time collaboration, version control, hundreds of LaTeX templates, and more.}, langid = {english}, keywords = {lowcode} } @incollection{loza14recsys, title = {A Hybrid Multi-Strategy Recommender System Using Linked Open Data}, booktitle = {Semantic Web Evaluation Challenge, Proceedings ({{ESWC}} 2014)}, author = {Ristoski, Petar and Loza Mencía, Eneldo and Paulheim, Heiko}, date = {2014-05}, series = {Communications in Computer and Information Science}, volume = {475}, pages = {150--156}, publisher = {Springer}, url = {http://2014.eswc-conferences.org/sites/default/files/eswc2014-challenges_rs_submission_12.pdf}, abstract = {In this paper, we discuss the development of a hybrid multi-strategy book recommendation system using Linked Open Data. Our approach builds on training individual base recommenders and using global popularity scores as generic recommenders. The results of the individual recommenders are combined using stacking regression and rank aggregation. We show that this approach delivers very good results in different recommendation settings and also allows for incorporating diversity of recommendations.}, isbn = {978-3-319-12023-2}, nodoi = {10.1007/978-3-319-12024-9₁9} } @article{Lu2007, title = {Node Similarity in the Citation Graph}, author = {Lu, Wangzhong and Janssen, J. and Milios, E. and Japkowicz, N. and Zhang, Yongzheng}, date = {2007-01-01}, journaltitle = {Knowl. Inf. Syst.}, volume = {11}, number = {1}, pages = {105--129}, issn = {0219-3116}, doi = {10.1007/s10115-006-0023-9}, abstract = {Published scientific articles are linked together into a graph, the citation graph, through their citations. This paper explores the notion of similarity based on connectivity alone, and proposes several algorithms to quantify it. Our metrics take advantage of the local neighborhoods of the nodes in the citation graph. Two variants of link-based similarity estimation between two nodes are described, one based on the separate local neighborhoods of the nodes, and another based on the joint local neighborhood expanded from both nodes at the same time. The algorithms are implemented and evaluated on a subgraph of the citation graph of computer science in a retrieval context. The results are compared with text-based similarity, and demonstrate the complementarity of link-based and text-based retrieval.} } @article{lucasCollabRDLLanguageCoordinate2017, title = {{{CollabRDL}}: {{A}} Language to Coordinate Collaborative Reuse}, shorttitle = {{{CollabRDL}}}, author = {Lucas, Edson M. and Oliveira, Toacy C. and Farias, Kleinner and Alencar, Paulo S.C.}, date = {2017-02}, journaltitle = {J. Syst. Softw.}, issn = {01641212}, doi = {10.1016/j.jss.2017.01.031}, langid = {english}, keywords = {collavorative modeling} } @article{lucene, title = {Apache Lucene Core}, url = {https://lucene.apache.org/core/}, note = {last access 26.04.2019 \par last access 26.04.2019 \par last access 26.04.2019 \par last access 26.04.2019 \par last access 26.04.2019} } @article{LucioADLSSSW16, title = {Model Transformation Intents and Their Properties}, author = {Lúcio, Levi and Amrani, Moussa and Dingel, Juergen and Lambers, Leen and Salay, Rick and Selim, Gehan M. K. and Syriani, Eugene and Wimmer, Manuel}, date = {2016}, journaltitle = {Software Syst. Model.}, volume = {15}, number = {3}, pages = {647--684}, doi = {10.1007/S10270-014-0429-X}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {TL;DR \par A framework for the description of model transformation intents is defined, which includes a description of properties a model transformation has to satisfy to qualify as a suitable realization of an intent.}, timestamp = {Fri, 18 Sep 2020 11:19:42 +0200} } @incollection{lucioFTG+PMIntegratedFramework2013, title = {{{FTG}}+{{PM}}: {{An Integrated Framework}} for {{Investigating Model Transformation Chains}}}, shorttitle = {{{FTG}}+{{PM}}}, booktitle = {{{SDL}} 2013: {{Model-Driven Dependability Engineering}}}, author = {Lúcio, Levi and Mustafiz, Sadaf and Denil, Joachim and Vangheluwe, Hans and Jukss, Maris}, editor = {Khendek, Ferhat and Toeroe, Maria and Gherbi, Abdelouahed and Reed, Rick}, date = {2013}, series = {Lecture {{Notes}} in {{Computer Science}}}, number = {7916}, pages = {182--202}, publisher = {Springer Berlin Heidelberg}, url = {http://link.springer.com/chapter/10.1007/978-3-642-38911-5_11}, urldate = {2015-03-24}, abstract = {In this paper, we describe our ongoing work on model transformation chains. Model transformation chains refer to the sequences of model transformations in Model Driven Engineering (MDE). The transformations represent and formalise typical model/software engineering activities, and their chaining is the natural composition of such activities. Model transformation chains found in industrial practice vary widely, depending on the specific domain they are used in. By explicitly modelling development activities, these activities can be analysed and the MDE process may be improved. As a step towards such analyses, we propose an integrated framework to describe all the artifacts involved in model transformation chains, as well as the means to execute “enact” those chains. We describe the Formalism Transformation Graph + Process Model (FTG+PM) which is at the heart of our framework in detail.}, isbn = {978-3-642-38910-8 978-3-642-38911-5}, langid = {english}, keywords = {software engineering}, note = {TL;DR \par An integrated framework to describe all the artifacts involved in model transformation chains, as well as the means to execute “enact” those chains is proposed and the Formalism Transformation Graph + Process Model (FTG+PM) is described which is at the heart of the framework in detail.} } @article{lucioModelTransformationIntents2014, title = {Model Transformation Intents and Their Properties}, author = {Lúcio, Levi and Amrani, Moussa and Dingel, Jürgen and Lambers, Leen and Salay, Rick and Selim, Gehan MK and Syriani, Eugene and Wimmer, Manuel}, date = {2014}, journaltitle = {Softw. Syst. Model.}, pages = {1--38}, url = {http://link.springer.com/article/10.1007/s10270-014-0429-x}, urldate = {2015-03-20} } @article{lucioTechniqueAutomaticValidation2010, title = {A {{Technique}} for {{Automatic Validation}} of {{Model Transformations}}}, author = {Lúcio, Levi and Barroca, Bruno and Amaral, Vasco}, date = {2010}, journaltitle = {Model Driven Eng. Lang. Syst.}, volume = {6394}, pages = {136--150}, doi = {10.1007/978-3-642-16145-2_10} } @inproceedings{luckeyHighqualitySpecificationSelfadaptive2013, title = {High-Quality Specification of Self-Adaptive Software Systems}, booktitle = {Proc. 8th {{Int}}. {{Symp}}. {{Softw}}. {{Eng}}. {{Adapt}}. {{Self-Manag}}. {{Syst}}.}, author = {Luckey, Markus and Engels, Gregor}, date = {2013}, pages = {143--152}, publisher = {IEEE Press}, url = {http://dl.acm.org/citation.cfm?id=2487359}, urldate = {2016-09-21} } @article{lucredioMOOGLEMetamodelbasedModel2010, title = {{{MOOGLE}}: A Metamodel-Based Model Search Engine}, author = {Lucrédio, Daniel and M. Fortes, Renata P. and Whittle, Jon}, date = {2010}, journaltitle = {Softw. Syst. Model.}, volume = {11}, number = {2}, pages = {183--208}, doi = {10.1007/s10270-010-0167-7}, note = {TL;DR \par Moogle is presented, a model search engine that uses metamodeling information to create richer search indexes and to allow more complex queries to be performed and to improve the accuracy of the search.} } @online{ludvigsenHowEstimateReduce2022, title = {How to Estimate and Reduce the Carbon Footprint of Machine Learning Models}, author = {Ludvigsen, Kasper Groes Albin}, date = {2022-12-06T14:23:31}, url = {https://towardsdatascience.com/how-to-estimate-and-reduce-the-carbon-footprint-of-machine-learning-models-49f24510880}, urldate = {2024-04-11}, abstract = {Two ways to easily estimate the carbon footprint of machine learning models and 17 ideas for how you might reduce it}, langid = {english}, organization = {Medium} } @article{LUNG2004227, title = {Applications of Clustering Techniques to Software Partitioning, Recovery and Restructuring}, author = {Lung, Chung-Horng and Zaman, Marzia and Nandi, Amit}, date = {2004}, journaltitle = {J. Syst. Softw.}, volume = {73}, number = {2}, pages = {227--244}, issn = {0164-1212}, url = {http://www.sciencedirect.com/science/article/pii/S0164121203002346}, abstract = {The artifacts constituting a software system are sometimes unnecessarily coupled with one another or may drift over time. As a result, support of software partitioning, recovery, and restructuring is often necessary. This paper presents studies on applying the numerical taxonomy clustering technique to software applications. The objective is to facilitate those activities just mentioned and to improve design, evaluation and evolution. Numerical taxonomy is mathematically simple and yet it is a useful mechanism for component clustering and software partitioning. The technique can be applied at various levels of abstraction or to different software life-cycle phases. We have applied the technique to: (1) software partitioning at the software architecture design phase; (2) grouping of components based on the source code to recover the software architecture in the reverse engineering process; (3) restructuring of a software to support evolution in the maintenance stage; and (4) improving cohesion and reducing coupling for source code. In this paper, we provide an introduction to the numerical taxonomy, discuss our experiences in applying the approach to various areas, and relate the technique to the context of similar work.}, nodoi = {https://doi.org/10.1016/S0164-1212(03)00234-6}, keywords = {Clustering,Cohesion and coupling,Design recovery,Evolution,Restructuring,Reverse engineering,Software partitioning}, note = {Applications of statistics in software engineering \par Applications of statistics in software engineering \par Applications of statistics in software engineering \par Applications of statistics in software engineering \par Applications of statistics in software engineering} } @article{Luo2019504, title = {Deep Angular Embedding and Feature Correlation Attention for Breast {{MRI}} Cancer Analysis}, author = {Luo, L. and Chen, H. and Wang, X. and Dou, Q. and Lin, H. and Zhou, J. and Li, G. and Heng, P.-A.}, editor = {Shen D., Yap P.-T., Peters T.M., Khan A., Staib L.H., Essert C., Zhou S., Liu T.}, date = {2019}, journaltitle = {Lect. Notes Comput. Sci. Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinforma.}, volume = {11767 LNCS}, pages = {504--512}, publisher = {{Springer Science and Business Media Deutschland GmbH}}, issn = {03029743}, doi = {10.1007/978-3-030-32251-9_55}, abstract = {Accurate and automatic analysis of breast MRI plays a vital role in early diagnosis and successful treatment planning for breast cancer. Due to the heterogeneity nature, precise diagnosis of tumors remains a challenging task. In this paper, we propose to identify breast tumor in MRI by Cosine Margin Sigmoid Loss (CMSL) with deep learning (DL) and localize possible cancer lesion by COrrelation Attention Map (COAM) based on the learned features. The CMSL embeds tumor features onto a hyper-sphere and imposes a decision margin through cosine constraints. In this way, the DL model could learn more separable inter-class features and more compact intra-class features in the angular space. Furthermore, we utilize the correlations among feature vectors to generate attention maps that could accurately localize cancer candidates with only image-level labels. We build the largest breast cancer dataset involving 10,290 DCE-MRI scan volumes for developing and evaluating the proposed methods. The model driven by CMSL achieved a classification accuracy of 0.855 and AUC of 0.902 on the testing set, with sensitivity and specificity of 0.857 and 0.852, respectively, outperforming competitive methods overall. In addition, the proposed COAM accomplished more accurate localization of the cancer center compared with other state-of-the-art weakly supervised localization method. © Springer Nature Switzerland AG 2019.}, document_type = {Conference Paper}, isbn = {9783030322502}, source = {Scopus}, note = {cited By 9} } @inproceedings{luoCharacteristicsChallengesLowCode2021, title = {Characteristics and {{Challenges}} of {{Low-Code Development}}: {{The Practitioners}}' {{Perspective}}}, shorttitle = {Characteristics and {{Challenges}} of {{Low-Code Development}}}, booktitle = {Proc. 15th {{ACM IEEE Int}}. {{Symp}}. {{Empir}}. {{Softw}}. {{Eng}}. {{Meas}}. {{ESEM}}}, author = {Luo, Yajing and Liang, Peng and Wang, Chong and Shahin, Mojtaba and Zhan, Jing}, date = {2021-10-11}, pages = {1--11}, publisher = {ACM}, location = {Bari Italy}, doi = {10.1145/3475716.3475782}, eventtitle = {{{ESEM}} '21: {{ACM}} / {{IEEE International Symposium}} on {{Empirical Software Engineering}} and {{Measurement}}}, isbn = {978-1-4503-8665-4}, langid = {english}, keywords = {LOGSEQ}, note = {TL;DR \par The findings suggest that researchers should clearly define the terms when they refer to LCD, and developers should consider whether the characteristics of LCD are appropriate for their projects.} } @article{luongFACOSFindingAPI2021, title = {{{FACOS}}: {{Finding API Relevant Contents}} on {{Stack Overflow}} with {{Semantic}} and {{Syntactic Analysis}}}, shorttitle = {{{FACOS}}}, author = {Luong, Kien and Hadi, Mohammad and Thung, Ferdian and Fard, Fatemeh and Lo, David}, date = {2021-11-13}, journaltitle = {ArXiv211107238 Cs}, eprint = {2111.07238}, eprinttype = {arXiv}, eprintclass = {cs}, url = {http://arxiv.org/abs/2111.07238}, urldate = {2021-11-21}, abstract = {Collecting API examples, usages, and mentions relevant to a specific API method over discussions on venues such as Stack Overflow is not a trivial problem. It requires efforts to correctly recognize whether the discussion refers to the API method that developers/tools are searching for. The content of the thread, which consists of both text paragraphs describing the involvement of the API method in the discussion and the code snippets containing the API invocation, may refer to the given API method. Leveraging this observation, we develop FACOS, a context-specific algorithm to capture the semantic and syntactic information of the paragraphs and code snippets in a discussion. FACOS combines a syntactic word-based score with a score from a predictive model fine-tuned from CodeBERT. FACOS beats the state-of-the-art approach by 13.9\% in terms of F1-score.}, langid = {english}, keywords = {Computer Science - Artificial Intelligence,Computer Science - Programming Languages,Computer Science - Software Engineering}, note = {TL;DR \par FACOS is a context-specific algorithm to capture the semantic and syntactic information of the paragraphs and code snippets in a discussion that beats the state-of-the-art approach by 13.9\% in terms of F1-score.} } @article{luoOnlineAdaptationAutonomous2022, title = {Online Adaptation for Autonomous Unmanned Systems Driven by Requirements Satisfaction Model}, author = {Luo, Yixing and Zhou, Yuan and Zhao, Haiyan and Jin, Zhi and Zhang, Tianwei and Liu, Yang and Barthaud, Danny and Yu, Yijun}, date = {2022-08}, journaltitle = {Softw Syst Model}, volume = {21}, number = {4}, pages = {1295--1319}, issn = {1619-1366, 1619-1374}, doi = {10.1007/s10270-022-00981-7}, abstract = {Autonomous unmanned systems (AUSs) emerge to replace human operators for achieving better safety, efficiency, and effectiveness in harsh and difficult missions. They usually run in a highly open and dynamic operating environment, in which some unexpected situations may occur, leading to violations of predefined requirements. In order to maintain stable performance, the AUS control software needs to predict in advance whether the requirements will be violated and then make adaptations to maximize requirements satisfaction. We propose Captain, a model-driven and control-based online adaptation approach, for the AUS control software. At the modeling phase, apart from the system behavior model and the operating environment model, we construct a requirements satisfaction model. At runtime, based on the requirements satisfaction model, Captain first predicts whether the requirements will be violated in the upcoming situation; then identifies the unsatisfiable requirements that need to be accommodated; and finally, finds an optimal adaptation for the upcoming situation. We evaluate Captain in both simulated scenarios and the real world. For the former, we use two cases of UAV Delivery and UUV Ocean Surveillance, whose results demonstrate the Captain’s robustness, scalability, and real-time performance. For the latter, we have successfully implemented Captain in the DJI Matrice 100 UAV with real-world workloads.}, langid = {english}, note = {TL;DR \par This work proposes Captain, a model-driven and control-based online adaptation approach, for the AUS control software that predicts whether the requirements will be violated in the upcoming situation; identifies the unsatisfiable requirements that need to be accommodated; and finally, finds an optimal adaptation for the forthcoming situation.} } @online{luResponsibleAIEra2023, title = {Towards {{Responsible AI}} in the {{Era}} of {{ChatGPT}}: {{A Reference Architecture}} for {{Designing Foundation Model-based AI Systems}}}, shorttitle = {Towards {{Responsible AI}} in the {{Era}} of {{ChatGPT}}}, author = {Lu, Qinghua and Zhu, Liming and Xu, Xiwei and Xing, Zhenchang and Whittle, Jon}, date = {2023-04-13}, eprint = {2304.11090}, eprinttype = {arXiv}, eprintclass = {cs}, url = {http://arxiv.org/abs/2304.11090}, urldate = {2023-05-18}, abstract = {The release of ChatGPT, Bard, and other large language model (LLM)-based chatbots has drawn huge attention on foundations models worldwide. There is a growing trend that foundation models will serve as the fundamental building blocks for most of the future AI systems. However, incorporating foundation models in AI systems raises significant concerns about responsible AI due to their black box nature and rapidly advancing super-intelligence. Additionally, the foundation model's growing capabilities can eventually absorb the other components of AI systems, introducing the moving boundary and interface evolution challenges in architecture design. To address these challenges, this paper proposes a pattern-oriented responsible-AI-by-design reference architecture for designing foundation model-based AI systems. Specially, the paper first presents an architecture evolution of AI systems in the era of foundation models, from "foundation-model-as-a-connector" to "foundation-model-as-a-monolithic architecture". The paper then identifies the key design decision points and proposes a pattern-oriented reference architecture to provide reusable responsible-AI-by-design architectural solutions to address the new architecture evolution and responsible AI challenges. The patterns can be embedded as product features of foundation model-based AI systems and can enable organisations to capitalise on the potential of foundation models while minimising associated risks.}, pubstate = {prepublished}, keywords = {Computer Science - Artificial Intelligence,Computer Science - Computation and Language,Computer Science - Software Engineering,LOGSEQ}, note = {TL;DR \par The paper identifies key design decisions and proposes a pattern-oriented reference architecture for designing responsible foundation-model-based systems, which can enable the potential of foundation models while minimising associated risks.} } @inproceedings{Lutz2021583, title = {How Many Costly Simulations Do We Need to Create Accurate Metamodels? {{A}} Case Study on Predicting Hiv Viral Load in Response to Clinically Relevant Intervention Scenarios}, author = {Lutz, C.B. and Giabbanelli, P.J. and Fisher, A. and Mago, V.K.}, editor = {Martin C.R., Blas M.J., Psijas A.I.}, date = {2021}, series = {Simulation {{Series}}}, volume = {53}, number = {2}, pages = {583--594}, publisher = {{The Society for Modeling and Simulation International}}, issn = {07359276}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118574816&partnerID=40&md5=9eea6773e6551052e5a35c095cf09935}, abbrev_source_title = {Simul. Ser.}, affiliation = {Dept. of Computer Science and Software Engineering, Miami University, 205 Benton Hall, 510 E. High Street, Oxford, OH, United States; Dept. of Computer Science, Lakehead University, FB 1009B, 955 Oliver Rd, Thunder Bay, ON, Canada}, document_type = {Conference Paper}, langid = {english}, source = {Scopus}, note = {cited By 0} } @inproceedings{lv_codehow:_2015, ids = {DBLP:conf/kbse/LvZLWZZ15}, title = {{{CodeHow}}: {{Effective Code Search Based}} on {{API Understanding}} and {{Extended Boolean Model}} ({{E}})}, shorttitle = {{{CodeHow}}}, booktitle = {2015 30th {{IEEEACM Int}}. {{Conf}}. {{Autom}}. {{Softw}}. {{Eng}}. {{ASE}}}, author = {Lv, Fei and Zhang, Hongyu and Lou, Jian-guang and Wang, Shaowei and Zhang, Dongmei and Zhao, Jianjun}, date = {2015-11}, pages = {260--270}, publisher = {IEEE}, location = {Lincoln, NE, USA}, url = {http://ieeexplore.ieee.org/document/7372014/}, urldate = {2019-09-11}, abstract = {Over the years of software development, a vast amount of source code has been accumulated. Many code search tools were proposed to help programmers reuse previouslywritten code by performing free-text queries over a large-scale codebase. Our experience shows that the accuracy of these code search tools are often unsatisfactory. One major reason is that existing tools lack of query understanding ability. In this paper, we propose CodeHow, a code search technique that can recognize potential APIs a user query refers to. Having understood the potentially relevant APIs, CodeHow expands the query with the APIs and performs code retrieval by applying the Extended Boolean model, which considers the impact of both text similarity and potential APIs on code search. We deploy the backend of CodeHow as a Microsoft Azure service and implement the frontend as a Visual Studio extension. We evaluate CodeHow on a large-scale codebase consisting of 26K C\# projects downloaded from GitHub. The experimental results show that when the top 1 results are inspected, CodeHow achieves a precision score of 0.794 (i.e., 79.4\% of the first returned results are relevant code snippets). The results also show that CodeHow outperforms conventional code search tools. Furthermore, we perform a controlled experiment and a survey of Microsoft developers. The results confirm the usefulness and effectiveness of CodeHow in programming practices.}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/bib/conf/kbse/LvZLWZZ15}, isbn = {978-1-5090-0025-8}, langid = {english}, nodoi = {10.1109/ASE.2015.42}, note = {TL;DR \par This paper proposes CodeHow, a code search technique that can recognize potential APIs a user query refers to and performs code retrieval by applying the Extended Boolean model, which considers the impact of both text similarity and potential APIs on code search.}, timestamp = {Fri, 01 Mar 2019 13:05:18 +0100} } @article{Ma20212388, title = {Model-Driven Deep Learning Based Channel Estimation and Feedback for Millimeter-Wave Massive Hybrid {{MIMO}} Systems}, author = {Ma, X. and Gao, Z. and Gao, F. and DI Renzo, M.}, date = {2021}, journaltitle = {IEEE J. Sel. Areas Commun.}, volume = {39}, number = {8}, pages = {2388--2406}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {07338716}, doi = {10.1109/JSAC.2021.3087269}, abstract = {This paper proposes a model-driven deep learning (MDDL)-based channel estimation and feedback scheme for wideband millimeter-wave (mmWave) massive hybrid multiple-input multiple-output (MIMO) systems, where the angle-delay domain channels' sparsity is exploited for reducing the overhead. First, we consider the uplink channel estimation for time-division duplexing systems. To reduce the uplink pilot overhead for estimating high-dimensional channels from a limited number of radio frequency (RF) chains at the base station (BS), we propose to jointly train the phase shift network and the channel estimator as an auto-encoder. Particularly, by exploiting the channels' structured sparsity from an a priori model and learning the integrated trainable parameters from the data samples, the proposed multiple-measurement-vectors learned approximate message passing (MMV-LAMP) network with the devised redundant dictionary can jointly recover multiple subcarriers' channels with significantly enhanced performance. Moreover, we consider the downlink channel estimation and feedback for frequency-division duplexing systems. Similarly, the pilots at the BS and channel estimator at the users can be jointly trained as an encoder and a decoder, respectively. Besides, to further reduce the channel feedback overhead, only the received pilots on part of the subcarriers are fed back to the BS, which can exploit the MMV-LAMP network to reconstruct the spatial-frequency channel matrix. Numerical results show that the proposed MDDL-based channel estimation and feedback scheme outperforms state-of-the-art approaches. © 1983-2012 IEEE.}, art_number = {9452036}, coden = {ISACE}, document_type = {Article}, source = {Scopus}, note = {cited By 8 \par TL;DR \par A model-driven deep learning (MDDL)-based channel estimation and feedback scheme for wideband millimeter-wave (mmWave) massive hybrid multiple-input multiple-output (MIMO) systems, where the angle-delay domain channels’ sparsity is exploited for reducing the overhead.} } @article{Maarek:1991:IRA:126244.126254, title = {An Information Retrieval Approach for Automatically Constructing Software Libraries}, author = {Maarek, Yoëlle S. and Berry, Daniel M. and Kaiser, Gail E.}, date = {1991-08}, journaltitle = {IEEE Trans. Softw. Eng.}, volume = {17}, number = {8}, pages = {800--813}, publisher = {IEEE Press}, address = {Piscataway, NJ, USA}, issn = {0098-5589}, url = {http://dx.doi.org/10.1109/32.83915}, acmid = {126254}, issue_date = {August 1991}, nodoi = {10.1109/32.83915}, numpages = {14}, keywords = {attributes,automatic programming,browsing,clustering technique,free-style natural language queries,free-text indexing scheme,indexing scheme,information retrieval approach,information retrieval systems,large software libraries,lexical affinities,natural language documentation,natural languages,software reusability,software reuse,subroutines}, note = {TL;DR \par A technology for automatically assembling large software libraries which promote software reuse by helping the user locate the components closest to her/his needs is described.} } @article{maAssessingQualityMetamodels2013, ids = {maAssessingQualityMetamodels2013a}, title = {Assessing the Quality of Metamodels}, author = {Ma, Zhiyi and He, Xiao and Liu, Chao}, date = {2013-08}, journaltitle = {Front. Comput. Sci.}, volume = {7}, number = {4}, pages = {558--570}, issn = {2095-2228, 2095-2236}, doi = {10.1007/s11704-013-1151-5}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {TL;DR \par A quality model, which systematically characterizes and classifies quality attributes, and an operable measuring mechanism for effectively assessing the quality of metamodels based on the quality model are presented, using UML as the main example.} } @inproceedings{maccioniQUEPAQUeryingExploring2016, title = {{{QUEPA}}: {{QUerying}} and {{Exploring}} a {{Polystore}} by {{Augmentation}}}, shorttitle = {{{QUEPA}}}, author = {Maccioni, Antonio and Basili, Edoardo and Torlone, Riccardo}, date = {2016}, pages = {2133--2136}, publisher = {ACM Press}, doi = {10.1145/2882903.2899393}, abstract = {Polystore systems (or simply polystores) have been recently proposed to support a common scenario in which enterprise data are stored in a variety of database technologies relying on different data models and languages. Polystores provide a loosely coupled integration of data sources and support the direct access, with the local language, to each specific storage engine to exploit its distinctive features. Given the absence of a global schema, new challenges for accessing data arise in these environments. In fact, it is usually hard to know in advance if a query to a specific data store can be satisfied with data stored elsewhere in the polystore.}, isbn = {978-1-4503-3531-7}, langid = {english}, keywords = {TYPHONML}, note = {TL;DR \par QUEPA implements in this way a lightweight mechanism for data integration in the polystore and operates in a plug-and-play mode, thus reducing the need for ad-hoc configurations and for middleware layers involving standard APIs, unified query languages or shared data models.} } @inproceedings{Macdonald2012, title = {Allocation of Simulation Effort for Neural Network vs. Regression Metamodels}, author = {Macdonald, C. and Gunn, E.A.}, date = {2012}, series = {Proceedings - {{Winter Simulation Conference}}}, issn = {08917736}, doi = {10.1109/WSC.2012.6464998}, abbrev_source_title = {Proc. Winter Simul. Conf.}, affiliation = {Dalhousie University, PO Box 15000, Halifax, NS B3H 4R2, Canada}, art_number = {6464998}, coden = {WSCPD}, correspondence_address1 = {Macdonald, C.; Dalhousie University, PO Box 15000, Halifax, NS B3H 4R2, Canada; email: corinne.macdonald@dal.ca}, document_type = {Conference Paper}, isbn = {978-1-4673-4779-2}, langid = {english}, source = {Scopus}, keywords = {notion}, note = {cited By 1 \par TL;DR \par It is shown that a distribution of simulation effort over a larger sample of input points may result in better neural network metamodels; this conclusion differs from other studies involving regression metAModels.} } @article{MacDonellSKM10, title = {How Reliable Are Systematic Reviews in Empirical Software Engineering?}, author = {MacDonell, Stephen G. and Shepperd, Martin J. and Kitchenham, Barbara A. and Mendes, Emilia}, date = {2010}, journaltitle = {IEEE Trans Softw. Eng}, volume = {36}, number = {5}, pages = {676--687}, doi = {10.1109/TSE.2010.28}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/journals/tse/MacDonellSKM10.bib}, timestamp = {Fri, 27 Mar 2020 08:44:36 +0100} } @online{MachineLearningAutomation, title = {Machine {{Learning Automation}} - {{Run}}:{{AI}}}, url = {https://www.run.ai/guides/machine-learning-operations/machine-learning-automation/}, urldate = {2021-04-21} } @book{MachineLearningBurkov_s, title = {Machine {{Learning}} - {{Burkov}}\_s Book} } @online{MachineLearningPipelines, title = {Machine {{Learning Pipelines}}: {{Provenance}}, {{Reproducibility}} and {{FAIR Data Principles}}}, shorttitle = {(17) ({{PDF}}) {{Machine Learning Pipelines}}}, url = {https://www.researchgate.net/publication/342377391_Machine_Learning_Pipelines_Provenance_Reproducibility_and_FAIR_Data_Principles}, urldate = {2021-03-18}, abstract = {ResearchGate is a network dedicated to science and research. Connect, collaborate and discover scientific publications, jobs and conferences. All for free.}, langid = {english}, organization = {ResearchGate}, keywords = {DONE} } @article{macias-escrivaSelfadaptiveSystemsSurvey2013, title = {Self-Adaptive Systems: {{A}} Survey of Current Approaches, Research Challenges and Applications}, shorttitle = {Self-Adaptive Systems}, author = {Macías-Escrivá, Frank D. and Haber, Rodolfo and family=Toro, given=Raul, prefix=del, useprefix=true and Hernandez, Vicente}, date = {2013-12}, journaltitle = {Expert Syst. Appl.}, volume = {40}, number = {18}, pages = {7267--7279}, issn = {09574174}, doi = {10.1016/j.eswa.2013.07.033}, langid = {english} } @online{MagenticoneGeneralistMultiagent2024, title = {Magentic-One: A Generalist Multi-Agent System for Solving Complex Tasks}, shorttitle = {Magentic-One}, date = {2024-11-04}, url = {https://www.microsoft.com/en-us/research/articles/magentic-one-a-generalist-multi-agent-system-for-solving-complex-tasks/}, urldate = {2024-12-11}, abstract = {By Adam Fourney, Principal Researcher; Gagan Bansal, Senior Researcher; Hussein Mozannar, Senior Researcher; Victor Dibia, Principal Research Software Engineer; Saleema Amershi, Partner Research Manager Contributors: Adam Fourney, Gagan Bansal, Hussein Mozannar, Cheng Tan, Eduardo Salinas, Erkang (Eric) Zhu, Friederike Niedtner, Grace Proebsting, Griffin Bassman, Jack Gerrits, Jacob Alber, Peter Chang, Ricky Loynd, Robert West, Victor […]}, langid = {english}, organization = {Microsoft Research} } @article{mahdavinejadMachineLearningInternet2018, title = {Machine Learning for Internet of Things Data Analysis: A Survey}, shorttitle = {Machine Learning for Internet of Things Data Analysis}, author = {Mahdavinejad, Mohammad Saeid and Rezvan, Mohammadreza and Barekatain, Mohammadamin and Adibi, Peyman and Barnaghi, Payam and Sheth, Amit P.}, date = {2018-08}, journaltitle = {Digital Communications and Networks}, volume = {4}, number = {3}, pages = {161--175}, issn = {23528648}, doi = {10.1016/j.dcan.2017.10.002}, abstract = {Rapid developments in hardware, software, and communication technologies have facilitated the emergence of Internet-connected sensory devices that provide observations and data measurements from the physical world. By 2020, it is estimated that the total number of Internet-connected devices being used will be between 25 and 50 billion. As these numbers grow and technologies become more mature, the volume of data being published will increase. The technology of Internet-connected devices, referred to as Internet of Things (IoT), continues to extend the current Internet by providing connectivity and interactions between the physical and cyber worlds. In addition to an increased volume, the IoT generates big data characterized by its velocity in terms of time and location dependency, with a variety of multiple modalities and varying data quality. Intelligent processing and analysis of this big data are the key to developing smart IoT applications. This article assesses the various machine learning methods that deal with the challenges presented by IoT data by considering smart cities as the main use case. The key contribution of this study is the presentation of a taxonomy of machine learning algorithms explaining how different techniques are applied to the data in order to extract higher level information. The potential and challenges of machine learning for IoT data analytics will also be discussed. A use case of applying a Support Vector Machine (SVM) to Aarhus smart city traffic data is presented for a more detailed exploration.}, langid = {english} } @article{Mahmoudi20161, title = {Benefits of Metamodel-Reduction for Nonlinear Dynamic Response Analysis of Damaged Composite Structures}, author = {Mahmoudi, S. and Trivaudey, F. and Bouhaddi, N.}, date = {2016}, journaltitle = {Finite Elem. Anal. Des.}, volume = {119}, pages = {1--14}, publisher = {Elsevier B.V.}, issn = {0168874X}, doi = {10.1016/j.finel.2016.05.001}, abbrev_source_title = {Finite Elem Anal Des}, affiliation = {FEMTO-ST Institute, UMR 6174, Department of Applied Mechanics, University of Franche-Comté, UBFC, 24 rue de lÉpitaphe, Besançon, 25000, France}, coden = {FEADE}, correspondence_address1 = {Mahmoudi, S.; FEMTO-ST Institute, 24 rue de lÉpitaphe, France; email: saber.mahmoudi@femto-st.fr}, document_type = {Article}, langid = {english}, source = {Scopus}, note = {cited By 6} } @inproceedings{maiaDragonflyToolSimulating2019, title = {Dragonfly: A {{Tool}} for {{Simulating Self-Adaptive Drone Behaviours}}}, shorttitle = {Dragonfly}, booktitle = {2019 {{IEEEACM}} 14th {{Int}}. {{Symp}}. {{Softw}}. {{Eng}}. {{Adapt}}. {{Self-Manag}}. {{Syst}}. {{SEAMS}}}, author = {Maia, Paulo Henrique and Vieira, Lucas and Chagas, Matheus and Yu, Yijun and Zisman, Andrea and Nuseibeh, Bashar}, date = {2019-05}, pages = {107--113}, publisher = {IEEE}, location = {Montreal, QC, Canada}, doi = {10.1109/SEAMS.2019.00022}, abstract = {Drone simulators can provide an abstraction of different applications of drones and facilitate reasoning about distinct situations, in order to evaluate the effectiveness of these applications. In this paper we describe Dragonfly, a simulator of the behaviours of individual and collection of drones in various environments, involving random contextual variables and different environmental settings. Dragonfly supports the use of several drones in applications and evaluates the satisfaction of requirements under normal and exceptional situations. It simulates adaptive behaviours of drones due to exceptional situations. The adaption of drones is based on the use of wrappers implemented using aspect-oriented programming.}, eventtitle = {2019 {{IEEE}}/{{ACM}} 14th {{International Symposium}} on {{Software Engineering}} for {{Adaptive}} and {{Self-Managing Systems}} ({{SEAMS}})}, isbn = {978-1-7281-3368-3}, langid = {english}, note = {TL;DR \par Dragonfly is described, a simulator of the behaviours of individual and collection of drones in various environments, involving random contextual variables and different environmental settings, which simulates adaptive behaviours of drones due to exceptional situations.} } @incollection{maiwaldWhatAreReal2019, title = {What {{Are Real JSON Schemas Like}}?: {{An Empirical Analysis}} of {{Structural Properties}}}, shorttitle = {What {{Are Real JSON Schemas Like}}?}, booktitle = {Advances in {{Conceptual Modeling}}}, author = {Maiwald, Benjamin and Riedle, Benjamin and Scherzinger, Stefanie}, editor = {Guizzardi, Giancarlo and Gailly, Frederik and Suzana Pitangueira Maciel, Rita}, date = {2019}, volume = {11787}, pages = {95--105}, publisher = {Springer International Publishing}, location = {Cham}, doi = {10.1007/978-3-030-34146-6_9}, isbn = {978-3-030-34145-9 978-3-030-34146-6}, langid = {english}, note = {TL;DR \par A first empirical analysis of a curated collection of real-world JSON Schemas is presented, which helps practitioners and researchers in making realistic assumptions when building tools for JSON Schema processing.} } @article{maiya_ktrain_2020, title = {Ktrain: {{A Low-Code Library}} for {{Augmented Machine Learning}}}, shorttitle = {Ktrain}, author = {Maiya, Arun S.}, date = {2020-07}, journaltitle = {ArXiv200410703 Cs}, eprint = {2004.10703}, eprinttype = {arXiv}, eprintclass = {cs}, abstract = {We present ktrain, a low-code Python library that makes machine learning more accessible and easier to apply. As a wrapper to TensorFlow and many other libraries (e.g., transformers, scikit-learn, stellargraph), it is designed to make sophisticated, state-of-the-art machine learning models simple to build, train, inspect, and apply by both beginners and experienced practitioners. Featuring modules that support text data (e.g., text classification, sequence tagging, open-domain question-answering), vision data (e.g., image classification), graph data (e.g., node classification, link prediction), and tabular data, ktrain presents a simple unified interface enabling one to quickly solve a wide range of tasks in as little as three or four "commands" or lines of code.}, keywords = {Computer Science - Computation and Language,Computer Science - Computer Vision and Pattern Recognition,Computer Science - Machine Learning,Computer Science - Social and Information Networks}, note = {arXiv: 2004.10703 \par Comment: 9 pages} } @article{maki_context, title = {Context {{Extraction}} in {{Recommendation Systems}} in {{Software Engineering}}: {{A Preliminary Survey}}}, author = {Maki, Sana and Kpodjedo, Sègla and Boussaidi, Ghizlane El}, date = {2015}, pages = {10}, abstract = {Recommendation System in Software Engineering (RSSE) represents a new promising research area, whose goal is to help software developers in their tasks by providing them with contextdependent insights extracted from their current project or taken from best practices. A key challenge here is to retrieve the context from the programming task in order to provide useful recommendations. In this paper, we conduct a survey of RSSEs with a particular focus on different approaches used to extract the context. We propose a feature model to represent some important characteristics of such extraction and identify some open issues.}, langid = {english}, note = {TL;DR \par A survey of RSSEs is conducted with a particular focus on different approaches used to extract the context, and a feature model is proposed to represent some important characteristics of such extraction and identify some open issues.} } @inproceedings{maLibRadarFastAccurate2016, ids = {7883363}, title = {{{LibRadar}}: Fast and Accurate Detection of Third-Party Libraries in {{Android}} Apps}, shorttitle = {{{LibRadar}}}, author = {Ma, Ziang and Wang, Haoyu and Guo, Yao and Chen, Xiangqun}, date = {2016}, pages = {653--656}, publisher = {ACM Press}, doi = {10.1145/2889160.2889178}, isbn = {978-1-4503-4205-6}, langid = {english} } @inproceedings{mangharamThreeChallengesCyberphysical2016, title = {Three Challenges in Cyber-Physical Systems}, booktitle = {2016 8th {{Int}}. {{Conf}}. {{Commun}}. {{Syst}}. {{Netw}}. {{COMSNETS}}}, author = {Mangharam, Rahul and Abbas, Houssam and Behl, Madhur and Jang, Kuk and Pajic, Miroslav and Jiang, Zhihao}, date = {2016}, pages = {1--8}, publisher = {IEEE}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7440015}, urldate = {2016-08-21}, note = {TL;DR \par This work discusses five challenges which require creative insights and application of model-based design, control systems, scheduling theory, formal methods, statistical machine learning and domain-specific experimentation.} } @book{Manning:2008:IIR:1394399, ids = {10.5555/1394399}, title = {Introduction to Information Retrieval}, author = {Manning, Christopher D. and Raghavan, Prabhakar and Schütze, Hinrich}, date = {2008}, publisher = {Cambridge University Press}, location = {New York, NY, USA}, isbn = {0-521-86571-9 978-0-521-86571-5}, note = {TL;DR \par This textbook teaches classical and web information retrieval, including web search and the related areas of text classification and text clustering from basic concepts, making it perfect for introductory courses in information retrieval for advanced undergraduates and graduate students in computer science.} } @article{mansoNoredundantMetricsUML2003, title = {No-Redundant {{Metrics}} for {{UML Class Diagram Structural Complexity}}}, author = {Manso, Ma Esperanza and Genero, Marcela and Piattini, Mario}, date = {2003}, journaltitle = {Adv. Inf. Syst. Eng.}, volume = {2681}, pages = {127--142}, doi = {10.1007/3-540-45017-3_11}, note = {TL;DR \par The obtained results show that the metrics related to associations, aggregations, generalizations and dependencies, are the most relevant whilst those related to size seem to be redundant.} } @article{MansoorKLWBD15, title = {{{MOMM}}: {{Multi-objective}} Model Merging}, shorttitle = {{{MOMM}}}, author = {Mansoor, Usman and Kessentini, Marouane and Langer, Philip and Wimmer, Manuel and Bechikh, Slim and Deb, Kalyanmoy}, date = {2015-05}, journaltitle = {J. Syst. Softw.}, volume = {103}, pages = {423--439}, issn = {01641212}, doi = {10.1016/j.jss.2014.11.043}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Mon, 24 Feb 2020 15:58:20 +0100} } @article{mansouryFeedbackLoopBias2020, title = {Feedback {{Loop}} and {{Bias Amplification}} in {{Recommender Systems}}}, author = {Mansoury, Masoud and Abdollahpouri, Himan and Pechenizkiy, Mykola and Mobasher, Bamshad and Burke, Robin}, date = {2020-07-25}, journaltitle = {ArXiv200713019 Cs}, eprint = {2007.13019}, eprinttype = {arXiv}, eprintclass = {cs}, url = {http://arxiv.org/abs/2007.13019}, urldate = {2022-03-24}, abstract = {Recommendation algorithms are known to suffer from popularity bias; a few popular items are recommended frequently while the majority of other items are ignored. These recommendations are then consumed by the users, their reaction will be logged and added to the system: what is generally known as a feedback loop. In this paper, we propose a method for simulating the users interaction with the recommenders in an offline setting and study the impact of feedback loop on the popularity bias amplification of several recommendation algorithms. We then show how this bias amplification leads to several other problems such as declining the aggregate diversity, shifting the representation of users' taste over time and also homogenization of the users experience. In particular, we show that the impact of feedback loop is generally stronger for the users who belong to the minority group.}, keywords = {Computer Science - Information Retrieval}, note = {TL;DR \par A method for simulating the users interaction with the recommenders in an offline setting is proposed and the impact of feedback loop on the popularity bias amplification of several recommendation algorithms is studied.} } @article{mantzCoevolvingMetamodelsTheir2015, title = {Co-Evolving Meta-Models and Their Instance Models: {{A}} Formal Approach Based on Graph Transformation}, shorttitle = {Co-Evolving Meta-Models and Their Instance Models}, author = {Mantz, Florian and Taentzer, Gabriele and Lamo, Yngve and Wolter, Uwe}, date = {2015-06}, journaltitle = {Sci. Comput. Program.}, volume = {104}, pages = {2--43}, issn = {01676423}, doi = {10.1016/j.scico.2015.01.002}, langid = {english} } @article{mantzCustomizingModelMigrations2013, title = {Customizing Model Migrations by Rule Schemes}, author = {Mantz, Florian and Taentzer, Gabriele and Lamo, Yngve}, date = {2013}, journaltitle = {Proc. 2013 Int. Workshop Princ. Softw. Evol. - IWPSE 2013}, pages = {1}, doi = {10.1145/2501543.2501545} } @online{ManuallyConfigureTelegraf, title = {Manually Configure {{Telegraf}} for {{InfluxDB}} v2.0 | {{InfluxDB OSS}} 2.0 {{Documentation}}}, url = {https://docs.influxdata.com/influxdb/v2.0/write-data/no-code/use-telegraf/manual-config/}, urldate = {2021-01-11} } @online{Many, title = {Many 𝘈𝘐 𝘢𝘨𝘦𝘯𝘵𝘴 Shared on {{LinkedIn}} Are 𝘈𝘐 𝘸𝘰𝘳𝘬𝘧𝘭𝘰𝘸𝘴 or 𝘢𝘶𝘵𝘰𝘮𝘢𝘵𝘪𝘰𝘯𝘴 in Disguise. {{Here}}'s Why This Distinction 𝘮𝘢𝘵𝘵𝘦𝘳𝘴:}, url = {https://www.linkedin.com/posts/akantjas_many-%F0%9D%98%88%F0%9D%98%90-%F0%9D%98%A2%F0%9D%98%A8%F0%9D%98%A6%F0%9D%98%AF%F0%9D%98%B5%F0%9D%98%B4-shared-on-linkedin-activity-7279075047271522304-2MU6?utm_source=share&utm_medium=member_android}, keywords = {source\_mobile} } @article{MAO201757, title = {A Survey of the Use of Crowdsourcing in Software Engineering}, author = {Mao, Ke and Capra, Licia and Harman, Mark and Jia, Yue}, date = {2017}, journaltitle = {J. Syst. Softw.}, volume = {126}, pages = {57--84}, issn = {0164-1212}, url = {http://www.sciencedirect.com/science/article/pii/S0164121216301832}, nodoi = {https://doi.org/10.1016/j.jss.2016.09.015} } @article{Mao20222870, title = {Joint Channel Estimation and Active-User Detection for Massive Access in Internet of Things-{{A}} Deep Learning Approach}, author = {Mao, Z. and Liu, X. and Peng, M. and Chen, Z. and Wei, G.}, date = {2022}, journaltitle = {IEEE Internet Things J.}, volume = {9}, number = {4}, pages = {2870--2881}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {23274662}, doi = {10.1109/JIOT.2021.3097133}, abstract = {For conventional signaling, the length of the orthogonal pilot is required at least equal to the total number of user antennas. However, it is not recommended in the Internet of Things (IoT) due to the expensive cost paid in massive connectivities. Thanks to the sporadic nature of the massive connected users where a considerable fraction of users are inactive within a coherence time, the nonorthogonal pilot can be utilized with the joint channel estimation and active-user detection being modeled as a compressive sensing problem. According to the different antenna configuration methods employed by the base station, the constructed problems in this work are formulated into the single measurement vector and the multiple measurement vectors recovery problems. Also, we develop a model-driven deep learning algorithm to solve the problems based on the traditional alternative direction method of multipliers (ADMM) algorithm, where the iteration operation is unfolded into the network layer. The network parameters are learned with the help of the stochastic gradient descent algorithm. Simulation results show that the proposed approach can achieve better performance than an ADMM algorithm under the same computational complexity. © 2014 IEEE.}, document_type = {Article}, source = {Scopus}, note = {cited By 0 \par TL;DR \par A model-driven deep learning algorithm is developed to solve the problems based on the traditional alternative direction method of multipliers algorithm, where the iteration operation is unfolded into the network layer and the network parameters are learned with the help of the stochastic gradient descent algorithm.} } @article{MAOFrameworkProcess2017, title = {{{MAO}}: {{A Framework}} for {{Process Model Generation}} with {{Multi-Agent Orchestration}}}, date = {2017}, abstract = {Process models are frequently used in software engineering to describe business requirements, guide software testing and control system improvement. However, traditional process modeling methods often require the participation of numerous experts, which is expensive and time-consuming. Therefore, the exploration of a more efficient and cost-effective automated modeling method has emerged as a focal point in current research. This article explores a framework for automatically generating process models with multi-agent orchestration (MAO), aiming to enhance the efficiency of process modeling and offer valuable insights for domain experts. Our framework MAO leverages large language models as the cornerstone for multi-agent, employing an innovative prompt strategy to ensure efficient collaboration among multi-agent. Specifically, 1) generation. The first phase of MAO is to generate a slightly rough process model from the text description; 2) refinement. The agents would continuously refine the initial process model through multiple rounds of dialogue; 3) reviewing. Large language models are prone to hallucination phenomena among multi-turn dialogues, so the agents need to review and repair semantic hallucinations in process models; 4) testing. The representation of process models is diverse. Consequently, the agents utilize external tools to test whether the generated process model contains format errors, namely format hallucinations, and then adjust the process model to conform to the output paradigm. The experiments demonstrate that the process models generated by our framework outperform existing methods and surpass manual modeling by 89\%, 61\%, 52\%, and 75\% on four different datasets, respectively.}, langid = {english} } @article{maozFrameworkRelatingSyntactic, title = {A {{Framework}} for {{Relating Syntactic}} and {{Semantic Model Differences}}}, author = {Maoz, Shahar and Ringert, Jan Oliver}, url = {http://www.cs.tau.ac.il/~ringert/publications/MR15synsemdiff.pdf}, urldate = {2015-09-10}, note = {TL;DR \par The Diffuse framework provides a novel foundation for combining syntactic and semantic differencing, a language-independent, abstract framework which relates syntactic change operations and semantic difference witnesses.} } @article{maqbool2007hierarchical, title = {Hierarchical Clustering for Software Architecture Recovery}, author = {Maqbool, Onaiza and Babri, Haroon}, date = {2007}, journaltitle = {IEEE Trans. Softw. Eng.}, volume = {33}, number = {11}, pages = {759--780}, publisher = {IEEE}, note = {TL;DR \par This paper provides a detailed analysis of the behavior of various similarity and distance measures that may be employed for software clustering, and analyzes the clustering process of various well-known clustering algorithms by using multiple criteria.} } @article{marcenSystematicLiteratureReview2024, title = {A Systematic Literature Review of Model-Driven Engineering Using Machine Learning}, author = {Marcén, Ana C. and Iglesias, Antonio and Lapeña, Raúl and Pérez, Francisca and Cetina, Carlos}, date = {2024-09}, journaltitle = {IEEE Trans. Softw. Eng.}, volume = {50}, number = {9}, pages = {2269--2293}, issn = {1939-3520}, doi = {10.1109/TSE.2024.3430514}, abstract = {Model-driven engineering (MDE) is a software engineering paradigm based on the systematic use of models. Over the past few years, engineers have significantly increased the use of MDE, which has been reported as a successful paradigm for developing industrial software. Recently, there have also been remarkable advancements in the Artificial Intelligence (AI) domain, with a significant increase in advanced Machine Learning (ML) techniques. The advances in both fields have led to a surge in works that dwell within the intersection of ML and MDE. This work places the focus on systematically reviewing works that leverage ML to solve MDE problems. We have reviewed a total of 9,194 papers, selecting 98 studies for further analysis. The results of our Systematic Literature Review (SLR) bring light to the current state of the art and trends in the field, discussing the drift in the usage of the different available ML techniques along with the remaining research gaps and open challenges. Our SLR has the potential to produce a positive impact in the research community by steering it towards ML techniques that have been successfully applied to solve MDE challenges.}, eventtitle = {{{IEEE Transactions}} on {{Software Engineering}}}, langid = {english}, keywords = {/unread,Analytical models,Bibliographies,Codes,machine learning,Machine learning,Model-driven engineering,systematic literature review,Systematics,Unified modeling language,Vectors} } @online{marcusDeepLearningCritical2018, title = {Deep {{Learning}}: {{A Critical Appraisal}}}, shorttitle = {Deep {{Learning}}}, author = {Marcus, Gary}, date = {2018-01-02}, eprint = {1801.00631}, eprinttype = {arXiv}, eprintclass = {cs, stat}, url = {http://arxiv.org/abs/1801.00631}, urldate = {2023-03-17}, abstract = {Although deep learning has historical roots going back decades, neither the term "deep learning" nor the approach was popular just over five years ago, when the field was reignited by papers such as Krizhevsky, Sutskever and Hinton's now classic (2012) deep network model of Imagenet. What has the field discovered in the five subsequent years? Against a background of considerable progress in areas such as speech recognition, image recognition, and game playing, and considerable enthusiasm in the popular press, I present ten concerns for deep learning, and suggest that deep learning must be supplemented by other techniques if we are to reach artificial general intelligence.}, pubstate = {prepublished}, keywords = {97R40,Computer Science - Artificial Intelligence,Computer Science - Machine Learning,I.2.0,I.2.6,Statistics - Machine Learning} } @book{margariaLeveragingApplicationsFormal2021, title = {Leveraging {{Applications}} of {{Formal Methods}}, {{Verification}} and {{Validation}}: 10th {{International Symposium}} on {{Leveraging Applications}} of {{Formal Methods}}, {{ISoLA}} 2021, {{Rhodes}}, {{Greece}}, {{October}} 17–29, 2021, {{Proceedings}}}, shorttitle = {Leveraging {{Applications}} of {{Formal Methods}}, {{Verification}} and {{Validation}}}, editor = {Margaria, Tiziana and Steffen, Bernhard}, date = {2021}, series = {Lecture {{Notes}} in {{Computer Science}}}, volume = {13036}, publisher = {Springer International Publishing}, location = {Cham}, doi = {10.1007/978-3-030-89159-6}, isbn = {978-3-030-89158-9 978-3-030-89159-6}, langid = {english} } @article{marozzoWorkflowManagementSystem2018, title = {A {{Workflow Management System}} for {{Scalable Data Mining}} on {{Clouds}}}, author = {Marozzo, Fabrizio and Talia, Domenico and Trunfio, Paolo}, date = {2018-05-01}, journaltitle = {IEEE Trans. Serv. Comput.}, volume = {11}, number = {3}, pages = {480--492}, issn = {1939-1374}, doi = {10.1109/TSC.2016.2589243}, abstract = {The extraction of useful information from data is often a complex process that can be conveniently modeled as a data analysis workflow. When very large data sets must be analyzed and/or complex data mining algorithms must be executed, data analysis workflows may take very long times to complete their execution. Therefore, efficient systems are required for the scalable execution of data analysis workflows, by exploiting the computing services of the Cloud platforms where data is increasingly being stored. The objective of the paper is to demonstrate how Cloud software technologies can be integrated to implement an effective environment for designing and executing scalable data analysis workflows. We describe the design and implementation of the Data Mining Cloud Framework (DMCF), a data analysis system that integrates a visual workflow language and a parallel runtime with the Software-as-aService (SaaS) model. DMCF was designed taking into account the needs of real data mining applications, with the goal of simplifying the development of data mining applications compared to generic workflow management systems that are not specifically designed for this domain. The result is a high-level environment that, through an integrated visual workflow language, minimizes the programming effort, making easier to domain experts the use of common patterns specifically designed for the development and the parallel execution of data mining applications. The DMCF’s visual workflow language, system architecture and runtime mechanisms are presented. We also discuss several data mining workflows developed with DMCF and the scalability obtained executing such workflows on a public Cloud.}, langid = {english}, keywords = {STAR}, note = {\textbf{Blue Annotations (3/2/2022, 15:37:35)} \par "Several systems have been proposed to design and execute workflow-based applications [10], [11], [12], but only some of them currently work on the Cloud and support visual workflow programming. In the following we discuss representative visual workflow management systems that can be used in Cloud environments." (\href{zotero://open-pdf/library/items/XTEYZECW?page=2}{Marozzo et al 2018:481}) \par "Galaxy" (\href{zotero://open-pdf/library/items/XTEYZECW?page=2}{Marozzo et al 2018:481}) \par "averna [15] is a workflow management system mostly used in the life sciences community" (\href{zotero://open-pdf/library/items/XTEYZECW?page=2}{Marozzo et al 2018:481}) \par "Orange4WS [17] is a service-oriented workflow system that extends Orange, a data mining toolbox and a visual programming environment for the visual composition of data mining workflows." (\href{zotero://open-pdf/library/items/XTEYZECW?page=2}{Marozzo et al 2018:481}) \par "Kepler [18] is a visual workflow management system that provides a graphical user interface for designing scientific" (\href{zotero://open-pdf/library/items/XTEYZECW?page=2}{Marozzo et al 2018:481}) \par "workflows" (\href{zotero://open-pdf/library/items/XTEYZECW?page=3}{Marozzo et al 2018:482}) \par "E-Science Central (e-SC) [19] allows scientists to store," (\href{zotero://open-pdf/library/items/XTEYZECW?page=3}{Marozzo et al 2018:482}) \par "analyze and share data in the Cloud." (\href{zotero://open-pdf/library/items/XTEYZECW?page=3}{Marozzo et al 2018:482}) \par "ClowdFlows [20] is a Cloud-based platform for the composition, execution, and sharing of interactive data mining workflows." (\href{zotero://open-pdf/library/items/XTEYZECW?page=3}{Marozzo et al 2018:482}) \par "set of technologies to execute workflow-based applications over clusters and Grid" (\href{zotero://open-pdf/library/items/XTEYZECW?page=3}{Marozzo et al 2018:482}) \par "RapidMiner1 is a powerful commercial platform through which users can exploit many analytics tools to visually create predictive analytics workflows." (\href{zotero://open-pdf/library/items/XTEYZECW?page=3}{Marozzo et al 2018:482}) \par "each tool node of a workflow can be a Hadoop program, and therefore it can exploit the Hadoop parallelism" (\href{zotero://open-pdf/library/items/XTEYZECW?page=3}{Marozzo et al 2018:482}) \par "DMCF workflow, task Yes PaaS Yes" (\href{zotero://open-pdf/library/items/XTEYZECW?page=3}{Marozzo et al 2018:482}) \par "RapidMiner" (\href{zotero://open-pdf/library/items/XTEYZECW?page=3}{Marozzo et al 2018:482}) \par TL;DR \par The design and implementation of the Data Mining Cloud Framework (DMCF), a data analysis system that integrates a visual workflow language and a parallel runtime with the Software-as-a-Service (SaaS) model is described. \par \textbf{Yellow Annotations (3/2/2022, 15:37:35)} \par "The extraction of useful information from data is often a complex process that can be conveniently modeled as a data analysis workflow" (\href{zotero://open-pdf/library/items/XTEYZECW?page=1}{Marozzo et al 2018:480}) \par "data analysis workflows may take very long times to complete their execution" (\href{zotero://open-pdf/library/items/XTEYZECW?page=1}{Marozzo et al 2018:480}) \par "efficient systems are required for the scalable execution of data analysis workflows, by exploiting the computing services of the Cloud platforms where data is increasingly being stored." (\href{zotero://open-pdf/library/items/XTEYZECW?page=1}{Marozzo et al 2018:480}) \par "Cloud software technologies can be integrated to implement an effective environment for designing and executing scalable data analysis workflows" (\href{zotero://open-pdf/library/items/XTEYZECW?page=1}{Marozzo et al 2018:480}) \par "Data Mining Cloud Framework (DMCF), a data analysis system that integrates a visual workflow language and a parallel runtime with the Software-as-aService (SaaS) model" (\href{zotero://open-pdf/library/items/XTEYZECW?page=1}{Marozzo et al 2018:480}) \par "eal data mining applications" (\href{zotero://open-pdf/library/items/XTEYZECW?page=1}{Marozzo et al 2018:480}) \par "simplifying the development of data mining applications compared to generic workflow management systems that are not specifically designed for this domain" (\href{zotero://open-pdf/library/items/XTEYZECW?page=1}{Marozzo et al 2018:480}) \par "minimizes the programming effort, making easier to domain experts the use of common patterns specifically designed for the development and the parallel execution of data mining applications." (\href{zotero://open-pdf/library/items/XTEYZECW?page=1}{Marozzo et al 2018:480}) \par "DMCF's visual workflow language, system architecture and runtime mechanisms are presented. We also discuss several data mining workflows developed with DMCF and the scalability obtained executing such workflows on a public Cloud" (\href{zotero://open-pdf/library/items/XTEYZECW?page=1}{Marozzo et al 2018:480}) \par "distributed datasets, preprocessing tools, data mining algorithms and knowledge models" (\href{zotero://open-pdf/library/items/XTEYZECW?page=1}{Marozzo et al 2018:480}) \par "Workflows provide a declarative way of specifying the high-level logic of an application, hiding the low-level details that are not fundamental for application design" (\href{zotero://open-pdf/library/items/XTEYZECW?page=1}{Marozzo et al 2018:480}) \par "DMCF minimizes the programming effort," (\href{zotero://open-pdf/library/items/XTEYZECW?page=1}{Marozzo et al 2018:480}) \par "use of common patterns specifically designed for the development and the parallel execution of data mining applications" (\href{zotero://open-pdf/library/items/XTEYZECW?page=2}{Marozzo et al 2018:481}) \par \mkbibemph{DEFINITION OF COMMONG PATTERNS FOR THE PARALLEL EXECUTION OF DATA MINING APPLICATINS (\href{zotero://open-pdf/library/items/XTEYZECW?page=2}{note on p.481})} \par "isual patterns useful in real data mining applications, in particular: data pre-processing (data partitioning and filtering); parameter sweeping (the concurrent execution of many instances of the same tool with different parameters to find the best result); input sweeping (the concurrent execution of many instances of the same tool with different input data); tool sweeping (the concurrent execution of different tools on same data); combinations of parameter, input, and tool sweeping patterns for the highest flexibility; data and models aggregation (e.g., models evaluations, voting operations, models aggregation)." (\href{zotero://open-pdf/library/items/XTEYZECW?page=2}{Marozzo et al 2018:481}) \par "design of parallel data analysis workflows." (\href{zotero://open-pdf/library/items/XTEYZECW?page=2}{Marozzo et al 2018:481})} } @article{martinez-lasacaDandelionScalableCloudbased2023, title = {Dandelion: {{A}} Scalable, Cloud-Based Graphical Language Workbench for Industrial Low-Code Development}, shorttitle = {Dandelion}, author = {Martínez-Lasaca, Francisco and Díez, Pablo and Guerra, Esther and De Lara, Juan}, date = {2023-05}, journaltitle = {Journal of Computer Languages}, pages = {101217}, issn = {25901184}, doi = {10.1016/j.cola.2023.101217}, abstract = {There is an increasing demand nowadays for low-code development platforms (LCDPs). As they rely heavily on graphical languages rather than writing code, these platforms enable citizen developers to participate in software development. However, creating new LCDPs is very costly, since it requires building support for graphical modelling and its integration with services like model validation, recommendation systems, or code generation. While Model-driven Engineering (MDE) has developed technologies to create these components, most of them are not cloud-based, as required by LCDPs. In particular, a cloud-based graphical workbench capable of providing the scalability required by industrial applications and adequately supporting technological heterogeneity is currently missing.}, langid = {english}, keywords = {LOGSEQ} } @article{martinez-lasacaLowcoBotChattingLowCode, title = {{{LowcoBot}}: {{Towards Chatting With Low-Code Platforms}}}, author = {Martínez-Lasaca, Francisco and Díez, Pablo and Guerra, Esther and family=Lara, given=Juan, prefix=de, useprefix=true}, abstract = {Low-code platforms are gaining momentum, allowing the creation of complex applications directly from the browser. This allows their use by individuals with a wide range of backgrounds, but they pose some problems. On the one hand, newcomers may want to grasp the platform capabilities or pinpoint where can they access some functionality, which may prove difficult without guidance. On the other hand, their navigability can be complex: their functionality can be distributed across many webpages and user interfaces, each managing different concepts. Additionally, users may deem more convenient addressing some tasks using natural language instead of navigating visual interfaces. For these reasons, we introduce LowcoBot, a model-driven solution to generate LLM-based chatbots out of low-code platform design models. We demonstrate its capabilities by generating a chatbot for Dandelion+, a low-code platform built within an industrial context, and showing the range of tasks it supports out-of-the-box.}, langid = {english}, keywords = {LOGSEQ}, note = {\section{Annotazioni\\ (10/6/2024, 12:25:34)} \par - “their functionality can be distributed across many webpages and user interfaces, each managing different concepts.” (Martínez-Lasaca et al., p. 1) \#5fb236 \par - “integrating conversational assistants – or chatbots – into low-code platforms can improve their navigability by providing users with more ways to interact with them –” (Martínez-Lasaca et al., p. 1) \#5fb236 \par - “odel-driven engineering (MDE) approach [3] that allows generating chatbots automatically” (Martínez-Lasaca et al., p. 2) \#5fb236 \par - “contents of the LLM agent vary between platforms.” (Martínez-Lasaca et al., p. 3) \#5fb236 \par - “o automatically populate tools and the initial prompt from a low-code platform model specification” (Martínez-Lasaca et al., p. 3) \#5fb236 \par - “visual metaphors (e.g., tables, diagrams, or forms)” (Martínez-Lasaca et al., p. 4) \#5fb236 \par - “capturing the relationships of the entities between the three technological spaces” (Martínez-Lasaca et al., p. 5) \#5fb236 \par - “initial prompt” (Martínez-Lasaca et al., p. 5) \#5fb236 \par - “shallow way” (Martínez-Lasaca et al., p. 5) \#ffd400\\ \mkbibemph{I wold define such a concept.} \par - “igure 4:” (Martínez-Lasaca et al., p. 6) \#ffd400\\ \mkbibemph{Who create this graph and when? Can we explicitly link this graph with the explanatory example presented in Fig. 1?} \par - “Conclusion” (Martínez-Lasaca et al., p. 10) \#ffd400\\ \mkbibemph{You are assuming that the LLM knows the lowcode platform you are considering. in case you are developing a new one or you are targeting a lowcode platform the LLM is not aware of, you should support also the training phase, isn't it? I think you should discuss this side situation somewhere in the paper.}} } @inproceedings{martinezAutomatingExtractionModelBased2015, title = {Automating the {{Extraction}} of {{Model-Based Software Product Lines}} from {{Model Variants}} ({{T}})}, author = {Martinez, Jabier and Ziadi, Tewfik and Bissyande, Tegawende F. and Klein, Jacques and family=Traon, given=Yves, prefix=le, useprefix=false}, date = {2015-11}, pages = {396--406}, publisher = {IEEE}, doi = {10.1109/ASE.2015.44}, isbn = {978-1-5090-0025-8} } @article{martinoClassificationChallengesNonFunctional, title = {Classification and {{Challenges}} of {{Non-Functional Requirements}} in {{ML-Enabled Systems}}: {{A Systematic Literature Review}}}, author = {Martino, Vincenzo De and Palomba, Fabio}, abstract = {Objective: In this respect, we notice the lack of a comprehensive synthesis of the non-functional requirements affecting ML-enabled systems, other than the major challenges faced to deal with them. Such a synthesis may not only provide a comprehensive summary of the state of the art, but also drive further research on the analysis, management, and optimization of non-functional requirements of ML-intensive systems. Method: In this paper, we propose a systematic literature review targeting two key aspects such as (1) the classification of the non-functional requirements investigated so far, and (2) the challenges to be faced when developing models in ML-enabled systems. Through the combination of well-established guidelines for conducting systematic literature reviews and additional search criteria, we survey a total amount of 69 research articles. Results: Our findings report that current research identified 30 different non-functional requirements, which can be grouped into six main classes. We also compiled a catalog of more than 23 software engineering challenges, based on which further research should consider the nonfunctional requirements of machine learning-enabled systems. Conclusion: We conclude our work by distilling implications and a future outlook on the topic.}, langid = {english} } @article{marussySpecificationLanguageConsistent2020, title = {A {{Specification Language}} for {{Consistent Model Generation}} Based on {{Partial Models}}.}, author = {Marussy, Kristóf and Semeráth, Oszkár and A. Babikian, Aren and Varró, Dániel}, date = {2020}, journaltitle = {JOT}, volume = {19}, number = {3}, pages = {3:1}, issn = {1660-1769}, doi = {10.5381/jot.2020.19.3.a12}, langid = {english}, note = {TL;DR \par A novel specification language for partial models used in consistent graph model generation by combining partial models, graph predicates and model metrics with mutual dependencies between them is proposed.} } @article{maSOGPTSpotterDetectingChatGPTGenerated, title = {{{SOGPTSpotter}}: {{Detecting ChatGPT-Generated Answers}} on {{Stack Overflow}}}, author = {Ma, Suyu and Chen, Chunyang and Khalajzadeh, Hourieh and Grundy, John}, volume = {1}, number = {1}, langid = {english}, keywords = {LOGSEQ}, note = {\section{Annotazioni\\ (6/12/2024, 21:19:11)} \par - “human” (Ma et al., p. 1) \#2ea8e5 \par - “exchange of knowledge and providing solutions to programming challenges” (Ma et al., p. 1) \#5fb236 \par - “SOGPTSpotter” (Ma et al., p. 1) \#a28ae5 \par () \par - “Recently, there has been a significant increase in the number of answers generated by ChatGPT, which can lead to incorrect and unreliable information being posted on the site.” (Ma et al., p. 1) \#5fb236 \par - “detecting whether a post is ChatGPT-generated remains a challenging task” (Ma et al., p. 1) \#a28ae5 \par - “answers” (Ma et al., p. 1) \#2ea8e5 \par - “ChatGPT answers” (Ma et al., p. 1) \#2ea8e5 \par - “We also conducted a real-world case study on Stack Overflow” (Ma et al., p. 1) \#5fb236 \par - “Therefore, maintaining the quality of these answers is essential to ensure the reliability of the platform and its continued ser” (Ma et al., p. 2) \#5fb236 \par - “Reinforcement Learning from Human Feedback” (Ma et al., p. 2) \#5fb236 \par - “software enginee” (Ma et al., p. 2) \#ffd400\\ \mkbibemph{Please replace arxiv references with the corresponding published version (if any).} \par - “dramatic reduction in traffic to Stack Overflow” (Ma et al., p. 2) \#5fb236 \par - “Another major impact is ChatGPT-generated answers to Stack Overflow posts.” (Ma et al., p. 2) \#a28ae5 \par - “However, the site soon banned all answers produced by the model due to concerns about the accuracy of the information provided.” (Ma et al., p. 2) \#a28ae5 \par - “"substantially harmful"” (Ma et al., p. 2) \#a28ae5 \par - “The main issue lies in the fact that while the responses generated by ChatGPT often appear to be plausible, they are frequently incorrect.” (Ma et al., p. 2) \#e56eee \par - “non-existent command, git delete commit,” (Ma et al., p. 3) \#a28ae5 \par - “t. This information is incorrect and could lead to confusion for users who follow these steps” (Ma et al., p. 3) \#e56eee \par - “ack the necessary expertise to validate the accuracy of these responses before posting them.” (Ma et al., p. 3) \#a28ae5 \par - “detecting ChatGPT content is challenging in many domains, as the content is designed to mimic human language and structure.” (Ma et al., p. 3) \#a28ae5 \par - “high-quality” (Ma et al., p. 3) \#ffd400\\ \mkbibemph{what do you mean?} \par - “detects ChatGPT-generated content on Stack Overflow;” (Ma et al., p. 4) \#a28ae5 \par - “Generative Pretrained Transforme” (Ma et al., p. 5) \#5fb236 \par - “It generates responses by predicting the likelihood of a word given the previous words used in the tex” (Ma et al., p. 5) \#5fb236 \par - “Siamese Neural Network” (Ma et al., p. 6) \#5fb236 \par - “reference answer” (Ma et al., p. 6) \#ffd400\\ \mkbibemph{THis is not clear. THe reference answer should be by humans, isn't it?} \par - “comprehensive benchmark that enables the accurate detection of ChatGPT-generated content in Stack Overflow” (Ma et al., p. 6) \#5fb236 \par - “A high-quality answer generated by ChatGPT, designed to serve as a benchmark with distinct non-human features for comparison.” (Ma et al., p. 6) \#ffd400\\ \mkbibemph{THis is a bit obscure. It is not clear what are the characteristics of the answers available in this set. Especially, how to ensure to make it different from the ChatGPT answer. It is necessary to explain why it is necessary to have this, instead of having simply pairs of human and chatGPT answers.} \par - “o” (Ma et al., p. 6) \#5fb236 \par - “he question title and question body are regarded together as the complete question. The dataset preparation process involved several key steps: selecting and filtering data from Stack Overflow, generating reference answers, and” (Ma et al., p. 7) \#ffd400\\ \mkbibemph{It's not clear why we have two sets of questions generated by ChatGPT. This needs to be rechecked.....} \par - “16,847 high-quality post questions and their corresponding high-quality answers based on the specified criteria.” (Ma et al., p. 7) \#5fb236 \par - “et, we generated a reference answer designed to encapsulate the typical features of non-human answers, as identified in related empirical research [9, 32, 37, 49, 59, 69]. The reference answers serve as a” (Ma et al., p. 8) \#ffd400\\ \mkbibemph{Maybe the goal is training the model with couples of human and ChatGPT generated answers? If yes this needs to be clarified earlier! TO BE CHECKED!} \par - “6000 questions” (Ma et al., p. 8) \#5fb236 \par - “diversity of tags” (Ma et al., p. 8) \#5fb236 \par - “6,000 high-quality question” (Ma et al., p. 8) \#ffd400\\ \mkbibemph{what was the quested quality?} \par - “xceed 1000 tokens in length.” (Ma et al., p. 9) \#ffd400\\ \mkbibemph{Is this respected? Sometimes LLMs do not follow these kids of requests.} \par - “Chain-of-Thought ChatGPT Prompt” (Ma et al., p. 9) \#ffd400\\ \mkbibemph{I don't see a substantial difference with the "Reference Prompt"} \par - “Persona Prompt: For the last segment, we used a persona approach [19], framing ChatGPT as a professional and experienced developer. This prompt aimed to produce answers that demonstrate deep expertise and” (Ma et al., p. 10) \#ffd400\\ \mkbibemph{I don't see how such a requirement can be actually satisfied.} \par - “a reference answer, a human answer, and a ChatGPT answer.” (Ma et al., p. 10) \#5fb236 \par - “similarity between the reference answer and the other answers.” (Ma et al., p. 10) \#5fb236 \par - “key similarities and differences” (Ma et al., p. 10) \#5fb236 \par - “By using BigBird, we ensure that our model can capture the comprehensive context and nuances of lengthy texts.” (Ma et al., p. 11) \#5fb236 \par - “11GB NVidia GeForce RTX 2080 Ti graphics card” (Ma et al., p. 12) \#5fb236 \par - “various baseline models for comparison” (Ma et al., p. 12) \#5fb236 \par - “accuracy” (Ma et al., p. 12) \#5fb236 \par - “precision” (Ma et al., p. 12) \#5fb236 \par - “recall” (Ma et al., p. 12) \#5fb236 \par - “F1 score” (Ma et al., p. 12) \#5fb236 \par - “RQ1:” (Ma et al., p. 12) \#5fb236 \par - “RQ2:” (Ma et al., p. 12) \#5fb236 \par - “RQ3:” (Ma et al., p. 12) \#a28ae5\\ \mkbibemph{INTERESTING} \par - “RQ4” (Ma et al., p. 12) \#ffd400\\ \mkbibemph{This overlaps with RQ1, isn't it?} \par - “RQ5” (Ma et al., p. 12) \#5fb236 \par - “The splitting process was carried out randomly” (Ma et al., p. 13) \#a28ae5 \par - “: The validation set is used to tune the hyperparameters and make decisions regarding early stopping during the training process.” (Ma et al., p. 13) \#5fb236 \par - “the predictability of” (Ma et al., p. 13) \#ffd400 \par - “This high recall ensures that most ChatGPT-generated content is identified, and high precision ensures that the identified content is indeed ChatGPTgenerated.” (Ma et al., p. 14) \#5fb236 \par - “ChatGPT-generated content without misclassifying human-generated content.” (Ma et al., p. 14) \#5fb236 \par - “length of the input text affect the performance of SOGPTSpotter?” (Ma et al., p. 16) \#ffd400\\ \mkbibemph{lenght of the text used for training or during the inference phase?} \par - “improved F1-scores with longer input texts.” (Ma et al., p. 16) \#5fb236 \par - “which were applied to the answers to make them more challenging to detect.” (Ma et al., p. 18) \#5fb236 \par - “For each type of adversarial attack, we ran SOGPTSpotter and the baseline models on our modified test dataset and measured their detection accuracy using F1-score. This allowed us to quantify the impact of each type of adversarial attack on the performance of our tool and the baseline models, providing valuable insights into their robustness against such attacks. The results are shown in Table 4.” (Ma et al., p. 18) \#ffd400\\ \mkbibemph{Details on how attacks have been performed and measured are needed. For instance, are those performed on the same dataset used for the previous research questions? How questions have been selected?} \par - “ignificantly influences the performance of SOGPTSpotter, with longer texts providing better F1-scores.” (Ma et al., p. 18) \#5fb236 \par - “substitution” (Ma et al., p. 18) \#a28ae5 \par - “Deep-Word-Bug” (Ma et al., p. 18) \#5fb236 \par - “Answer to RQ5: The field study results demonstrate the practical effectiveness of our SOGPTSpotter tool in a real-world setting on Stack Overflow. Our model successfully identified ChatGPT-generated content with a high acceptance rate of 94\% for post edits submitted for community review. Despite some challenges with shorter answers and posts containing long code snippets, the overall high acceptance rate indicates that our tool can significantly aid in moderating AI-generated content on Q\&A platforms.” (Ma et al., p. 22) \#ffd400\\ \mkbibemph{This research question is not convincing. The users trusted that the 47 posts were generated but we cannot be sure that they were so, isn't it? It is necessary to provide a better argumentation for support such research question.} \par - “the selection of post answers from Stack Overflow as human answers.” (Ma et al., p. 22) \#5fb236 \par - “While our SOGPTSpotter performed well on both the experimental datasets and real-world case studies, improvements in LLMs might lead to more sophisticated AI-generated content that is harder to detect.” (Ma et al., p. 22) \#ffd400\\ \mkbibemph{Yes, this require monitoring efforts as future work.}} } @article{Masthead2017, title = {Masthead}, date = {2017-01}, journaltitle = {IEEE Softw.}, volume = {34}, number = {1}, pages = {8--8}, issn = {0740-7459}, doi = {10.1109/MS.2017.5}, langid = {english} } @article{Masthead2018, title = {Masthead}, date = {2018-09}, journaltitle = {IEEE Softw.}, volume = {35}, number = {5}, pages = {c2-c2}, issn = {0740-7459}, doi = {10.1109/MS.2018.3571248}, abstract = {Describes the above-named upcoming conference event. May include topics to be covered or calls for papers.} } @article{Masthead2020, title = {Masthead}, date = {2020-09}, journaltitle = {IEEE Softw.}, volume = {37}, number = {5}, pages = {C2-C2}, issn = {0740-7459, 1937-4194}, doi = {10.1109/MS.2020.2972672}, langid = {english} } @article{Masthead2020a, title = {Masthead}, date = {2020-07}, journaltitle = {IEEE Softw.}, volume = {37}, number = {4}, pages = {C2-C2}, issn = {0740-7459, 1937-4194}, doi = {10.1109/MS.2020.2972660}, langid = {english} } @misc{mastropaolo2023robustness, title = {On the Robustness of Code Generation Techniques: {{An}} Empirical Study on {{GitHub}} Copilot}, author = {Mastropaolo, Antonio and Pascarella, Luca and Guglielmi, Emanuela and Ciniselli, Matteo and Scalabrino, Simone and Oliveto, Rocco and Bavota, Gabriele}, date = {2023}, journaltitle = {International conference on software engineering (ICSE 2023), may 17-19 2023, melbourne, australia}, note = {TL;DR \par This paper presents an empirical study in which it is aimed at understanding whether different but semantically equivalent natural language descriptions result in the same recommended function in the generated code function.} } @online{mastropaoloAutomaticallyCompletingGitHub2023, title = {Toward {{Automatically Completing GitHub Workflows}}}, author = {Mastropaolo, Antonio and Zampetti, Fiorella and Di Penta, Massimiliano and Bavota, Gabriele}, date = {2023-08-31}, eprint = {2308.16774}, eprinttype = {arXiv}, eprintclass = {cs}, url = {http://arxiv.org/abs/2308.16774}, urldate = {2023-09-05}, abstract = {Continuous integration and delivery (CI/CD) are nowadays at the core of software development. Their benefits come at the cost of setting up and maintaining the CI/CD pipeline, which requires knowledge and skills often orthogonal to those entailed in other software-related tasks. While several recommender systems have been proposed to support developers across a variety of tasks, little automated support is available when it comes to setting up and maintaining CI/CD pipelines. We present GH-WCOM (GitHub Workflow COMpletion), a Transformer-based approach supporting developers in writing a specific type of CI/CD pipelines, namely GitHub workflows. To deal with such a task, we designed an abstraction process to help the learning of the transformer while still making GH-WCOM able to recommend very peculiar workflow elements such as tool options and scripting elements. Our empirical study shows that GH-WCOM provides up to 34.23\% correct predictions, and the model's confidence is a reliable proxy for the recommendations' correctness likelihood.}, pubstate = {prepublished}, keywords = {Computer Science - Software Engineering,LOGSEQ} } @article{mastropaoloAutomatingCoderelatedTasks, title = {Automating Code-Related Tasks via Pre-Trained Models of Code - {{THESIS PROPOSAL}}}, author = {Mastropaolo, Antonio}, abstract = {Developers are often faced with the challenge of writing high-quality code while meeting strong time constraints. Recent literature exploits Deep Learning (DL) models to support developers in code-related tasks. For example, DL-based approaches have been proposed to automate bug-fixing activities, code summarization, and code review. Some of these tasks require to work with both code and technical natural language (e.g., code summarization), posing additional challenges in the training of DL models which must deal with bi-modal data. Our goal is to widen the support given to developers when dealing with code-related tasks characterized by technical natural language and code. To this extent, we started investigating the benefits brought by the “pretrain-then-finetune” paradigm when using DL models to automate code-related activities. The basic idea of this paradigm is to first pre-train the model with self-supervised tasks with the only goal of learning the languages of interest (e.g., technical English and code). Then, the fine-tuning phase takes care of specializing the model for the specific task of interest (e.g., code summarization). Given the positive results we achieved, we focused our research on two code-related tasks characterized by both code and natural language. The first is the already mentioned code summarization which consists in generating a code summary for a given piece of code at hand (e.g., method, code snippet). The second is the generation and injection of complete log statements in which the DL model takes as input a code component and is in charge of recommending to developers which log statements may be beneficial to inject, thus taking care of generating the log statement (including a meaningful log message) and inject it in the correct code location.}, langid = {english} } @online{mastropaoloEvaluatingCodeSummarization2023, title = {Evaluating {{Code Summarization Techniques}}: {{A New Metric}} and an {{Empirical Characterization}}}, shorttitle = {Evaluating {{Code Summarization Techniques}}}, author = {Mastropaolo, Antonio and Ciniselli, Matteo and Di Penta, Massimiliano and Bavota, Gabriele}, date = {2023-12-24}, eprint = {2312.15475}, eprinttype = {arXiv}, eprintclass = {cs}, url = {http://arxiv.org/abs/2312.15475}, urldate = {2024-01-18}, abstract = {Several code summarization techniques have been proposed in the literature to automatically document a code snippet or a function. Ideally, software developers should be involved in assessing the quality of the generated summaries. However, in most cases, researchers rely on automatic evaluation metrics such as BLEU, ROUGE, and METEOR. These metrics are all based on the same assumption: The higher the textual similarity between the generated summary and a reference summary written by developers, the higher its quality. However, there are two reasons for which this assumption falls short: (i) reference summaries, e.g., code comments collected by mining software repositories, may be of low quality or even outdated; (ii) generated summaries, while using a different wording than a reference one, could be semantically equivalent to it, thus still being suitable to document the code snippet. In this paper, we perform a thorough empirical investigation on the complementarity of different types of metrics in capturing the quality of a generated summary. Also, we propose to address the limitations of existing metrics by considering a new dimension, capturing the extent to which the generated summary aligns with the semantics of the documented code snippet, independently from the reference summary. To this end, we present a new metric based on contrastive learning to capture said aspect. We empirically show that the inclusion of this novel dimension enables a more effective representation of developers’ evaluations regarding the quality of automatically generated summaries.}, langid = {english}, pubstate = {prepublished}, keywords = {Computer Science - Software Engineering,LOGSEQ}, note = {\section{Annotazioni\\ (19/1/2024, 11:16:08)} \par “Evaluating Code Summarization Techniques: A New Metric and an Empirical Characterization” (Mastropaolo et al., 2023, p. 1) \#5fb236 \\ \mkbibemph{This is related to project [[PROJECTS/MOSAICO]]} \par “Several code summarization techniques have been proposed in the literature to document a code snippet or a function automatically.” (Mastropaolo et al., 2023, p. 1) \#ffd400 \\ \mkbibemph{This is a comment for a yellow highlight [[P1]] [[STAR]]} \par “oftware developers should be involved in assessing the quality of the generated summaries. However, in most cases, researchers rely on a” (Mastropaolo et al., 2023, p. 1) \#ff6666 \\ \mkbibemph{This is not good [[people/phuong]]} \par “falls short: (i) reference summaries, e.g., code comments collected by mining software repositories, may be of low quality or even outdated; (ii)” (Mastropaolo et al., 2023, p. 1) \#5fb236 \par “m a thorough empirical investigation on the complementarity of different types of metrics in capturing the quality of a generated summary. Also, we propose to address the limitations of existing metrics by consid” (Mastropaolo et al., 2023, p. 1) \#ffd400 \par TL;DR \par A thorough empirical investigation on the complementarity of different types of metrics in capturing the quality of a generated summary is performed, and a new metric based on contrastive learning to capture said aspect is presented.} } @inproceedings{mastropaoloStudyingUsageTextToText2021, ids = {T5_Oliveto_Bavota_2021}, title = {Studying the {{Usage}} of {{Text-To-Text Transfer Transformer}} to {{Support Code-Related Tasks}}}, booktitle = {2021 {{IEEEACM}} 43rd {{Int}}. {{Conf}}. {{Softw}}. {{Eng}}. {{ICSE}}}, author = {Mastropaolo, Antonio and Scalabrino, Simone and Cooper, Nathan and Nader Palacio, David and Poshyvanyk, Denys and Oliveto, Rocco and Bavota, Gabriele}, date = {2021-05}, pages = {336--347}, publisher = {IEEE}, location = {Madrid, ES}, doi = {10.1109/ICSE43902.2021.00041}, eventtitle = {2021 {{IEEE}}/{{ACM}} 43rd {{International Conference}} on {{Software Engineering}} ({{ICSE}})}, isbn = {978-1-6654-0296-5} } @online{MathematicsMachineLearning, title = {Mathematics for {{Machine Learning}}}, url = {https://www.linkedin.com/feed/update/urn:li:activity:7294898366449143808?utm_source=share&utm_medium=member_android&rcm=ACoAAACaoXoBMvdj060syLix4vC3rwOgjAE9MpU}, keywords = {source\_mobile} } @article{mathewSoftwareEngineeringsTop2018, title = {Software {{Engineering}}’s {{Top Topics}}, {{Trends}}, and {{Researchers}}}, author = {Mathew, G. and Menzies, T.}, date = {2018-09}, journaltitle = {IEEE Softw.}, volume = {35}, number = {5}, pages = {88--93}, issn = {0740-7459}, doi = {10.1109/MS.2018.3571230}, abstract = {For this theme issue on the 50th anniversary of software engineering (SE), Redirections offers an overview of the twists, turns, and numerous redirections seen over the years in the SE research literature. Nearly a dozen topics have dominated the past few decades of SE research—and these have been redirected many times. Some are gaining popularity, whereas others are becoming increasingly rare. This article is part of a theme issue on software engineering’s 50th anniversary.}, keywords = {software engineering}, note = {TL;DR \par This theme issue on the 50th anniversary of software engineering (SE) offers an overview of the twists, turns, and numerous redirections seen over the years in the SE research literature.} } @article{Matic2021, title = {Extensible Chatbot Architecture Using Metamodels of Natural Language Understanding}, author = {Matic, R. and Kabiljo, M. and Zivkovic, M. and Cabarkapa, M.}, date = {2021}, journaltitle = {Electron. Switz.}, volume = {10}, number = {18}, publisher = {MDPI}, issn = {20799292}, doi = {10.3390/electronics10182300}, abbrev_source_title = {Electronics (Switzerland)}, affiliation = {Department for Information Systems and Technologies, Belgrade Academy for Business and Arts Applied Studies, Kraljice Marije 73, Belgrade, 11000, Serbia; Faculty of Informatics and Computing, Singidunum University, Danijelova 32, Belgrade, 11000, Serbia; School of Electrical Engineering, University of Belgrade, Bulevar Kralja Aleksandra 73, Belgrade, 11000, Serbia}, art_number = {2300}, correspondence_address1 = {Zivkovic, M.; Faculty of Informatics and Computing, Danijelova 32, Serbia; email: mzivkovic@singidunum.ac.rs; Cabarkapa, M.; School of Electrical Engineering, Bulevar Kralja Aleksandra 73, Serbia; email: cabmilan@etf.bg.ac.rs}, document_type = {Article}, langid = {english}, source = {Scopus}, keywords = {GOAL\_Model-Assistance,notion}, note = {cited By 1 \par TL;DR \par The paper describes overall chatbot architecture and provides corresponding metamodels as well as rules for mapping between the proposed and two commonly used NLU metAModels and could be easily extended with new NLU services and communication channels. \par to be excluded} } @article{mattihalliPlantLeafDiseases2018, title = {Plant Leaf Diseases Detection and Auto-Medicine}, author = {Mattihalli, Channamallikarjuna and Gedefaye, Edemialem and Endalamaw, Fasil and Necho, Adugna}, date = {2018-09}, journaltitle = {Internet Things}, volume = {1--2}, pages = {67--73}, issn = {25426605}, doi = {10.1016/j.iot.2018.08.007}, abstract = {Leaf diseases in plants cause major production and economic losses as well as reduction in both quality and quantity of agricultural crop. It’s better to detect the leaf diseases in early on leaf health and disease detection can facilitate the control of diseases through proper management strategies.}, langid = {english} } @article{mattsonDemonstratingBigDAWGPolystore, title = {Demonstrating the {{BigDAWG Polystore System}} for {{Ocean Metagenomic Analysis}}}, author = {Mattson, Tim and Gadepally, Vijay and She, Zuohao and Dziedzic, Adam and Parkhurst, Jeff}, pages = {9}, abstract = {In most Big Data applications, the data is heterogeneous. As we have been arguing in a series of papers, storage engines should be well suited to the data they hold. Therefore, a system supporting Big Data applications should be able to expose multiple storage engines through a single interface. We call such systems, polystore systems. Our reference implementation of the polystore concept is called BigDAWG (short for the Big Data Analytics Working Group). In this demonstration, we will show the BigDAWG system and a number of polystore applications built to help ocean metagenomics researchers handle their heterogenous Big Data.}, langid = {english}, note = {TL;DR \par This demonstration will show the BigDAWG system and a number of polystore applications built to help ocean metage-nomics researchers handle their heterogenous Big Data.} } @inproceedings{MayerhoferLWK13, title = {{{xMOF}}: Executable {{DSMLs}} Based on {{fUML}}}, booktitle = {Softw. {{Lang}}. {{Eng}}. - 6th {{Int}}. {{Conf}}. {{SLE}} 2013 {{Indianap}}. {{USA Oct}}. 26-28 2013 {{Proc}}.}, author = {Mayerhofer, Tanja and Langer, Philip and Wimmer, Manuel and Kappel, Gerti}, editor = {Erwig, Martin and Paige, Richard F. and Wyk, Eric Van}, date = {2013}, series = {Lecture Notes in Computer Science}, volume = {8225}, pages = {56--75}, publisher = {Springer}, doi = {10.1007/978-3-319-02654-1\_4}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {TL;DR \par This paper integrates fUML, a standardized executable subset of UML, with MOF leading to a new metamodeling language xMOF, and proposes a methodology for developing executable DSMLs fostering the separation of abstract syntax and behavioral semantics.}, timestamp = {Tue, 14 May 2019 10:00:40 +0200} } @article{mazaheriRecommenderSystemScientific2021, title = {A {{Recommender System}} for {{Scientific Datasets}} and {{Analysis Pipelines}}}, author = {Mazaheri, Mandana and Kiar, Gregory and Glatard, Tristan}, date = {2021-08-20}, journaltitle = {2021 IEEE Workshop Work. Support Large-Scale Sci. WORKS}, volume = {7}, pages = {62011--62021}, doi = {10.1109/WORKS54523.2021.00006}, abstract = {Scientific datasets and analysis pipelines are increasingly being shared publicly in the interest of open science. However, mechanisms are lacking to reliably identify which pipelines and datasets can appropriately be used together. Given the increasing number of high-quality public datasets and pipelines, this lack of clear compatibility threatens the findability and reusability of these resources. We investigate the feasibility of a collaborative filtering system to recommend pipelines and datasets based on provenance records from previous executions. We evaluate our system using datasets and pipelines extracted from the Canadian Open Neuroscience Platform, a national initiative for open neuroscience. The recommendations provided by our system (AUC\$=0.83\$) are significantly better than chance and outperform recommendations made by domain experts using their previous knowledge as well as pipeline and dataset descriptions (AUC\$=0.63\$). In particular, domain experts often neglect low-level technical aspects of a pipeline-dataset interaction, such as the level of pre-processing, which are captured by a provenance-based system. We conclude that provenance-based pipeline and dataset recommenders are feasible and beneficial to the sharing and usage of open-science resources. Future work will focus on the collection of more comprehensive provenance traces, and on deploying the system in production.}, langid = {english}, keywords = {Computer Science - Information Retrieval,Computer Science - Machine Learning} } @incollection{mazanekToolDemonstrationTransformation2011, title = {Tool Demonstration of the Transformation Judge}, booktitle = {Applications of {{Graph Transformations}} with {{Industrial Relevance}}}, author = {Mazanek, Steffen and Rutetzki, Christian and Minas, Mark}, date = {2011}, pages = {97--104}, publisher = {Springer}, url = {http://link.springer.com/chapter/10.1007/978-3-642-34176-2_10}, urldate = {2016-02-09}, note = {TL;DR \par The transformation judge is a novel system for the automatic evaluation and comparison of graph and model transformations that have been submitted as solutions for common transformation tasks such as those accepted as case studies for the transformation tool contest.} } @article{mccallumBuildingDomainSpecificSearch, title = {Building {{Domain-Specific Search Engines}} with {{Machine Learning Techniques}}}, author = {McCallum, Andrew and Nigam, Kamal and Rennie, Jason and Seymore, Kristie}, pages = {12}, abstract = {Domain-speci c search engines are growing in popularity because they o er increased accuracy and extra functionality not possible with the general, Web-wide search engines. For example, www.campsearch.com allows complex queries by age-group, size, location and cost over summer camps. Unfortunately these domain-speci c search engines are di cult and timeconsuming to maintain. This paper proposes the use of machine learning techniques to greatly automate the creation and maintenance of domain-speci c search engines. We describe new research in reinforcement learning, information extraction and text classi cation that enables e cient spidering, identifying informative text segments, and populating topic hierarchies. Using these techniques, we have built a demonstration system: a search engine for computer science research papers. It already contains over 50,000 papers and is publicly available at www.cora.justresearch.com.}, langid = {english}, keywords = {GOAL-Model\_Search,notion,TECHNIQUE\_ReinforcementLearning}, note = {TL;DR \par New research in reinforcement learning, information extraction and text classification that enables efficient spidering, identifying informative text segments, and populating topic hierarchies is described. \par TOO OLD, 1999 \par \href{https://www.ri.cmu.edu/pub_files/pub1/mccallum_andrew_1999_2/mccallum_andrew_1999_2.pdf}{cora.dvi (cmu.edu)}} } @inproceedings{mcdonnell_empirical_2013, title = {An {{Empirical Study}} of {{API Stability}} and {{Adoption}} in the {{Android Ecosystem}}}, booktitle = {2013 {{IEEE Int Conf Softw}}. {{Maint}}.}, author = {McDonnell, Tyler and Ray, Baishakhi and Kim, Miryung}, date = {2013-09}, pages = {70--79}, doi = {10.1109/ICSM.2013.18}, abstract = {When APIs evolve, clients make corresponding changes to their applications to utilize new or updated APIs. Despite the benefits of new or updated APIs, developers are often slow to adopt the new APIs. As a first step toward understanding the impact of API evolution on software ecosystems, we conduct an in-depth case study of the co-evolution behavior of Android API and dependent applications using the version history data found in github. Our study confirms that Android is evolving fast at a rate of 115 API updates per month on average. Client adoption, however, is not catching up with the pace of API evolution. About 28\% of API references in client applications are outdated with a median lagging time of 16 months. 22\% of outdated API usages eventually upgrade to use newer API versions, but the propagation time is about 14 months, much slower than the average API release interval (3 months). Fast evolving APIs are used more by clients than slow evolving APIs but the average time taken to adopt new versions is longer for fast evolving APIs. Further, API usage adaptation code is more defect prone than the one without API usage adaptation. This may indicate that developers avoid API instability.}, keywords = {Android API coevolution behavior,Android ecosystem,Androids,API evolution,API stability,API usage adaptation code,application program interfaces,github,Google,History,Humanoid robots,Mobile communication,mobile computing,operating systems (computers),Smart phones,Software,software ecosystems,software maintenance,version history data}, note = {TL;DR \par An in-depth case study of the co-evolution behavior of Android API and dependent applications using the version history data found in github confirms that Android is evolving fast at a rate of 115 API updates per month on average, but client adoption, however, is not catching up with the pace of API evolution.} } @book{mcewenDesigningInternetThings2014, title = {Designing the {{Internet}} of Things}, author = {McEwen, Adrian and Cassimally, Hakim}, date = {2014}, edition = {Reprinted with corrections}, publisher = {Wiley}, location = {Chichester}, isbn = {978-1-118-43062-0 978-1-118-43063-7 978-1-118-43065-1}, langid = {english}, pagetotal = {324}, note = {TL;DR \par Designing the Internet of Things helps software engineers, web designers, product designers, and electronics engineers start designing products using the Internet-of-Things approach and explains how to combine sensors, servos, robotics, Arduino chips, and more with various networks or the Internet to create interactive, cutting-edge devices.} } @article{mckinney2011pandas, title = {Pandas: A Foundational {{Python}} Library for Data Analysis and Statistics}, author = {McKinney, Wes and others}, date = {2011}, journaltitle = {Python High Perform. Sci. Comput.}, volume = {14}, number = {9}, pages = {1--9}, publisher = {Seattle} } @inproceedings{McMillan:2011:CSA:2117694.2119646, ids = {6080801}, title = {Categorizing Software Applications for Maintenance}, booktitle = {Proc. 2011 27th {{IEEE Int}}. {{Conf}}. {{Softw}}. {{Maint}}.}, author = {McMillan, Collin and Linares-Vasquez, Mario and Poshyvanyk, Denys and Grechanik, Mark}, date = {2011}, series = {{{ICSM}} '11}, pages = {343--352}, publisher = {IEEE Computer Society}, location = {Washington, DC, USA}, issn = {1063-6773}, url = {https://doi.org/10.1109/ICSM.2011.6080801}, acmid = {2119646}, isbn = {978-1-4577-0663-9}, nodoi = {10.1109/ICSM.2011.6080801}, numpages = {10}, keywords = {API calls,application program interfaces,application programming interface,automatic categorization,binary executables,byte-code,categorizing software applications,closed source repository,closed-source,closed-source repository,Companies,Entropy,Java,Java repository,learning (artificial intelligence),legal reasons,Libraries,machine learning,maintenance tasks,open-source,open-source repository,organizational reasons,predict maintenance problems,project management,public domain software,Software,software categorization,software maintenance,software management,software projects,software repository,source code,Support vector machines,third-party library} } @inproceedings{mcmillanDetectingSimilarSoftware2012, title = {Detecting Similar Software Applications}, booktitle = {2012 34th {{Int}}. {{Conf}}. {{Softw}}. {{Eng}}. {{ICSE}}}, author = {McMillan, Collin and Grechanik, Mark and Poshyvanyk, Denys}, date = {2012-06}, pages = {364--374}, publisher = {IEEE}, location = {Zurich}, doi = {10.1109/ICSE.2012.6227178}, eventtitle = {2012 34th {{International Conference}} on {{Software Engineering}} ({{ICSE}} 2012)}, isbn = {978-1-4673-1066-6 978-1-4673-1067-3}, note = {TL;DR \par The results show with strong statistical significance that CLAN automatically detects similar applications from a large repository of 8,310 Java applications with a higher precision than MUDABlue.} } @inproceedings{mcmillanRecommendingSourceCode2010, title = {Recommending Source Code Examples via {{API}} Call Usages and Documentation}, booktitle = {Proc. {{2Nd Int}}. {{Workshop Recomm}}. {{Syst}}. {{Softw}}. {{Eng}}.}, author = {McMillan, Collin and Poshyvanyk, Denys and Grechanik, Mark}, date = {2010}, series = {{{RSSE}} '10}, pages = {21--25}, publisher = {ACM}, location = {New York, NY, USA}, url = {http://doi.acm.org/10.1145/1808920.1808925}, acmid = {1808925}, isbn = {978-1-60558-974-9}, nodoi = {10.1145/1808920.1808925}, numpages = {5} } @article{mcnemar, title = {Note on the Sampling Error of the Difference between Correlated Proportions or Percentages}, author = {McNemar, Quinn}, date = {1947}, journaltitle = {Psychometrika}, volume = {12}, number = {2}, pages = {153--157}, note = {TL;DR \par Two formulas are presented for judging the significance of the difference between correlated proportions and the chi square equivalent of one of the developed formulas.} } @online{MCP, title = {{{MCP}}}, url = {https://www.linkedin.com/posts/genai-works_artificialintelligence-machinelearning-activity-7316146549502181376-xKZZ}, urldate = {2025-04-11}, abstract = {2025: MCP is quietly becoming the API moment for AI agents Since OpenAI dropped function calling in 2023, we’ve been watching a clear pattern unfold: more intelligence in models, but more friction in getting agents to actually do anything useful across tools, systems, and APIs. The problem? There’s no standard execution layer for AI. Developers are stuck wiring agents into every system manually. But that’s starting to change. In late 2024, the Model Context Protocol (MCP) entered the chat. Think: an open protocol that lets agents talk to external tools using a common interface. Autonomously. Across apps. With human-in-the-loop control. It’s like LSP for language tools—but built for agents that act, not just respond. Today’s traction is strong, especially among devs: - Cursor turned into a “do-anything” IDE by wiring in MCP servers for Slack, Resend, Replicate, and more. - Developers use Postgres + Upstash servers to stay in flow while checking DBs, managing cache, or debugging live environments. - Claude Desktop and Highlight show how this moves beyond code—into business workflows and visual UX. But we’re still in the early innings. What MCP needs next: Multi-tenant hosting, built-in authentication, and permission layers Tool discovery protocols so agents can find what they need Gateway infrastructure to route, manage, and scale tool access Standard UX patterns so every client doesn’t reinvent tool selection If done right, MCP won’t just make agents smarter—it’ll reshape how tools are built, discovered, and monetized. 📌 Imagine a future where: Your product ships with a tool layer for AI agents by default Tool choice is based on speed, price, and precision—not brand recognition Documentation becomes machine-readable and instantly operational We’re entering the agent-native architecture era. And the new stack is just getting started. MCP might just be the interface that unifies it all. ☄️Boost your business with us—advertise where 10M+ AI leaders engage https://lnkd.in/dj8iSyJ8 \#artificialintelligence \#machinelearning \#developerexperience \#aiagents \#technology \#innovation | 13 commenti su LinkedIn}, keywords = {MOSAICO} } @online{MDD4DRESProgram, title = {{{MDD4DRES Program}}}, url = {http://www.mdd4dres.org/program/#JM}, urldate = {2016-03-10} } @article{MDE, ids = {Schmidt06}, title = {Guest Editor's Introduction: {{Model-driven}} Engineering}, author = {Schmidt, D. C.}, date = {2006-02}, journaltitle = {Computer}, volume = {39}, number = {2}, pages = {25--31}, issn = {1558-0814}, doi = {10.1109/MC.2006.58}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Wed, 12 Aug 2020 10:30:10 +0200} } @article{meadHalfCenturySoftware2018, title = {Half a {{Century}} of {{Software Engineering Education}}: {{The CMU Exemplar}}}, shorttitle = {Half a {{Century}} of {{Software Engineering Education}}}, author = {Mead, N. R. and Garlan, D. and Shaw, M.}, date = {2018-09}, journaltitle = {IEEE Softw.}, volume = {35}, number = {5}, pages = {25--31}, issn = {0740-7459}, doi = {10.1109/MS.2018.290110743}, abstract = {From the aspirational title of the 1968 NATO conference, software engineering has evolved to a well-defined engineering discipline with strong educational underpinnings. The supporting educational foundation has grown from a few courses in programming languages and data structures, evolving through structured programming, correctness formalisms, and state machine abstractions to full curricula and degree programs. With this context in mind, the authors discuss the evolution of software engineering education and pedagogy, software engineering principles, and future needs, drawing specifically on their experience at Carnegie Mellon University. Reflecting on the software development profession today, they believe that formal software engineering education is needed at least as much as it was in earlier decades. However, it must address the increasing diversity of the developer community, and it must be an education based on the enduring principles that will last a lifetime. This article is part of a theme issue on software engineering’s 50th anniversary.}, keywords = {software engineering}, note = {TL;DR \par Reflecting on the software development profession today, the authors believe that formal software engineering education is needed at least as much as it was in earlier decades, but it must address the increasing diversity of the developer community, and it must be an education based on the enduring principles that will last a lifetime.} } @online{MeasuringFidelityPhysical, title = {Measuring the Fidelity of a Physical and a Digital Twin Using Trace Alignments}, url = {https://www.computer.org/csdl/journal/ts/2024/12/10682975/20lmmhCIqKk}, langid = {english}, keywords = {source\_mobile} } @online{MechanismDesignLarge, title = {Mechanism Design for Large Language Models}, url = {https://research.google/blog/mechanism-design-for-large-language-models/}, urldate = {2025-05-05}, langid = {english} } @article{mehrabiSurveyBiasFairness2022, title = {A {{Survey}} on {{Bias}} and {{Fairness}} in {{Machine Learning}}}, author = {Mehrabi, Ninareh and Morstatter, Fred and Saxena, Nripsuta and Lerman, Kristina and Galstyan, Aram}, date = {2022-07-31}, journaltitle = {ACM Comput. Surv.}, volume = {54}, number = {6}, pages = {1--35}, issn = {0360-0300, 1557-7341}, doi = {10.1145/3457607}, abstract = {With the widespread use of artificial intelligence (AI) systems and applications in our everyday lives, accounting for fairness has gained significant importance in designing and engineering of such systems. AI systems can be used in many sensitive environments to make important and life-changing decisions; thus, it is crucial to ensure that these decisions do not reflect discriminatory behavior toward certain groups or populations. More recently some work has been developed in traditional machine learning and deep learning that address such challenges in different subdomains. With the commercialization of these systems, researchers are becoming more aware of the biases that these applications can contain and are attempting to address them. In this survey, we investigated different real-world applications that have shown biases in various ways, and we listed different sources of biases that can affect AI applications. We then created a taxonomy for fairness definitions that machine learning researchers have defined to avoid the existing bias in AI systems. In addition to that, we examined different domains and subdomains in AI showing what researchers have observed with regard to unfair outcomes in the state-of-the-art methods and ways they have tried to address them. There are still many future directions and solutions that can be taken to mitigate the problem of bias in AI systems. We are hoping that this survey will motivate researchers to tackle these issues in the near future by observing existing work in their respective fields.}, langid = {english}, keywords = {LOGSEQ}, note = {TL;DR \par This survey investigated different real-world applications that have shown biases in various ways, and created a taxonomy for fairness definitions that machine learning researchers have defined to avoid the existing bias in AI systems.} } @article{meijerCorelationalModelData2011, title = {A Co-Relational Model of Data for Large Shared Data Banks}, author = {Meijer, Erik and Bierman, Gavin}, date = {2011-04-01}, journaltitle = {Commun. ACM}, volume = {54}, number = {4}, pages = {49}, issn = {00010782}, doi = {10.1145/1924421.1924436}, langid = {english}, note = {TL;DR \par A model based on n-ary relations, a normal form for data base relations, and the concept of a universal data sublanguage are introduced and certain operations on relations are discussed and applied to the problems of redundancy and consistency in the user's model.} } @inproceedings{Mekala2020410, title = {Metamorphic Filtering of Black-Box Adversarial Attacks on Multi-Network Face Recognition Models}, author = {Mekala, R.R. and Porter, A. and Lindvall, M.}, date = {2020}, series = {Proceedings - 2020 {{IEEE}}/{{ACM}} 42nd {{International Conference}} on {{Software Engineering Workshops}}, {{ICSEW}} 2020}, pages = {410--417}, publisher = {Association for Computing Machinery, Inc}, doi = {10.1145/3387940.3391483}, abstract = {Adversarial examples pose a serious threat to the robustness of machine learning models in general and of deep learning models in particular. These carefully designed perturbations of input images can cause targeted misclassifications to a label of the attacker's choice, without being detectable to the naked eye. A particular class of adversarial attacks called black box attacks can be used to fool a target model despite not having access to the model parameters or to the input data used to train the model. In this paper, we first build a black box attack against robust multi-model face recognition pipelines and then test it against Google's FaceNet. We then present a novel metamorphic defense pipeline relying on nonlinear image transformations to detect adversarial attacks with a high degree of accuracy. We further use the results to create probabilistic metamorphic relations that define efficient decision boundaries between the safe and adversarial examples; achieving adversarial classification accuracy of up to 96\%. © 2020 ACM.}, author_keywords = {adversarial attacks; computer vision; deep learning; machine learning}, document_type = {Conference Paper}, isbn = {978-1-4503-7963-2}, source = {Scopus}, keywords = {Adversarial classifications,Decision boundary,Deep learning,Face recognition,High degree of accuracy,Image transformations,Machine learning models,Mathematical transformations,Metamorphic relations,Misclassifications,Pipelines,Recognition models,Software engineering,Technical presentations}, note = {cited By 2 \par TL;DR \par This paper builds a black box attack against robust multi-model face recognition pipelines and then presents a novel metamorphic defense pipeline relying on nonlinear image transformations to detect adversarial attacks with a high degree of accuracy.} } @article{melanconReusableSemanticsImplementation, title = {Reusable Semantics for Implementation of Python Optimizing Compilers}, author = {Melançon, Olivier}, langid = {french}, keywords = {LOGSEQ} } @inproceedings{meloContextAugmentedSoftwareDevelopment2019, title = {Context-{{Augmented Software Development}} in {{Traditional}} and {{Big Data Projects}}: {{Literature Review}} and {{Preliminary Framework}}}, shorttitle = {Context-{{Augmented Software Development}} in {{Traditional}} and {{Big Data Projects}}}, booktitle = {2019 {{IEEE Int}}. {{Conf}}. {{Big Data Big Data}}}, author = {Melo, Glaucia and Alencar, Paulo and Cowan, Don}, date = {2019-12}, pages = {3449--3457}, publisher = {IEEE}, location = {Los Angeles, CA, USA}, doi = {10.1109/BigData47090.2019.9006245}, eventtitle = {2019 {{IEEE International Conference}} on {{Big Data}} ({{Big Data}})}, isbn = {978-1-7281-0858-2}, note = {TL;DR \par It is believed supporting contextual knowledge through its representation and mining for recommendation and real-time provision can significantly improve traditional and big data software project development.} } @incollection{melvilleRecommenderSystems2010, ids = {melville_recommender_2010}, title = {Recommender Systems.}, booktitle = {Encyclopedia of Machine Learning}, author = {Melville, Prem and Sindhwani, Vikas}, editor = {Sammut, Claude and Webb, Geoffrey I.}, date = {2010}, pages = {829--838}, publisher = {Springer}, url = {http://dblp.uni-trier.de/db/reference/ml/ml2010.html#MelvilleS10}, added-at = {2011-11-25T00:00:00.000+0100}, biburl = {https://www.bibsonomy.org/bibtex/226b0f0d297e0d3302c28cbf58efad665/dblp}, ee = {http://dx.doi.org/10.1007/978-0-387-30164-8₇05}, interhash = {50b623dcfae0cd344f966cb9e9d7b9c0}, intrahash = {26b0f0d297e0d3302c28cbf58efad665}, isbn = {978-0-387-30768-8}, keywords = {dblp}, note = {TL;DR \par An overview of recommender system, types of RS, problems in RS and future scope of RS are presented, and the future of Recommender System is envisions which may open up new research directions in this domain.}, timestamp = {2011-11-26T11:38:23.000+0100} } @article{mendoncaDevelopingSelfAdaptiveMicroservice2021, title = {Developing {{Self-Adaptive Microservice Systems}}: {{Challenges}} and {{Directions}}}, shorttitle = {Developing {{Self-Adaptive Microservice Systems}}}, author = {Mendonca, Nabor C. and Jamshidi, Pooyan and Garlan, David and Pahl, Claus}, date = {2021-03}, journaltitle = {IEEE Softw.}, volume = {38}, number = {2}, pages = {70--79}, issn = {0740-7459, 1937-4194}, doi = {10.1109/MS.2019.2955937}, langid = {english}, keywords = {LOGSEQ}, note = {TL;DR \par Key challenges for the development of microservice applications as self-adaptive systems are identified, using a cloud-based intelligent video-surveillance application as a motivating example and potential new directions for addressing most of the identified challenges are suggested.} } @inproceedings{Meng2019, title = {Power Allocation in Multi-User Cellular Networks with Deep {{Q}} Learning Approach}, author = {Meng, F. and Chen, P. and Wu, L.}, date = {2019}, series = {{{IEEE International Conference}} on {{Communications}}}, volume = {2019-May}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {15503607}, doi = {10.1109/ICC.2019.8761431}, abstract = {The model-driven power allocation (PA) algorithms in the wireless cellular networks with interfering multiple-access channel (IMAC) have been investigated for decades. Nowadays, the data-driven model-free machine learning-based approaches are rapidly developing in this field, and among them the deep reinforcement learning (DRL) is proved to be of great potential. Different from supervised learning, the DRL takes advantages of exploration and exploitation to maximize the objective function under certain constraints. In our paper, we propose a two-step training framework. First, with the off-line learning in simulated environment, a deep Q network (DQN) is trained with deep Q learning (DQL) algorithm, which is well-designed to be in consistent with this PA issue. Second, the DQN will be further fine-tuned with real data in on-line training procedure. The simulation results show that the proposed DQN achieves the highest averaged sum-rate, comparing to the ones with present standard DQL training. With different user densities, our DQN outperforms benchmark algorithms and thus a good generalization ability is verified. © 2019 IEEE.}, art_number = {8761431}, document_type = {Conference Paper}, isbn = {978-1-5386-8088-9}, source = {Scopus}, note = {cited By 33 \par TL;DR \par A deep Q network (DQN) is trained with deep Q learning (DQL) algorithm, which is well-designed to be in consistent with this PA issue, and results show that the proposed DQN achieves the highest averaged sum-rate, comparing to the ones with present standard DQL training.} } @inproceedings{Mens06, title = {On the Use of Graph Transformations for Model Refactoring}, booktitle = {Gener. {{Transform}}. {{Tech}}. {{Softw}}. {{Eng}}. {{Int}}. {{Summer Sch}}. {{GTTSE}} 2005 {{Braga Port}}. {{July}} 4-8 2005 {{Revis}}. {{Pap}}.}, author = {Mens, Tom}, editor = {Lämmel, Ralf and Saraiva, João and Visser, Joost}, date = {2005}, series = {Lecture Notes in Computer Science}, volume = {4143}, pages = {219--257}, publisher = {Springer}, doi = {10.1007/11877028\_7}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {TL;DR \par This work focuses on the activity of model refactoring, and shows how graph transformation theory can provide formal support for this activity, and how such support can be implemented in state-of-the-art graph transformation tools such as AGG and Fujaba.}, timestamp = {Sun, 26 Apr 2020 17:09:18 +0200} } @article{MenziesBCMLSTZ13, title = {Local versus Global Lessons for Defect Prediction and Effort Estimation}, author = {Menzies, Tim and Butcher, Andrew and Cok, David R. and Marcus, Andrian and Layman, Lucas and Shull, Forrest and Turhan, Burak and Zimmermann, Thomas}, date = {2013}, journaltitle = {IEEE Trans Softw. Eng}, volume = {39}, number = {6}, pages = {822--834}, note = {TL;DR \par It is concluded that when researchers attempt to draw lessons from some historical data source, they should ignore any existing local divisions into multiple sources, cluster across all available data, then restrict the learning of lessons to the clusters from other sources that are nearest to the test data.} } @article{menziesFiveLawsSE2020, ids = {menziesFiveLawsSE2020a,menziesFiveLawsSE2020b}, title = {The {{Five Laws}} of {{SE}} for {{AI}}}, author = {Menzies, Tim}, date = {2020-01}, journaltitle = {IEEE Softw.}, volume = {37}, number = {1}, pages = {81--85}, issn = {0740-7459, 1937-4194}, doi = {10.1109/MS.2019.2954841}, langid = {english}, keywords = {DONE}, note = {TL;DR \par It is time to talk about software engineering (SE) for artificial intelligence (AI) as industry is becoming increasingly dependent on AI software.} } @article{menziesShockinglySimpleKEYS2021, ids = {menziesShockinglySimpleKEYS2021a}, title = {Shockingly {{Simple}}:"{{KEYS}}" for {{Better AI}} for {{SE}}}, shorttitle = {Shockingly {{Simple}}}, author = {Menzies, Tim}, date = {2021-03}, journaltitle = {IEEE Softw.}, volume = {38}, number = {2}, pages = {114--118}, issn = {0740-7459, 1937-4194}, doi = {10.1109/MS.2020.3043014}, langid = {english}, note = {TL;DR \par As 2020 drew to a close, I was thinking about what lessons the authors have learned about software engineering (SE) for artificial intelligence (AI)-things that they can believe now but, in the last century, would have seemed somewhat shocking.} } @article{menziesSoftwareAnalyticsWhats2018, title = {Software {{Analytics}}: {{What}}’s {{Next}}?}, shorttitle = {Software {{Analytics}}}, author = {Menzies, T. and Zimmermann, T.}, date = {2018-09}, journaltitle = {IEEE Softw.}, volume = {35}, number = {5}, pages = {64--70}, issn = {0740-7459}, doi = {10.1109/MS.2018.290111035}, abstract = {Knowing what factors control software projects is very useful because humans might not understand those factors. Developers sometimes develop their own ideas about good and bad software, on the basis of just a few past projects. Using software analytics, we can correct those misconceptions. Software analytics lets software engineers learn about AI techniques. Once they learn those techniques, they can build and ship innovative AI tools. That is, software analytics is the training ground for the next generation of AI-literate software engineers. This article is part of a special issue on software engineering’s 50th anniversary.}, keywords = {artificial intelligence,software engineering}, note = {TL;DR \par Software analytics lets software engineers learn about AI techniques, and once they learn those techniques, they can build and ship innovative AI tools.} } @inproceedings{merilinnaStateArtPractice2006, title = {State of the Art and Practice of Opensource Component Integration}, booktitle = {Softw. {{Eng}}. {{Adv}}. {{Appl}}. 2006 {{SEAA06}} 32nd {{EUROMICRO Conf}}. {{On}}}, author = {Merilinna, Janne and Matinlassi, Mari}, date = {2006}, pages = {170--177}, publisher = {IEEE}, url = {http://ieeexplore.ieee.org/abstract/document/1690138/}, urldate = {2017-02-25}, note = {TL;DR \par The main observations are that the lack of documentation and heterogeneity of platforms are problems that neither the state of the art or practice could solve and both techniques and methods for predicting and solving both architecture and component level integration problems were not used in practice.} } @article{mernik2005when, title = {When and How to Develop Domain-Specific Languages}, author = {Mernik, Marjan and Heering, Jan and Sloane, Anthony M}, date = {2005}, journaltitle = {ACM Comput. Surv. CSUR}, volume = {37}, number = {4}, pages = {316--344}, publisher = {ACM} } @book{MessageRoSE20182018, ids = {MessageRoSE20182018a}, title = {Message from the {{RoSE}} 2018 {{Co-Organizers}}}, date = {2018}, journaltitle = {Proceedings - International Conference on Software Engineering}, volume = {137815}, publisher = {IEEE Computer Society} } @article{meyersGenericPlaceTransformationbased, title = {A Generic In-Place Transformation-Based Approach to Structured Model Co-Evolution}, author = {Meyers, Bart and Wimmer, Manuel and Cicchetti, Antonio and Sprinkle, Jonathan}, abstract = {In MDE not only models but also metamodels are subject to evolution. More specifically, they need to be adapted to correct errors, support new and/or update language features. The direct consequence of such evolutionary steps comprises the problem of managing the co-evolution of existing model instances, which may no longer conform to the new metamodel version. This model migration is intrinsically complex and results in a time-consuming and error-prone process if no adequate support is provided.}, langid = {english} } @book{meyerSoftwareEngineering2015, ids = {laser_software_2015}, title = {Software {{Engineering}}}, editor = {Meyer, Bertrand and Nordio, Martin}, date = {2015}, series = {Lecture {{Notes}} in {{Computer Science}}}, volume = {8987}, publisher = {Springer International Publishing}, location = {Cham}, doi = {10.1007/978-3-319-28406-4}, isbn = {978-3-319-28405-7 978-3-319-28406-4}, note = {OCLC: 944106474 \par OCLC: 944106474 \par TL;DR \par The need for automation of software development is discussed in the context of next-generation computing and the lag in the use of available tools is pointed out.} } @inproceedings{miEmpiricalCharacterizationIFTTT2017, title = {An Empirical Characterization of {{IFTTT}}: Ecosystem, Usage, and Performance}, shorttitle = {An Empirical Characterization of {{IFTTT}}}, booktitle = {Proc. 2017 {{Internet Meas}}. {{Conf}}. - {{IMC}} 17}, author = {Mi, Xianghang and Qian, Feng and Zhang, Ying and Wang, XiaoFeng}, date = {2017}, pages = {398--404}, publisher = {ACM Press}, location = {London, United Kingdom}, doi = {10.1145/3131365.3131369}, abstract = {IFTTT is a popular trigger-action programming platform whose applets can automate more than 400 services of IoT devices and web applications. We conduct an empirical study of IFTTT using a combined approach of analyzing data collected for 6 months and performing controlled experiments using a custom testbed. We profile the interactions among different entities, measure how applets are used by end users, and test the performance of applet execution. Overall we observe the fast growth of the IFTTT ecosystem and its increasing usage for automating IoT-related tasks, which correspond to 52\% of all services and 16\% of the applet usage. We also observe several performance inefficiencies and identify their causes.}, eventtitle = {The 2017 {{Internet Measurement Conference}}}, isbn = {978-1-4503-5118-8}, langid = {english} } @online{MigliorareConcentrazioneCombattere, title = {Migliorare Concentrazione e Combattere La Fatica Mentale Nella Scrittura Accademica}, url = {https://univaq-my.sharepoint.com/personal/davide_diruscio_univaq_it/Documents/Migliorare%20Concentrazione%20e%20Combattere%20la%20Fatica%20Mentale%20nella%20Scrittura%20Accademica.pdf}, urldate = {2025-04-22}, langid = {english} } @inproceedings{Mihalcea:2006:CKM:1597538.1597662, title = {Corpus-Based and Knowledge-Based Measures of Text Semantic Similarity}, booktitle = {Proc. 21st {{Natl}}. {{Conf}}. {{Artif}}. {{Intell}}. - {{Vol}}. 1}, author = {Mihalcea, Rada and Corley, Courtney and Strapparava, Carlo}, date = {2006}, series = {{{AAAI}}'06}, pages = {775--780}, publisher = {AAAI Press}, location = {Boston, Massachusetts}, url = {http://dl.acm.org/citation.cfm?id=1597538.1597662}, acmid = {1597662}, isbn = {978-1-57735-281-5}, numpages = {6} } @article{millerWordNetLexicalDatabase1995, title = {{{WordNet}}: {{A}} Lexical Database for English}, author = {Miller, George A.}, date = {1995-11}, journaltitle = {Commun. ACM}, volume = {38}, number = {11}, pages = {39--41}, publisher = {ACM}, address = {New York, NY, USA}, issn = {0001-0782}, url = {http://doi.acm.org/10.1145/219717.219748}, acmid = {219748}, issue_date = {Nov. 1995}, nodoi = {10.1145/219717.219748}, numpages = {3}, note = {TL;DR \par WordNet1 provides a more effective combination of traditional lexicographic information and modern computing, and is an online lexical database designed for use under program control.} } @article{minoliBlockchainMechanismsIoT2018, title = {Blockchain Mechanisms for {{IoT}} Security}, author = {Minoli, Daniel and Occhiogrosso, Benedict}, date = {2018-09}, journaltitle = {Internet Things}, volume = {1--2}, pages = {1--13}, issn = {25426605}, doi = {10.1016/j.iot.2018.05.002}, abstract = {The deployment of Internet of Things (IoT) results in an enlarged attack surface that requires end-to-end security mitigation. IoT applications range from mission-critical predicaments (e.g., Smart Grid, Intelligent Transportation Systems, video surveillance, e-health) to business-oriented applications (e.g., banking, logistics, insurance, and contract law). There is a need for comprehensive support of security in the IoT, especially for mission-critical applications, but also for the down-stream business applications. A number of security techniques and approaches have been proposed and/or utilized. Blockchain mechanisms (BCMs) play a role in securing many IoT-oriented applications by becoming part of a security mosaic, in the context of a defenses-in-depth/Castle Approach. A blockchain is a database that stores all processed transactions – or data – in chronological order, in a set of computer memories that are tamperproof to adversaries. These transactions are then shared by all participating users. Information is stored and/or published as a public ledger that is infeasible to modify; every user or node in the system retains the same ledger as all other users or nodes in the network. This paper highlights some IoT environments where BCMs play an important role, while at the same time pointing out that BCMs are only part of the IoT Security (IoTSec) solution.}, langid = {english} } @article{miorandiInternetThingsVision2012, title = {Internet of Things: {{Vision}}, Applications and Research Challenges}, shorttitle = {Internet of Things}, author = {Miorandi, Daniele and Sicari, Sabrina and De Pellegrini, Francesco and Chlamtac, Imrich}, date = {2012-09}, journaltitle = {Ad Hoc Networks}, volume = {10}, number = {7}, pages = {1497--1516}, issn = {15708705}, doi = {10.1016/j.adhoc.2012.02.016}, abstract = {The term ‘‘Internet-of-Things’’ is used as an umbrella keyword for covering various aspects related to the extension of the Internet and the Web into the physical realm, by means of the widespread deployment of spatially distributed devices with embedded identification, sensing and/or actuation capabilities. Internet-of-Things envisions a future in which digital and physical entities can be linked, by means of appropriate information and communication technologies, to enable a whole new class of applications and services. In this article, we present a survey of technologies, applications and research challenges for Internetof-Things.}, langid = {english}, keywords = {relevant} } @inproceedings{miorNoSESchemaDesign2016, title = {{{NoSE}}: {{Schema}} Design for {{NoSQL}} Applications}, shorttitle = {{{NoSE}}}, booktitle = {2016 {{IEEE}} 32nd {{Int}}. {{Conf}}. {{Data Eng}}. {{ICDE}}}, author = {Mior, Michael J. and Salem, Kenneth and Aboulnaga, Ashraf and Liu, Rui}, date = {2016-05}, pages = {181--192}, publisher = {IEEE}, location = {Helsinki, Finland}, doi = {10.1109/ICDE.2016.7498239}, eventtitle = {2016 {{IEEE}} 32nd {{International Conference}} on {{Data Engineering}} ({{ICDE}})}, isbn = {978-1-5090-2020-1}, note = {TL;DR \par This work presents a system for recommending database schemas for NoSQL applications that is able to capture rules of thumb used by expert designers without explicitly encoding the rules, and implemented a prototype of this approach for the Cassandra extensible record store.} } @inproceedings{Miranda:2008:ICF:1486927.1487083, title = {Incremental Collaborative Filtering for Binary Ratings}, booktitle = {Proc. 2008 {{IEEEWICACM Int}}. {{Conf}}. {{Web Intell}}. {{Intell}}. {{Agent Technol}}. - {{Vol}}. 01}, author = {Miranda, Catarina and Jorge, Alípio M.}, date = {2008}, series = {{{WI-IAT}} '08}, pages = {389--392}, publisher = {IEEE Computer Society}, location = {Washington, DC, USA}, url = {http://dx.doi.org/10.1109/WIIAT.2008.263}, acmid = {1487083}, isbn = {978-0-7695-3496-1}, nodoi = {10.1109/WIIAT.2008.263}, numpages = {4}, keywords = {Incremental Collaborative Filtering,Web Recommender Systems}, note = {TL;DR \par An incremental version of item-based CF for implicit binary ratings is proposed, and it is observed that recall and precision tend to improve when the authors continuously add information to the recommender model, and that the time spent for recommendation does not degrade.} } @online{MiSE2017ProgramPreparatory, title = {{{MiSE2017}} - {{Program}} and Preparatory Emails}, url = {https://docs.google.com/document/d/1FVBtlKZzdkNVYqea-9nB2YCpicTohzntjEcx2ZKcqjA/edit?usp=sharing&usp=embed_facebook}, urldate = {2017-05-08}, abstract = {Program 1 Registrants (33) 3 Preparatory emails 5 To presenters 6 To attendees 6 Program Sunday 21 May 2017 09:00 - 09:05 Welcome from the organizers 09:05 - 10:30 Keynote 1: Empirical Studies into UML in Practice: Pitfalls and Prospects, Michel Chaudron [abstract] [Session chair: Davide] ...}, organization = {Google Docs} } @inproceedings{misraSoftwareClusteringUnifying2012, title = {Software Clustering: {{Unifying}} Syntactic and Semantic Features}, booktitle = {2012 19th {{Work}}. {{Conf}}. {{Reverse Eng}}.}, author = {Misra, J. and Annervaz, K. M. and Kaulgud, V. and Sengupta, S. and Titus, G.}, date = {2012-10}, pages = {113--122}, issn = {2375-5369}, doi = {10.1109/WCRE.2012.21}, keywords = {application portfolios,architectural recovery,automated component labeling,cluster selection,component discovery,distance estimation,feature extraction,feature location problem,graph theory,high level component architecture extraction,information quality enhancement,inter-component interaction generation,latent semantic indexing,lexical analysis,multiobjective global modularity criterion,multiple hierarchical levels,noise reduction,object-oriented programming,pattern clustering,program comprehension,program diagnostics,Reverse engineering,semantic features,software architecture,software clustering,source code elements,syntactic features,vector space model,weighted graph partitioning} } @online{MisurazioneDiGas, title = {Misurazione Di Gas o Di Ulteriore Protocollo Di Studio {{Vaillant}} e Di E-{{Bus}} Di Controllo / {{Riscaldamento}} / {{Casa}} Intelligente 1-Wire Con Le Proprie Mani / Ab-Log.Ru}, url = {http://www.ab-log.ru/smart-house/heating-automation/gaz_meter}, urldate = {2015-04-04} } @book{mitchellConceptsProgrammingLanguages2003, title = {Concepts in Programming Languages}, author = {Mitchell, John C.}, date = {2003}, publisher = {Cambridge University Press}, location = {Cambridge, UK ; New York}, isbn = {978-0-521-78098-8}, langid = {english}, pagetotal = {529}, keywords = {Programming languages (Electronic computers)} } @article{mitchellFAIRDataPipeline2021, title = {{{FAIR Data Pipeline}}: Provenance-Driven Data Management for Traceable Scientific Workflows}, shorttitle = {{{FAIR Data Pipeline}}}, author = {Mitchell, Sonia Natalie and Lahiff, Andrew and Cummings, Nathan and Hollocombe, Jonathan and Boskamp, Bram and Reddyhoff, Dennis and Field, Ryan and Zarebski, Kristian and Wilson, Antony and Burke, Martin and Archibald, Blair and Bessell, Paul and Blackwell, Richard and Boden, Lisa A. and Brett, Alys and Brett, Sam and Dundas, Ruth and Enright, Jessica and Gonzalez-Beltran, Alejandra N. and Harris, Claire and Hinder, Ian and Hughes, Christopher David and Knight, Martin and Mano, Vino and McMonagle, Ciaran and Mellor, Dominic and Mohr, Sibylle and Marion, Glenn and Matthews, Louise and McKendrick, Iain J. and Pooley, Christopher Mark and Porphyre, Thibaud and Reeves, Aaron and Townsend, Edward and Turner, Robert and Walton, Jeremy and Reeve, Richard}, date = {2021-10-13}, journaltitle = {ArXiv211007117 Cs Q-Bio}, eprint = {2110.07117}, eprinttype = {arXiv}, eprintclass = {cs, q-bio}, url = {http://arxiv.org/abs/2110.07117}, urldate = {2022-02-24}, abstract = {Modern epidemiological analyses to understand and combat the spread of disease depend critically on access to, and use of, data. Rapidly evolving data, such as data streams changing during a disease outbreak, are particularly challenging. Data management is further complicated by data being imprecisely identified when used. Public trust in policy decisions resulting from such analyses is easily damaged and is often low, with cynicism arising where claims of "following the science" are made without accompanying evidence. Tracing the provenance of such decisions back through open software to primary data would clarify this evidence, enhancing the transparency of the decision-making process. Here, we demonstrate a Findable, Accessible, Interoperable and Reusable (FAIR) data pipeline developed during the COVID-19 pandemic that allows easy annotation of data as they are consumed by analyses, while tracing the provenance of scientific outputs back through the analytical source code to data sources. Such a tool provides a mechanism for the public, and fellow scientists, to better assess the trust that should be placed in scientific evidence, while allowing scientists to support policy-makers in openly justifying their decisions. We believe that tools such as this should be promoted for use across all areas of policy-facing research.}, keywords = {Computer Science - Digital Libraries,Quantitative Biology - Quantitative Methods} } @article{mittelmannPersonalKnowledgeManagement2016, title = {Personal {{Knowledge Management}} as {{Basis}} for {{Successful Organizational Knowledge Management}} in the {{Digital Age}}}, author = {Mittelmann, Angelika}, date = {2016}, journaltitle = {Procedia Computer Science}, volume = {99}, pages = {117--124}, issn = {18770509}, doi = {10.1016/j.procs.2016.09.105}, langid = {english} } @article{mitwalliStreamliningEngineeringDistribution, title = {Streamlining the {{Engineering}} and {{Distribution}} of {{Graphical DSLs}} Based on {{Recurring Patterns}}}, author = {Mitwalli, Daniel Sami and Krumrey, Marco and Teumert, Sebastian}, abstract = {In the context of general purpose LCNC platforms, where the visual modeling languages can be defined possibly easily and supported in a possibly systematic and uniform fashion, we examine the challenges of creating and distributing graphical Domain-Specific Languages (DSLs) and identify key patterns within the process that form the basis of Cinco Cloud, our cloud based LCNC platform. First, we outline essential functionalities for Integrated Modeling Environments (IMEs) for graphical DSLs, most of which can be derived from the highlevel DSL specifications. This recurring task can be simplified by automatically generating or configuring IMEs from these specifications, or by programmatically extending them with additional semantics. Second, we explores how an integrated language repository within the Cinco Cloud architecture can facilitate the distribution and reuse of DSLs, while streamlining the overall language engineering process. Architectural concerns are in fact at the core of the tension between uniformity and specialization. Finally, we discuss future opportunities for enhancing language versioning, improving discoverability, and integrating AI tools to support the design and use of DSLs.}, langid = {english}, keywords = {LOGSEQ}, note = {\section{Annotazioni\\ (20/1/2025, 17:50:34)} \par - “Abstract—In” (Mitwalli et al., p. 4) \#ffd400\\ \mkbibemph{The abstract is not effective in presenting what are the problems in the focus of the paper in making a clear sttement of what is the proposed solution of the work. } \par - “LCNC” (Mitwalli et al., p. 4) \#ffd400\\ \mkbibemph{This is the first occurrence of this acronym and as a such it is necessary to put it here in the extended manner. } \par - “possibly” (Mitwalli et al., p. 4) \#ffd400\\ \mkbibemph{ } \par - “key patterns” (Mitwalli et al., p. 4) \#ffd400\\ \mkbibemph{what kind of patterns are you looking for? Patterns of what? } \par - “project 1” (Mitwalli et al., p. 4) \#ff6666\\ \mkbibemph{ } \par - “In that sense, we will demonstrate the potential of Cinco Cloud in streamlining language engineering processes and discuss the future development of such repositories in the language engineering landscape.” (Mitwalli et al., p. 4) \#5fb236\\ \mkbibemph{ } \par - “Several metamodeling tools address this problem by generating dedicated IMEs from a high-level specification that are then tailored to a specific set of graphical DSLs such as Eclipse Sirius Web [2] 2, WebGME [10] 3 and others [11, 1, 9, 7].” (Mitwalli et al., p. 5) \#ffd400\\ \mkbibemph{ } \par - “[2] 2” (Mitwalli et al., p. 5) \#ff6666\\ \mkbibemph{ } \par - “There, language engineers can specify graphical languages using two high-level textual specification languages:” (Mitwalli et al., p. 5) \#ffd400\\ \mkbibemph{The novelty is not clarified. } \par - “Meta Graph Language (MGL)” (Mitwalli et al., p. 5) \#2ea8e5\\ \mkbibemph{ } \par - “Meta Style Language (MSL)” (Mitwalli et al., p. 5) \#2ea8e5\\ \mkbibemph{ } \par - “TypeScript” (Mitwalli et al., p. 5) \#5fb236\\ \mkbibemph{ } \par - “By abstracting the complexities of IME development, Cinco Cloud allows users to focus on designing DSLs and deploying intuitive graphical modeling tools.” (Mitwalli et al., p. 5) \#ffd400\\ \mkbibemph{Still not clear what are the limitations of existing approaches that authors aim at addressing, and thus what are the novelties of Cinco Cloud concerning the development of domain-specific modeling languages. } \par - “uploading the associated language to a central language repository.” (Mitwalli et al., p. 5) \#ffd400\\ \mkbibemph{THis is potentially interesting even though it is not clear what are the provided reusability mechanisms. } \par - “A domain expert simply selects the language through a form and a dedicated IME is automatically deployed in the Cinco Cloud, ready to use. Under the hood, a general purpose IME is deployed, the selected language(s) fetched from the central language repository into the IME, and thereby the IME is configured for the language(s).” (Mitwalli et al., p. 5) \#ffd400\\ \mkbibemph{What's new with what MetaEdit++ or other modeling platforms already provide? } \par - “These are provided automatically by Cinco Cloud 4” (Mitwalli et al., p. 5) \#a28ae5\\ \mkbibemph{ } \par - “abstract and concrete syntax of a graphical DSL” (Mitwalli et al., p. 5) \#5fb236\\ \mkbibemph{ } \par - “Eclipse Layout Kernel (ELK)” (Mitwalli et al., p. 6) \#a28ae5\\ \mkbibemph{ } \par - “. Dyamic Components and Functionalities” (Mitwalli et al., p. 6) \#ffd400\\ \mkbibemph{Over the last few years there has been a proliferation of low-code platforms that permit to develop applications by reusing components that embody recurring behaviours. It is necessary to compare the propose approach with such a line of research. } \par - “CertBot 8” (Mitwalli et al., p. 7) \#ffd400\\ \mkbibemph{ } \par - “Langium-based 11 language server” (Mitwalli et al., p. 7) \#ffd400\\ \mkbibemph{ } \par - “easing the distribution and composition of languages, both from language-designer to language-designer, as well as from language-designer to domain-expert (a holistic approach)” (Mitwalli et al., p. 8) \#ffd400\\ \mkbibemph{This concept of language composition is not clear. It is relevant, but it is not clear how the proposed approach has addressed such a problem. } \par - “Sprotty Client 13 and GLSP server” (Mitwalli et al., p. 9) \#a28ae5\\ \mkbibemph{ } \par - “As mentioned before, our language repository currently only supports a single version of each language.” (Mitwalli et al., p. 10) \#5fb236\\ \mkbibemph{ } \par - “Although the current state of the central language repository is an independent microservice, it is not yet completely independent from the overall Cinco Cloud archetecture.” (Mitwalli et al., p. 10) \#5fb236\\ \mkbibemph{ } \par - “archetecture” (Mitwalli et al., p. 10) \#ff6666\\ \mkbibemph{ } \par - “an independently deployable service that can be integrated with other cloud-based archetectures for LCNC platforms, including a generalized API.” (Mitwalli et al., p. 10) \#ffd400\\ \mkbibemph{I'm not sure I got the plan of the authors. The repository is supposed to store languages developed with the Cinco Cloud and to enable their reuse. What does it mean enabling the integration of the repository in other LCNC platforms? How to to ensure the their interoperability/compatibility? } \par - “common patterns” (Mitwalli et al., p. 10) \#ffd400\\ \mkbibemph{it is not clear in the paper what are the common patterns that have been identified with respect to already existing primitives that existing (meta)modeling platforms already implement. } \par - “recurring patterns” (Mitwalli et al., p. 10) \#ffd400\\ \mkbibemph{if the authors are referring to the content given in Section III, it is not clear what's their novelty. } \par - “the externalization of a centralized language repository.” (Mitwalli et al., p. 10) \#ffd400\\ \mkbibemph{I see this as potential novelty of the proposed approach and as a such I would restructure the paper and rebalance the content and focus in this direction, instead of pushing the recuccring patterns concepts, that instead are recurring functionalities that are expected by any modeling environments nowadays. }} } @inproceedings{MMR02, title = {Similarity Flooding: A Versatile Graph Matching Algorithm and Its Application to Schema Matching}, booktitle = {Proc. 18th {{Int}}. {{Conf}}. {{Data Eng}}. 2002}, author = {Melnik, S. and Garcia-Molina, H. and Rahm, E.}, date = {2002}, pages = {117--128}, issn = {1063-6382}, doi = {10.1109/ICDE.2002.994702}, keywords = {accuracy metric,biochemical applications,Biochemistry,Bioinformatics,Catalogs,data handling,data schemas,data structures,Data structures,data warehouses,data warehousing,e-business,filters,fixpoint computation,Floods,graph matching algorithm,high-level operators,Humans,Information management,information models,mappings,Matched filters,pattern matching,schema matching,similarity flooding,Testing,user labor savings,Warehousing}, note = {TL;DR \par This paper presents a matching algorithm based on a fixpoint computation that is usable across different scenarios and conducts a user study, in which the accuracy metric was used to estimate the labor savings that the users could obtain by utilizing the algorithm to obtain an initial matching.} } @incollection{mmsim2, title = {Metamodel Matching for Automatic Model Transformation Generation}, booktitle = {Model Driven Engineering Languages and Systems}, author = {Falleri, Jean-Rémy and Huchard, Marianne and Lafourcade, Mathieu and Nebut, Clémentine}, editor = {Czarnecki, Krzysztof and Ober, Ileana and Bruel, Jean-Michel and Uhl, Axel and Völter, Markus}, date = {2008}, series = {Lecture Notes in Computer Science}, volume = {5301}, pages = {326--340}, publisher = {Springer Berlin Heidelberg}, doi = {10.1007/978-3-540-87875-9_24}, isbn = {978-3-540-87874-2}, note = {TL;DR \par This paper proposes an approach that automatically detects mappings between two metamodels and uses them to generate an alignment between those metAModels, built on the Similarity Flooding algorithm used in the fields of schema matching and ontology alignment.} } @article{mobasherAttacksRemediesCollaborative2007, ids = {4216981,mobasher_attacks_2007}, title = {Attacks and {{Remedies}} in {{Collaborative Recommendation}}}, author = {Mobasher, Bamshad and Burke, Robin and Bhaumik, Runa and Sandvig, J.J.}, date = {2007-05}, journaltitle = {IEEE Intell. Syst.}, volume = {22}, number = {3}, pages = {56--63}, issn = {1541-1672}, doi = {10.1109/MIS.2007.45}, langid = {english} } @online{MobileAutonomousSystems, title = {Mobile {{Autonomous Systems Laboratory}} | {{Electrical Engineering}} and {{Computer Science}} | {{MIT OpenCourseWare}}}, url = {http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-186-mobile-autonomous-systems-laboratory-january-iap-2005/index.htm}, urldate = {2016-01-23} } @article{mocriiIoTbasedSmartHomes2018, title = {{{IoT-based}} Smart Homes: {{A}} Review of System Architecture, Software, Communications, Privacy and Security}, shorttitle = {{{IoT-based}} Smart Homes}, author = {Mocrii, Dragos and Chen, Yuxiang and Musilek, Petr}, date = {2018-09}, journaltitle = {Internet Things}, volume = {1--2}, pages = {81--98}, issn = {25426605}, doi = {10.1016/j.iot.2018.08.009}, abstract = {This article presents a review of major technologies of IoT-based smart homes. It starts with definition of the smart home that sets the perspective adopted in the review. In addition to describing the complementary user and system functions of the smart home, it introduces its general, IoT-based architecture and sets smart homes within the larger context of the smart grid. The following sections concentrate on software solutions and components of smart home management systems, related communication technologies, and issues of privacy and security associated with the connected nature of modern smart homes. A separate section presents current challenges of smart home technologies and their dispersion, and points to some interesting solutions and future trends.}, langid = {english} } @online{ModeldrivenEngineeringScientific, title = {Model-Driven {{Engineering}} of {{Scientific Applications}} - {{Mohamed Almorsy Abdelrazek}}}, url = {https://sites.google.com/site/mohamedalmorsy/home/research/model-driven-engineering-of-scientific-applications}, urldate = {2017-02-23} } @online{ModelDrivenSustainabilityEvaluation2020, title = {Toward {{Model-Driven Sustainability Evaluation}} – {{Communications}} of the {{ACM}}}, date = {2020-03-01}, url = {https://cacm.acm.org/research/toward-model-driven-sustainability-evaluation/}, urldate = {2024-04-02}, langid = {american} } @misc{MODELS_2025_paper_100, title = {{{MODELS}}\_2025\_paper\_100}, langid = {english}, keywords = {LOGSEQ}, note = {I'm reviewing a research paper and I took the following notes: \par \section{Annotazioni\\ (22/5/2025, 11:21:13)} \par - “Integrating AI Development Process” (“MODELS\_2025\_paper\_100”, p. 1) \#5fb236 \par - “current practices often separate the development of ML and non-ML components, resulting in fragmented process development.” (“MODELS\_2025\_paper\_100”, p. 1) \#5fb236 \par - “establishment of effective feedback loops” (“MODELS\_2025\_paper\_100”, p. 1) \#5fb236 \par - “The framework aims to improve the management of AI system development by providing a unified and formalized representation of its constituent processes.” (“MODELS\_2025\_paper\_100”, p. 1) \#a28ae5 \par - “We demonstrate the feasibility of our framework through a case study, illustrating how it facilitates more systematic, transparent, and traceable process management in AI system development.” (“MODELS\_2025\_paper\_100”, p. 1) \#a28ae5 \par - “The integration of ML into software products introduces new software engineering (SE) challenges and intensifies existing ones” (“MODELS\_2025\_paper\_100”, p. 1) \#e56eee \par - “The SE challenges introduced by ML integration largely stem from the need to coordinate multiple engineering pipelines, including system and software engineering, data engineering, and ML training” (“MODELS\_2025\_paper\_100”, p. 1) \#e56eee \par - “However, integrating such heterogeneous artifacts into a cohesive development process remains inherently challenging” (“MODELS\_2025\_paper\_100”, p. 1) \#5fb236 \par - “These integration challenges are further exacerbated by current AI development practices, which frequently treat the development of ML and non-ML components in isolation [6].” (“MODELS\_2025\_paper\_100”, p. 1) \#5fb236 \par - “In order to address the challenges stemming from the lack of co-development between ML and non-ML components, we introduce AIEngOrchestrator, a model-based framework that orchestrates AI system development through an artifactcentric lens.” (“MODELS\_2025\_paper\_100”, p. 1) \#ffd400\\ \mkbibemph{SO the focus of the orchestrator is the AI system development? Not the system being executed?} \par - “Central to the framework is a domain-specific modeling language (DSL), GSM4SE4AI, an artifact-centric process modeling language that enables precise modeling of both the structural and behavioral aspects of AI development artifacts.” (“MODELS\_2025\_paper\_100”, p. 1) \#a28ae5 \par - “it establishes a unified structure that makes relationships between artifacts clear and manageable.” (“MODELS\_2025\_paper\_100”, p. 1) \#ffd400\\ \mkbibemph{manageable for what?} \par - “This integration offers end-to-end traceability, concurrent change propagation within and across development pipelines, and effective change impact analysis. A case study is used to demonstrate the applicability of the proposed approach.” (“MODELS\_2025\_paper\_100”, p. 1) \#ffd400\\ \mkbibemph{What is the final goal of the approach?} \par - “key principles from Software Engineering for AI” (“MODELS\_2025\_paper\_100”, p. 1) \#5fb236 \par - “core concepts in process modeling” (“MODELS\_2025\_paper\_100”, p. 1) \#5fb236 \par - “These systems are typically characterized by their reliance on machine learning (ML) models or other AI techniques to achieve their functionality.” (“MODELS\_2025\_paper\_100”, p. 1) \#5fb236 \par - “ML Module: Thi” (“MODELS\_2025\_paper\_100”, p. 2) \#2ea8e5 \par - “classification, prediction, recommendation, or anomaly detection” (“MODELS\_2025\_paper\_100”, p. 2) \#5fb236 \par - “Non-ML Module:” (“MODELS\_2025\_paper\_100”, p. 2) \#2ea8e5 \par - “Software Engineering Pipeline is responsible for the specification, design, implementation, and testing of the non-ML module” (“MODELS\_2025\_paper\_100”, p. 2) \#5fb236 \par - “Data Engineering Pipeline focuses on the collection, curation, and preprocessing of data required for training machine learning models.” (“MODELS\_2025\_paper\_100”, p. 2) \#5fb236 \par - “ML Training Pipeline handles model selection, training, and validation.” (“MODELS\_2025\_paper\_100”, p. 2) \#5fb236 \par - “System Engineering Pipeline oversees the integration of all system components—both ML and non-ML—as well as their overall verification and validation” (“MODELS\_2025\_paper\_100”, p. 2) \#5fb236 \par - “Despite this interdependence, these pipelines are often developed in isolation with limited coordination” (“MODELS\_2025\_paper\_100”, p. 2) \#a28ae5 \par - “This fragmentation disrupts the overall development lifecycle, primarily by hindering traceability, which in turn leads to cascading errors, costly rework, and increased integration risks. These challenges highlight the urgent need for a unified development approach that ensures traceability across pipelines and promotes coordinated engineering efforts.” (“MODELS\_2025\_paper\_100”, p. 2) \#ffd400\\ \mkbibemph{Can you make some examples of problems due to the mentioned limited coordination? What are the currently available technologies that are exploited to mitigate such issues?} \par - “Process modeling refers to the construction of a structured representation of a process, designed to enhance its comprehension, analysis, and optimization [9]. This representation typically identifies the actions to be executed (activities), the roles responsible for executing them, and the corresponding inputs and outputs (artifacts).” (“MODELS\_2025\_paper\_100”, p. 2) \#5fb236 \par - “Rather than prescribing a strict sequence of tasks, activities are triggered dynamically in response to the presence or changes in data values” (“MODELS\_2025\_paper\_100”, p. 2) \#a28ae5 \par - “We selected GSM as the foundational modeling approach for our framework due to its strong alignment with the dynamic, data-centric nature of AI system development. Building on these foundations, our framework adopts GSM to model the AI development process declaratively, using data-driven conditions to govern execution, as detailed in the following sections.” (“MODELS\_2025\_paper\_100”, p. 2) \#a28ae5 \par - “This section establishes the modeling foundation of the AIEngOrchestrator framework by introducing a domain-specific modeling language, GSM4SE4AI (Guard-Stage-Milestone for Software Engineering for AI).” (“MODELS\_2025\_paper\_100”, p. 2) \#5fb236 \par - “GSM4SE4AI is to provide a structured and coordinated process modeling approach specifically tailored to the integrated development of AI systems” (“MODELS\_2025\_paper\_100”, p. 2) \#5fb236 \par - “this language facilitates comprehensive end-toend traceability across the entire AI development lifecycle.” (“MODELS\_2025\_paper\_100”, p. 2) \#5fb236 \par - “detailed exploration of GSM4SE4AI: (A) the abstract syntax; (B) the concrete syntax of the macro-level model, illustrating the high-level representation and relations between artifacts; and (C) the concrete syntax of the micro-level model, detailing the lifecycle and information modeling within individual artifacts.” (“MODELS\_2025\_paper\_100”, p. 2) \#5fb236 \par - “A. GSM4SE4AI Abstract Syntax” (“MODELS\_2025\_paper\_100”, p. 2) \#2ea8e5 \par - “AI Development Process” (“MODELS\_2025\_paper\_100”, p. 3) \#2ea8e5 \par - “(1) Guard:” (“MODELS\_2025\_paper\_100”, p. 3) \#5fb236 \par - “(2) Stage: a unit of work that is activated when its guard condition is satisfied.” (“MODELS\_2025\_paper\_100”, p. 3) \#5fb236 \par - “(3) Milestone: a set of business rules also following the ECA pattern, which determines when a stage should be completed.” (“MODELS\_2025\_paper\_100”, p. 3) \#5fb236 \par - “its concrete syntax, defined across two distinct levels of granularity” (“MODELS\_2025\_paper\_100”, p. 3) \#ffd400\\ \mkbibemph{Is this actually implemented? Is it tool supported?} \par - “the concrete syntax captures: (i) the structural relationships among multiple artifact types—referred to as the macro-level representation, and (ii) the internal behavior of individual artifacts—referred to as the micro-level representation.” (“MODELS\_2025\_paper\_100”, p. 3) \#5fb236 \par - “high-level traceability and impact analysis across development pipelines” (“MODELS\_2025\_paper\_100”, p. 3) \#a28ae5 \par - “tracking of specific data attributes within artifacts (e.g., learning rate used in a hyperparameter tuning artifact) enabling precise verification and runtime introspection” (“MODELS\_2025\_paper\_100”, p. 3) \#a28ae5 \par - “Concrete Syntax” (“MODELS\_2025\_paper\_100”, p. 3) \#ffd400\\ \mkbibemph{This is supposed to include actual modeling constructs that modelers can use to specify models conforming to the proposed metamodel. Actually by reading the descriptive text of the whole "Macro-Level Concrete Syntax" section, the abstraction level is still high and I don't see any modeling language or some constructs of it that can be used. The whole subsection is lenghty and not easy to grasp. At the end of the sub-section is not clear what's can be used out of the different presented concepts.} \par - “This subsection introduces the concrete syntax used to represent the macro-level model in GSM4SE4AI, with a focus on the definition of AI artifact and their relationships.” (“MODELS\_2025\_paper\_100”, p. 3) \#5fb236 \par - “GSM4SE4AI, the core artifacts—along with their relationships and the rationale behind their inclusion—are modeled to reflect the objective of constructing a Traceability Information Model (TIM) tailored specifically for AI system” (“MODELS\_2025\_paper\_100”, p. 3) \#5fb236 \par - “A core component of any software development process is the definition and application of a TIM. Such models provide guidance on which development artifacts should be created and maintained, as well as the relationships that need to be established among them.” (“MODELS\_2025\_paper\_100”, p. 3) \#5fb236 \par - “TIM is designed to ultimately support essential project analyses, including change impact assessment, consistency checking, and requirements validation” (“MODELS\_2025\_paper\_100”, p. 4) \#5fb236 \par - “To realize this TIM within the context of SE4AI, GSM4SE4AI introduces a set of traceable AI artifacts and their corresponding links (traceable elements), organized around four complementary traceability approaches. Each approach targets a distinct aspect of traceability, and the associated artifacts are modeled to reflect that objective.” (“MODELS\_2025\_paper\_100”, p. 4) \#5fb236 \par - “Approach I: Full-Scope Coverage of AI Development” (“MODELS\_2025\_paper\_100”, p. 4) \#2ea8e5 \par - “(MLA1) ML Requirement Specification (MLRS)” (“MODELS\_2025\_paper\_100”, p. 4) \#2ea8e5 \par - “(MLA2) Data Splitting Artifact” (“MODELS\_2025\_paper\_100”, p. 4) \#2ea8e5 \par - “(MLA3) Training Algorithm Artifact” (“MODELS\_2025\_paper\_100”, p. 4) \#2ea8e5 \par - “(MLA4) Hyperparameter Tuning Artifact” (“MODELS\_2025\_paper\_100”, p. 4) \#2ea8e5 \par - “(MLA5) ML Module Artifact” (“MODELS\_2025\_paper\_100”, p. 5) \#2ea8e5 \par - “(MLA6) ML Validation Artifact” (“MODELS\_2025\_paper\_100”, p. 5) \#2ea8e5 \par - “System Requirement Specification (SRS),” (“MODELS\_2025\_paper\_100”, p. 5) \#5fb236 \par - “This subsection introduces the concrete syntax used to represent the micro-level model, with a focus on the definition of AI artifact behaviors.” (“MODELS\_2025\_paper\_100”, p. 6) \#a28ae5 \par - “As the artifact progresses, subsequent Stages are triggered based on Guard conditions that evaluate the values of associated Data Attributes.” (“MODELS\_2025\_paper\_100”, p. 6) \#5fb236 \par - “is automatically generated to collect the necessary input from the user.” (“MODELS\_2025\_paper\_100”, p. 6) \#ffd400\\ \mkbibemph{What is the execution environmnet? When? at which stage of which process? We are missing an overview presentation about the provided framework supporting I guess the different pipelines presented earlier in the paper. The different artifacts of modeling constructs are presented without a proper context and enviroment presentation.} \par - “Data attributes are explicitly selected and modeled to enhance traceability across different dimensions of AI system development.” (“MODELS\_2025\_paper\_100”, p. 6) \#ffd400\\ \mkbibemph{See my previous comment.} \par - “These attributes enable developers to trace how specific model configurations were derived and to revisit the reasoning behind them for future audits or refinements.” (“MODELS\_2025\_paper\_100”, p. 7) \#5fb236 \par - “This section explains how the framework supports runtime execution that is driven by changes in artifact data attributes, rather than by fixed activity flows.” (“MODELS\_2025\_paper\_100”, p. 7) \#ffd400\\ \mkbibemph{Unfortunately the framework (intended as a software that can be used) has not been presented.} \par - “Fig. 7. SRS-ML artifact behavior defined through data-driven stages. Transitions are shown for illustration, but control flow is implicit” (“MODELS\_2025\_paper\_100”, p. 7) \#ffd400\\ \mkbibemph{How this process, that it is supposed to be an ideal one, can be actually put in practice also in relation with the existing technology? As a general comment we are missing concrete links from the presented ides with existing technologies and tools that cannot be completely substituted. It is necessary to ensure a link with them.} \par - “Execution coordination is governed by the artifact relationship layer (see Section III). For instance, once a higherlevel artifact—such as the SRS-ML—has completed all its mandatory stages, related lower-level artifacts (e.g., MLRS, DRS, or SWRS) become eligible for instantiation.” (“MODELS\_2025\_paper\_100”, p. 8) \#ffd400\\ \mkbibemph{What executes such coordination?} \par - “enabling consistent propagation of modifications across artifact boundaries.” (“MODELS\_2025\_paper\_100”, p. 8) \#ffd400\\ \mkbibemph{How such propagation is done? How changes are represented and executed/analyzed?} \par - “IntentClassifierModule” (“MODELS\_2025\_paper\_100”, p. 8) \#5fb236 \par - “TextbasedChat” (“MODELS\_2025\_paper\_100”, p. 8) \#5fb236 \par - “DialogManagerModule” (“MODELS\_2025\_paper\_100”, p. 8) \#5fb236 \par - “A. Scenario 1. Change Impact Analysis across AI Pipelines” (“MODELS\_2025\_paper\_100”, p. 8) \#2ea8e5 \par - “This scenario exemplifies how the framework facilitates the concurrent propagation of changes across pipelines, supporting the systematic determination of how a change in one artifact may affect related artifacts across the AI development pipeline.” (“MODELS\_2025\_paper\_100”, p. 8) \#ffd400\\ \mkbibemph{The representation and management of such changes is not clear.} \par - “A subsequent update introduced a new requirement mandating the logging of all user interactions during runtime.” (“MODELS\_2025\_paper\_100”, p. 8) \#5fb236 \par - “Thanks to the explicit traceability links between artifacts across AI development pipelines supported by GSM4SE4AI, structured change impact analysis across pipelines becomes feasible and systematic. As illustrated in Fig. 9, the change is initiated within the SWRS of the TextbasedChat module. Through inter-transverse change propagation, this softwarelevel change triggers an update in the DRS, evolving it from Version 1.0 to Version 2.0.” (“MODELS\_2025\_paper\_100”, p. 8) \#ffd400\\ \mkbibemph{This mechanism is very abstract and it's not evident how it happens in practice. I don't think software migrations due to change propagation happen automatically.} \par - “s(” (“MODELS\_2025\_paper\_100”, p. 9) \#ff6666\\ \mkbibemph{Missing space} \par - “VI. PROOF-OF-CONCEPT PROTOTYPE” (“MODELS\_2025\_paper\_100”, p. 9) \#ffd400\\ \mkbibemph{what's the envisioned architecture of the orchestrator? Section 6 shows some proof-of-concept dialog boxes without presenting a possible architecture actually involving all the different pipelines and change propagation requirements. Also this section is too high level.} \par - “proof-of-concep” (“MODELS\_2025\_paper\_100”, p. 9) \#ff6666\\ \mkbibemph{proof-of-concept} \par - “A case study and prototype show improved traceability, change propagation, and consistency.” (“MODELS\_2025\_paper\_100”, p. 10) \#ffd400\\ \mkbibemph{Improved with respect to what? It is necessary to convince about the mentioned improved by considering existing technologies.} \par Consider that those that are tagged with \#5fb236 are just highlights, those that are tagged with \#e56eee and \#a28ae5 are important sentences. Please pay attention instead to the notes that are tagged with \#ffd400. Those that are tagged with \#ff6666 are typos or errors. Could you please draft a review by organizing it as follows: SUMMARY: Just a few sentences to summarize the work. COMMENTS: Organize the notes, especially those that contain issues or typos. Moreover, list the strengths and weaknesses of the work (no more than 3 items each). At the end, list 3 questions for the authors that might be involved in a rebuttal phase.} } @misc{MODELS_2025_paper_19, title = {{{MODELS}}\_2025\_paper\_19}, langid = {english}, keywords = {LOGSEQ}, note = {I'm reviewing a research paper and I took the following notes: \par \section{Annotazioni\\ (5/5/2025, 16:40:59)} \par - “Model-Driven Approach” (“MODELS\_2025\_paper\_19”, p. 1) \#ffd400\\ \mkbibemph{The paper makes an overview of different technologies for supporting multi agent systems and propose a taxonomy including and organizing different terms that are involved in the development of multi agents systems based on LLMs. As a such the title does not reflect the actual content of the paper, which is more a survey than an approach.} \par - “LLM Agent Design” (“MODELS\_2025\_paper\_19”, p. 1) \#5fb236 \par - “most agents are assembled in an ad-hoc manner without a coherent architectural foundation.” (“MODELS\_2025\_paper\_19”, p. 1) \#5fb236 \par - “The challenge lies in structuring task decomposition, agent delegation, tool integration, and evaluation into coherent, extensible agent behaviors.” (“MODELS\_2025\_paper\_19”, p. 1) \#e56eee \par - “we propose a modeldriven framework for LLM agent design that distinguishes structural components from cognitive modules and integrates them into a unified architecture.” (“MODELS\_2025\_paper\_19”, p. 1) \#e56eee \par - “transforming how people interacted with LLMs and how developers began to architect intelligent capabilities atop existing and new software.” (“MODELS\_2025\_paper\_19”, p. 1) \#5fb236 \par - “With the rise of frameworks like LangChain1 and LlamaIndex2, developers could integrate LLMs into applications independent of LLM vendor lock-in” (“MODELS\_2025\_paper\_19”, p. 1) \#5fb236 \par - “from LLM workflows to agentic systems powered by LLMs capable of contextual, tool-mediated, and adaptive behavior.” (“MODELS\_2025\_paper\_19”, p. 1) \#e56eee \par - “these approaches were functionally and architecturally distinct from LLM-integrated software.” (“MODELS\_2025\_paper\_19”, p. 1) \#5fb236 \par - “LLMs were used to classify model repositories (classification)” (“MODELS\_2025\_paper\_19”, p. 1) \#5fb236 \par - “LLM agents—components that use LLMs not just to respond, but to plan, act, and adapt” (“MODELS\_2025\_paper\_19”, p. 1) \#e56eee \par - “OpenAI Swarm6, now replaced by Agents SDK7” (“MODELS\_2025\_paper\_19”, p. 1) \#5fb236 \par - “reintroduces concepts from multi-agent systems and cognitive architectures,” (“MODELS\_2025\_paper\_19”, p. 1) \#e56eee \par - “the focus for developers moves beyond prompt writing and pipeline design to architectural and design choices involving planning modules, delegation logic, and tool interfaces.” (“MODELS\_2025\_paper\_19”, p. 2) \#e56eee\\ \mkbibemph{THAT'S CRUCIAL! IMPORTANT. Maybe tool interfaces is related to MCP?} \par - “architectural perspective” (“MODELS\_2025\_paper\_19”, p. 2) \#a28ae5 \par - “agents” (“MODELS\_2025\_paper\_19”, p. 2) \#2ea8e5 \par - “tasks” (“MODELS\_2025\_paper\_19”, p. 2) \#2ea8e5 \par - “tools” (“MODELS\_2025\_paper\_19”, p. 2) \#2ea8e5 \par - “orchestra” (“MODELS\_2025\_paper\_19”, p. 2) \#2ea8e5 \par - “cognitive modules” (“MODELS\_2025\_paper\_19”, p. 2) \#2ea8e5 \par - “architectural model for LLM agent design” (“MODELS\_2025\_paper\_19”, p. 2) \#2ea8e5 \par - “systematized framework of structural components” (“MODELS\_2025\_paper\_19”, p. 2) \#2ea8e5 \par - “integrated behavioral scaffold” (“MODELS\_2025\_paper\_19”, p. 2) \#2ea8e5 \par - “Plan–Act–Evaluate” (“MODELS\_2025\_paper\_19”, p. 2) \#5fb236 \par - “Explore–Exploit–Learn” (“MODELS\_2025\_paper\_19”, p. 2) \#5fb236 \par - “Together, these contributions present a principled, modeldriven view of LLM-native systems, clarifying core elements and enabling deliberate design of orchestration, reasoning, and adaptability.” (“MODELS\_2025\_paper\_19”, p. 2) \#e56eee \par - “LLM usage to agentic integration” (“MODELS\_2025\_paper\_19”, p. 2) \#2ea8e5 \par - “agent design architecture” (“MODELS\_2025\_paper\_19”, p. 2) \#2ea8e5 \par - “structural components and cognitive modules” (“MODELS\_2025\_paper\_19”, p. 2) \#2ea8e5 \par - “orchestration, decomposition, collaboration, and tool use” (“MODELS\_2025\_paper\_19”, p. 2) \#2ea8e5 \par - “explore–exploit–learn dynamics” (“MODELS\_2025\_paper\_19”, p. 2) \#2ea8e5 \par - “experimentation” (“MODELS\_2025\_paper\_19”, p. 2) \#2ea8e5 \par - “future directions and conclusions” (“MODELS\_2025\_paper\_19”, p. 2) \#2ea8e5 \par - “distributed autonomy and coordination” (“MODELS\_2025\_paper\_19”, p. 2) \#5fb236 \par - “LLM-powered agents now exhibit memory, planning, and reasoning capabilities [18], with simulation pipelines embedding LLMs across their full lifecycle [19].” (“MODELS\_2025\_paper\_19”, p. 2) \#5fb236 \par - “Frameworks have emerged that decompose agents into modules for planning, memory, tool use, and reflection [23],” (“MODELS\_2025\_paper\_19”, p. 2) \#e56eee \par - “showing convergence with MAS principles—but realized through LLM-native methods.” (“MODELS\_2025\_paper\_19”, p. 2) \#e56eee \par - “Although effective for interleaved reasoning and acting, these early agents lacked orchestration, memory-backed decision-making, or modular delegation logic—essential features for handling complex, long-horizon tasks. Without shared execution contexts or rolebased coordination, they could not generalize beyond isolated task flows.” (“MODELS\_2025\_paper\_19”, p. 2) \#5fb236 \par - “foundational planning primitive embedded within structured architectures.” (“MODELS\_2025\_paper\_19”, p. 3) \#5fb236 \par - “memory (short- and long-term),” (“MODELS\_2025\_paper\_19”, p. 3) \#a28ae5 \par - “profile module” (“MODELS\_2025\_paper\_19”, p. 3) \#a28ae5 \par - “adaptive planning” (“MODELS\_2025\_paper\_19”, p. 3) \#a28ae5 \par - “Architectural Perspectives on LLM Agent Desig” (“MODELS\_2025\_paper\_19”, p. 3) \#2ea8e5 \par - “Early frameworks introduced cognitive loops with modular memory, internal/external actions, and decision cycles [26]” (“MODELS\_2025\_paper\_19”, p. 3) \#e56eee \par - “while others organized agents around core functions of reasoning, perception, and action” (“MODELS\_2025\_paper\_19”, p. 3) \#e56eee \par - “Learning, Knowledge, and Adaptation in LLM Agents” (“MODELS\_2025\_paper\_19”, p. 3) \#2ea8e5 \par - “As LLM agents gain traction, what remains missing is a cohesive, architectural model that ties these capabilities together with clarity and intentionality. Rather than introducing entirely new mechanisms, our work offers a model-driven perspective that unifies these components into a coherent design framework.” (“MODELS\_2025\_paper\_19”, p. 3) \#e56eee \par - “We believe this will enable developers to build context-aware, tool-mediated, and adaptive LLM agents with deliberate modularity and strategic coordination,” (“MODELS\_2025\_paper\_19”, p. 3) \#f19837 \par - “The knowledge component supports semantic reasoning, and memory provides continuity and personalization across interactions.” (“MODELS\_2025\_paper\_19”, p. 3) \#a28ae5 \par - “Task execution is governed by a modular orchestration layer that coordinates subtasks, tool usage, and inter-agent collaboration.” (“MODELS\_2025\_paper\_19”, p. 3) \#5fb236 \par - “A. Operational Roles and Interfaces” (“MODELS\_2025\_paper\_19”, p. 4) \#5fb236 \par - “It carries with it an evaluative dimension, as each goal is associated with specific criteria that define what success looks like.” (“MODELS\_2025\_paper\_19”, p. 4) \#5fb236 \par - “This distinction is important because the same goal can be pursued through many different tasks, and tasks may need to be recomposed, reprioritized, or even reinterpreted as conditions change or new information arises. From a system perspective, this allows the agent to dynamically adjust its operational plan (tasks) while maintaining alignment with the overarching goal.” (“MODELS\_2025\_paper\_19”, p. 4) \#a28ae5 \par - “The agent does not use tools indiscriminately; rather, tool invocation is governed by its internal capabilities, activated based on task requirements and environmental context” (“MODELS\_2025\_paper\_19”, p. 4) \#5fb236 \par - “It maintains a shared context over the internal states of all agents, manages workflows, handles branching or fallback logic, and bridges perception, cognition, and action—enabling sustained, adaptive reasoning across stages and modalities.” (“MODELS\_2025\_paper\_19”, p. 4) \#5fb236 \par - “Goal” (“MODELS\_2025\_paper\_19”, p. 5) \#2ea8e5 \par - “Sufficiency” (“MODELS\_2025\_paper\_19”, p. 5) \#2ea8e5 \par - “Preferences” (“MODELS\_2025\_paper\_19”, p. 5) \#2ea8e5 \par - “Guardrails” (“MODELS\_2025\_paper\_19”, p. 5) \#2ea8e5 \par - “Memory” (“MODELS\_2025\_paper\_19”, p. 5) \#2ea8e5 \par - “Learning” (“MODELS\_2025\_paper\_19”, p. 5) \#2ea8e5 \par - “Knowledge” (“MODELS\_2025\_paper\_19”, p. 5) \#2ea8e5 \par - “epresentative types for each module” (“MODELS\_2025\_paper\_19”, p. 5) \#5fb236 \par - “Goals” (“MODELS\_2025\_paper\_19”, p. 5) \#2ea8e5 \par - “Sufficiency” (“MODELS\_2025\_paper\_19”, p. 5) \#2ea8e5 \par - “Learning” (“MODELS\_2025\_paper\_19”, p. 5) \#2ea8e5 \par - “Guardrails” (“MODELS\_2025\_paper\_19”, p. 5) \#2ea8e5 \par - “memory” (“MODELS\_2025\_paper\_19”, p. 5) \#2ea8e5 \par - “Episodic memory” (“MODELS\_2025\_paper\_19”, p. 5) \#5fb236 \par - “Reflective memory” (“MODELS\_2025\_paper\_19”, p. 5) \#5fb236 \par - “Preference memory” (“MODELS\_2025\_paper\_19”, p. 5) \#5fb236 \par - “main orchestration variations” (“MODELS\_2025\_paper\_19”, p. 6) \#2ea8e5 \par - “task decomposition type” (“MODELS\_2025\_paper\_19”, p. 6) \#2ea8e5 \par - “delegation and collaboration mechanisms” (“MODELS\_2025\_paper\_19”, p. 6) \#2ea8e5 \par - “tool usage” (“MODELS\_2025\_paper\_19”, p. 6) \#2ea8e5 \par - “orchestration architectures” (“MODELS\_2025\_paper\_19”, p. 6) \#5fb236 \par - “publish-subscribe” (“MODELS\_2025\_paper\_19”, p. 6) \#2ea8e5 \par - “graph-based” (“MODELS\_2025\_paper\_19”, p. 6) \#2ea8e5 \par - “handoff-based” (“MODELS\_2025\_paper\_19”, p. 6) \#2ea8e5 \par - “Publish-Subscribe” (“MODELS\_2025\_paper\_19”, p. 6) \#2ea8e5 \par - “Graph-Based” (“MODELS\_2025\_paper\_19”, p. 6) \#2ea8e5 \par - “Execution is centrally orchestrated based on graph logic, with a persistent shared Execution Context capturing state updates” (“MODELS\_2025\_paper\_19”, p. 6) \#5fb236 \par - “Handoff-Based” (“MODELS\_2025\_paper\_19”, p. 6) \#2ea8e5 \par - “Fig. 5.” (“MODELS\_2025\_paper\_19”, p. 7) \#ffd400\\ \mkbibemph{A legend with the used color schema is needed.} \par - “As shown in Figure 7, a central agent interprets userprovided instructions and orchestrates the task through a mix of direct action, delegation to specialized multi-agent teams, and internal deliberation.” (“MODELS\_2025\_paper\_19”, p. 7) \#a28ae5 \par - “A key mechanism in this setup is nested chat, which is an agentic version of prompt chaining. The nested chat can involve launching sub-conversations with other agents, running inner verification or refinement loops, or resolving ambiguities in the original instruction.” (“MODELS\_2025\_paper\_19”, p. 7) \#ffd400\\ \mkbibemph{How about governance or in general the resolution of possible conflicting situations?} \par - “Figure 7 is just one instantiation; delegation and collaboration patterns can vary widely depending on the agentic framework and problem space.” (“MODELS\_2025\_paper\_19”, p. 7) \#5fb236 \par - “Generic Tools” (“MODELS\_2025\_paper\_19”, p. 8) \#5fb236 \par - “Function Tools” (“MODELS\_2025\_paper\_19”, p. 8) \#5fb236 \par - “Agent-as-Tool” (“MODELS\_2025\_paper\_19”, p. 8) \#5fb236 \par - “Agent Role Typology” (“MODELS\_2025\_paper\_19”, p. 8) \#2ea8e5 \par - “exploreexploit-learn paradigm” (“MODELS\_2025\_paper\_19”, p. 8) \#5fb236 \par - “Plan–Act–Evaluate (PAE)” (“MODELS\_2025\_paper\_19”, p. 8) \#2ea8e5 \par - “Autogen, OpenAI Agents SDK, CrewAI, PydanticAI, and LangGraph” (“MODELS\_2025\_paper\_19”, p. 9) \#ffd400\\ \mkbibemph{What are the criteria that have been used to select them?} \par - “12We note two concerns, beyond this paper’s scope: (a) open-source models (e.g., available via Ollama) are less capable than frontier models, often lacking tool use, vision, and reasoning, and tend to produce more erratic agent behavior; and (b) frontier model costs can become prohibitive as agents and messages proliferate.” (“MODELS\_2025\_paper\_19”, p. 9) \#e56eee \par - “hands-on experiences with agentic frameworks” (“MODELS\_2025\_paper\_19”, p. 10) \#5fb236 \par - “design architecture across diverse domains to assess robustness, adaptability, and goal alignment in real-world settings.” (“MODELS\_2025\_paper\_19”, p. 10) \#5fb236 \par - “benchmarks that evaluate orchestration quality” (“MODELS\_2025\_paper\_19”, p. 10) \#5fb236 \par - “This paper responds to that shift by proposing a principled, model-driven framework for LLM agent design.” (“MODELS\_2025\_paper\_19”, p. 10) \#a28ae5 \par - “we observed operational differences and early indications of recurring design tendencies” (“MODELS\_2025\_paper\_19”, p. 10) \#a28ae5 \par - “set of practical guidelines for structuring agent behavior” (“MODELS\_2025\_paper\_19”, p. 10) \#a28ae5 \par - “Rather than prescribing a monolithic architecture, we offer scaffolds that support flexible composition and architectural clarity.” (“MODELS\_2025\_paper\_19”, p. 10) \#ffd400\\ \mkbibemph{Indeed, I appreciate the effort, even though the proposed description is generic and vague. There are not specific guidelines on how to actually decompose monolithic architectures.} \par - “REFERENCES” (“MODELS\_2025\_paper\_19”, p. 11) \#ffd400\\ \mkbibemph{34 references out of 50 come from arxive. I understand this is a very fast growing topic, however it is necessary to keep basing our research on peer reviewed sources. This is a general comment.} \par COnsider that those that are tagget with \#5fb236 are just highlights, those that are tagged with \#e56eee and \#a28ae5 are imporant sentences. Please pay attention instead to the notes that are tagged with \#ffd400. Those that are tagged with \#ff6666 are typos or errors. Could you please draft a review by organizing it as follows: SUMMARY: Just a few sentence to summarize the work COMMENTS: Organize the notes especially those that contain issues or typos.} } @misc{MODELS_2025_paper_3, title = {{{MODELS}}\_2025\_paper\_3}, langid = {english}, keywords = {LOGSEQ}, note = {I'm reviewing a research paper and I took the following notes: \par \section{Annotazioni\\ (21/5/2025, 09:48:15)} \par - “autonomous reasoning and human-like conversational abilities.” (“MODELS\_2025\_paper\_3”, p. 1) \#5fb236 \par - “We discuss the conceptual foundations and key design principles of LLMA-UML and evaluate our extension by modeling a representative case study and discussing the benefits of our approach” (“MODELS\_2025\_paper\_3”, p. 1) \#ffd400\\ \mkbibemph{What's the goal of such modeling effort?} \par - “Incorporating such agents into software systems presents new challenges for system design and modeling.” (“MODELS\_2025\_paper\_3”, p. 1) \#5fb236 \par - “standardized way to model an AI agent that relies on a massive learned model and engages in open-ended natural language interactions.” (“MODELS\_2025\_paper\_3”, p. 1) \#a28ae5 \par - “However, these implementations follow ad-hoc architectures, and a general modeling method to describe their design has yet to mature.” (“MODELS\_2025\_paper\_3”, p. 1) \#5fb236 \par - “. We identify why existing UML multi-agent modeling techniques are insufficient for this new class of agents” (“MODELS\_2025\_paper\_3”, p. 1) \#2ea8e5 \par - “propose a UML profile with new modeling elements to address these deficiencies.” (“MODELS\_2025\_paper\_3”, p. 1) \#2ea8e5 \par - “communicate” (“MODELS\_2025\_paper\_3”, p. 1) \#ffd400\\ \mkbibemph{what do you mean? Communicate you mean presenting a system under design/development? What's the goal of the modeling phase here? At what stage of the development you see modeling~ being imporant when developing LLM-based multi-agent systems?} \par - “currently model LLM-agent based systems in the domain of software engineering.” (“MODELS\_2025\_paper\_3”, p. 1) \#2ea8e5 \par - “outlines the related work” (“MODELS\_2025\_paper\_3”, p. 1) \#2ea8e5 \par - “problem and outlines the limitations of current UML models in this context.” (“MODELS\_2025\_paper\_3”, p. 1) \#2ea8e5 \par - “proposed UML extension for LLM-agents” (“MODELS\_2025\_paper\_3”, p. 1) \#2ea8e5 \par - “semantics and usage guidelines for LLMA-UML stereotypes.” (“MODELS\_2025\_paper\_3”, p. 1) \#a28ae5 \par - “papers that propose the development of LLM-agents” (“MODELS\_2025\_paper\_3”, p. 2) \#e56eee \par - “agent interaction protocols (AIP).” (“MODELS\_2025\_paper\_3”, p. 2) \#a28ae5 \par - “o represent internal agent processing, they recommend using activity diagrams and statecharts and dashed notations for interfacing with other agents” (“MODELS\_2025\_paper\_3”, p. 2) \#5fb236 \par - “cloning, mitosis, reproduction and parasitic and symbiotic repationships.” (“MODELS\_2025\_paper\_3”, p. 2) \#5fb236 \par - “In particular, these UML models assume clearly defined internal state representations and a limited set of message types, which is a poor fit for LLM-based agents that operate via free-form natural language and possess an implicit knowledge model.” (“MODELS\_2025\_paper\_3”, p. 2) \#a28ae5 \par - “existing UML-based multi-agent modeling techniques lack concepts for the kinds of interactions and internal structures that LLM-based agents exhibit” (“MODELS\_2025\_paper\_3”, p. 2) \#a28ae5 \par - “ReAct is an approach that combines reasoning and acting by combining an LLM’s chainof-thought with actions in an environment” (“MODELS\_2025\_paper\_3”, p. 2) \#5fb236 \par - “advanced LLM-based agents are essentially hybrid systems combining a language model with additional modules or external services” (“MODELS\_2025\_paper\_3”, p. 2) \#e56eee \par - “LLM-Agent-UMF” (“MODELS\_2025\_paper\_3”, p. 2) \#5fb236 \par - “They propose LLM-Agent-UMF, a unified conceptual framework to classify an agent’s core modules (planning, memory, profile, action, security) and distinguish the LLM and tool component” (“MODELS\_2025\_paper\_3”, p. 2) \#e56eee \par - “key issues that need to be addressed to accurately represent LLM agents” (“MODELS\_2025\_paper\_3”, p. 2) \#a28ae5 \par - “Implicit Knowledge and Reasoning” (“MODELS\_2025\_paper\_3”, p. 3) \#2ea8e5 \par - “Current UML abstractions (e.g. the attributes/methods of a class) cannot directly encapsulate the complexity of an LLM’s knowledge base or the probabilistic nature of its reasoning” (“MODELS\_2025\_paper\_3”, p. 3) \#a28ae5 \par - “Existing agent profiles that include beliefs and goals [3] treat knowledge as discrete data, which does not capture the rich and diffuse knowledge embedded in an LLM.” (“MODELS\_2025\_paper\_3”, p. 3) \#e56eee \par - “Natural Language Interaction:” (“MODELS\_2025\_paper\_3”, p. 3) \#2ea8e5 \par - “UML sequence diagrams and communication diagrams lack constructs to express that a message is, for example, a prompt or a free-form query to be interpreted by the agen” (“MODELS\_2025\_paper\_3”, p. 3) \#5fb236 \par - “Tool Use and Environment Actions” (“MODELS\_2025\_paper\_3”, p. 3) \#2ea8e5 \par - “LLM-agents can significantly extend their functionality beyond simple text generation by integrating and utilising external tools” (“MODELS\_2025\_paper\_3”, p. 3) \#5fb236 \par - “but they have not provided for on-the-fly tool usage driven by an agent’s internal reasoning.” (“MODELS\_2025\_paper\_3”, p. 3) \#5fb236 \par - “This makes it difficult to capture the loop where an LLM agent decides it lacks information, calls an external service, and then incorporates the result into its next prompt.” (“MODELS\_2025\_paper\_3”, p. 3) \#a28ae5 \par - “Dynamic Goals and Non-deterministic Behavior” (“MODELS\_2025\_paper\_3”, p. 3) \#2ea8e5 \par - “it does not readily accommodate the emergence of new goals at runtime” (“MODELS\_2025\_paper\_3”, p. 3) \#a28ae5 \par - “This limitation highlights the need for modeling techniques that can capture the dynamic and evolving nature of LLM-agent behavior.” (“MODELS\_2025\_paper\_3”, p. 3) \#a28ae5 \par - “UML-agent profiles are not well suited for representing the internal mechanisms and interaction patterns of LLM-based agents.” (“MODELS\_2025\_paper\_3”, p. 3) \#a28ae5 \par - “To address the limitations outlined in section IV, we introduce a UML extension, realized as a UML profile, specifically adapted to the modeling of LLM-agents and their interactions” (“MODELS\_2025\_paper\_3”, p. 4) \#a28ae5 \par - “stereotypes and model elements that capture the unique needs of LLM-based agents.” (“MODELS\_2025\_paper\_3”, p. 4) \#a28ae5 \par - “{$<<$}LLMAgent{$>>$} stereotype encapsulates the autonomous reasoning and decision-making capabilities” (“MODELS\_2025\_paper\_3”, p. 4) \#5fb236 \par - “{$<<$}Prompt{$>>$} stereotype facilitates natural languagebased communication, enabling agents to coordinate tasks and share information effectively within the system” (“MODELS\_2025\_paper\_3”, p. 4) \#5fb236 \par - “{$<<$}Tool{$>>$} stereotype represents external resources that agents leverage collaboratively, supporting task delegation and enhancing system flexibility as well as extending the autonomous capabilities of the {$<<$}LLMAgent{$>>$}” (“MODELS\_2025\_paper\_3”, p. 4) \#5fb236 \par - “{$<<$}Memory{$>>$} stereotype ensures that agents retain context and historical data across interactions” (“MODELS\_2025\_paper\_3”, p. 4) \#5fb236 \par - “{$<<$}LLMAgent{$>>$} is a stereotype used to clearly identify an autonomous agent component whose core reasoning capabilities are driven by a large language model.” (“MODELS\_2025\_paper\_3”, p. 4) \#5fb236 \par - “{$<<$}Prompt{$>>$}” (“MODELS\_2025\_paper\_3”, p. 4) \#ffd400\\ \mkbibemph{It's not appropriate using this stereotype also to represent amswers from agents. Prompts are string given as input to LLMs and not answers.} \par - “distinguish user queries, agent responses,” (“MODELS\_2025\_paper\_3”, p. 4) \#ffd400\\ \mkbibemph{This is not consistent with Fig 2 where {$<<$}prompt{$>>$} is used to represent at the same manner both user queries and agent responses.} \par - “this profile includes conventions for modeling the internal reasoning cycle of an LLM agent” (“MODELS\_2025\_paper\_3”, p. 4) \#ffd400\\ \mkbibemph{How this conventions are prescriptive? How to check if they are actually used? IN general, at this point of the paper is not clear if the proposed UML profile is tool supported. Moreover, what the intended use of the profile, who is the main intended stakeholder?} \par - “UML Activity Diagrams or Interaction Overview Diagrams can be used to describe the control flow of the agent.” (“MODELS\_2025\_paper\_3”, p. 4) \#ffd400\\ \mkbibemph{Typically, processes involving orchestration or choreographis of services are specified by using BPMN, Can you comment the performed choice with respect to the possible use of BPMN? \#question} \par - “These diagrams can be annotated to represent reasoning steps such as interpret prompt, evaluate context, select tool, invoke tool, and generate response” (“MODELS\_2025\_paper\_3”, p. 4) \#ffd400\\ \mkbibemph{How can the annotation be done?} \par - “Using the proposed profile, modelers can develop both structural and behavioral UML diagrams that explicitly capture the characteristics of LLM-based agents” (“MODELS\_2025\_paper\_3”, p. 4) \#5fb236 \par - “Component Diagrams” (“MODELS\_2025\_paper\_3”, p. 5) \#2ea8e5 \par - “This structural representation clearly identifies the core agent, its external tool dependencies and its connection to the memory components, providing more insight than a standard UML diagram where the LLM nature and specific dependencies may be obscured” (“MODELS\_2025\_paper\_3”, p. 5) \#5fb236 \par - “Sequence Diagrams:” (“MODELS\_2025\_paper\_3”, p. 5) \#2ea8e5 \par - “The agent then sends its response back to the user, modeled as another {$<<$}Prompt{$>>$} message with content such as “Your meeting is scheduled for tomorrow at 10am”.” (“MODELS\_2025\_paper\_3”, p. 5) \#ffd400\\ \mkbibemph{The stereotype {$<<$}Prompt{$>>$} is used also to represent responses from Agents} \par - “Figure 2:” (“MODELS\_2025\_paper\_3”, p. 5) \#ffd400 \par - “Sequence Diagram: Scheduling a Meeting, illustrating {$<<$}Prompt{$>>$} messages, {$<<$}Tool{$>>$} interaction, and a clarification loop.” (“MODELS\_2025\_paper\_3”, p. 5) \#ffd400\\ \mkbibemph{Why do you need to specify loops for giving instructins on the calendar to be used? Why not including this information in the initial prompt? By the way the example is not effective in presenting the case of emerging behaviours, decisions that need to be taken at runtime etc. as motivated earlier in the paper.} \par - “Activity Diagrams:” (“MODELS\_2025\_paper\_3”, p. 5) \#2ea8e5 \par - “Overall, the proposed UML profile and its diagramming guidelines allow modelers to create representations of LLMbased systems that explicitly indicate the presence and role of the LLM through stereotypes such as {$<<$}LLMAgent{$>>$}. The profile highlights natural language interactions using {$<<$}Prompt{$>>$}, clarifies how agents use external capabilities using {$<<$}Tool{$>>$} elements, represents the function of conversational context or persistent state using {$<<$}Memory{$>>$} artifacts, and illustrates the flow of reasoning and action within both activity and sequence diagrams. Crucially, these enhancements are integrated within the standard UML framework, facilitating adoption by software engineers working within established modeling environments.” (“MODELS\_2025\_paper\_3”, p. 6) \#ffd400\\ \mkbibemph{This is a repetition.} \par - “A {$<<$}Tool{$>>$} provides specific external functionality with at least one defined interface operation.” (“MODELS\_2025\_paper\_3”, p. 7) \#5fb236 \par - “a {$<<$}LLMAgent{$>>$} enforces session coherence by linking to {$<<$}Memory{$>>$} and processing requests in sequence.” (“MODELS\_2025\_paper\_3”, p. 7) \#5fb236 \par - “knowledge representation includes both implicit knowledge within {$<<$}LLMAgent{$>>$} structures and explicit knowledge stored in {$<<$}Memory{$>>$}” (“MODELS\_2025\_paper\_3”, p. 7) \#5fb236 \par - “interaction dynamics revolve around {$<<$}Prompt{$>>$} message sequences and {$<<$}Tool{$>>$} invocations, with context management achieved through structured {$<<$}Memory{$>>$} access.” (“MODELS\_2025\_paper\_3”, p. 7) \#5fb236 \par - “reasoning processes appear as nested activations within sequence diagrams, annotated activity flows for chain-ofthought logic, or alternatively combined fragments for nondeterministic behavior.” (“MODELS\_2025\_paper\_3”, p. 7) \#5fb236 \par - “When implemented through a UML profile, these semantics help to avoid modeling oversights (such as missing memory links or undefined tool interfaces) and ensure a coherent architecture for LLM agent design.” (“MODELS\_2025\_paper\_3”, p. 7) \#a28ae5 \par - “a theoretical modeling extension, evaluation is conducted by analyzing its expressiveness and consistency rather than quantitative metrics.” (“MODELS\_2025\_paper\_3”, p. 7) \#a28ae5 \par - “We illustrate the usefulness of the proposed UML profile through an example case study and discuss how it addresses the previously identified issues.” (“MODELS\_2025\_paper\_3”, p. 7) \#ffd400\\ \mkbibemph{WHat are the research questions that you wanted to answer with the case study?} \par - “chapter” (“MODELS\_2025\_paper\_3”, p. 7) \#ff6666\\ \mkbibemph{Section} \par - “By comparing LLMA-UML diagrams with their conventional UML counterparts, we evaluate the expressiveness, clarity, and practical benefits of the extension.” (“MODELS\_2025\_paper\_3”, p. 7) \#a28ae5 \par - “To illustrate the benefits of LLMA-UML, we model a software development pipeline where multiple LLM-based agents collaborate to analyze requirements, generate code, and validate results. This case study highlights the unique attributes of LLM-agents such as natural language interaction, memorydriven reasoning, tool integration and adaptive workflows” (“MODELS\_2025\_paper\_3”, p. 7) \#ffd400\\ \mkbibemph{To illustrate the benefits of LLMA-UML it is necessary to discuss the existing challenges that the approach aims to address. What's the difficulties that the defined UML profiles permits to address? Who is the main stakeholder? What are the criticalities due to the lack of a modeling language like LLMA-UML. As mentioned many times by the authors, one of the peculiar characteristics of the LLM\_bases systems is their emerging behaviour, their reasoning and planning. Why modeling them? What's the benefit?} \par - “The shared memory component ({$<<$}Memory{$>>$}) is central to the operation of the system. It holds project-specific context that agents retrieve or update during task execution.” (“MODELS\_2025\_paper\_3”, p. 8) \#5fb236 \par - “As illustrated in Figure 6, the activity diagram models the decision logic within the pipeline.” (“MODELS\_2025\_paper\_3”, p. 8) \#ffd400\\ \mkbibemph{How is this used in practice?} \par - “precise modeling of LLM-agent systems where traditional UML falls short.” (“MODELS\_2025\_paper\_3”, p. 9) \#5fb236 \par - “The case study also demonstrates the scalability of LLMAUML, with complex interactions, such as simultaneous tool use by multiple agents, remaining readable through stereotyped elements.” (“MODELS\_2025\_paper\_3”, p. 9) \#ffd400\\ \mkbibemph{The consideration of multiple agents is indeed relevant even though it is not properly covered. In particualar, it can be the case that Agents interact each other and in this case it is necessary to introduce some governance mechanisms in order to manage conflicting situations (e.g., different outputs from agents that are supposed to support the same task and from the same query they produce conflicting results).} \par - “In summary, LLMA-UML bridges the gap between traditional software modeling and the unique requirements of LLM-agent systems, providing a structured yet flexible framework for designing, analyzing and communicating AI-driven workflows.” (“MODELS\_2025\_paper\_3”, p. 9) \#5fb236 \par - “Our proposed UML profile introduces the {$<<$}LLMAgent{$>>$}, {$<<$}Prompt{$>>$}, {$<<$}Tool{$>>$} and {$<<$}Memory{$>>$} stereotypes and corresponding semantics which together enable modelers to create clear and expressive models of LLM-agent architectures and interactions.” (“MODELS\_2025\_paper\_3”, p. 10) \#5fb236 \par - “theoretical foundation for describing systems that integrate LLM-agents using a standard modeling language.” (“MODELS\_2025\_paper\_3”, p. 10) \#ffd400\\ \mkbibemph{There are arelady different frameworks that permits to specify workflows of different agents. How do you compare with them? What's the benefit of the proposed approach with respect to them?} \par - “Furthermore, to address the socio-technical aspects of LLM agents, future extensions could include concepts such as a {$<<$}Policy{$>>$} stereotype to explicitly model ethical constraints or alignment rules governing agent behavior.” (“MODELS\_2025\_paper\_3”, p. 10) \#ffd400\\ \mkbibemph{Yes, this is related to one of my comments above related to governance.} \par - “[6] J. Liu, K. Wang, Y. Chen, X. Peng, Z. Chen, L. Zhang, and Y. Lou, “Large Language Model-Based Agents for Software Engineering: A Survey.”” (“MODELS\_2025\_paper\_3”, p. 10) \#ffd400\\ \mkbibemph{please complete references with all the details} \par COnsider that those that are tagget with \#5fb236 are just highlights, those that are tagged with \#e56eee and \#a28ae5 are imporant sentences. Please pay attention instead to the notes that are tagged with \#ffd400. Those that are tagged with \#ff6666 are typos or errors. Could you please draft a review by organizing it as follows: SUMMARY: Just a few sentence to summarize the work COMMENTS: Organize the notes especially those that contain issues or typos.} } @misc{MODELS_2025_paper_8, title = {{{MODELS}}\_2025\_paper\_8}, langid = {english}, keywords = {LOGSEQ}, note = {I'm reviewing a research paper and I took the following notes: \par \section{Annotazioni\\ (23/5/2025, 17:16:01)} \par - “Abstract—Verification” (“MODELS\_2025\_paper\_8”, p. 1) \#ffd400\\ \mkbibemph{The abstract needs to be reworked. It's not convey exactly the point, it's not clear and it does not present what the paper is about.} \par - “The Data Transfer Architecture Model [1] is a formal model that can describe both specifications and architectures of RESTful microservices, and can generate executable prototypes of the services with selecting data transfer methods between PUSH and PULL styles.” (“MODELS\_2025\_paper\_8”, p. 1) \#ffd400 \par - “However, the model does not have sufficient capability to specify practical microservices as it cannot directly represent the creation and deletion of resources or their hierarchical structure.” (“MODELS\_2025\_paper\_8”, p. 1) \#ffd400 \par - “we extend the model to allow hierarchical access to the internal structure of resources, as well as the creation and deletion of resources by structuring resource states” (“MODELS\_2025\_paper\_8”, p. 1) \#5fb236 \par - “verification and validation of specifications have been emphasized in the development of large-scale systems that require high reliability.” (“MODELS\_2025\_paper\_8”, p. 1) \#5fb236 \par - “However, little work has done on formal specification languages for microservices.” (“MODELS\_2025\_paper\_8”, p. 1) \#ffd400\\ \mkbibemph{Are you sure? I think it's important to better support such a sentence. it is important to clarify what kind of specification the authors are referring to, the intended goal of the specification, etc.} \par - “whether the choice of PUSH or PULL data transfer conforms to a given formal specification” (“MODELS\_2025\_paper\_8”, p. 1) \#ffd400\\ \mkbibemph{These terms PUSH and PULL suddenly appear even in the abstract without proper context.} \par - “Therefore, in this paper, we extend the Data Transfer Architecture Model to allow hierarchical access to the internal structure of resources, as well as the creation and deletion of resources by structuring resource states. In this paper, we call the extended model the extended Data Transfer Architecture Model or simply DTRAM.” (“MODELS\_2025\_paper\_8”, p. 1) \#ff6666\\ \mkbibemph{PLease make the paragarph more clear. There are many occurrences of sentences that are not properly written and that go directly to the point. It is necessary to provide preliminary and background information. For isntance DTRAM is mentioned without giving a proper context or discussing the main concept of the approach and what's its focus/goal.} \par - “we conducted an experiment that compares DTRAM with a well-known formal specification language Alloy [2] with 24 professional engineers who have 3 or more years of Web services development experience.” (“MODELS\_2025\_paper\_8”, p. 1) \#5fb236 \par - “DTRAM is significantly better suited than Alloy for describing the specifications of the microservices.” (“MODELS\_2025\_paper\_8”, p. 1) \#ffd400\\ \mkbibemph{With respect to what criteria? To what exteent DTRAM is better than Alloy. What are the comparative criteria that have been considered?} \par - “II. MOTIVATING EXAMPLE” (“MODELS\_2025\_paper\_8”, p. 2) \#ffd400\\ \mkbibemph{BY reading section 2 I don't see what's the challenge, what are the issues that authors want to focus. It is necessary to explicitly state what's the problem with the presented motivating example.} \par - “The system is constructed as RESTful microservices that have the resource hierarchies shown in Fig. 1.” (“MODELS\_2025\_paper\_8”, p. 2) \#ffd400\\ \mkbibemph{instead of using a figure I would use a listing.} \par - “A. Overview of Data Transfer Architecture Model” (“MODELS\_2025\_paper\_8”, p. 2) \#ffd400\\ \mkbibemph{By reading this section, I don't what's novel with respect to existing modeling languages to specify architectures (e.g., AADL, etc)} \par - “specifications and architectures of RESTful microservices.” (“MODELS\_2025\_paper\_8”, p. 2) \#5fb236 \par - “At the specification level, 1) and 2) are needed to describe external behavior. Therefore, resources and their state transitions are the basis of the specification model.” (“MODELS\_2025\_paper\_8”, p. 2) \#5fb236 \par - “The model has another construct, channel.” (“MODELS\_2025\_paper\_8”, p. 2) \#5fb236 \par - “the input (in), output (out) and reference (ref) ports,” (“MODELS\_2025\_paper\_8”, p. 2) \#5fb236 \par - “arbitrary number of resources can connect to each port.” (“MODELS\_2025\_paper\_8”, p. 2) \#5fb236 \par - “An event channel directly receives an event from the outside of the system as a message, and the states of all outputside resources immediately change.” (“MODELS\_2025\_paper\_8”, p. 3) \#5fb236 \par - “In the Data Transfer Architecture Model, such a chain of message transfers is assumed to be performed simultaneously within a single event frame” (“MODELS\_2025\_paper\_8”, p. 3) \#5fb236 \par - “Here, v(\_, \_) and x(\_, \_) represent the state transition functions of resources v and x, respectively” (“MODELS\_2025\_paper\_8”, p. 3) \#5fb236 \par - “Since resources of the Data Transfer Architecture Model do not support hierarchical composition, they cannot directly represent the hierarchical structures of ‘resources’ in RESTful microservices. Therefore, we intend to extend the Data Transfer Architecture Model to allow hierarchical access to the internal structure of resources, as well as the creation and deletion of resources.” (“MODELS\_2025\_paper\_8”, p. 3) \#5fb236 \par - “To bind each path parameter to the enclosing channel, we allow a channel to have its specific parameter named selector.” (“MODELS\_2025\_paper\_8”, p. 3) \#5fb236 \par - “In addition, to support one to many transfer (i.e., distribution of data) and many to one transfer (i.e., collection of data), we introduce hierarchical structures into channels.” (“MODELS\_2025\_paper\_8”, p. 3) \#5fb236 \par - “the tool allows the user to choose a data transfer method between the PULL and PUSH-styles for each data transfer, and to generate an executable JAX-RS prototype that conforms to the chosen data transfer methods.” (“MODELS\_2025\_paper\_8”, p. 7) \#a28ae5 \par - “Although a described model can be tested by generating, compiling and deploying a prototype, this process is too burdensome for frequent iteration.” (“MODELS\_2025\_paper\_8”, p. 7) \#ffd400\\ \mkbibemph{What's the prototype is able to do?} \par - “We design the simulation feature to virtually execute the described model in an interactive manner.” (“MODELS\_2025\_paper\_8”, p. 7) \#ffd400\\ \mkbibemph{What's the goal of this simulation?} \par - “As shown in Fig. 7, each resource is represented as an ellipse within the simulation window.” (“MODELS\_2025\_paper\_8”, p. 7) \#ffd400\\ \mkbibemph{Who is supposed to be the user of the developed simulation tool? in which development process/phase?} \par - “To evaluate the suitability of DTRAM for describing specifications of RESTful microservices and the effectiveness of the simulation feature, we pose the following research questions.” (“MODELS\_2025\_paper\_8”, p. 8) \#ffd400 \par - “RQ1: Which representation model, DTRAM or Alloy, is more suitable for describing behavior of RESTful microservices?” (“MODELS\_2025\_paper\_8”, p. 8) \#5fb236 \par - “RQ2: Is the visualization of the simulation by visual modeling tool helpful for understanding DTRAM models?” (“MODELS\_2025\_paper\_8”, p. 8) \#5fb236 \par - “model representation rather than the specifications of each description language in the evaluation in RQ1.” (“MODELS\_2025\_paper\_8”, p. 8) \#5fb236 \par - “The effectiveness of the simulation will depend on the method of visualization. Therefore, we examine the effectiveness of the visualization in RQ2.” (“MODELS\_2025\_paper\_8”, p. 8) \#5fb236 \par - “We recruited 24 professional Web service engineers as participants through a crowdsourcing service on the condition of having 3 or more years of development experience” (“MODELS\_2025\_paper\_8”, p. 8) \#5fb236 \par - “We provided the participants with the experiment procedure and the details of the experimental tasks on a website. The participants were asked to answer the questionnaire after completing each task. All tasks can be completed within a web browser. Each task is related to a Web service. As target Web services, we prepared two services; SimpleTwitter (ST for short) as a SNS and InventoryManagement (IM for short) as an enterprise microservice. ST is a simple tweet system and IM is a simplified inventory management system for a liquor store. Also, we prepared four models that are written in Alloy and DTRAM and describe the specifications of two target Web services (in the following, abbreviated as ‘ST Alloy’, ‘ST DTRAM’, ‘IM Alloy’, ‘IM DTRAM’)” (“MODELS\_2025\_paper\_8”, p. 8) \#ffd400\\ \mkbibemph{What was the goal of the task? this is not clear. For what?} \par - “As a whole, each participant was asked to work on four tasks. Considering order effects of the tasks, we divided the participants into four groups in Table I, and changed the order of the tasks for each group.” (“MODELS\_2025\_paper\_8”, p. 8) \#5fb236 \par - “1) Read the outline page. 2) Read the page explaining Alloy. 3) Read the page explaining the specification of IM. 4) Work on IM Alloy (1st task). 5) Read the page explaining DTRAM. 6) Work on IM DTRAM (2nd task). 7) Read the page explaining the specification of ST. 8) Work on ST Alloy (3rd task). 9) Work on ST DTRAM (4th task).” (“MODELS\_2025\_paper\_8”, p. 8) \#5fb236 \par - “VII. CASE STUDIES” (“MODELS\_2025\_paper\_8”, p. 9) \#ffd400\\ \mkbibemph{It's not clear the contribution of the case studies with respect to the whole experiments and to the given research questions. It is not clear to what extent the case studies help to answer the research questions.} \par - “Both applications are online Android applications developed in our laboratory for project-based learning, with their backends implemented as RESTful services using JAX-RS.” (“MODELS\_2025\_paper\_8”, p. 9) \#5fb236 \par - “We think that the results are due to the difference in the underlying computation models (semiprocedural style of DTRAM and declarative style of Alloy). For specification descriptions of RESTful microservices, semiprocedural language is considered suitable.” (“MODELS\_2025\_paper\_8”, p. 10) \#5fb236 \par - “In this paper, we presented a formal specification and architecture model for RESTful microservices, DTRAM, which extends the Data Transfer Architecture Model to represent the creation and deletion of resources and their hierarchical structure.” (“MODELS\_2025\_paper\_8”, p. 10) \#5fb236 \par - “we enhanced the visual modeling tool to support DTRAM models and enable direct simulation of the described model.” (“MODELS\_2025\_paper\_8”, p. 10) \#ffd400\\ \mkbibemph{With respect to this clarification. What are the enhancements that have been operated on the visual modeling tool? what are the challenges that the previous version was affected with?} \par - “The results show that DTRAM is significantly better suited than Alloy for describing their specifications” (“MODELS\_2025\_paper\_8”, p. 10) \#ffd400\\ \mkbibemph{This seems quite obvious. You are comparing a general-purpose approach (Alloy) with a domain specific one (DTRAM as presented in the paper). Thus is not surprising that DTRAM performs better than Alloy with respect to the usability aspect.} \par - “confirmed that nearly the entire APIs were regenerated correctly from newly described DTRAM models” (“MODELS\_2025\_paper\_8”, p. 10) \#5fb236 \par Consider that those that are tagged with \#5fb236 are just highlights, those that are tagged with \#e56eee and \#a28ae5 are important sentences. Please pay attention instead to the notes that are tagged with \#ffd400. Those that are tagged with \#ff6666 are typos or errors. Could you please draft a review by organizing it as follows: SUMMARY: Just a few sentences to summarize the work. COMMENTS: Organize the notes, especially those that contain issues or typos. Moreover, list the strengths and weaknesses of the work (no more than 3 items each). At the end, list 3 questions for the authors that might be involved in a rebuttal phase.} } @misc{MODELS_2025_paper_85, title = {{{MODELS}}\_2025\_paper\_85}, langid = {english}, keywords = {LOGSEQ}, note = {I'm reviewing a research paper and I took the following notes: \par \section{Annotazioni\\ (22/5/2025, 16:21:55)} \par - “Requirement Extraction” (“MODELS\_2025\_paper\_85”, p. 1) \#a28ae5 \par - “Use Case Modeling” (“MODELS\_2025\_paper\_85”, p. 1) \#a28ae5 \par - “from User Stories” (“MODELS\_2025\_paper\_85”, p. 1) \#a28ae5 \par - “Early-stage software modeling plays a crucial role in capturing stakeholders’ intentions and ensuring that the system aligns with user requirements.” (“MODELS\_2025\_paper\_85”, p. 1) \#5fb236 \par - “transitioning from the natural language of user stories to structured artifacts, such as UML use case diagrams and full use case descriptions, remains challenging for analysts—especially for novices—due to its time-consuming, tedious, and susceptible to errors.” (“MODELS\_2025\_paper\_85”, p. 1) \#a28ae5 \par - “Story2Spec, a DeepSeek-based tool that classifies user stories into requirements according to the FURPS model (functional, usability, performance, reliability, and supportability).” (“MODELS\_2025\_paper\_85”, p. 1) \#a28ae5 \par - “it generates use case diagrams following UML syntax language and use cases descriptions based on a predefined template.” (“MODELS\_2025\_paper\_85”, p. 1) \#5fb236 \par - “Story2Spec tool generates consistent and interpretable use case diagrams with clear and detailed use case descriptions” (“MODELS\_2025\_paper\_85”, p. 1) \#5fb236 \par - “the proposed tool can significantly support the specification phase and, thus, mitigate the burden on analysts when identifying software artifacts” (“MODELS\_2025\_paper\_85”, p. 1) \#5fb236 \par - “A clear definition of user needs and system interactions and behavior paves the way for understandable and well-structured UML diagrams, which can positively impact communication among stakeholders, users, and developers” (“MODELS\_2025\_paper\_85”, p. 1) \#5fb236 \par - “Several studies indicate that including UML in agile methods significantly enhances the consistency and comprehensibility of software designs, thus decreasing the uncertainty and misconceptions throughout the project life cycle.” (“MODELS\_2025\_paper\_85”, p. 1) \#5fb236 \par - “In agile development, developers rely on user stories and succinct descriptions of software requirements to understand the system’s different functionalities.” (“MODELS\_2025\_paper\_85”, p. 1) \#5fb236 \par - “User stories basically boost cross-team clarity by clearly identifying the target users and the significance of functions, often proposing a delivery timeline for delivery according to priority.” (“MODELS\_2025\_paper\_85”, p. 1) \#5fb236 \par - “Nevertheless, it can pose new challenges if the user stories are not written properly or are misinterpreted due to their complexity and ambiguity, leading to increased workload, delays, and unmet user expectations [4].” (“MODELS\_2025\_paper\_85”, p. 1) \#5fb236 \par - “As a {$<$}actor/user{$>$} I want {$<$}goal/action{$>$} so that {$<$}reason/value{$>$}.” (“MODELS\_2025\_paper\_85”, p. 1) \#5fb236 \par - “time ,” (“MODELS\_2025\_paper\_85”, p. 1) \#ff6666 \par - “In this context, we introduce our tool, Story2Spec, powered by Deepseek, which automatically extracts requirements following the FURPS model [14]. This model is particularly beneficial for handling user stories because of their informal and varied nature.” (“MODELS\_2025\_paper\_85”, p. 1) \#a28ae5 \par - “Classifies user stories into requirements according to the FURPS model (functional, usability, performance, reliability, and supportability).” (“MODELS\_2025\_paper\_85”, p. 2) \#5fb236 \par - “Generates use case diagrams based on the extracted software requirements following the UML syntax language.” (“MODELS\_2025\_paper\_85”, p. 2) \#5fb236 \par - “Generate a complete description of the use cases based on a predefined template.” (“MODELS\_2025\_paper\_85”, p. 2) \#5fb236 \par - “A within-subjects study involving 12 software engineering analysts was conducted to assess the efficiency and usability of the Story2Spec tool compared to the baseline ChatGPT.” (“MODELS\_2025\_paper\_85”, p. 2) \#5fb236 \par - “[10] developed an NLP-based approach that integrates ontological modeling and Prolog rules to convert user stories into structured UML diagrams, including class, use case, and package diagrams.” (“MODELS\_2025\_paper\_85”, p. 2) \#5fb236 \par - “However, the model may struggle with user stories that are ambiguous or poorly organized, which could necessitate additional refinement or even human intervention.” (“MODELS\_2025\_paper\_85”, p. 2) \#5fb236 \par - “[15] utilized machine learning and NLP techniques to generate UML use case diagrams.” (“MODELS\_2025\_paper\_85”, p. 2) \#ffd400\\ \mkbibemph{The subject of a sentence cannot be a bibliographic reference. This issue occurs many times in the related work section.} \par - “ChatGPT requires high computational resources and encounters difficulties in appropriately interpreting domainspecific terms without further customized training or modification.” (“MODELS\_2025\_paper\_85”, p. 2) \#5fb236 \par - “. [17] presented a method based on Stanford CoreNLP to generate a sequence diagram and a collaborative diagram from natural language requirements” (“MODELS\_2025\_paper\_85”, p. 2) \#5fb236 \par - “Despite the progress made in deriving UML from user stories, the critical step of extracting detailed requirements is often overlooked [8], [9], [20], despite its importance.” (“MODELS\_2025\_paper\_85”, p. 3) \#a28ae5 \par - “This section describes the motivation example that drives our study as shown in 1” (“MODELS\_2025\_paper\_85”, p. 3) \#ff6666\\ \mkbibemph{... shown in *Fig.* 1.} \par - “rdadmap project” (“MODELS\_2025\_paper\_85”, p. 3) \#ffd400\\ \mkbibemph{What is it about? Can you introduce it?} \par - “analysts and experts usually extract these artifacts manually, which poses some challenges.” (“MODELS\_2025\_paper\_85”, p. 3) \#5fb236 \par - “Such manual work could be susceptible to human error, like overlooking some vital information or miscategorizing the requirements since these can differ from one analyst to another.” (“MODELS\_2025\_paper\_85”, p. 3) \#5fb236 \par - “Shifting toward automation can resolve the issues encountered with the manual approach.” (“MODELS\_2025\_paper\_85”, p. 3) \#5fb236 \par - “Story2Spec, which acts as a supportive solution for developers and analysts.” (“MODELS\_2025\_paper\_85”, p. 3) \#ffd400\\ \mkbibemph{Why developers directly? The tool is supposed to extract requirements, right? Requirements are not taken as input directly from developers.} \par - “Furthermore, our tool offers scalability over time since it can used for different projects of any size.” (“MODELS\_2025\_paper\_85”, p. 3) \#ff6666\\ \mkbibemph{Many grammatical errors.} \par - “1) Requirements classification, which aims to categorize requirements into functional, usability, performance, reliability, supportability, and others according to the FURPS model;” (“MODELS\_2025\_paper\_85”, p. 3) \#2ea8e5 \par - “2) Use case diagram generation: in this step, the deep seekbased model generates a UML use case diagram based on the input requirements;” (“MODELS\_2025\_paper\_85”, p. 3) \#2ea8e5 \par - “3) Use cases description Generation: each use case identified in the diagram will be systematically expanded into a comprehensive textual description including preconditions, postconditions, and flow of activities.” (“MODELS\_2025\_paper\_85”, p. 3) \#2ea8e5 \par - “Deriving requirements from user stories is an essential phase in the software development lifecycle, guaranteeing that user needs are comprehensively understood and converted into executable activities” (“MODELS\_2025\_paper\_85”, p. 3) \#5fb236 \par - “as shown in 2” (“MODELS\_2025\_paper\_85”, p. 3) \#ff6666\\ \mkbibemph{as shown in Fig. 2} \par - “shown in 3(I” (“MODELS\_2025\_paper\_85”, p. 3) \#ff6666 \par - “Fig. 2. User Requirement Classification Interface (Interface A)” (“MODELS\_2025\_paper\_85”, p. 4) \#ffd400\\ \mkbibemph{It's not needed to consume page space to include a figure showing a file upload form.} \par - “B. Use case diagram generation” (“MODELS\_2025\_paper\_85”, p. 4) \#ffd400\\ \mkbibemph{The generation processes need to be properly presented. For instance, it is not clear the links from the use cases given in Fig. 4 and the classified user stories given in Fig.2 . A clear mapping description needs to be given. Also the need for the user stories classification has to be clearly motivated.} \par - “V. EVALUATION” (“MODELS\_2025\_paper\_85”, p. 5) \#ffd400\\ \mkbibemph{I suggest adding a dedicated subsections to present the metrics that have been used for the evaluation (i.e., precision, recall, F1-score, etc)} \par - “To validate our work, we conducted a within-subjects study with 12 software engineering experts.” (“MODELS\_2025\_paper\_85”, p. 5) \#5fb236 \par - “as shown in I .” (“MODELS\_2025\_paper\_85”, p. 5) \#ff6666\\ \mkbibemph{as shown in Table I} \par - “We analyzed 10 projects that included 877 user stories.” (“MODELS\_2025\_paper\_85”, p. 5) \#ffd400\\ \mkbibemph{How are these projects selected.} \par - “Each expert was requested to evaluate the generated use case diagram based on the following evaluation question by rating them on a 5-point Likert scale (1 = Not at all, 2 = Slightly, 3 = Moderately, 4 = Mostly, 5 = Completely).” (“MODELS\_2025\_paper\_85”, p. 5) \#ffd400\\ \mkbibemph{In my opinion, in the whole evaluation process also artifacts produced by humans and mixed with the automated ones need to be considered.~ As it is the evaluation permits to evaluate the correctness of the considered artifacts, but not the completeness. The generation step from user stories can neglect some use stories making the generated use cases uncomplete.~ This is why it is necessary to include also manually created use cases.} \par - “by answering the following questions” (“MODELS\_2025\_paper\_85”, p. 5) \#ffd400\\ \mkbibemph{Add an explici reference to Table III that includes the questions that need to be answered.} \par - “To answer this question, the participants answer the questionnaire, which contains five questions ranging from (QE14 to QE18) by rating using the following scale: 1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, 5 = Strongly Agree.” (“MODELS\_2025\_paper\_85”, p. 6) \#ffd400\\ \mkbibemph{Add a reference to Table IV.} \par - “We evaluated six projects (P1-P6), with two projects assessed by each of the 12 experts. Each evaluation includes responses to seven questions (QE1–QE7). We calculate the mean (M) and standard deviation (SD) for the six projects for each evaluation question.” (“MODELS\_2025\_paper\_85”, p. 6) \#5fb236 \par - “VII .” (“MODELS\_2025\_paper\_85”, p. 6) \#ff6666\\ \mkbibemph{Table VII.} \par - “We studied the efficiency of our story2spec tool compared to ChatGPT’s performance as shown in VII . It identifies actors and uses cases (QE1, QE2) for specific projects like projects 1 and 3. Some standard deviation values are high, which indicates that some generated diagrams might be incomplete or difficult to interpret. Additionally, ChatGPT faces some difficulties in providing the include/extend relationships between use cases due to a lack of deep knowledge about UML structure.” (“MODELS\_2025\_paper\_85”, p. 6) \#ffd400\\ \mkbibemph{It is not clear if experts were aware or not of the language models that generated the artifacts under analysis. In other words, the experts were aware that were evaluating the use cases generated by ChatGPT or Story2Spec? This can be a potential bias. It is important to keep this aspect hidden.} \par - “in VIII.” (“MODELS\_2025\_paper\_85”, p. 7) \#ff6666\\ \mkbibemph{in Table VIII} \par - “in IX” (“MODELS\_2025\_paper\_85”, p. 7) \#ff6666\\ \mkbibemph{in Table IX} \par - “chatGPT” (“MODELS\_2025\_paper\_85”, p. 7) \#ff6666\\ \mkbibemph{ChatGPT. Please check carefully the paper to fix errors like this one.} \par - “In this section, the 12 participants evaluate the usage of our tool story2spec and the baseline ChatGPT in terms of time, ease of use, and workload reduction, as shown in X. Each participant rates the evaluation questions on a 5-point Likert scale.” (“MODELS\_2025\_paper\_85”, p. 7) \#ff6666\\ \mkbibemph{Typo and rephrase.} \par - “This study introduces Story2Spec, a DeepSeek-based tool for helping analysts transform natural language user stories into structured UML diagram modeling. It initially classifies user stories into requirements according to the FURPS model (functional, usability, performance, reliability, and supportability). Next, it produces use case diagrams following the UML syntax and generates case descriptions based on predefined. To evaluate the performance of our tool, we conducted a withinsubjects study involving 12 software engineering experts with varying levels of experience. We compared our results with the baseline ChatGPT. The findings indicate that the Story2Spec tool is able to create clear and consistent use case diagrams and use case descriptions that comply with UML standards. Furthermore, the story2spec tool proves to be efficient in saving time and reducing the workload of the analysts. In future work, we aim to explore other UML diagrams, such as the class and sequence diagrams.” (“MODELS\_2025\_paper\_85”, p. 8) \#5fb236 \par Consider that those that are tagged with \#5fb236 are just highlights, those that are tagged with \#e56eee and \#a28ae5 are important sentences. Please pay attention instead to the notes that are tagged with \#ffd400. Those that are tagged with \#ff6666 are typos or errors. Could you please draft a review by organizing it as follows: SUMMARY: Just a few sentences to summarize the work. COMMENTS: Organize the notes, especially those that contain issues or typos. Moreover, list the strengths and weaknesses of the work (no more than 3 items each). At the end, list 3 questions for the authors that might be involved in a rebuttal phase.} } @online{MODELS20, title = {{{MODELS20}}}, url = {https://www.overleaf.com/8979411763njfrwbxdyfcg}, urldate = {2020-02-13}, abstract = {An online LaTeX editor that's easy to use. No installation, real-time collaboration, version control, hundreds of LaTeX templates, and more.}, langid = {english} } @inproceedings{MODELS24_paper_2827pdf, title = {{{MODELS24}}\_paper\_2827.Pdf}, keywords = {LOGSEQ}, note = {\section{Annotazioni\\ (13/5/2024, 17:35:24)} \par - “unify the behaviour of domain-specific frameworks and evaluate it for two frameworks.” (“MODELS24\_paper\_2827.pdf”, p. 1) \#a28ae5\\ \mkbibemph{ } \par - “we enable structurally different meta-modeling approaches to exhibit identical behavior for the meta-model” (“MODELS24\_paper\_2827.pdf”, p. 1) \#a28ae5\\ \mkbibemph{ } \par - “for the user model” (“MODELS24\_paper\_2827.pdf”, p. 1) \#a28ae5\\ \mkbibemph{ } \par - “but exhibit different syntax and semantics” (“MODELS24\_paper\_2827.pdf”, p. 1) \#a28ae5\\ \mkbibemph{ } \par - “We identified 17 common concepts and defined artificial formal features for them.” (“MODELS24\_paper\_2827.pdf”, p. 1) \#a28ae5\\ \mkbibemph{ } \par - “Moreover, the formal nature of our interface prevents model inconsistencies and ensures safe meta-model modifications during runtime by forcing correct-by-construction.” (“MODELS24\_paper\_2827.pdf”, p. 1) \#a28ae5\\ \mkbibemph{ } \par - “language workbench, used to develop and validate the models” (“MODELS24\_paper\_2827.pdf”, p. 1) \#5fb236\\ \mkbibemph{ } \par - “run-time implementation exhibiting different syntax and semantics due to software or hardware constraints.” (“MODELS24\_paper\_2827.pdf”, p. 1) \#5fb236\\ \mkbibemph{ } \par - “Our scope is the typical domain-specific setup, where we have a meta-modeling language (M3 level), which is used to define the meta-model (M2 level) of a domain-specific language (DSL). With the DSL, user models (M1 level) can be instantiated, that comply to the rules defined in the M2 model [19], [1]. Respectively, the M2 model complies to the rules in the M3 model.” (“MODELS24\_paper\_2827.pdf”, p. 1) \#5fb236\\ \mkbibemph{It seems to be related to the coupled evolution setting. Let's see! [[check]] } \par - “The situation is less perfect and can be highly challenging, if bijectivity is not given.” (“MODELS24\_paper\_2827.pdf”, p. 2) \#a28ae5\\ \mkbibemph{ } \par - “behavior” (“MODELS24\_paper\_2827.pdf”, p. 2) \#5fb236\\ \mkbibemph{mmm.... be careful with the usage of behavior.... let's see!!! [[check]] } \par - “Artifacts expressible only in one framework are out of scope of our unified interface.” (“MODELS24\_paper\_2827.pdf”, p. 2) \#ffd400\\ \mkbibemph{This means that you are considering cases that can be managed with 1-1 mappings!!! } \par - “This does not mean that the mapping is one to one.” (“MODELS24\_paper\_2827.pdf”, p. 2) \#ffd400\\ \mkbibemph{ [[check]] } \par - “bijectiv” (“MODELS24\_paper\_2827.pdf”, p. 2) \#ff6666\\ \mkbibemph{ } \par - “Our first aim is a generic method for transferring a meaningful functional subset of artifacts of one M2 language lossless into another such that having the same expressiveness afterwards.” (“MODELS24\_paper\_2827.pdf”, p. 2) \#a28ae5\\ \mkbibemph{ } \par - “based on the transferred M2 models, we want a generic way to transfer essential parts of the M1 model content lossless between DSM frameworks” (“MODELS24\_paper\_2827.pdf”, p. 2) \#a28ae5\\ \mkbibemph{ } \par - “Maintaining the behavior of is not as easy as it seams” (“MODELS24\_paper\_2827.pdf”, p. 2) \#ff6666\\ \mkbibemph{ } \par - “an cl” (“MODELS24\_paper\_2827.pdf”, p. 2) \#ff6666\\ \mkbibemph{ } \par - “interacting with the models and respectively the frameworks has to be unified.” (“MODELS24\_paper\_2827.pdf”, p. 2) \#ffd400\\ \mkbibemph{I'm not sure how far it is possible to go with making this platform independent. Consider for instance interpreted vs compiled modeling frameworks. } \par - “Our proposal as depicted in Figure 1 is to identify common M2 and M1 concepts that will exist in most frameworks with the same intention.” (“MODELS24\_paper\_2827.pdf”, p. 2) \#a28ae5\\ \mkbibemph{ } \par - “majority” (“MODELS24\_paper\_2827.pdf”, p. 2) \#ffd400\\ \mkbibemph{How have you elicited/selected them? I expect to see some sort of survey. } \par - “RQ3: Is the implementation effort for such an interface feasible for Ecore and our embedded DSL framework?” (“MODELS24\_paper\_2827.pdf”, p. 2) \#ffd400\\ \mkbibemph{Why have questioned this only for Ecore and for your DSL framework? } \par - “In terms of common formats, only 9 \% of the frameworks were interoperable.” (“MODELS24\_paper\_2827.pdf”, p. 2) \#a28ae5\\ \mkbibemph{ } \par - “M2-level "common model concepts", e.g. unary and n-ary relations.” (“MODELS24\_paper\_2827.pdf”, p. 2) \#a28ae5\\ \mkbibemph{ } \par - “behavior of models” (“MODELS24\_paper\_2827.pdf”, p. 3) \#ffd400\\ \mkbibemph{The concept of "behavior of models" needs clarification! What do you mean with it? } \par - “(1) agreeing on one meta-modeling language that fulfills all needs and ensures consistency, and (2) developing a general method to convert from one DSM framework to another.” (“MODELS24\_paper\_2827.pdf”, p. 3) \#ffd400\\ \mkbibemph{This is the idea of "Power Model" from Brain Selic. } \par - “Our approach differs in two aspects from the state of the art: first, we do not rely on any constraint language for ensuring completeness and correctness, but rather on a correct-by-construction (and deletion) approach” (“MODELS24\_paper\_2827.pdf”, p. 3) \#a28ae5\\ \mkbibemph{ } \par - “we do not accept information loss between two frameworks; instead, we require that concepts not natively supported are augmented by a wrapper.” (“MODELS24\_paper\_2827.pdf”, p. 3) \#a28ae5\\ \mkbibemph{ } \par - “We define a "concept" as a modeling element with a defined meaning, e.g. an M2 level class which is the blueprint for instantiating objects at M1 level.” (“MODELS24\_paper\_2827.pdf”, p. 3) \#5fb236\\ \mkbibemph{ } \par - “In other words, a concept is a modeling intention, with defined behavior, but open realization.” (“MODELS24\_paper\_2827.pdf”, p. 3) \#ffd400\\ \mkbibemph{What's the granularity of concept? I guess it very much depends on the modeling expert, isn't it? (I see a degree of arbitrariness) } \par - “set of concepts as the common core” (“MODELS24\_paper\_2827.pdf”, p. 3) \#a28ae5\\ \mkbibemph{ } \par - “he properties and relations of our concepts are formally defined as an extended class diagram.” (“MODELS24\_paper\_2827.pdf”, p. 3) \#ffd400\\ \mkbibemph{I'm wondering how different can be this set of concepts than those available in EMOF. } \par - “The more advanced those concepts are, the higher will be the effort in wrapping them to specific implementations.” (“MODELS24\_paper\_2827.pdf”, p. 3) \#ffd400\\ \mkbibemph{I can understand the idea even though it is not clear the practical benefits. Why defining a new M3 formalism? For instance, why not considering KM3 and possibly refine it if needed to reach the goal of the authors? I see the M2 layer concepts considerably overlapping with those in KM3. } \par - “*M1OBJECT is the instance of an M2 class.” (“MODELS24\_paper\_2827.pdf”, p. 3) \#ffd400\\ \mkbibemph{This type of relation between M2 and M1 elements is not evident. } \par - “Besides M1 and M2, we define three concepts on an Mx layer (s. Table 1a), which means these exist on M2 and M1 level. *MXELEMENT is an interface that enables common properties (s. subsection 3.3) for all M1 and M2 concepts, e.g. all concepts can have constraints, *MXCONSTRAINT. Moreover, we define *MXMDB as a singleton concept to be the root for all modeling operations, i.e. a model database (MDB).” (“MODELS24\_paper\_2827.pdf”, p. 4) \#ffd400\\ \mkbibemph{Why not presenting them in m2 and m1 layers, for instance with M2MDB, M1MDB, M2ELEMENT, M1ELEMENT, etc. } \par - “build-in” (“MODELS24\_paper\_2827.pdf”, p. 4) \#ff6666\\ \mkbibemph{built-in } \par - “We defined the features of our concepts formally in Figure 3 using UML. Our class diagram, however, comes with some extras.” (“MODELS24\_paper\_2827.pdf”, p. 4) \#ffd400\\ \mkbibemph{I miss a comparison/discussion of the expressive power of the proposed modeling concepts with those available in existing metamodeling frameworks. Why not considering one of those as possible candidate for the "Common modeling concepts" shown in Fig. 1. } \par - “formally” (“MODELS24\_paper\_2827.pdf”, p. 4) \#ffd400\\ \mkbibemph{ } \par - “stereo type” (“MODELS24\_paper\_2827.pdf”, p. 4) \#ff6666\\ \mkbibemph{ } \par - “A UNIFIED MODELING INTERFACE BASED ON CONCEPTS” (“MODELS24\_paper\_2827.pdf”, p. 5) \#ffd400\\ \mkbibemph{This is related to our paper co-authored with J. De Lara. } \par - “Correct by construction / correct by deletion” (“MODELS24\_paper\_2827.pdf”, p. 5) \#ffd400\\ \mkbibemph{THis is not convincing. } \par - “consistent” (“MODELS24\_paper\_2827.pdf”, p. 5) \#ffd400\\ \mkbibemph{Do you mean correct? } \par - “build” (“MODELS24\_paper\_2827.pdf”, p. 5) \#ff6666\\ \mkbibemph{ } \par - “Consistent” (“MODELS24\_paper\_2827.pdf”, p. 5) \#ffd400\\ \mkbibemph{Correct. } \par - “Figure 4: Create graph of concepts: epimorphic mapping of concept and associations to a graph” (“MODELS24\_paper\_2827.pdf”, p. 7) \#ffd400\\ \mkbibemph{How the graphs that are represented in Fig 4 have a concrete implementation? What are the hypothesis that we should consider to say that by looking at Fig. 4 we can conclude that defined CRUD operations are complete and correct? Who define them? How? } \par - “Implementation effort” (“MODELS24\_paper\_2827.pdf”, p. 7) \#ffd400\\ \mkbibemph{I don't get the points of the numbers shown in Table 2, especially those in the third column. How should we interpret such numbers? What's the research questions you wanted to answer? }} } @inproceedings{MODELS24_paper_2991pdf, title = {{{MODELS24}}\_paper\_2991.Pdf}, keywords = {LOGSEQ}, note = {\section{Annotazioni\\ (1/5/2024, 11:22:22)} \par - “require” (“MODELS24\_paper\_2991.pdf”, p. 1) \#ff4400\\ \mkbibemph{Not very clear and convincing} \par - “Occasionally, a few languages solve this as an example of their technological space.” (“MODELS24\_paper\_2991.pdf”, p. 1) \#f0ff00\\ \mkbibemph{Uml profile? Whats new? So far is not clear} \par - “concludes” (“MODELS24\_paper\_2991.pdf”, p. 2) \#f0ff00\\ \mkbibemph{What?} \par - “MaCoCo” (“MODELS24\_paper\_2991.pdf”, p. 2) \#f0ff00\\ \mkbibemph{What's the link with monticore?} \par - “This design is a foundation of the self-extension mechanism, which we describe in more detail in the following sections.” (“MODELS24\_paper\_2991.pdf”, p. 3) \#f0ff00\\ \mkbibemph{Up to now it I not clear.} \par - “Extendable concepts:” (“MODELS24\_paper\_2991.pdf”, p. 3) \#f0ff00\\ \mkbibemph{Are they linked to some application domain?} \par - “While this feature is essential for providing reusable libraries, it does not necessarily have to be part of the DSL. It often makes sense to reuse the packaging mechanisms” (“MODELS24\_paper\_2991.pdf”, p. 3) \#f0ff00\\ \mkbibemph{What does it mean?} \par - “reprocessed” (“MODELS24\_paper\_2991.pdf”, p. 3) \#f0ff00\\ \mkbibemph{What?} \par - “GUI” (“MODELS24\_paper\_2991.pdf”, p. 4) \#f0ff00\\ \mkbibemph{What's the need for mentioning the two versions of the gui dsl?} \par - “GUI component declaration with its implementation, where the implementation consists of components imported from other models and instantiated as a part of the component.” (“MODELS24\_paper\_2991.pdf”, p. 4) \#f0ff00\\ \mkbibemph{Needs to be clarified with examples. It is not clear until now.} \par - “The presented approach differs from the GUI DSL 1, where components are a part of the DSL grammar and models only” (“MODELS24\_paper\_2991.pdf”, p. 4) \#f0ff00\\ \mkbibemph{Is this relevant? I would present dsl2 without the née for mentioning dsl1. What do we loose if we have only dsl2 in the discussion} \par - “self-extension mechanism eliminates the need to modify artifacts at the” (“MODELS24\_paper\_2991.pdf”, p. 4) \#f0ff00\\ \mkbibemph{Why self? What de the characteristics that distinguish the self? What' the difference between self and not self?} \par - “Data structure” (“MODELS24\_paper\_2991.pdf”, p. 5) \#f0ff00\\ \mkbibemph{No associations?} \par - “despite having two versions of the pages)” (“MODELS24\_paper\_2991.pdf”, p. 5) \#f0ff00\\ \mkbibemph{Why is it relevant to see both?} \par - “After the component is added to the language, the app modeler integrates it into the models.” (“MODELS24\_paper\_2991.pdf”, p. 6) \#f0ff00\\ \mkbibemph{How?} \par - “The app modeler moves the common layout to a new model specifying the component. (3) The app modeler directly” (“MODELS24\_paper\_2991.pdf”, p. 7) \#f0ff00\\ \mkbibemph{How is the semantics of the component specified? Even in this case language engineer might be required because users might not have the knowledge to add components.} \par - “self-extension” (“MODELS24\_paper\_2991.pdf”, p. 7) \#f0ff00\\ \mkbibemph{Why self?} \par - “Table 2: Lines of code (LoC) for GUI components” (“MODELS24\_paper\_2991.pdf”, p. 7) \#f0ff00\\ \mkbibemph{I'm not convinced about this metric. Comments have to be developed anyhow and such a metric is not linked and do not leverage the main novelty of the approach I. E. The possibility of adding new components by relying on some reusable libraries. Te way components are concretely added is not explained. For instance is it possible to do so during modeling sessions? Or it is necessary to import all the required components before starting the specification of models?} \par - “implementation is always higher and stays within 20\% difference, which, besides GUI DSL 2 syntax being more compact than Java in” (“MODELS24\_paper\_2991.pdf”, p. 7) \#f0ff00\\ \mkbibemph{A qualitative discussion about such a 20\% is missing.} \par - “Since the amount of code remains within the same bounds for both GUI DSL versions and the code does not change its meaning, the effort spent on the component implementation is similar.” (“MODELS24\_paper\_2991.pdf”, p. 7) \#f0ff00\\ \mkbibemph{Exactly, that's the point!!!} \par - “adding or changing the component by a language engineer.” (“MODELS24\_paper\_2991.pdf”, p. 7) \#f0ff00\\ \mkbibemph{I think that the required competences are almost the same, so what's the point here.} \par - “Workflow in GUI DSL 2” (“MODELS24\_paper\_2991.pdf”, p. 8) \#f0ff00\\ \mkbibemph{In this case the app modeler should still have language engineering competences. If not you should convincingly prove it.} \par - “This communication includes providing the specification to the language engineer and notifying the app modeler of deploying the new language version. In our experience working with GUI DSL 1, the development delay introduced by the communication between two teams varied from several minutes to several full working days. Such a delay due to communication needs quickly accumulates with continuous requirement changes and new language users.” (“MODELS24\_paper\_2991.pdf”, p. 8) \#f0ff00\\ \mkbibemph{Communication seems the main aspect that the proposed approachd deals with. However so far the validation is mainly on LOC and is not about communication aspects and does not show in quantitative extent how it is improved.} \par - “To summarize, the self-extension mechanism simplifies the workflow for introducing a new domain concept, such as a GUI component in our example. We observe the benefits when dealing with isolated cases of component creation. However, the mechanism has tradeoffs, which we discuss in the next section.” (“MODELS24\_paper\_2991.pdf”, p. 8) \#f0ff00\\ \mkbibemph{This is not so evident. You should involve people in doing so.} \par - “against” (“MODELS24\_paper\_2991.pdf”, p. 8) \#f0ff00\\ \mkbibemph{Why against?} \par - “s” (“MODELS24\_paper\_2991.pdf”, p. 8) \#f0ff00\\ \mkbibemph{Extra s} \par - “Developing, deploying, integrating, and maintaining the infrastructure for a model library creates additional work since it requires additional scripts that implement the importing” (“MODELS24\_paper\_2991.pdf”, p. 8) \#f0ff00\\ \mkbibemph{That's a good point and the paper does not discuss such an infrastructure to manage/store/provision model libraries.} \par - “In our example, the overview component is implemented by a language developer (GUI DSL 1) or an app modeler (GUI DSL 2). GUI DSL 2 that has the self-extension mechanism demands a” (“MODELS24\_paper\_2991.pdf”, p. 8) \#ff4400\\ \mkbibemph{It does not parse.} \par - “created 38 library components.” (“MODELS24\_paper\_2991.pdf”, p. 8) \#f0ff00\\ \mkbibemph{Can you mention their competencies?} \par - “If a DSL is built for modelers with narrow expertise, a selfextension mechanism will introduce a challenge since it pushes concepts to a modeling level, bringing the DSL closer to a GPL.” (“MODELS24\_paper\_2991.pdf”, p. 9) \#f0ff00\\ \mkbibemph{I agree see my previous comments.} \par - “With GUI DSL 2, the modeler must understand how to define a new layout template in a model to utilize the selfextension mechanism efficiently. If the component implementation is entirely delegated to a library developer, the advantage of the reduced communication overhead discussed in this work has no effect.” (“MODELS24\_paper\_2991.pdf”, p. 9) \#f0ff00\\ \mkbibemph{I agree!!!!} \par - “Our work specifies conditions and creates a model library.” (“MODELS24\_paper\_2991.pdf”, p. 9) \#f0ff00\\ \mkbibemph{Where?} \par - “whereas our approach is to create a new DSL or rework an existing one.” (“MODELS24\_paper\_2991.pdf”, p. 9) \#f0ff00\\ \mkbibemph{I think it is similar to the uml idea.} \par - “mechanism that supports a model library without modifying the DSL.” (“MODELS24\_paper\_2991.pdf”, p. 9) \#f0ff00\\ \mkbibemph{What are the concepts that you allow to extend?} \par - “prerequisites, including self-extension, import, and packaging mechanisms.” (“MODELS24\_paper\_2991.pdf”, p. 9) \#f0ff00\\ \mkbibemph{I the paper only the concept of self extension is discussed. The other two concepts are not prsenteded technically.}} } @inproceedings{MODELS24_paper_8050pdf, title = {{{MODELS24}}\_paper\_8050.Pdf}, keywords = {LOGSEQ}, note = {\section{Annotazioni\\ (1/5/2024, 22:11:32)} \par - “challenges” (“MODELS24\_paper\_8050.pdf”, p. 1) \#f0ff00\\ \mkbibemph{These should be described better. What challenges in particular? } \par - “an” (“MODELS24\_paper\_8050.pdf”, p. 1) \#ff6666\\ \mkbibemph{a } \par - “an” (“MODELS24\_paper\_8050.pdf”, p. 1) \#f0ff00\\ \mkbibemph{A } \par - “automatic data and schema migration between all kinds of data stores, including necessary optimizations of the schema.” (“MODELS24\_paper\_8050.pdf”, p. 1) \#5fb236\\ \mkbibemph{ } \par - “all kinds of data stores,” (“MODELS24\_paper\_8050.pdf”, p. 1) \#f0ff00\\ \mkbibemph{All kinds? } \par - “optimizations” (“MODELS24\_paper\_8050.pdf”, p. 1) \#f0ff00\\ \mkbibemph{What kinds of optimization? } \par - “suitable architecture represents an important basis regarding an implementation.” (“MODELS24\_paper\_8050.pdf”, p. 1) \#ffd400\\ \mkbibemph{suitable architecture for what? } \par - “The biggest challenges are how to handle the complexity of such a system, the required flexibility in terms of extensions and interchangeability of the individual "components", as well as the scalability to process large amounts of data.” (“MODELS24\_paper\_8050.pdf”, p. 1) \#f0ff00\\ \mkbibemph{Very vague so far. } \par - “First approaches to a flexible and scalable architecture based on microservices” (“MODELS24\_paper\_8050.pdf”, p. 1) \#ffd400\\ \mkbibemph{Does not parse } \par - “First” (“MODELS24\_paper\_8050.pdf”, p. 1) \#f0ff00\\ \mkbibemph{First? } \par - “architecture to satisfy the previously established criteria is discussed.” (“MODELS24\_paper\_8050.pdf”, p. 1) \#f0ff00\\ \mkbibemph{ } \par - “we using a subset of” (“MODELS24\_paper\_8050.pdf”, p. 2) \#ff4400\\ \mkbibemph{Many grammatical errors. } \par - “AUTOMATED SCHEMA AND DATA MIGRATION” (“MODELS24\_paper\_8050.pdf”, p. 2) \#f0ff00\\ \mkbibemph{Check if typhon and alike are considered as starting point. } \par - “In addition to our own implementation of a schema extraction algorithm with focus on reference detection and support for all types of data stores (including polystores), we have integrated the graph extraction of [16] and the implementation of [8], which support various aggregator-oriented data stores.” (“MODELS24\_paper\_8050.pdf”, p. 2) \#ffd400\\ \mkbibemph{This means that you have developed your own schema extraction approach by integrating [16] and [8]. As it is the section, is not effective in distilling the peculiarities of what you have done. I suggest to revise the section to directly presenting what you have done and in what respect to improve existing techniques/approaches. Currently, the section contains too many references and it is not self-container by compromising the readability of the whole section. The mentioned polystore concept is not supported by the technical discussion. Managing polystores is very difficult and currently the paper is not exploring and showing how the proposed approach is able to deal with polystores. } \par - “For the schema management and data migration in our approach, we use the M-model from [9]. In addition, Darwin’s schema evolution language has been extended to include further schema modification operations (SMO)s, which are required in our migration and optimization process.” (“MODELS24\_paper\_8050.pdf”, p. 2) \#ffd400\\ \mkbibemph{See my previous comment. It applies also to this section. } \par - “continious example.” (“MODELS24\_paper\_8050.pdf”, p. 3) \#ff4400\\ \mkbibemph{Continuous } \par - “target-specific meta model” (“MODELS24\_paper\_8050.pdf”, p. 3) \#ffd400\\ \mkbibemph{At this stage, it is not clear what is the target-specific metamodel. Moreover, Fig. 5 and FIg, 6 should be represented in terms of object diagrams showing the instances of metaclasses of the source and target metamodels (which unfortunately have not shown in the paper). } \par - “Figure 6 shows the target-specific meta model after applying the SMOs and Figure 7 illustrates what the physical model of Neo4j would look like at this point.” (“MODELS24\_paper\_8050.pdf”, p. 3) \#ffd400\\ \mkbibemph{The implemented operations need to be explained. Currently, the reaser is supposed to read Listing 1 and draw her considerations/conclusions on her own. } \par - “.3 Data \& Workload Analysis, Schema Optimization” (“MODELS24\_paper\_8050.pdf”, p. 3) \#ffd400\\ \mkbibemph{The whole section, and the second half in particular, is very verbose and does not clearly presents what are the novelties and the contributions of the authors concerning the work done for data and workload analysis and the consequent schema optimization. I suggest the authors to clearly state early in the paper what are the main technical contributions that goes beyond the usage and application of existing techniques to implement the workflow shown in Fig. 4. } \par - “Listing 1: Schema modification operations of the continious example,” (“MODELS24\_paper\_8050.pdf”, p. 3) \#f0ff00\\ \mkbibemph{Who writ this? } \par - “generated by the automated schema optimization, a” (“MODELS24\_paper\_8050.pdf”, p. 5) \#ffd400\\ \mkbibemph{Who performs the generation of the SMOs? } \par - “Listing 3 shows the SMOs of these manual optimizations,” (“MODELS24\_paper\_8050.pdf”, p. 5) \#ffd400\\ \mkbibemph{This sentence seems to be contracting the previous one. It is not clear if the SMOs shown in Listing 3 are (fully/partially) automatically generated or manually specified. Moreover, a detailed presentation of the logic underpinning the performed optimization is required. } \par - “operstions” (“MODELS24\_paper\_8050.pdf”, p. 5) \#ff6666\\ \mkbibemph{ } \par - “aplying” (“MODELS24\_paper\_8050.pdf”, p. 5) \#ff6666\\ \mkbibemph{ } \par - “operstions” (“MODELS24\_paper\_8050.pdf”, p. 5) \#ff6666\\ \mkbibemph{ } \par - “aplying” (“MODELS24\_paper\_8050.pdf”, p. 6) \#ff6666\\ \mkbibemph{ } \par - “apying” (“MODELS24\_paper\_8050.pdf”, p. 6) \#ff6666\\ \mkbibemph{ } \par - “apying” (“MODELS24\_paper\_8050.pdf”, p. 6) \#ff6666\\ \mkbibemph{ } \par - “The migration of data primarily concerns the mapping of the data structures of the source store to the data structures of the target store. With the use of the meta model (M-model), the schema changes can be easily applied during the data migration. Figure 4 illustrates the process of data migration in detail.” (“MODELS24\_paper\_8050.pdf”, p. 6) \#ffd400\\ \mkbibemph{The paper contains several sentences like this one that vaguously present a concept without providing technical and convincing arguments supporting it. } \par - “We developing” (“MODELS24\_paper\_8050.pdf”, p. 7) \#ff6666\\ \mkbibemph{ } \par - “a generic approach based on Apache Spark2 that on the one hand enables scalable data migration between different relational and NoSQL stores (in all directions) and on the other hand can handle schema changes (optimizations) during data migration. The use of Apache Spark also has the advantage that a large number of connectors for relational and NoSQL database systems are already available.” (“MODELS24\_paper\_8050.pdf”, p. 7) \#ffd400\\ \mkbibemph{The related work part of this section can be shortened to give more details about what the authors have done as novel contribution concerning the schema and data migration task. } \par - “The choice of a suitable design is an important prerequisite for the implementation. Since automatic optimization and migration between different data stores is a complex task consisting of different components (see Figure 4), for which various research prototypes are available, one goal is to easily integrate existing approaches in such a system.” (“MODELS24\_paper\_8050.pdf”, p. 7) \#ffd400\\ \mkbibemph{It seems that integration is main contribution of this paper, isn't it? } \par - “all types of data stores including automated schema optimizations.” (“MODELS24\_paper\_8050.pdf”, p. 7) \#ffd400\\ \mkbibemph{As mentioned by the authors while overviewing the different related work, there are many approaches addressing the problem of the authors. It is not clear, what are the novelties of the proposed approach. } \par - “highly flexible and scalable design for an automated schema and data migration process between all types of data stores including schema optimizations.” (“MODELS24\_paper\_8050.pdf”, p. 8) \#ffd400\\ \mkbibemph{No quantitative and qualitative analysis supporting sentences like this one! } \par - “The Currently implemented components are: Migration Manager (includes core logic), Web GUI, various Schema Extraction Implementation, Schema Manager (currently without the use the extended schema modification language), our Schema Optimizer approach and Data Migration. Furthermore, a first prototype of the Schema Editor has been implemented.” (“MODELS24\_paper\_8050.pdf”, p. 8) \#ffd400\\ \mkbibemph{Such clarifications should not come to the conclusion section. They should be presented earlier in the paper. }} } @online{ModelTypingSpringer, title = {On Model Typing - {{Springer}}}, url = {http://link.springer.com/article/10.1007%2Fs10270-006-0036-6}, urldate = {2015-04-01} } @online{MoDeS3, title = {{{MoDeS3}}}, url = {http://modes3.tumblr.com/}, urldate = {2016-08-21} } @inproceedings{MohagheghiD07, title = {Developing a Quality Framework for Model-Driven Engineering}, booktitle = {Models {{Softw}}. {{Eng}}. {{Workshop Symp}}. {{MoDELS}} 2007 {{Nashv}}. {{TN USA Sept}}. 30 - {{Oct}}. 5 2007 {{Rep}}. {{Revis}}. {{Sel}}. {{Pap}}.}, author = {Mohagheghi, Parastoo and Dehlen, Vegard}, editor = {Giese, Holger}, date = {2007}, series = {Lecture Notes in Computer Science}, volume = {5002}, pages = {275--286}, publisher = {Springer}, doi = {10.1007/978-3-540-69073-3\_29}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Wed, 23 Feb 2022 12:58:02 +0100} } @inproceedings{mohagheghiMetamodelSpecifyingQuality2008, title = {A Metamodel for Specifying Quality Models in Model-Driven Engineering}, booktitle = {Proc {{Nord}}. {{Workshop Model Driven Eng}}.}, author = {Mohagheghi, Parastoo and Dehlen, Vegard}, date = {2008}, pages = {51--65}, url = {http://www.sintef-group.com/globalassets/upload/ikt/9012/qualitymetamodel_final.pdf}, urldate = {2015-12-02} } @inproceedings{Mohanty2015239, title = {Ultra-Fast Variability-Aware Optimization of Mixed-Signal Designs Using Bootstrapped Kriging}, author = {Mohanty, S.P. and Kougianos, E. and Yanambaka, V.P.}, date = {2015}, series = {Proceedings - {{International Symposium}} on {{Quality Electronic Design}}, {{ISQED}}}, volume = {2015-April}, pages = {239--242}, publisher = {IEEE Computer Society}, issn = {19483287}, doi = {10.1109/ISQED.2015.7085432}, abbrev_source_title = {Proc. - Int. Symp. Qual. Electron. Des., ISQED}, affiliation = {NanoSystem Design Laboratory (NSDL), Department of Computer Science and Engineering, University of North Texas, Denton, TX 76207, United States; NanoSystem Design Laboratory (NSDL), Department of Electrical Engineering Technology, University of North Texas, Denton, TX 76207, United States}, art_number = {7085432}, document_type = {Conference Paper}, isbn = {978-1-4799-7581-5}, langid = {english}, source = {Scopus}, note = {cited By 1 \par TL;DR \par A process-variation aware design flow for ultra-fast variability-aware optimization of nano-CMOS based physical design of analog circuits and combines Kriging bootstrapped Neural Network metamodels with a Particle Swarm Optimization (PSO) algorithm in the design optimization flow.} } @inproceedings{Moin2022144, title = {{{ML-Quadrat}} \& {{DriotData}}: {{A}} Model-Driven Engineering Tool and a Low-Code Platform for Smart {{IoT}} Services}, author = {Moin, A. and Mituca, A. and Challenger, M. and Badii, A. and Gunnemann, S.}, date = {2022}, series = {Proceedings - {{International Conference}} on {{Software Engineering}}}, pages = {144--148}, publisher = {IEEE Computer Society}, issn = {02705257}, doi = {10.1109/ICSE-Companion55297.2022.9793752}, abbrev_source_title = {Proc Int Conf Software Eng}, affiliation = {Technical Univ. of Munich (TUM), Dept. of Informatics, Germany; DriotData Ug, Munich, Germany; Univ. of Antwerp \& Flanders Make, Dept. of Computer Science, Belgium; Univ. of Reading, Dept. of Computer Science, United Kingdom; Tum, Science Institute, Dept. of Informatics \& Munich Data, Germany}, coden = {PCSED}, document_type = {Conference Paper}, isbn = {978-1-6654-9598-1}, langid = {english}, source = {Scopus}, note = {cited By 0} } @article{moinMDE4QAIModelDrivenEngineering2021, title = {{{MDE4QAI}}: {{Towards Model-Driven Engineering}} for {{Quantum Artificial Intelligence}}}, shorttitle = {{{MDE4QAI}}}, author = {Moin, Armin and Challenger, Moharram and Badii, Atta and Günnemann, Stephan}, date = {2021}, publisher = {arXiv}, doi = {10.48550/ARXIV.2107.06708}, abstract = {Over the past decade, Artificial Intelligence (AI) has provided enormous new possibilities and opportunities, but also new demands and requirements for software systems. In particular, Machine Learning (ML) has proven useful in almost every vertical application domain. Although other sub-disciplines of AI, such as intelligent agents and Multi-Agent Systems (MAS) did not become promoted to the same extent, they still possess the potential to be integrated into the mainstream technology stacks and ecosystems, for example, due to the ongoing prevalence of the Internet of Things (IoT) and smart Cyber-Physical Systems (CPS). However, in the decade ahead, an unprecedented paradigm shift from classical computing towards Quantum Computing (QC) is expected, with perhaps a quantum-classical hybrid model. We expect the Model-Driven Engineering (MDE) paradigm to be an enabler and a facilitator, when it comes to the quantum and the quantum-classical hybrid applications as it has already proven beneficial in the highly complex domains of IoT, smart CPS and AI with inherently heterogeneous hardware and software platforms, and APIs. This includes not only automated code generation, but also automated model checking and verification, as well as model analysis in the early design phases, and model-to-model transformations both at the design-time and at the runtime. In this paper, the vision is focused on MDE for Quantum AI, and a holistic approach integrating all of the above.}, version = {1}, keywords = {notion}, note = {\subsection{Other} Preliminary Version - Vision Paper} } @inproceedings{mokaddem2018recommending, title = {Recommending Model Refactoring Rules from Refactoring Examples}, booktitle = {Proc. 21th {{ACMIEEE Int}}. {{Conf}}. {{Model Driven Eng}}. {{Lang}}. {{Syst}}.}, author = {family=Mokaddem, given=Chihab, prefix=eddine, useprefix=false and Sahraoui, Houari and Syriani, Eugene}, date = {2018}, pages = {257--266}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {TL;DR \par This paper proposes an approach to recommend refactoring rules that lean automatically fromRefactoring examples, and shows that, in general, the learned rules are accurate.} } @inproceedings{mokos2010ontology, title = {Ontology-Based Model Driven Engineering for Safety Verification}, booktitle = {2010 36th {{EUROMICRO Conf}}. {{Softw}}. {{Eng}}. {{Adv}}. {{Appl}}.}, author = {Mokos, Konstantinos and Meditskos, George and Katsaros, Panagiotis and Bassiliades, Nick and Vasiliades, Vangelis}, date = {2010}, pages = {47--54}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found} } @article{molino_ludwig_2019, title = {Ludwig: A Type-Based Declarative Deep Learning Toolbox}, shorttitle = {Ludwig}, author = {Molino, Piero and Dudin, Yaroslav and Miryala, Sai Sumanth}, date = {2019-09}, journaltitle = {ArXiv190907930 Cs Stat}, eprint = {1909.07930}, eprinttype = {arXiv}, eprintclass = {cs, stat}, abstract = {In this work we present Ludwig, a flexible, extensible and easy to use toolbox which allows users to train deep learning models and use them for obtaining predictions without writing code. Ludwig implements a novel approach to deep learning model building based on two main abstractions: data types and declarative configuration files. The data type abstraction allows for easier code and sub-model reuse, and the standardized interfaces imposed by this abstraction allow for encapsulation and make the code easy to extend. Declarative model definition configuration files enable inexperienced users to obtain effective models and increase the productivity of expert users. Alongside these two innovations, Ludwig introduces a general modularized deep learning architecture called Encoder-Combiner-Decoder that can be instantiated to perform a vast amount of machine learning tasks. These innovations make it possible for engineers, scientists from other fields and, in general, a much broader audience to adopt deep learning models for their tasks, concretely helping in its democratization.}, keywords = {Computer Science - Artificial Intelligence,Computer Science - Computation and Language,Computer Science - Computer Vision and Pattern Recognition,Computer Science - Machine Learning,Computer Science - Software Engineering,Statistics - Machine Learning}, note = {arXiv: 1909.07930} } @unpublished{mongodbSchemaDesignExample10:25:57UTC, title = {Schema {{Design By Example}}}, author = {MongoDB}, year = {10:25:57 UTC}, url = {https://www.slideshare.net/mongodb/schema-design-by-example}, urldate = {2018-04-30}, abstract = {One of the challenges that comes with moving to MongoDB is figuring how to} } @unpublished{mongodbTransitioningSQLMongoDB10:56:52UTC, type = {Technology}, title = {Transitioning from {{SQL}} to {{MongoDB}}}, author = {MongoDB}, year = {10:56:52 UTC}, url = {https://www.slideshare.net/mongodb/transition-sql2mongo-1?next_slideshow=1}, urldate = {2018-04-30}, abstract = {Learn how to transition from SQL to MongoDB with this presentation.} } @online{MonitoringYourHome, title = {Monitoring Your Home Network with {{InfluxDB}} on {{Raspberry Pi}} with {{Docker}} | by {{Pete Shima}} | {{Medium}}}, url = {https://medium.com/@petey5000/monitoring-your-home-network-with-influxdb-on-raspberry-pi-with-docker-78a23559ffea}, urldate = {2021-01-07} } @article{monperrusMeasuringModels, title = {Measuring Models}, author = {Monperrus, Martin and Jezequel, Jean-Marc and Champeau, Joel and Hoeltzener, Brigitte} } @article{mora_segura_extremo_2019, title = {Extremo: {{An Eclipse}} Plugin for Modelling and Meta-Modelling Assistance}, shorttitle = {Extremo}, author = {Mora Segura, Àngel and family=Lara, given=Juan, prefix=de, useprefix=true}, date = {2019}, journaltitle = {Sci. Comput. Program.}, volume = {180}, pages = {71--80}, issn = {0167-6423}, doi = {10.1016/j.scico.2019.05.003}, abstract = {Modelling is a core activity in software development paradigms like Model-driven Engineering (MDE). Therefore, the quality of (meta-)models is crucial for the success of software projects. However, many times, modelling becomes a purely manual activity, which does not take advantage of information embedded in heterogeneous information sources, such as XML documents, ontologies, or other models and meta-models. In order to improve this situation, we present Extremo, an Eclipse plugin aimed at gathering the information stored in heterogeneous sources in a common data model, to facilitate the reuse of information chunks in the model being built. The tool covers the steps needed to incorporate this knowledge within an external modelling tool, supporting the uniform query of the heterogeneous sources and the evaluation of constraints. Flexibility of the main features (e.g., supported data formats, queries) is achieved by means of extensible mechanisms. To illustrate the usefulness of Extremo, we describe a practical case study in the financial domain and evaluate its performance and scalability.}, langid = {english} } @online{MoralEducationSelfManagement, title = {E {{M C I}}: {{Moral Education}}: {{Self-Management}} - {{Lecture}} 5}, url = {http://spu.edu/depts/iccs/emci/courses/lectures/self_management_lec5.htm}, urldate = {2016-09-21} } @article{morales_repor_2020, title = {{{RePOR}}: {{Mimicking}} Humans on Refactoring Tasks. {{Are}} We There Yet?}, shorttitle = {{{RePOR}}}, author = {Morales, Rodrigo and Khomh, Foutse and Antoniol, Giuliano}, date = {2020-07}, journaltitle = {Empir. Softw. Eng.}, volume = {25}, number = {4}, pages = {2960--2996}, issn = {1573-7616}, doi = {10.1007/s10664-020-09826-7}, abstract = {Refactoring is a maintenance activity that aims to improve design quality while preserving the behavior of a system. Several (semi)automated approaches have been proposed to support developers in this maintenance activity, based on the correction of anti-patterns, which are “poor” solutions to recurring design problems. However, little quantitative evidence exists about the impact of automatically refactored code on program comprehension, and in which context automated refactoring can be as effective as manual refactoring. Leveraging RePOR, an automated refactoring approach based on partial order reduction techniques, we performed an empirical study to investigate whether automated refactoring code structure affects the understandability of systems during comprehension tasks. (1) We surveyed 80 developers, asking them to identify from a set of 20 refactoring changes if they were generated by developers or by a tool, and to rate the refactoring changes according to their design quality; (2) we asked 30 developers to complete code comprehension tasks on 10 systems that were refactored by either a freelancer or an automated refactoring tool. To make comparison fair, for a subset of refactoring actions that introduce new code entities, only synthetic identifiers were presented to practitioners. We measured developers’ performance using the NASA task load index for their effort, the time that they spent performing the tasks, and their percentages of correct answers. Our findings, despite current technology limitations, show that it is reasonable to expect a refactoring tools to match developer code. Indeed, results show that for 3 out of the 5 anti-pattern types studied, developers could not recognize the origin of the refactoring (i.e., whether it was performed by a human or an automatic tool). We also observed that developers do not prefer human refactorings over automated refactorings, except when refactoring Blob classes; and that there is no statistically significant difference between the impact on code understandability of human refactorings and automated refactorings. We conclude that automated refactorings can be as effective as manual refactorings. However, for complex anti-patterns types like the Blob, the perceived quality achieved by developers is slightly higher.}, langid = {english}, keywords = {Anti-patterns,Empirical software engineering studies,Program Comprehension,Refactoring,Software maintenance}, note = {TL;DR \par An empirical study to investigate whether automated refactoring code structure affects the understandability of systems during comprehension tasks concludes that automated refactorings can be as effective as manualRefactoring, except for complex anti-patterns types like the Blob.} } @incollection{moralesDSLAIEngineering2022, title = {Towards a {{DSL}} for {{AI Engineering Process Modeling}}}, booktitle = {Product-{{Focused Software Process Improvement}}}, author = {Morales, Sergio and Clarisó, Robert and Cabot, Jordi}, editor = {Taibi, Davide and Kuhrmann, Marco and Mikkonen, Tommi and Klünder, Jil and Abrahamsson, Pekka}, date = {2022}, volume = {13709}, pages = {53--60}, publisher = {Springer International Publishing}, location = {Cham}, doi = {10.1007/978-3-031-21388-5_4}, isbn = {978-3-031-21387-8 978-3-031-21388-5}, langid = {english} } @incollection{moreno-llorenaFunctionalCharacterizationCollaborative2011, title = {Towards a Functional Characterization of Collaborative Systems}, booktitle = {Cooperative {{Design}}, {{Visualization}}, and {{Engineering}}}, author = {Moreno-Llorena, Jaime and Claros, Iván and Martín, Rafael and Cobos, Ruth and family=Lara, given=Juan, prefix=de, useprefix=true and Guerra, Esther}, date = {2011}, pages = {182--185}, publisher = {Springer}, url = {http://link.springer.com/chapter/10.1007/978-3-642-23734-8_30}, urldate = {2015-04-01} } @inproceedings{Moreno:2015:IUT:2818754.2818860, title = {How Can {{I}} Use This Method?}, booktitle = {37th {{Int}}. {{Conf}}. {{Softw}}. {{Eng}}.}, author = {Moreno, Laura and Bavota, Gabriele and Di Penta, Massimiliano and Oliveto, Rocco and Marcus, Andrian}, date = {2015}, pages = {880--890}, publisher = {IEEE}, location = {Piscataway}, acmid = {2818860}, isbn = {978-1-4799-1934-5}, nodoi = {10.1109/ICSE.2015.98}, numpages = {11} } @inproceedings{Morin20162160, title = {Machine Learning-Based Metamodels for Sawing Simulation}, author = {Morin, M. and Paradis, F. and Rolland, A. and Wery, J. and Gaudreault, J. and Laviolette, F.}, date = {2016}, series = {Proceedings - {{Winter Simulation Conference}}}, volume = {2016-February}, pages = {2160--2171}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {08917736}, doi = {10.1109/WSC.2015.7408329}, abbrev_source_title = {Proc. Winter Simul. Conf.}, affiliation = {FORAC Research Consortium, Department of Computer Science, Software Engineering, 1065, avodela Medecine, Quebec, QC G1VOA6, Canada}, art_number = {7408329}, coden = {WSCPD}, document_type = {Conference Paper}, isbn = {978-1-4673-9743-8}, langid = {english}, source = {Scopus}, note = {cited By 10} } @article{morinModelBasedSoftwareEngineering2017, title = {Model-{{Based Software Engineering}} to {{Tame}} the {{IoT Jungle}}}, author = {Morin, Brice and Harrand, Nicolas and Fleurey, Franck}, date = {2017-01}, journaltitle = {IEEE Softw.}, volume = {34}, number = {1}, pages = {30--36}, issn = {0740-7459}, doi = {10.1109/MS.2017.11}, langid = {english}, note = {TL;DR \par The ThingML (Internet of Things Modeling Language) approach, which was inspired by UML, integrates concepts targeted at the IoT in a model-driven, generative approach.} } @article{morrisonFrameworkSupportingDynamic2007, title = {A Framework for Supporting Dynamic Systems Co-Evolution}, author = {Morrison, Ron and Balasubramaniam, Dharini and Kirby, Graham and Mickan, Kath and Warboys, Brian and Greenwood, R. Mark and Robertson, Ian and Snowdon, Bob}, date = {2007-09-11}, journaltitle = {Autom. Software Eng.}, volume = {14}, number = {3}, pages = {261--292}, issn = {0928-8910, 1573-7535}, doi = {10.1007/s10515-007-0011-7}, langid = {english} } @book{morrisonSoftwareArchitecture2nd2005, title = {Software {{Architecture}}, 2nd {{European Workshop}}, {{EWSA}} 2005, {{Pisa}}, {{Italy}}, {{June}} 13-14, 2005, {{Proceedings}}}, editor = {Morrison, Ronald and Oquendo, Flávio}, date = {2005}, series = {Lecture {{Notes}} in {{Computer Science}}}, volume = {3527}, publisher = {Springer}, doi = {10.1007/b136986}, isbn = {3-540-26275-X}, note = {TL;DR \par The ArchWare Tower: the Implementation of an Active Software Engineering Environment using a ?} } @article{mosses2004modular, title = {Modular Structural Operational Semantics}, author = {Mosses, Peter D}, date = {2004}, journaltitle = {J. Log. Algebr. Program.}, volume = {60}, pages = {195--228}, publisher = {Elsevier} } @article{mostermanCyberphysicalSystemsChallenges2016, title = {Cyber-Physical Systems Challenges: A Needs Analysis for Collaborating Embedded Software Systems}, shorttitle = {Cyber-Physical Systems Challenges}, author = {Mosterman, Pieter J. and Zander, Justyna}, date = {2016-02}, journaltitle = {Softw. Syst. Model.}, volume = {15}, number = {1}, pages = {5--16}, issn = {1619-1366, 1619-1374}, doi = {10.1007/s10270-015-0469-x}, langid = {english} } @article{mostermanIndustry40CyberPhysical2016, title = {Industry 4.0 as a {{Cyber-Physical System}} Study}, author = {Mosterman, Pieter J. and Zander, Justyna}, date = {2016-02}, journaltitle = {Softw. Syst. Model.}, volume = {15}, number = {1}, pages = {17--29}, issn = {1619-1366, 1619-1374}, doi = {10.1007/s10270-015-0493-x}, langid = {english}, note = {TL;DR \par This work focuses on the collaborative function dimension and presents a set of concrete examples of CPS challenges based on a pick and place machine that solves a distributed version of the Towers of Hanoi puzzle.} } @online{mozannarReadingLinesModeling2024, title = {Reading {{Between}} the {{Lines}} - {{Modeling User Behavior}} and {{Costs}} in {{AI-Assisted Programming}}}, shorttitle = {Reading {{Between}} the {{Lines}}}, author = {Mozannar, Hussein and Bansal, Gagan and Fourney, Adam and Horvitz, Eric}, date = {2024-04-22}, eprint = {2210.14306}, eprinttype = {arXiv}, eprintclass = {cs}, url = {http://arxiv.org/abs/2210.14306}, urldate = {2024-04-26}, abstract = {Code-recommendation systems, such as Copilot and CodeWhisperer, have the potential to improve programmer productivity by suggesting and auto-completing code. However, to fully realize their potential, we must understand how programmers interact with these systems and identify ways to improve that interaction. To make progress, we studied GitHub Copilot, a code-recommendation system used by millions of programmers daily. We developed CUPS, a taxonomy of common programmer activities when interacting with Copilot. Our study of 21 programmers, who completed coding tasks and retrospectively labeled their sessions with CUPS, showed that CUPS can help us understand how programmers interact with code-recommendation systems, revealing inefficiencies and time costs. Our insights reveal how programmers interact with Copilot and motivate new interface designs and metrics.}, langid = {english}, pubstate = {prepublished}, keywords = {Computer Science - Human-Computer Interaction,Computer Science - Machine Learning,Computer Science - Software Engineering} } @article{Mozejko201819, title = {Traffic Signal Settings Optimization Using Gradient Descent}, author = {Mozejko, M. and Brzeski, M. and Madry, L. and Skowronek, L. and Gora, P.}, date = {2018}, journaltitle = {Schedae Informaticae}, volume = {27}, pages = {19--30}, publisher = {Jagiellonian University}, issn = {17323916}, doi = {10.4467/20838476SI.18.002.10407}, abbrev_source_title = {Schedae Informaticae}, affiliation = {TensorCell; Faculty of Mathematics and Computer Science, Jagiellonian University, Poland; Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Poland}, document_type = {Article}, langid = {english}, source = {Scopus}, note = {cited By 2} } @article{MQLLM4DSLModuleQuality2018, title = {{{MQ-LLM4DSL}}: Module and Quality Driven Domain-Specific Language Design with Large Language Models}, date = {2018}, abstract = {Domain-Specific Languages (DSLs) are essential in software engineering, offering precise expression and excellent usability for specific domains, significantly improving software development efficiency. However, DSL design is challenging, requiring deep domain knowledge and language expertise to ensure quality. Mainstream DSL design methods use intermediate models like ontologies and metamodels to guide construction through manually defined rules, demanding substantial expert effort. Although some approaches transform ontologies into DSLs using explicit rules, ensuring full requirement retention remains a challenge. These methods primarily focus on the expressiveness of DSLs, while considerations for other quality aspects are insufficient. Some methods emphasize the evaluation of DSL during the design process and the improvement based on the evaluation results, incorporating this process as a part of the DSL design method. However, these methods do not provide clear improvement approaches based on specific evaluations. We propose the MQ-LLM4DSL method. This method utilizes the functional modules of domain corpus as an intermediate model for DSL design. It also provides specific improvement methods based on the core quality factors of the DSL and iteratively optimizes the quality of each step in the DSL design. Experimental results show that DSLs designed with MQ-LLM4DSL outperform those created using the baseline method, achieving an improvement: 22\% in Expressiveness, 16\% in Flexibility and 9\% in EaseofLearn.}, langid = {english}, keywords = {LOGSEQ}, note = {I'm reviewing a research paper and I took the following notes: \par \section{Annotazioni\\ (16/5/2025, 19:27:20)} \par - “Although some approaches transform ontologies into DSLs using explicit rules, ensuring full requirement retention remains a challenge.” (“MQ-LLM4DSL: module and quality driven domain-specific language design with large language models”, 2018, p. 1) \#5fb236 \par - “Some methods emphasize the evaluation of DSL during the design process and the improvement based on the evaluation results, incorporating this process as a part of the DSL design method. However, these methods do not provide clear improvement approaches based on specific evaluations.” (“MQ-LLM4DSL: module and quality driven domain-specific language design with large language models”, 2018, p. 1) \#5fb236 \par - “Automatically generating high-quality DSLs from domain-specific corpora has long been an open research challenge, attracting significant interest from both academia and industry.” (“MQ-LLM4DSL: module and quality driven domain-specific language design with large language models”, 2018, p. 1) \#5fb236 \par - “domain corpora as the functional requirements for DSL design” (“MQ-LLM4DSL: module and quality driven domain-specific language design with large language models”, 2018, p. 1) \#5fb236 \par - “Other quality requirements consist of usability, flexibility, etc” (“MQ-LLM4DSL: module and quality driven domain-specific language design with large language models”, 2018, p. 1) \#5fb236 \par - “Corpus2DSL” (“MQ-LLM4DSL: module and quality driven domain-specific language design with large language models”, 2018, p. 1) \#2ea8e5 \par - “Model2DSL” (“MQ-LLM4DSL: module and quality driven domain-specific language design with large language models”, 2018, p. 1) \#2ea8e5 \par - “The presence of conceptual models allows designers to build precise results based on their understanding of the requirements, which also achieves traceability of the design and reduces complexity by breaking down tasks” (“MQ-LLM4DSL: module and quality driven domain-specific language design with large language models”, 2018, p. 1) \#5fb236 \par - “Unfortunately, these methods focus on discussing the rationality of conceptual models and lack specific steps for building models and transforming them into DSLs.” (“MQ-LLM4DSL: module and quality driven domain-specific language design with large language models”, 2018, p. 1) \#5fb236 \par - “Ontology2DSL” (“MQ-LLM4DSL: module and quality driven domain-specific language design with large language models”, 2018, p. 1) \#2ea8e5 \par - “[17, 33, 33]” (“MQ-LLM4DSL: module and quality driven domain-specific language design with large language models”, 2018, p. 1) \#ff6666\\ \mkbibemph{Reference [33] occurs twice.} \par - “Existing DSL design methods still require a significant amount of work from experts at different design stages.” (“MQ-LLM4DSL: module and quality driven domain-specific language design with large language models”, 2018, p. 1) \#5fb236 \par - “Corpus2Model2DSL” (“MQ-LLM4DSL: module and quality driven domain-specific language design with large language models”, 2018, p. 1) \#ffd400\\ \mkbibemph{It's not shown in Fig. 1, isn't it?} \par - “The above DSL design methods lack focus on improving DSL quality.” (“MQ-LLM4DSL: module and quality driven domain-specific language design with large language models”, 2018, p. 2) \#5fb236 \par - “lack approaches to improve DSL quality through quality evaluation.” (“MQ-LLM4DSL: module and quality driven domain-specific language design with large language models”, 2018, p. 2) \#5fb236 \par - “However, these methods do not provide clear improvement approaches based on specific evaluations” (“MQ-LLM4DSL: module and quality driven domain-specific language design with large language models”, 2018, p. 2) \#5fb236 \par - “two limitations of existing methods” (“MQ-LLM4DSL: module and quality driven domain-specific language design with large language models”, 2018, p. 2) \#ffd400\\ \mkbibemph{Which one exactly?} \par - “domain corpora to DSL design, and to ensure the expressiveness and other qualities of DSLs.” (“MQ-LLM4DSL: module and quality driven domain-specific language design with large language models”, 2018, p. 2) \#5fb236 \par - “MQ-LLM4DSL(Figure 2)” (“MQ-LLM4DSL: module and quality driven domain-specific language design with large language models”, 2018, p. 2) \#ff6666\\ \mkbibemph{A space is missing before the open parenthesis. There are many occurrences of the same problem.} \par - “Based on this, clear executable steps for designing DSLs are provided so that LLMs can easily understand and execute the steps and present results. By summarizing and analyzing existing DSL evaluation methods, we have selected quality indicators and evaluation methods that can guide DSL improvement. Then, combining the DSL design steps with the evaluation methods, we provide methods for evaluation-feedback to further improve DSL quality.” (“MQ-LLM4DSL: module and quality driven domain-specific language design with large language models”, 2018, p. 2) \#ffd400\\ \mkbibemph{This description is vague and requires a concrete example to show what you plan to improve. A motivating and explanatory example would help understand the challenges the work aims to address.} \par - “The method consists of the following four parts:” (“MQ-LLM4DSL: module and quality driven domain-specific language design with large language models”, 2018, p. 2) \#5fb236 \par - “1. Pattern Extraction: Use LLMs to extract entities, relationships, and other abstract concepts (referred to as elements) from domain corpora;” (“MQ-LLM4DSL: module and quality driven domain-specific language design with large language models”, 2018, p. 2) \#2ea8e5 \par - “2. Restructuring and Refining of the Functional Module Architecture: Use patterns as the initial functional modules, restructure the architecture based on the common parts of the patterns and the similar relationships among functional modules;” (“MQ-LLM4DSL: module and quality driven domain-specific language design with large language models”, 2018, p. 2) \#2ea8e5 \par - “3. Module DSL Design and Integration:” (“MQ-LLM4DSL: module and quality driven domain-specific language design with large language models”, 2018, p. 2) \#2ea8e5 \par - “M DSL” (“MQ-LLM4DSL: module and quality driven domain-specific language design with large language models”, 2018, p. 2) \#ffd400\\ \mkbibemph{Is this a typo?} \par - “Evaluation and Improvement:” (“MQ-LLM4DSL: module and quality driven domain-specific language design with large language models”, 2018, p. 2) \#2ea8e5 \par - “Figure 2:” (“MQ-LLM4DSL: module and quality driven domain-specific language design with large language models”, 2018, p. 2) \#ffd400\\ \mkbibemph{According to the figure, it seems that the DSL quality evaluation process gives some input to the DSL development process. However, I would expect that there is also a connection on the other way round.} \par - “quality during use and quality during maintenance.” (“MQ-LLM4DSL: module and quality driven domain-specific language design with large language models”, 2018, p. 2) \#5fb236 \par - “Therefore, we cannot improve the DSL based on the evaluation results of each quality indicator individually.” (“MQ-LLM4DSL: module and quality driven domain-specific language design with large language models”, 2018, p. 3) \#5fb236 \par - “we need to determine the relationship between various quality indicators and identify which quality assessments can guide DSL improvements” (“MQ-LLM4DSL: module and quality driven domain-specific language design with large language models”, 2018, p. 3) \#5fb236 \par - “requirements, which is Expressiveness.” (“MQ-LLM4DSL: module and quality driven domain-specific language design with large language models”, 2018, p. 3) \#ffd400\\ \mkbibemph{Only expressiveness? It seems you wanted to focus on more than one requirement.} \par - “Expressiveness” (“MQ-LLM4DSL: module and quality driven domain-specific language design with large language models”, 2018, p. 3) \#5fb236 \par - “Therefore, improvements to the DSL should prioritize Expressiveness. Then, consider quality indicators that have little to no impact on the existing Expressiveness during improvement. Finally, consider improvements that potentially affect Expressiveness. Based on this, select quality indicators that can provide information for DSL improvement. Following the aforementioned approach to filter existing DSL evaluation methods, we obtain the results(Table 2).” (“MQ-LLM4DSL: module and quality driven domain-specific language design with large language models”, 2018, p. 3) \#ffd400\\ \mkbibemph{this is not clear, it needs rewording.} \par - “MQ-LLM4DSL ,” (“MQ-LLM4DSL: module and quality driven domain-specific language design with large language models”, 2018, p. 3) \#ff6666\\ \mkbibemph{extra space before ","} \par - “3 Approach” (“MQ-LLM4DSL: module and quality driven domain-specific language design with large language models”, 2018, p. 3) \#ffd400\\ \mkbibemph{This section is high level and it looks like more a project proposal than a developed approach presentation. I suggest revise the whole section (and also the other ones) with the aim of making the paper more concrete and robust. It is necessary to present explanatory examples and real DSL cases that can be used to make the paper more concrete. Currently the paper is about DSL design and development, and there is no "trace" of DSL in the paper.} \par - “Table 2: DSL evaluation methods for quality improvement” (“MQ-LLM4DSL: module and quality driven domain-specific language design with large language models”, 2018, p. 4) \#ffd400\\ \mkbibemph{The content of Table 2 is not properly supported by proper evidence. How have authors defined such quality indicators? I'm not convinced about them. For instance, many aspects are related to "Understanding" including the supporting tool of the considered DSL. You might have different implementations of the same DSL, and quality aspects may change. For instance, how would you cover graphical vs textual DSLs?} \par - “Figure 4: MQ-LLM4DSL overview” (“MQ-LLM4DSL: module and quality driven domain-specific language design with large language models”, 2018, p. 4) \#ffd400\\ \mkbibemph{In my opinion, the process cannot be only unidirectional. Also back-propagation should be supported. During the development of the DSL it can be required to refine requirements in order to add some unforeseen ones or refine existing ones.} \par - “3.1.1 Pattern Extraction.” (“MQ-LLM4DSL: module and quality driven domain-specific language design with large language models”, 2018, p. 4) \#2ea8e5 \par - “3.1.2 Functional Module Architecture Restructuring and Refinement.” (“MQ-LLM4DSL: module and quality driven domain-specific language design with large language models”, 2018, p. 4) \#2ea8e5 \par - “Refinement is then performed on the existing functional module architecture, further subdividing and designing modules that represent large, complex, or insufficiently clear functions, based on the existing architecture.” (“MQ-LLM4DSL: module and quality driven domain-specific language design with large language models”, 2018, p. 4) \#5fb236 \par - “3.1.3 Module DSLs Design and Integration.” (“MQ-LLM4DSL: module and quality driven domain-specific language design with large language models”, 2018, p. 4) \#2ea8e5 \par - “3.1.4 Quality Evaluation and Improvement.” (“MQ-LLM4DSL: module and quality driven domain-specific language design with large language models”, 2018, p. 5) \#2ea8e5 \par - “Quality Evaluation and Improvement” (“MQ-LLM4DSL: module and quality driven domain-specific language design with large language models”, 2018, p. 6) \#ffd400\\ \mkbibemph{The approach is supposed to be quality driven, thus I would expect to have quality checks in different stages of the process, instead of having quality evaluations only the very end of the approach. It is difficult to understand how improvement are guided with the aim of improving the quality of the DSL under development. Moreover, it is not clear if the process shown in Fig. 4 has been actually implemented and tool supported.} \par - “4.5 Detailed Experimental Setup” (“MQ-LLM4DSL: module and quality driven domain-specific language design with large language models”, 2018, p. 8) \#ffd400\\ \mkbibemph{Also this section needs details. It is not clear how the experiments have been actually executed. Currently, readers are supposed to download the package from zenodo and figure out on their own how the approach has been actually developed, what are the software components related to the what has been presented in the paper etc. It is necessary to make the paper self-explanatory and then readers taht want know more can explore the code. Not the vice versa.\\ } \par COnsider that those that are tagget with \#5fb236 are just highlights, those that are tagged with \#e56eee and \#a28ae5 are imporant sentences. Please pay attention instead to the notes that are tagged with \#ffd400. Those that are tagged with \#ff6666 are typos or errors. Could you please draft a review by organizing it as follows: SUMMARY: Just a few sentence to summarize the work STRENGHTS: \par WEAKNESSES: \par COMMENTS: Organize the notes with respect to the following criteria: \par \begin{itemize} \item \texttt{Novelty} \item \texttt{Rigor} \item \texttt{Relevance (of the contribution)} \item \texttt{Verifiability and Transparency} \item \texttt{Presentation} \end{itemize} \par And then add a Detailed Comments section to report the notets that contain issues or typos.} } @online{MSCAITN2016, title = {{{MSCA-ITN-2016}}}, url = {https://ec.europa.eu/research/participants/portal/desktop/en/opportunities/h2020/topics/2056-msca-itn-2016.html}, urldate = {2015-11-19} } @inproceedings{mucciniSelfadaptationCyberphysicalSystems2016, title = {Self-Adaptation for Cyber-Physical Systems: A Systematic Literature Review}, shorttitle = {Self-Adaptation for Cyber-Physical Systems}, author = {Muccini, Henry and Sharaf, Mohammad and Weyns, Danny}, date = {2016}, pages = {75--81}, publisher = {ACM Press}, doi = {10.1145/2897053.2897069}, isbn = {978-1-4503-4187-5}, langid = {english} } @incollection{mullerAutonomicComputingNow2009, title = {Autonomic {{Computing Now You See It}}, {{Now You Don}}’t}, booktitle = {Software {{Engineering}}}, author = {Müller, Hausi A. and Kienle, Holger M. and Stege, Ulrike}, editor = {De Lucia, Andrea and Ferrucci, Filomena}, editora = {Hutchison, David and Kanade, Takeo and Kittler, Josef and Kleinberg, Jon M. and Mattern, Friedemann and Mitchell, John C. and Naor, Moni and Nierstrasz, Oscar and Pandu Rangan, C. and Steffen, Bernhard and Sudan, Madhu and Terzopoulos, Demetri and Tygar, Doug and Vardi, Moshe Y. and Weikum, Gerhard}, editoratype = {redactor}, date = {2009}, volume = {5413}, pages = {32--54}, publisher = {Springer Berlin Heidelberg}, location = {Berlin, Heidelberg}, url = {http://link.springer.com/10.1007/978-3-540-95888-8_2}, urldate = {2016-09-29}, isbn = {978-3-540-95887-1 978-3-540-95888-8}, note = {TL;DR \par In this article, it is argued that both design perspectives are needed and necessary for autonomic system design.} } @online{MultiagentSwarmsLangGraph, title = {Multi-Agent Swarms with {{LangGraph}}}, url = {https://youtube.com/watch?v=JeyDrn1dSUQ&si=Q1M2muJ_BSZmtGyL}, keywords = {source\_mobile} } @article{Mumuni2022191, title = {Bayesian Cue Integration of Structure from Motion and {{CNN-based}} Monocular Depth Estimation for Autonomous Robot Navigation}, author = {Mumuni, F. and Mumuni, A.}, date = {2022}, journaltitle = {Int. J. Intell. Robot. Appl.}, volume = {6}, number = {2}, pages = {191--206}, publisher = {Springer}, issn = {23665971}, doi = {10.1007/s41315-022-00226-2}, abstract = {Monocular depth estimation (MDE) provides information (from a single image) about overall scene layout, and is useful in robotics for autonomous navigation and vision-aided guidance. Advancements in deep learning, particularly self-supervised convolutional neural networks (CNNs), have led to the development of MDE models capable of providing highly accurate per-pixel depth maps. However, these models are typically tuned for specific datasets, leading to sharp performance degradation in real-world scenarios, particularly in robot vision tasks—where the natural environments are too varied and complex to be sufficiently described by standard datasets. Motivated by the approach of biological vision, whose immense success relies on optimal combination of multiple depth cues and knowledge about the underlying environments, we exploit structure from motion (SfM) through optical flow as an additional depth cue and prior knowledge about depth distribution in the environment to improve monocular depth prediction. Meanwhile, there is a general incompatibility between the outputs of these models—whereas SfM measures absolute distances, MDE is scale ambiguous, returning only depth ratios. Consequently, we show how it is possible to promote MDE cue from ordinal scale to the same metric scale as SfM, thus, enabling their optimal integration in a Bayesian optimal manner. Additionally, we generalize the relationship between camera tilt angles and resulting MDE distortions, and show how this can be used to further improve depth perception robustness and accuracy (up to 6.2\%) for a mobile robot whose heading is subject to arbitrary angular inclinations. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.}, document_type = {Article}, source = {Scopus}, note = {cited By 0 \par TL;DR \par This work shows how it is possible to promote MDE cue from ordinal scale to the same metric scale as SfM, thus, enabling their optimal integration in a Bayesian optimal manner, and generalizes the relationship between camera tilt angles and resulting MDE distortions.} } @incollection{munappyDataPipelineManagement2020, title = {Data {{Pipeline Management}} in {{Practice}}: {{Challenges}} and {{Opportunities}}}, shorttitle = {Data {{Pipeline Management}} in {{Practice}}}, booktitle = {Product-{{Focused Software Process Improvement}}}, author = {Munappy, Aiswarya Raj and Bosch, Jan and Olsson, Helena Homström}, editor = {Morisio, Maurizio and Torchiano, Marco and Jedlitschka, Andreas}, date = {2020}, volume = {12562}, pages = {168--184}, publisher = {Springer International Publishing}, location = {Cham}, doi = {10.1007/978-3-030-64148-1_11}, abstract = {Data pipelines involve a complex chain of interconnected activities that starts with a data source and ends in a data sink. Data pipelines are important for data-driven organizations since a data pipeline can process data in multiple formats from distributed data sources with minimal human intervention, accelerate data life cycle activities, and enhance productivity in data-driven enterprises. However, there are challenges and opportunities in implementing data pipelines but practical industry experiences are seldom reported. The findings of this study are derived by conducting a qualitative multiple-case study and interviews with the representatives of three companies. The challenges include data quality issues, infrastructure maintenance problems, and organizational barriers. On the other hand, data pipelines are implemented to enable traceability, fault-tolerance, and reduce human errors through maximizing automation thereby producing high-quality data. Based on multiplecase study research with five use cases from three case companies, this paper identifies the key challenges and benefits associated with the implementation and use of data pipelines.}, isbn = {978-3-030-64147-4 978-3-030-64148-1}, langid = {english}, keywords = {STAR}, note = {\textbf{Blue Annotations (24/2/2022, 11:01:03)} \par "Most of the organizations have already realized that big data is an essential factor for success and consequently, they use big data for business decisions [9] [13]. However, high-quality data is critical for excellent data products [3]." (\href{zotero://open-pdf/library/items/BL2NMPE3?page=2}{Munappy et al 2020:169}) \par "Companies relying on data for making decisions should be able to collect, store, and process high-quality data." (\href{zotero://open-pdf/library/items/BL2NMPE3?page=2}{Munappy et al 2020:169}) \par "it automates the operations involved in the selection, extraction, transformation, aggregation, validation, and loading of data for further analysis and visualization [11]. It oers end to end speed by removing errors and resisting bottlenecks or delay. Data pipelines can process multiple streams of data simultaneously [14]." (\href{zotero://open-pdf/library/items/BL2NMPE3?page=3}{Munappy et al 2020:170}) \par "It can route data through a dierent application like visualization or machine learning or deep learning model." (\href{zotero://open-pdf/library/items/BL2NMPE3?page=3}{Munappy et al 2020:170}) \par "Data pipelines in production should run iteratively for a longer duration due to which it has to manage process and performance monitoring, validation, fault detection, and mitigation." (\href{zotero://open-pdf/library/items/BL2NMPE3?page=3}{Munappy et al 2020:170}) \par "Data Collection Pipeline" (\href{zotero://open-pdf/library/items/BL2NMPE3?page=6}{Munappy et al 2020:173}) \par "Data Governance Pipeline" (\href{zotero://open-pdf/library/items/BL2NMPE3?page=7}{Munappy et al 2020:174}) \par "Data Pipeline for Machine learning Applications" (\href{zotero://open-pdf/library/items/BL2NMPE3?page=8}{Munappy et al 2020:175}) \par "Data for this pipeline is obtained from the devices that are sent to the repair center. Data pipelines for machine learning applications has four main steps namely ingest, store, transform and aggregate." (\href{zotero://open-pdf/library/items/BL2NMPE3?page=8}{Munappy et al 2020:175}) \par "These new les are then loaded into the archive directory of the data cluster." (\href{zotero://open-pdf/library/items/BL2NMPE3?page=8}{Munappy et al 2020:175}) \par "Data transformation checks for the new les in the archive directory of the data cluster and when found, it is fetched, uncompressed and processed to convert it to an appropriate format" (\href{zotero://open-pdf/library/items/BL2NMPE3?page=8}{Munappy et al 2020:175}) \par "Data Collection Pipeline" (\href{zotero://open-pdf/library/items/BL2NMPE3?page=8}{Munappy et al 2020:175}) \par "Data Quality analysis Pipeline" (\href{zotero://open-pdf/library/items/BL2NMPE3?page=9}{Munappy et al 2020:176}) \par "Integrating new data sources:" (\href{zotero://open-pdf/library/items/BL2NMPE3?page=10}{Munappy et al 2020:177}) \par "nodes can have more than one capability." (\href{zotero://open-pdf/library/items/BL2NMPE3?page=10}{Munappy et al 2020:177}) \par \textbf{Red Annotations (24/2/2022, 11:01:03)} \par "Collecting data from multiple assorted sources to producing useful insights is challenging [1]." (\href{zotero://open-pdf/library/items/BL2NMPE3?page=2}{Munappy et al 2020:169}) \par "Moreover, big data is dicult to congure, deploy, and manage due to its volume, velocity, and variety [12]." (\href{zotero://open-pdf/library/items/BL2NMPE3?page=2}{Munappy et al 2020:169}) \par "Data ow can be precarious, because there are several things that can go wrong during the transportation of data from one node to another: data can become corrupted, it can cause latency, or data sources may overlap and/or generate duplicates [5]. These problems increase in scale and impact as the number of data sources multiplies and complexity of the requirements grows." (\href{zotero://open-pdf/library/items/BL2NMPE3?page=3}{Munappy et al 2020:170}) \par "data pipeline creation, management, and maintenance is a complicated task which demands a considerable amount of time and eort." (\href{zotero://open-pdf/library/items/BL2NMPE3?page=3}{Munappy et al 2020:170}) \par "Most of the companies do this maintenance manually by appointing a dedicated person to guard the data ow through the pipeline." (\href{zotero://open-pdf/library/items/BL2NMPE3?page=3}{Munappy et al 2020:170}) \par "data pipeline challenges including infrastructural, organizational, and technical ones." (\href{zotero://open-pdf/library/items/BL2NMPE3?page=3}{Munappy et al 2020:170}) \par "Data errors and their impact on machine learning models are described in [6] by Caveness et. al. They also propose a data validation framework that validates the data owing through the machine learning pipeline" (\href{zotero://open-pdf/library/items/BL2NMPE3?page=4}{Munappy et al 2020:171}) \par "The collected data can have sensitive information like use details which needs responsible attention" (\href{zotero://open-pdf/library/items/BL2NMPE3?page=6}{Munappy et al 2020:173}) \par "data generated by sources can be of dierent formats and frequencies" (\href{zotero://open-pdf/library/items/BL2NMPE3?page=6}{Munappy et al 2020:173}) \par "data collection pipeline that collects data from distributed devices." (\href{zotero://open-pdf/library/items/BL2NMPE3?page=6}{Munappy et al 2020:173}) \par "Data pipeline scalability:" (\href{zotero://open-pdf/library/items/BL2NMPE3?page=10}{Munappy et al 2020:177}) \par "ability of a data pipeline to scale with the increased amount of ingested data, while keeping the cost low is a real challenge experienced by the data pipeline developers" (\href{zotero://open-pdf/library/items/BL2NMPE3?page=10}{Munappy et al 2020:177}) \par "Increased number of nodes and connectors in upstream:" (\href{zotero://open-pdf/library/items/BL2NMPE3?page=10}{Munappy et al 2020:177}) \par "the easy detection of faults, each of the nodes should be preferably assigned a single capability. Thus" (\href{zotero://open-pdf/library/items/BL2NMPE3?page=10}{Munappy et al 2020:177}) \par "number of nodes and connectors increases in the upstream in relation to the data product yielded from the pipeline." (\href{zotero://open-pdf/library/items/BL2NMPE3?page=10}{Munappy et al 2020:177}) \par ""With the increased number of components in the data pipeline which in turn makes it dicult to understand and maintain. It is dicult to" (\href{zotero://open-pdf/library/items/BL2NMPE3?page=10}{Munappy et al 2020:177}) \par "10 Aiswarya et al. attain the right balance between robustness and complexity" (\href{zotero://open-pdf/library/items/BL2NMPE3?page=11}{Munappy et al 2020:178}) \par "Trade-o between data pipeline complexity and robustness:" (\href{zotero://open-pdf/library/items/BL2NMPE3?page=11}{Munappy et al 2020:178}) \par "Repeated alarms:" (\href{zotero://open-pdf/library/items/BL2NMPE3?page=11}{Munappy et al 2020:178}) \par "Data Quality Challenges" (\href{zotero://open-pdf/library/items/BL2NMPE3?page=12}{Munappy et al 2020:179}) \par "Missing data les:" (\href{zotero://open-pdf/library/items/BL2NMPE3?page=12}{Munappy et al 2020:179}) \par ""Data quality is a challenge that is being discussed over years. But, at industry level we still struggle to achieve desired level of data quality" - Senior Data Scientist(R1)" (\href{zotero://open-pdf/library/items/BL2NMPE3?page=12}{Munappy et al 2020:179}) \par "Operational errors:" (\href{zotero://open-pdf/library/items/BL2NMPE3?page=13}{Munappy et al 2020:180}) \par "Logical changes:" (\href{zotero://open-pdf/library/items/BL2NMPE3?page=13}{Munappy et al 2020:180}) \par "Solve data accessibility challenges" (\href{zotero://open-pdf/library/items/BL2NMPE3?page=13}{Munappy et al 2020:180}) \par "Save time and eort of human resources" (\href{zotero://open-pdf/library/items/BL2NMPE3?page=13}{Munappy et al 2020:180}) \par "inbuilt monitoring capability" (\href{zotero://open-pdf/library/items/BL2NMPE3?page=13}{Munappy et al 2020:180}) \par "Improves traceability of data work ow" (\href{zotero://open-pdf/library/items/BL2NMPE3?page=14}{Munappy et al 2020:181}) \par "Supports heterogeneous data sources" (\href{zotero://open-pdf/library/items/BL2NMPE3?page=14}{Munappy et al 2020:181}) \par "Accelerates Data life cycle activities" (\href{zotero://open-pdf/library/items/BL2NMPE3?page=15}{Munappy et al 2020:182}) \par "Standardize the Data Work ow" (\href{zotero://open-pdf/library/items/BL2NMPE3?page=15}{Munappy et al 2020:182}) \par "Improved Data Analytics and Machine Learning Models" (\href{zotero://open-pdf/library/items/BL2NMPE3?page=15}{Munappy et al 2020:182}) \par "Data pipelines are traceable since the stages are predened yielding better quality data for the models." (\href{zotero://open-pdf/library/items/BL2NMPE3?page=15}{Munappy et al 2020:182}) \par "data pipelines ensure a smooth ow of data unless it fails in one of the steps." (\href{zotero://open-pdf/library/items/BL2NMPE3?page=15}{Munappy et al 2020:182}) \par TL;DR \par The challenges include data quality issues, infrastructure maintenance problems, and organizational barriers, which are implemented to enable traceability, fault-tolerance, and reduce human errors through maximizing automation thereby producing high-quality data. \par \textbf{Yellow Annotations (24/2/2022, 11:01:03)} \par "Data pipelines involve a complex chain of interconnected activities that starts with a data source and ends in a data sink." (\href{zotero://open-pdf/library/items/BL2NMPE3?page=2}{Munappy et al 2020:169}) \par "process data in multiple formats" (\href{zotero://open-pdf/library/items/BL2NMPE3?page=2}{Munappy et al 2020:169}) \par "minimal human intervention," (\href{zotero://open-pdf/library/items/BL2NMPE3?page=2}{Munappy et al 2020:169}) \par "distributed data sources" (\href{zotero://open-pdf/library/items/BL2NMPE3?page=2}{Munappy et al 2020:169}) \par "data life cycle activities" (\href{zotero://open-pdf/library/items/BL2NMPE3?page=2}{Munappy et al 2020:169}) \par "enhance productivity in data-driven enterprises" (\href{zotero://open-pdf/library/items/BL2NMPE3?page=2}{Munappy et al 2020:169}) \par "data quality issues" (\href{zotero://open-pdf/library/items/BL2NMPE3?page=2}{Munappy et al 2020:169}) \par "infrastructure maintenance problems" (\href{zotero://open-pdf/library/items/BL2NMPE3?page=2}{Munappy et al 2020:169}) \par "organizational barriers" (\href{zotero://open-pdf/library/items/BL2NMPE3?page=2}{Munappy et al 2020:169}) \par "automation thereby producing high-quality data" (\href{zotero://open-pdf/library/items/BL2NMPE3?page=2}{Munappy et al 2020:169}) \par "Data is being increasingly used by industries for decision making, training machine learning(ML)/deep learning(DL) models, creating reports, and generating insights." (\href{zotero://open-pdf/library/items/BL2NMPE3?page=2}{Munappy et al 2020:169}) \par "In other words, data pipelines are the connected chain of processes where the output of one or more processes becomes an input for another [19]" (\href{zotero://open-pdf/library/items/BL2NMPE3?page=3}{Munappy et al 2020:170}) \par "large scale companies like Google, Amazon, LinkedIn, and Facebook have recognized the importance of pipelines for their daily activities" (\href{zotero://open-pdf/library/items/BL2NMPE3?page=4}{Munappy et al 2020:171})} } @online{muradHowBuildGeneralpurpose2024, title = {How to Build a General-Purpose {{LLM}} Agent}, author = {Murad, Maya}, date = {2024-12-05}, url = {https://towardsdatascience.com/build-a-general-purpose-ai-agent-c40be49e7400/}, urldate = {2025-03-14}, abstract = {A Step-by-Step Guide}, langid = {english}, organization = {Towards Data Science} } @article{murLineeGuidaIniziative, title = {Linee Guida per le iniziative di sistema Missione 4: Istruzione e ricerca Componente 2: Dalla ricerca all’impresa}, author = {Mur, Pnrr}, pages = {47}, langid = {italian} } @inproceedings{Murphy-HillMG10, title = {Understanding Context: Creating a Lasting Impact in Experimental Software Engineering Research}, booktitle = {Proc. {{FoSER}} 2010 {{FSE}} 2010 {{St}}. {{Fe NM USA Novemb}}. 7-11 2010}, author = {Murphy-Hill, Emerson R. and Murphy, Gail C. and Griswold, William G.}, date = {2010}, pages = {255--258}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/sigsoft/Murphy-HillMG10.bib}, timestamp = {Tue, 06 Nov 2018 16:59:23 +0100} } @inproceedings{Murphy09, title = {Attacking Information Overload in Software Development}, booktitle = {{{IEEE Symp}}. {{Vis}}. {{Lang}}. {{Hum}}.-{{Centric Comput}}. {{VLHCC}} 2009 {{Corvallis USA}} 20-24 {{Sept}}. 2009 {{Proc}}.}, author = {Murphy, Gail C.}, date = {2009}, pages = {4}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/vl/Murphy09.bib}, note = {TL;DR \par The overload faced by programmers today is described and several approaches to attack the problem are discussed, some of which may also pertain beyond the domain of software development.}, timestamp = {Wed, 16 Oct 2019 14:14:55 +0200} } @article{Murtagh2012, title = {Algorithms for Hierarchical Clustering: {{An}} Overview}, author = {Murtagh, Fionn and Contreras, Pedro}, date = {2012-01}, journaltitle = {Wiley Interdisc Rew Data Min. Knowl. Discov.}, volume = {2}, pages = {86--97}, nodoi = {10.1002/widm.53}, note = {TL;DR \par A recently developed very efficient (linear time) hierarchical clustering algorithm is described, which can also be viewed as a hierarchical grid‐based algorithm.} } @inproceedings{MussbacherABBCCCFHHKSSSW14, title = {The Relevance of Model-Driven Engineering Thirty Years from Now}, booktitle = {Model-{{Driven Eng}}. {{Lang}}. {{Syst}}. - 17th {{Int}}. {{Conf}}. {{MODELS}} 2014 {{Valencia Spain Sept}}. 28 - {{Oct}}. 3 2014 {{Proc}}.}, author = {Mussbacher, Gunter and Amyot, Daniel and Breu, Ruth and Bruel, Jean-Michel and Cheng, Betty H. C. and Collet, Philippe and Combemale, Benoît and France, Robert B. and Heldal, Rogardt and Hill, James H. and Kienzle, Jörg and Schöttle, Matthias and Steimann, Friedrich and Stikkolorum, Dave R. and Whittle, Jon}, editor = {Dingel, Jürgen and Schulte, Wolfram and Ramos, Isidro and Abrahão, Silvia and Insfrán, Emilio}, date = {2014}, series = {Lecture Notes in Computer Science}, volume = {8767}, pages = {183--200}, publisher = {Springer}, doi = {10.1007/978-3-319-11653-2\_12}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Tue, 24 May 2022 15:28:49 +0200} } @inproceedings{mussbacherAssessmentGridIntelligent2020, title = {Towards an Assessment Grid for Intelligent Modeling Assistance}, booktitle = {Proc. 23rd {{ACMIEEE Int}}. {{Conf}}. {{Model Driven Eng}}. {{Lang}}. {{Syst}}. {{Companion Proc}}.}, author = {Mussbacher, Gunter and Combemale, Benoit and Abrahão, Silvia and Bencomo, Nelly and Burgueño, Loli and Engels, Gregor and Kienzle, Jörg and Kühn, Thomas and Mosser, Sébastien and Sahraoui, Houari and Weyssow, Martin}, date = {2020-10-16}, pages = {1--10}, publisher = {ACM}, location = {Virtual Event Canada}, doi = {10.1145/3417990.3421396}, eventtitle = {{{MODELS}} '20: {{ACM}}/{{IEEE}} 23rd {{International Conference}} on {{Model Driven Engineering Languages}} and {{Systems}}}, isbn = {978-1-4503-8135-2}, langid = {english}, keywords = {GOAL\_Model-Assistance,notion}, note = {THIS IS A “META” PAPER} } @misc{mylyn, title = {Eclipse {{Mylyn}}}, url = {https://projects.eclipse.org/projects/tools.mylyn} } @inproceedings{naghiaei_cpfair_2022, title = {{{CPFair}}: {{Personalized Consumer}} and {{Producer Fairness Re-ranking}} for {{Recommender Systems}}}, shorttitle = {{{CPFair}}}, booktitle = {Proc. 45th {{Int}}. {{ACM SIGIR Conf}}. {{Res}}. {{Dev}}. {{Inf}}. {{Retr}}.}, author = {Naghiaei, Mohammadmehdi and Rahmani, Hossein A. and Deldjoo, Yashar}, date = {2022-07}, pages = {770--779}, publisher = {ACM}, location = {Madrid Spain}, doi = {10.1145/3477495.3531959}, abstract = {Recently, there has been a rising awareness that when machine learning (ML) algorithms are used to automate choices, they may treat/affect individuals unfairly, with legal, ethical, or economic consequences. Recommender systems are prominent examples of such ML systems that assist users in making high-stakes judgments. A common trend in the previous literature research on fairness in recommender systems is that the majority of works treat user and item fairness concerns separately, ignoring the fact that recommender systems operate in a two-sided marketplace. In this work, we present an optimization-based re-ranking approach that seamlessly integrates fairness constraints from both the consumer and producer-side in a joint objective framework. We demonstrate through large-scale experiments on 8 datasets that our proposed method is capable of improving both consumer and producer fairness without reducing overall recommendation quality, demonstrating the role algorithms may play in minimizing data biases.}, isbn = {978-1-4503-8732-3}, langid = {english} } @article{nagnathpaiMapItsImpact2023, title = {Map and {{Its Impact}} on the {{Functional Safety}} of {{Automated Driving Vehicles}}}, author = {Nagnath Pai, Vishwanath and Barosan, Ion and Khabbaz Saberi, Arash}, date = {2023-09-25}, journaltitle = {J. Softw. Eng. Auton. Syst.}, publisher = {Athena Publishing}, issn = {2949-9372}, doi = {10.55060/j.jseas.230925.001}, abstract = {The increasing progress of Automated Driving (AD) technologies emphasises the significance of maps in ensuring the safety of these AD systems. While research has been conducted on the safety of AD systems themselves, the role of maps has not been thoroughly explored. In this article, we aim to address this gap by conducting an analysis to quantify the...}, langid = {english}, keywords = {LOGSEQ}, note = {TL;DR \par This research successfully identified unsafe scenarios along with their corresponding map features and showcased the admissible error margins in the map for the selected map feature, ensuring the secure operation of an AD system.} } @article{nagornyBigDataAnalysis, title = {Big {{Data Analysis}} in {{Smart Manufacturing}}: {{A Review}}}, author = {Nagorny, Kevin and Lima-Monteiro, Pedro and Barata, Jose and Colombo, Armando Walter}, pages = {29}, abstract = {The technological evolution emerges a unified (Industrial) Internet of Things network, where loosely coupled smart manufacturing devices build smart manufacturing systems and enable comprehensive collaboration possibilities that increase the dynamic and volatility of their ecosystems. On the one hand, this evolution generates a huge field for exploitation, but on the other hand also increases complexity including new challenges and requirements demanding for new approaches in several issues. One challenge is the analysis of such systems that generate huge amounts of (continuously generated) data, potentially containing valuable information useful for several use cases, such as knowledge generation, key performance indicator (KPI) optimization, diagnosis, predication, feedback to design or decision support. This work presents a review of Big Data analysis in smart manufacturing systems. It includes the status quo in research, innovation and development, next challenges, and a comprehensive list of potential use cases and exploitation possibilities.}, langid = {english}, keywords = {big data,DONE,smart manufacturing} } @article{nairFindingFasterConfigurations, title = {Finding {{Faster Configurations}} Using {{FLASH}}}, author = {Nair, Vivek and Yu, Zhe and Menzies, Tim and Siegmund, Norbert and Apel, Sven}, pages = {17}, abstract = {Finding good configurations of a software system is often challenging since the number of configuration options can be large. Software engineers often make poor choices about configuration or, even worse, they usually use a sub-optimal configuration in production, which leads to inadequate performance. To assist engineers in finding the better configuration, this article introduces FLASH, a sequential model-based method that sequentially explores the configuration space by reflecting on the configurations evaluated so far to determine the next best configuration to explore. FLASH scales up to software systems that defeat the prior state-of-the-art model-based methods in this area. FLASH runs much faster than existing methods and can solve both single-objective and multi-objective optimization problems. The central insight of this article is to use the prior knowledge of the configuration space (gained from prior runs) to choose the next promising configuration. This strategy reduces the effort (i.e., number of measurements) required to find the better configuration. We evaluate FLASH using 30 scenarios based on 7 software systems to demonstrate that FLASH saves effort in 100\% and 80\% of cases in single-objective and multi-objective problems respectively by up to several orders of magnitude compared to state-of-the-art techniques.}, langid = {english} } @article{najdataeiEfficientDataStreaming, title = {Efficient {{Data Streaming Analytic Designs}} for {{Parallel}} and {{Distributed Processing}}}, author = {Najdataei, Hannaneh}, abstract = {Today, ubiquitously sensing technologies enable inter-connection of physical objects, as part of Internet of Things (IoT), and provide massive amounts of data streams. In such scenarios, the demand for timely analysis has resulted in a shift of data processing paradigms towards continuous, parallel, and multitier computing. However, these paradigms are followed by several challenges especially regarding analysis speed, precision, costs, and deterministic execution. This thesis studies a number of such challenges to enable efficient continuous processing of streams of data in a decentralized and timely manner. In the first part of the thesis, we investigate techniques aiming at speeding up the processing without a loss in precision. The focus is on continuous machine learning/data mining types of problems, appearing commonly in IoT applications, and in particular continuous clustering and monitoring, for which we present novel algorithms; (i) Lisco, a sequential algorithm to cluster data points collected by LiDAR (a distance sensor that creates a 3D mapping of the environment), (ii) p-Lisco, the parallel version of Lisco to enhance pipeline- and data-parallelism of the latter, (iii) pi-Lisco, the parallel and incremental version to reuse the information and prevent redundant computations, (iv) g-Lisco, a generalized version of Lisco to cluster any data with spatio-temporal locality by leveraging the implicit ordering of the data, and (v) Amble, a continuous monitoring solution in an industrial process.}, langid = {english}, keywords = {LOGSEQ} } @article{nanayakkaraSurveyFindingTrends2021, title = {A {{Survey}} of {{Finding Trends}} in {{Data Mining Techniques}} for {{Social Media Analysis}}}, author = {Nanayakkara, A. C. and Kumara, B. T. G. S. and Rathnayaka, R. M. K. T.}, date = {2021-08-01}, journaltitle = {SL J. Soc. Sci. Hum.}, volume = {1}, number = {2}, pages = {37}, issn = {2773-692X, 2773-6911}, doi = {10.4038/sljssh.v1i2.36}, abstract = {Social media have become very popular in the last few decades. Users rely on social network sites like Twitter, Facebook, YouTube, and LinkedIn for both information and entertainment needs. Social media analytics with data mining technology could be an analysis axis centered on extracting trends, patterns, and rules from the social media pool, to serve the people and organizations to have optimum choices concerning many disciplines. The traditional media analytical techniques appear obsolete and inadequate to gratify this immense array of unstructured social media knowledge characterized by three key problems namely; size, noise, and dynamism, predominantly shifting from the batch scale to the streaming one. The objective of this study is to investigate the data mining techniques that were used by social media networks during the years 2010 and 2020. The effort is a systematic review of content analysis in studies within the field of social media analytics that was published in principal databases. 125 articles were reviewed in this paper. Content analysis was implemented based on their approach, tools utilized, language, the dataset used, country, year, and nature of the experiment. The review discovered that 22 data mining techniques were employed with social media data while frequently used in Artificial Neural Network (ANN), Bayesian networks (BN) and Support Vector Machine (SVM), K-means Clustering, and Neuro-Fuzzy Logic Approach. The study has focused to assist the involved analyzers and educators to capture the research trends and problems associated with the Social media analytics process with future research initiatives.}, langid = {english}, note = {TL;DR \par The review discovered that 22 data mining techniques were employed with social media data while frequently used in Artificial Neural Network, Bayesian networks, and Support Vector Machine, K-means Clustering, and Neuro-Fuzzy Logic Approach.} } @article{nanDDASRDeepDiverse, title = {{{DDASR}}: Deep Diverse {{API}} Sequence Recommendation}, author = {Nan, Siyu and Wang, Jian and Zhang, Neng and Li, Duantengchuan and Li, Bing}, langid = {english} } @article{narayanan_multi-view_2018, title = {A Multi-View Context-Aware Approach to {{Android}} Malware Detection and Malicious Code Localization}, author = {Narayanan, Annamalai and Chandramohan, Mahinthan and Chen, Lihui and Liu, Yang}, date = {2018-06}, journaltitle = {Empir. Softw. Eng.}, volume = {23}, number = {3}, pages = {1222--1274}, issn = {1573-7616}, doi = {10.1007/s10664-017-9539-8}, abstract = {Many existing Machine Learning (ML) based Android malware detection approaches use a variety of features such as security-sensitive APIs, system calls, control-flow structures and information flows in conjunction with ML classifiers to achieve accurate detection. Each of these feature sets provides a unique semantic perspective (or view) of apps’ behaviors with inherent strengths and limitations. Meaning, some views are more amenable to detect certain attacks but may not be suitable to characterize several other attacks. Most of the existing malware detection approaches use only one (or a selected few) of the aforementioned feature sets which prevents them from detecting a vast majority of attacks. Addressing this limitation, we propose MKLDroid, a unified framework that systematically integrates multiple views of apps for performing comprehensive malware detection and malicious code localization. The rationale is that, while a malware app can disguise itself in some views, disguising in every view while maintaining malicious intent will be much harder. MKLDroid uses a graph kernel to capture structural and contextual information from apps’ dependency graphs and identify malice code patterns in each view. Subsequently, it employs Multiple Kernel Learning (MKL) to find a weighted combination of the views which yields the best detection accuracy. Besides multi-view learning, MKLDroid’s unique and salient trait is its ability to locate fine-grained malice code portions in dependency graphs (e.g., methods/classes). Malicious code localization caters several important applications such as supporting human analysts studying malware behaviors, engineering malware signatures, and other counter-measures. Through our large-scale experiments on several datasets (incl. wild apps), we demonstrate that MKLDroid outperforms three state-of-the-art techniques consistently, in terms of accuracy while maintaining comparable efficiency. In our malicious code localization experiments on a dataset of repackaged malware, MKLDroid was able to identify all the malice classes with 94\% average recall. Our work opens up two new avenues in malware research: (i) enables the research community to elegantly look at Android malware behaviors in multiple perspectives simultaneously, and (ii) performing precise and scalable malicious code localization.}, langid = {english} } @inproceedings{Narayanankutty202192, title = {Self-Adapting Model-Based {{SDSec}} for {{IoT}} Networks Using Machine Learning}, author = {Narayanankutty, H.}, date = {2021}, series = {Proceedings - 2021 {{IEEE}} 18th {{International Conference}} on {{Software Architecture Companion}}, {{ICSA-C}} 2021}, pages = {92--93}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/ICSA-C52384.2021.00023}, abstract = {IoT networks today face a myriad of security vulnerabilities in their infrastructure due to its wide attack surface. Large-scale networks are increasingly adopting a Software-Defined Networking approach, it allows for simplified network control and management through network virtualization. Since traditional security mechanisms are incapable of handling virtualized environments, SDSec or Software-Defined Security is introduced as a solution to support virtualized infrastructure, specifically aimed at providing security solutions to SDN frameworks. To further aid large scale design and development of SDN frameworks, Model-Driven Engineering (MDE) has been proposed to be used at the design phase, since abstraction, automation and analysis are inherently key aspects of MDE. This provides an efficient approach to reducing large problems through models that abstract away the complex technicality of the total system. Making adaptations to these models to address security issues faced in IoT networks, largely reduces cost and improves efficiency. These models can be simulated, analysed and supports architecture model adaptation; model changes are then reflected back to the real system. We propose a model-driven security approach for SDSec networks that can self-Adapt using machine learning to mitigate security threats. The overall design time changes can be monitored at run time through machine learning techniques (e.g. deep, reinforcement learning) for real time analysis. This approach can be tested in IoT simulation environments, for instance using the CAPS IoT modeling and simulation framework. Using self-Adaptation of models and advanced machine learning for data analysis would ensure that the SDSec architecture adapts and improves over time. This largely reduces the overall attack surface to achieve improved end-To-end security in IoT environments. © 2021 IEEE.}, art_number = {9425853}, document_type = {Conference Paper}, isbn = {978-1-6654-3910-7}, source = {Scopus}, note = {cited By 0 \par TL;DR \par A model-driven security approach for SDSec networks that can self-adapt using machine learning to mitigate security threats is proposed, which largely reduces the overall attack surface to achieve improved end-to-end security in IoT environments.} } @article{nasabStudyFairnessConcerns, title = {A Study of Fairness Concerns in {{AI-based}} Mobile App Reviews}, author = {Nasab, Ali Rezaei and Dashti, Maedeh and Shahin, Mojtaba and Zahedi, Mansooreh and Khalajzadeh, Hourieh and Arora, Chetan and Liang, Peng}, journaltitle = {J. ACM}, volume = {0}, number = {0}, abstract = {Fairness is one of the socio-technical concerns that must be addressed in AI-based systems. Unfair AI-based systems, particularly unfair AI-based mobile apps, can pose difficulties for a significant proportion of the global population. This paper aims to analyze fairness concerns in AI-based app reviews. We first manually constructed a ground-truth dataset, including a statistical sample of fairness and non-fairness reviews. Leveraging the ground-truth dataset, we developed and evaluated a set of machine learning and deep learning classifiers that distinguish fairness reviews from non-fairness reviews. Our experiments show that our best-performing classifier can detect fairness reviews with a precision of 94\%. We then applied the best-performing classifier on approximately 9.5M reviews collected from 108 AI-based apps and identified around 92K fairness reviews. Next, applying the K-means clustering technique to the 92K fairness reviews, followed by manual analysis, led to the identification of six distinct types of fairness concerns (e.g., ‘receiving different quality of features and services in different platforms and devices’ and ‘lack of transparency and fairness in dealing with user-generated content’). Finally, the manual analysis of 2,248 app owners’ responses to the fairness reviews identified six root causes (e.g., ‘copyright issues’) that app owners report to justify fairness concerns. CCS Concepts: • Software and its engineering → Software post-development issues.}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {\section{Annotazioni\\ (15/4/2024, 15:57:12)} \par - “unfair AI-based mobile apps” (Nasab et al., p. 2) \#a28ae5\\ \mkbibemph{ } \par - “global AI market size, particularly in the form of Machine Learning (ML) and Deep Learning (DL), is predicted to exceed USD 1.6 trillion by 2030” (Nasab et al., p. 2) \#5fb236\\ \mkbibemph{ } \par - “fairness concerns in AI-based app reviews” (Nasab et al., p. 2) \#a28ae5\\ \mkbibemph{ } \par - “fairness” (Nasab et al., p. 2) \#a28ae5\\ \mkbibemph{ } \par - “non-fairness reviews.” (Nasab et al., p. 2) \#a28ae5\\ \mkbibemph{ } \par - “fairness concerns in AI systems cannot only stem from technical components and data but can also be attributed to the process of building AI systems, humans, and governance (e.g., decisions made by developers or policies adopted by providers).” (Nasab et al., p. 3) \#a28ae5\\ \mkbibemph{ } \par - “With 6.64 billion smartphone users worldwide (83.32\% of the current world population) [86], mobile software apps are the most prevailing type of software system used by a wide range of individuals and groups with different characteristics (e.g., age, education level, cultural background, race, and gender)” (Nasab et al., p. 3) \#a28ae5\\ \mkbibemph{ } \par - “This increased risk is attributed to the complex nature of AI technologies, which can inadvertently encode and perpetuate biases present in their training data or opaque decision-making algorithms” (Nasab et al., p. 3) \#a28ae5\\ \mkbibemph{ } \par - “no work has focused on providing an accurate and fine-grained view of different types of fairness concerns and their possible root causes in AI-based apps” (Nasab et al., p. 3) \#e56eee\\ \mkbibemph{ } \par - “a growing concern exists about ensuring the responsible behavior of AI solutions” (Nasab et al., p. 3) \#a28ae5\\ \mkbibemph{ } \par - “possibility that they could produce biased results, have errors, or lack sufficient transparency” (Nasab et al., p. 3) \#a28ae5\\ \mkbibemph{ } \par - “responsible use and adoption of AI” (Nasab et al., p. 3) \#a28ae5\\ \mkbibemph{ } \par - “Fairness is a key element of responsible AI” (Nasab et al., p. 3) \#a28ae5\\ \mkbibemph{ } \par - “no single definition of fairness in AI systems, and varies in different domain” (Nasab et al., p. 3) \#5fb236\\ \mkbibemph{ } \par - “fairness is defined as “the absence of any prejudice or favoritism towards an individual or group based on their inherent or acquired characteristics” (Nasab et al., p. 3) \#a28ae5\\ \mkbibemph{\#card } \par - “socio-technical challenge” (Nasab et al., p. 3) \#a28ae5\\ \mkbibemph{ } \par - “TensorFlow Lite that enable executing AI tasks solely on smartph” (Nasab et al., p. 3) \#5fb236\\ \mkbibemph{ } \par - “investigate fairness concerns in AI-based app reviews.” (Nasab et al., p. 3) \#e56eee\\ \mkbibemph{ } \par - “fairness review is a review that discusses a fairness concern (topic)” (Nasab et al., p. 3) \#5fb236\\ \mkbibemph{ } \par - “our best-performing classifier can detect fairness reviews with a precision of 94\%” (Nasab et al., p. 3) \#a28ae5\\ \mkbibemph{ } \par - “a finegrained view of different fairness concerns.” (Nasab et al., p. 3) \#ffd400\\ \mkbibemph{Should it be defined as a multi-classification problem? } \par - “the top six fairness concerns in AI-based app reviews” (Nasab et al., p. 3) \#ffd400\\ \mkbibemph{Why *the* top six? Where have they been introduced/defined? } \par - “K-means clustering technique to summarize fairness concerns for answering RQ1 in Section 5” (Nasab et al., p. 4) \#ffd400\\ \mkbibemph{I would not mention RQs here since they have not been introduced. For instance, I would write "... We report about the root causes of fairness concerns in Section 6."... } \par - “n,” (Nasab et al., p. 4) \#ff6666\\ \mkbibemph{ } \par - “six root causes of the fairness concerns: ‘copyright issues’, ‘development complexity’, ‘buggy code’, ‘external factors’, ‘development cost’, and ‘user usage and awareness’.” (Nasab et al., p. 4) \#ffd400\\ \mkbibemph{The definition of these 6 concerns needs to be introduced before the usage done before without any explanation. } \par - “However, it is evident that ‘fairness’ is a complicated notion with different definitions” (Nasab et al., p. 4) \#5fb236\\ \mkbibemph{ } \par - “more work is required to clarify different fairness definitions and how they are employed in different scenarios” (Nasab et al., p. 4) \#a28ae5\\ \mkbibemph{ } \par - “improving the mitigation techniques on fairness-aware ML models requires a thorough understanding of the root causes of the unintended behaviour of these models” (Nasab et al., p. 5) \#a28ae5\\ \mkbibemph{ } \par - “biases in the representation, measurement, and learning phase of a model to biases in aggregation, evaluation of results and deployment of model.” (Nasab et al., p. 5) \#e56eee\\ \mkbibemph{ } \par - “evaluating the fairness of AI systems comes with three challenges for practitioners: determining the appropriate performance metrics, identifying relevant stakeholders and demographic groups, and collecting datasets.” (Nasab et al., p. 5) \#e56eee\\ \mkbibemph{ } \par - “Bird et al. [16] proposed the Fairlearn toolkit to help AI practitioners make a trade-off between fairness and model performance.” (Nasab et al., p. 5) \#a28ae5\\ \mkbibemph{ } \par - “Fairify” (Nasab et al., p. 5) \#a28ae5\\ \mkbibemph{ } \par - “This paper aims to deeply analyze the fairness of AI-based apps. This will be achieved by answering the following overarching” (Nasab et al., p. 6) \#ffd400\\ \mkbibemph{"Fairness of AI-based apps" OR "Fairness of AI-based apps review?" } \par - “We argue that user reviews of AI-based mobile apps can be a rich resource to uncover fairness concerns from the users’ perspective.” (Nasab et al., p. 6) \#5fb236\\ \mkbibemph{ } \par - “socio-technical issues” (Nasab et al., p. 6) \#5fb236\\ \mkbibemph{ } \par - “none of these automated techniques has focused on fairness concerns” (Nasab et al., p. 6) \#a28ae5\\ \mkbibemph{ } \par - “we plan to experiment with several ML and DL binary classifiers and a clustering technique that detect and summarize fairness concerns from the user reviews of AI-based apps.” (Nasab et al., p. 6) \#5fb236\\ \mkbibemph{ } \par - “we decided to use the most relevant reviews sorted by Google Play Store.” (Nasab et al., p. 7) \#5fb236\\ \mkbibemph{ } \par - “fairness and non-fairness app reviews” (Nasab et al., p. 7) \#5fb236\\ \mkbibemph{ } \par - “we collected the top 500K reviews from each app with more than 500K reviews using a Google Play Crawler” (Nasab et al., p. 7) \#5fb236\\ \mkbibemph{ } \par - “17,968,298 reviews.” (Nasab et al., p. 7) \#5fb236\\ \mkbibemph{ } \par - “removed reviews with less than 4 words and non-English reviews” (Nasab et al., p. 7) \#5fb236\\ \mkbibemph{ } \par - “9,475,506 reviews that potentially covered several topics such as feature requests, bug reports, non-technical concerns, etc.” (Nasab et al., p. 7) \#a28ae5\\ \mkbibemph{ } \par - “‘fair’, ‘discrimination’, and ‘bias’ are three commonly used keywords to refer to fairness.” (Nasab et al., p. 8) \#5fb236\\ \mkbibemph{ } \par - “30,019 ‘potential fairness reviews’.” (Nasab et al., p. 8) \#ffd400\\ \mkbibemph{I would also checked if some filtered out reviewes contain relevant text that could have been included. } \par - “the 30,019 potential fairness reviews” (Nasab et al., p. 8) \#ffd400\\ \mkbibemph{By following my previous comment, why not applying KeyBERT to the initial dataset? } \par - “46,700 potential fairness reviews.” (Nasab et al., p. 8) \#ffd400\\ \mkbibemph{Why not analyse the initial 9M reviews with the found keywords? } \par - “TF-IDF calculates the significance of a word in a text” (Nasab et al., p. 9) \#5fb236\\ \mkbibemph{ } \par - “1,132 fairness and 1,473 non-fairness reviews.” (Nasab et al., p. 9) \#a28ae5\\ \mkbibemph{ } \par - “4.1.1 Dataset.” (Nasab et al., p. 9) \#5fb236\\ \mkbibemph{ } \par - “Word2Vec is a word embedding technique that captures the semantic and syntactic relationships between words in a large corpus of text” (Nasab et al., p. 9) \#5fb236\\ \mkbibemph{ } \par - “USE works at the sentence level and utilizes pre-trained sentence embedding models to produce sentence vectors” (Nasab et al., p. 9) \#5fb236\\ \mkbibemph{ } \par - “average” (Nasab et al., p. 11) \#5fb236\\ \mkbibemph{ } \par - “Performance of the cla” (Nasab et al., p. 11) \#5fb236\\ \mkbibemph{ } \par - “has low precision of 0.63,” (Nasab et al., p. 11) \#5fb236\\ \mkbibemph{ } \par - “high false positives” (Nasab et al., p. 11) \#5fb236\\ \mkbibemph{ } \par - “Logistic Regression with TF-IDF, USE, and Word2Vec” (Nasab et al., p. 11) \#5fb236\\ \mkbibemph{ } \par - “best-performing model,” (Nasab et al., p. 12) \#5fb236\\ \mkbibemph{ } \par - “Hence, we aimed to cluster and summarize fairness reviews” (Nasab et al., p. 13) \#a28ae5\\ \mkbibemph{ } \par - “such unfair behaviours may lead to the exclusion of or discrimination towards some users.” (Nasab et al., p. 13) \#5fb236\\ \mkbibemph{ } \par - “binary classifier to detect fairness reviews” (Nasab et al., p. 13) \#5fb236\\ \mkbibemph{ } \par - “Table 2.” (Nasab et al., p. 14) \#ffd400\\ \mkbibemph{The distribution of the identified six categories should be also discussed with respect to the categories of Tab 2. Currently, the reader has to wait the discussion section, to get some hints about this aspect. } \par - “maximizing the similarity between the individual reviews in each cluster and the center of that cluster” (Nasab et al., p. 14) \#5fb236\\ \mkbibemph{ } \par - “summarization metric suggested by Nema et al. [72] to identify the suitable value of k in our work.” (Nasab et al., p. 14) \#5fb236\\ \mkbibemph{ } \par - “minimizing the influence of the following two issues in data, i.e., (i) a data point can appear in multiple clusters produced by K-means” (Nasab et al., p. 14) \#5fb236\\ \mkbibemph{ } \par - “(ii) a data point discusses several topics or is an outlier.” (Nasab et al., p. 14) \#5fb236\\ \mkbibemph{ } \par - “The silhouette score is a measure of how similar a data point is to the other data points in its cluster compared to how similar it is to the data points in other clusters” (Nasab et al., p. 15) \#a28ae5\\ \mkbibemph{ } \par - “Our experiments showed that the highest value for the summarization metric is achieved when k=10” (Nasab et al., p. 15) \#a28ae5\\ \mkbibemph{ } \par - “2 to 10” (Nasab et al., p. 15) \#5fb236\\ \mkbibemph{ } \par - “To understand the main fairness concern discussed in each of the eight compact clusters, we descendingly sorted the reviews in each compact cluster based on their silhouette scores. The first author manually analyzed the 30 reviews with the highest silhouette scores in each cluster (8 ∗ 30 = 240 reviews in total) and suggested a topic for each cluster. Then, three other authors rechecked all these 240 reviews and the proposed topics. The first author and the rest of authors held several meetings to discuss the topics, refined them and solved any disagreements. Although we had eight compact clusters, as shown in Fig. 3, the qualitative analysis process led to the identification of six distinct fairness concerns that are discussed next in Section 5.2.” (Nasab et al., p. 15) \#ffd400\\ \mkbibemph{The cluster analysis should be better presented, including the including the details that lead to focus on six concerns starting from initial 10 clusters. } \par - “Fairness Concern 2: Feeling linguistic d” (Nasab et al., p. 16) \#a28ae5\\ \mkbibemph{ } \par - “Fairness Concern 1: Receiving different quality of features and services in different platforms and devices” (Nasab et al., p. 16) \#a28ae5\\ \mkbibemph{ } \par - “Our analysis found that many users complain about the difference between the quality of features and services that an app offers on different platforms and devices.” (Nasab et al., p. 16) \#5fb236\\ \mkbibemph{ } \par - “expect to be treated equally by app providers.” (Nasab et al., p. 16) \#5fb236\\ \mkbibemph{ } \par - “feeling of inequality” (Nasab et al., p. 16) \#5fb236\\ \mkbibemph{ } \par - “Fairness Concern 3: Lack of transparency and fairness in dealing with user-generated content.” (Nasab et al., p. 16) \#a28ae5\\ \mkbibemph{ } \par - “Feeling” (Nasab et al., p. 17) \#a28ae5\\ \mkbibemph{ } \par - “Fairness Concern 6: Unfair and non-transparent advertisement and subscription poli- cies” (Nasab et al., p. 17) \#a28ae5\\ \mkbibemph{ } \par - “Fairness Concern 4: Feeling gender and racial discrimination” (Nasab et al., p. 17) \#a28ae5\\ \mkbibemph{ } \par - “Fairness Concern 5:” (Nasab et al., p. 17) \#a28ae5\\ \mkbibemph{ } \par - “hip and promotion” (Nasab et al., p. 17) \#a28ae5\\ \mkbibemph{ } \par - “copyright issues imposed on the apps, platforms, or” (Nasab et al., p. 18) \#a28ae5\\ \mkbibemph{ } \par - “discuss six types of fairness concerns in AI-based app reviews, among which ‘receiving different quality of features and services in different platforms and devices’ and ‘lack of transparency and fairness in dealing with user-generated content’ are the most widely raised fairness concerns.” (Nasab et al., p. 18) \#a28ae5\\ \mkbibemph{ } \par - “the used AI models may make mistakes in identifying the context of a conversation, leading to saying inappropriate messages.” (Nasab et al., p. 19) \#a28ae5\\ \mkbibemph{ } \par - “App owners report six root causes, namely ‘copyright issues’, ‘development complexity’, ‘buggy code’, ‘external factors’, ‘development cost’, and ‘user usage and awareness’, to justify the fairness concerns raised by users.” (Nasab et al., p. 20) \#a28ae5\\ \mkbibemph{ } \par - “We suggest future research efforts to investigate this relationship and delve deeper into the fairness concerns specific to each category.” (Nasab et al., p. 20) \#a28ae5\\ \mkbibemph{ } \par - “Other researchers may apply this iterative and multi-faceted approach to reduce false positives, particularly when working with infrequently discussed topics in app reviews.” (Nasab et al., p. 21) \#a28ae5\\ \mkbibemph{ } \par - “We argue that the popularity of DL models should not result in less attention being paid to traditional ML classifiers, which may outperform DL models in some datasets.” (Nasab et al., p. 22) \#a28ae5\\ \mkbibemph{ } \par - “We started with a literature-driven set of keywords and expanded this using KeyBERT.” (Nasab et al., p. 22) \#ffd400\\ \mkbibemph{I would have used KeyBERT on the initial dataset consisting of 9M app reviews. } \par - “≈9.5 million reviews” (Nasab et al., p. 22) \#5fb236\\ \mkbibemph{ }} } @article{nassifAutomaticallyCategorizingSoftware2018, title = {Automatically {{Categorizing Software Technologies}}}, author = {Nassif, Mathieu and Treude, Christoph and Robillard, Martin}, date = {2018}, journaltitle = {IEEE Trans. Softw. Eng.}, pages = {1--1}, issn = {0098-5589, 1939-3520}, doi = {10.1109/TSE.2018.2836450}, abstract = {Informal language and the absence of a standard taxonomy for software technologies make it difficult to reliably analyze technology trends on discussion forums and other on-line venues. We propose an automated approach called Witt for the categorization of software technology (an expanded version of the hypernym discovery problem). Witt takes as input a phrase describing a software technology or concept and returns a general category that describes it (e.g., integrated development environment), along with attributes that further qualify it (commercial, php, etc.). By extension, the approach enables the dynamic creation of lists of all technologies of a given type (e.g., web application frameworks). Our approach relies on Stack Overflow and Wikipedia, and involves numerous original domain adaptations and a new solution to the problem of normalizing automatically-detected hypernyms. We compared Witt with six independent taxonomy tools and found that, when applied to software terms, Witt demonstrated better coverage than all evaluated alternate solutions, without a corresponding degradation in false positive rate.}, langid = {english}, note = {TL;DR \par This work proposes an automated approach to the categorization of software technologies that relies on Stack Overflow and Wikipedia, and involves numerous original domain adaptations and a new solution to the problem of normalizing automatically-detected hypernyms.} } @inproceedings{nasticPatRICIANovelProgramming2013, title = {{{PatRICIA}} -- {{A Novel Programming Model}} for {{IoT Applications}} on {{Cloud Platforms}}}, author = {Nastic, Stefan and Sehic, Sanjin and Vogler, Michael and Truong, Hong-Linh and Dustdar, Schahram}, date = {2013-12}, pages = {53--60}, publisher = {IEEE}, doi = {10.1109/SOCA.2013.48}, isbn = {978-1-4799-2702-9 978-1-4799-2701-2}, note = {TL;DR \par PatRICIA is outlined, which aims at providing an end-to-end solution for high-level programming and provisioning of IoT applications on cloud platforms, and presents a novel programming model, based on the concept of intent and intent scope, for dealing with the complexity, diversity and scale of IoT systems in the cloud.} } @inproceedings{nasticProvisioningSoftwareDefinedIoT2014, title = {Provisioning {{Software-Defined IoT Cloud Systems}}}, author = {Nastic, Stefan and Sehic, Sanjin and Le, Duc-Hung and Truong, Hong-Linh and Dustdar, Schahram}, date = {2014-08}, pages = {288--295}, publisher = {IEEE}, doi = {10.1109/FiCloud.2014.52}, isbn = {978-1-4799-4357-9}, note = {TL;DR \par This paper introduces the concept of software-defined IoT units - a novel approach to IoT cloud computing that encapsulates fine-grained IoT resources and IoT capabilities in well-defined APIs in order to provide a unified view on accessing, configuring and operating IoT cloud systems.} } @article{naumovDeepLearningRecommendation2019, title = {Deep {{Learning Recommendation Model}} for {{Personalization}} and {{Recommendation Systems}}}, author = {Naumov, Maxim and Mudigere, Dheevatsa and Shi, Hao-Jun Michael and Huang, Jianyu and Sundaraman, Narayanan and Park, Jongsoo and Wang, Xiaodong and Gupta, Udit and Wu, Carole-Jean and Azzolini, Alisson G. and Dzhulgakov, Dmytro and Mallevich, Andrey and Cherniavskii, Ilia and Lu, Yinghai and Krishnamoorthi, Raghuraman and Yu, Ansha and Kondratenko, Volodymyr and Pereira, Stephanie and Chen, Xianjie and Chen, Wenlin and Rao, Vijay and Jia, Bill and Xiong, Liang and Smelyanskiy, Misha}, date = {2019-05-31}, journaltitle = {ArXiv190600091 Cs}, eprint = {1906.00091}, eprinttype = {arXiv}, eprintclass = {cs}, url = {http://arxiv.org/abs/1906.00091}, urldate = {2021-06-07}, abstract = {With the advent of deep learning, neural network-based recommendation models have emerged as an important tool for tackling personalization and recommendation tasks. These networks differ significantly from other deep learning networks due to their need to handle categorical features and are not well studied or understood. In this paper, we develop a state-of-the-art deep learning recommendation model (DLRM) and provide its implementation in both PyTorch and Caffe2 frameworks. In addition, we design a specialized parallelization scheme utilizing model parallelism on the embedding tables to mitigate memory constraints while exploiting data parallelism to scale-out compute from the fully-connected layers. We compare DLRM against existing recommendation models and characterize its performance on the Big Basin AI platform, demonstrating its usefulness as a benchmark for future algorithmic experimentation and system co-design.}, langid = {english}, keywords = {68T05,Computer Science - Information Retrieval,Computer Science - Machine Learning,H.3.3,H.3.4,I.2.6,I.5.0}, note = {Comment: 10 pages, 6 figures} } @article{navarreteIntroducingSubjectiveKnowledge, title = {Introducing {{Subjective Knowledge Graphs}}}, author = {Navarrete, Francisco J and Vallecillo, Antonio}, pages = {10}, abstract = {Knowledge-based applications that deal with uncertainty usually represent it by means of a confidence score that expresses the probability that a given fact is true. However, different users may have distinct opinions about the same fact, something that is not considered in existing proposals. This is critical in a number of areas where individual opinions need to be taken into account when making informed decisions, particularly when these are to be made by consensus. This paper introduces Subjective Knowledge Graphs (SKG), an extension to Probabilistic Knowledge Graphs that considers the individual opinions of separate users about the same facts, and allows reasoning about them. We show how SKGs can be implemented using standard graph databases and how the results of the queries can be enriched with the associated degrees of uncertainty.}, langid = {english} } @article{nazabalDataEngineeringData2020, title = {Data {{Engineering}} for {{Data Analytics}}: {{A Classification}} of the {{Issues}}, and {{Case Studies}}}, shorttitle = {Data {{Engineering}} for {{Data Analytics}}}, author = {Nazabal, Alfredo and Williams, Christopher K. I. and Colavizza, Giovanni and Smith, Camila Rangel and Williams, Angus}, date = {2020-04-27}, journaltitle = {ArXiv200412929 Cs}, eprint = {2004.12929}, eprinttype = {arXiv}, eprintclass = {cs}, url = {http://arxiv.org/abs/2004.12929}, urldate = {2020-07-21}, abstract = {Consider the situation where a data analyst wishes to carry out an analysis on a given dataset. It is widely recognized that most of the analyst’s time will be taken up with data engineering tasks such as acquiring, understanding, cleaning and preparing the data. In this paper we provide a description and classification of such tasks into high-levels groups, namely data organization, data quality and feature engineering. We also make available four datasets and example analyses that exhibit a wide variety of these problems, to help encourage the development of tools and techniques to help reduce this burden and push forward research towards the automation or semi-automation of the data engineering process.}, langid = {english}, note = {Comment: 24 pages, 1 figure, submitted to IEEE Transactions on Knowledge and Data Engineering} } @article{nejatiNextGenerationSoftwareVerification2021, title = {Next-{{Generation Software Verification}}: {{An AI Perspective}}}, shorttitle = {Next-{{Generation Software Verification}}}, author = {Nejati, Shiva}, date = {2021-05-01}, journaltitle = {IEEE Softw.}, volume = {38}, number = {03}, pages = {126--130}, publisher = {IEEE Computer Society}, issn = {0740-7459}, doi = {10.1109/MS.2021.3049322}, abstract = {In recent years, automated software verification has progressed significantly. We can now effectively explore complex software structures through automated testing or to prove properties of complex programs, such as compilers using formal methods. But, for the most part, software testing and formal software verification techniques have advanced independently with relatively few insights on how their research thrusts compare or can be combined.}, langid = {english}, note = {TL;DR \par This paper presents a meta-modelling framework that automates the very labor-intensive and therefore time-heavy and expensive process of manually verifying the properties of complex programs, such as compilers using formal methods.} } @misc{neo4j_942_2020, title = {9.4.2. {{The Shortest Path}} Algorithm - 9.4. {{Path}} Finding Algorithms}, author = {{Neo4j}}, date = {2020-06}, nourl = {https://neo4j.com/docs/graph-algorithms/current/labs-algorithms/shortest-path/} } @article{Neto2017293, title = {A Framework for Data Transformation in {{Credit Behavioral Scoring}} Applications Based on {{Model Driven Development}}}, author = {Neto, R. and Jorge Adeodato, P. and Carolina Salgado, A.}, date = {2017}, journaltitle = {Expert Syst. Appl.}, volume = {72}, pages = {293--305}, publisher = {Elsevier Ltd}, issn = {09574174}, doi = {10.1016/j.eswa.2016.10.059}, abstract = {The preprocessing stage in knowledge discovery projects is costly, normally taking between 50\% and 80\% of the total project time. It is in this stage that data in a relational database are transformed for applying a data mining technique. This stage is a complex task that demands from database designers a strong interaction with experts having a broad knowledge about the application domain. Frameworks aiming to systemize this stage have significant limitations when applied to Credit Behavioral Scoring solutions. This paper proposes a framework based on the Model Driven Development approach to systemize the mentioned stage. This work has three main contributions: 1) improving the discriminant power of data mining techniques by means of the construction of new input variables which embed temporal knowledge for the technique; 2) reducing the time of data transformation using automatic code generation, and 3) allowing artificial intelligence and statistics modelers to perform the data transformation without the help of database experts. In order to validate the proposed framework, two comparative studies were conducted. Experiments showed that the proposed framework delivers a performance equivalent or superior to those of existing frameworks and reduces the time of data transformation with a confidence level of 95\%. © 2016}, coden = {ESAPE}, document_type = {Article}, source = {Scopus}, note = {cited By 11} } @inproceedings{netzNaturalLanguageWeb2024, title = {From {{Natural Language}} to {{Web Applications}}: {{Using Large Language Models}} for {{Model-Driven Software Engineering}}}, shorttitle = {From {{Natural Language}} to {{Web Applications}}}, author = {Netz, Lukas and Michael, Judith and Rumpe, Bernhard}, date = {2024}, pages = {179--195}, publisher = {Gesellschaft für Informatik e.V.}, url = {https://dl.gi.de/handle/20.500.12116/43620}, urldate = {2024-08-02}, abstract = {We evaluate the usage of Large Language Models (LLMs) to transform natural language into models of a predefined domain-specific language within the context of model-driven software engineering. In this work we test systematically the reliability and correctness of the developed tooling, to ensure its usability in an automated model-driven engineering context. Up to now, LLMs such as ChatGPT were not sophisticated enough to yield promising results. The new API-Access and the release of GPT-4, enabled us to develop improved tooling that can be evaluated systematically. This paper introduces an approach that can produce a running web application based on simple informal specifications, that is provided by a domain expert with no prior knowledge of any DSL. We extended our toolchain to include ChatGPT and provided the AI with additional DSL-specific contexts in order to receive models that can be further processed. We performed tests to ensure the semantic and syntactic correctness of the created models. This approach shows the potential of LLMs to successfully bridge the gap between domain experts and developers and discusses its current limitations.}, eventtitle = {Modellierung 2024}, isbn = {978-3-88579-742-5}, langid = {english} } @article{neumannHowMightWe2019, title = {How Might We Increase System Trustworthiness?}, author = {Neumann, Peter G.}, date = {2019-09-24}, journaltitle = {Commun. ACM}, volume = {62}, number = {10}, pages = {23--25}, issn = {0001-0782, 1557-7317}, doi = {10.1145/3357225}, abstract = {Summarizing some of the changes that seem increasingly necessary to address known system and network deficiencies and anticipate currently unknown vulnerabilities.}, langid = {english} } @online{NewSimilarityMeasure, title = {A {{New Similarity Measure}} for an {{Ontology Matching System}} - {{Springer}}}, url = {http://link.springer.com/chapter/10.1007/978-3-319-25840-9_17?wt_mc=alerts.TOCseries}, urldate = {2015-11-02} } @online{NewSoftRobot, title = {New {{Soft Robot}} Is {{Completely Autonomous}} and {{Has No Electronics}}!}, url = {http://sciencenewsjournal.com/new-soft-robot-completely-autonomous-no-electronics/}, urldate = {2016-08-29} } @article{Ng:2002:CMC:627342.628263, title = {{{CLARANS}}: {{A}} Method for Clustering Objects for Spatial Data Mining}, author = {Ng, Raymond T. and Han, Jiawei}, date = {2002-09}, journaltitle = {IEEE Trans Knowl Data Eng}, volume = {14}, number = {5}, pages = {1003--1016}, publisher = {IEEE Educational Activities Department}, address = {Piscataway, NJ, USA}, issn = {1041-4347}, url = {http://dx.doi.org/10.1109/TKDE.2002.1033770}, acmid = {628263}, issue_date = {September 2002}, nodoi = {10.1109/TKDE.2002.1033770}, numpages = {14}, keywords = {clustering algorithms,computational geometry.,randomized search,Spatial data mining}, note = {TL;DR \par A new clustering method is proposed, called CLARANS, whose aim is to identify spatial structures that may be present in the data, and two spatial data mining algorithms that aim to discover relationships between spatial and nonspatial attributes are developed.} } @inproceedings{Nguyen:2015:CRV:2942298.2942305, title = {Content-Based Recommendations via {{DBpedia}} and Freebase: {{A}} Case Study in the Music Domain}, booktitle = {Proc. 14th {{Int}}. {{Conf}}. {{Semantic Web}} - {{ISWC}} 2015 - {{Vol}}. 9366}, author = {Nguyen, Phuong T. and Tomeo, Paolo and Di Noia, Tommaso and Di Sciascio, Eugenio}, date = {2015}, pages = {605--621}, publisher = {Springer-Verlag New York, Inc.}, location = {New York, NY, USA}, url = {http://dx.doi.org/10.1007/978-3-319-25007-6_35}, acmid = {2942305}, isbn = {978-3-319-25006-9}, nodoi = {10.1007/978-3-319-25007-6₃5}, numpages = {17}, keywords = {Content-based recommender systems,Linked open data,Quality assessment,Semantic similarity}, note = {TL;DR \par This paper investigates how the choice of one of the two datasets may influence the performance of a recommendation engine not only in terms of precision of the results but also in termsof their diversity and novelty.} } @inproceedings{Nguyen:2015:ESP:2740908.2742141, title = {An Evaluation of {{SimRank}} and Personalized {{PageRank}} to Build a Recommender System for the Web of Data}, booktitle = {Proc. 24th {{Int}}. {{Conf}}. {{World Wide Web}}}, author = {Nguyen, Phuong T. and Tomeo, Paolo and Di Noia, Tommaso and Di Sciascio, Eugenio}, date = {2015}, series = {{{WWW}} '15 Companion}, pages = {1477--1482}, publisher = {ACM}, location = {New York, NY, USA}, url = {http://doi.acm.org/10.1145/2740908.2742141}, acmid = {2742141}, isbn = {978-1-4503-3473-0}, nodoi = {10.1145/2740908.2742141}, numpages = {6}, keywords = {personalized pagerank,recommender systems,simrank,web of data}, note = {TL;DR \par Two existing metrics, SimRank and PageRank, are reviewed and investigated and their suitability and performance for computing similarity between resources in RDF graphs and their usage to feed a content-based recommender system are investigated.} } @inproceedings{Nguyen:2016:ACR:2950290.2950333, title = {{{API}} Code Recommendation Using Statistical Learning from Fine-Grained Changes}, booktitle = {Proc. 2016 24th {{ACM SIGSOFT Int}}. {{Symp}}. {{Found}}. {{Softw}}. {{Eng}}.}, author = {Nguyen, Anh Tuan and Hilton, Michael and Codoban, Mihai and Nguyen, Hoan Anh and Mast, Lily and Rademacher, Eli and Nguyen, Tien N. and Dig, Danny}, date = {2016}, series = {{{FSE}} 2016}, pages = {511--522}, publisher = {ACM}, location = {New York, NY, USA}, url = {http://doi.acm.org/10.1145/2950290.2950333}, acmid = {2950333}, isbn = {978-1-4503-4218-6}, nodoi = {10.1145/2950290.2950333}, numpages = {12}, keywords = {API Recommendation,Fine-grained Code Changes,Statistical Learning}, note = {TL;DR \par A novel API recommendation approach that taps into the predictive power of repetitive code changes to provide relevant API recommendations for developers based on statistical learning from fine-grained code changes and from the context in which those changes were made.} } @inproceedings{Nguyen:2017:ACD:3098344.3098399, title = {Automatic Categorization with Deep Neural Network for Open-Source Java Projects}, booktitle = {Proc. 39th {{Int}}. {{Conf}}. {{Softw}}. {{Eng}}. {{Companion}}}, author = {Nguyen, Anh Tuan and Nguyen, Tien N.}, date = {2017}, series = {{{ICSE-C}} '17}, pages = {164--166}, publisher = {IEEE Press}, location = {Piscataway, NJ, USA}, url = {https://doi.org/10.1109/ICSE-C.2017.118}, acmid = {3098399}, isbn = {978-1-5386-1589-8}, nodoi = {10.1109/ICSE-C.2017.118}, numpages = {3} } @article{Nguyen:2019:JSS:CrossRec, title = {{{CrossRec}}: {{Recommending}} Highly Relevant Third-Party Libraries - Manuscript under Review}, author = {Nguyen, Phuong T. and Di Rocco, Juri and Di Ruscio, Davide and Di Penta, Massimiliano}, date = {2019}, journaltitle = {J. Syst. Softw.} } @article{Nguyen:2019:JSS:CrossSim, title = {An Automated Approach to Assess the Similarity of {{GitHub}} Repositories - Manuscript under Revision}, author = {Nguyen, Phuong T. and Di Rocco, Juri and Rubei, Riccardo and Di Ruscio, Davide}, date = {2019}, journaltitle = {Softw. Qual. J.} } @article{Nguyen20217333, title = {Linear and Deep Neural Network-Based Receivers for Massive {{MIMO}} Systems with One-Bit {{ADCs}}}, author = {Nguyen, L.V. and Swindlehurst, A.L. and Nguyen, D.H.N.}, date = {2021}, journaltitle = {IEEE Trans. Wirel. Commun.}, volume = {20}, number = {11}, pages = {7333--7345}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {15361276}, doi = {10.1109/TWC.2021.3082844}, abstract = {The use of one-bit analog-to-digital converters (ADCs) is a practical solution for reducing cost and power consumption in massive Multiple-Input-Multiple-Output (MIMO) systems. However, the distortion caused by one-bit ADCs makes the data detection task much more challenging. In this paper, we propose a two-stage detection method for massive MIMO systems with one-bit ADCs. In the first stage, we present several linear receivers based on the Bussgang decomposition that show significant performance gains over conventional linear receivers. Next, we reformulate the maximum-likelihood (ML) detection problem to address its non-robustness. Based on the reformulated ML detection problem, we propose a model-driven deep neural network-based detector, namely OBMNet, whose performance is comparable with an existing support vector machine-based receiver, albeit with a much lower computational complexity. A nearest-neighbor search method is then proposed for the second stage to refine the first stage solution. Unlike existing search methods that typically perform the search over a large candidate set, the proposed search method generates a limited number of most likely candidates and thus limits the search complexity. Numerical results confirm the low complexity, efficiency, and robustness of the proposed two-stage detection method. © 2002-2012 IEEE.}, document_type = {Article}, source = {Scopus}, note = {cited By 2 \par TL;DR \par A model-driven deep neural network-based detector, namely OBMNet, is proposed whose performance is comparable with an existing support vector machine-based receiver, albeit with a much lower computational complexity.} } @article{nguyenAdversarialAttacksAPI, title = {Adversarial {{Attacks}} to {{API Recommender Systems}}: {{Time}} to {{Wake Up}} and {{Smell}} the {{Coffee}}?}, author = {Nguyen, Phuong T and Sipio, Claudio Di and Rocco, Juri Di and Ruscio, Davide Di}, pages = {13}, abstract = {Recommender systems in software engineering provide developers with a wide range of valuable items to help them complete their tasks. Among others, API recommender systems have gained momentum in recent years as they became more successful at suggesting API calls or code snippets. While these systems have proven to be effective in terms of prediction accuracy, there has been less attention for what concerns such recommenders’ resilience against adversarial attempts. In fact, by crafting the recommenders’ learning material, e.g., data from large open-source software (OSS) repositories, hostile users may succeed in injecting malicious data, putting at risk the software clients adopting API recommender systems. In this paper, we present an empirical investigation of adversarial machine learning techniques and their possible influence on recommender systems. The evaluation performed on three state-of-the-art API recommender systems reveals a worrying outcome: all of them are not immune to malicious data. The obtained result triggers the need for effective countermeasures to protect recommender systems against hostile attacks disguised in training data.}, langid = {english} } @book{nguyenAdversarialAttacksCode2023, title = {Adversarial {{Attacks}} on {{Code Models}} with {{Discriminative Graph Patterns}}}, author = {Nguyen, Thanh-Dat and Zhou, Yang and D Le, Xuan Bach and {Patanamon} and Thongtanunam, Patanamon and Lo, David}, date = {2023-08-21}, abstract = {Pre-trained language models of code are now widely used in various software engineering tasks such as code generation, code completion, vulnerability detection, etc. This, in turn, poses security and reliability risks to these models. One of the important threats is \textbackslash textit\{adversarial attacks\}, which can lead to erroneous predictions and largely affect model performance on downstream tasks. Current adversarial attacks on code models usually adopt fixed sets of program transformations, such as variable renaming and dead code insertion, leading to limited attack effectiveness. To address the aforementioned challenges, we propose a novel adversarial attack framework, GraphCodeAttack, to better evaluate the robustness of code models. Given a target code model, GraphCodeAttack automatically mines important code patterns, which can influence the model's decisions, to perturb the structure of input code to the model. To do so, GraphCodeAttack uses a set of input source codes to probe the model's outputs and identifies the \textbackslash textit\{discriminative\} ASTs patterns that can influence the model decisions. GraphCodeAttack then selects appropriate AST patterns, concretizes the selected patterns as attacks, and inserts them as dead code into the model's input program. To effectively synthesize attacks from AST patterns, GraphCodeAttack uses a separate pre-trained code model to fill in the ASTs with concrete code snippets. We evaluate the robustness of two popular code models (e.g., CodeBERT and GraphCodeBERT) against our proposed approach on three tasks: Authorship Attribution, Vulnerability Prediction, and Clone Detection. The experimental results suggest that our proposed approach significantly outperforms state-of-the-art approaches in attacking code models such as CARROT and ALERT.} } @inproceedings{nguyenEmpiricalEvaluationGitHub2022, ids = {10.1145/3524842.3528470}, title = {An Empirical Evaluation of {{GitHub}} Copilot's Code Suggestions}, booktitle = {Proc. 19th {{Int}}. {{Conf}}. {{Min}}. {{Softw}}. {{Repos}}.}, author = {Nguyen, Nhan and Nadi, Sarah}, date = {2022-05-23}, pages = {1--5}, publisher = {ACM}, location = {Pittsburgh Pennsylvania}, doi = {10.1145/3524842.3528470}, eventtitle = {{{MSR}} '22: 19th {{International Conference}} on {{Mining Software Repositories}}}, isbn = {978-1-4503-9303-4}, langid = {english}, pagetotal = {5}, keywords = {codex,empirical evaluation,GitHub copilot,program synthesis} } @article{nguyenEvaluationMachineLearning, title = {Evaluation of {{Machine Learning Classifiers}} for {{Metamodels}}}, author = {Nguyen, Phuong T and Rocco, Juri Di and Iovino, Ludovico and Ruscio, Davide Di and Pierantonio, Alfonso}, pages = {25}, abstract = {Modeling is a ubiquitous activity in the process of software development. In recent years, such an activity has reached a high degree of intricacy, guided by the heterogeneity of the components, data sources, and tasks. The democratized use of models has led to the necessity for suitable machinery for mining modeling repositories. Among others, the classification of metamodels into independent categories facilitates personalized searches by boosting the visibility of metamodels. Nevertheless, the manual classification of metamodels is not only a tedious but also an error-prone task. According to our observation, misclassification is the norm which leads to a reduction in reachability as well as re-usability of metamodels. Handling such complexity requires suitable tooling to leverage raw data into practical knowledge that can help modelers with their daily tasks. In our previous work, we proposed AURORA as a Machine Learning classifier for metamodels repositories. In this paper, we present a thorough evaluation of the system by taking into consideration different settings as well as evaluation metrics.}, langid = {english} } @inproceedings{Nicolae2021, title = {Metamodel-Based Prediction of {{On Resistance}} for Microelectronic Power Switches}, author = {Nicolae, G. and Buzo, A. and Feuerbaum, C. and Diaconu, C.V. and Cucu, H. and Pelz, G. and Burileanu, C.}, date = {2021}, series = {{{IEEE Electrical Design}} of {{Advanced Packaging}} and {{Systems Symposium}}}, volume = {2021-December}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {21511225}, doi = {10.1109/EDAPS53774.2021.9656996}, abbrev_source_title = {IEEE Electr. Des. Adv. Packag. Syst. Symp.}, affiliation = {University Politehnica of Bucharest, Romania; Infineon Technologies, Neubiberg, Germany}, correspondence_address1 = {Nicolae, G.; University Politehnica of BucharestRomania; email: georgian.nicolae@ieee.org}, document_type = {Conference Paper}, isbn = {978-1-6654-6613-4}, langid = {english}, source = {Scopus}, note = {cited By 1} } @article{nielsenNeuralNetworksDeep2018, type = {misc}, title = {Neural Networks and Deep Learning}, author = {Nielsen, Michael A.}, date = {2018}, publisher = {Determination Press}, url = {http://neuralnetworksanddeeplearning.com/}, added-at = {2019-01-15T22:46:49.000+0100}, biburl = {https://www.bibsonomy.org/bibtex/274383acee84241145ff4ffede9658206/slicside}, interhash = {04d527cadd39f888fc3babcad3343362}, intrahash = {74383acee84241145ff4ffede9658206}, keywords = {ba-2018-hahnrico}, note = {TL;DR \par An overview of some of the different kinds of networks and their applications is given and how these architectures are used for business applications such as recommender systems is highlighted.}, timestamp = {2019-01-15T22:46:49.000+0100} } @inproceedings{Niemann:2013:NCF:2487575.2487656, ids = {10.1145/2487575.2487656}, title = {A New Collaborative Filtering Approach for Increasing the Aggregate Diversity of Recommender Systems}, booktitle = {Proc. 19th {{ACM SIGKDD Int}}. {{Conf}}. {{Knowl}}. {{Discov}}. {{Data Min}}.}, author = {Niemann, Katja and Wolpers, Martin}, date = {2013}, series = {{{KDD}} '13}, pages = {955--963}, publisher = {ACM}, location = {New York, NY, USA}, url = {http://doi.acm.org/10.1145/2487575.2487656}, acmid = {2487656}, isbn = {978-1-4503-2174-7}, nodoi = {10.1145/2487575.2487656}, numpages = {9}, pagetotal = {9}, keywords = {aggregate diversity,item-item similarity,long tail,niche items,recommender systems,usage context}, note = {TL;DR \par A new collaborative filtering approach that is based on the items' usage contexts is proposed that increases the rating predictions for niche items with fewer usage data available and improves the aggragate diversity of the recommendations.} } @inproceedings{Niggemann201221, title = {Solving Modeling Problems with Machine Learning a Classification Scheme of Model Learning Approaches for Technical Systems}, author = {Niggemann, O. and Stein, B. and Maier, A.}, date = {2012}, series = {Tagungsband - {{Dagstuhl-Workshop MBEES}}: {{Modellbasierte Entwicklung}} Eingebetteter {{Systeme VIII}}, {{MBEES}} 2012}, pages = {21--29}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84873361603&partnerID=40&md5=dd7dfd4d9c468d595a967b9a1c21ce38}, abbrev_source_title = {Tagungsband - Dagstuhl-Workshop MBEES: Model. Entwickl. eingebetteter Systeme, MBEES}, affiliation = {Fraunhofer IOSB - Competence Center Industrial Automation, Lemgo, Germany; IT - Institut Industrial IT, Hochschule Ostwestfalen-Lippe, Lemgo, Germany; Bauhaus-Universität Weimar, Germany}, correspondence_address1 = {Niggemann, O.; Fraunhofer IOSB - Competence Center Industrial Automation, Lemgo, Germany; email: oliver.niggemann@iosb-ina.fraunhofer.de}, document_type = {Conference Paper}, langid = {english}, source = {Scopus}, keywords = {GOAL\_Model-Classification,LOGSEQ,notion}, note = {cited By 0} } @article{nikanjamFaultsDeepReinforcement2021, title = {Faults in {{Deep Reinforcement Learning Programs}}: {{A Taxonomy}} and {{A Detection Approach}}}, shorttitle = {Faults in {{Deep Reinforcement Learning Programs}}}, author = {Nikanjam, Amin and Morovati, Mohammad Mehdi and Khomh, Foutse and Braiek, Houssem Ben}, date = {2021-01-05}, journaltitle = {ArXiv210100135 Cs}, eprint = {2101.00135}, eprinttype = {arXiv}, eprintclass = {cs}, url = {http://arxiv.org/abs/2101.00135}, urldate = {2021-01-09}, abstract = {A growing demand is witnessed in both industry and academia for employing Deep Learning (DL) in various domains to solve real-world problems. Deep Reinforcement Learning (DRL) is the application of DL in the domain of Reinforcement Learning (RL). Like any software systems, DRL applications can fail because of faults in their programs. In this paper, we present the first attempt to categorize faults occurring in DRL programs. We manually analyzed 761 artifacts of DRL programs (from Stack Overflow posts and GitHub issues) developed using well-known DRL frameworks (OpenAI Gym, Dopamine, Keras-rl, Tensorforce) and identified faults reported by developers/users. We labeled and taxonomized the identified faults through several rounds of discussions. The resulting taxonomy is validated using an online survey with 19 developers/researchers. To allow for the automatic detection of faults in DRL programs, we have defined a meta-model of DRL programs and developed DRLinter, a model-based fault detection approach that leverages static analysis and graph transformations. The execution flow of DRLinter consists in parsing a DRL program to generate a model conforming to our meta-model and applying detection rules on the model to identify faults occurrences. The effectiveness of DRLinter is evaluated using 15 synthetic DRLprograms in which we injected faults observed in the analyzed artifacts of the taxonomy. The results show that DRLinter can successfully detect faults in all synthetic faulty programs.}, note = {TL;DR \par A meta-model of DRL programs is defined and a model-based fault detection approach that leverages static analysis and graph transformations is developed, DRLinter, which can successfully detect faults in both synthesized and real-world examples.} } @article{Niknam202046, ids = {niknamFederatedLearningWireless2020a}, title = {Federated Learning for Wireless Communications: {{Motivation}}, Opportunities, and Challenges}, author = {Niknam, S. and Dhillon, H.S. and Reed, J.H.}, date = {2020}, journaltitle = {IEEE Commun. Mag.}, volume = {58}, number = {6}, pages = {46--51}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {01636804}, doi = {10.1109/MCOM.001.1900461}, abstract = {There is a growing interest in the wireless communications community to complement the traditional model-driven design approaches with data-driven machine learning (ML)-based solutions. While conventional ML approaches rely on the assumption of having the data and processing heads in a central entity, this is not always feasible in wireless communications applications because of the inaccessibility of private data and large communication overhead required to transmit raw data to central ML processors. As a result, decentralized ML approaches that keep the data where it is generated are much more appealing. Due to its privacy-preserving nature, federated learning is particularly relevant for many wireless applications, especially in the context of fifth generation (5G) networks. In this article, we provide an accessible introduction to the general idea of federated learning, discuss several possible applications in 5G networks, and describe key technical challenges and open problems for future research on federated learning in the context of wireless communications. © 1979-2012 IEEE.}, art_number = {9141214}, coden = {ICOMD}, document_type = {Article}, source = {Scopus}, keywords = {Central-entity,Communication overheads,Data handling,Privacy preserving,Private data,Technical challenges,Traditional models,Wireless application,Wireless communications}, note = {cited By 126 \par cited By 126} } @article{nikolovConceptualizationScalableExecution2021, title = {Conceptualization and Scalable Execution of Big Data Workflows Using Domain-Specific Languages and Software Containers}, author = {Nikolov, Nikolay and Dessalk, Yared Dejene and Khan, Akif Quddus and Soylu, Ahmet and Matskin, Mihhail and Payberah, Amir H. and Roman, Dumitru}, date = {2021-12}, journaltitle = {Internet of Things}, volume = {16}, pages = {100440}, issn = {25426605}, doi = {10.1016/j.iot.2021.100440}, abstract = {Big Data processing, especially with the increasing proliferation of Internet of Things (IoT) technologies and convergence of IoT, edge and cloud computing technologies, involves handling massive and complex data sets on heterogeneous resources and incorporating different tools, frameworks, and processes to help organizations make sense of their data collected from various sources. This set of operations, referred to as Big Data workflows, requires taking advantage of Cloud infrastructures’ elasticity for scalability. In this article, we present the design and prototype implementation of a Big Data workflow approach based on the use of software container technologies, message-oriented middleware (MOM), and a domain-specific language (DSL) to enable highly scalable workflow execution and abstract workflow definition. We demonstrate our system in a use case and a set of experiments that show the practical applicability of the proposed approach for the specification and scalable execution of Big Data workflows. Furthermore, we compare our proposed approach’s scalability with that of Argo Workflows – one of the most prominent tools in the area of Big Data workflows – and provide a qualitative evaluation of the proposed DSL and overall approach with respect to the existing literature.}, langid = {english}, keywords = {LOGSEQ} } @incollection{NIPS2019_8307, title = {Adversarial Examples Are Not Bugs, They Are Features}, booktitle = {Advances in Neural Information Processing Systems 32}, author = {Ilyas, Andrew and Santurkar, Shibani and Tsipras, Dimitris and Engstrom, Logan and Tran, Brandon and Madry, Aleksander}, editor = {Wallach, H. and Larochelle, H. and Beygelzimer, A. and family=Buc, given=F., prefix=dAlché-, useprefix=true and Fox, E. and Garnett, R.}, date = {2019}, pages = {125--136}, publisher = {Curran Associates, Inc.}, url = {http://papers.nips.cc/paper/8307-adversarial-examples-are-not-bugs-they-are-features.pdf} } @article{niuAPIUsagePattern2017, ids = {Niu2017API,Niu:2017:AUP:3104915.3104977}, title = {{{API}} Usage Pattern Recommendation for Software Development}, author = {Niu, Haoran and Keivanloo, Iman and Zou, Ying}, date = {2017-07}, journaltitle = {J. Syst. Softw.}, volume = {129}, pages = {127--139}, publisher = {Elsevier}, issn = {01641212}, doi = {10.1016/j.jss.2016.07.026}, acmid = {3104977}, issue_date = {July 2017}, langid = {english}, noaddress = {New York, NY, USA}, nodoi = {10.1016/j.jss.2016.07.026}, numpages = {13}, keywords = {Clustering,Object usage,Usage pattern} } @misc{noauthor_borrowing_nodate, title = {Borrowing Your Enemy’s Arrows: The Case of Code Reuse in {{Android}} via Direct Inter-App Code Invocation | {{Proceedings}} of the 28th {{ACM Joint Meeting}} on {{European Software Engineering Conference}} and {{Symposium}} on the {{Foundations}} of {{Software Engineering}}}, url = {https://dl-acm-org.univaq.clas.cineca.it/doi/abs/10.1145/3368089.3409745}, urldate = {2021-03-11} } @misc{noauthor_neo4jneo4j-java-driver_2020, title = {Neo4j/Neo4j-Java-Driver}, date = {2020-06}, abstract = {Neo4j Bolt driver for Java. Contribute to neo4j/neo4j-java-driver development by creating an account on GitHub.}, copyright = {Apache-2.0}, nopublisher = {Neo4j}, nourl = {https://github.com/neo4j/neo4j-java-driver}, keywords = {bolt,database,driver,java,neo4j,neo4j-driver}, note = {original-date: 2015-04-15T17:08:15Z} } @inproceedings{noiaRecommenderSystemsLinked2015, title = {Recommender Systems and Linked Open Data}, booktitle = {Reason. {{Web Web Log}}. {{Rules}} - 11th {{Int}}. {{Summer Sch}}. 2015 {{Berl}}. {{Ger}}. {{July}} 31 - {{August}} 4 2015 {{Tutor}}. {{Lect}}.}, author = {Noia, Tommaso Di and Ostuni, Vito Claudio}, date = {2015}, pages = {88--113}, doi = {10.1007/978-3-319-21768-0_4}, bibsource = {dblp computer science bibliography, http://dblp.org}, biburl = {http://dblp.org/rec/bib/conf/rweb/NoiaO15}, timestamp = {Sun, 21 May 2017 00:20:32 +0200} } @book{nonamiAutonomousControlSystems2013, title = {Autonomous Control Systems and Vehicles: Intelligent Unmanned Systems ; [{{International Conference}} on {{Intelligent Unmanned Systems}} ({{ICIUS}}) 2011 ... {{Chiba University}}, {{Japan}} ; Collection of Excellent Papers That Where Updated after Presentation]}, shorttitle = {Autonomous Control Systems and Vehicles}, editor = {Nonami, Kenzo and {International Conference on Intelligent Unmanned Systems} and {International Society of Intelligent Unmanned Systems}}, date = {2013}, series = {International Series on Intelligent Systems, Control and Automation: Science and Engineering}, number = {65}, publisher = {Springer}, location = {Tokyo}, isbn = {978-4-431-54276-6 978-4-431-54275-9}, langid = {english}, pagetotal = {315}, note = {Organized by the International Society of Intelligent Unmanned Systems (ISIUS) and locally by the Center for Bio-Micro Robotics Research at Chiba University, Japan. - The event was the 7th conference continuing from previous conferences held in Seoul, Korea (2005, 2006), Bali, Indonesia (2007), Nanjing, China (2008), Jeju, Korea (2009), and Bali, Indonesia (2010) Literaturangaben} } @incollection{nordmannSurveyDomainspecificLanguages2014, title = {A Survey on Domain-Specific Languages in Robotics}, booktitle = {Simulation, {{Modeling}}, and {{Programming}} for {{Autonomous Robots}}}, author = {Nordmann, Arne and Hochgeschwender, Nico and Wrede, Sebastian}, date = {2014}, pages = {195--206}, publisher = {Springer}, url = {http://link.springer.com/chapter/10.1007/978-3-319-11900-7_17}, urldate = {2015-04-21} } @book{northropUltralargescaleSystemsSoftware2006, title = {Ultra-Large-Scale Systems: The Software Challenge of the Future}, shorttitle = {Ultra-Large-Scale Systems}, author = {Northrop, Linda and Feiler, Peter H and Pollak, Bill and Pipitone, Daniel}, date = {2006}, publisher = {Software Engineering Institute, Carnegie Mellon University}, location = {Pittsburgh, Pa.}, isbn = {978-0-9786956-0-6}, langid = {english} } @online{NoSQLDataModeling, title = {{{NoSQL Data Modeling}} | {{eBay Tech Blog}}}, url = {http://www.ebaytechblog.com/2014/10/10/nosql-data-modeling/#.VRPVNfmJtrc}, urldate = {2015-03-26} } @online{NoSQLDataModelinga, title = {{{NoSQL Data Modeling Techniques}} – {{Highly Scalable Blog}}}, url = {https://highlyscalable.wordpress.com/2012/03/01/nosql-data-modeling-techniques/}, urldate = {2018-05-06} } @misc{notinfringing, title = {{{GitHub Copilot}} Is Not Infringing Your Copyright {{https://felixreda.eu/2021/07/github-copilot-is-not-infringing-your-copyright/}}}, author = {Reda, Felix} } @misc{NotionNotes, title = {Notion Notes}, url = {https://www.notion.so/Publications-SoSyM-and-Visions-81b70721668c4e5d83b78bac2dbde571} } @article{novielliLoveJoyAnger2020, title = {Love, {{Joy}}, {{Anger}}, {{Sadness}}, {{Fear}}, and {{Surprise}}: {{SE Needs Special Kinds}} of {{AI}}: {{A Case Study}} on {{Text Mining}} and {{SE}}}, shorttitle = {Love, {{Joy}}, {{Anger}}, {{Sadness}}, {{Fear}}, and {{Surprise}}}, author = {Novielli, Nicole and Calefato, Fabio and Lanubile, Filippo}, date = {2020-05}, journaltitle = {IEEE Softw.}, volume = {37}, number = {3}, pages = {86--91}, issn = {0740-7459, 1937-4194}, doi = {10.1109/MS.2020.2968557}, langid = {english}, note = {TL;DR \par Is it possible to monitor the mood of a large team of coders to determine when and where a codebase needs additional help?} } @article{ntiMinireviewMachineLearning2022, title = {A Mini-Review of Machine Learning in Big Data Analytics: {{Applications}}, Challenges, and Prospects}, shorttitle = {A Mini-Review of Machine Learning in Big Data Analytics}, author = {Nti, Isaac Kofi and Quarcoo, Juanita Ahia and Aning, Justice and Fosu, Godfred Kusi}, date = {2022-06}, journaltitle = {Big Data Min. Anal.}, volume = {5}, number = {2}, pages = {81--97}, issn = {2096-0654}, doi = {10.26599/BDMA.2021.9020028}, abstract = {The availability of digital technology in the hands of every citizenry worldwide makes an available unprecedented massive amount of data. The capability to process these gigantic amounts of data in real-time with Big Data Analytics (BDA) tools and Machine Learning (ML) algorithms carries many paybacks. However, the high number of free BDA tools, platforms, and data mining tools makes it challenging to select the appropriate one for the right task. This paper presents a comprehensive mini-literature review of ML in BDA, using a keyword search; a total of 1512 published articles was identified. The articles were screened to 140 based on the study proposed novel taxonomy. The study outcome shows that deep neural networks (15\%), support vector machines (15\%), artificial neural networks (14\%), decision trees (12\%), and ensemble learning techniques (11\%) are widely applied in BDA. The related applications fields, challenges, and most importantly the openings for future research, are detailed.}, langid = {english} } @online{Numediart, title = {Numediart}, url = {http://www.numediart.org/2015/06/23/hci-seminar-research-advances-in-interactive-systems-modeling-%C2%BB/}, urldate = {2016-01-23} } @online{NuoveIndicazioni2025, title = {Nuove {{Indicazioni}} 2025 {{Scuola}} Dell’infanzia e {{Primo}} Ciclo Di Istruzione {{Materiali}} per Il Dibattito Pubblico}, url = {https://www.mim.gov.it/documents/20182/0/Nuove+indicazioni+2025.pdf/cebce5de-1e1d-12de-8252-79758c00a50b?version=1.0&t=1741684578272}, urldate = {2025-03-19}, langid = {english} } @online{NuoveIndicazioniNazionali2025, title = {Nuove Indicazioni Nazionali/1. {{Il}} Testo in Sintesi}, date = {2025-03-17}, url = {https://www.tuttoscuola.com/nuove-indicazioni-nazionali-1-il-testo-in-sintesi/}, urldate = {2025-03-19}, abstract = {La Commissione incaricata dal ministro Valditara di aggiornare le linee guida delle Indicazioni Nazionali, varate dal ministro~Francesco Profumo~nel 2012, ha consegnato il suo lavoro. Si tratta di un testo base, peraltro assai strutturato (153 pagine), sul quale – come ha annunciato il Capo Dipartimento del Ministero dell’istruzione e del merito Carmela Palumbo al convegno organizzato …}, langid = {english}, organization = {Tuttoscuola}, keywords = {/unread} } @article{oakesBuildingDomainspecificMachine2022, ids = {oakesBuildingDomainSpecificMachine2023}, title = {Building Domain-Specific Machine Learning Workflows: A Conceptual Framework for the State-of-the-Practice}, shorttitle = {Building {{Domain-Specific Machine Learning Workflows}}}, author = {Oakes, Bentley James and Famelis, Michalis and Sahraoui, Houari}, date = {2022-03-16}, journaltitle = {arXiv:2203,08638 [cs]}, eprint = {2203.08638}, eprinttype = {arXiv}, eprintclass = {cs}, url = {http://arxiv.org/abs/2203.08638}, urldate = {2022-03-22}, abstract = {Domain experts are increasingly employing machine learning to solve their domain-specific problems. This article presents six key challenges that a domain expert faces in transforming their problem into a computational workflow, and then into an executable implementation. These challenges arise out of our conceptual framework which presents the "route" of options that a domain expert may choose to take while developing their solution. To ground our conceptual framework in the state-of-the-practice, this article discusses a selection of available textual and graphical workflow systems and their support for these six challenges. Case studies from the literature in various domains are also examined to highlight the tools used by the domain experts as well as a classification of the domain-specificity and machine learning usage of their problem, workflow, and implementation. The state-of-the-practice informs our discussion of the six key challenges, where we identify which challenges are not sufficiently addressed by available tools. We also suggest possible research directions for software engineering researchers to increase the automation of these tools and disseminate best-practice techniques between software engineering and various scientific domains.}, langid = {english}, keywords = {Computer Science - Software Engineering,GOAL\_MDE4AI}, note = {Comment: 33 pages 14 figures \par \textbf{Contents} \par \begin{itemize} \item \href{zotero://open-pdf/0_8KTK8B7W/1}{1 Introduction} \item \href{zotero://open-pdf/0_8KTK8B7W/3}{2 Background} \item \href{zotero://open-pdf/0_8KTK8B7W/4}{3 Overview of Our Framework} \item \href{zotero://open-pdf/0_8KTK8B7W/6}{4 Layers and Intra-Layer Transformations} \item \href{zotero://open-pdf/0_8KTK8B7W/10}{5 Inter-layer Transformations} \item \href{zotero://open-pdf/0_8KTK8B7W/12}{6 Workflow Standards and Tools} \item \href{zotero://open-pdf/0_8KTK8B7W/17}{7 Case Studies} \item \href{zotero://open-pdf/0_8KTK8B7W/25}{8 Discussion} \item \href{zotero://open-pdf/0_8KTK8B7W/29}{9 Conclusion} \end{itemize} \par TL;DR \par This article presents to software engineering researchers the six key challenges that a domain expert faces in addressing their problem with a computational workflow, and the underlying executable implementation and identifies which challenges and transformations are not sufficiently addressed by available tools.} } @article{obrenovicQuotesIEEESoftware2018, title = {Quotes from {{IEEE Software History}}}, author = {Obrenović, Ž}, date = {2018-09}, journaltitle = {IEEE Softw.}, volume = {35}, number = {5}, pages = {10--13}, issn = {0740-7459}, doi = {10.1109/MS.2018.3571243}, abstract = {This alternative view of IEEE Software history presents quotes organized in conversations. Each conversation pairs a quote from the magazine’s early days (1984–1990) with a more contemporary quote, with at least 20 years between the two. The aim is to illustrate that some key ideas and topics are classic and have value even decades later. Additional pairs of quotes are available in the Web Extra at https://extras.computer.org/extra/mso2018050010s1.pdf. This article is part of a theme issue on software engineering’s 50th anniversary.}, note = {TL;DR \par This alternative view of IEEE Software history presents quotes organized in conversations, illustrating that some key ideas and topics are classic and have value even decades later.} } @inproceedings{ODMD14a, title = {A Linked Data Recommender System Using a Neighborhood-Based Graph Kernel}, booktitle = {15th {{Int}}. {{Conf}}. {{Electron}}. {{Commer}}. {{Web Technol}}.}, author = {Ostuni, Vito Claudio and Di Noia, Tommaso and Mirizzi, Roberto and Di Sciascio, Eugenio}, date = {2014}, series = {Lecture Notes in Business Information Processing}, publisher = {Springer}, url = {http://sisinflab.poliba.it/sisinflab/publications/ 2014/ODMD14a} } @article{odonovanIndustrialBigData2015, title = {An Industrial Big Data Pipeline for Data-Driven Analytics Maintenance Applications in Large-Scale Smart Manufacturing Facilities}, author = {O’Donovan, P. and Leahy, K. and Bruton, K. and O’Sullivan, D. T. J.}, date = {2015-12}, journaltitle = {Journal of Big Data}, volume = {2}, number = {1}, pages = {25}, issn = {2196-1115}, doi = {10.1186/s40537-015-0034-z}, abstract = {The term smart manufacturing refers to a future-state of manufacturing, where the real-time transmission and analysis of data from across the factory creates manufacturing intelligence, which can be used to have a positive impact across all aspects of operations. In recent years, many initiatives and groups have been formed to advance smart manufacturing, with the most prominent being the Smart Manufacturing Leadership Coalition (SMLC), Industry 4.0, and the Industrial Internet Consortium. These initiatives comprise industry, academic and government partners, and contribute to the development of strategic policies, guidelines, and roadmaps relating to smart manufacturing adoption. In turn, many of these recommendations may be implemented using data-centric technologies, such as Big Data, Machine Learning, Simulation, Internet of Things and Cyber Physical Systems, to realise smart operations in the factory. Given the importance of machine uptime and availability in smart manufacturing, this research centres on the application of data-driven analytics to industrial equipment maintenance. The main contributions of this research are a set of data and system requirements for implementing equipment maintenance applications in industrial environments, and an information system model that provides a scalable and fault tolerant big data pipeline for integrating, processing and analysing industrial equipment data. These contributions are considered in the context of highly regulated large-scale manufacturing environments, where legacy (e.g. automation controllers) and emerging instrumentation (e.g. internet-aware smart sensors) must be supported to facilitate initial smart manufacturing efforts.}, langid = {english} } @inproceedings{Ogden202151, title = {Many Models at the Edge: {{Scaling}} Deep Inference via Model-Level Caching}, author = {Ogden, S.S. and Gilman, G.R. and Walls, R.J. and Guo, T.}, editor = {El-Araby E., Kalogeraki V., Lassabe F., Porter B., Ghahremani S., Nunes I., Bakhouya M., Tomforde S., Pianini D.}, date = {2021}, series = {Proceedings - 2021 {{IEEE International Conference}} on {{Autonomic Computing}} and {{Self-Organizing Systems}}, {{ACSOS}} 2021}, pages = {51--60}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/ACSOS52086.2021.00027}, abstract = {Deep learning (DL) models are rapidly expanding in popularity in large part due to rapid innovations in model accuracy, as well as companies' enthusiasm in integrating deep learning into the existing application logic. This trend will inevitably lead to a deployment scenario, akin to the content delivery network for web objects, where many deep learning models-each with different popularity-run on a shared edge with limited resources. In this paper, we set out to answer the key question of how to manage many deep learning models at the edge effectively. Via an empirical study based on profiling more than twenty deep learning models and extrapolating from an open-source Microsoft Azure workload trace, we pinpoint a promising avenue of leveraging cheaper CPUs, rather than commonly promoted accelerators, for edge-based deep inference serving. Based on our empirical insights, we formulate the DL model management problem as a classical caching problem, which we refer to as model-level caching. As an initial step towards realizing model-level caching, we propose a simple cache eviction policy, called CremeBrulee, by adapting BeladyMIN to explicitly consider DL model-specific factors when calculating each in-cache object's utility. Using a small-scale testbed, we demonstrate that CremeBrulee can achieve a 50\% reduction in memory while keeping load latency below 92\% of execution latency and less than 36\% of the penalty of using a random approach to model eviction. Further, when scaling to more models and requests in a simulation, we demonstrate that CremeBrulee can keep the model load delay lower than other eviction policies that only consider workload characteristics by up to 16.6\%. Relevant research artifacts are available at https://github.com/cake-lab/CremeBrulee © 2021 IEEE.}, document_type = {Conference Paper}, isbn = {978-1-6654-1261-2}, source = {Scopus}, note = {cited By 0 \par TL;DR \par This paper proposes a simple cache eviction policy, called CremeBrulee, by adapting BeladyMIN to explicitly consider DL model-specific factors when calculating each in-cache object's utility, and formulate the DL model management problem as a classical caching problem, which it refers to as model-level caching.} } @article{Okewu2020273, title = {A Software Engineering Approach to Implementation of {{SDG}} 6 in Adum-Aiona Community of Nigeria}, author = {Okewu, E. and Misra, S. and Lius, F.-S.}, editor = {Gervasi O., Murgante B., Garau C., Blecic I., Taniar D., Apduhan B.O., Rocha A.M.A.C., Tarantino E., Torre C.M., Karaca Y., Misra S.}, date = {2020}, journaltitle = {Lect. Notes Comput. Sci. Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinforma.}, volume = {12254 LNCS}, pages = {273--288}, publisher = {{Springer Science and Business Media Deutschland GmbH}}, issn = {03029743}, doi = {10.1007/978-3-030-58817-5_21}, abbrev_source_title = {Lect. Notes Comput. Sci.}, affiliation = {Centre for Information Technology and Systems, University of Lagos, Lagos, Nigeria; Department of Electrical and Information Engineering, Covenant University, Ota, Nigeria; Department of Computer Sciences, University of Alcala, Henares, Spain}, correspondence_address1 = {Okewu, E.; Centre for Information Technology and Systems, Nigeria; email: eokewu@unilag.edu.ng}, document_type = {Conference Paper}, isbn = {9783030588168}, langid = {english}, source = {Scopus}, note = {cited By 1 \par TL;DR \par An engineering problem-solving approach to the open-air defecation health problem and it is demonstrated that besides being used to model software systems, computational models (software architecture) are useful in documenting and promoting understanding of concepts in virtually all fields of human endeavour.} } @inproceedings{Okobiah2014365, title = {Kriging Bootstrapped Neural Network Training for Fast and Accurate Process Variation Analysis}, author = {Okobiah, O. and Mohanty, S.P. and Kougianos, E.}, date = {2014}, series = {Proceedings - {{International Symposium}} on {{Quality Electronic Design}}, {{ISQED}}}, pages = {365--372}, publisher = {IEEE Computer Society}, issn = {19483287}, doi = {10.1109/ISQED.2014.6783349}, abbrev_source_title = {Proc. - Int. Symp. Qual. Electron. Des., ISQED}, affiliation = {NanoSystem Design Laboratory (NSDL), University of North Texas, Denton, TX 76207, United States}, art_number = {6783349}, document_type = {Conference Paper}, isbn = {978-1-4799-3946-6}, langid = {english}, source = {Scopus}, note = {cited By 3} } @online{oladeleMLModelRegistry2022, title = {{{ML Model Registry}}: {{The Ultimate Guide}}}, shorttitle = {{{ML Model Registry}}}, author = {Oladele, Stephen}, date = {2022-08-03T12:21:51+00:00}, url = {https://neptune.ai/blog/ml-model-registry}, urldate = {2024-01-31}, abstract = {Imagine you are the only data scientist on your team, you start working on a machine learning project and perform a series of experiments that produce various ML models (and artifacts) that you “track” through non-standard naming conventions. Since the naming conventions you used for your model files were unclear, it took you a while…}, langid = {american}, organization = {neptune.ai} } @article{olsonPMLBLargeBenchmark2017, title = {{{PMLB}}: A Large Benchmark Suite for Machine Learning Evaluation and Comparison}, shorttitle = {{{PMLB}}}, author = {Olson, Randal S. and La Cava, William and Orzechowski, Patryk and Urbanowicz, Ryan J. and Moore, Jason H.}, date = {2017-12}, journaltitle = {BioData Mining}, volume = {10}, number = {1}, pages = {36}, issn = {1756-0381}, doi = {10.1186/s13040-017-0154-4}, abstract = {Background: The selection, development, or comparison of machine learning methods in data mining can be a difficult task based on the target problem and goals of a particular study. Numerous publicly available real-world and simulated benchmark datasets have emerged from different sources, but their organization and adoption as standards have been inconsistent. As such, selecting and curating specific benchmarks remains an unnecessary burden on machine learning practitioners and data scientists. Results: The present study introduces an accessible, curated, and developing public benchmark resource to facilitate identification of the strengths and weaknesses of different machine learning methodologies. We compare meta-features among the current set of benchmark datasets in this resource to characterize the diversity of available data. Finally, we apply a number of established machine learning methods to the entire benchmark suite and analyze how datasets and algorithms cluster in terms of performance. From this study, we find that existing benchmarks lack the diversity to properly benchmark machine learning algorithms, and there are several gaps in benchmarking problems that still need to be considered. Conclusions: This work represents another important step towards understanding the limitations of popular benchmarking suites and developing a resource that connects existing benchmarking standards to more diverse and efficient standards in the future.}, langid = {english}, keywords = {LOGSEQ} } @article{omaEnergyefficientModelFog2018, title = {An Energy-Efficient Model for Fog Computing in the {{Internet}} of {{Things}} ({{IoT}})}, author = {Oma, Ryuji and Nakamura, Shigenari and Duolikun, Dilawaer and Enokido, Tomoya and Takizawa, Makoto}, date = {2018-09}, journaltitle = {Internet Things}, volume = {1--2}, pages = {14--26}, issn = {25426605}, doi = {10.1016/j.iot.2018.08.003}, abstract = {A huge number of devices like sensors in addition to computers are interconnected in the IoT (Internet of Things). In the cloud computing model, sensor data is transmitted to servers in networks and processed on the servers in a cloud. Here, networks are congested and servers are overloaded due to heavy traffic from sensors. In order to reduce the delay time and network traffic and increase the performance of the system, data and processes are distributed to not only servers in a cloud but also fog nodes in fog computing models. While the traffic of servers in a cloud can be reduced, the total electric energy consumed by fog nodes increases to process sensor data. In this paper, we newly propose a treebased fog computing (TBFC) model to distribute processes and data to servers and fog nodes so that the total electric energy consumption of nodes can be reduced in the IoT. In the evaluation, we show the total electric energy consumption of nodes in the TBFC model is smaller than the cloud computing model.}, langid = {english} } @online{OpenAIAgents, title = {{{OpenAI Agents}}}, url = {https://www.linkedin.com/posts/conorgrennan_breaking-openai-has-released-ai-agents-ugcPost-7288287296993255424-6ndO?utm_source=share&utm_medium=member_android}, keywords = {source\_mobile} } @online{OpenVsClosedloop, title = {Open- vs. Closed-Loop Control~|~{{Control Engineering}}}, url = {http://www.controleng.com/single-article/open-vs-closed-loop-control/f8d8023a15738d0fcfe78d6a2d71dd60.html}, urldate = {2016-11-01} } @article{OrchestratingATLModel, title = {Orchestrating {{ATL Model Transformations}}}, note = {TL;DR \par Wires*, a graphical executable language for the orchestration of ATL transformations, which provides appropriate mechanisms to enable the modular and compositional specification and execution of complex model transformations chains.} } @misc{osgi, title = {Eclipse {{Equinox}}}, url = {https://www.eclipse.org/equinox/} } @online{OSGiModularityTutorial, title = {{{OSGi Modularity}} - {{Tutorial}}}, url = {http://www.vogella.com/tutorials/OSGi/article.html#introduction-into-software-modularity-with-osgi}, urldate = {2016-12-02} } @article{osmanSATToSE2017Postproceedings2017, ids = {osmanSATToSE2017Postproceedings2017a}, title = {{{SATToSE}} 2017: {{The}} Post-Proceedings Editorial}, author = {Osman, H. and Chis, A. and Ruscio, D.D. and Zaytsev, V.}, date = {2017}, journaltitle = {CEUR Workshop Proc.}, volume = {2070}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85045455178&partnerID=40&md5=6babe4f6ad63c4a50a18de55f9115cc3}, note = {cited By 0 \par cited By 0} } @inproceedings{ostuniTopnRecommendationsImplicit2013, title = {Top-n Recommendations from Implicit Feedback Leveraging Linked Open Data}, booktitle = {Proc. 7th {{ACM Conf}}. {{Recomm}}. {{Syst}}.}, author = {Ostuni, Vito Claudio and Di Noia, Tommaso and Di Sciascio, Eugenio and Mirizzi, Roberto}, date = {2013}, series = {{{RecSys}} '13}, pages = {85--92}, publisher = {ACM}, location = {New York, NY, USA}, url = {http://doi.acm.org/10.1145/2507157.2507172}, acmid = {2507172}, isbn = {978-1-4503-2409-0}, nodoi = {10.1145/2507157.2507172}, numpages = {8}, keywords = {dbpedia,hybrid recommender system,implicit feedback,learning to rank,linked data,top-n recommendations}, note = {TL;DR \par SPrank is presented, a novel hybrid recommendation algorithm able to compute top-N item recommendations from implicit feedback exploiting the information available in the so called Web of Data.} } @article{oubelliScalableModelBased2018, title = {A Scalable Model Based Approach for Data Model Evolution: {{Application}} to Space Missions Data Models}, shorttitle = {A Scalable Model Based Approach for Data Model Evolution}, author = {Oubelli, Lynda Ait and Aït Ameur, Yamine and Bedouet, Judicael and Kervarc, Romain and Chausserie-Laprée, Benoit and Larzul, Béatrice}, date = {2018-08}, journaltitle = {Comput. Lang. Syst. Struct.}, issn = {14778424}, doi = {10.1016/j.cl.2018.08.001}, langid = {english} } @inproceedings{ouelletControlSwarmsAutonomous2011, title = {Control of Swarms of Autonomous Robots Using {{Model Driven Development-A}} State-Based Approach}, booktitle = {Syst. {{Conf}}. {{SysCon}} 2011 {{IEEE Int}}.}, author = {Ouellet, Dany and Givigi, Sidney N. and Beaulieu, Alain JG}, date = {2011}, pages = {512--519}, publisher = {IEEE}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5929129}, urldate = {2016-08-21}, note = {TL;DR \par This work focuses on swarms of robots, defined as the capability of robots to keep close to each other in formation, without colliding with neighbors and obstacles, and uses IBM Rational Rose Real-Time™ (RoseRT) to implement such a controller in emulation following the formalism of Model-Driven Development.} } @article{ouniSearchbasedSoftwareLibrary2017, ids = {Ouni:2017:SSL:3032135.3032325}, title = {Search-Based Software Library Recommendation Using Multi-Objective Optimization}, author = {Ouni, Ali and Kula, Raula Gaikovina and Kessentini, Marouane and Ishio, Takashi and German, Daniel M. and Inoue, Katsuro}, date = {2017-03}, journaltitle = {Inf. Softw. Technol.}, volume = {83}, pages = {55--75}, publisher = {Butterworth-Heinemann}, address = {Newton, MA, USA}, location = {Newton, MA, USA}, issn = {09505849}, doi = {10.1016/j.infsof.2016.11.007}, acmid = {3032325}, issue_date = {March 2017}, langid = {english}, nodoi = {10.1016/j.infsof.2016.11.007}, numpages = {21}, pagetotal = {21}, keywords = {Multi-objective optimization,Search-based software engineering,Software library,Software reuse} } @online{OurGoalAccelerate, title = {Our Goal Is to Accelerate Scientific Breakthroughs at Scale by Harnessing the Power of {{AI}}.}, url = {https://bit.ly/3Zn6jkV}, keywords = {source\_mobile} } @online{OverviewAutonomousSystems, title = {Overview of the {{Autonomous Systems Area}} | {{Wallenberg ASP}}}, url = {http://wasp-sweden.org/research/overview-of-autonomous-systems-area/}, urldate = {2016-08-26} } @article{ozkayaApplicationLargeLanguage2023, title = {Application of {{Large Language Models}} to {{Software Engineering Tasks}}: {{Opportunities}}, {{Risks}}, and {{Implications}}}, shorttitle = {Application of {{Large Language Models}} to {{Software Engineering Tasks}}}, author = {Ozkaya, Ipek}, date = {2023-05}, journaltitle = {IEEE Softw.}, volume = {40}, number = {3}, pages = {4--8}, issn = {0740-7459, 1937-4194}, doi = {10.1109/MS.2023.3248401}, langid = {english}, keywords = {LOGSEQ} } @inproceedings{Ozyegen2020148, title = {Generative Adversarial Networks in Designing Electromagnetic Engineered Surfaces for Mm-Wave Band Spectrum Environments}, author = {Ozyegen, O. and Ethier, J. and Kavurmacioglu, E. and Basar, A.}, editor = {Pakfetrat T., Jourdan G.-V., Enenkel R., Kontogiannis K.}, date = {2020}, series = {{{CASCON}} 2019 {{Proceedings}} - {{Conference}} of the {{Centre}} for {{Advanced Studies}} on {{Collaborative Research}} - {{Proceedings}} of the 29th {{Annual International Conference}} on {{Computer Science}} and {{Software Engineering}}}, pages = {148--155}, publisher = {Center for Advanced Studies on Collaborative Research}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087415807&partnerID=40&md5=2f9c4b54b7bb3b545fa2d9492edd3929}, abstract = {AIM: In this paper, we set out to understand the capabilities of conditional generative adversarial networks (cGANs) in the generation of electromagnetic engineered surfaces (EES). BACKGROUND: As the use of radio spectrum is increasing, one direction to increase the supply is by tapping into available high frequency spectrum and engineer the propagation environment through carefully designed electromagnetic engineered surfaces. Generative models such as GANs, can learn to generate new designs by training on existing dataset but have been traditionally applied in the domains of image processing and text generation. METHODOLOGY: We propose to train a cGAN for EES generation. The cGAN is first tested on the MNIST dataset to ensure convergent training. Next EES transfer functions categories of interest are defined and k-means clustering is used to assign EES designs in our dataset. The cGAN is then trained on the 9x9 EES dataset and its performance is evaluated using several metrics. RESULTS: Our results indicate that the proposed cGAN is able to generate different engineered surface designs given a desired transfer function, improving the accuracy by at least 3 fold compared to a random generation process. CONCLUSION: The application of cGANs on EES shows the applicability of machine learning such as generative models in engineering applications with design constraints. © 2019 Copyright held by the owner/author(s).}, author_keywords = {Electromagnetic engineered surfaces; Generative adversarial networks; Machine learning; Mm-Wave spectrum; Smart environments}, document_type = {Conference Paper}, source = {Scopus}, keywords = {Adversarial networks,Design constraints,Engineered surfaces,Engineering applications,Engineering education,Generative model,High frequency spectrum,Image processing,K-means clustering,Millimeter waves,Propagation environment,Random generation,Software engineering,Transfer functions}, note = {cited By 1} } @online{PaaSword, title = {{{PaaSword}}}, url = {https://sites.google.com/site/paaswordeu/}, urldate = {2015-04-08} } @inproceedings{Padget201435, title = {On Requirements Representation and Reasoning Using Answer Set Programming}, author = {Padget, J. and Elakehal, E.E. and Satoh, K. and Ishikawa, F.}, date = {2014}, series = {2014 {{IEEE}} 1st {{International Workshop}} on {{Artificial Intelligence}} for {{Requirements Engineering}}, {{AIRE}} 2014 - {{Proceedings}}}, pages = {35--42}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/AIRE.2014.6894854}, abbrev_source_title = {IEEE Int. Workshop Artif. Intell. Requir. Eng., AIRE - Proc.}, affiliation = {Department of Computer Science, University of Bath, United Kingdom; National Institute of Informatics and Sokendai, Japan; National Institute of Informatics, Japan}, art_number = {6894854}, correspondence_address1 = {Padget, J.; Department of Computer Science, University of BathUnited Kingdom}, document_type = {Conference Paper}, isbn = {978-1-4799-6355-3}, langid = {english}, source = {Scopus}, keywords = {GOAL\_Model-Requirements,notion,TECHNIQUE\_ASP}, note = {ASP \par cited By 3} } @inproceedings{pageLearningAutonomousSystems2017, title = {Learning Autonomous Systems — {{An}} Interdisciplinary Project-Based Experience}, booktitle = {2017 {{IEEE Front}}. {{Educ}}. {{Conf}}. {{FIE}}}, author = {Page, Brian R. and Ziaeefard, Saeedeh and Moridian, Barzin and Mahmoudian, Nina}, date = {2017-10}, pages = {1--7}, publisher = {IEEE}, location = {Indianapolis, IN}, doi = {10.1109/FIE.2017.8190555}, eventtitle = {2017 {{IEEE Frontiers}} in {{Education Conference}} ({{FIE}})}, isbn = {978-1-5090-5920-1} } @article{paigeEvolvingModelsModelDriven2015, ids = {paigeEvolvingModelsModelDriven2016,wrro110199}, title = {Evolving {{Models}} in {{Model-Driven Engineering}}: {{State-of-the-Art}} and {{Future Challenges}}}, shorttitle = {Evolving {{Models}} in {{Model-Driven Engineering}}}, author = {Paige, Richard F. and Matragkas, Nicholas and Rose, Louis M.}, date = {2015}, journaltitle = {J. Syst. Softw.}, url = {http://www.sciencedirect.com/science/article/pii/S0164121215001909}, urldate = {2015-10-19} } @article{paigeRigorousIdentificationEncoding2010, title = {Rigorous Identification and Encoding of Trace-Links in Model-Driven Engineering}, author = {Paige, Richard F. and Drivalos, Nikolaos and Kolovos, Dimitrios S. and Fernandes, Kiran J. and Power, Christopher and Olsen, Goran K. and Zschaler, Steffen}, date = {2010}, journaltitle = {Softw. Syst. Model.}, volume = {10}, number = {4}, pages = {469--487}, doi = {10.1007/s10270-010-0158-8}, note = {TL;DR \par This paper demonstrates how to identify the different kinds of trace-links that may appear in an end-to-end MDE process and describes a rigorous approach to defining semantically rich trace- links between models, where the models themselves may be constructed using diverse modelling languages.} } @inproceedings{pakdeetrakulwongRecommendationSystemsSoftware2014, title = {Recommendation Systems for Software Engineering: {{A}} Survey from Software Development Life Cycle Phase Perspective}, shorttitle = {Recommendation Systems for Software Engineering}, author = {Pakdeetrakulwong, Udsanee and Wongthongtham, Pornpit and Siricharoen, Waralak V.}, date = {2014-12}, pages = {137--142}, publisher = {IEEE}, url = {http://ieeexplore.ieee.org/document/7038793/}, urldate = {2017-06-19}, isbn = {978-1-908320-39-1}, nodoi = {10.1109/ICITST.2014.7038793}, note = {TL;DR \par A survey of recommendation systems for software engineering focusing in particular on what they can assist users in each software development life cycle phase is conducted.} } @article{palombaGentleIntroductionAI, title = {A {{Gentle Introduction}} to {{AI}}}, author = {Palomba, Fabio}, langid = {english} } @inproceedings{palyartMDE4HPCApproachUsing2011, title = {{{MDE4HPC}}: An Approach for Using Model-Driven Engineering in High-Performance Computing}, shorttitle = {{{MDE4HPC}}}, booktitle = {Int. {{SDL Forum}}}, author = {Palyart, Marc and Lugato, David and Ober, Ileana and Bruel, Jean-Michel}, date = {2011}, pages = {247--261}, publisher = {Springer}, url = {http://link.springer.com/10.1007%2F978-3-642-25264-8_19}, urldate = {2017-02-23} } @article{PAM, title = {{{PAM}}: {{Probabilistic API}} Miner}, author = {Fowkes, Jaroslav and Sutton, Charles}, url = {https://github.com/mast-group/api-mining}, note = {last access 24.08.2018 \par last access 24.08.2018 \par last access 24.08.2018 \par last access 24.08.2018 \par last access 24.08.2018} } @article{panachEvaluatingModelDrivenDevelopment2021, title = {Evaluating {{Model-Driven Development Claims}} with {{Respect}} to {{Quality}}: {{A Family}} of {{Experiments}}}, shorttitle = {Evaluating {{Model-Driven Development Claims}} with {{Respect}} to {{Quality}}}, author = {Panach, Jose Ignacio and Dieste, Oscar and Marin, Beatriz and Espana, Sergio and Vegas, Sira and Pastor, Oscar and Juristo, Natalia}, date = {2021-01-01}, journaltitle = {IIEEE Trans. Software Eng.}, volume = {47}, number = {1}, pages = {130--145}, issn = {0098-5589, 1939-3520, 2326-3881}, doi = {10.1109/TSE.2018.2884706}, note = {TL;DR \par Six replications of the baseline are reported to study the impact of problem complexity on software quality in the context of model-driven development, suggesting that MDD yields better quality for more complex problems.} } @article{panDevelopingHybridIntrusion2015, title = {Developing a {{Hybrid Intrusion Detection System Using Data Mining}} for {{Power Systems}}}, author = {Pan, Shengyi and Morris, Thomas and Adhikari, Uttam}, date = {2015-11}, journaltitle = {IEEE Trans. Smart Grid}, volume = {6}, number = {6}, pages = {3104--3113}, issn = {1949-3053, 1949-3061}, doi = {10.1109/TSG.2015.2409775}, abstract = {Synchrophasor systems provide an immense volume of data for wide area monitoring and control of power systems to meet the increasing demand of reliable energy. The construction of traditional intrusion detection systems (IDSs) that use manually created rules based upon expert knowledge is knowledge-intensive and is not suitable in the context of this big data problem. This paper presents a systematic and automated approach to build a hybrid IDS that learns temporal state-based specifications for power system scenarios including disturbances, normal control operations, and cyber-attacks. A data mining technique called common path mining is used to automatically and accurately learn patterns for scenarios from a fusion of synchrophasor measurement data, and power system audit logs. As a proof of concept, an IDS prototype was implemented and validated. The IDS prototype accurately classifies disturbances, normal control operations, and cyber-attacks for the distance protection scheme for a two-line three-bus power transmission system.}, langid = {english}, note = {TL;DR \par A systematic and automated approach to build a hybrid IDS that learns temporal state-based specifications for power system scenarios including disturbances, normal control operations, and cyber-attacks is presented.} } @inproceedings{Panichella:2013:EUT:2486788.2486857, title = {How to Effectively Use Topic Models for Software Engineering Tasks? {{An}} Approach Based on Genetic Algorithms}, booktitle = {Proc. 2013 {{Int}}. {{Conf}}. {{Softw}}. {{Eng}}.}, author = {Panichella, Annibale and Dit, Bogdan and Oliveto, Rocco and Di Penta, Massimiliano and Poshyvanyk, Denys and De Lucia, Andrea}, date = {2013}, series = {{{ICSE}} '13}, pages = {522--531}, publisher = {IEEE Press}, location = {Piscataway, NJ, USA}, url = {http://dl.acm.org.univaq.clas.cineca.it/citation.cfm?id=2486788.2486857}, acmid = {2486857}, isbn = {978-1-4673-3076-3}, numpages = {10} } @article{Pant2020504, title = {Co-Modelling Strategy for Development of Airpath Metamodel on Multi-Physics Simulation Platform}, author = {Pant, G. and Campean, F. and Korsunovs, A. and Neagu, D. and Garcia-Afonso, O.}, editor = {Ju Z., Zhou D., Yang L., Yang C., Gegov A.}, date = {2020}, journaltitle = {Adv. Intell. Syst. Comput.}, volume = {1043}, pages = {504--516}, publisher = {Springer Verlag}, issn = {21945357}, doi = {10.1007/978-3-030-29933-0_42}, abbrev_source_title = {Adv. Intell. Sys. Comput.}, affiliation = {University of Bradford, Bradford, United Kingdom; University of La Laguna, Tenerife, Spain}, correspondence_address1 = {Pant, G.; University of BradfordUnited Kingdom; email: g.pant@bradford.ac.uk}, document_type = {Conference Paper}, isbn = {9783030299323}, langid = {english}, source = {Scopus}, keywords = {notion}, note = {cited By 1} } @inproceedings{papagelisQualitativeAnalysisUserbased2005, title = {Qualitative Analysis of User-Based and Item-Based Prediction Algorithms for Recommendation Agents}, booktitle = {Eng. {{Appl}}. {{Artif}}. {{Intell}}.}, author = {Papagelis, Manos and Plexousakis, Dimitris}, editor = {Klusch, Matthias and Ossowski, Sascha and Kashyap, Vipul and Unland, Rainer}, date = {2005-10}, volume = {18}, pages = {152--166}, publisher = {Pergamon Press, Inc.}, address = {Tarrytown, NY, USA}, location = {Berlin, Heidelberg}, url = {http://dx.doi.org/10.1016/j.engappai.2005.06.010}, abstract = {Recommendation agents employ prediction algorithms to provide users with items that match their interests. In this paper, we describe and evaluate several prediction algorithms, some of which are novel in that they combine user-based and item-based similarity measures derived from either explicit or implicit ratings. We compare both statistical and decision-support accuracy metrics of the algorithms against different levels of data sparsity and different operational thresholds. The first metric evaluates the accuracy in terms of average absolute deviation, while the second evaluates how effectively predictions help users to select high-quality items. Our experimental results indicate better performance of item-based predictions derived from explicit ratings in relation to both metrics. Category-boosted predictions can lead to slightly better predictions when combined with explicit ratings, while implicit ratings (in the sense that we have defined them here) perform much worse than explicit ratings.}, acmid = {1707132}, isbn = {978-3-540-30104-2}, issue_date = {October, 2005}, nodoi = {10.1016/j.engappai.2005.06.010}, numpages = {9}, keywords = {Collaborative filtering,Recommendation algorithms,Similarity measures} } @online{PaperPageSWELancer, title = {Paper Page - {{SWE-Lancer}}: {{Can Frontier LLMs Earn}} \$1 {{Million}} from {{Real-World Freelance Software Engineering}}?}, url = {https://huggingface.co/papers/2502.12115}, keywords = {source\_mobile} } @inproceedings{papineniBleuMethodAutomatic2002, title = {Bleu: A {{Method}} for {{Automatic Evaluation}} of {{Machine Translation}}}, shorttitle = {Bleu}, booktitle = {Proc. 40th {{Annu}}. {{Meet}}. {{Assoc}}. {{Comput}}. {{Linguist}}.}, author = {Papineni, Kishore and Roukos, Salim and Ward, Todd and Zhu, Wei-Jing}, date = {2002-07}, pages = {311--318}, publisher = {Association for Computational Linguistics}, location = {Philadelphia, Pennsylvania, USA}, doi = {10.3115/1073083.1073135}, eventtitle = {{{ACL}} 2002}, note = {TL;DR \par This work proposes a method of automatic machine translation evaluation that is quick, inexpensive, and language-independent, that correlates highly with human evaluation, and that has little marginal cost per run.} } @online{PapyrusIoTModeling, title = {Papyrus for {{IoT}} – {{A Modeling Solution}} for {{IoT}}}, url = {https://www.eclipse.org/community/eclipse_newsletter/2016/april/article3.php}, urldate = {2016-08-21}, note = {TL;DR \par A highlight to the current status of Papyrus, an open source Modeling Environment to tackle specific IoT’s challenges: from heterogeneous applications development and management, realtime models to supervision of critical running system.} } @inproceedings{Park2015, title = {A Prediction Modeling Framework: {{Toward}} Integration of Noisy Manufacturing Data and Product Design}, author = {Park, J. and Kim, K.-Y. and Sohmshetty, R.}, date = {2015}, series = {Proceedings of the {{ASME Design Engineering Technical Conference}}}, volume = {2A-2015}, publisher = {American Society of Mechanical Engineers (ASME)}, doi = {10.1115/DETC201546236}, abbrev_source_title = {Proc. ASME Des. Eng. Tech. Conf.}, affiliation = {Department of Industrial and Systems Engineering, Wayne State University, Detroit, MI 48202, United States; Group Leader, Advanced Steel Technology R and A Engineering, Ford Motor Company, Dearborn, MI 48124, United States}, document_type = {Conference Paper}, isbn = {978-0-7918-5707-6}, langid = {english}, source = {Scopus}, keywords = {notion}, note = {cited By 6} } @article{Park202083, title = {Designing the Ai Developing System through Ecological Interface Design}, author = {Park, D. and Park, H. and Song, S.}, editor = {Ahram T., Falcao C.}, date = {2020}, journaltitle = {Adv. Intell. Syst. Comput.}, volume = {1217 AISC}, pages = {83--96}, publisher = {Springer}, issn = {21945357}, doi = {10.1007/978-3-030-51828-8_12}, abstract = {In recent years, several kinds of machine learning tools have developed, each involving complex functions and tasks, which means usage knowledge varies between tools. Integrating the environment for effective AI machine learning can be regarded as a complicated task and may even consist of several separate tasks, such as building a test environment, data acquisition, data cleansing, machine learning training, and model management. In terms of the cognitive engineering approach, most tasks not only require knowledge-based cognitive control over skill-based or rule-based behaviours higher cognitive loads and workloads as well. Since complex knowledge and higher cognitive loads are required, the use of AI machine learning is limited and leads to ineffective work procedures. Thus, this research analysed the AI development process via various methods of cognitive task analysis in order to identify which tasks induce cognitive workload. Then, a new integrated AI development system was created, which was expected to reduce the number of ineffective tasks and workload. Experiments were conducted twice to validate the system’s effectiveness, and the results indicate that there were significant differences between the several different AI development tasks. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020.}, document_type = {Conference Paper}, isbn = {9783030518271}, source = {Scopus}, note = {cited By 1 \par TL;DR \par This research analysed the AI development process via various methods of cognitive task analysis in order to identify which tasks induce cognitive workload, and created a new integrated AI development system, which was expected to reduce the number of ineffective tasks and workload.} } @inproceedings{parkIoTRoutingArchitecture2014, title = {{{IoT}} Routing Architecture with Autonomous Systems of Things}, booktitle = {Internet {{Things WF-IoT}} 2014 {{IEEE World Forum On}}}, author = {Park, Soochang and Crespi, Noel and Park, Hosung and Kim, Sang-Ha}, date = {2014}, pages = {442--445}, publisher = {IEEE}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6803207}, urldate = {2016-11-02}, note = {TL;DR \par A future-driven routing architecture for Internet of Things, addressing classification of diverse features of ASoTs, and exploring new challenges especially on inter-domain routing is presented.} } @inproceedings{parkSelfmanagementSystemBased2006, title = {Self-Management System Based on Self-Healing Mechanism}, booktitle = {Asia-{{Pac}}. {{Netw}}. {{Oper}}. {{Manag}}. {{Symp}}.}, author = {Park, Jeongmin and Yoo, Giljong and Jeong, Chulho and Lee, Eunseok}, date = {2006}, pages = {372--382}, publisher = {Springer}, url = {http://link.springer.com/chapter/10.1007/11876601_38}, urldate = {2016-09-21} } @report{Parnas1971Information, title = {Information Distribution Aspects of Design Methodology}, author = {Parnas, David L.}, date = {1971}, institution = {Departement of Computer Science, Carnegie Mellon University}, location = {Pittsburgh} } @article{parnasCriteriaBeUsed1972, title = {On the {{Criteria To Be Used}} in {{Decomposing Systems}} into {{Modules}}}, author = {Parnas, D L}, date = {1972}, volume = {15}, number = {12}, pages = {6}, abstract = {This paper discusses modularization as a mechanism for improving the flexibility and comprehensibility of a system while allowing the shortening of its development time. The effectiveness of a "modularization" is dependent upon the criteria used in dividing the system into modules. A system design problem is presented and both a conventional and unconventional decomposition are described. It is shown that the unconventional decompositions have distinct advantages for the goals outlined. The criteria used in arriving at the decompositions are discussed. The unconventional decomposition, if implemented with the conventional assumption that a module consists of one or more subroutines, will be less efficient in most cases. An alternative approach to implementation which does not have this effect is sketched.}, langid = {english} } @article{parnin2012crowd, title = {Crowd Documentation: {{Exploring}} the Coverage and the Dynamics of {{API}} Discussions on {{Stack Overflow}}}, author = {Parnin, Chris and Treude, Christoph and Grammel, Lars and Storey, Margaret-Anne}, date = {2012}, journaltitle = {Ga. Inst. Technol. Tech Rep} } @article{parra-ullauriEventdrivenTemporalModels2022, title = {Event-Driven Temporal Models for Explanations - {{ETeMoX}}: Explaining Reinforcement Learning}, shorttitle = {Event-Driven Temporal Models for Explanations - {{ETeMoX}}}, author = {Parra-Ullauri, Juan Marcelo and García-Domínguez, Antonio and Bencomo, Nelly and Zheng, Changgang and Zhen, Chen and Boubeta-Puig, Juan and Ortiz, Guadalupe and Yang, Shufan}, date = {2022-06}, journaltitle = {Softw Syst Model}, volume = {21}, number = {3}, pages = {1091--1113}, issn = {1619-1366, 1619-1374}, doi = {10.1007/s10270-021-00952-4}, abstract = {Abstract Modern software systems are increasingly expected to show higher degrees of autonomy and self-management to cope with uncertain and diverse situations. As a consequence, autonomous systems can exhibit unexpected and surprising behaviours. This is exacerbated due to the ubiquity and complexity of Artificial Intelligence (AI)-based systems. This is the case of Reinforcement Learning (RL), where autonomous agents learn through trial-and-error how to find good solutions to a problem. Thus, the underlying decision-making criteria may become opaque to users that interact with the system and who may require explanations about the system’s reasoning. Available work for eXplainable Reinforcement Learning (XRL) offers different trade-offs: e.g. for runtime explanations, the approaches are model-specific or can only analyse results after-the-fact. Different from these approaches, this paper aims to provide an online model-agnostic approach for XRL towards trustworthy and understandable AI. We present ETeMoX, an architecture based on temporal models to keep track of the decision-making processes of RL systems. In cases where the resources are limited (e.g. storage capacity or time to response), the architecture also integrates complex event processing, an event-driven approach, for detecting matches to event patterns that need to be stored, instead of keeping the entire history. The approach is applied to a mobile communications case study that uses RL for its decision-making. In order to test the generalisability of our approach, three variants of the underlying RL algorithms are used: Q-Learning, SARSA and DQN. The encouraging results show that using the proposed configurable architecture, RL developers are able to obtain explanations about the evolution of a metric, relationships between metrics, and were able to track situations of interest happening over time windows.}, langid = {english}, keywords = {GOAL\_ExplainableReinforcementLearning,notion,TECHNIQUE\_ReinforcementLearning} } @inproceedings{ParreirasSW07, title = {On Marrying Ontological and Metamodeling Technical Spaces}, booktitle = {Proc. 6th {{Jt}}. {{Meet}}. {{Eur}}. {{Softw}}. {{Eng}}. {{Conf}}. {{ACM SIGSOFT Int}}. {{Symp}}. {{Found}}. {{Softw}}. {{Eng}}. 2007 {{Dubrov}}. {{Croat}}. {{Sept}}. 3-7 2007}, author = {Parreiras, Fernando Silva and Staab, Steffen and Winter, Andreas}, editor = {Crnkovic, Ivica and Bertolino, Antonia}, date = {2007}, pages = {439--448}, publisher = {ACM}, doi = {10.1145/1287624.1287687}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Tue, 01 Feb 2022 10:45:16 +0100} } @inproceedings{Passant:2010:DMR:1940334.1940349, title = {Dbrec: {{Music}} Recommendations Using {{DBpedia}}}, booktitle = {Proc. 9th {{Int}}. {{Semantic Web Conf}}. {{Semantic Web}} - {{Vol}}. {{Part II}}}, author = {Passant, Alexandre}, date = {2010}, series = {{{ISWC}}'10}, pages = {209--224}, publisher = {Springer-Verlag}, location = {Berlin, Heidelberg}, url = {http://dl.acm.org/citation.cfm?id=1940334.1940349}, acmid = {1940349}, isbn = {3-642-17748-4 978-3-642-17748-4}, numpages = {16}, keywords = {DBpedia,linked data,recommendation systems,semantic distance,semantic web applications} } @inproceedings{passantMeasuringSemanticDistance2010, title = {Measuring Semantic Distance on Linking Data and Using It for Resources Recommendations.}, booktitle = {{{AAAI Spring Symp}}. {{Linked Data Meets Artif}}. {{Intell}}.}, author = {Passant, Alexandre}, date = {2010}, publisher = {AAAI}, url = {http://dblp.uni-trier.de/db/conf/aaaiss/aaaiss2010-7.html#Passant10}, added-at = {2012-02-17T00:00:00.000+0100}, biburl = {http://www.bibsonomy.org/bibtex/2cdd5d7e0e615eb104fe56a4c90ceb96a/dblp}, ee = {http://www.aaai.org/ocs/index.php/SSS/SSS10/paper/view/1147}, interhash = {eee04621d061bb7f9143ce6b36b9ded6}, intrahash = {cdd5d7e0e615eb104fe56a4c90ceb96a}, keywords = {dblp}, note = {TL;DR \par This paper demonstrates how to measure semantic distance on Linked Data in order to identify relatedness between resources, and how such measures can be used to provide a new kind of self-explanatory recommendations.}, timestamp = {2012-02-17T00:00:00.000+0100} } @book{pastorAdvancedInformationSystems2005, title = {Advanced {{Information Systems Engineering}}, 17th {{International Conference}}, {{CAiSE}} 2005, {{Porto}}, {{Portugal}}, {{June}} 13-17, 2005, {{Proceedings}}}, editor = {Pastor, Oscar and family=Cunha, given=João Falcão, prefix=e, useprefix=false}, date = {2005}, series = {Lecture {{Notes}} in {{Computer Science}}}, volume = {3520}, publisher = {Springer}, doi = {10.1007/b136788}, isbn = {3-540-26095-1} } @article{patelEnablingHighlevelApplication2015, title = {Enabling High-Level Application Development for the {{Internet}} of {{Things}}}, author = {Patel, Pankesh and Cassou, Damien}, date = {2015}, journaltitle = {J. Syst. Softw.}, volume = {103}, pages = {62--84}, url = {http://www.sciencedirect.com/science/article/pii/S0164121215000187}, urldate = {2016-05-30} } @article{pattersonCarbonEmissionsLarge2021, title = {Carbon {{Emissions}} and {{Large Neural Network Training}}}, author = {Patterson, David and Gonzalez, Joseph and Le, Quoc and Liang, Chen and Munguia, Lluis-Miquel and Rothchild, Daniel and So, David and Texier, Maud and Dean, Jeff}, date = {2021-04-23}, journaltitle = {ArXiv210410350 Cs}, eprint = {2104.10350}, eprinttype = {arXiv}, eprintclass = {cs}, url = {http://arxiv.org/abs/2104.10350}, urldate = {2022-04-04}, abstract = {The computation demand for machine learning (ML) has grown rapidly recently, which comes with a number of costs. Estimating the energy cost helps measure its environmental impact and finding greener strategies, yet it is challenging without detailed information. We calculate the energy use and carbon footprint of several recent large models-T5, Meena, GShard, Switch Transformer, and GPT-3-and refine earlier estimates for the neural architecture search that found Evolved Transformer. We highlight the following opportunities to improve energy efficiency and CO2 equivalent emissions (CO2e): Large but sparsely activated DNNs can consume {$<$}1/10th the energy of large, dense DNNs without sacrificing accuracy despite using as many or even more parameters. Geographic location matters for ML workload scheduling since the fraction of carbon-free energy and resulting CO2e vary \textasciitilde 5X-10X, even within the same country and the same organization. We are now optimizing where and when large models are trained. Specific datacenter infrastructure matters, as Cloud datacenters can be \textasciitilde 1.4-2X more energy efficient than typical datacenters, and the ML-oriented accelerators inside them can be \textasciitilde 2-5X more effective than off-the-shelf systems. Remarkably, the choice of DNN, datacenter, and processor can reduce the carbon footprint up to \textasciitilde 100-1000X. These large factors also make retroactive estimates of energy cost difficult. To avoid miscalculations, we believe ML papers requiring large computational resources should make energy consumption and CO2e explicit when practical. We are working to be more transparent about energy use and CO2e in our future research. To help reduce the carbon footprint of ML, we believe energy usage and CO2e should be a key metric in evaluating models, and we are collaborating with MLPerf developers to include energy usage during training and inference in this industry standard benchmark.}, keywords = {Computer Science - Computers and Society,Computer Science - Machine Learning}, note = {TL;DR \par The energy use and carbon footprint of several recent large models are calculated-T5, Meena, GShard, Switch Transformer, and GPT-3-and earlier estimates for the neural architecture search that found Evolved Transformer are refined to avoid miscalculations.} } @article{pautassoMicroservicesPracticePart2017, entrysubtype = {magazine}, title = {Microservices in {{Practice}}, {{Part}} 1: {{Reality Check}} and {{Service Design}}}, shorttitle = {Microservices in {{Practice}}, {{Part}} 1}, author = {Pautasso, Cesare and Zimmermann, Olaf and Amundsen, Mike and Lewis, James and Josuttis, Nicolai and {undefined} and {undefined} and {undefined} and {undefined}}, date = {2017}, journaltitle = {IEEE Software}, volume = {34}, number = {1}, pages = {91--98}, issn = {0740-7459}, abstract = {Service-oriented architecture (SOA) and microservices insiders Mike Amundsen, James Lewis, and Nicolai Josuttis share their experiences and predictions with department editors Cesare Pautasso and Olaf Zimmermann.}, keywords = {software development,software engineering}, note = {TL;DR \par Service-oriented architecture and microservices insiders Mike Amundsen, James Lewis, and Nicolai Josuttis share their experiences and predictions with department editors Cesare Pautasso and Olaf Zimmermann.} } @incollection{Pazzani2007, title = {Content-Based Recommendation Systems}, booktitle = {The Adaptive Web: {{Methods}} and Strategies of Web Personalization}, author = {Pazzani, Michael J. and Billsus, Daniel}, editor = {Brusilovsky, Peter and Kobsa, Alfred and Nejdl, Wolfgang}, date = {2007}, pages = {325--341}, publisher = {Springer Berlin Heidelberg}, location = {Berlin, Heidelberg}, doi = {10.1007/978-3-540-72079-9₁0}, isbn = {978-3-540-72079-9}, note = {TL;DR \par This chapter discusses content-based recommendation systems, i.e., systems that recommend an item to a user based upon a description of the item and a profile of the user's interests, which are used in a variety of domains ranging from recommending web pages, news articles, restaurants, television programs, and items for sale.} } @article{Pearce_Ahmad_Tan_Dolan-Gavitt_Karri_2021, title = {Asleep at the Keyboard? {{Assessing}} the Security of {{GitHub}} Copilot’s Code Contributions}, author = {Pearce, Hammond and Ahmad, Baleegh and Tan, Benjamin and Dolan-Gavitt, Brendan and Karri, Ramesh}, date = {2021-12}, publisher = {arXiv}, url = {http://arxiv.org/abs/2108.09293}, issue = {arXiv:2108.09293}, note = {TL;DR \par This work systematically investigates the prevalence and conditions that can cause GitHub Copilot to recommend insecure code, and explores Copilot’s performance on three distinct code generation axes—examining how it performs given diversity of weaknesses, diversity of prompts, and diversity of domains.} } @online{pearceAsleepKeyboardAssessing2021, title = {Asleep at the {{Keyboard}}? {{Assessing}} the {{Security}} of {{GitHub Copilot}}'s {{Code Contributions}}}, shorttitle = {Asleep at the {{Keyboard}}?}, author = {Pearce, Hammond and Ahmad, Baleegh and Tan, Benjamin and Dolan-Gavitt, Brendan and Karri, Ramesh}, date = {2021-12-16}, eprint = {2108.09293}, eprinttype = {arXiv}, eprintclass = {cs}, url = {http://arxiv.org/abs/2108.09293}, urldate = {2023-04-24}, abstract = {There is burgeoning interest in designing AI-based systems to assist humans in designing computing systems, including tools that automatically generate computer code. The most notable of these comes in the form of the first self-described `AI pair programmer', GitHub Copilot, a language model trained over open-source GitHub code. However, code often contains bugs - and so, given the vast quantity of unvetted code that Copilot has processed, it is certain that the language model will have learned from exploitable, buggy code. This raises concerns on the security of Copilot's code contributions. In this work, we systematically investigate the prevalence and conditions that can cause GitHub Copilot to recommend insecure code. To perform this analysis we prompt Copilot to generate code in scenarios relevant to high-risk CWEs (e.g. those from MITRE's "Top 25" list). We explore Copilot's performance on three distinct code generation axes -- examining how it performs given diversity of weaknesses, diversity of prompts, and diversity of domains. In total, we produce 89 different scenarios for Copilot to complete, producing 1,689 programs. Of these, we found approximately 40\% to be vulnerable.}, pubstate = {prepublished}, keywords = {Computer Science - Artificial Intelligence,Computer Science - Cryptography and Security,LOGSEQ}, note = {Comment: Accepted for publication in IEEE Symposium on Security and Privacy 2022 \par TL;DR \par This work systematically investigates the prevalence and conditions that can cause GitHub Copilot to recommend insecure code, and explores Copilot’s performance on three distinct code generation axes—examining how it performs given diversity of weaknesses, diversity of prompts, and diversity of domains.} } @article{pelliccioneArtificialIntelligenceSoftware, title = {Artificial {{Intelligence}} and {{Software Engineering}}}, author = {Pelliccione, Patrizio and Ruscio, Davide Di and Begel, Andrew and Crnkovic, Ivica}, pages = {5}, abstract = {ML and AI are increasingly dominating the high-tech industry. Organizations and technology companies are leveraging their big data to create new products or improve their processes to reach the next level in their market. However, ML and AI are not a silver bullet and Software 2.0 is not the end of software developers or software engineering. In this lecture I will introduce the course and I will argument on how software engineering can help ML and AI to become the key technology for (autonomous) systems of the near future. Software engineering best practices and achievements reached in the last decades might help, e.g., (i) democratising the use of ML/AI, (ii) composing, reusing, chaining ML/AI models to solve more complex problems, and (iii) supporting for reasoning about correctness, repeatability, explainability, traceability, fairness, ethics, while building an ML/AI pipeline.}, langid = {english}, note = {TL;DR \par Managers, business owners, computer literate individuals, and software developers alike are all seeking an understanding of artificial intelligence and wondering what its uses might be, and Derek Partridge helps to understand what AI can and cannot do.} } @inproceedings{penaModeldrivenArchitectureApproach2006, title = {A Model-Driven Architecture Approach for Modeling, Specifying and Deploying Policies in Autonomous and Autonomic Systems}, booktitle = {2006 2nd {{IEEE Int}}. {{Symp}}. {{Dependable Auton}}. {{Secure Comput}}.}, author = {Pena, Joaquin and Hinchey, Michael G. and Sterritt, Roy and Ruiz-Cortes, Antonio and Resinas, Manuel}, date = {2006}, pages = {19--30}, publisher = {IEEE}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4030862}, urldate = {2016-08-21}, note = {TL;DR \par This paper proposes a set of UML-based models to specify autonomic and autonomous features along with the necessary procedures, based on modification and composition of models, to deploy a policy as an executing system.} } @inproceedings{Peng20198935, title = {Domain Agnostic Learning with Disentangled Representations}, author = {Peng, X. and Huang, Z. and Sun, X. and Saenko, K.}, date = {2019}, series = {36th {{International Conference}} on {{Machine Learning}}, {{ICML}} 2019}, volume = {2019-June}, pages = {8935--8946}, publisher = {International Machine Learning Society (IMLS)}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078303667&partnerID=40&md5=68918f412d63c4ae07d47a24b7181b8f}, abstract = {Unsupervised model transfer has the potential to greatly improve the generalizability of deep models to novel domains. Yet the current literature assumes that the separation of target data into distinct domains is known as a priori. In this paper, we propose the task of Domain-Agnostic Learning (DAL): How to transfer knowledge from a labeled source domain to unlabeled data from arbitrary target domains? To tackle this problem, we devise a novel Deep Adversarial Disentangled Autoencoder (DADA) capable of disentangling domain-specific features from class identity. We demonstrate experimentally that when the target domain labels are unknown, DADA leads to state-of-the-art performance on several image classification datascts. © 2019 International Machine Learning Society (IMLS).}, document_type = {Conference Paper}, isbn = {978-1-5108-8698-8}, source = {Scopus}, keywords = {Artificial intelligence,Auto encoders,Domain agnostics,Domain specific,Machine learning,Model transfer,Novel domain,Software engineering,State-of-the-art performance,Target domain,Unlabeled data}, note = {cited By 54} } @inproceedings{Peng20206, title = {Detecting Adversarial Examples for Network Intrusion Detection System with {{GAN}}}, author = {Peng, Y. and Fu, G. and Luo, Y. and Hu, J. and Li, B. and Yan, Q.}, editor = {L., Wenzheng}, date = {2020}, series = {Proceedings of the {{IEEE International Conference}} on {{Software Engineering}} and {{Service Sciences}}, {{ICSESS}}}, volume = {2020-October}, pages = {6--10}, publisher = {IEEE Computer Society}, issn = {23270586}, doi = {10.1109/ICSESS49938.2020.9237728}, abstract = {With the increasing scale of network, attacks against network emerge one after another, and security problems become increasingly prominent. Network intrusion detection system is a widely used and effective security means at present. In addition, with the development of machine learning technology, various intelligent intrusion detection algorithms also start to sprout. By flexibly combining these intelligent methods with intrusion detection technology, the comprehensive performance of intrusion detection can be improved, but the vulnerability of machine learning model in the adversarial environment can not be ignored. In this paper, we study the defense problem of network intrusion detection system against adversarial samples. More specifically, we design a defense algorithm for NIDS against adversarial samples by using bidirectional generative adversarial network. The generator learns the data distribution of normal samples during training, which is an implicit model reflecting the normal data distribution. After training, the adversarial sample detection module calculates the reconstruction error and the discriminator matching error of sample. Then, the adversarial samples are removed, which improves the robustness and accuracy of NIDS in the adversarial environment. © 2020 IEEE.}, art_number = {9237728}, author_keywords = {adversarial sample; defense technology; machine learning; network and data security; network intrusion system}, document_type = {Conference Paper}, isbn = {978-1-7281-6578-3}, source = {Scopus}, keywords = {Adversarial environments,Comprehensive performance,Computer crime,Intrusion detection,Intrusion detection algorithms,Intrusion detection technologies,Machine learning,Machine learning models,Machine learning technology,Network intrusion detection systems,Network security,Reconstruction error,Software engineering}, note = {cited By 4 \par TL;DR \par A defense algorithm for NIDS against adversarial samples is designed by using bidirectional generative adversarial network, which improves the robustness and accuracy of NIDS in the adversarial environment.} } @inproceedings{pereiraPlatformEnableSelfadaptive2020, title = {A Platform to Enable Self-Adaptive Cloud Applications Using Trustworthiness Properties}, booktitle = {Proc. {{IEEEACM}} 15th {{Int}}. {{Symp}}. {{Softw}}. {{Eng}}. {{Adapt}}. {{Self-Manag}}. {{Syst}}.}, author = {Pereira, José D'Abruzzo and Silva, Rui and Antunes, Nuno and Silva, Jorge L. M. and family=França, given=Breno, prefix=de, useprefix=true and Moraes, Regina and Vieira, Marco}, date = {2020-06-29}, pages = {71--77}, publisher = {ACM}, location = {Seoul Republic of Korea}, doi = {10.1145/3387939.3391608}, abstract = {Self-Adaptive Systems (SASs) relect on both their state and on the environment and change their behavior to satisfy the expected objectives. Cloud systems are self-adaptive by nature, especially considering the resources used in a pay-as-you-go manner. Satisfying trustworthiness (worthiness of a service based on evidences of its trust) properties also demands self-adaptation capabilities. Unfortunately, developers lack an easy-to-use platform to support the assessment of such properties and to execute the required adaptions. This paper presents TMA, a platform that implements a MAPE-K control loop for cloud systems, supported by a distributed monitoring system based on probes. Quality Models are used to express trustworthiness properties, resulting in scores, which are used to plan adaptations through evaluation rules. These plans are executed by actuators. A demo shows the scaling up/down of the number of containers in a cloud application of a set of web services from TPC Benchmarks, as a result of changes observed in the environment.}, eventtitle = {{{SEAMS}} '20: {{IEEE}}/{{ACM}} 15th {{International Symposium}} on {{Software Engineering}} for {{Adaptive}} and {{Self-Managing Systems}}}, isbn = {978-1-4503-7962-5}, langid = {english}, keywords = {DONE}, note = {TL;DR \par TMA is presented, a platform that implements a MAPE-K control loop for cloud systems, supported by a distributed monitoring system based on probes, used to express trustworthiness properties and to plan adaptations through evaluation rules.} } @article{perez-sanchezOnlineLearningAlgorithm2013, title = {An Online Learning Algorithm for Adaptable Topologies of Neural Networks}, author = {Pérez-Sánchez, Beatriz and Fontenla-Romero, Oscar and Guijarro-Berdiñas, Bertha and Martínez-Rego, David}, date = {2013-12}, journaltitle = {Expert Syst. Appl.}, volume = {40}, number = {18}, pages = {7294--7304}, issn = {09574174}, doi = {10.1016/j.eswa.2013.06.066}, langid = {english} } @article{Pérez-Soler2020207, title = {Model-Driven Chatbot Development}, author = {Pérez-Soler, S. and Guerra, E. and family=Lara, given=J., prefix=de, useprefix=true}, editor = {Dobbie G., Frank U., Liddle S.W., Mayr H.C., Kappel G.}, date = {2020}, journaltitle = {Lect. Notes Comput. Sci. Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinforma.}, volume = {12400 LNCS}, pages = {207--222}, publisher = {{Springer Science and Business Media Deutschland GmbH}}, issn = {03029743}, doi = {10.1007/978-3-030-62522-1_15}, abbrev_source_title = {Lect. Notes Comput. Sci.}, affiliation = {Universidad Autónoma de Madrid, Madrid, Spain}, correspondence_address1 = {Pérez-Soler, S.; Universidad Autónoma de MadridSpain; email: Sara.PerezS@uam.es}, document_type = {Conference Paper}, isbn = {9783030625214}, langid = {english}, source = {Scopus}, keywords = {GOAL\_Model-Assistance,notion}, note = {cited By 7 \par TL;DR \par Chatbots are software services accessed via conversation in natural language that are increasingly used to help in all kinds of procedures like booking flights, querying visa information or assigning tasks to developers.} } @online{perryUsersWriteMore2022, title = {Do {{Users Write More Insecure Code}} with {{AI Assistants}}?}, author = {Perry, Neil and Srivastava, Megha and Kumar, Deepak and Boneh, Dan}, date = {2022-12-16}, eprint = {2211.03622}, eprinttype = {arXiv}, eprintclass = {cs}, url = {http://arxiv.org/abs/2211.03622}, urldate = {2023-03-10}, abstract = {We conduct the first large-scale user study examining how users interact with an AI Code assistant to solve a variety of security related tasks across different programming languages. Overall, we find that participants who had access to an AI assistant based on OpenAI's codex-davinci-002 model wrote significantly less secure code than those without access. Additionally, participants with access to an AI assistant were more likely to believe they wrote secure code than those without access to the AI assistant. Furthermore, we find that participants who trusted the AI less and engaged more with the language and format of their prompts (e.g. re-phrasing, adjusting temperature) provided code with fewer security vulnerabilities. Finally, in order to better inform the design of future AI-based Code assistants, we provide an in-depth analysis of participants' language and interaction behavior, as well as release our user interface as an instrument to conduct similar studies in the future.}, pubstate = {prepublished}, keywords = {Computer Science - Cryptography and Security}, note = {Comment: 18 pages, 16 figures, update adds names of statistical tests and survey questions \par TL;DR \par A user study to examine how users interact with AI code assistants to solve a variety of security related tasks finds that participants who had access to an AI assistant wrote significantly less secure code.} } @inproceedings{pervaizExaminingChallengesDevelopment2019, title = {Examining the Challenges in Development Data Pipeline}, booktitle = {Proc. {{Conf}}. {{Comput}}. {{Sustain}}. {{Soc}}. - {{COMPASS}} 19}, author = {Pervaiz, Fahad and Vashistha, Aditya and Anderson, Richard}, date = {2019}, pages = {13--21}, publisher = {ACM Press}, location = {Accra, Ghana}, doi = {10.1145/3314344.3332496}, abstract = {The developing world has increasingly relied on data driven policies. Numerous development agencies have pushed for on-ground data collection to support the development work they pursue. Many governments have launched their own efforts for frequent information gathering. Overall, the amount of data collected is tremendous, yet there are significant issues in doing useful analysis. Most of these barriers manifest in data cleaning and merging, and require a data engineer to support some parts of the analysis. In this paper, we investigate the challenges of cleaning development data through an interview based study. We conducted face to face interviews of 13 stakeholders, eight from international development organizations and five government workers from Pakistan, including both managers and data analysts. From analysis of the interviews we identified common challenges faced in processing development data including correcting open text fields, merging hierarchical data, and extracting data from textual formats such as PDF. We construct a basic taxonomy of data cleaning challenges, and identify areas where support tools can improve the process. Ultimately, the objective is to empower regular data users to easily do the necessary data cleaning and scrubbing for analysis.}, eventtitle = {The 2nd {{ACM SIGCAS Conference}}}, isbn = {978-1-4503-6714-1}, langid = {english}, note = {TL;DR \par A basic taxonomy of data cleaning challenges is constructed, and areas where support tools can improve the process are identified, to empower regular data users to easily do the necessary data cleaning and scrubbing for analysis.} } @inproceedings{pescador2016dsl, title = {{{DSL-maps}}: From Requirements to Design of Domain-Specific Languages}, booktitle = {Proc. 31st {{IEEEACM Int}}. {{Conf}}. {{Autom}}. {{Softw}}. {{Eng}}.}, author = {Pescador, Ana and family=Lara, given=Juan, prefix=de, useprefix=true}, date = {2016}, pages = {438--443}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {TL;DR \par DSL-maps is proposed, a notation inspired by mind-maps, to represent requirements for DSLs, and is supported by a tool, which helps in the automated transition into an initial meta-model design, using a customizable transformation and recommendations from a catalogue of meta- model design patterns.} } @inproceedings{pescadorPatternBasedDevelopmentDomainSpecific2015, title = {Pattern-{{Based Development}} of {{Domain-Specific Modelling Languages}}}, author = {Pescador, Ana and Garmendia, Antonio and Guerra, Esther and Cuadrado, Jesús Sánchez and family=Lara, given=Juan, prefix=de, useprefix=true}, date = {2015}, publisher = {MODELS}, url = {http://www.miso.es/pubs/DSLtao.pdf}, urldate = {2015-09-24}, note = {TL;DR \par This work proposes the construction of DSMLs and their modelling environments aided by patterns which gather knowledge of specific domains, design alternatives, concrete syntax, dynamic semantics and functionality for the modelling environment.} } @article{petcuProcessingExtremeData2016, title = {On {{Processing Extreme Data}}}, author = {Petcu, Dana and Iuhasz, Gabriel and Pop, Daniel and Talia, Domenico and Carretero, Jesus and Prodan, Radu and Fahringer, Thomas and Grasso, Ivan and Doallo, Ramon and Martin, Maria J. and Fraguela, Basilio B. and Trobec, Roman and Depolli, Matjaz and Rodriguez, Francisco Almeida and De Sande, Francisco and Da Costa, Georges and Pierson, Jean-Marc and Anastasiadis, Stergios and Bartzokas, Aristides and Lolis, Christos and Goncalves, Pedro and Brito, Fabrice and Brown, Nick}, date = {2016-01-30}, journaltitle = {SCPE}, volume = {16}, number = {4}, pages = {467--490}, issn = {1895-1767}, doi = {10.12694/scpe.v16i4.1134}, keywords = {STAR}, note = {TL;DR \par The starting point is the definition of new programming paradigms, APIs, runtime tools and methodologies for expressing data-intensive tasks on exascale systems, paving the way for the exploitation of massive parallelism over a simplified model of the system architecture.} } @inproceedings{Petroll2021, title = {Synthetic Data Generation for Deep Learning Models}, author = {Petroll, C. and Denk, M. and Holtmannspötter, J. and Paetzold, K. and Höfer, P.}, editor = {Krause D., Paetzold K., Wartzack S.}, date = {2021}, series = {Proceedings of the 32nd {{Symposium Design}} for {{X}}, {{DFX}} 2021}, publisher = {The Design Society}, doi = {10.35199/dfx2021.11}, abbrev_source_title = {Proc. Symp. Des. X, DFX}, affiliation = {Bundeswehr Research Institute for Materials, Fuels and Lubricants (WIWeB); Universität der Bundeswehr München (UniBwM), Germany; Technische Universität Dresden, Germany}, correspondence_address1 = {Petroll, C.Institutsweg 1, Germany; email: christophpetroll@bundeswehr.org}, document_type = {Conference Paper}, langid = {english}, source = {Scopus}, note = {cited By 0 \par TL;DR \par It is discussed what are the requirements for solving an MDO problem with a metamodel taking into account functional and production-specific boundary conditions and how to create a data set with the right quality and quantity.} } @article{pettigrewTastingProjectiveTechnique2008, title = {Tasting as a Projective Technique}, author = {Pettigrew, Simone and Charters, Stephen}, date = {2008}, journaltitle = {Qual. Mark. Res. Int. J.}, volume = {11}, number = {3}, eprint = {https://doi.org/10.1108/13522750810879048}, pages = {331--343}, url = {https://doi.org/10.1108/13522750810879048}, abstract = {Purpose – The purpose of this paper is to investigate the benefits of tasting as a projective technique (PT) in explicating consumers' thoughts and feelings towards food and beverage products.Design/methodology/approach – In total, ten focus groups were conducted with 35 consumers, 14 wine producers, and 13 mediators. The mediator category included those involved in marketing, wholesaling, retailing, and judging wine. Participants in each focus group were given the same four wines to taste. Initially they were invited to discuss their views on wine quality. The participants were then presented with the wines and asked to discuss their responses to them, particularly their perceptions of the quality of the wines.Findings – The primary findings related to: the changes in apparent certainty levels amongst professionals and high‐involvement informants; exposure of real and contradictory preferences; role of cognitive, affective, and sensory responses to wine; and interpretation of the language of tasting.Research limitations/implications – Tasting as a PT has the potential to generate additional and insightful data that can increase our appreciation of the complexities involved in consumption experiences. In particular, it can reveal the uncertainty that can affect consumers' product evaluations and explicate the multiple evaluation pathways that can be used by consumers of food and beverage products.Originality/value – The paper is of value in showing that the ability of PTs to yield both stated and actual preferences provides insight into the salient external factors that impact on consumption decisions and gives an indication of where marketers could most effectively focus their product development and promotional attention.}, nodoi = {10.1108/13522750810879048} } @thesis{pettinariProcessdrivenDevelopmentAnalysis, title = {Process-Driven {{Development}} and {{Analysis}} of {{Multi-Robot Systems}}.Pdf}, author = {Pettinari, Sara}, note = {\section{Annotazioni\\ (11/5/2024, 14:47:00)} \par - “considering robots’ behavior at a high level allows for conceptualizing their missions as process models.” (Pettinari, p. iii) \#a28ae5\\ \mkbibemph{ } \par - “Business Process Management (BPM) is the reference discipline that enables their usage to describe the overall workflow of a system, including its design, enactment, monitoring, analysis, and refinement.” (Pettinari, p. iii) \#a28ae5\\ \mkbibemph{ } \par - “designing processes to model the robotic mission, facilitating its execution according to the planned sequence” (Pettinari, p. iii) \#5fb236\\ \mkbibemph{ } \par - “following research objectives” (Pettinari, p. 6) \#f0ff00\\ \mkbibemph{What's your technical contribution then? } \par - “FaMe framework.” (Pettinari, p. 7) \#f0ff00\\ \mkbibemph{This should be mentioned also earlier in the document. } \par - “Concluding Remarks.” (Pettinari, p. 7) \#f0ff00\\ \mkbibemph{You conclude the thesis without mentioning evaluation activities that you might have done to evaluate both FAME and TALE. } \par - “support code reuse in robotics research” (Pettinari, p. 9) \#ff4400\\ \mkbibemph{ } \par - “Fig.2.1” (Pettinari, p. 10) \#ff4400\\ \mkbibemph{ } \par - “BPMN allows to design of different kinds” (Pettinari, p. 14) \#ff4400\\ \mkbibemph{ } \par - “Process discovery techniques take an event log and create a process model that adequately describes the underlying process. Nevertheless, the correctness of the result depends both on the log completeness and on the used discovery algorithm.” (Pettinari, p. 19) \#00b036\\ \mkbibemph{ } \par - “Figure 2.13: EKG example” (Pettinari, p. 21) \#f0ff00\\ \mkbibemph{A conclusion section is missing. } \par - “this thesis considers as a running scenario the application of an MRS in an agricultural field aiming to improve crop productivity.” (Pettinari, p. 23) \#ff4400\\ \mkbibemph{ } \par - “other frameworks.” (Pettinari, p. 23) \#f0ff00\\ \mkbibemph{you mean any framework , right? } \par - “existing model-driven approaches for robotic systems is provided.” (Pettinari, p. 29) \#f0ff00\\ \mkbibemph{I would have presented them earlier to highlight the limitations of existing approaches and thus to motivate the need for your proposal. } \par - “executable MRS collaboration diagram used in the following phase to command each robotic behavior, thus guaranteeing a distributed execution of the MRS.” (Pettinari, p. 30) \#f0ff00\\ \mkbibemph{DISTRIBUTED EXECUTION... THIS IS IMPORTANT. CHECK HOW SUCH A DISTRIBUTION IS SUPPORTED. } \par - “, minimize redundancy, and reduce the possibility of errors. By integrating the concept of building blocks, the FaMe framework keeps consistency with the characteristics of modularity and reusability of the ROS.” (Pettinari, p. 34) \#f0ff00\\ \mkbibemph{How the guidelines above are enforced? } \par - “the diagram with additional information. This information is mapped in each configured BPMN element as extension elements. Indeed, the extension element is part of the BPMN metamodel [62, 8.2.3]” (Pettinari, p. 34) \#f0ff00\\ \mkbibemph{What's the tool you have used to enforce this? } \par - “predefined values or with dynamically updated values in the form of \$tvariable\_idu.” (Pettinari, p. 34) \#f0ff00\\ \mkbibemph{Where? } \par - “Once these configurations have been done, the BPMN collaboration can be deployed and the MRS executed.” (Pettinari, p. 35) \#f0ff00\\ \mkbibemph{Again, the technical details of the execution environment are not clear here. } \par - “architecture of ROS, where a robot is a collection of nodes, a node implementing a BPMN engine has been incorporated into the robots. Each of these nodes receives the pool” (Pettinari, p. 36) \#f0ff00\\ \mkbibemph{What are the technical details to execute bpmn engines in synergy with ros? } \par - “Gazebo” (Pettinari, p. 36) \#f0ff00\\ \mkbibemph{I think it's the first time it is mentioned. } \par - “new” (Pettinari, p. 36) \#f0ff00\\ \mkbibemph{ } \par - “process at run-time,” (Pettinari, p. 36) \#f0ff00\\ \mkbibemph{New process at runtime? I'm not sure this possibility has been discussed previously. It is important to explain how this works. } \par - “Policy compatibility for” (Pettinari, p. 37) \#f0ff00\\ \mkbibemph{ } \par - “the reliability parameter” (Pettinari, p. 37) \#f0ff00\\ \mkbibemph{What's their meaning? } \par - “comply with the standards set by ROS for effective communication.” (Pettinari, p. 38) \#f0ff00\\ \mkbibemph{I can understand the meaning and the effect, however concrete examples (even by referring to the running example) is needed here. } \par - “them. This effort could be mitigated using a standard modeling language, like the BPMN notation used” (Pettinari, p. 39) \#f0ff00\\ \mkbibemph{Bpmn still needs to be learned!!! } \par - “Even if this means that the designers should learn the guidelines, the effort to learn them is not high.” (Pettinari, p. 39) \#f0ff00\\ \mkbibemph{This needs to be proved! } \par - “disciplined arrangements of BPMN elements,” (Pettinari, p. 39) \#f0ff00\\ \mkbibemph{If they are not enforced by the tool, then some threats arise related to the correct use of the approach. } \par - “Table 4.2: Features comparison of the related works” (Pettinari, p. 41) \#f0ff00\\ \mkbibemph{This analysis should come before presenting fame with the aim of analysing the short coming of existing approaches and motivating a novel technique. How would the work for the explanatory examples? What are their limitations? } \par - “5.1.” (Pettinari, p. 45) \#f0ff00\\ \mkbibemph{ } \par - “JavaScript as the predefined language for script tasks, while allowing language customization” (Pettinari, p. 46) \#f0ff00\\ \mkbibemph{This facility needs to be elaborated a bit. For instance you should discuss the scope of variables, the environment where such JavaScript code will be executed and more importantly, how to test this code which can do potentially anything. } \par - “, which allows connections only when the publisher’s policies align with the subscriber’s policies. Whenever there is a change in the QoS policy of a signal node, the modeler triggers the rule engine mechanism to evaluate the updated parameters.” (Pettinari, p. 48) \#f0ff00\\ \mkbibemph{So this means that at runtime ros entities can change their quality and the modeler has to do something via the modeling tool? } \par - “Figure 5.12: Update Closest script task configuration” (Pettinari, p. 56) \#f0ff00\\ \mkbibemph{This limit the support of the motivation related to the benefit of using bpmn as a well known language. } \par - “Enactment” (Pettinari, p. 57) \#f0ff00\\ \mkbibemph{It's important to stress somewhere the difference of distributed vs decentralised executions to highlights the benefits and the strengths of the proposed approach. } \par - “Analysis” (Pettinari, p. 74) \#f0ff00\\ \mkbibemph{Who elicited the requirements of this analysis. In other words, the three analisi you have devised and discussed have been defined to satisfy some user requirements or for some other reason? } \par - “flow:” (Pettinari, p. 74) \#f0ff00\\ \mkbibemph{ } \par - “Related Works” (Pettinari, p. 76) \#f0ff00\\ \mkbibemph{As for the FAME approach, I would present the related work section, earlier in the chapter to distill the limitations of existing approaches. The need and relevance of process mining have also to be pushed more. } \par - “Enhancement” (Pettinari, p. 84) \#f0ff00\\ \mkbibemph{What's the goal fir this? Is it application or domain specific? } \par - “extract activity-centric robotic event logs at the desired level of granularity and related to multiple perspectives of interest. The” (Pettinari, p. 96) \#f0ff00\\ \mkbibemph{Where is the goal taken into account? } \par - “merging the top-down and bottom-up approaches. Specifically, the BPMN collaboration diagram is used to guide the behavior of the MRS. During the system execution, exploiting the Tale methodology, event logs are generated to perform process mining-based system analysis. Finally, based on the insights provided during the analysis, a process adaptation step, supported by the MRS designer supervision, can be performed to re-design the model defining the mission and enact the improved MRS.” (Pettinari, p. 100) \#f0ff00\\ \mkbibemph{This is related to models@runtime field, which is not mentioned in the thesis. }} } @inproceedings{pezoaFoundationsJSONSchema2016, title = {Foundations of {{JSON Schema}}}, booktitle = {Proc. 25th {{Int}}. {{Conf}}. {{World Wide Web}}}, author = {Pezoa, Felipe and Reutter, Juan L. and Suarez, Fernando and Ugarte, Martín and Vrgoč, Domagoj}, date = {2016-04-11}, pages = {263--273}, publisher = {International World Wide Web Conferences Steering Committee}, location = {Montréal Québec Canada}, doi = {10.1145/2872427.2883029}, eventtitle = {{{WWW}} '16: 25th {{International World Wide Web Conference}}}, isbn = {978-1-4503-4143-1}, langid = {english}, note = {TL;DR \par This paper provides the first formal definition of syntax and semantics for JSON Schema and uses it to show that implementing this layer on top of JSON is feasible in practice.} } @article{pezzeTrailerACM2030, title = {The {{Trailer}} of the {{ACM}} 2030 {{Roadmap}} for {{Software Engineering}}}, author = {Pezzè, Mauro and Ciniselli, Matteo}, abstract = {The landscape of software engineering has dramatically changed. The recent advances in AI, the new opportunities of quantum computing, and the new challenges of sustainability and cyber security upset the software engineering research prospective. The 2030 SE-Roadmap special issue of ACM TOSEM Transactions on Software Engineering and Methodology gives a 360◦ view of the research challenges of the 30ties with a thorough editorial, four roadmap papers from the ACM TOSEM editorial board, and over 30 peer-reviewed papers from the research community.}, langid = {english} } @article{phuong_t_nguyen_2018_1476035, title = {An Automated Approach to Assess the Similarity of {{GitHub}} Repositories - Online Appendix}, author = {Rocco, Di and {Juri} and Nguyen, Phuong T. and Rubei, Riccardo and Di Ruscio, Davide}, date = {2018-10}, doi = {10.5281/zenodo.1476035}, note = {Last access 7.11.2018 \par Last access 7.11.2018 \par Last access 7.11.2018 \par Last access 7.11.2018 \par Last access 7.11.2018} } @inproceedings{piccioniIDEbasedIntegratedSolution2009, title = {An {{IDE-based}}, Integrated Solution to Schema Evolution of Object-Oriented Software}, booktitle = {2009 {{IEEEACM Int}}. {{Conf}}. {{Autom}}. {{Softw}}. {{Eng}}.}, author = {Piccioni, Marco and Orioly, Manuel and Meyer, Bertrand and Schneider, Teseo}, date = {2009-11}, pages = {650--654}, publisher = {IEEE}, location = {Auckland, New Zealand}, doi = {10.1109/ASE.2009.100}, abstract = {With the wide support for serialization in object-oriented programming languages, persistent objects have become common place. Retrieving previously ”persisted” objects from classes whose schema changed is however difficult. Naive solutions, such as initializing new fields to default values, risk to invalidate the consistency of the application. The ESCHER framework addresses this issues through an IDE-based approach that handles schema evolution by managing versions of the code and generating transformation functions automatically. The infrastructure also enforces class invariants to prevent the introduction of any corrupt objects. This article describes the principles behind invariant-safe schema evolution, and the design and implementation of the ESCHER system.}, eventtitle = {2009 24th {{IEEE}}/{{ACM International Conference}} on {{Automated Software Engineering}} ({{ASE}})}, isbn = {978-1-4244-5259-0}, langid = {english} } @article{pierantonioalfonsoKeynoteJCRSTAF, title = {Keynote {{JCR STAF}}}, author = {{Pierantonio, Alfonso}} } @article{pierantonioOpenAccessAll2020, title = {Open {{Access}}: All You Wanted to Know and Never Dared to Ask.}, shorttitle = {Open {{Access}}}, author = {Pierantonio, Alfonso and family=Brand, given=Mark, prefix=van den, useprefix=true and Combemale, Benoit}, date = {2020}, journaltitle = {JOT}, volume = {19}, number = {1}, pages = {1}, issn = {1660-1769}, doi = {10.5381/jot.2020.19.1.e1}, langid = {english}, note = {TL;DR \par This editorial presents the various forms of open access, discusses their pros and cons from the perspective of the Journal of Object Technology and its editors in chiefs, and illustrates how JOT implements a platinum open access model.} } @article{Pinna Puissant2015461, title = {Resolving Model Inconsistencies Using Automated Regression Planning}, author = {{J.Puissant} and Van Der Straeten, R. and Mens, T.}, date = {2015}, journaltitle = {Softw. Syst. Model.}, volume = {14}, number = {1}, pages = {461--481}, publisher = {Springer Verlag}, issn = {16191366}, doi = {10.1007/s10270-013-0317-9}, abbrev_source_title = {Softw. Syst. Model.}, affiliation = {Service de Génie Logiciel, Institut COMPLEXYS, Université de Mons, Place du Parc 20, Mons, 7000, Belgium; Software Languages Lab, Vrije Universiteit Brussel, Pleinlaan 2, Brussels, 1050, Belgium}, correspondence_address1 = {Pinna Puissant, J.; Service de Génie Logiciel, Place du Parc 20, Belgium}, document_type = {Article}, langid = {english}, source = {Scopus}, keywords = {GOAL\_Model-Repair,notion,TECHNIQUE\_AutomatedRegressionPlanning}, note = {cited By 32 \par TL;DR \par Badger is implemented, a regression planner in Prolog that generates resolution plans for UML models using both generated models and reverse-engineered models of varying sizes, the largest ones containing more than 10,000 model elements.} } @article{ploschICSOFT_2025_34, title = {{{ICSOFT}}\_2025\_34}, author = {Plösch, Reinhold and Ernst, Florian and Saft, Matthias}, abstract = {The starting point for this paper and service for the query-based generation of quality models was the requirement to be able to manage software quality models dynamically, as detailed domain knowledge is usually required for this task. We present new approaches regarding the query-based generation of software quality models and the creation of profiles for quality analyses using the code quality tool SonarQube. Furthermore, our support for the automatic assignment of software quality rules to entries of a hierarchical quality model simplifies the maintenance of the models with the help of machine learning models and large language models (HMCN and SciBERT in our case). The resulting findings were evaluated for their practical suitability using expert interviews. The results are promising and show that semantic management of quality models could help spreading the use of quality models, as it considerably reduces the maintenance effort.}, langid = {english}, keywords = {LOGSEQ}, note = {\section{Annotazioni\\ (9/3/2025, 19:47:45)} \par - “Abstract” (Plösch et al., p. 1) \#ff6666\\ \mkbibemph{This needs major revision. It is not effective in presenting neither the problem nor the solution. } \par - “The starting point for this paper and service for the query-based generation of quality models was the requirement to be able to manage software quality models dynamically, as detailed domain knowledge is usually required for this task.” (Plösch et al., p. 1) \#ff6666\\ \mkbibemph{It does not parse. } \par - “and service” (Plösch et al., p. 1) \#ff6666\\ \mkbibemph{ } \par - “(Wagner et al., 2012a)” (Plösch et al., p. 1) \#ff6666\\ \mkbibemph{tha authors of ... state.... } \par - “The Quamoco meta-model has been selected and, therefore, all further steps are going to be performed using this specific QM.” (Plösch et al., p. 1) \#ffd400\\ \mkbibemph{Which stepts, of what... what is the problem to be solved? } \par - “it changes relatively fast compared to more static-like data in SonarQube-like metrics and rules.” (Plösch et al., p. 1) \#ffd400\\ \mkbibemph{what does it mean that data changes dynamically? What kinds of data are you talking about? What are the types of systems of interest? } \par - “A quality profile can be seen as a collection of metrics and rules which are grouped into a profile. These profiles are then associated with projects” (Plösch et al., p. 1) \#5fb236\\ \mkbibemph{ } \par - “Based on this context, the following RQs were defined:” (Plösch et al., p. 2) \#ffd400\\ \mkbibemph{The used dictionary/terminology should be introduced early in the paper. The authors assume too many terms that have to be properly defined. Authors assume that readers are aware of SonarQube and constituting components. They also assume that the way quality models can be defined in SonarQube are in the knowledge of the readers. } \par - “categorization of software metrics based on machine learning algorithms suitable?” (Plösch et al., p. 2) \#ffd400\\ \mkbibemph{What does it mean? What's that? } \par - “”An effective software process applied in a manner that creates a useful product that provides measurable value for those who produce it and those who use it.”” (Plösch et al., p. 2) \#ffd400\\ \mkbibemph{Is this a definition of software quality? Is Software Quality a process? } \par - “per d” (Plösch et al., p. 2) \#ff6666\\ \mkbibemph{ } \par - “While there is still ongoing work in how to structure the abstract term Software Qualtiy, there are no approaches known from the scientific literature that support systematic tailoring of quality models to the quality requirements of a software project.” (Plösch et al., p. 2) \#ffd400\\ \mkbibemph{That's no true! } \par - “dynamic aspects is to collect usage data of rules’ issues in projects within SonarQube which then can be further used for tailoring QMs” (Plösch et al., p. 4) \#5fb236\\ \mkbibemph{ } \par - “where the ratio of related issues that are marked as won’t fix to the total number of issues of the corresponding rule is larger than x\%. This could make sense as it is an indicator that the related rule is not suitable for a specific application domain” (Plösch et al., p. 4) \#5fb236\\ \mkbibemph{ } \par - “Main component” (Plösch et al., p. 7) \#ff6666\\ \mkbibemph{ }} } @article{plotkin2004structural, title = {A Structural Approach to Operational Semantics}, author = {Plotkin, Gordon}, date = {2004}, journaltitle = {J. Log. Algebr. Program.}, volume = {60}, pages = {17--139}, publisher = {Elsevier} } @article{PMID:25142186, title = {Uncovering the Information Core in Recommender Systems}, author = {Zeng, Wei and Zeng, An and Liu, Hao and Shang, Ming-Sheng and Zhou, Tao}, date = {2014}, journaltitle = {Sci. Rep.}, volume = {4}, pages = {6140}, issn = {2045-2322}, doi = {10.1038/srep06140} } @thesis{poliniModelDrivenEngineeringIoT, title = {Model-{{Driven Engineering}} for {{IoT Applications}}: A Focus on {{Reusability}} and {{Portability}}}, author = {Polini, Andrea and Ruscio, Davide Di and Cleophas, Loek and Fedeli, Arianna}, langid = {english}, note = {\section{Annotazioni\\ (11/4/2024, 17:46:20)} \par - “The Internet of Things (IoT) has ushered in a paradigm shift, reshaping our worldview and unlocking new horizons for delivering advanced services by connecting billions of devices and sensors.” (Polini et al., p. iii) \#5fb236 \par - “heterogeneous devices with different scopes” (Polini et al., p. iii) \#5fb236 \par - “interoperability” (Polini et al., p. iii) \#5fb236 \par - “heterogeneity poses significant challenges in the design, development, and deployment of IoT solution” (Polini et al., p. iii) \#5fb236 \par - “proprietary architectures” (Polini et al., p. iii) \#5fb236 \par - “developing IoT solutions is highly dependent on a distinct vendor’s ecosystem and technologies” (Polini et al., p. iii) \#5fb236 \par - “model-driven engineering methodology for the design, development and deployment of reusable and portable IoT applications.” (Polini et al., p. iii) \#5fb236 \par - “facilitate the creation of IoT solutions that can be ported across different IoT platforms” (Polini et al., p. iii) \#5fb236 \par - “A domain-specific modelling language was developed to model IoT-related elements and their relations. This language abstracts from specific platform functionalities and facilitates platform-agnostic representations, allowing refinements and portability across target IoT platforms through model-to-text transformations” (Polini et al., p. iii) \#5fb236 \par - “The microservice architecture and the methodology are validated through a testing prototype and evaluated by 50 participants who shared the benefits of the proposed solution.” (Polini et al., p. iii) \#5fb236 \par - “designing, developing, and deploying IoT applications driven by the heterogeneity of devices, resources, and communication protocols.” (Polini et al., p. v) \#5fb236 \par - “Software Product Lines” (Polini et al., p. v) \#5fb236 \par - “IoT and Digital Twins” (Polini et al., p. vii) \#5fb236 \par - “DTMN: a Modelling Notation for Digital Twins” (Polini et al., p. vii) \#5fb236 \par - “The essence of IoT lies in the intricate network comprising diverse physical devices embedded with sensors, software, and other technologies, fostering communication and data exchange without human intervention” (Polini et al., p. 3) \#5fb236 \par - “given the high heterogeneity of the elements involved, the design, development and deployment of such IoT solutions brings several challenges that this thesis aims to overcome.” (Polini et al., p. 3) \#a28ae5 \par - “complexity is primarily attributed to the high degree of heterogeneity intrinsic to the IoT domain in handling different elements such as IoT devices, data types, protocols, cloud platforms, and many others.” (Polini et al., p. 4) \#a28ae5 \par - “enterprises, which tend to build ad-hoc solutions tailored to the specific use-case requirements” (Polini et al., p. 4) \#a28ae5 \par - “This way, while offering granular control and satisfying immediate customer needs, incurs considerable effort and costs in managing different heterogeneous elements that must be repeated for each new solution, even if similarities exist” (Polini et al., p. 4) \#5fb236 \par - “collaborations involving diverse experts, each contributing unique expertise, in the entire IoT application life-cycle” (Polini et al., p. 4) \#a28ae5 \par - “devices must be adapted to the physical environment, and integrated” (Polini et al., p. 4) \#5fb236 \par - “information systems to capture real-world data and make more informed decisions.” (Polini et al., p. 4) \#5fb236 \par - “Reusability mechanisms are based on the fact that once a given structure has been developed or a given knowledge acquired, it can be saved for subsequent reuse by developing modular and standardised components [160]” (Polini et al., p. 4) \#5fb236 \par - “necessitating a shift toward reusability as a mechanism to mitigate development barriers and enhance efficiency support” (Polini et al., p. 4) \#5fb236 \par - “reusability is not only a methodological concern but also a technological imperative.” (Polini et al., p. 5) \#5fb236 \par - “deciding which IoT platform to adopt is estimated to require almost one year of study” (Polini et al., p. 5) \#5fb236 \par - “Model-driven engineering (MDE) exhibits a promising potential to decrease the complexity of IoT application development” (Polini et al., p. 5) \#5fb236 \par - “1.2 Research Question and Contributions” (Polini et al., p. 6) \#ffd400\\ \mkbibemph{I recommend adding a figure in this section to graphical represent the different contributions and how they are connected, thus to show the contributions that underpin other ones. Moreover, I would label the contributions that have been concretized in a corresponding software and put its name. You can also link the different phase starting from design up to deployment etc.} \par - “Research Question. “How to facilitate a model-driven design, development and deployment of IoT applications focusing on reusability and portability?”” (Polini et al., p. 6) \#5fb236 \par - “gaps and opportunities for improvement in terms of reusability and portability.” (Polini et al., p. 6) \#a28ae5 \par - “ecosystem that covers the life-cycle of IoT applications, from the first architecture concepts up to the deployment of the application” (Polini et al., p. 6) \#a28ae5 \par - “Separation of Concerns focus.” (Polini et al., p. 6) \#a28ae5 \par - “Multi-view models to represent IoT applications” (Polini et al., p. 6) \#a28ae5 \par - “Reusability of IoT Knowledge, models and artefacts” (Polini et al., p. 7) \#a28ae5 \par - “repository of reusable assets that can be leveraged across multiple IoT projects” (Polini et al., p. 7) \#ffd400\\ \mkbibemph{That's interesting, repository of reusable assets... how to ensure platform independence?} \par - “Design Science Research (DSR) Methodology” (Polini et al., p. 9) \#a28ae5 \par - “An important aspect of the DSR methodology is its inherent flexibility, allowing for the repetition of each activity.” (Polini et al., p. 10) \#5fb236 \par - “Section 2.3 describes the IoT application life-cycle together with the mapping of the various actors involved for each step. Section 2.4 concludes the chapter with a detailed analysis of the open challenges in IoT application development.” (Polini et al., p. 11) \#5fb236 \par - “Section 3.2 sheds light on the interconnection of MDE and the IoT domain, presenting several models used to model the IoT domain. These models are domain-specific models, Business process models and feature models.” (Polini et al., p. 11) \#5fb236 \par - “comparison of the MDE approach targeting the development of IoT application” (Polini et al., p. 11) \#5fb236 \par - “limitations of the analysed approaches” (Polini et al., p. 11) \#5fb236 \par - “proposed methodology” (Polini et al., p. 11) \#5fb236 \par - “panoramic over the proposed approach” (Polini et al., p. 11) \#5fb236 \par - “use case scenario” (Polini et al., p. 11) \#5fb236 \par - “Design Phase” (Polini et al., p. 11) \#a28ae5 \par - “Development Phase” (Polini et al., p. 12) \#a28ae5 \par - “Development and the Deployment” (Polini et al., p. 12) \#a28ae5 \par - “discussion and limitations of the proposed methodology” (Polini et al., p. 12) \#a28ae5 \par - “complex, time-consuming, and challenging activity.” (Polini et al., p. 24) \#5fb236 \par - “IoT introduces highly intricate and dynamic relationships among experts.” (Polini et al., p. 24) \#a28ae5 \par - “hardware design, software development, network architecture, data analytics, and user experience, among others.” (Polini et al., p. 24) \#5fb236 \par - “Individual who represent” (Polini et al., p. 26) \#ff6666\\ \mkbibemph{Individuals} \par - “this decision takes eleven months to handle correctly, and no mechanisms to facilitate this decision have been proposed yet.” (Polini et al., p. 27) \#ffd400\\ \mkbibemph{How is you approaches able to improve this?} \par - “For example, an IoT application developed for AWS IoT Core may rely on AWS-specific services and APIs that are not easily transferable to another IoT platform, such as the Microsoft Azure IoT” (Polini et al., p. 35) \#5fb236 \par - “(” (Polini et al., p. 35) \#ff6666 \par - “these solutions provide execution platforms that are extremely coupled with a specific technology” (Polini et al., p. 45) \#a28ae5 \par - “Section 4.4 discusses the presented comparison and highlights the open challenges that these MDE approaches present.” (Polini et al., p. 47) \#a28ae5 \par - “Figure 4.1: Search and filtering process.” (Polini et al., p. 49) \#a28ae5\\ \mkbibemph{Fico} \par - “Were also considered four criteria for exclusion” (Polini et al., p. 49) \#ff6666 \par - “These specifications are then translated as platform-specific code that can be deployed on low-level IoT devices. The entire approach is supported by the CHESSIoT Tool.” (Polini et al., p. 53) \#5fb236 \par - “CHESSIoT” (Polini et al., p. 55) \#ff6666\\ \mkbibemph{I think the annotations in Tab 4.1 given to ChessIoT do not reflect the capabilities of ChessIoT. Please double check.} \par - “Table” (Polini et al., p. 60) \#ffd400\\ \mkbibemph{It is important to mention also possible early analysis activities that might be performed on the modeling artifacts to possibly detect potential faults. THe work is already dense and I'm suggesting to mention that this is an additional activities that need to be supported by ideal approaches.} \par - “While developers commonly face the complexity of adapting existing models or already implemented code, this methodology proposes a paradigm shift in which reusability is considered from the early stages of the application’s life cycle.” (Polini et al., p. 67) \#a28ae5 \par - “feature models as a semantic representation of all functionalities and IoT devices pertinent to that domain” (Polini et al., p. 68) \#a28ae5 \par - “IoT-enhanced business process models.” (Polini et al., p. 68) \#a28ae5 \par - “Figure 5.2: Microservice Architecture” (Polini et al., p. 74) \#ffd400\\ \mkbibemph{What are the implicated dependencies induced by the adopted BPMN Engine?} \par - “1FloWare web page: www.pros.unicam.it/floware” (Polini et al., p. 79) \#ff6666\\ \mkbibemph{The link is not working.} \par - “Business Process in the IoT” (Polini et al., p. 93) \#5fb236 \par - “integrate IoT elements inside business processe” (Polini et al., p. 93) \#5fb236 \par - “Business Engineer in making informed decisions during the modelling process.” (Polini et al., p. 94) \#5fb236 \par - “High-Level Events” (Polini et al., p. 101) \#ff6666\\ \mkbibemph{Put in bold} \par - “reusability and portability in the design, development, and deployment of these solutions.” (Polini et al., p. 125) \#a28ae5 \par - “single feature model for multiple customer requests enables reusability, streamlining development processes and enhancing scalability.” (Polini et al., p. 125) \#a28ae5 \par - “mapping of features and devices, companies can navigate solution requirements with clarity and precision, facilitating improved communication and alignment among stakeholders” (Polini et al., p. 125) \#a28ae5 \par - “platform-independent modelling strategy adept at capturing the heterogeneous IoT functionalities and devices and their integration within business processes with different views and domain-specific models” (Polini et al., p. 125) \#a28ae5 \par - “mitigates the risks associated with vendor lock-in” (Polini et al., p. 125) \#a28ae5 \par - “high-level representation of an IoT application that ensures portable deployment solutions compatible with several IoT platforms and middleware” (Polini et al., p. 125) \#a28ae5 \par - “refining platform-independent models with technology-specific information tailored to the IoT platform” (Polini et al., p. 125) \#a28ae5 \par - “evaluating the proposed methodology within the context of microservice architecture” (Polini et al., p. 131) \#5fb236 \par - “Supporting Architecture Prototype” (Polini et al., p. 131) \#ffd400\\ \mkbibemph{This chapter disrupts the flow maintained so far. In Chapter 8 you show the different steps of the process by finishing it with the the deployment of the modeled IoT applications on different platforms. What's the role of the architecture presented in chapter 10? Can it be seen as an alternative platform that you are proposing that can be considered at the same level of Azure or ThingsBoard? If this is the case, it is important to highlights the strengths and limitations of the proposed platform (i.e., architecture prototype) with respect to the alternative ones that have been considered earlier in the document.} \par - “According to [266], a way of preliminary evaluating the proposal of a new architecture is through developing a prototype” (Polini et al., p. 132) \#5fb236 \par - “Hypothesis. The proposed FloBP approach and the supporting infrastructure allow an effective collaboration to construct and deploy an IoT-enhanced BP, the flow of coordinated tasks, the IoT devices participating in the BP, and the interactions the process must have with IoT devices.” (Polini et al., p. 137) \#5fb236 \par - “participants were asked to define the corresponding model according to the role played.” (Polini et al., p. 137) \#a28ae5 \par - “A” (Polini et al., p. 137) \#ff6666 \par - “The number of recruited participants was designed to facilitate their distribution into two balanced groups of ten participants.” (Polini et al., p. 138) \#ffd400\\ \mkbibemph{This sentence is not consistent with the detail given below with the description of the three groups (30 + 10 + 10).} \par - “two balanced groups of ten participants” (Polini et al., p. 138) \#ffd400\\ \mkbibemph{Maybe two groups of 25 participants?} \par - “bench-marked” (Polini et al., p. 138) \#ff6666\\ \mkbibemph{benchmarked} \par - “NASA-TLX questionnaire” (Polini et al., p. 138) \#a28ae5 \par - “Mental demand” (Polini et al., p. 139) \#5fb236 \par - “Physical demand” (Polini et al., p. 139) \#5fb236 \par - “Temporal demand” (Polini et al., p. 139) \#5fb236 \par - “Effort” (Polini et al., p. 139) \#5fb236 \par - “Performance” (Polini et al., p. 139) \#5fb236 \par - “Frustration level” (Polini et al., p. 139) \#5fb236 \par - “presented model-driven approach allows an effective collaborative development to create IoT applications converging more modelling language as feature models, BPMN and DSL.” (Polini et al., p. 145) \#5fb236 \par - “As commented above, this experiment recruits participants so that members of the same group have similar profiles.” (Polini et al., p. 146) \#5fb236 \par - “The present and future trajectory of this thesis is firmly directed towards the application of the proposed methodology within new interrelated emergent topics, such as the realm of digital twin technology.” (Polini et al., p. 149) \#a28ae5 \par - “thesis aims to unlock new possibilities for innovation and advancement in diverse fields, from manufacturing and healthcare to smart cities and others.” (Polini et al., p. 149) \#a28ae5 \par - “This data enables digital twins to accurately capture and reflect the current state of the physical system, enhancing the capabilities of IoT applications and facilitating precise monitoring, analysis, and prediction of performance metrics” (Polini et al., p. 149) \#a28ae5 \par - “possible integration of the proposed methodology for designing and developing a Digital Twin Modelling Notation (DTMN) and to provide deployment support” (Polini et al., p. 150) \#5fb236 \par - “analysis between existing IoT platforms that can be used to implement digital twin solutions.” (Polini et al., p. 150) \#a28ae5 \par - “predict changes in the physical system to foster decision-making and provide the opportunity to apply preventive actions for optimising system behaviour and/or mitigating unexpected events.” (Polini et al., p. 150) \#a28ae5 \par - “This thesis presents a model-driven methodology to offer a systematic approach to the design, development, and deployment of IoT applications.” (Polini et al., p. 163) \#a28ae5 \par - “By adopting a model-driven engineering paradigm, this thesis aimed to streamline the development process, promote consistency, and facilitate the adoption of IoT solutions across diverse environments and use cases.” (Polini et al., p. 163) \#a28ae5 \par - “importance of abstraction layers and separation of concerns design principles to encapsulate functionalities and promote reuse by design” (Polini et al., p. 163) \#a28ae5 \par - “different types of models, such as feature models, IoT-enhanced business processes and a domain-specific modelling language tailored to the IoT domain, this methodology provided a structure for capturing the intricacies of the IoT domain while maintaining clarity and comprehensibility.” (Polini et al., p. 163) \#a28ae5 \par - “reusability” (Polini et al., p. 163) \#a28ae5 \par - “portability” (Polini et al., p. 163) \#a28ae5 \par - “IoT platforms receiving events resulting from data manipulation of the devices” (Polini et al., p. 164) \#a28ae5 \par - “this entire methodology has been validated through a case study involving a smart canteen.” (Polini et al., p. 164) \#a28ae5 \par - “[69] Flavio Corradini et al. “FloWare: a model-driven approach fostering reuse and customisation in IoT applications modelling and development”. In: Software and Systems Modeling (2022), pp. 131–258. [70] Flavio Corradini et al. “FloWare: An Approach for IoT Support and Application Development”. In: Enterprise, Business-Process and Information Systems Modeling. Cham: Springer International Publishing, 2021, pp. 350–365.” (Polini et al., p. 170) \#5fb236} } @article{polyakovMachineLearningCybersecurity, title = {Machine {{Learning}} for {{Cybersecurity}} 10}, author = {Polyakov, Alexander}, pages = {23}, langid = {english} } @online{PolystoreDatabasesBe, title = {Polystore {{Databases}} to Be {{Examined}} at {{IEEE}}, {{CIDR Conferences}} | {{Intel Science}} \& {{Technology Center}} for {{Big Data}}}, url = {http://istc-bigdata.org/index.php/polystore-databases-at-ieee-cidr-conferences/}, urldate = {2018-04-16} } @article{pontaMetadataCodecentricUsagebased2018, title = {Beyond {{Metadata}}: {{Code-centric}} and {{Usage-based Analysis}} of {{Known Vulnerabilities}} in {{Open-source Software}}}, shorttitle = {Beyond {{Metadata}}}, author = {Ponta, Serena E. and Plate, Henrik and Sabetta, Antonino}, date = {2018-06-15}, journaltitle = {ArXiv180605893 Cs}, eprint = {1806.05893}, eprinttype = {arXiv}, eprintclass = {cs}, url = {http://arxiv.org/abs/1806.05893}, urldate = {2018-10-08}, abstract = {The use of open-source software (OSS) is ever-increasing, and so is the number of open-source vulnerabilities being discovered and publicly disclosed. The gains obtained from the reuse of community-developed libraries may be offset by the cost of detecting, assessing, and mitigating their vulnerabilities in a timely fashion. In this paper we present a novel method to detect, assess and mitigate OSS vulnerabilities that improves on state-of-the-art approaches, which commonly depend on metadata to identify vulnerable OSS dependencies. Our solution instead is code-centric and combines static and dynamic analysis to determine the reachability of the vulnerable portion of libraries used (directly or transitively) by an application. Taking this usage into account, our approach then supports developers in choosing among the existing non-vulnerable library versions. VULAS, the tool implementing our code-centric and usage-based approach, is officially recommended by SAP to scan its Java software, and has been successfully used to perform more than 250000 scans of about 500 applications since December 2016. We report on our experience and on the lessons we learned when maturing the tool from a research prototype to an industrial-grade solution.}, note = {Comment: To appear in the Proc. of the 2018 IEEE International Conference on Software Maintenance and Evolution (ICSME) Added: - acknowledgements - citation to Dashevskyi et al. (TSE 2018), DOI: 10.1109/TSE.2018.2816033 \par TL;DR \par A novel method to detect, assess and mitigate OSS vulnerabilities that improves on state-of-the-art approaches that combines static and dynamic analysis to determine the reachability of the vulnerable portion of libraries used by an application.} } @article{ponzanelli_prompter:_2016, title = {Prompter: {{Turning}} the {{IDE}} into a Self-Confident Programming Assistant}, shorttitle = {Prompter}, author = {Ponzanelli, Luca and Bavota, Gabriele and Di Penta, Massimiliano and Oliveto, Rocco and Lanza, Michele}, date = {2016-10}, journaltitle = {Empir. Softw. Eng.}, volume = {21}, number = {5}, pages = {2190--2231}, issn = {1382-3256, 1573-7616}, doi = {10.1007/s10664-015-9397-1}, abstract = {Developers often require knowledge beyond the one they possess, which boils down to asking co-workers for help or consulting additional sources of information, such as Application Programming Interfaces (API) documentation, forums, and Q\&A websites. However, it requires time and energy to formulate one’s problem, peruse and process the results. We propose a novel approach that, given a context in the Integrated Development Environment (IDE), automatically retrieves pertinent discussions from Stack Overflow, evaluates their relevance using a multi-faceted ranking model, and, if a given confidence threshold is surpassed, notifies the developer. We have implemented our approach in PROMPTER, an Eclipse plug-in. PROMPTER was evaluated in two empirical studies. The first study was aimed at evaluatingPROMPTER’s ranking model and involved 33 participants.}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, timestamp = {Thu, 15 Jun 2017 21:30:27 +0200} } @inproceedings{ponzanelliMiningStackOverflowTurn2014, ids = {Ponzanelli:2014:MST:2597073.2597077,ponzanelliMiningStackOverflowTurn2014a}, title = {Mining {{StackOverflow}} to Turn the {{IDE}} into a Self-Confident Programming Prompter}, author = {Ponzanelli, Luca and Bavota, Gabriele and Di Penta, Massimiliano and Oliveto, Rocco and Lanza, Michele}, date = {2014}, pages = {102--111}, publisher = {ACM Press}, location = {Hyderabad, India}, doi = {10.1145/2597073.2597077}, acmid = {2597077}, isbn = {978-1-4503-2863-0}, langid = {english}, nodoi = {10.1145/2597073.2597077}, numpages = {10}, keywords = {Developers Support,Empirical Studies,LOGSEQ,Recommender Systems}, note = {\section{Annotazioni\\ (14/1/2024, 21:41:37)} \par “sources of information like” (Ponzanelli et al., 2014, p. 1) \par “API” (Ponzanelli et al., 2014, p. 1) \par “documentation” (Ponzanelli et al., 2014, p. 1) \par “forums” (Ponzanelli et al., 2014, p. 1) \par “Q\&A websites” (Ponzanelli et al., 2014, p. 1) \par “Knowing what to search for and how is nontrivial,” (Ponzanelli et al., 2014, p. 1) \par “given a context in the IDE,” (Ponzanelli et al., 2014, p. 1) \par “automatically retrieves pertinent discussions from Stack Overflow,” (Ponzanelli et al., 2014, p. 1) \par “valuates their relevance, and, if a given confidence threshold is surpassed, notifies the developer about the available help.” (Ponzanelli et al., 2014, p. 1) \par “ranking model” (Ponzanelli et al., 2014, p. 1) \par “usefulness” (Ponzanelli et al., 2014, p. 1) \par “Every time developers need to look for information, they interrupt their work flow,” (Ponzanelli et al., 2014, p. 1) \par “eave the IDE” (Ponzanelli et al., 2014, p. 1) \par “Web browser to perform and refine searches, and assess the results” (Ponzanelli et al., 2014, p. 1) \par “the problem context in the IDE. The information is retrieved from differen” (Ponzanelli et al., 2014, p. 1) This is a test [[P1]] \par “s forums, mailing lists [2], blogs, Q\&A websites, bug trackers [1]” (Ponzanelli et al., 2014, p. 1) \par “Recommender systems” (Ponzanelli et al., 2014, p. 1) \par “eR” (Ponzanelli et al., 2014, p. 1) \par “ose” (Ponzanelli et al., 2014, p. 1) \par “eepI” (Ponzanelli et al., 2014, p. 1) \par “suggest project artifacts in the IDE aiming at providing developers with additional information on specific parts of the system.” (Ponzanelli et al., 2014, p. 1) \par “Ideally, a recommender system should behave like a prompter in a theatre: Ready to provide suggestions whenever the actor needs them, and ready to autonomously give suggestions if it feels something is going wrong.” (Ponzanelli et al., 2014, p. 1) \par “driver” (Ponzanelli et al., 2014, p. 1) \par “e” (Ponzanelli et al., 2014, p. 1) \par “observer,” (Ponzanelli et al., 2014, p. 1) \par “interrupts the driver by giving suggestions.” (Ponzanelli et al., 2014, p. 1) \par “the driver can consult the observer whenever she needs it, making the observer” (Ponzanelli et al., 2014, p. 1) \par “the programming prompter of the programming actor.” (Ponzanelli et al., 2014, p. 1) \par “push notifications” (Ponzanelli et al., 2014, p. 1) \par “relevant Stack Overflow discussions to the developer. Prompter makes” (Ponzanelli et al., 2014, p. 1) \par “IDE” (Ponzanelli et al., 2014, p. 1) \par “silently observes and analyzes” (Ponzanelli et al., 2014, p. 1) \par “the code context in the IDE” (Ponzanelli et al., 2014, p. 1) \par “automatically searches for Stack Over-” (Ponzanelli et al., 2014, p. 1) \par “flow discussions on the Web” (Ponzanelli et al., 2014, p. 1) \par “evaluates their relevance by taking i” (Ponzanelli et al., 2014, p. 1) \par “consideration code aspects” (Ponzanelli et al., 2014, p. 1) \par “(i) receive and track notifications” (Ponzanelli et al., 2014, p. 2) \par “(ii) read the suggested Stack Overflow discussions.” (Ponzanelli et al., 2014, p. 2) \par “code contexts (1) every time a change in the source code occurs” (Ponzanelli et al., 2014, p. 2) \par “formulates a query starting from the code context (2)” (Ponzanelli et al., 2014, p. 2) \par “rompter submits a new search only if the query diff” (Ponzanelli et al., 2014, p. 2) \par “plug-in,” (Ponzanelli et al., 2014, p. 2) \par “the search engines to which the query is sent,” (Ponzanelli et al., 2014, p. 2) \par “Stack Overflow API” (Ponzanelli et al., 2014, p. 2) \par “Web search on the Stack Overflow website” (Ponzanelli et al., 2014, p. 2) \par “All resulting URLs are collected and duplicates removed (5).” (Ponzanelli et al., 2014, p. 2) \par “The service uses the Stack Overflow question ID to retrieve the discussion via the Stack Overflow API (6)” (Ponzanelli et al., 2014, p. 2) \par “discussion is, given the code context, ranked (7) a” (Ponzanelli et al., 2014, p. 3) \par “he ranked list of URLs is sent back to the plug-in where Prompter decides whether to fire a” (Ponzanelli et al., 2014, p. 3) \par “new notification in the IDE (8).” (Ponzanelli et al., 2014, p. 3) \par “Our previous work in this context [28] only used text similarity as a means to retrieve Stack Overflow discussions related to the actual code” (Ponzanelli et al., 2014, p. 3) \par “The approach we present here relies on several combined aspects, and has proven to be more robust and less error-prone.” (Ponzanelli et al., 2014, p. 3) \par “rompter waits until the developer stops writing” (Ponzanelli et al., 2014, p. 3) \par “current code ele-” (Ponzanelli et al., 2014, p. 3) \par “ment” (Ponzanelli et al., 2014, p. 3) \par “extracts the current context” (Ponzanelli et al., 2014, p. 3) \par “,packageName.ClassName f” (Ponzanelli et al., 2014, p. 3) \par “me for classes” (Ponzanelli et al., 2014, p. 3) \par “packageName.ClassName.methodSignature for methods;” (Ponzanelli et al., 2014, p. 3) \par “the source code of the modified element” (Ponzanelli et al., 2014, p. 3) \par “the types of the used API,” (Ponzanelli et al., 2014, p. 3) \par “the names of methods invoked in the API, again considering only external libraries and JDK only.” (Ponzanelli et al., 2014, p. 3) \par “automate the triggering of searche” (Ponzanelli et al., 2014, p. 3) \par “es” (Ponzanelli et al., 2014, p. 3) \par “treat the code as a bag of words” (Ponzanelli et al., 2014, p. 3) \par “(i) splitting identifiers and removing stop words” (Ponzanelli et al., 2014, p. 3) \par “(ii) ranking the obtained terms according to their frequency” (Ponzanelli et al., 2014, p. 3) \par “(iii) selecting the top-n most frequent terms” (Ponzanelli et al., 2014, p. 3) \par “d more discriminative terms” (Ponzanelli et al., 2014, p. 3) \par “term entropy can be used to lower the prominence of frequent terms that do not suffi” (Ponzanelli et al., 2014, p. 3) \par “the Query Generation Service selects the top n terms to devise the query, plus the word java.” (Ponzanelli et al., 2014, p. 3) \par “To overcome this problem, before selecting the n terms to create the query, we use the Levenshtein distance [21] to verify if in the context there are terms with a very high textual similarity.” (Ponzanelli et al., 2014, p. 3) \par “1. Textual Similarity:” (Ponzanelli et al., 2014, p. 3) \par “2. Code Similarity:” (Ponzanelli et al., 2014, p. 3) \par “3. API Types Similarity” (Ponzanelli et al., 2014, p. 3) \par “4. API Methods Similarity:” (Ponzanelli et al., 2014, p. 3) \par “JDT” (Ponzanelli et al., 2014, p. 3) \par “5. Question Score:” (Ponzanelli et al., 2014, p. 4) \par “sigmoid function:” (Ponzanelli et al., 2014, p. 4) \par “where x is the score and ̄ x is the average of the scores of all the questions in Stack Overflow according to the data dump of June 2013.” (Ponzanelli et al., 2014, p. 4) \par “6. Accepted Answer Score:” (Ponzanelli et al., 2014, p. 4) \par “7. User Reputation:” (Ponzanelli et al., 2014, p. 4) \par “8. Tags Similarity: T” (Ponzanelli et al., 2014, p. 4) \par “M. Robillard, R. Walker, and T. Zimmermann. Recommendation systems for software engineering. IEEE Software, pages 80–86, 2010.” (Ponzanelli et al., 2014, p. 10) \par “[42] L. Williams. Integrating pair programming into a software development process. In Proceedings of CSEET 2001, pages 27–36. IEEE, 2001.” (Ponzanelli et al., 2014, p. 10) \par \section{Annotazioni\\ (14/1/2024, 22:35:16)} \par “sources of information like” (Ponzanelli et al., 2014, p. 1) \par “API” (Ponzanelli et al., 2014, p. 1) \par “documentation” (Ponzanelli et al., 2014, p. 1) \par “forums” (Ponzanelli et al., 2014, p. 1) \par “Q\&A websites” (Ponzanelli et al., 2014, p. 1) \par “Knowing what to search for and how is nontrivial,” (Ponzanelli et al., 2014, p. 1) \par “given a context in the IDE,” (Ponzanelli et al., 2014, p. 1) \par “automatically retrieves pertinent discussions from Stack Overflow,” (Ponzanelli et al., 2014, p. 1) \par “valuates their relevance, and, if a given confidence threshold is surpassed, notifies the developer about the available help.” (Ponzanelli et al., 2014, p. 1) \par “ranking model” (Ponzanelli et al., 2014, p. 1) \par “usefulness” (Ponzanelli et al., 2014, p. 1) \par “Every time developers need to look for information, they interrupt their work flow,” (Ponzanelli et al., 2014, p. 1) \par “eave the IDE” (Ponzanelli et al., 2014, p. 1) \par “Web browser to perform and refine searches, and assess the results” (Ponzanelli et al., 2014, p. 1) \par “the problem context in the IDE. The information is retrieved from differen” (Ponzanelli et al., 2014, p. 1) This is a test [[P1]] \par “s forums, mailing lists [2], blogs, Q\&A websites, bug trackers [1]” (Ponzanelli et al., 2014, p. 1) \par “Recommender systems” (Ponzanelli et al., 2014, p. 1) \par “eR” (Ponzanelli et al., 2014, p. 1) \par “ose” (Ponzanelli et al., 2014, p. 1) \par “eepI” (Ponzanelli et al., 2014, p. 1) \par “suggest project artifacts in the IDE aiming at providing developers with additional information on specific parts of the system.” (Ponzanelli et al., 2014, p. 1) \par “Ideally, a recommender system should behave like a prompter in a theatre: Ready to provide suggestions whenever the actor needs them, and ready to autonomously give suggestions if it feels something is going wrong.” (Ponzanelli et al., 2014, p. 1) \par “driver” (Ponzanelli et al., 2014, p. 1) \par “e” (Ponzanelli et al., 2014, p. 1) \par “observer,” (Ponzanelli et al., 2014, p. 1) \par “interrupts the driver by giving suggestions.” (Ponzanelli et al., 2014, p. 1) \par “the driver can consult the observer whenever she needs it, making the observer” (Ponzanelli et al., 2014, p. 1) \par “the programming prompter of the programming actor.” (Ponzanelli et al., 2014, p. 1) \par “push notifications” (Ponzanelli et al., 2014, p. 1) \par “relevant Stack Overflow discussions to the developer. Prompter makes” (Ponzanelli et al., 2014, p. 1) \par “IDE” (Ponzanelli et al., 2014, p. 1) \par “silently observes and analyzes” (Ponzanelli et al., 2014, p. 1) \par “the code context in the IDE” (Ponzanelli et al., 2014, p. 1) \par “automatically searches for Stack Over-” (Ponzanelli et al., 2014, p. 1) \par “flow discussions on the Web” (Ponzanelli et al., 2014, p. 1) \par “evaluates their relevance by taking i” (Ponzanelli et al., 2014, p. 1) \par “consideration code aspects” (Ponzanelli et al., 2014, p. 1) \par (Ponzanelli et al., 2014, p. 2) This is very interesting \par “(i) receive and track notifications” (Ponzanelli et al., 2014, p. 2) \par “(ii) read the suggested Stack Overflow discussions.” (Ponzanelli et al., 2014, p. 2) \par “code contexts (1) every time a change in the source code occurs” (Ponzanelli et al., 2014, p. 2) \par “formulates a query starting from the code context (2)” (Ponzanelli et al., 2014, p. 2) \par “rompter submits a new search only if the query diff” (Ponzanelli et al., 2014, p. 2) \par “plug-in,” (Ponzanelli et al., 2014, p. 2) \par “the search engines to which the query is sent,” (Ponzanelli et al., 2014, p. 2) \par “Stack Overflow API” (Ponzanelli et al., 2014, p. 2) \par “Web search on the Stack Overflow website” (Ponzanelli et al., 2014, p. 2) \par “All resulting URLs are collected and duplicates removed (5).” (Ponzanelli et al., 2014, p. 2) \par “The service uses the Stack Overflow question ID to retrieve the discussion via the Stack Overflow API (6)” (Ponzanelli et al., 2014, p. 2) \par “discussion is, given the code context, ranked (7) a” (Ponzanelli et al., 2014, p. 3) \par “he ranked list of URLs is sent back to the plug-in where Prompter decides whether to fire a” (Ponzanelli et al., 2014, p. 3) \par “new notification in the IDE (8).” (Ponzanelli et al., 2014, p. 3) \par “Our previous work in this context [28] only used text similarity as a means to retrieve Stack Overflow discussions related to the actual code” (Ponzanelli et al., 2014, p. 3) \par “The approach we present here relies on several combined aspects, and has proven to be more robust and less error-prone.” (Ponzanelli et al., 2014, p. 3) \par “rompter waits until the developer stops writing” (Ponzanelli et al., 2014, p. 3) \par “current code ele-” (Ponzanelli et al., 2014, p. 3) \par “ment” (Ponzanelli et al., 2014, p. 3) \par “extracts the current context” (Ponzanelli et al., 2014, p. 3) \par “,packageName.ClassName f” (Ponzanelli et al., 2014, p. 3) \par “me for classes” (Ponzanelli et al., 2014, p. 3) \par “packageName.ClassName.methodSignature for methods;” (Ponzanelli et al., 2014, p. 3) \par “the source code of the modified element” (Ponzanelli et al., 2014, p. 3) \par “the types of the used API,” (Ponzanelli et al., 2014, p. 3) \par “the names of methods invoked in the API, again considering only external libraries and JDK only.” (Ponzanelli et al., 2014, p. 3) \par “automate the triggering of searche” (Ponzanelli et al., 2014, p. 3) \par “es” (Ponzanelli et al., 2014, p. 3) \par “treat the code as a bag of words” (Ponzanelli et al., 2014, p. 3) \par “(i) splitting identifiers and removing stop words” (Ponzanelli et al., 2014, p. 3) \par “(ii) ranking the obtained terms according to their frequency” (Ponzanelli et al., 2014, p. 3) \par “(iii) selecting the top-n most frequent terms” (Ponzanelli et al., 2014, p. 3) \par “d more discriminative terms” (Ponzanelli et al., 2014, p. 3) \par “term entropy can be used to lower the prominence of frequent terms that do not suffi” (Ponzanelli et al., 2014, p. 3) \par “the Query Generation Service selects the top n terms to devise the query, plus the word java.” (Ponzanelli et al., 2014, p. 3) \par “To overcome this problem, before selecting the n terms to create the query, we use the Levenshtein distance [21] to verify if in the context there are terms with a very high textual similarity.” (Ponzanelli et al., 2014, p. 3) \par “1. Textual Similarity:” (Ponzanelli et al., 2014, p. 3) \par “2. Code Similarity:” (Ponzanelli et al., 2014, p. 3) \par “3. API Types Similarity” (Ponzanelli et al., 2014, p. 3) \par “4. API Methods Similarity:” (Ponzanelli et al., 2014, p. 3) \par “JDT” (Ponzanelli et al., 2014, p. 3) \par “5. Question Score:” (Ponzanelli et al., 2014, p. 4) \par “sigmoid function:” (Ponzanelli et al., 2014, p. 4) \par “where x is the score and ̄ x is the average of the scores of all the questions in Stack Overflow according to the data dump of June 2013.” (Ponzanelli et al., 2014, p. 4) \par “6. Accepted Answer Score:” (Ponzanelli et al., 2014, p. 4) \par “7. User Reputation:” (Ponzanelli et al., 2014, p. 4) \par “8. Tags Similarity: T” (Ponzanelli et al., 2014, p. 4) \par “M. Robillard, R. Walker, and T. Zimmermann. Recommendation systems for software engineering. IEEE Software, pages 80–86, 2010.” (Ponzanelli et al., 2014, p. 10) \par “[42] L. Williams. Integrating pair programming into a software development process. In Proceedings of CSEET 2001, pages 27–36. IEEE, 2001.” (Ponzanelli et al., 2014, p. 10)} } @inproceedings{ponzanelliSeahawkStackOverflow2013, title = {Seahawk: {{Stack}} Overflow in the {{IDE}}}, booktitle = {2013 35th {{Int}}. {{Conf}}. {{Softw}}. {{Eng}}. {{ICSE}}}, author = {Ponzanelli, L. and Bacchelli, A. and Lanza, M.}, date = {2013-05}, pages = {1295--1298}, issn = {0270-5257}, doi = {10.1109/ICSE.2013.6606701}, note = {TL;DR \par Seahawk is an Eclipse plugin that supports an integrated and largely automated approach to assist programmers using Stack Overflow, and formulates queries automatically from the active context in the IDE, presents a ranked and interactive list of results, and lets users import code samples in discussions through drag \& drop.} } @article{poojaraServerlessDataPipeline2022, title = {Serverless Data Pipeline Approaches for {{IoT}} Data in Fog and Cloud Computing}, author = {Poojara, Shivananda R. and Dehury, Chinmaya Kumar and Jakovits, Pelle and Srirama, Satish Narayana}, date = {2022-05}, journaltitle = {Future Generation Computer Systems}, volume = {130}, pages = {91--105}, issn = {0167739X}, doi = {10.1016/j.future.2021.12.012}, abstract = {With the increasing number of Internet of Things (IoT) devices, massive amounts of raw data is being generated. The latency, cost, and other challenges in cloud-based IoT data processing have driven the adoption of Edge and Fog computing models, where some data processing tasks are moved closer to data sources. Properly dealing with the flow of such data requires building data pipelines, to control the complete life cycle of data streams from data acquisition at the data source, edge and fog processing, to Cloud side storage and analytics. Data analytics tasks need to be executed dynamically at different distances from the data sources and often on very heterogeneous hardware devices. This can be streamlined by the use of a Serverless (or FaaS) cloud computing model, where tasks are defined as virtual functions, which can be migrated from edge to cloud (and vice versa) and executed in an event-driven manner on data streams. In this work, we investigate the benefits of building Serverless data pipelines (SDP) for IoT data analytics and evaluate three different approaches for designing SDPs: (1) Off-the-shelf data flow tool (DFT) based, (2) Object storage service (OSS) based and (3) MQTT based. Further, we applied these strategies on three fog applications (Aeneas, PocketSphinx, and custom Video processing application) and evaluated the performance by comparing their processing time (computation time, network communication and disk access time), and resource utilization. Results show that DFT is unsuitable for compute-intensive applications such as video or image processing, whereas OSS is best suitable for this task. However, DFT is nicely fit for bandwidthintensive applications due to the minimum use of network resources. On the other hand, MQTT-based SDP is observed with increase in CPU and Memory usage as the number of users rose, and experienced a drop in data units in the pipeline for PocketSphinx and custom video processing applications, however it performed well for Aeneas which had low size data units.}, langid = {english} } @inproceedings{PopoolaKR16, title = {{{EMG}}: A Domain-Specific Transformation Language for Synthetic Model Generation}, booktitle = {Theory {{Pract}}. {{Model Transform}}. - 9th {{Int}}. {{Conf}}. {{ICMTSTAF}} 2016 {{Vienna Austria July}} 4-5 2016 {{Proc}}.}, author = {Popoola, Saheed and Kolovos, Dimitrios S. and Rodriguez, Horacio Hoyos}, editor = {Gorp, Pieter Van and Engels, Gregor}, date = {2016}, series = {Lecture Notes in Computer Science}, volume = {9765}, pages = {36--51}, publisher = {Springer}, doi = {10.1007/978-3-319-42064-6\_3}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Mon, 05 Feb 2024 20:35:50 +0100} } @inproceedings{Porres03, title = {Model Refactorings as Rule-Based Update Transformations}, booktitle = {«{{UML}}» 2003 - {{Unified Model}}. {{Lang}}. {{Model}}. {{Lang}}. {{Appl}}. 6th {{Int}}. {{Conf}}. {{San Franc}}. {{CA USA Oct}}. 20-24 2003 {{Proc}}.}, author = {Porres, Ivan}, editor = {Stevens, Perdita and Whittle, Jon and Booch, Grady}, date = {2003}, series = {Lecture Notes in Computer Science}, volume = {2863}, pages = {159--174}, publisher = {Springer}, doi = {10.1007/978-3-540-45221-8\_16}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {TL;DR \par This paper discusses how to define and execute model refactorings as rule-based transformations and presents an experimental tool to execute these transformations.} } @article{portugalUseMachineLearning2015, ids = {DBLP:journals/corr/PortugalAC15a}, title = {The {{Use}} of {{Machine Learning Algorithms}} in {{Recommender Systems}}: {{A Systematic Review}}}, shorttitle = {The {{Use}} of {{Machine Learning Algorithms}} in {{Recommender Systems}}}, author = {Portugal, Ivens and Alencar, Paulo and Cowan, Donald}, date = {2015}, journaltitle = {ArXiv Prepr. ArXiv151105263}, eprint = {1511.05263}, eprinttype = {arXiv}, url = {https://arxiv.org/abs/1511.05263}, urldate = {2017-03-10}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/bib/journals/corr/PortugalAC15a}, timestamp = {Mon, 13 Aug 2018 16:46:19 +0200} } @online{PostLinkedIn, title = {Post | {{LinkedIn}}}, url = {https://www.linkedin.com/posts/genai-works_aiagents-mcp-generativeai-activity-7318202020664958977-NDsE/?utm_source=share&utm_medium=member_desktop&rcm=ACoAAACaoXoBMvdj060syLix4vC3rwOgjAE9MpU}, urldate = {2025-04-16}, langid = {english}, keywords = {/unread} } @article{pottsSoftwareengineeringResearchRevisited1993, title = {Software-Engineering Research Revisited}, author = {Potts, Colin}, date = {1993}, journaltitle = {IEEE Softw.}, volume = {10}, number = {5}, pages = {19--28}, url = {http://ieeexplore.ieee.org/abstract/document/232392/}, urldate = {2017-07-03} } @article{potvinWhyGoogleStores2016, title = {Why {{Google}} Stores Billions of Lines of Code in a Single Repository}, author = {Potvin, Rachel and Levenberg, Josh}, date = {2016}, journaltitle = {Commun. ACM}, volume = {59}, number = {7}, pages = {78--87}, url = {http://dl.acm.org/citation.cfm?id=2854146}, urldate = {2017-05-25}, note = {TL;DR \par Google's monolithic repository provides a common source of truth for tens of thousands of developers around the world.} } @article{Pourpanah:2016:HMF:2884077.2884195, title = {A Hybrid Model of Fuzzy {{ARTMAP}} and Genetic Algorithm for Data Classification and Rule Extraction}, author = {Pourpanah, Farhad and Lim, Chee Peng and Saleh, Junita Mohamad}, date = {2016-05}, journaltitle = {Expert Syst. Appl.}, volume = {49}, number = {C}, pages = {74--85}, publisher = {Pergamon Press, Inc.}, address = {Tarrytown, NY, USA}, issn = {0957-4174}, url = {https://doi.org/10.1016/j.eswa.2015.11.009}, acmid = {2884195}, issue_date = {May 2016}, nodoi = {10.1016/j.eswa.2015.11.009}, numpages = {12}, keywords = {Data classification,Fuzzy ARTMAP,Genetic algorithm,Q-learning,reinforcement learning,Rule extraction} } @misc{Practices_for_Managing_Machine_Learning_Products_a, title = {Practices\_for\_{{Managing}}\_{{Machine}}\_{{Learning}}\_{{Products}}\_a}, langid = {english} } @article{prasadConvolutionalNeuralNetworks, title = {Convolutional {{Neural Networks}} with {{Tensor}} Ow}, author = {Prasad, Ashu}, pages = {28}, langid = {english} } @inproceedings{Pretschner05, title = {One Evaluation of Model-Based Testing and Its Automation}, booktitle = {Proc. 27th {{Int}}. {{Conf}}. {{Softw}}. {{Eng}}.}, author = {Pretschner, A. and Prenninger, W. and Wagner, S. and Kühnel, C. and Baumgartner, M. and Sostawa, B. and Zölch, R. and Stauner, T.}, date = {2005}, series = {{{ICSE}} '05}, pages = {392--401}, publisher = {Association for Computing Machinery}, location = {New York, NY, USA}, doi = {10.1145/1062455.1062529}, abstract = {Model-based testing relies on behavior models for the generation of model traces: input and expected output—test cases—for an implementation. We use the case study of an automotive network controller to assess different test suites in terms of error detection, model coverage, and implementation coverage. Some of these suites were generated automatically with and without models, purely at random, and with dedicated functional test selection criteria. Other suites were derived manually, with and without the model at hand. Both automatically and manually derived model-based test suites detected significantly more requirements errors than hand-crafted test suites that were directly derived from the requirements. The number of detected programming errors did not depend on the use of models. Automatically generated model-based test suites detected as many errors as hand-crafted model-based suites with the same number of tests. A sixfold increase in the number of model-based tests led to an 11\% increase in detected errors.}, isbn = {1-58113-963-2}, langid = {english}, pagetotal = {10}, keywords = {/unread,⛔ No INSPIRE recid found,abstraction,automotive software,CASE,coverage,model-based development,test case generation} } @article{prev-93837, title = {{{MDE Adoption}}—{{A}} Three-Legged Chair}, booktitle = {Grand Challenges in Modeling}, date = {2017} } @inproceedings{prism, title = {{{PRISM}} 4.0: Verification of Probabilistic Real-Time Systems}, booktitle = {Comput. {{Aided Verification}} - 23rd {{Int}}. {{Conf}}. {{CAV}}}, author = {Kwiatkowska, Marta Z. and Norman, Gethin and Parker, David}, date = {2011}, volume = {6806}, pages = {585--591}, publisher = {Springer}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {TL;DR \par A major new release of the PRISMprobabilistic model checker is described, adding, in particular, quantitative verification of (priced) probabilistic timed automata.} } @online{ProductivityCareerJob, title = {\#productivity \#career \#job | {{Harish}} Kumar😊 | 74 Commenti}, url = {https://www.linkedin.com/posts/harishkumar-sh_productivity-career-job-activity-7326908466827010048-KZAR}, urldate = {2025-05-11}, abstract = {🛑Stop using Coursera to purchase courses. 🚨Google launches no-cost AI courses—learn the future today 🚨 No sign-up fees or prior skills required. Don\&\#39;t miss these 10 must-see courses: 1. Introduction to Generative AI Master the basics of Generative AI in a short, beginner-friendly course. Learn how to build AI-powered applications using Google resources Complete this short 45-minute course to start your AI journey! https://lnkd.in/gzqt7ehQ 2. Introduction to Large Language Models Join this short course to understand big language models (LLMs). Learn where LLMs are useful and how to improve them. Finish this 45-minute course to build your own AI apps! https://lnkd.in/gFWMHWiW 3. Introduction to Responsible AI Learn how Google builds AI that\&\#39;s fair and helpful. Understand the 7 principles that guide ethical AI development. Perfect for anyone interested in AI ethics. https://lnkd.in/g5-VJgkT 4. Prompt Design in Vertex AI Master the art of talking to AI to get exactly what you want. Learn how to create images and text with simple instructions. Try hands-on exercises using Google\&\#39;s latest AI tools. https://lnkd.in/g4HaZaqy 5. Introduction to Image Generation See how computers can create realistic images from scratch. Learn about the physics-inspired AI that powers image creation. https://lnkd.in/gk2cPVFA 6. Encoder-Decoder Architecture Discover how machines translate languages and summarize texts. Learn the building blocks of modern language processing. Build your own text generator with simple code examples. https://lnkd.in/giKmcvd8 7. Attention Mechanism See how AI learns to focus on what matters in text and images. Understand the magic behind modern language models. Quick 45-minute intro to an essential AI concept. https://lnkd.in/gEx4A56p 8. Transformer Models and BERT Model Learn the technology that revolutionized language AI. See how BERT understands context in sentences. Earn a cool digital badge when you finish! https://lnkd.in/gzaQZgpZ 9. Create Image Captioning Models Build AI that can describe what it sees in photos. Learn how to train models that connect images and language. https://lnkd.in/g8bfnWUp 10. Introduction to Generative AI Studio Try Google\&\#39;s easy-to-use tool for building AI applications. See demos of how to turn ideas into working AI projects. https://lnkd.in/gtkjed96 Boost your skills, earn certifications, and unlock high-paying jobs! All the best 👍 What is your favourite ❓ Tell me in comments 👉 Follow me Harish kumar😊 for more and 🔄 Repost this to help others for career growth 📈 📍 Telegram 👇 https://t.me/Hariskumar0 📍 Twitter 👇 https://lnkd.in/gysRJ8cB Job Alert OpenAI \#Productivity \#Career \#job | 74 commenti su LinkedIn} } @online{ProgrammaDiRicerca, title = {Programma Di Ricerca e Formazione Dottorale - Finanziamento Dottorati Di Ricerca | Coesione Abruzzo}, url = {https://coesione.regione.abruzzo.it/avvisi-pubblici/fse/programma-di-ricerca-e-formazione-dottorale-finanziamento-dottorati-di-ricerca}, urldate = {2025-04-17}, langid = {english}, keywords = {P1} } @online{ProgrammiScolasticiNon2025, title = {I Programmi Scolastici Non Esistono Più, Lo Volete Capire?}, date = {2025-02-27T10:32:50+01:00}, url = {https://www.ilpost.it/2025/02/27/raimo-i-programmi-scolastici-non-esistono-piu-lo-volete-capire/}, urldate = {2025-03-20}, abstract = {Ci sono le “indicazioni nazionali”. Se si usano come sinonimi è perché chi lavora alla “riforma Valditara” vuole tornare all'insegnamento di un secolo fa} } @online{Project2ACLAutonomous, title = {Project2 - {{ACL}} - {{Autonomous Systems}} and {{Robotics}} - {{Research Groups}} - {{Research}} - {{ACSE}} - {{The University}} of {{Sheffield}}}, url = {https://www.sheffield.ac.uk/acse/research/groups/asrg/acl/project2}, urldate = {2016-08-26} } @incollection{prokschHowBuildRecommendation2015, title = {How to {{Build}} a {{Recommendation System}} for {{Software Engineering}}}, booktitle = {Software {{Engineering}}}, author = {Proksch, Sebastian and Bauer, Veronika and Murphy, Gail C.}, editor = {Meyer, Bertrand and Nordio, Martin}, date = {2015}, volume = {8987}, pages = {1--42}, publisher = {Springer International Publishing}, location = {Cham}, doi = {10.1007/978-3-319-28406-4_1}, abstract = {Software developers must interact with large amounts of different types of information and perform many different activities to build a software system. To ease the finding of information and hone workflows, there has been growing interest in building recommenders that are intended to help software developers work more effectively. Building an effective recommender requires a deep understanding of the problem that is the target of a recommender, analysis of different aspects of the approach taken to perform the recommendations and design and evaluation of the mechanisms used to present recommendations to a developer. In this chapter, we outline the different steps that must be taken to develop an effective recommender system to aid software development.}, isbn = {978-3-319-28405-7 978-3-319-28406-4}, langid = {english} } @book{PromptEngineering, title = {Prompt {{Engineering}}}, url = {https://media.licdn.com/dms/document/media/v2/D561FAQEIJBr2epmSTw/feedshare-document-pdf-analyzed/B56ZYhFS60GsAc-/0/1744311801539?e=1745452800&v=beta&t=OJwr--ukyhuLZHhBf_mWJ_Oee8uHNaZA7OCQupIBecE}, urldate = {2025-04-11}, langid = {english}, keywords = {[[LLM]]}, note = {This needs to be investigated. \par - “softmax function used in machine learning” (“Prompt Engineering”, p. 10) \#e56eee \par This needs to be investigated. \par - “softmax function used in machine learning” (“Prompt Engineering”, p. 10) \#e56eee\\ \mkbibemph{ }} } @inproceedings{PromptingFinetuningComparative2021, title = {Prompting or {{Fine-tuning}}? {{A Comparative Study}} of {{Large Language Models}} for {{Taxonomy Construction}}}, booktitle = {2021 {{ACMIEEE Int}}. {{Conf}}. {{Model Driven Eng}}. {{Lang}}. {{Syst}}. {{Companion MODELS-C}}}, date = {2021}, publisher = {IEEE}, location = {Fukuoka, Japan}, abstract = {Taxonomies represent hierarchical relations between entities, frequently applied in various software modeling and natural language processing (NLP) activities. They are typically subject to a set of structural constraints restricting their content. However, manual taxonomy construction can be time-consuming, incomplete, and costly to maintain. Recent studies of large language models (LLMs) have demonstrated that appropriate user inputs (called prompting) can effectively guide LLMs, such as GPT-3, in diverse NLP tasks without explicit (re-)training. However, existing approaches for automated taxonomy construction typically involve fine-tuning a language model by adjusting model parameters. In this paper, we present a general framework for taxonomy construction that takes into account structural constraints. We subsequently conduct a systematic comparison between the prompting and fine-tuning approaches performed on a hypernym taxonomy and a novel computer science taxonomy dataset. Our result reveals the following: (1) Even without explicit training on the dataset, the prompting approach outperforms fine-tuning-based approaches. Moreover, the performance gap between prompting and fine-tuning widens when the training dataset is small. However, (2) taxonomies generated by the fine-tuning approach can be easily post-processed to satisfy all the constraints, whereas handling violations of the taxonomies produced by the prompting approach can be challenging. These evaluation findings provide guidance on selecting the appropriate method for taxonomy construction and highlight potential enhancements for both approaches.}, eventtitle = {2021 {{ACM}}/{{IEEE International Conference}} on {{Model Driven Engineering Languages}} and {{Systems Companion}} ({{MODELS-C}})}, isbn = {978-1-6654-2484-4}, langid = {english}, keywords = {LOGSEQ}, note = {TL;DR \par A general framework for taxonomy construction that takes into account structural constraints is presented and a systematic comparison between the prompting and fine-tuning approaches performed on a hypernym taxonomy and a novel computer science taxonomy dataset is conducted.} } @inproceedings{Protasiewicz2020, title = {A Neural Network Toolbox for Electricity Consumption Forecasting}, author = {Protasiewicz, J.}, date = {2020}, series = {Proceedings of the {{International Joint Conference}} on {{Neural Networks}}}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/IJCNN48605.2020.9206996}, abbrev_source_title = {Proc Int Jt Conf Neural Networks}, affiliation = {National Information Processing Institute, Warsaw, Poland}, art_number = {9206996}, coden = {85OFA}, correspondence_address1 = {Protasiewicz, J.; National Information Processing InstitutePoland}, document_type = {Conference Paper}, isbn = {978-1-7281-6926-2}, langid = {english}, source = {Scopus}, keywords = {notion}, note = {cited By 0 \par TL;DR \par The results and flexibility of use suggest that the neural toolbox should help users to develop prediction systems of electricity consumption more conveniently, as it is designed for that particular purpose.} } @inproceedings{provoostDingNetSelfAdaptiveInternetofThings2019, title = {{{DingNet}}: {{A Self-Adaptive Internet-of-Things Exemplar}}}, shorttitle = {{{DingNet}}}, booktitle = {2019 {{IEEEACM}} 14th {{Int}}. {{Symp}}. {{Softw}}. {{Eng}}. {{Adapt}}. {{Self-Manag}}. {{Syst}}. {{SEAMS}}}, author = {Provoost, Michiel and Weyns, Danny}, date = {2019-05}, pages = {195--201}, publisher = {IEEE}, location = {Montreal, QC, Canada}, doi = {10.1109/SEAMS.2019.00033}, eventtitle = {2019 {{IEEE}}/{{ACM}} 14th {{International Symposium}} on {{Software Engineering}} for {{Adaptive}} and {{Self-Managing Systems}} ({{SEAMS}})}, isbn = {978-1-7281-3368-3}, keywords = {DONE} } @dataset{PTMTorrentDatasetMining2023, title = {{{PTMTorrent}}: {{A Dataset}} for {{Mining Open-source Pre-trained Model Packages}}}, shorttitle = {{{PTMTorrent}}}, date = {2023-02-04}, publisher = {figshare}, doi = {10.6084/m9.figshare.22009880.v4}, abstract = {Due to the cost of developing and training deep learning models from scratch, machine learning engineers have begun to reuse pre-trained models (PTMs) and fine-tune them for downstream tasks. PTM registries known as “model hubs” support engineers in distributing and reusing deep learning models. PTM packages include pre-trained weights, documentation, model architectures, datasets, and metadata. Mining the information in PTM packages will enable the discovery of engineering phenomena and tools to support software engineers. However, accessing this information is difficult — there are many PTM registries, and both the registries and the individual packages may have rate limiting for accessing the data. We present an open-source dataset, PTMTorrent, to facilitate the evaluation and understanding of PTM packages. This paper describes the creation, structure, usage, and limitations of the dataset. The dataset includes a snapshot of 5 model hubs and a total of 15,913 PTM packages. These packages are represented in a uniform data schema for cross-hub mining. We describe prior uses of this data and suggest research opportunities for mining using our dataset. We provide links to the PTM Dataset and PTM Torrent Source Code.}, langid = {english} } @online{PtolemyProjectHome, title = {Ptolemy {{Project Home Page}}}, url = {http://ptolemy.eecs.berkeley.edu/}, urldate = {2016-01-26} } @online{PublicationsGEMOCInitiative, title = {Publications » {{The GEMOC Initiative}}}, url = {http://gemoc.org/publications/}, urldate = {2015-09-28} } @article{pulgattiDataMigrationDifferent, title = {Data {{Migration Between Different Data Models}} of {{NoSql Databases}}}, author = {Pulgatti, Leandro Duarte}, pages = {80}, langid = {english} } @article{Pylianidis202145, title = {Location-Specific vs Location-Agnostic Machine Learning Metamodels for Predicting Pasture Nitrogen Response Rate}, author = {Pylianidis, C. and Snow, V. and Holzworth, D. and Bryant, J. and Athanasiadis, I.N.}, editor = {Del Bimbo A., Cucchiara R., Farinella G.M., Mei T., Bertini M., Escalante H.J., Vezzani R., Sclaroff S.}, date = {2021}, journaltitle = {Lect. Notes Comput. Sci. Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinforma.}, volume = {12666 LNCS}, pages = {45--54}, publisher = {{Springer Science and Business Media Deutschland GmbH}}, issn = {03029743}, doi = {10.1007/978-3-030-68780-9_5}, abbrev_source_title = {Lect. Notes Comput. Sci.}, affiliation = {Wageningen University, Wageningen, Netherlands; AgResearch, Christchurch, New Zealand; CSIRO, Brisbane, Australia}, correspondence_address1 = {Pylianidis, C.; Wageningen UniversityNetherlands; email: christos.pylianidis@wur.nl}, document_type = {Conference Paper}, isbn = {9783030687793}, langid = {english}, source = {Scopus}, note = {cited By 1} } @online{PyTorch, title = {{{PyTorch}}}, url = {https://media.licdn.com/dms/document/media/v2/D561FAQEWwLy_yNkACA/feedshare-document-pdf-analyzed/B56ZYvb5_ZH0AY-/0/1744552587199?e=1745452800&v=beta&t=tYrrnkFZ0EIJ2bpkm1b5jtwJc9umrv8UdPCpQZoU45E}, urldate = {2025-04-13}, langid = {english} } @online{qianCommunicativeAgentsSoftware2023, title = {Communicative Agents for Software Development}, author = {Qian, Chen and Cong, Xin and Liu, Wei and Yang, Cheng and Chen, Weize and Su, Yusheng and Dang, Yufan and Li, Jiahao and Xu, Juyuan and Li, Dahai and Liu, Zhiyuan and Sun, Maosong}, date = {2023-12-19}, eprint = {2307.07924}, eprinttype = {arXiv}, eprintclass = {cs}, doi = {10.48550/arXiv.2307.07924}, abstract = {Software engineering is a domain characterized by intricate decision-making processes, often relying on nuanced intuition and consultation. Recent advancements in deep learning have started to revolutionize software engineering practices through elaborate designs implemented at various stages of software development. In this paper, we present an innovative paradigm that leverages large language models (LLMs) throughout the entire software development process, streamlining and unifying key processes through natural language communication, thereby eliminating the need for specialized models at each phase. At the core of this paradigm lies ChatDev, a virtual chat-powered software development company that mirrors the established waterfall model, meticulously dividing the development process into four distinct chronological stages: designing, coding, testing, and documenting. Each stage engages a team of "software agents", such as programmers, code reviewers, and test engineers, fostering collaborative dialogue and facilitating a seamless workflow. The chat chain acts as a facilitator, breaking down each stage into atomic subtasks. This enables dual roles, allowing for proposing and validating solutions through context-aware communication, leading to efficient resolution of specific subtasks. The instrumental analysis of ChatDev highlights its remarkable efficacy in software generation, enabling the completion of the entire software development process in under seven minutes at a cost of less than one dollar. It not only identifies and alleviates potential vulnerabilities but also rectifies potential hallucinations while maintaining commendable efficiency and cost-effectiveness. The potential of ChatDev unveils fresh possibilities for integrating LLMs into the realm of software development. Our code is available at https://github.com/OpenBMB/ChatDev.}, langid = {english}, pubstate = {prepublished}, keywords = {Computer Science - Computation and Language,Computer Science - Multiagent Systems,Computer Science - Software Engineering}, note = {Comment: https://github.com/OpenBMB/ChatDev \par Comment: https://github.com/OpenBMB/ChatDev \par TL;DR \par An innovative paradigm that leverages large language models (LLMs) throughout the entire software development process, streamlining and unifying key processes through natural language communication, thereby eliminating the need for specialized models at each phase.} } @article{Qiang20202508, title = {A Model-Driven Deep Learning Algorithm for Joint Activity Detection and Channel Estimation}, author = {Qiang, Y. and Shao, X. and Chen, X.}, date = {2020}, journaltitle = {IEEE Commun. Lett.}, volume = {24}, number = {11}, pages = {2508--2512}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {10897798}, doi = {10.1109/LCOMM.2020.3011571}, abstract = {This letter provides a deep learning framework for massive grant-free random access in 6G cellular internet of things (IoT) networks. A model-driven deep learning algorithm for joint activity detection and channel estimation is proposed based on the principle of approximate massage passing (AMP). This algorithm only needs to learn four key parameters, but not the whole algorithm architecture. More importantly, it does not require the prior information about active probabilities and channel variance, and can significantly improve the performance with a finite number of training data. Simulation results validate the effectiveness of the proposed deep learning algorithm. © 1997-2012 IEEE.}, art_number = {9146533}, coden = {ICLEF}, document_type = {Article}, source = {Scopus}, note = {cited By 10 \par TL;DR \par A model-driven deep learning algorithm for joint activity detection and channel estimation is proposed based on the principle of approximate massage passing that does not require the prior information about active probabilities and channel variance, and can significantly improve the performance with a finite number of training data.} } @online{qianNextTokenPromptrobust2025, title = {Beyond the next Token: Towards Prompt-Robust Zero-Shot Classification via Efficient Multi-Token Prediction}, shorttitle = {Beyond the next Token}, author = {Qian, Junlang and Zhu, Zixiao and Zhou, Hanzhang and Feng, Zijian and Zhai, Zepeng and Mao, Kezhi}, date = {2025-04-04}, eprint = {2504.03159}, eprinttype = {arXiv}, eprintclass = {cs}, doi = {10.48550/arXiv.2504.03159}, abstract = {Zero-shot text classification typically relies on prompt engineering, but the inherent prompt brittleness of large language models undermines its reliability. Minor changes in prompt can cause significant discrepancies in model performance. We attribute this prompt brittleness largely to the narrow focus on nexttoken probabilities in existing methods. To address this, we propose Placeholding Parallel Prediction (P3 ), a novel approach that predicts token probabilities across multiple positions and simulates comprehensive sampling of generation paths in a single run of a language model. Experiments show improved accuracy and up to 98\% reduction in the standard deviation across prompts, boosting robustness. Even without a prompt, P3 maintains comparable performance, reducing the need for prompt engineering.}, langid = {english}, pubstate = {prepublished}, keywords = {/unread,Computer Science - Computation and Language}, note = {Comment: Accepted in NAACL 2025 (main Oral)} } @inproceedings{Qiao2021282, title = {Research on Motion Modeling and Control of Tracking Car Based on Neural Network}, author = {Qiao, J.}, date = {2021}, series = {Proceedings - 2021 2nd {{International Conference}} on {{Computing}} and {{Data Science}}, {{CDS}} 2021}, pages = {282--286}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/CDS52072.2021.00056}, abbrev_source_title = {Proc. - Int. Conf. Comput. Data Sci., CDS}, affiliation = {Research Institute of Micro/Nano Science and Technology, Shanghai Jiao Tong University, Shanghai, China}, art_number = {9463303}, correspondence_address1 = {Qiao, J.; Research Institute of Micro/Nano Science and Technology, China; email: qiao\textsubscript{j}ing\textsubscript{y}ang@163.com}, document_type = {Conference Paper}, isbn = {978-1-6654-0428-0}, langid = {english}, source = {Scopus}, note = {cited By 0} } @article{Qin2022, title = {Fuzzing-Based Hard-Label Black-Box Attacks against Machine Learning Models}, author = {Qin, Y. and Yue, C.}, date = {2022}, journaltitle = {Comput. Secur.}, volume = {117}, publisher = {Elsevier Ltd}, issn = {01674048}, doi = {10.1016/j.cose.2022.102694}, abstract = {Machine learning models are vulnerable to adversarial examples. We study the most realistic hard-label black-box attacks in this paper. The main limitation of the existing attacks is that they need a large number of model queries, making them inefficient and even infeasible in practice. Inspired by the very successful fuzz testing approach in traditional software engineering and computer security domains, we propose fuzzing-based hard-label black-box attacks against machine learning models. We design an AdvFuzzer to explore multiple paths between a source image and a guidance image, and design a LocalFuzzer to explore the nearby space around a given input for identifying potential adversarial examples. We demonstrate that our fuzzing attacks are feasible and effective in generating successful adversarial examples with significantly reduced number of model queries and L0 distance. More interestingly, given a successful example generated by either our or other attacks, LocalFuzzer can immediately generate more successful adversarial examples even with smaller L2 distance from the source example. © 2022 Elsevier Ltd}, art_number = {102694}, author_keywords = {Adversarial example; Adversarial machine learning; Black-box attack; Fuzzing; Neural network}, coden = {CPSED}, document_type = {Article}, source = {Scopus}, keywords = {Adversarial example,Adversarial machine learning,Black boxes,Black-box attack,Black-box testing,Fuzz Testing,Fuzzing,Machine learning,Machine learning models,Multiple-path,Neural-networks,Security domains,Security of data}, note = {cited By 2} } @misc{QualitativeClusteringSoftware, title = {Qualitative Clustering of Software Repositories Based on Software Metrics}, langid = {english} } @article{Quinlan:1986:IDT:637962.637969, title = {Induction of Decision Trees}, author = {Quinlan, J. R.}, date = {1986-03}, journaltitle = {Mach. Learn.}, volume = {1}, number = {1}, pages = {81--106}, publisher = {Kluwer Academic Publishers}, address = {Hingham, MA, USA}, issn = {0885-6125}, url = {http://dx.doi.org/10.1023/A:1022643204877}, acmid = {637969}, nodoi = {10.1023/A:1022643204877}, numpages = {26}, keywords = {classification,decision trees,expert systems,induction,information theory,knowledge acquisition} } @article{Quintero2016219, title = {Knowledge Management Metamodel from Social Analysis of Lessons Learnt Registered in the Cloud}, author = {Quintero, J.L. and García, V.H.M. and García, C.P.}, editor = {Liberona D., Feldmann B., Uden L.}, date = {2016}, journaltitle = {Commun. Comput. Inf. Sci.}, volume = {620}, pages = {219--232}, publisher = {Springer Verlag}, issn = {18650929}, doi = {10.1007/978-3-319-42147-6_19}, abbrev_source_title = {Commun. Comput. Info. Sci.}, affiliation = {CUN-Corporación Unificada Nacional de Educación Superior, Bogotá, Colombia; Universidad Distrital Francisco José de Caldas, Bogotá, Colombia; Universidad de Oviedo, Oviedo, Spain}, correspondence_address1 = {García, V.H.M.; Universidad Distrital Francisco José de CaldasColombia; email: vmedina@udistrital.edu.co}, document_type = {Conference Paper}, isbn = {9783319421469}, langid = {english}, source = {Scopus}, note = {cited By 1 \par TL;DR \par The final result is the design of a functional architecture that permits integrating the Web 2.0 Application and a semantic analysis algorithm from unstructured information by applying machine learning techniques.} } @manual{QVT, type = {manual}, title = {{{MOF QVT}} Final Adopted Specification}, author = {{Omg}}, date = {2005}, number = {ptc/05-11-01}, publisher = {Object Management Group}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {OMG doc. ptc/05-11-01} } @inproceedings{Rabbah2020, title = {A New Classification Model Based on Stacknet and Deep Learning for Fast Detection of {{COVID}} 19 through {{X}} Rays Images}, author = {Rabbah, J. and Ridouani, M. and Hassouni, L.}, editor = {Oubenaalla Y., Nfaoui E.H., Loqman C., Riffi J., Kozma R., Mestari M., Alippi C., Joumhidi J.}, date = {2020}, series = {4th {{International Conference}} on {{Intelligent Computing}} in {{Data Sciences}}, {{ICDS}} 2020}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/ICDS50568.2020.9268777}, abbrev_source_title = {Int. Conf. Intell. Comput. Data Sci., ICDS}, affiliation = {Hassan Ii University, Ritm Laboratory, Ced Engineering Sciences, Casablanca, Morocco}, art_number = {9268777}, document_type = {Conference Paper}, isbn = {978-1-7281-8084-7}, langid = {english}, source = {Scopus}, note = {cited By 6} } @article{Rabbi201749, title = {An {{MDE}} Approach for Modelling and Reasoning about Multi-Agent Systems}, author = {Rabbi, F. and Lamo, Y. and Kristensen, L.M.}, editor = {Carrascosa C., Julian Inglada V., Osman N., Criado Pacheco N.}, date = {2017}, journaltitle = {Lect. Notes Comput. Sci. Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinforma.}, volume = {10207 LNAI}, pages = {49--57}, publisher = {Springer Verlag}, issn = {03029743}, doi = {10.1007/978-3-319-59294-7_5}, abbrev_source_title = {Lect. Notes Comput. Sci.}, affiliation = {Western Norway University of Applied Sciences, Bergen, Norway; University of Oslo, Oslo, Norway}, correspondence_address1 = {Rabbi, F.; Western Norway University of Applied SciencesNorway; email: Fazle.Rabbi@hvl.no}, document_type = {Conference Paper}, isbn = {9783319592930}, langid = {english}, source = {Scopus}, note = {cited By 0} } @article{radfordLanguageModelsAre, title = {Language Models Are Unsupervised Multitask Learners}, author = {Radford, Alec and Wu, Jeffrey and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya}, abstract = {Natural language processing tasks, such as question answering, machine translation, reading comprehension, and summarization, are typically approached with supervised learning on taskspecific datasets. We demonstrate that language models begin to learn these tasks without any explicit supervision when trained on a new dataset of millions of webpages called WebText. When conditioned on a document plus questions, the answers generated by the language model reach 55 F1 on the CoQA dataset - matching or exceeding the performance of 3 out of 4 baseline systems without using the 127,000+ training examples. The capacity of the language model is essential to the success of zero-shot task transfer and increasing it improves performance in a log-linear fashion across tasks. Our largest model, GPT-2, is a 1.5B parameter Transformer that achieves state of the art results on 7 out of 8 tested language modeling datasets in a zero-shot setting but still underfits WebText. Samples from the model reflect these improvements and contain coherent paragraphs of text. These findings suggest a promising path towards building language processing systems which learn to perform tasks from their naturally occurring demonstrations.}, langid = {english} } @article{Rafique2018D126, title = {Machine Learning for Network Automation: {{Overview}}, Architecture, and Applications [{{Invited Tutorial}}]}, author = {Rafique, D. and Velasco, L.}, date = {2018}, journaltitle = {J. Opt. Commun. Netw.}, volume = {10}, number = {10}, pages = {D126-D143}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {19430620}, doi = {10.1364/JOCN.10.00D126}, abstract = {Networks are complex interacting systems involving cloud operations, core and metro transport, and mobile connectivity all the way to video streaming and similar user applications.With localized and highly engineered operational tools, it is typical of these networks to take days to weeks for any changes, upgrades, or service deployments to take effect. Machine learning, a sub-domain of artificial intelligence, is highly suitable for complex system representation. In this tutorial paper, we review several machine learning concepts tailored to the optical networking industry and discuss algorithm choices, data and model management strategies, and integration into existing network control and management tools. We then describe four networking case studies in detail, covering predictive maintenance, virtual network topology management, capacity optimization, and optical spectral analysis. © 2009-2012 OSA.}, art_number = {8501533}, document_type = {Article}, source = {Scopus}, note = {cited By 166 \par TL;DR \par This tutorial paper reviews several machine learning concepts tailored to the optical networking industry and discusses algorithm choices, data and model management strategies, and integration into existing network control and management tools.} } @article{Ragkhitwetsagul2018, title = {A Comparison of Code Similarity Analysers}, author = {Ragkhitwetsagul, Chaiyong and Krinke, Jens and Clark, David}, date = {2018-08-01}, journaltitle = {Empir. Softw. Eng.}, volume = {23}, number = {4}, pages = {2464--2519}, issn = {1573-7616}, doi = {10.1007/s10664-017-9564-7}, abstract = {Copying and pasting of source code is a common activity in software engineering. Often, the code is not copied as it is and it may be modified for various purposes; e.g. refactoring, bug fixing, or even software plagiarism. These code modifications could affect the performance of code similarity analysers including code clone and plagiarism detectors to some certain degree. We are interested in two types of code modification in this study: pervasive modifications, i.e. transformations that may have a global effect, and local modifications, i.e. code changes that are contained in a single method or code block. We evaluate 30 code similarity detection techniques and tools using five experimental scenarios for Java source code. These are (1) pervasively modified code, created with tools for source code and bytecode obfuscation, and boiler-plate code, (2) source code normalisation through compilation and decompilation using different decompilers, (3) reuse of optimal configurations over different data sets, (4) tool evaluation using ranked-based measures, and (5) local + global code modifications. Our experimental results show that in the presence of pervasive modifications, some of the general textual similarity measures can offer similar performance to specialised code similarity tools, whilst in the presence of boiler-plate code, highly specialised source code similarity detection techniques and tools outperform textual similarity measures. Our study strongly validates the use of compilation/decompilation as a normalisation technique. Its use reduced false classifications to zero for three of the tools. Moreover, we demonstrate that optimal configurations are very sensitive to a specific data set. After directly applying optimal configurations derived from one data set to another, the tools perform poorly on the new data set. The code similarity analysers are thoroughly evaluated not only based on several well-known pair-based and query-based error measures but also on each specific type of pervasive code modification. This broad, thorough study is the largest in existence and potentially an invaluable guide for future users of similarity detection in source code.}, note = {TL;DR \par This study strongly validates the use of compilation/decompilation as a normalisation technique and reduced false classifications to zero for three of the tools, and demonstrates that optimal configurations are very sensitive to a specific data set.} } @inproceedings{ragoneSchemasummarizationLinkeddatabasedFeature2017, title = {Schema-Summarization in Linked-Data-Based Feature Selection for Recommender Systems}, booktitle = {Proc. {{Symp}}. {{Appl}}. {{Comput}}.}, author = {Ragone, Azzurra and Tomeo, Paolo and Magarelli, Corrado and Di Noia, Tommaso and Palmonari, Matteo and Maurino, Andrea and Di Sciascio, Eugenio}, date = {2017}, series = {{{SAC}} '17}, pages = {330--335}, publisher = {ACM}, location = {New York, NY, USA}, url = {http://doi.acm.org/10.1145/3019612.3019837}, acmid = {3019837}, isbn = {978-1-4503-4486-9}, nodoi = {10.1145/3019612.3019837}, numpages = {6}, keywords = {information gain,linked data,ontology summarization} } @inproceedings{RahimiTRB23, title = {Towards Generating Structurally Realistic Models by Generative Adversarial Networks}, booktitle = {{{ACMIEEE Int}}. {{Conf}}. {{Model Driven Eng}}. {{Lang}}. {{Syst}}. {{MODELS}} 2023 {{Companion Västerås Swed}}. {{Oct}}. 1-6 2023}, author = {Rahimi, Abbas and Tisi, Massimo and Rahimi, Shekoufeh Kolahdouz and Berardinelli, Luca}, date = {2023}, pages = {597--604}, publisher = {IEEE}, doi = {10.1109/MODELS-C59198.2023.00098}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {TL;DR \par Generative deep learning, in particular, Generative Adversarial Networks (GANs), are utilized to present an approach for generating new structurally realistic models, and preliminary statistical results illustrate that using GAN s can be promising for creating new realistic models.}, timestamp = {Fri, 05 Jan 2024 16:35:45 +0100} } @misc{Raise2025250504163755ef169965, title = {Raise2025-250504163755-Ef169965}, langid = {english} } @inproceedings{Rajaei2021149, title = {A {{DSL}} for Encoding Models for Graph-Learning Processes}, author = {Rajaei, Z. and Kolahdouz-Rahimi, S. and Tisi, M. and Jouault, F.}, editor = {Iovino L., Kristensen L.M.}, date = {2021}, series = {{{CEUR Workshop Proceedings}}}, volume = {2999}, pages = {149--161}, publisher = {CEUR-WS}, issn = {16130073}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118923715&partnerID=40&md5=0392ba00df3c85c19cc98510b244e3b9}, abbrev_source_title = {CEUR Workshop Proc.}, affiliation = {MDSE Research Group, Department of Software Engineering, University of Isfahan, Iran; IMT Atlantique, LS2N (UMR CNRS 6004), Nantes, France; ERIS, ESEO-TECH, Angers, France}, correspondence_address1 = {Rajaei, Z.; MDSE Research Group, Iran; email: z.rajaei@eng.ui.ac.ir}, document_type = {Conference Paper}, langid = {english}, source = {Scopus}, note = {cited By 0 \par TL;DR \par A Domain-Specific Language (DSL) is introduced for configuring the encoding of models into suitable input for graph-learning tools, interpreted to automatically translate MDE datasets, enabling their use in machine-learning pipelines.} } @book{rajInternetThingsEnabling2017, title = {The {{Internet}} of Things: Enabling Technologies, Platforms, and Use Cases}, shorttitle = {The {{Internet}} of Things}, author = {Raj, Pethuru and Raman, Anupama C.}, date = {2017}, publisher = {CRC Press/Taylor \& Francis Group}, location = {Boca Raton}, isbn = {978-1-4987-6128-4}, langid = {english}, pagetotal = {364}, keywords = {internet of things} } @article{rajputEnhancingEnergyAwarenessDeep, title = {Enhancing {{Energy-Awareness}} in {{Deep Learning}} through {{Fine-Grained Energy Measurement}}}, author = {Rajput, Saurabhsingh and Widmayer, Tim and Shang, Ziyuan and Kechagia, Maria and Sarro, Federica and Sharma, Tushar}, volume = {1}, number = {1}, abstract = {SAURABHSINGH RAJPUT, Dalhousie University, Canada TIM WIDMAYER, University College London, UK ZIYUAN SHANG, Nanyang Technological University, Singapore MARIA KECHAGIA, University College London, UK FEDERICA SARRO, University College London, UK TUSHAR SHARMA, Dalhousie University, Canada With the increasing usage, scale, and complexity of Deep Learning (dl) models, their rapidly growing energy consumption has become a critical concern. Promoting green development and energy awareness at different granularities is the need of the hour to limit carbon emissions of dl systems. However, the lack of standard and repeatable tools to accurately measure and optimize energy consumption at fine granularity (e.g., at the api level) hinders progress in this area. This paper introduces FECoM (Fine-grained Energy Consumption Meter), a framework for finegrained dl energy consumption measurement. FECoM enables researchers and developers to profile dl apis from energy perspective. FECoM addresses the challenges of fine-grained energy measurement using static instrumentation while considering factors such as computational load and temperature stability. We assess FECoM’s capability for fine-grained energy measurement for one of the most popular open-source dl frameworks, namely TensorFlow. Using FECoM, we also investigate the impact of parameter size and execution time on energy consumption, enriching our understanding of TensorFlow apis’ energy profiles. Furthermore, we elaborate on the considerations and challenges while designing and implementing a finegrained energy measurement tool. This work will facilitate further advances in dl energy measurement and the development of energy-aware practices for dl systems.}, langid = {english} } @article{rajputGreenlightHighlightingTensorFlow2024, title = {Greenlight: Highlighting {{TensorFlow APIs}} Energy Footprint}, author = {Rajput, Saurabhsingh and Kechagia, Maria and Sarro, Federica and Sharma, Tushar}, date = {2024}, abstract = {Deep learning (dl) models are being widely deployed in real-world applications, but their usage remains computationally intensive and energy-hungry. While prior work has examined model-level energy usage, the energy footprint of the dl frameworks, such as TensorFlow and PyTorch, used to train and build these models, has not been thoroughly studied. We present Greenlight, a large-scale dataset containing fine- grained energy profiling information of 1284 TensorFlow api calls. We developed a command line tool called CodeGreen to curate such a dataset. CodeGreen is based on our previously proposed framework FECoM, which employs static analysis and code instrumentation to isolate invocations of TensorFlow operations and measure their energy consumption precisely. By executing api calls on representative workloads and measuring the consumed energy, we construct detailed energy profiles for the apis. Several factors, such as input data size and the type of operation, significantly impact energy footprints. Greenlight provides a ground-truth dataset capturing energy consumption along with relevant factors such as input parameter size to take the first step towards optimization of energy-intensive TensorFlow code. The Greenlight dataset opens up new research directions such as predicting api energy consumption, automated optimization, modeling efficiency trade-offs, and empirical studies into energy-aware dl system design.}, langid = {english} } @article{ramasamyWorkflowAnalysisData2023, title = {Workflow Analysis of Data Science Code in Public {{GitHub}} Repositories}, author = {Ramasamy, Dhivyabharathi and Sarasua, Cristina and Bacchelli, Alberto and Bernstein, Abraham}, date = {2023-01}, journaltitle = {Empir Software Eng}, volume = {28}, number = {1}, pages = {7}, issn = {1382-3256, 1573-7616}, doi = {10.1007/s10664-022-10229-z}, abstract = {Despite the ubiquity of data science, we are far from rigorously understanding how coding in data science is performed. Even though the scientific literature has hinted at the iterative and explorative nature of data science coding, we need further empirical evidence to understand this practice and its workflows in detail. Such understanding is critical to recognise the needs of data scientists and, for instance, inform tooling support. To obtain a deeper understanding of the iterative and explorative nature of data science coding, we analysed 470 Jupyter notebooks publicly available in GitHub repositories. We focused on the extent to which data scientists transition between different types of data science activities, or steps (such as data preprocessing and modelling), as well as the frequency and co-occurrence of such transitions. For our analysis, we developed a dataset with the help of five data science experts, who manually annotated the data science steps for each code cell within the aforementioned 470 notebooks. Using the first-order Markov chain model, we extracted the transitions and analysed the transition probabilities between the different steps. In addition to providing deeper insights into the implementation practices of data science coding, our results provide evidence that the steps in a data science workflow are indeed iterative and reveal specific patterns. We also evaluated the use of the annotated dataset to train machinelearning classifiers to predict the data science step(s) of a given code cell. We investigate the representativeness of the classification by comparing the workflow analysis applied to (a) the predicted data set and (b) the data set labelled by experts, finding an F1-score of about 71\% for the 10-class data science step prediction problem.}, langid = {english}, keywords = {LOGSEQ}, note = {TL;DR \par The results provide evidence that the steps in a data science workflow are indeed iterative and reveal specific patterns, and the use of the annotated dataset to train machine-learning classifiers to predict the data science step(s) of a given code cell is evaluated.} } @inproceedings{Ramaswamy201450, title = {Modeling Non-Functional Properties for Human-Machine Systems}, author = {Ramaswamy, A. and Monsuez, B. and Tapus, A.}, date = {2014}, series = {{{AAAI Spring Symposium}} - {{Technical Report}}}, volume = {SS-14-02}, pages = {50--55}, publisher = {AI Access Foundation}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84904888679&partnerID=40&md5=8255705b33be09a555b7c43d9218c3a7}, abbrev_source_title = {AAAI Spring Symp. Tech. Rep.}, affiliation = {Department of Computer and System Engineering, ENSTA-ParisTech, 828 Blvd Marechaux, Palaiseau, France; VeDeCom Institute, 77 rue des Chantiers, 78000 Versailles, France}, document_type = {Conference Paper}, isbn = {978-1-57735-655-4}, langid = {english}, source = {Scopus}, note = {cited By 6 \par TL;DR \par The importance of non- functional properties in humanmachine systems are highlighted and a metamodel for modeling those properties is proposed and a case study on assistive lane keeping in automobiles is presented to demonstrate how the non-functional properties can be modeled.} } @article{ramosUsingTFIDFDetermine1999, title = {Using {{TF-IDF}} to Determine Word Relevance in Document Queries}, author = {Ramos, Juan}, date = {1999}, added-at = {2011-08-09T16:33:51.000+0200}, biburl = {https://www.bibsonomy.org/bibtex/2e757decbf13ecf55dee0b5eae56f9ccd/reynares.e}, interhash = {59140639220e47ed6a9636c3ee35ac1a}, intrahash = {e757decbf13ecf55dee0b5eae56f9ccd}, keywords = {ontologyₗearning}, note = {TL;DR \par This paper examines the results of applying Term Frequency Inverse Document Frequency to determine what words in a corpus of documents might be more favorable to use in a query and provides evidence that this simple algorithm efficiently categorizes relevant words that can enhance query retrieval.}, timestamp = {2013-05-16T22:34:47.000+0200} } @article{randObjectiveCriteriaEvaluation1971, title = {Objective Criteria for the Evaluation of Clustering Methods}, author = {Rand, W.M.}, date = {1971}, journaltitle = {J. Am. Stat. Assoc.}, volume = {66}, number = {336}, pages = {846--850}, publisher = {JSTOR}, issn = {0162-1459}, added-at = {2011-03-28T19:20:19.000+0200}, biburl = {https://www.bibsonomy.org/bibtex/26967eb207406719bb83669ebbebb2099/dunarel}, interhash = {1afaf0170bc705a9e49b625f67679ee2}, intrahash = {6967eb207406719bb83669ebbebb2099}, owner = {root}, keywords = {imported}, timestamp = {2011-03-28T19:20:21.000+0200} } @article{Rangel-Patino2019733, title = {Post-Silicon Receiver Equalization Metamodeling by Artificial Neural Networks}, author = {Rangel-Patino, F.E. and Rayas-Sanchez, J.E. and Viveros-Wacher, A. and Chavez-Hurtado, J.L. and Vega-Ochoa, E.A. and Hakim, N.}, date = {2019}, journaltitle = {IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.}, volume = {38}, number = {4}, pages = {733--740}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {02780070}, doi = {10.1109/TCAD.2018.2834403}, abbrev_source_title = {IEEE Trans Comput Aided Des Integr Circuits Syst}, affiliation = {Intel Corporation, Zapopan, 45109, Mexico; Department of Electronics, Systems, and Informatics, ITESO-The Jesuit University of Guadalajara, Tlaquepaque, 45604, Mexico; Intel Corporation, Santa Clara, CA 95052, United States}, art_number = {8355951}, coden = {ITCSD}, correspondence_address1 = {Rayas-Sanchez, J.E.; Department of Electronics, Mexico; email: erayas@iteso.mx}, document_type = {Article}, langid = {english}, source = {Scopus}, keywords = {notion}, note = {cited By 4 \par TL;DR \par A metamodeling approach based on neural networks is proposed to efficiently simulate the effects of a receiver equalizer PHY tuning settings and is evaluated by comparing with measured responses on a real server HSIO link.} } @online{rasheedCodeporiLargescaleSystem2024, title = {Codepori: Large-Scale System for Autonomous Software Development Using Multi-Agent Technology}, shorttitle = {Codepori}, author = {Rasheed, Zeeshan and Malik, Sami and Kemell, Kai-Kristian and Muhammad, Waseem and Saari, Mika and Systä, Kari and Abrahamsson, Pekka}, date = {2024}, eprinttype = {SSRN}, doi = {10.2139/ssrn.4979510}, abstract = {Objective: This paper aims to investigate the potential of LLM-based agents in the software industry, particularly in enhancing productivity and reducing time-to-market for complex software solutions. Our primary objective is to gain insights into how these agents can fundamentally transform the development of large-scale software. Methods: We introduce CodePori, a novel system designed to automate code generation for large and complex software projects based on functional and non-functional requirements defined by stakeholders through natural language prompts. We utilized LLM-based multi-agents, where each agent engages with a specific task, including system design, code development, code review, code verification, and test engineering. To assess the proposed system performance, we utilized the HumanEval benchmark and manually tested the CodePori model, providing 20 different project descriptions as input and then evaluated the code accuracy by manually executing the code. Results: CodePori is able to generate running code for large-scale projects, aligned with the typical software development process within minutes, and at a cost of a few dollars. The HumanEval benchmark results indicate that CodePori improves code accuracy and efficiency by 89\%. A manual assessment conducted by the first author shows that the CodePori system achieved an accuracy rate of 85\%. Conclusion: Based on the results, our conclusion is that CodePori provides a contribution to both industry and academia by automating software development. Additionally, our study demonstrates the transformative potential of LLM-based agents in SE, highlighting their practical applications and opening new opportunities for broader adoption in both industry and academia. This suggests a paradigm shift in how software is developed and deployed. Our project is publicly available at https://github.com/GPT-Laboratory/CodePori.}, langid = {english}, pubstate = {prepublished}, keywords = {/unread} } @article{Rasiman202235, title = {How Effective Is Automated Trace Link Recovery in Model-Driven Development?}, author = {Rasiman, R. and Dalpiaz, F. and España, S.}, editor = {Gervasi V., Vogelsang A.}, date = {2022}, journaltitle = {Lect. Notes Comput. Sci. Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinforma.}, volume = {13216 LNCS}, pages = {35--51}, publisher = {{Springer Science and Business Media Deutschland GmbH}}, issn = {03029743}, doi = {10.1007/978-3-030-98464-9_4}, abbrev_source_title = {Lect. Notes Comput. Sci.}, affiliation = {Utrecht University, Utrecht, Netherlands}, correspondence_address1 = {Dalpiaz, F.; Utrecht UniversityNetherlands; email: f.dalpiaz@uu.nl}, document_type = {Conference Paper}, isbn = {9783030984632}, langid = {english}, source = {Scopus}, keywords = {GOAL\_Trace-link-recovery,notion,TECHNIQUE\_Gradient\_Boosted\_Decision\_Trees,TECHNIQUE\_RandomForests}, note = {cited By 0} } @article{Rausch2021127, title = {Evaluating the Effectiveness of Metamodeling in Emulating Quantitative Models}, author = {Rausch, M. and Sanders, W.H.}, editor = {Abate A., Marin A.}, date = {2021}, journaltitle = {Lect. Notes Comput. Sci. Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinforma.}, volume = {12846 LNCS}, pages = {127--145}, publisher = {{Springer Science and Business Media Deutschland GmbH}}, issn = {03029743}, doi = {10.1007/978-3-030-85172-9_7}, abbrev_source_title = {Lect. Notes Comput. Sci.}, affiliation = {University of Illinois at Urbana-Champaign, Urbana, IL, United States; Carnegie Mellon University, Pittsburgh, PA, United States}, correspondence_address1 = {Rausch, M.; University of Illinois at Urbana-ChampaignUnited States; email: mjrausc2@illinois.edu}, document_type = {Conference Paper}, isbn = {9783030851712}, langid = {english}, source = {Scopus}, note = {cited By 0 \par TL;DR \par Evaluated metamodels found that the metamodels are reasonably accurate and are several thousand times faster than the corresponding models they emulate, and that stacking-based meta-models are significantly more accurate than state-of-the-practice meta-models.} } @inproceedings{Raychev2014Code, title = {Code Completion with Statistical Language Models}, booktitle = {35th {{ACM SIGPLAN Conf}}. {{Program}}. {{Lang}}. {{Des}}. {{Implement}}.}, author = {Raychev, Veselin and Vechev, Martin and Yahav, Eran}, date = {2014}, pages = {419--428}, publisher = {ACM}, location = {New York}, doi = {10.1145/2594291.2594321}, isbn = {978-1-4503-2784-8} } @article{raySurveyInternetThings2018, title = {A Survey on {{Internet}} of {{Things}} Architectures}, author = {Ray, P.P.}, date = {2018-07}, journaltitle = {Journal of King Saud University - Computer and Information Sciences}, volume = {30}, number = {3}, pages = {291--319}, issn = {13191578}, doi = {10.1016/j.jksuci.2016.10.003}, abstract = {Internet of Things is a platform where every day devices become smarter, every day processing becomes intelligent, and every day communication becomes informative. While the Internet of Things is still seeking its own shape, its effects have already stared in making incredible strides as a universal solution media for the connected scenario. Architecture specific study does always pave the conformation of related field. The lack of overall architectural knowledge is presently resisting the researchers to get through the scope of Internet of Things centric approaches. This literature surveys Internet of Things oriented architectures that are capable enough to improve the understanding of related tool, technology, and methodology to facilitate developer’s requirements. Directly or indirectly, the presented architectures propose to solve real-life problems by building and deployment of powerful Internet of Things notions. Further, research challenges have been investigated to incorporate the lacuna inside the current trends of architectures to motivate the academics and industries get involved into seeking the possible way outs to apt the exact power of Internet of Things. A main contribution of this survey paper is that it summarizes the current state-of-the-art of Internet of Things architectures in various domains systematically.}, langid = {english}, note = {TL;DR \par This surveying paper’s central aspect is its comprehensive summary of the government in Internet of Things designs across multiple disciplines, and research gaps have also been examined to accommodate the gaps in the present architectural trends.} } @article{raySurveyIoTCloud2016, title = {A Survey of {{IoT}} Cloud Platforms}, author = {Ray, Partha Pratim}, date = {2016-12}, journaltitle = {Future Computing and Informatics Journal}, volume = {1}, number = {1--2}, pages = {35--46}, issn = {23147288}, doi = {10.1016/j.fcij.2017.02.001}, abstract = {Internet of Things (IoT) envisages overall merging of several “things” while utilizing internet as the backbone of the communication system to establish a smart interaction between people and surrounding objects. Cloud, being the crucial component of IoT, provides valuable application specific services in many application domains. A number of IoT cloud providers are currently emerging into the market to leverage suitable and specific IoT based services. In spite of huge possible involvement of these IoT clouds, no standard cum comparative analytical study has been found across the literature databases. This article surveys popular IoT cloud platforms in light of solving several service domains such as application development, device management, system management, heterogeneity management, data management, tools for analysis, deployment, monitoring, visualization, and research. A comparison is presented for overall dissemination of IoT clouds according to their applicability. Further, few challenges are also described that the researchers should take on in near future. Ultimately, the goal of this article is to provide detailed knowledge about the existing IoT cloud service providers and their pros and cons in concrete form.}, langid = {english} } @online{razzaqAutoAgentFullyautomatedHighly2025, title = {{{AutoAgent}}: A Fully-Automated and Highly Self-Developing Framework That Enables Users to Create and Deploy {{LLM}} Agents through Natural Language Alone}, shorttitle = {{{AutoAgent}}}, author = {Razzaq, Asif}, date = {2025-03-07}, url = {https://www.marktechpost.com/2025/03/07/autoagent-a-fully-automated-and-highly-self-developing-framework-that-enables-users-to-create-and-deploy-llm-agents-through-natural-language-alone/}, urldate = {2025-03-14}, abstract = {From business processes to scientific studies, AI agents can process huge datasets, streamline processes, and help in decision-making. Yet, even with all these developments, building and tailoring LLM agents is still a daunting task for most users. The main reason is that AI agent platforms require programming skills, restricting access to a mere fraction of the population. With a mere 0.03\% of the world's population having the necessary coding skills, the mass deployment of LLM agents is beyond the reach of non-technical users. While AI is increasingly becoming an essential tool in different industries, non-programming professionals cannot tap into its}, langid = {english}, organization = {Marktechpost} } @online{RealWorldIoT, title = {Real {{World IoT}}: {{Architectures}} and {{Projects}} with {{Eclipse IoT}} | {{EclipseCon Europe}} 2016}, url = {https://www.eclipsecon.org/europe2016/session/real-world-iot-architectures-and-projects-eclipse-iot}, urldate = {2016-09-27} } @article{REBY199735, title = {Artificial Neural Networks as a Classification Method in the Behavioural Sciences}, author = {Reby, David and Lek, Sovan and Dimopoulos, Ioannis and Joachim, Jean and Lauga, Jacques and Aulagnier, Stéphane}, date = {1997}, journaltitle = {Behav. Processes}, volume = {40}, number = {1}, pages = {35--43}, issn = {0376-6357}, abstract = {The classification and recognition of individual characteristics and behaviours constitute a preliminary step and is an important objective in the behavioural sciences. Current statistical methods do not always give satisfactory results. To improve performance in this area, we present a methodology based on one of the principles of artificial neural networks: the backpropagation gradient. After summarizing the theoretical construction of the model, we describe how to parameterize a neural network using the example of the individual recognition of vocalizations of four fallow deer (Dama dama). With 100\% recognition and \%90\% prediction success, the results are very promising.}, keywords = {Classification,Deer,Mammal,Modelling,Neural network,Vocalization} } @misc{RecommendingAlternativeAPI, title = {Recommending Alternative {{API}} Usages for Code Customization Based on Code Pre-Trained Models}, langid = {english} } @misc{RecSys_2024_paper_779, title = {{{RecSys}}\_2024\_paper\_779}, note = {\section{Annotazioni\\ (17/8/2024, 07:42:13)} \par - “(1) integration of diverse environmental data” (“RecSys\_2024\_paper\_779”, p. 1) \#5fb236 \par - “(2) a dynamic weighting algorithm allowing fine-grained user control over routing parameters;” (“RecSys\_2024\_paper\_779”, p. 1) \#5fb236 \par - “(3) transparent decision-making via an interactive interface displaying edge-level information;” (“RecSys\_2024\_paper\_779”, p. 1) \#5fb236 \par - “(4) a community-driven feedback mechanism that continuously refines recommendations.” (“RecSys\_2024\_paper\_779”, p. 1) \#5fb236 \par - “These improvements come with slight trade-offs in distance, especially in walking mode” (“RecSys\_2024\_paper\_779”, p. 1) \#5fb236 \par - “comprehensive framework for multi-criteria route recommendation, balancing individual preferences, environmental factors, and community insights in urban navigation.” (“RecSys\_2024\_paper\_779”, p. 1) \#5fb236 \par - “predominantly prioritize route efficiency” (“RecSys\_2024\_paper\_779”, p. 1) \#5fb236 \par - “typically measured by shortest travel time or distance” (“RecSys\_2024\_paper\_779”, p. 1) \#ffd400\\ \mkbibemph{Google Maps, I'm not aware of other systems, give also information about Co2 emission for each alternative route.} \par - “multifaceted human-centric factors such as environmental quality, public health, and personal preferences.” (“RecSys\_2024\_paper\_779”, p. 1) \#5fb236 \par - “user empowerment and environmental considerations.” (“RecSys\_2024\_paper\_779”, p. 1) \#5fb236 \par - “these systems frequently consider such factors in isolation” (“RecSys\_2024\_paper\_779”, p. 1) \#a28ae5 \par - “without a holistic integration of multiple user-defined preferences” (“RecSys\_2024\_paper\_779”, p. 1) \#a28ae5 \par - “black-box nature of many optimization algorithms” (“RecSys\_2024\_paper\_779”, p. 1) \#a28ae5 \par - “offering users little insight into how recommended routes align with their personal and environmental values,” (“RecSys\_2024\_paper\_779”, p. 1) \#a28ae5 \par - “Our research introduces Routify1 (Route intelligence for you), an open-source route recommendation system that fundamentally shifts the paradigm from the optimization to the democratization of route selection.” (“RecSys\_2024\_paper\_779”, p. 1) \#ffd400 \par - “air quality, noise levels, greenery, altitude, and real-time traffic conditions.” (“RecSys\_2024\_paper\_779”, p. 1) \#a28ae5 \par - “The problem with optimization in urban navigation often stems from its narrow focus on efficiency metrics (such as distance or travel time), neglecting qualitative and environmental aspects.” (“RecSys\_2024\_paper\_779”, p. 1) \#f0ff00\\ \mkbibemph{Not fully true, systems like Google maps also suggest cow concerned recommendations} \par - “This can lead to suboptimal outcomes, for example, routes that save time but expose users to higher pollution levels or less appealing environments.” (“RecSys\_2024\_paper\_779”, p. 1) \#a28ae5 \par - “one-size-fits-all recommendations can create new congestion situations” (“RecSys\_2024\_paper\_779”, p. 1) \#a28ae5 \par - “multi-criteria recommendation system” (“RecSys\_2024\_paper\_779”, p. 1) \#a28ae5 \par - “Providing custom parametrization that allows users to weigh different factors according to their preferences” (“RecSys\_2024\_paper\_779”, p. 1) \#a28ae5 \par - “Promoting transparency and trust through its open-source nature and user-centric, value-sensitive design,” (“RecSys\_2024\_paper\_779”, p. 1) \#e56eee \par - “empowers users to make choices that reflect their personal values.” (“RecSys\_2024\_paper\_779”, p. 2) \#e56eee \par - “prioritize routing for motor vehicles and public transit” (“RecSys\_2024\_paper\_779”, p. 2) \#a28ae5 \par - “The most accurate proxies of routing preferences [12, 13] also depend on subjective, cultural, and environmental factors [14].” (“RecSys\_2024\_paper\_779”, p. 2) \#a28ae5 \par - “the scarcity of data on relevant features” (“RecSys\_2024\_paper\_779”, p. 2) \#f0ff00\\ \mkbibemph{How have you addressed that?} \par - “these services offer limited personalization, often extending the logic of vehicular routing to cycling and pedestrian mobility” (“RecSys\_2024\_paper\_779”, p. 2) \#a28ae5 \par - “Moreover, despite their differences, the above services remain opaque regarding the parameters and factors used in route computation” (“RecSys\_2024\_paper\_779”, p. 2) \#2ea8e5 \par - “Personalized routing algorithms can provide alternatives that are more aligned to user preferences” (“RecSys\_2024\_paper\_779”, p. 2) \#a28ae5 \par - “aggregated synthetic metrics” (“RecSys\_2024\_paper\_779”, p. 2) \#5fb236 \par - “. First, the integration of real-time, hyper-local environmental data (such as air quality and noise levels) into routing algorithms is still limited.” (“RecSys\_2024\_paper\_779”, p. 2) \#5fb236 \par - “First, the integration of real-time, hyper-local environmental data (such as air quality and noise levels) into routing algorithms is still limited.” (“RecSys\_2024\_paper\_779”, p. 2) \#f0ff00\\ \mkbibemph{Is it because of the lack of data or what?} \par - “systems lack transparency in their decision-making processes, operating as "black boxes" that users must trust without understanding them. Third,” (“RecSys\_2024\_paper\_779”, p. 2) \#f0ff00\\ \mkbibemph{But this is not a scientific contribution, isn't it?} \par - “Routify addresses these gaps by providing a transparent, highly customizable system that integrates real-time environmental data and leverages community input.” (“RecSys\_2024\_paper\_779”, p. 2) \#a28ae5 \par - “goals of sustainable urban development and citizen empowerment in smart cities.” (“RecSys\_2024\_paper\_779”, p. 2) \#5fb236 \par - “diverse data sources to construct a comprehensive representation of the urban environment.” (“RecSys\_2024\_paper\_779”, p. 2) \#5fb236 \par - “his heterogeneous data is synthesized into a unified G” (“RecSys\_2024\_paper\_779”, p. 2) \#5fb236 \par - “Graph” (“RecSys\_2024\_paper\_779”, p. 2) \#f0ff00\\ \mkbibemph{What are the nodes of the graph?} \par - “ges” (“RecSys\_2024\_paper\_779”, p. 3) \#f0ff00\\ \mkbibemph{Are these defined on es for all? Some more details on the graph based representation would help understand better the approach.} \par - “The Application Layer serves as the interface between the system and the user. It presents intuitive visualizations and statistics derived from the routing results, allowing users to make informed decisions. Users interact with the system by inputting their preferences for various factors, represented by intuitive icons (e.g. green index, noise levels, etc.). Additionally, users can provide feedback on suggested routes, contributing” (“RecSys\_2024\_paper\_779”, p. 3) \#f0ff00\\ \mkbibemph{The main focus of the paper is the algorithm, the encoding in a graph based representation of all data, the application users can exploit, all of them?} \par - “RESULTS” (“RecSys\_2024\_paper\_779”, p. 3) \#00b036\\ \mkbibemph{I'm not sure how to interpret the obtained results. Are they good, bad? What's the baseline to compare with?} \par - “control the family-wise error rate due to multiple comparisons, we applied the Bonferroni correction [30] to adjust the p-values.” (“RecSys\_2024\_paper\_779”, p. 3) \#f0ff00 \par - “Future work will focus on expanding to further environmental factors, developing advanced preference learning algorithms, and exploring multi-objective optimization techniques. We aim to integrate contextual factors like time-of-day and weather conditions, and investigate collaborative filtering approaches to enhance recommendations while maintaining privacy.” (“RecSys\_2024\_paper\_779”, p. 4) \#f0ff00\\ \mkbibemph{Indeed it is interesting even though I'm missing missing a clear discussion about the sciebtific challenges author had while conceiving the proposed approach.}} } @misc{RecSys_2024_paper_791, title = {{{RecSys}}\_2024\_paper\_791}, note = {\section{Annotazioni\\ (17/8/2024, 08:11:31)} \par - “Maximal Marginal Relevance (MMR): Integrates diversity by balancing relevance with the novelty of recommendations, crucial for varied user engagement. • Determinantal Point Processes (DPP): Promotes diversity through a probabilistic model that favors less similar items, enhancing the breadth of recommendations. • Generalist-Specialist (GS) Scores: Tailors recommendations by assessing user interest breadth, enabling personalized diversity. • Contextual Bandits (LinUCB): Adapts to real-time user feedback, effectively managing the trade-off between exploring new items and exploiting known preferences.” (“RecSys\_2024\_paper\_791”, p. 2) \#f0ff00\\ \mkbibemph{This is a repetition of what has been already said above.} \par - “ecommendation effectiveness” (“RecSys\_2024\_paper\_791”, p. 4) \#f0ff00\\ \mkbibemph{The improvement is marginal, isn't it?} \par - “this paper, we assessed the problem of limited diversity in the real-world e-commerce eye-wear platform, Lenskart. To overcome this issue, we assessed the impact of re-ranking at injecting diversity in the recommendation lists, via offline experiments.” (“RecSys\_2024\_paper\_791”, p. 4) \#f0ff00\\ \mkbibemph{The paper does not discuss the Generalizability of the performed study. A discussion on the threats to validity is required to make the paper convincing and scientifically relevant.}} } @misc{RecSys_2024_paper_810, title = {{{RecSys}}\_2024\_paper\_810}, note = {\section{Annotazioni\\ (17/8/2024, 07:03:27)} \par - “four fairness metrics – Demographic Parity, Equal Accuracy, Equalized Odds, and Positive Predictive Value” (“RecSys\_2024\_paper\_810”, p. 1) \#00b036\\ \mkbibemph{ } \par - “need for enhanced information and education on algorithmic fairness to support informed decision-making in using these systems.” (“RecSys\_2024\_paper\_810”, p. 1) \#00b036\\ \mkbibemph{ } \par - “Algorithmic fairness, in particular, has become a major issue, especially when these systems affect individuals’ lives and well-being” (“RecSys\_2024\_paper\_810”, p. 1) \#00b036\\ \mkbibemph{ } \par - “Demographic Parity focuses on equal representation, while Equalized Odds ensures equal error rates across groups.” (“RecSys\_2024\_paper\_810”, p. 1) \#00b036\\ \mkbibemph{ } \par - “This divergence underscores a fundamental challenge in designing recommender systems that are perceived as fair by all users.” (“RecSys\_2024\_paper\_810”, p. 3) \#f0ff00\\ \mkbibemph{Is it really possible? Does it make sense? } \par - “This disparity in perceptions also points to a broader issue in the deployment of recommender systems: the communication and understanding of algorithmic decisions.” (“RecSys\_2024\_paper\_810”, p. 4) \#f0ff00\\ \mkbibemph{It can be also the conte t and the gravity. The question is if metrics alone are enough or we need something note sophisticated that for instance can choose metrics that make sense with respect to the context. } \par - “explanations of how algorithms function and the trade-offs they entail. Users should understand not only the mechanics of the algorithms but also the ethical considerations involved in choosing one fairness metric over another. Furthermore, the” (“RecSys\_2024\_paper\_810”, p. 4) \#f0ff00\\ \mkbibemph{Yes exactly. } \par - “further complicating the task of developing universally fair systems. The apparent lack of a universally” (“RecSys\_2024\_paper\_810”, p. 4) \#f0ff00\\ \mkbibemph{I'm not sure this can be done. }} } @misc{Recsys2024workshops_paper_130, title = {Recsys2024-Workshops\_paper\_130}, keywords = {LOGSEQ}, note = {\section{Annotazioni\\ (16/9/2024, 10:39:36)} \par - “Empowering shilling attacks” (“recsys2024-workshops\_paper\_130”, p. 1) \#5fb236 \par - “Knowledge graphs (KGs).” (“recsys2024-workshops\_paper\_130”, p. 1) \#5fb236 \par - “A malicious agent can add fake user profiles into the platform, altering the genuine similarity values and the corresponding recommendation lists” (“recsys2024-workshops\_paper\_130”, p. 1) \#a28ae5 \par - “KGs relatedness measures, i.e., Katz and Exclusivity-based” (“recsys2024-workshops\_paper\_130”, p. 1) \#5fb236 \par - “Thanks to the appearance of wideranging Linked Datasets like DBpedia and Wikidata, we have witnessed the flourishing of novel techniques in several research fields, like Machine Learning, Information Retrieval, and Recommender Systems.” (“recsys2024-workshops\_paper\_130”, p. 1) \#5fb236 \par - “ocal solution to assist users’ decision-making process” (“recsys2024-workshops\_paper\_130”, p. 1) \#5fb236 \par - “RSs support and ease the decisional process.” (“recsys2024-workshops\_paper\_130”, p. 1) \#5fb236 \par - “heir rationale is to analyze products experienced by similar users to produce tailored recommendations.” (“recsys2024-workshops\_paper\_130”, p. 1) \#5fb236 \par - “malicious users may want to jeopardize the operation of the recommendation platform.” (“recsys2024-workshops\_paper\_130”, p. 1) \#a28ae5 \par - “Whatever they are motivated by, the problem is that these similarities are vulnerable to the insertion of fake profiles” (“recsys2024-workshops\_paper\_130”, p. 1) \#a28ae5 \par - “shilling attack” (“recsys2024-workshops\_paper\_130”, p. 1) \#5fb236 \par - “generate more influential fake profiles to threaten CF models’ performance” (“recsys2024-workshops\_paper\_130”, p. 1) \#a28ae5 \par - “semantics-aware shilling attack SAShA” (“recsys2024-workshops\_paper\_130”, p. 1) \#e56eee \par - “Katz centrality and Exclusivity-based relatedness.” (“recsys2024-workshops\_paper\_130”, p. 2) \#a28ae5 \par - “ontological, categorical, and factual relations” (“recsys2024-workshops\_paper\_130”, p. 2) \#a28ae5 \par - “two novel graph topological and semantic approaches” (“recsys2024-workshops\_paper\_130”, p. 2) \#a28ae5 \par - “a novel semantic shilling attack strategy” (“recsys2024-workshops\_paper\_130”, p. 2) \#a28ae5 \par - “a deeper discussion of the experimental results involving several dimensions” (“recsys2024-workshops\_paper\_130”, p. 2) \#a28ae5 \par - “SAShA” (“recsys2024-workshops\_paper\_130”, p. 2) \#5fb236 \par - “Definition 1 (Recommendation Problem)” (“recsys2024-workshops\_paper\_130”, p. 2) \#5fb236 \par - “g : U × I → R” (“recsys2024-workshops\_paper\_130”, p. 2) \#5fb236 \par - “A KG is a heterogeneous network that encodes multiple relationships, edges, nodes, and links items at high-level relationships, making them a strong item representation technique.” (“recsys2024-workshops\_paper\_130”, p. 2) \#a28ae5 \par - “Knowledge-aware Recommender Systems (KaRS [22, 23]).” (“recsys2024-workshops\_paper\_130”, p. 2) \#a28ae5 \par - “Path-based methods” (“recsys2024-workshops\_paper\_130”, p. 3) \#5fb236 \par - “KG embedding-based techniques” (“recsys2024-workshops\_paper\_130”, p. 3) \#5fb236 \par - “fake profiles to poison the data and alter the recommendation behavior toward malicious goals” (“recsys2024-workshops\_paper\_130”, p. 3) \#a28ae5 \par - “shilling attack (injection of malicious profiles)” (“recsys2024-workshops\_paper\_130”, p. 3) \#a28ae5 \par - “proposing and investigating attack strategies” (“recsys2024-workshops\_paper\_130”, p. 3) \#a28ae5 \par - “exploring defensive mechanisms” (“recsys2024-workshops\_paper\_130”, p. 3) \#a28ae5 \par - “adversary’s knowledge is related only to the recommender system’s user-item interaction matrix” (“recsys2024-workshops\_paper\_130”, p. 3) \#a28ae5 \par - “SAShA framework to verify the possible improvement of the adversary’s efficacy when processing the KG information with semantic similarity measures.” (“recsys2024-workshops\_paper\_130”, p. 3) \#a28ae5 \par - “shilling attacks, which are hand-engineered strategies to study recommender systems’ security.” (“recsys2024-workshops\_paper\_130”, p. 3) \#a28ae5 \par - “This research line is different from machine-learned data poisoning attack [70, 71, 72, 73, 74] and adversarial machine-learned attacks [75, 76, 61, 77? ] where adversaries adopt optimization techniques to create perturbations.” (“recsys2024-workshops\_paper\_130”, p. 3) \#5fb236 \par - “shilling attacks against targeted items integrating information obtained from a knowledge graph (KG).” (“recsys2024-workshops\_paper\_130”, p. 3) \#a28ae5 \par - “In general, the three metrics cover different aspects of the similarity between the resource a signal of the overlap of the descriptions and a semanticsaware signal that highlights the specificity of the relations between the resources.” (“recsys2024-workshops\_paper\_130”, p. 3) \#ff6666\\ \mkbibemph{Does not parse, please, revise.} \par - “The smaller the angle, the higher is the cosine, and thus the similarity.” (“recsys2024-workshops\_paper\_130”, p. 4) \#a28ae5 \par - “/” (“recsys2024-workshops\_paper\_130”, p. 4) \#ff6666 \par - “Katz centrality” (“recsys2024-workshops\_paper\_130”, p. 4) \#2ea8e5 \par - “the probability of the path between two nodes can indicate the effectiveness of the link” (“recsys2024-workshops\_paper\_130”, p. 4) \#a28ae5 \par - “Given a constant probability for a single-hop path” (“recsys2024-workshops\_paper\_130”, p. 4) \#ffd400\\ \mkbibemph{how is this calculated/assessed. Who provide it?} \par - “Katz relatedness” (“recsys2024-workshops\_paper\_130”, p. 4) \#a28ae5 \par - “accumulated score over the top-t shortest paths between them.” (“recsys2024-workshops\_paper\_130”, p. 4) \#a28ae5 \par - “where SP (t) ij is the set of the top-t shortest paths between items i and j. This is the first novel similarity metric tested in this work.” (“recsys2024-workshops\_paper\_130”, p. 4) \#5fb236 \par - “Exclusivity-based semantic relatedness” (“recsys2024-workshops\_paper\_130”, p. 4) \#2ea8e5 \par - “two concepts are strongly connected if the type of relations between them is different from the type of relations they have with other concepts.” (“recsys2024-workshops\_paper\_130”, p. 4) \#a28ae5 \par - “predicate ρ of type τ” (“recsys2024-workshops\_paper\_130”, p. 4) \#ffd400\\ \mkbibemph{So predicates have also types?} \par - “IS denotes the selected item set containing items identified by the attacker to maximize the effectiveness of the attack” (“recsys2024-workshops\_paper\_130”, p. 4) \#5fb236 \par - “SP = IS + IF + Iφ + IT (6) where IS denotes the selected item set containing items identified by the attacker to maximize the effectiveness of the attack, IF is the filler item set, containing a set of randomly selected items to which rating scores are assigned to make them imperceptible. IT is the target item, for which the recommendation model will make a prediction, aimed to be maximal (for push attack) or minimum (for nuke attack). Finally, Iφ is the unrated item set, holding a number of items without any ratings.” (“recsys2024-workshops\_paper\_130”, p. 4) \#ffd400\\ \mkbibemph{An illustrative example would help here.}} } @online{redazioneScritturaManoMemorizzazione2025, title = {La Scrittura a Mano, La Memorizzazione e La Lettura Contro Il "Marciume Cerebrale" Causato Dagli Smartphone. {{Così}} Le Nuove Indicazioni Nazionali Combattono Gli Effetti Negativi Della Tecnologia}, author = {{redazione}}, date = {2025-03-18T07:10:55+00:00}, url = {https://www.orizzontescuola.it/la-scrittura-a-mano-la-memorizzazione-e-la-lettura-contro-il-marciume-cerebrale-causato-dagli-smartphone-cosi-le-nuove-indicazioni-nazionali-combattono-gli-effetti-negativi-della-tecnologia/}, urldate = {2025-03-21}, abstract = {Non solo critiche e suggerimenti, le nuove Indicazioni nazionali proposte dal Ministero Valditara, incassano un giudizio estremamente positivo da parte di Andrea Cangini, direttore dell’Osservatorio Carta, Penna \& Digitale della Fondazione Einaudi. Il quale, in un intervento su Italia oggi di Martedì 18 Marzo 2025 a Pagina 33, definisce l'enfasi sulla scrittura e la lettura individuate nelle Indicazioni come pratiche che possono contrastare gli effetti negativi derivanti dall’eccessivo utilizzo della tecnologia da parte delle nuove generazioni.}, note = {\#Clip \par 21-03-2025 \par 19:48 \href{https://www.orizzontescuola.it/la-scrittura-a-mano-la-memorizzazione-e-la-lettura-contro-il-marciume-cerebrale-causato-dagli-smartphone-cosi-le-nuove-indicazioni-nazionali-combattono-gli-effetti-negativi-della-tecnologia/}{La scrittura a mano, la memorizzazione e la lettura contro il "marciume cerebrale" causato dagli smartphone. Così le nuove indicazioni nazionali combattono gli effetti negativi della tecnologia - Orizzonte Scuola Notizie} \par scrittura e la lettura individuate nelle Indicazioni come pratiche che possono contrastare gli effetti negativi derivanti dall’eccessivo utilizzo della tecnologia da parte delle nuove generazioni. \par \#Clip \par 21-03-2025 \par 19:48 \href{https://www.orizzontescuola.it/la-scrittura-a-mano-la-memorizzazione-e-la-lettura-contro-il-marciume-cerebrale-causato-dagli-smartphone-cosi-le-nuove-indicazioni-nazionali-combattono-gli-effetti-negativi-della-tecnologia/}{La scrittura a mano, la memorizzazione e la lettura contro il "marciume cerebrale" causato dagli smartphone. Così le nuove indicazioni nazionali combattono gli effetti negativi della tecnologia - Orizzonte Scuola Notizie} \par “\textbf{brain rot}” o “marciscenza al cervello” \par \#Clip \par 21-03-2025 \par 19:49 \href{https://www.orizzontescuola.it/la-scrittura-a-mano-la-memorizzazione-e-la-lettura-contro-il-marciume-cerebrale-causato-dagli-smartphone-cosi-le-nuove-indicazioni-nazionali-combattono-gli-effetti-negativi-della-tecnologia/}{La scrittura a mano, la memorizzazione e la lettura contro il "marciume cerebrale" causato dagli smartphone. Così le nuove indicazioni nazionali combattono gli effetti negativi della tecnologia - Orizzonte Scuola Notizie} \par \textbf{deterioramento delle facoltà mentali}, causato dall’abitudine di scorrere rapidamente contenuti superficiali, rendendo più difficile la memorizzazione e la concentrazione. \par \#Clip \par 21-03-2025 \par 19:50 \href{https://www.orizzontescuola.it/la-scrittura-a-mano-la-memorizzazione-e-la-lettura-contro-il-marciume-cerebrale-causato-dagli-smartphone-cosi-le-nuove-indicazioni-nazionali-combattono-gli-effetti-negativi-della-tecnologia/}{La scrittura a mano, la memorizzazione e la lettura contro il "marciume cerebrale" causato dagli smartphone. Così le nuove indicazioni nazionali combattono gli effetti negativi della tecnologia - Orizzonte Scuola Notizie} \par \textbf{Scrittura manuale, memorizzazione, lettura}, tutte attività che favoriscono il potenziamento dell’emisfero sinistro del cervello, responsabile del \textbf{pensiero logico e analitico}. \par \#Clip \par 21-03-2025 \par 19:50 \href{https://www.orizzontescuola.it/la-scrittura-a-mano-la-memorizzazione-e-la-lettura-contro-il-marciume-cerebrale-causato-dagli-smartphone-cosi-le-nuove-indicazioni-nazionali-combattono-gli-effetti-negativi-della-tecnologia/}{La scrittura a mano, la memorizzazione e la lettura contro il "marciume cerebrale" causato dagli smartphone. Così le nuove indicazioni nazionali combattono gli effetti negativi della tecnologia - Orizzonte Scuola Notizie} \par i ragazzi rischiano di dipendere esclusivamente dalla sfera emotiva, con un impatto negativo sulla loro capacità di valutazione critica e razionale. \par \#Clip \par 21-03-2025 \par 19:51 \href{https://www.orizzontescuola.it/la-scrittura-a-mano-la-memorizzazione-e-la-lettura-contro-il-marciume-cerebrale-causato-dagli-smartphone-cosi-le-nuove-indicazioni-nazionali-combattono-gli-effetti-negativi-della-tecnologia/}{La scrittura a mano, la memorizzazione e la lettura contro il "marciume cerebrale" causato dagli smartphone. Così le nuove indicazioni nazionali combattono gli effetti negativi della tecnologia - Orizzonte Scuola Notizie} \par effetti della calligrafia sullo sviluppo cognitivo dei bambini. \par \#Clip \par 21-03-2025 \par 19:57 \href{https://www.orizzontescuola.it/la-scrittura-a-mano-la-memorizzazione-e-la-lettura-contro-il-marciume-cerebrale-causato-dagli-smartphone-cosi-le-nuove-indicazioni-nazionali-combattono-gli-effetti-negativi-della-tecnologia/}{La scrittura a mano, la memorizzazione e la lettura contro il "marciume cerebrale" causato dagli smartphone. Così le nuove indicazioni nazionali combattono gli effetti negativi della tecnologia - Orizzonte Scuola Notizie} \par \textbf{scrivere a mano} attiva aree cerebrali legate all’elaborazione, all’attenzione e al linguaggio, migliorando l’apprendimento e la memoria. \par \#Clip \par 21-03-2025 \par 19:57 \href{https://www.orizzontescuola.it/la-scrittura-a-mano-la-memorizzazione-e-la-lettura-contro-il-marciume-cerebrale-causato-dagli-smartphone-cosi-le-nuove-indicazioni-nazionali-combattono-gli-effetti-negativi-della-tecnologia/}{La scrittura a mano, la memorizzazione e la lettura contro il "marciume cerebrale" causato dagli smartphone. Così le nuove indicazioni nazionali combattono gli effetti negativi della tecnologia - Orizzonte Scuola Notizie} \par uno sviluppo cognitivo più completo \par \#Clip \par 21-03-2025 \par 19:58 \href{https://www.orizzontescuola.it/la-scrittura-a-mano-la-memorizzazione-e-la-lettura-contro-il-marciume-cerebrale-causato-dagli-smartphone-cosi-le-nuove-indicazioni-nazionali-combattono-gli-effetti-negativi-della-tecnologia/}{La scrittura a mano, la memorizzazione e la lettura contro il "marciume cerebrale" causato dagli smartphone. Così le nuove indicazioni nazionali combattono gli effetti negativi della tecnologia - Orizzonte Scuola Notizie} \par Per quanto riguarda la \textbf{calligrafia}, questa non solo migliora la qualità della scrittura, ma contribuisce anche allo sviluppo delle capacità motorie e cognitive.} } @inproceedings{reedTFICFNewTerm2006, title = {{{TF-ICF}}: {{A}} New Term Weighting Scheme for Clustering Dynamic Data Streams}, booktitle = {Proc. 5th {{Int}}. {{Conf}}. {{Mach}}. {{Learn}}. {{Appl}}.}, author = {Reed, Joel W. and Jiao, Yu and Potok, Thomas E. and Klump, Brian A. and Elmore, Mark T. and Hurson, Ali R.}, date = {2006}, series = {{{ICMLA}} '06}, pages = {258--263}, publisher = {IEEE Computer Society}, location = {Washington, DC, USA}, url = {http://dx.doi.org/10.1109/ICMLA.2006.50}, acmid = {1193734}, isbn = {0-7695-2735-3}, nodoi = {10.1109/ICMLA.2006.50}, numpages = {6} } @misc{REGOLAMENTO_Dottorati_Ricerca_Modifica_DR_rep_597_01_06_2023pdf, title = {{{REGOLAMENTO}}\_{{Dottorati}}\_{{Ricerca}}\_{{Modifica}}\_{{DR}}\_rep\_597\_01\_06\_2023.Pdf}, langid = {english}, keywords = {⛔ No INSPIRE recid found,LOGSEQ} } @misc{RegolamentoConferimentoDi, title = {Regolamento per Il Conferimento Di Contratti Di Ricerca}, url = {https://www.univaq.it/include/utilities/blob.php?table=regolamento&id=200&item=file}, urldate = {2025-04-17}, keywords = {/unread} } @article{ReliableDataProcessing2021, title = {Reliable {{Data Processing}} with {{Minimal Toil}}}, date = {2021}, pages = {15}, langid = {english} } @inproceedings{Rendon:2011:CIE:1959666.1959695, title = {A Comparison of Internal and External Cluster Validation Indexes}, booktitle = {Proc. 2011 {{Am}}. {{Conf}}. {{Appl}}. {{Math}}. 5th {{WSEAS Int}}. {{Conf}}. {{Comput}}. {{Eng}}. {{Appl}}.}, author = {Rendón, Eréndira and Abundez, Itzel M. and Gutierrez, Citlalih and Zagal, Sergio Díaz and Arizmendi, Alejandra and Quiroz, Elvia M. and Arzate, H. Elsa}, date = {2011}, series = {{{AMERICAN-MATH}}'11/{{CEA}}'11}, pages = {158--163}, publisher = {{World Scientific and Engineering Academy and Society (WSEAS)}}, location = {Stevens Point, Wisconsin, USA}, url = {http://dl.acm.org/citation.cfm?id=1959666.1959695}, acmid = {1959695}, isbn = {978-960-474-270-7}, numpages = {6}, keywords = {cluster validity,clustering algorithm,external indexes,internal indexes,k-means}, note = {TL;DR \par Results obtained in this study indicate that internal indexes are more accurate in group determining in a given clustering structure.} } @article{Rendon2011, title = {Internal versus {{External}} Cluster Validation Indexes}, author = {Rendón, Eréndira and Abundez, Itzel and Arizmendi, Alejandra and Quiroz, Elvia M.}, date = {2011-03}, journaltitle = {Int J. Compt Comm}, volume = {5}, number = {1}, pages = {27--34}, address = {Hingham, MA, USA}, abstract = {.}, acmid = {1061908}, added-at = {2011-09-18T22:25:48.000+0200}, biburl = {http://www.bibsonomy.org/bibtex/21afe6065cc536f52534a7c15eed599c3/jil}, numpages = {8}, keywords = {cluster clustering entropy evaluation f-score fscore measure measures purity}, note = {TL;DR \par A comparison between external and internal indexes is shown and results obtained indicate that internal indexes are more accurate in group determining in a given clustering structure.} } @article{RePEc:eee:intfor:v:14:y:1998:i:1:p:35-62, title = {Forecasting with Artificial Neural Networks:: {{The}} State of the Art}, author = {Zhang, Guoqiang and Eddy Patuwo, B. and Y. Hu, Michael}, date = {1998}, journaltitle = {Int. J. Forecast.}, volume = {14}, number = {1}, pages = {35--62}, note = {TL;DR \par The planning, budgeting, and controlling processes (PBCP) largely subsume all of the planning and controlling activities of an organization within the context of a single management control system is discussed.} } @online{RepubblicaitNewsTempo, title = {La Repubblica.it - News in tempo reale - Le notizie e i video di politica, cronaca, economia, sport}, url = {http://www.repubblica.it/}, urldate = {2020-01-15}, abstract = {Repubblica è il quotidiano online aggiornato 24 ore su 24 su politica, cronaca, economia, sport, esteri, spettacoli, musica, cultura, scienza, tecnologia.}, langid = {italian}, organization = {Repubblica.it} } @online{ResearchInsightsServerless, title = {({{Research}}) {{Insights}} for {{Serverless Application Engineering}}}, url = {https://www.computer.org/csdl/magazine/so/2021/01/09305894/1pNkwYVzrUc}, urldate = {2021-01-17}, note = {TL;DR \par The “Practitioners’ Digest” department reports on papers about serverless application engineering from Journal of Systems and Software, the 2020 European Conference on Software Architecture, and the 19th International Conference on Middleware.} } @book{RESEARCHMETHODS, title = {{{RESEARCH}} {{METHODS}}} } @inproceedings{resnikUsingInformationContent1995, title = {Using Information Content to Evaluate Semantic Similarity in a Taxonomy}, booktitle = {Proc. 14th {{Int}}. {{Jt}}. {{Conf}}. {{Artif}}. {{Intell}}. - {{Vol}}. 1}, author = {Resnik, Philip}, date = {1995}, series = {{{IJCAI}}'95}, pages = {448--453}, publisher = {Morgan Kaufmann Publishers Inc.}, location = {San Francisco, CA, USA}, url = {http://dl.acm.org/citation.cfm?id=1625855.1625914}, acmid = {1625914}, isbn = {1-55860-363-8 978-1-55860-363-9}, numpages = {6}, note = {TL;DR \par This paper presents a new measure of semantic similarity in an IS-A taxonomy, based on the notion of information content, which performs encouragingly well and is significantly better than the traditional edge counting approach.} } @online{Results1stCall, title = {Results of the 1st Call on {{Smart System Integration}} under {{H2020}}}, url = {ec.europa.eu//digital-agenda/en/news/results-1st-call-smart-system-integration-under-h2020}, urldate = {2015-04-08}, abstract = {10 projects were selected for co-financing under the first H2020 call for proposals on Smart System Integration.}, organization = {Digital Agenda for Europe} } @article{ReuseAutomatedIntegration2023, title = {Reuse and {{Automated Integration}} of {{Recommenders}} for {{Modelling Languages}}}, date = {2023}, langid = {english} } @inproceedings{revault1995metamodeling, title = {A Metamodeling Technique: The {{METAGEN}} System}, booktitle = {Proc. {{TOOLS}}}, author = {Revault, Nicolas and Sahraoui, Houari A and Blain, Gilles and Perrot, Jean-François}, date = {1995}, volume = {16}, pages = {127--139}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found} } @article{riahisfarRoadmapSecurityChallenges2018, title = {A Roadmap for Security Challenges in the {{Internet}} of {{Things}}}, author = {Riahi Sfar, Arbia and Natalizio, Enrico and Challal, Yacine and Chtourou, Zied}, date = {2018-04}, journaltitle = {Digital Communications and Networks}, volume = {4}, number = {2}, pages = {118--137}, issn = {23528648}, doi = {10.1016/j.dcan.2017.04.003}, abstract = {Unquestionably, communicating entities (object, or things) in the Internet of Things (IoT) context are playing an active role in human activities, systems and processes. The high connectivity of intelligent objects and their severe constraints lead to many security challenges, which are not included in the classical formulation of security problems and solutions. The Security Shield for IoT has been identified by DARPA (Defense Advanced Research Projects Agency) as one of the four projects with a potential impact broader than the Internet itself. To help interested researchers contribute to this research area, an overview of the IoT security roadmap overview is presented in this paper based on a novel cognitive and systemic approach. The role of each component of the approach is explained, we also study its interactions with the other main components, and their impact on the overall. A case study is presented to highlight the components and interactions of the systemic and cognitive approach. Then, security questions about privacy, trust, identification, and access control are discussed. According to the novel taxonomy of the IoT framework, different research challenges are highlighted, important solutions and research activities are revealed, and interesting research directions are proposed. In addition, current standardization activities are surveyed and discussed to the ensure the security of IoT components and applications.}, langid = {english} } @incollection{Ricci2011, title = {Introduction to Recommender Systems Handbook}, booktitle = {Recommender Systems Handbook}, author = {Ricci, Francesco and Rokach, Lior and Shapira, Bracha}, editor = {Ricci, Francesco and Rokach, Lior and Shapira, Bracha and Kantor, Paul B.}, date = {2011}, pages = {1--35}, publisher = {Springer US}, location = {Boston, MA}, doi = {10.1007/978-0-387-85820-3_1}, abstract = {Recommender Systems (RSs) are software tools and techniques providing suggestions for items to be of use to a user. In this introductory chapter we briefly discuss basic RS ideas and concepts. Our main goal is to delineate, in a coherent and structured way, the chapters included in this handbook and to help the reader navigate the extremely rich and detailed content that the handbook offers.}, isbn = {978-0-387-85820-3} } @article{richardsonVendorLandscapeFractured2016, title = {Vendor {{Landscape}}: {{The Fractured}}, {{Fertile Terrain Of Low-Code Application Platforms}}}, author = {Richardson, Clay and Rymer, John R}, date = {2016}, pages = {23}, langid = {english} } @article{rieglerModelbasedModeswitchingFramework2023, title = {A Model-Based Mode-Switching Framework Based on Security Vulnerability Scores}, author = {Riegler, Michael and Sametinger, Johannes and Vierhauser, Michael and Wimmer, Manuel}, date = {2023-06}, journaltitle = {Journal of Systems and Software}, volume = {200}, pages = {111633}, issn = {01641212}, doi = {10.1016/j.jss.2023.111633}, abstract = {Software vulnerabilities can affect critical systems within an organization impacting processes, workflows, privacy, and safety. When a software vulnerability becomes known, affected systems are at risk until appropriate updates become available and eventually deployed. This period can last from a few days to several months, during which attackers can develop exploits and take advantage of the vulnerability. It is tedious and time-consuming to keep track of vulnerabilities manually and perform necessary actions to shut down, update, or modify systems. Vulnerabilities affect system components, such as a web server, but sometimes only target specific versions or component combinations.}, langid = {english} } @inproceedings{Ries202141, title = {An {{MDE}} Method for Improving Deep Learning Dataset Requirements Engineering Using Alloy and {{UML}}}, author = {Ries, B. and Guelfi, N. and Jahić, B.}, editor = {Hammoudi S., Pires L.F., Soley R., Seidewitz E.}, date = {2021}, series = {{{MODELSWARD}} 2021 - {{Proceedings}} of the 9th {{International Conference}} on {{Model-Driven Engineering}} and {{Software Development}}}, pages = {41--52}, publisher = {SciTePress}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103060952&partnerID=40&md5=4945b5c1de311257ad57a8d74cfc36ba}, abbrev_source_title = {MODELSWARD - Proc. Int. Conf. Model-Driven Eng. Softw. Dev.}, affiliation = {University of Luxembourg, Esch-sur-Alzette, Luxembourg}, document_type = {Conference Paper}, isbn = {978-989-758-487-9}, langid = {english}, source = {Scopus}, note = {cited By 1 \par TL;DR \par By analysing the datasets and learning outcomes of the training of neural networks, it is discovered that many issues were related to the poor specification of the datasets’ structure.} } @inproceedings{Rigou2020, title = {A Sketch of a Deep Learning Approach for Discovering {{UML}} Class Diagrams from System's Textual Specification}, author = {Rigou, Y. and Lamontagne, D. and Khriss, I.}, editor = {Benhala B., Mansouri K., Qbadou M., Raihani A.}, date = {2020}, series = {2020 1st {{International Conference}} on {{Innovative Research}} in {{Applied Science}}, {{Engineering}} and {{Technology}}, {{IRASET}} 2020}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/IRASET48871.2020.9092144}, abbrev_source_title = {Int. Conf. Innov. Res. Appl. Sci., Eng. Technol., IRASET}, affiliation = {D'informatique et de génie, UQAR, Département de mathématiques, Rimouski, Canada}, art_number = {9092144}, document_type = {Conference Paper}, isbn = {978-1-7281-4979-0}, langid = {english}, source = {Scopus}, keywords = {GOAL\_Model-Search,notion,TECHNIQUE\_DNN}, note = {cited By 2} } @inproceedings{Rivera2020631, title = {On the Engineering of {{IoT-Intensive}} Digital Twin Software Systems}, author = {Rivera, L.F. and Müller, H.A. and Villegas, N.M. and Tamura, G. and Jiménez, M.}, date = {2020}, series = {Proceedings - 2020 {{IEEE}}/{{ACM}} 42nd {{International Conference}} on {{Software Engineering Workshops}}, {{ICSEW}} 2020}, pages = {631--638}, publisher = {Association for Computing Machinery, Inc}, doi = {10.1145/3387940.3392195}, abbrev_source_title = {Proc. - IEEE/ACM Int. Conf. Softw. Eng. Workshops, ICSEW}, affiliation = {University of Victoria, Victoria, BC, Canada; Universidad Icesi, Cali, Valle del Cauca, Colombia}, document_type = {Conference Paper}, isbn = {978-1-4503-7963-2}, langid = {english}, source = {Scopus}, keywords = {notion}, note = {cited By 5 \par TL;DR \par An architectural reference model that adopts self-adaptation, control, and model-driven engineering techniques to specify the structural and behavioural aspects of DTs and enable the evolution of their internal models is proposed, and an approach for engineering IoT-intensive Digital Twin Software Systems (DTSS) is introduced using GEMINIS' capabilities to deal with uncertain conditions that might compromise the fidelity of a DT.} } @article{RiveraDV09, title = {Formal Specification and Analysis of Domain Specific Models Using Maude}, author = {Rivera, José Eduardo and Durán, Francisco and Vallecillo, Antonio}, date = {2009}, journaltitle = {Simul,}, volume = {85}, number = {11--12}, pages = {778--792}, doi = {10.1177/0037549709341635}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {TL;DR \par This paper explores the use of Maude as a formal notation for describing models, metamodels, and their dynamic behavior, making models amenable to formal analysis, reasoning, and simulation.}, timestamp = {Thu, 14 Oct 2021 08:52:54 +0200} } @inproceedings{RiveraRLB09, title = {Wires* : A Tool for Orchestrating Model Transformations}, booktitle = {{{XIV Jorn}}. {{Ing}}. {{Softw}}. {{Bases Datos JISBD}} 2009 {{San Sebastián Spain Sept}}. 8-11 2009}, author = {Rivera, José Eduardo and Ruiz-González, Daniel and López-Romero, Fernando and Bautista, José María}, editor = {Vallecillo, Antonio and Sagardui, Goiuria}, date = {2009}, pages = {158--161}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {TL;DR \par An arm attachment for an arm of a backhoe that attaches directly to a shovel to permit large objects to be picked up.}, timestamp = {Fri, 18 Sep 2009 13:16:59 +0200} } @article{Rivolli2021471, title = {A Study of the Correlation of Metafeatures Used for Metalearning}, author = {Rivolli, A. and Garcia, L.P.F. and Lorena, A.C. and family=Carvalho, given=A.C.P.L.F., prefix=de, useprefix=true}, editor = {Rojas I., Joya G., Catala A.}, date = {2021}, journaltitle = {Lect. Notes Comput. Sci. Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinforma.}, volume = {12861 LNCS}, pages = {471--483}, publisher = {{Springer Science and Business Media Deutschland GmbH}}, issn = {03029743}, doi = {10.1007/978-3-030-85030-2_39}, abbrev_source_title = {Lect. Notes Comput. Sci.}, affiliation = {Computing Department, Universidade Tecnológica Federal Do Paraná, Av. Alberto Carazzai, 1640, Cornélio Procópio, Paraná, 86300-000, Brazil; Department of Computer Science, University of Brasília, Campus Darcy Ribeiro, Asa Norte, Brasília, 70910-900, Brazil; Aeronautics Institute of Technology, Praça Marechal Eduardo Gomes, 50, São José dos Campos, São Paulo, 12228-900, Brazil; Institute of Mathematical and Computer Sciences, University of São Paulo, Av. Trabalhador São-carlense, 400, São Carlos, São Paulo, 13560-970, Brazil}, correspondence_address1 = {Rivolli, A.; Computing Department, Av. Alberto Carazzai, 1640, Brazil; email: rivolli@utfpr.edu.br}, document_type = {Conference Paper}, isbn = {9783030850296}, langid = {english}, source = {Scopus}, note = {cited By 1} } @article{robertsonProbabilisticRelevanceFramework2009, title = {The Probabilistic Relevance Framework: {{BM25}} and Beyond}, author = {Robertson, Stephen and Zaragoza, Hugo}, date = {2009-04}, journaltitle = {Found. Trends Inf. Retr.}, volume = {3}, number = {4}, pages = {333--389}, publisher = {Now Publishers Inc.}, address = {Hanover, MA, USA}, issn = {1554-0669}, url = {http://dx.doi.org/10.1561/1500000019}, acmid = {1704810}, issue_date = {April 2009}, nodoi = {10.1561/1500000019}, numpages = {57}, note = {TL;DR \par This work presents the PRF from a conceptual point of view, describing the probabilistic modelling assumptions behind the framework and the different ranking algorithms that result from its application: the binary independence model, relevance feedback models, BM25 and BM25F.} } @article{Robillard:2013:AAP:2498733.2498776, title = {Automated {{API}} Property Inference Techniques}, author = {Robillard, Martin P. and Bodden, Eric and Kawrykow, David and Mezini, Mira and Ratchford, Tristan}, date = {2013-05}, journaltitle = {IEEE Trans. Softw. Eng.}, volume = {39}, number = {5}, pages = {613--637}, publisher = {IEEE Press}, address = {Piscataway, NJ, USA}, issn = {0098-5589}, url = {http://dx.doi.org/10.1109/TSE.2012.63}, acmid = {2498776}, issue_date = {May 2013}, nodoi = {10.1109/TSE.2012.63}, numpages = {25}, keywords = {API evolution,API property,API usage pattern,Association rules,Context,data mining,interface,Itemsets,pattern mining,Programming,programming rules,protocols,Protocols,software engineering,specifications} } @incollection{robillardIntroductionRecommendationSystems2014, title = {An Introduction to Recommendation Systems in Software Engineering}, booktitle = {Recommendation {{Systems}} in {{Software Engineering}}}, author = {Robillard, Martin P. and Walker, Robert J.}, date = {2014}, pages = {1--11}, publisher = {Springer}, url = {http://link.springer.com/chapter/10.1007/978-3-642-45135-5_1}, urldate = {2017-03-08}, note = {TL;DR \par This introduction presents an overview of the issues and considerations involved in creating, evaluating, and using RSSEs, and presents a general outlook on the current state of research and development in the field of recommendation systems for highly technical domains.} } @article{robillardRecommendationSystemsSoftware2010, ids = {5235134}, title = {Recommendation Systems for Software Engineering}, author = {Robillard, Martin and Walker, Robert and Zimmermann, Thomas}, date = {2010}, journaltitle = {IEEE Softw.}, volume = {27}, number = {4}, pages = {80--86}, url = {http://ieeexplore.ieee.org/abstract/document/5235134/}, urldate = {2017-06-08}, keywords = {bug reports,coding tools and techniques,design tools and techniques,development tools,information space,programming environments,recommendation systems,recommender systems,software construction tools,software development,software engineering,software tool,software tools,time seeking information,value-producing task}, note = {TL;DR \par The authors provide an overview of recommendation systems for software engineering: what they are, what they can do for developers, and what they might do in the future.} } @book{robillardRecommendationSystemsSoftware2014, ids = {robillard_recommendation_2014}, title = {Recommendation {{Systems}} in {{Software Engineering}}}, editor = {Robillard, Martin P. and Maalej, Walid and Walker, Robert J. and Zimmermann, Thomas}, date = {2014}, publisher = {Springer Berlin Heidelberg}, location = {Berlin, Heidelberg}, doi = {10.1007/978-3-642-45135-5}, isbn = {978-3-642-45134-8 978-3-642-45135-5}, langid = {english}, note = {DOI: 10.1007/978-3-642-45135-5 \par TL;DR \par This introduction presents an overview of the issues and considerations involved in creating, evaluating, and using RSSEs, and presents a general outlook on the current state of research and development in the field of recommendation systems for highly technical domains.} } @incollection{robles-encisoTaskOffloadingComputing2022, title = {Task {{Offloading}} in {{Computing Continuum Using Collaborative Reinforcement Learning}}}, booktitle = {Internet of {{Things}}}, author = {Robles-Enciso, Alberto and Skarmeta, Antonio F.}, editor = {González-Vidal, Aurora and Mohamed Abdelgawad, Ahmed and Sabir, Essaid and Ziegler, Sébastien and Ladid, Latif}, date = {2022}, volume = {13533}, pages = {82--95}, publisher = {Springer International Publishing}, location = {Cham}, doi = {10.1007/978-3-031-20936-9_7}, isbn = {978-3-031-20935-2 978-3-031-20936-9}, langid = {english} } @article{robles2012towards, title = {Towards an Ontology-Based Retrieval of {{UML}} Class Diagrams}, author = {Robles, Karina and Fraga, Anabel and Morato, Jorge and Llorens, Juan}, date = {2012}, journaltitle = {Inf. Software Technol.}, volume = {54}, number = {1}, pages = {72--86}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found} } @inproceedings{roblesExtensiveDatasetUML2017, ids = {robles2017extensive}, title = {An {{Extensive Dataset}} of {{UML Models}} in {{GitHub}}}, booktitle = {2017 {{IEEEACM}} 14th {{Int}}. {{Conf}}. {{Min}}. {{Softw}}. {{Repos}}. {{MSR}}}, author = {Robles, Gregorio and Ho-Quang, Truong and Hebig, Regina and Chaudron, Michel R.V. and Fernandez, Miguel Angel}, date = {2017-05}, pages = {519--522}, publisher = {IEEE}, location = {Buenos Aires, Argentina}, doi = {10.1109/MSR.2017.48}, eventtitle = {2017 {{IEEE}}/{{ACM}} 14th {{International Conference}} on {{Mining Software Repositories}} ({{MSR}})}, isbn = {978-1-5386-1544-7}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {TL;DR \par This work systematically mined over 12 million GitHub projects to find UML files in them, and presents a semi-automated approach to collect UML stored in images, .xmi, and .uml files.} } @online{RoboticsAutonomousSystems, title = {Robotics and Autonomous Systems: Apply for Innovation Funding - {{News}} Stories - {{GOV}}.{{UK}}}, url = {https://www.gov.uk/government/news/robotics-and-autonomous-systems-apply-for-innovation-funding}, urldate = {2016-08-26} } @online{RoboticsProgrammingLaboratory, title = {Robotics {{Programming Laboratory}}}, url = {http://se.inf.ethz.ch/courses/2013b_fall/rpl/#lectures}, urldate = {2016-01-12} } @article{roccoUseLargeLanguage, title = {On the Use of Large Language Models in Model-Driven Engineering}, author = {Rocco, Juri Di and Ruscio, Davide Di and Sipio, Claudio Di and Nguyen, Phuong T and Rubei, Riccardo}, langid = {english}, keywords = {/unread} } @article{rocklovDataScienceMachine2021, title = {Data {{Science}} and {{Machine Learning}}: {{Mathematical}} and {{Statistical Methods}}}, shorttitle = {Data {{Science}} and {{Machine Learning}}}, author = {Rocklöv, Joacim and Gayle, Albert A}, date = {2021-01-23}, journaltitle = {Int. J. Epidemiol.}, volume = {49}, number = {6}, pages = {2096--2096}, issn = {0300-5771, 1464-3685}, doi = {10.1093/ije/dyaa111}, langid = {english}, keywords = {LOGSEQ} } @inproceedings{ROCKRobustClustering1999, title = {{{ROCK}}: {{A}} Robust Clustering Algorithm for Categorical Attributes}, booktitle = {Proc. 15th {{Int}}. {{Conf}}. {{Data Eng}}.}, date = {1999}, series = {{{ICDE}} '99}, pages = {512-}, publisher = {IEEE Computer Society}, location = {Washington, DC, USA}, url = {http://dl.acm.org/citation.cfm?id=846218.847264}, acmid = {847264}, isbn = {0-7695-0071-4}, key = {!!}, note = {TL;DR \par This work develops a robust hierarchical clustering algorithm, ROCK, that employs links and not distances when merging clusters, and shows that ROCK not only generates better quality clusters than traditional algorithms, but also exhibits good scalability properties.} } @article{rodriguez-graciaCollaborativeTestbedWeb2014, title = {A Collaborative Testbed Web Tool for Learning Model Transformation in Software Engineering Education}, author = {Rodríguez-Gracia, D. and Criado, J. and Iribarne, L. and Padilla, N.}, date = {2014-12}, journaltitle = {Comput. Hum. Behav.}, issn = {07475632}, doi = {10.1016/j.chb.2014.11.096}, langid = {english} } @incollection{rodriguezMetamodelDependenciesExecutable2011, title = {Metamodel {{Dependencies}} for {{Executable Models}}}, booktitle = {Objects, {{Models}}, {{Components}}, {{Patterns}}}, author = {Rodríguez, Carlos and Sánchez, Mario and Villalobos, Jorge}, editor = {Bishop, Judith and Vallecillo, Antonio}, date = {2011}, volume = {6705}, pages = {83--98}, publisher = {Springer Berlin Heidelberg}, location = {Berlin, Heidelberg}, doi = {10.1007/978-3-642-21952-8_8}, isbn = {978-3-642-21951-1 978-3-642-21952-8}, note = {TL;DR \par The goal of this paper is to discuss two alternative ways to establish dependencies, and illustrate their usage, benefits, and drawbacks in a concrete example.} } @article{rohrModeldrivenDevelopmentSelfmanaging2006, title = {Model-Driven Development of Self-Managing Software Systems}, author = {Rohr, Matthias and Boskovic, Marko and Giesecke, Simon and Hasselbring, Wilhelm}, date = {2006}, url = {http://eprints.uni-kiel.de/14544/1/MODELS2006.pdf}, urldate = {2016-09-21} } @book{Rojas:1996:NNS:235222, title = {Neural Networks: {{A}} Systematic Introduction}, author = {Rojas, Raúl}, date = {1996}, publisher = {Springer-Verlag}, location = {Berlin, Heidelberg}, isbn = {3-540-60505-3} } @inproceedings{Rokon2020SourceFinderFM, title = {{{SourceFinder}}: {{Finding}} Malware Source-Code from Publicly Available Repositories in {{GitHub}}}, booktitle = {23rd {{Int}}. {{Symp}}. {{Res}}. {{Attacks Intrusions Def}}. {{RAID}} 2020 {{San Sebastian Spain Oct}}. 14-15 2020}, author = {Rokon, Md Omar Faruk and Islam, Risul and Darki, Ahmad and Papalexakis, Evangelos E. and Faloutsos, Michalis}, editor = {Egele, Manuel and Bilge, Leyla}, date = {2020}, pages = {149--163}, publisher = {USENIX Association}, url = {https://www.usenix.org/conference/raid2020/presentation/omar}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/raid/RokonIDPF20.bib}, timestamp = {Thu, 17 Jun 2021 16:04:48 +0200} } @article{Roldán2020, title = {Integrating Complex Event Processing and Machine Learning: {{An}} Intelligent Architecture for Detecting {{IoT}} Security Attacks}, author = {Roldán, J. and Boubeta-Puig, J. and Luis Martínez, J. and Ortiz, G.}, date = {2020}, journaltitle = {Expert Syst. Appl.}, volume = {149}, publisher = {Elsevier Ltd}, issn = {09574174}, doi = {10.1016/j.eswa.2020.113251}, abstract = {The Internet of Things (IoT) is growing globally at a fast pace: people now find themselves surrounded by a variety of IoT devices such as smartphones and wearables in their everyday lives. Additionally, smart environments, such as smart healthcare systems, smart industries and smart cities, benefit from sensors and actuators interconnected through the IoT. However, the increase in IoT devices has brought with it the challenge of promptly detecting and combating the cybersecurity attacks and threats that target them, including malware, privacy breaches and denial of service attacks, among others. To tackle this challenge, this paper proposes an intelligent architecture that integrates Complex Event Processing (CEP) technology and the Machine Learning (ML) paradigm in order to detect different types of IoT security attacks in real time. In particular, such an architecture is capable of easily managing event patterns whose conditions depend on values obtained by ML algorithms. Additionally, a model-driven graphical tool for security attack pattern definition and automatic code generation is provided, hiding all the complexity derived from implementation details from domain experts. The proposed architecture has been applied in the case of a healthcare IoT network to validate its ability to detect attacks made by malicious devices. The results obtained demonstrate that this architecture satisfactorily fulfils its objectives. © 2020 Elsevier Ltd}, art_number = {113251}, coden = {ESAPE}, document_type = {Article}, source = {Scopus}, note = {cited By 38} } @inproceedings{romanBigDataPipelines2021, title = {Big {{Data Pipelines}} on the {{Computing Continuum}}: {{Ecosystem}} and {{Use Cases Overview}}}, shorttitle = {Big {{Data Pipelines}} on the {{Computing Continuum}}}, booktitle = {2021 {{IEEE Symp}}. {{Comput}}. {{Commun}}. {{ISCC}}}, author = {Roman, Dumitru and Nikolov, Nikolay and Soylu, Ahmet and Elvesaeter, Brian and Song, Hui and Prodan, Radu and Kimovski, Dragi and Marrella, Andrea and Leotta, Francesco and Matskin, Mihhail and Ledakis, Giannis and Theodosiou, Konstantinos and Simonet-Boulogne, Anthony and Perales, Fernando and Kharlamov, Evgeny and Ulisses, Alexandre and Solberg, Arnor and Ceccarelli, Raffaele}, date = {2021-09-05}, pages = {1--4}, publisher = {IEEE}, location = {Athens, Greece}, doi = {10.1109/ISCC53001.2021.9631410}, abstract = {Organisations possess and continuously generate huge amounts of static and stream data, especially with the proliferation of Internet of Things technologies. Collected but unused data, i.e., Dark Data, mean loss in value creation potential. In this respect, the concept of Computing Continuum extends the traditional more centralised Cloud Computing paradigm with Fog and Edge Computing in order to ensure low latency pre-processing and filtering close to the data sources. However, there are still major challenges to be addressed, in particular related to management of various phases of Big Data processing on the Computing Continuum. In this paper, we set forth an ecosystem for Big Data pipelines in the Computing Continuum and introduce five relevant real-life example use cases in the context of the proposed ecosystem.}, eventtitle = {2021 {{IEEE Symposium}} on {{Computers}} and {{Communications}} ({{ISCC}})}, isbn = {978-1-6654-2744-9}, langid = {english}, keywords = {LOGSEQ}, note = {TL;DR \par This paper sets forth an ecosystem for Big Data pipelines in the Computing Continuum and introduces five relevant real-life example use cases in the context of the proposed ecosystem.} } @book{romeroOptimisingSoftwareDevelopment2023, title = {Optimising the Software Development Process with Artificial Intelligence}, editor = {Romero, José Raúl and Medina-Bulo, Inmaculada and Chicano, Francisco}, date = {2023}, series = {Natural {{Computing Series}}}, publisher = {Springer Nature Singapore}, location = {Singapore}, doi = {10.1007/978-981-19-9948-2}, isbn = {978-981-19-9947-5 978-981-19-9948-2}, langid = {english}, keywords = {/unread} } @online{ronankiPromptSmellsOmen2024, ids = {ronankiPromptSmellsOmen2024a}, title = {Prompt {{Smells}}: {{An Omen}} for {{Undesirable Generative AI Outputs}}}, shorttitle = {Prompt {{Smells}}}, author = {Ronanki, Krishna and Cabrero-Daniel, Beatriz and Berger, Christian}, date = {2024-01-23}, eprint = {2401.12611}, eprinttype = {arXiv}, eprintclass = {cs}, url = {http://arxiv.org/abs/2401.12611}, urldate = {2024-02-23}, abstract = {Recent Generative Artificial Intelligence (GenAI) trends focus on various applications, including creating stories, illustrations, poems, articles, computer code, music compositions, and videos. Extrinsic hallucinations are a critical limitation of such GenAI, which can lead to significant challenges in achieving and maintaining the trustworthiness of GenAI. In this paper, we propose two new concepts that we believe will aid the research community in addressing limitations associated with the application of GenAI models. First, we propose a definition for the "desirability" of GenAI outputs and three factors which are observed to influence it. Second, drawing inspiration from Martin Fowler's code smells, we propose the concept of "prompt smells" and the adverse effects they are observed to have on the desirability of GenAI outputs. We expect our work will contribute to the ongoing conversation about the desirability of GenAI outputs and help advance the field in a meaningful way.}, pubstate = {prepublished}, keywords = {Computer Science - Machine Learning,Computer Science - Software Engineering}, note = {Comment: Accepted at CAIN 2024: Poster Track \par Comment: Accepted at CAIN 2024: Poster Track} } @article{rosa-bilbaoCEPEDALoCoEventdrivenArchitecture2023, title = {{{CEPEDALoCo}}: {{An}} Event-Driven Architecture for Integrating {{Complex Event Processing}} and Blockchain through Low-Code}, shorttitle = {{{CEPEDALoCo}}}, author = {Rosa-Bilbao, Jesús and Boubeta-Puig, Juan and Rutle, Adrian}, date = {2023-05-01}, journaltitle = {Internet of Things}, volume = {22}, pages = {100802}, doi = {10.1016/j.iot.2023.100802}, keywords = {LOGSEQ} } @incollection{rosaSelfmanagementDistributedSystems2013, title = {Self-Management of {{Distributed Systems Using High-Level Goal Policies}}}, booktitle = {Software {{Engineering}} for {{Self-Adaptive Systems II}}}, author = {Rosa, Liliana and Rodrigues, Luís and Lopes, Antónia}, date = {2013}, pages = {162--190}, publisher = {Springer}, url = {http://link.springer.com/chapter/10.1007/978-3-642-35813-5_7}, urldate = {2016-09-21}, note = {TL;DR \par This chapter presents an approach for the self-management of systems built from customizable components based on high-level goal policies, in response to changes in the execution context, the necessary system adaptations are automatically selected and deployed.} } @article{roseAnalysisApproachesModel, title = {An Analysis of Approaches to Model Migration}, author = {Rose, Louis M and Paige, Richard F and Kolovos, Dimitrios S and Polack, Fiona A C}, abstract = {Changing a metamodel can cause inconsistency with instance models. Modelling frameworks such as EMF that implement the OMG’s metamodelling architecture cannot be used to manipulate models that are inconsistent with their metamodel: inconsistent models cannot be loaded. Consequently, developers have to invest effort in migrating inconsistent models, to re-establish consistency. This is an example of model and metamodel evolution – indeed, of co-evolution. In their recent book, Mens and Demeyer note that there are new challenges for MDE in controlling and managing model and metamodel evolution. Various approaches to automating model migration have been proposed. In this paper, we demonstrate some of the important limitations of automatic generation of model migration strategies, and identify requirements for improved model and metamodel co-evolution support.}, langid = {english} } @incollection{roseComparisonModelMigration2010, title = {A Comparison of Model Migration Tools}, booktitle = {Model {{Driven Engineering Languages}} and {{Systems}}}, author = {Rose, Louis M. and Herrmannsdoerfer, Markus and Williams, James R. and Kolovos, Dimitrios S. and Garcés, Kelly and Paige, Richard F. and Polack, Fiona AC}, date = {2010}, pages = {61--75}, publisher = {Springer}, url = {http://link.springer.com/chapter/10.1007/978-3-642-16145-2_5}, urldate = {2015-03-20}, note = {TL;DR \par A representative sample of migration tools - AML, COPE, Ecore2Ecore and Epsilon Flock - are compared using common migration examples to support users in selecting the most appropriate tool for their situation.} } @article{roseGenericityModelManagement2011, title = {Genericity for Model Management Operations}, author = {Rose, Louis and Guerra, Esther and Lara, Juan and Etien, Anne and Kolovos, Dimitris and Paige, Richard}, date = {2011}, journaltitle = {Softw. Syst. Model.}, volume = {12}, number = {1}, pages = {201--219}, doi = {10.1007/s10270-011-0203-2} } @article{roseInternetThingsOverview2015, title = {The Internet of Things: {{An}} Overview}, shorttitle = {The Internet of Things}, author = {Rose, Karen and Eldridge, Scott and Chapin, Lyman}, date = {2015}, journaltitle = {Internet Soc. ISOC}, url = {http://www.internetsociety.org/sites/default/files/ISOC-IoT-Overview-20151014_0.pdf}, urldate = {2016-05-30} } @article{RoseKPPP14, title = {Epsilon Flock: A Model Migration Language}, author = {Rose, Louis M. and Kolovos, Dimitrios S. and Paige, Richard F. and Polack, Fiona A. C. and Poulding, Simon M.}, date = {2014}, journaltitle = {Software Syst. Model.}, volume = {13}, number = {2}, pages = {735--755}, doi = {10.1007/S10270-012-0296-2}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Fri, 18 Sep 2020 11:19:36 +0200} } @inproceedings{Rossi:2012:DPU:2367861.2367871, title = {Dynamic Pagerank Using Evolving Teleportation}, booktitle = {Proc. 9th {{Int}}. {{Conf}}. {{Algorithms Models Web Graph}}}, author = {Rossi, Ryan A. and Gleich, David F.}, date = {2012}, series = {{{WAW}}'12}, pages = {126--137}, publisher = {Springer-Verlag}, location = {Berlin, Heidelberg}, url = {http://dx.doi.org/10.1007/978-3-642-30541-2_10}, acmid = {2367871}, isbn = {978-3-642-30540-5}, nodoi = {10.1007/978-3-642-30541-2₁0}, numpages = {12} } @article{roughan10Lessons102011, title = {10 {{Lessons}} from 10 {{Years}} of {{Measuring}} and {{Modeling}} the {{Internet}}'s {{Autonomous Systems}}}, author = {Roughan, Matthew and Willinger, Walter and Maennel, Olaf and Perouli, Debbie and Bush, Randy}, date = {2011-10}, journaltitle = {IEEE J. Sel. Areas Commun.}, volume = {29}, number = {9}, pages = {1810--1821}, issn = {0733-8716}, doi = {10.1109/JSAC.2011.111006} } @article{rousseeuwSilhouettesGraphicalAid1987, title = {Silhouettes: {{A}} Graphical Aid to the Interpretation and Validation of Cluster Analysis}, author = {Rousseeuw, Peter J.}, date = {1987}, journaltitle = {J. Comput. Appl. Math.}, volume = {20}, pages = {53--65}, issn = {0377-0427}, url = {http://www.sciencedirect.com/science/article/pii/0377042787901257}, abstract = {A new graphical display is proposed for partitioning techniques. Each cluster is represented by a so-called silhouette, which is based on the comparison of its tightness and separation. This silhouette shows which objects lie well within their cluster, and which ones are merely somewhere in between clusters. The entire clustering is displayed by combining the silhouettes into a single plot, allowing an appreciation of the relative quality of the clusters and an overview of the data configuration. The average silhouette width provides an evaluation of clustering validity, and might be used to select an ‘appropriate’ number of clusters.}, nodoi = {https://doi.org/10.1016/0377-0427(87)90125-7}, keywords = {classification,cluster analysis,clustering validity,Graphical display} } @article{roy-hubaraDesignMethodsNew2020, title = {Design Methods for the New Database Era: A Systematic Literature Review}, shorttitle = {Design Methods for the New Database Era}, author = {Roy-Hubara, Noa and Sturm, Arnon}, date = {2020-03}, journaltitle = {Softw Syst Model}, volume = {19}, number = {2}, pages = {297--312}, issn = {1619-1366, 1619-1374}, doi = {10.1007/s10270-019-00739-8}, langid = {english}, keywords = {DONE,TYPHONML}, note = {TL;DR \par The study found that current database design methods do not address non-functional requirements; tend to refer to a preselected database; and are lacking in their evaluation.} } @incollection{roy-hubaraMethodDatabaseModel2019, title = {A {{Method}} for {{Database Model Selection}}}, booktitle = {Enterprise, {{Business-Process}} and {{Information Systems Modeling}}}, author = {Roy-Hubara, Noa and Shoval, Peretz and Sturm, Arnon}, editor = {Reinhartz-Berger, Iris and Zdravkovic, Jelena and Gulden, Jens and Schmidt, Rainer}, date = {2019}, volume = {352}, pages = {261--275}, publisher = {Springer International Publishing}, location = {Cham}, doi = {10.1007/978-3-030-20618-5_18}, isbn = {978-3-030-20617-8 978-3-030-20618-5}, langid = {english}, keywords = {TYPHONML}, note = {TL;DR \par This paper proposes a structured method for database model selection that considers a variety of factors, including data-related requirements, functional requirements and non-functional requirements, and proposes the most appropriate database models for that application.} } @article{roy-hubaraModelingGraphDatabase2017, title = {Modeling {{Graph Database Schema}}}, author = {Roy-Hubara, Noa and Rokach, Lior and Shapira, Bracha and Shoval, Peretz}, date = {2017-11}, journaltitle = {IT Prof.}, volume = {19}, number = {6}, pages = {34--43}, issn = {1520-9202}, doi = {10.1109/MITP.2017.4241458}, note = {TL;DR \par The authors present a new method for creating a graph database schema (GDBS) based on an entity-relationship diagram (ERD) of the application domain, which is mapped to a GDBS in a two-step process.} } @article{roy-hubaraQuestDatabaseSelection, title = {The {{Quest}} for a {{Database Selection}} and {{Design Method}}}, author = {Roy-Hubara, Noa}, pages = {9}, abstract = {New types of database have emerged over the last decade, aimed at answering new requirements in the Big Data era. The new databases, in additional to the Relational model, may fit to specific types of applications. Therefore, new challenges have also emerged, including the issue of which database model to select for a given application, and how to design the database based on the selected model. To the best of our knowledge, these two challenges have not been addressed by any systematic method. In this research we plan to devise a structured method for database model selection and design based on variety of factors, including data-related requirements, functional requirements, and non-functional requirements. Based on these requirements the method will recommend which database models are the most appropriate for that application and will suggest a design for the recommended models.}, langid = {english}, keywords = {DONE,TYPHONML}, note = {TL;DR \par This research plans to devise a structured method for database model selection and design based on variety of factors, including data-related requirements, functional requirements, and non-functional requirements, which will recommend which database models are the most appropriate for that application and will suggest a design for the recommended models.} } @inproceedings{Rubei:ASE:2019, title = {Recommeding Highly Relevant {{StackOverflow}} Posts with Boosted Multi-Facet Queries - Manuscript under Review}, booktitle = {34th {{IEEEACM Int}}. {{Conf}}. {{Autom}}. {{Softw}}. {{Eng}}. {{ASE}} 2019 {{San Diego Calif}}. {{USA}} 2019}, author = {Rubei, Riccardo and Di Sipio, Claudio and Nguyen, Phuong T. and Di Rocco, Juri and {Di Ruscio}} } @inproceedings{rubinDeclarativeApproachModel2008, title = {Declarative Approach for Model Composition}, booktitle = {Proc. 2008 {{Int}}. {{Workshop Models Softw}}. {{Eng}}.}, author = {Rubin, Julia and Chechik, Marsha and Easterbrook, Steve M.}, date = {2008}, pages = {7--14}, publisher = {ACM}, url = {http://dl.acm.org/citation.cfm?id=1370734}, urldate = {2015-09-24}, note = {TL;DR \par This paper proposes a declarative approach for model composition, which augments and strengthens existing structural and heuristic approaches, and defines a proof-of-concept prototype implementation of theDeclarative model composition framework using the Alloy Analyzer.} } @article{ruscioACMStudentResearch2017, title = {{{ACM}} Student Research Competition at {{MoDELS}} 2017}, author = {Ruscio, D.D. and Greenyer, J.}, date = {2017}, journaltitle = {CEUR Workshop Proc.}, volume = {2019}, pages = {547--548}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85041444549&partnerID=40&md5=7fa947db7e86266ce08af757982fb3e6}, note = {cited By 0 \par cited By 0} } @article{ruscioStudioSullaScalabilita, title = {Studio Sulla Scalabilita` Di Sistemi {{MLOps}} per Applicazioni {{AI}} Nel Dominio Climate Change}, author = {Ruscio, Davide Di and Sicchio, Giovanni}, langid = {english} } @inproceedings{Růžička2021105, title = {Combining Gaussian Processes with Neural Networks for Active Learning in Optimization}, author = {Růžička, J. and Koza, J. and Tumpach, J. and Pitra, Z. and Holeňa, M.}, editor = {Krempl G., Lemaire V., Holzinger A., Hammer B., Kottke D.}, date = {2021}, series = {{{CEUR Workshop Proceedings}}}, volume = {3079}, pages = {105--120}, publisher = {CEUR-WS}, issn = {16130073}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124088084&partnerID=40&md5=489c84189b9e10b74826f211a50decf3}, abbrev_source_title = {CEUR Workshop Proc.}, affiliation = {Czech Technical University, Prague, Czech Republic; Charles University, Prague, Czech Republic; Institute of Computer Science, Czech Academy of Sciences, Prague, Czech Republic}, document_type = {Conference Paper}, langid = {english}, source = {Scopus}, note = {cited By 0} } @article{rymerForresterWaveLowCode2019, title = {The {{Forrester Wave}}™: {{Low-Code Development Platforms For AD}}\&{{D Professionals}}, {{Q1}} 2019}, author = {Rymer, John R and Koplowitz, Rob}, date = {2019}, pages = {17}, langid = {english}, keywords = {lowcode} } @online{sabataleAutomateBPMNDiagrams2024, title = {Automate {{BPMN Diagrams}} and {{Note-taking}} in {{Cloud ALM}} with {{LLM}}}, author = {{sabatale}}, date = {2024-01-10T22:10:46}, url = {https://community.sap.com/t5/technology-blogs-by-members/automate-bpmn-diagrams-and-note-taking-in-cloud-alm-with-llm/ba-p/13574979}, urldate = {2024-05-02}, abstract = {AI Text Generation Large Language Models (LLM) have gained great momentum in 2023, particularly in the Text generation field. In a few months, summarizing and translating large volumes of content with AI models became somewhat common - no matter your industry or size. They perform especially well wh...}, langid = {english}, organization = {SAP Community} } @article{sahooOntologybasedFeatureEngineering2022, title = {Ontology-Based Feature Engineering in Machine Learning Workflows for Heterogeneous Epilepsy Patient Records}, author = {Sahoo, Satya S. and Kobow, Katja and Zhang, Jianzhe and Buchhalter, Jeffrey and Dayyani, Mojtaba and Upadhyaya, Dipak P. and Prantzalos, Katrina and Bhattacharjee, Meenakshi and Blumcke, Ingmar and Wiebe, Samuel and Lhatoo, Samden D.}, date = {2022-11-12}, journaltitle = {Sci. Rep.}, volume = {12}, number = {1}, pages = {19430}, publisher = {Nature Publishing Group}, issn = {2045-2322}, doi = {10.1038/s41598-022-23101-3}, abstract = {Biomedical ontologies are widely used to harmonize heterogeneous~data and integrate large volumes of clinical data from multiple sources. This study analyzed the utility of ontologies beyond their traditional roles, that is, in addressing a challenging and currently underserved field of feature engineering in machine learning workflows. Machine learning workflows are being increasingly used to analyze medical records with heterogeneous phenotypic, genotypic, and related medical terms to improve patient care. We performed a retrospective study using neuropathology reports from the German Neuropathology Reference Center for Epilepsy Surgery at Erlangen, Germany. This cohort included 312 patients who underwent epilepsy surgery and were labeled with one or more diagnoses, including dual pathology, hippocampal sclerosis, malformation of cortical dysplasia, tumor, encephalitis, and gliosis. We modeled the diagnosis terms together with their microscopy, immunohistochemistry, anatomy, etiologies, and imaging findings using the description logic-based Web Ontology Language (OWL) in the Epilepsy and Seizure Ontology (EpSO). Three tree-based machine learning models were used to classify the neuropathology reports into one or more diagnosis classes with and without ontology-based feature engineering. We used five-fold cross validation to avoid overfitting with a fixed number of repetitions while leaving out one subset of data for testing, and we used recall, balanced accuracy, and hamming loss as performance metrics for the multi-label classification task. The epilepsy ontology-based feature engineering approach improved the performance of all the three learning models with an improvement of 35.7\%, 54.5\%, and 33.3\% in logistics regression, random forest, and gradient tree boosting models respectively. The run time performance of all three models improved significantly with ontology-based feature engineering with gradient tree boosting model showing a 93.8\% reduction in the time required for training and testing of the model. Although, all three models showed an overall improved performance across the three-performance metrics using ontology-based feature engineering, the rate of improvement was not consistent across all input features. To analyze this variation in performance, we computed feature importance scores and found that microscopy had the highest importance score across the three models, followed by imaging, immunohistochemistry, and anatomy in a decreasing order of importance scores. This study showed that ontologies have an important role in feature engineering to make heterogeneous clinical data accessible to machine learning models and also improve~the performance of machine learning models in multilabel multiclass classification tasks.}, issue = {1}, langid = {english}, keywords = {Epilepsy,Information technology,Scientific data,Software} } @online{sahooSystematicSurveyPrompt2025, ids = {sahooSystematicSurveyPrompt2024}, title = {A {{Systematic Survey}} of {{Prompt Engineering}} in {{Large Language Models}}: {{Techniques}} and {{Applications}}}, shorttitle = {A {{Systematic Survey}} of {{Prompt Engineering}} in {{Large Language Models}}}, author = {Sahoo, Pranab and Singh, Ayush Kumar and Saha, Sriparna and Jain, Vinija and Mondal, Samrat and Chadha, Aman}, date = {2025-03-16}, eprint = {2402.07927}, eprinttype = {arXiv}, eprintclass = {cs}, doi = {10.48550/arXiv.2402.07927}, abstract = {Prompt engineering has emerged as an indispensable technique for extending the capabilities of large language models (LLMs) and vision-language models (VLMs). This approach leverages task-specific instructions, known as prompts, to enhance model efficacy without modifying the core model parameters. Rather than updating the model parameters, prompts allow seamless integration of pre-trained models into downstream tasks by eliciting desired model behaviors solely based on the given prompt. Prompts can be natural language instructions that provide context to guide the model or learned vector representations that activate relevant knowledge. This burgeoning field has enabled success across various applications, from question-answering to commonsense reasoning. However, there remains a lack of systematic organization and understanding of the diverse prompt engineering methods and techniques. This survey paper addresses the gap by providing a structured overview of recent advancements in prompt engineering, categorized by application area. For each prompting approach, we provide a summary detailing the prompting methodology, its applications, the models involved, and the datasets utilized. We also delve into the strengths and limitations of each approach and include a taxonomy diagram and table summarizing datasets, models, and critical points of each prompting technique. This systematic analysis enables a better understanding of this rapidly developing field and facilitates future research by illuminating open challenges and opportunities for prompt engineering.}, langid = {english}, pubstate = {prepublished}, keywords = {⛔ No INSPIRE recid found,Computer Science - Artificial Intelligence,Computer Science - Computation and Language,Computer Science - Human-Computer Interaction}, note = {Comment: 9 pages, 2 figures \par Comment: 12 pages, 2 figures} } @inproceedings{saidComparativeRecommenderSystem2014, title = {Comparative Recommender System Evaluation: Benchmarking Recommendation Frameworks}, shorttitle = {Comparative Recommender System Evaluation}, booktitle = {Proc. 8th {{ACM Conf}}. {{Recomm}}. {{Syst}}. - {{RecSys}} 14}, author = {Said, Alan and Bellogín, Alejandro}, date = {2014}, pages = {129--136}, publisher = {ACM Press}, location = {Foster City, Silicon Valley, California, USA}, doi = {10.1145/2645710.2645746}, eventtitle = {The 8th {{ACM Conference}}}, isbn = {978-1-4503-2668-1}, langid = {english}, note = {TL;DR \par This work compares common recommendation algorithms as implemented in three popular recommendation frameworks and shows the necessity of clear guidelines when reporting evaluation of recommender systems to ensure reproducibility and comparison of results.} } @inproceedings{Saied2015Could, title = {Could We Infer Unordered {{API}} Usage Patterns Only Using the Library Source Code?}, booktitle = {23rd {{Int}}. {{Conf}}. {{Program Comprehension}}}, author = {Saied, Mohamed Aymen and Abdeen, Hani and Benomar, Omar and Sahraoui, Houari}, date = {2015}, pages = {71--81}, publisher = {IEEE}, location = {Piscataway}, nodoi = {10.1109/ICPC.2015.16} } @article{SAIED2018164, title = {Improving Reusability of Software Libraries through Usage Pattern Mining}, author = {Saied, Mohamed Aymen and Ouni, Ali and Sahraoui, Houari and Kula, Raula Gaikovina and Inoue, Katsuro and Lo, David}, date = {2018}, journaltitle = {J. Syst. Softw.}, volume = {145}, pages = {164--179}, issn = {0164-1212}, url = {http://www.sciencedirect.com/science/article/pii/S0164121218301699}, abstract = {Modern software systems are increasingly dependent on third-party libraries. It is widely recognized that using mature and well-tested third-party libraries can improve developers’ productivity, reduce time-to-market, and produce more reliable software. Today’s open-source repositories provide a wide range of libraries that can be freely downloaded and used. However, as software libraries are documented separately but intended to be used together, developers are unlikely to fully take advantage of these reuse opportunities. In this paper, we present a novel approach to automatically identify third-party library usage patterns, i.e., collections of libraries that are commonly used together by developers. Our approach employs a hierarchical clustering technique to group together software libraries based on external client usage. To evaluate our approach, we mined a large set of over 6000 popular libraries from Maven Central Repository and investigated their usage by over 38,000 client systems from the Github repository. Our experiments show that our technique is able to detect the majority (77\%) of highly \%consistent and cohesive library usage patterns across a considerable \%number \%of client systems.}, nodoi = {https://doi.org/10.1016/j.jss.2018.08.032}, keywords = {Clustering,Software libraries,Software reuse,Usage patterns} } @inproceedings{saiedMiningMultilevelAPI2015, title = {Mining Multi-Level {{API}} Usage Patterns}, booktitle = {22nd {{Int}}. {{Conf}}. {{Softw}}. {{Anal}}. {{Evol}}. {{Reengineering}}}, author = {Saied, M. A. and Benomar, O. and Abdeen, H. and Sahraoui, H.}, date = {2015}, pages = {23--32}, publisher = {IEEE}, location = {Piscataway}, issn = {1534-5351}, keywords = {API Documentation,API Usage,application program interfaces,application programming interface,Clustering algorithms,Context,data mining,Documentation,Graphical user interfaces,Java,Layout,MLUP,multilevel API usage pattern mining,Security,Software Clustering,software libraries,Usage Pattern} } @inproceedings{Saini2019714, ids = {sainiTeachingModellingLiteracy2019}, title = {Teaching Modelling Literacy: {{An}} Artificial Intelligence Approach}, author = {Saini, R. and Mussbacher, G. and Guo, J.L.C. and Kienzle, J.}, editor = {Burgueno L., Burgueno L., Voss S., Chaudron M., Kienzle J., Volter M., Gerard S., Zahedi M., Bousse E., Rensink A., Polack F., Engels G., Kappel G., Pretschner A.}, date = {2019}, series = {Proceedings - 2019 {{ACM}}/{{IEEE}} 22nd {{International Conference}} on {{Model Driven Engineering Languages}} and {{Systems Companion}}, {{MODELS-C}} 2019}, pages = {714--719}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/MODELS-C.2019.00108}, abbrev_source_title = {Proc. - ACM/IEEE Int. Conf. Model Driven Eng. Lang. Syst. Companion, MODELS-C}, affiliation = {Dept. of ECE, McGill University, Montréal, QC, Canada; School of Computer Science, McGill University, Montréal, QC, Canada}, art_number = {8904688}, document_type = {Conference Paper}, isbn = {978-1-7281-5125-0}, langid = {english}, source = {Scopus}, keywords = {GOAL\_Model-Teaching,notion,TECHNIQUE\_DependencyGraph}, note = {cited By 10 \par TL;DR \par This paper proposes a framework called ModBud (a modelling buddy) to educate novice modellers about the art of abstraction and uses natural language processing and machine learning to create modelling bots with the aim of improving the modelling skills of novicemodellers and assisting other practitioners, too.} } @article{Saini20221015, ids = {SainiMGK22}, title = {Automated, Interactive, and Traceable Domain Modelling Empowered by Artificial Intelligence}, author = {Saini, R. and Mussbacher, G. and Guo, J.L.C. and Kienzle, J.}, date = {2022}, journaltitle = {Software Syst. Model.}, volume = {21}, number = {3}, pages = {1015--1045}, publisher = {{Springer Science and Business Media Deutschland GmbH}}, issn = {16191366}, doi = {10.1007/s10270-021-00942-6}, abbrev_source_title = {Softw. Syst. Model.}, affiliation = {Department of Electrical and Computer Engineering, McGill University, Montréal, Canada; School of Computer Science, McGill University, Montréal, Canada}, bibsource = {dblp computer science bibliography, https://dblp.org}, correspondence_address1 = {Saini, R.; Department of Electrical and Computer Engineering, Canada; email: rijul.saini@mail.mcgill.ca}, document_type = {Article}, langid = {english}, source = {Scopus}, keywords = {/unread,⛔ No INSPIRE recid found,GOAL\_Model-Assistance,notion,TECHNIQUE\_Gradient\_Boosted\_Decision\_Trees,TECHNIQUE\_RandomForests}, note = {cited By 0 \par TL;DR \par This paper proposes an algorithm to discover alternative configurations during bot-modeller interactions and uses this algorithm to find alternative configurations and then present these configurations in the form of suggestions to modellers.}, timestamp = {Fri, 29 Apr 2022 09:14:31 +0200} } @inproceedings{sainiArtificialIntelligenceEmpowered2020, title = {Artificial Intelligence Empowered Domain Modelling Bot}, booktitle = {Proc. 23rd {{ACMIEEE Int}}. {{Conf}}. {{Model Driven Eng}}. {{Lang}}. {{Syst}}. {{Companion Proc}}.}, author = {Saini, Rijul}, date = {2020-10-16}, pages = {1--6}, publisher = {ACM}, location = {Virtual Event Canada}, doi = {10.1145/3417990.3419486}, eventtitle = {{{MODELS}} '20: {{ACM}}/{{IEEE}} 23rd {{International Conference}} on {{Model Driven Engineering Languages}} and {{Systems}}}, isbn = {978-1-4503-8135-2}, langid = {english}, keywords = {GOAL\_Model-Assistance,notion,TECHNIQUE\_Clustering,TECHNIQUE\_NLP} } @article{salehieSelfadaptiveSoftwareLandscape2009, title = {Self-Adaptive Software: {{Landscape}} and Research Challenges}, shorttitle = {Self-Adaptive Software}, author = {Salehie, Mazeiar and Tahvildari, Ladan}, date = {2009}, journaltitle = {ACM Trans. Auton. Adapt. Syst. TAAS}, volume = {4}, number = {2}, pages = {14}, url = {http://dl.acm.org/citation.cfm?id=1516538}, urldate = {2016-01-12}, note = {TL;DR \par A taxonomy of research in self-adaptive software is presented, based on concerns of adaptation, that is, how, what, when and where, towards providing a unified view of this emerging area.} } @online{saleiroAequitasBiasFairness2019, title = {Aequitas: {{A Bias}} and {{Fairness Audit Toolkit}}}, shorttitle = {Aequitas}, author = {Saleiro, Pedro and Kuester, Benedict and Hinkson, Loren and London, Jesse and Stevens, Abby and Anisfeld, Ari and Rodolfa, Kit T. and Ghani, Rayid}, date = {2019-04-29}, eprint = {1811.05577}, eprinttype = {arXiv}, eprintclass = {cs}, url = {http://arxiv.org/abs/1811.05577}, urldate = {2023-03-09}, abstract = {Recent work has raised concerns on the risk of unintended bias in AI systems being used nowadays that can affect individuals unfairly based on race, gender or religion, among other possible characteristics. While a lot of bias metrics and fairness definitions have been proposed in recent years, there is no consensus on which metric/definition should be used and there are very few available resources to operationalize them. Therefore, despite recent awareness, auditing for bias and fairness when developing and deploying AI systems is not yet a standard practice. We present Aequitas, an open source bias and fairness audit toolkit that is an intuitive and easy to use addition to the machine learning workflow, enabling users to seamlessly test models for several bias and fairness metrics in relation to multiple population sub-groups. Aequitas facilitates informed and equitable decisions around developing and deploying algorithmic decision making systems for both data scientists, machine learning researchers and policymakers.}, pubstate = {prepublished}, keywords = {Computer Science - Artificial Intelligence,Computer Science - Computers and Society,Computer Science - Machine Learning}, note = {Comment: Aequitas website: http://dsapp.uchicago.edu/aequitas} } @article{Salemi2016, title = {Moving Least Squares Regression for High-Dimensional Stochastic Simulation Metamodeling}, author = {Salemi, P. and Nelson, B.L. and Staum, J.}, date = {2016}, journaltitle = {ACM Trans. Model. Comput. Simul.}, volume = {26}, number = {3}, publisher = {Association for Computing Machinery}, issn = {10493301}, doi = {10.1145/2724708}, abstract = {Simulation metamodeling is building a statistical model based on simulation output as an approximation to the system performance measure being estimated by the simulation model. In high-dimensional metamodeling problems, larger numbers of design points are needed to build an accurate and precise metamodel. Metamodeling techniques that are functions of all of these design points experience difficulties because of numerical instabilities and high computation times. We introduce a procedure to implement a local smoothing method called Moving Least Squares (MLS) regression in high-dimensional stochastic simulation metamodeling problems. Although MLS regression is known to work well when there are a very large number of design points, current procedures are focused on two- and three-dimensional cases. Furthermore, our procedure accounts for the fact that we can make replications and control the placement of design points in stochastic simulation. We provide a bound on the expected approximation error, show that the MLS predictor is consistent under certain conditions, and test the procedure with two examples that demonstrate better results than other existing simulation metamodeling techniques. © 2016 ACM.}, art_number = {16}, coden = {ATMCE}, document_type = {Article}, source = {Scopus}, note = {cited By 8 \par TL;DR \par This work introduces a procedure to implement a local smoothing method called Moving Least Squares (MLS) regression in high-dimensional stochastic simulation metamodeling problems and provides a bound on the expected approximation error and shows that the MLS predictor is consistent under certain conditions.} } @article{salman_controlled_2019, title = {A Controlled Experiment on Time Pressure and Confirmation Bias in Functional Software Testing}, author = {Salman, Iflaah and Turhan, Burak and Vegas, Sira}, date = {2019-08}, journaltitle = {Empir. Softw. Eng.}, volume = {24}, number = {4}, pages = {1727--1761}, issn = {1573-7616}, doi = {10.1007/s10664-018-9668-8}, abstract = {Confirmation bias is a person’s tendency to look for evidence that strengthens his/her prior beliefs rather than refutes them. Manifestation of confirmation bias in software testing may have adverse effects on software quality. Psychology research suggests that time pressure could trigger confirmation bias.}, note = {TL;DR \par It is found it necessary that testers develop self-awareness of confirmation bias and counter its potential adverse effects with a disconfirmatory attitude and further replications are recommended to investigate the effect of time pressure as a potential contributor to the manifestation of confirmation bias.} } @article{samadControlSystemsInternet2016, title = {Control {{Systems}} and the {{Internet}} of {{Things}} [{{Technical Activities}}]}, author = {Samad, Tariq}, date = {2016-02}, journaltitle = {IEEE Control Syst.}, volume = {36}, number = {1}, pages = {13--16}, issn = {1066-033X}, doi = {10.1109/MCS.2015.2495022}, note = {TL;DR \par The relationship between control systems and the Internet of Things is discussed, including hybrid systems, embedded systems, cyberphysical systems (CPS), and systems of systems.} } @article{sammetProgrammingLanguagesHistory1972, title = {Programming Languages: History and Future}, shorttitle = {Programming Languages}, author = {Sammet, Jean E.}, date = {1972-07}, journaltitle = {Commun. ACM}, volume = {15}, number = {7}, pages = {601--610}, issn = {0001-0782, 1557-7317}, doi = {10.1145/361454.361485}, abstract = {This paper discusses both the history and future of programming languages ( = higher level languages). Some of the difficulties in writing such a history are indicated. A key part of the paper is a tree showing the chronological development of languages and their interrelationships. Reasons for the proliferation of languages are given. The major languages are listed with the reasons for their importance. A section on chronology indicates the happenings of the significant previous time periods and the major topics of 1972. Key concepts other than specific languages are discussed.}, langid = {english} } @thesis{Samuel_2019, title = {A Provenance-Based Semantic Approach to Support Understandability, Reproducibility, and Reuse of Scientific Experiments}, author = {Samuel, Sheeba}, date = {2019}, location = {Jena}, doi = {10.22032/dbt.40396}, abstract = {Understandability and reproducibility of scientific results are vital in every field of science. Several reproducibility measures are being taken to make the data used in the publications findable and accessible. However, there are many challenges faced by scientists from the beginning of an experiment to the end in particular for data management. The explosive growth of heterogeneous research data and understanding how this data has been derived is one of the research problems faced in this context. Interlinking the data, the steps and the results from the computational and non-computational processes of a scientific experiment is important for the reproducibility. We introduce the notion of end-to-end provenance management’’ of scientific experiments to help scientists understand and reproduce the experimental results. The main contributions of this thesis are: (1) We propose a provenance modelREPRODUCE-ME’’ to describe the scientific experiments using semantic web technologies by extending existing standards. (2) We study computational reproducibility and important aspects required to achieve it. (3) Taking into account the REPRODUCE-ME provenance model and the study on computational reproducibility, we introduce our tool, ProvBook, which is designed and developed to demonstrate computational reproducibility. It provides features to capture and store provenance of Jupyter notebooks and helps scientists to compare and track their results of different executions. (4) We provide a framework, CAESAR (CollAborative Environment for Scientific Analysis with Reproducibility) for the end-to-end provenance management. This collaborative framework allows scientists to capture, manage, query and visualize the complete path of a scientific experiment consisting of computational and non-computational steps in an interoperable way. We apply our contributions to a set of scientific experiments in microscopy research projects.} } @article{sanchez-cuadradoBottomUpMetaModellingInteractive2012, title = {Bottom-{{Up Meta-Modelling}}: {{An Interactive Approach}}}, author = {Sánchez-Cuadrado, Jesús and Lara, Juan and Guerra, Esther}, date = {2012}, journaltitle = {Model Driven Eng. Lang. Syst.}, volume = {7590}, pages = {3--19}, doi = {10.1007/978-3-642-33666-9_2} } @article{sanchezBuildingModularYAWL2012, title = {Building a Modular {{YAWL}} Engine with {{Cumbia}}}, author = {Sanchez, Mario and Puentes, Diana and Villalobos, Jorge}, date = {2012}, journaltitle = {IJBPIM}, volume = {6}, number = {1}, pages = {41}, issn = {1741-8763, 1741-8771}, doi = {10.1504/IJBPIM.2012.047912}, abstract = {Nowadays, novel strategies to develop and adapt workflow engines in efficient ways are required in order to have BPM and workflow solutions with the capacity to support frequent changes in the corporate environment. One key strategy is to build new engines by reusing as much as possible from existing components. This requires two things. Firstly, the mechanisms and technologies to build a library of reusable, extensible and adaptable workflow components. And secondly, a platform to integrate those components and form full applications. In this paper we show that Cumbia, being a platform for the development of workflow engines based on the modularisation of workflows according to concerns, suits this task. This is illustrated with YOC, a Cumbia-based implementation of YAWL.}, langid = {english}, note = {TL;DR \par Cumbia, being a platform for the development of workflow engines based on the modularisation of workflows according to concerns, suits this task and is illustrated with YOC, a Cumbia-based implementation of YAWL.} } @article{sanchezcuadradoApproachesModelTransformation2008, title = {Approaches for {{Model Transformation Reuse}}: {{Factorization}} and {{Composition}}}, author = {Sánchez Cuadrado, Jesús and García Molina, Jesús}, date = {2008}, journaltitle = {Theory Pract. Model Transform.}, volume = {5063}, pages = {168--182}, doi = {10.1007/978-3-540-69927-9_12}, note = {TL;DR \par Two approaches for reusing model transformation definitions are presented, tackling the creation of related model transformations and the composition of existing, separated transformation definitions so that they can be used to solve a concrete transformation problem.} } @article{sanchezcuadradoComponentModelModel2014, title = {A {{Component Model}} for {{Model Transformations}}}, author = {Sanchez Cuadrado, J. and Guerra, E. and De Lara, J.}, date = {2014-11}, journaltitle = {IEEE Trans. Softw. Eng.}, volume = {40}, number = {11}, pages = {1042--1060}, issn = {0098-5589}, doi = {10.1109/TSE.2014.2339852}, abstract = {Model-driven engineering promotes an active use of models to conduct the software development process. In this way, models are used to specify, simulate, verify, test and generate code for the final systems. Model transformations are key enablers for this approach, being used to manipulate instance models of a certain modelling language. However, while other development paradigms make available techniques to increase productivity through reutilization, there are few proposals for the reuse of model transformations across different modelling languages. As a result, transformations have to be developed from scratch even if other similar ones exist. In this paper, we propose a technique for the flexible reutilization of model transformations. Our proposal is based on generic programming for the definition and instantiation of transformation templates, and on component-based development for the encapsulation and composition of transformations. We have designed a component model for model transformations, supported by an implementation currently targeting the Atlas Transformation Language (ATL). To evaluate its reusability potential, we report on a generic transformation component to analyse workflow models through their transformation into Petri nets, which we have reused for eight workflow languages, including UML Activity Diagrams, YAWL and two versions of BPMN.}, note = {TL;DR \par This paper designs a component model for model transformations, supported by an implementation currently targeting the Atlas Transformation Language (ATL), and reports on a generic transformation component to analyse workflow models through their transformation into Petri nets, which has reused for eight workflow languages.} } @article{sanchezSemanticbasedPrivacySettings2020, title = {Semantic-Based Privacy Settings Negotiation and Management}, author = {Sanchez, Odnan Ref and Torre, Ilaria and Knijnenburg, Bart P.}, date = {2020-10}, journaltitle = {Future Generation Computer Systems}, volume = {111}, pages = {879--898}, issn = {0167739X}, doi = {10.1016/j.future.2019.10.024}, abstract = {By 2020, an individual is expected to own an average of 6.58 devices that share and integrate a wealth of personal user data. The management of privacy preferences across these devices is a complex task for which users are ill-equipped, which increases privacy risks. In this paper we propose an approach that exploits Semantic Web (SW) technology to manage the user’s IoT privacy preferences and negotiate the permissions for data sharing with third parties. SW technology comprises a web of data that can be processed by machines through a formal, universally shared representation. In our approach, SW enables a lightweight and interoperable communication between a Personal Data Manager (PDM) and the Third Parties (TPs) that request access to the user’s personal data. The PDM can handle multiple heterogeneous personal IoT devices and manages the negotiation process between the user and the TPs in a way that can relieve users from the burden of specifying their privacy requirement for each TP. The core of the approach is the definition of the Privacy Preference for IoT (PPIoT) Ontology which is based on the Privacy Preference Ontology, the W3C Semantic Sensor Network Ontology, the Fair Information Practices (FIP) principles, and state-of-the-art recommendation techniques for privacy protection in the IoT. This ontology aims to capture the complexity of privacy management in the IoT paradigm in light of the recent General Data Protection Regulation (GDPR) of the European Union. Along with presenting the ontology, in this paper we will provide an example on how to use the PPIoT ontology for the management of privacy preferences in the fitness IoT domain and we will show how the PDM handles the process of negotiation between the user and the TPs. The approach is based on an interactive PPIoT-based Privacy Preference Model (PPM) that meets the requirements of the GDPR to have transparent and simple TP privacy policies. Finally, we will report the results of an evaluation on a mockup fitness app that implements this PPM. The main contributions of this paper are: (i) to propose an ontology for privacy preference in the IoT context, which covers a knowledge gap in existing literature and can be used for IoT privacy management, (ii) to propose an interactive PPIoT-based Privacy Preference Model, which is in accordance with the GDPR objectives.}, langid = {english} } @article{sandhuBigDataCloud2022, title = {Big {{Data}} with {{Cloud Computing}}: {{Discussions}} and {{Challenges}}}, author = {Sandhu, Amanpreet Kaur}, date = {2022}, journaltitle = {Big Data Min. Anal.}, pages = {9}, abstract = {With the recent advancements in computer technologies, the amount of data available is increasing day by day. However, excessive amounts of data create great challenges for users. Meanwhile, cloud computing services provide a powerful environment to store large volumes of data. They eliminate various requirements, such as dedicated space and maintenance of expensive computer hardware and software. Handling big data is a time-consuming task that requires large computational clusters to ensure successful data storage and processing. In this work, the definition, classification, and characteristics of big data are discussed, along with various cloud services, such as Microsoft Azure, Google Cloud, Amazon Web Services, International Business Machine cloud, Hortonworks, and MapR. A comparative analysis of various cloud-based big data frameworks is also performed. Various research challenges are defined in terms of distributed database storage, data security, heterogeneity, and data visualization.}, langid = {english} } @inproceedings{sandhuIntegrationArtificialIntelligence2021, title = {Integration of {{Artificial Intelligence}} into Software Reuse: {{An}} Overview of {{Software Intelligence}}}, shorttitle = {Integration of {{Artificial Intelligence}} into Software Reuse}, booktitle = {2021 2nd {{Int}}. {{Conf}}. {{Comput}}. {{Autom}}. {{Knowl}}. {{Manag}}. {{ICCAKM}}}, author = {Sandhu, Amandeep Kaur and Batth, Ranbir Singh}, date = {2021-01-19}, pages = {357--362}, publisher = {IEEE}, location = {Dubai, United Arab Emirates}, doi = {10.1109/ICCAKM50778.2021.9357738}, eventtitle = {2021 2nd {{International Conference}} on {{Computation}}, {{Automation}} and {{Knowledge Management}} ({{ICCAKM}})}, isbn = {978-1-7281-9491-2}, note = {TL;DR \par This paper provides a roadmap of conventional reuse approaches to Software Intelligence approaches and concludes that Components Based Development fits best for modern applications as it supports object-oriented paradigm.} } @article{santhanamBotsSoftwareEngineering2022, title = {Bots in Software Engineering: A Systematic Mapping Study}, shorttitle = {Bots in Software Engineering}, author = {Santhanam, Sivasurya and Hecking, Tobias and Schreiber, Andreas and Wagner, Stefan}, date = {2022-02-09}, journaltitle = {PeerJ Comput. Sci.}, volume = {8}, pages = {e866}, issn = {2376-5992}, doi = {10.7717/peerj-cs.866}, abstract = {Bots have emerged from research prototypes to deployable systems due to the recent developments in machine learning, natural language processing and understanding techniques. In software engineering, bots range from simple automated scripts to decision-making autonomous systems. The spectrum of applications of bots in software engineering is so wide and diverse, that a comprehensive overview and categorization of such bots is needed. Existing works considered selective bots to be analyzed and failed to provide the overall picture. Hence it is significant to categorize bots in software engineering through analyzing why, what and how the bots are applied in software engineering. We approach the problem with a systematic mapping study based on the research articles published in this topic. This study focuses on classification of bots used in software engineering, the various dimensions of the characteristics, the more frequently researched area, potential research spaces to be explored and the perception of bots in the developer community. This study aims to provide an introduction and a broad overview of bots used in software engineering. Discussions of the feedback and results from several studies provide interesting insights and prospective future directions.}, langid = {english}, note = {TL;DR \par This study focuses on classification of bots used in software engineering, the various dimensions of the characteristics, the more frequently researched area, potential research spaces to be explored and the perception of bots in the developer community.} } @online{sapkotaAIAgentsVs2025, title = {{{AI Agents}} vs. {{Agentic AI}}: {{A Conceptual Taxonomy}}, {{Applications}} and {{Challenges}}}, shorttitle = {{{AI Agents}} vs. {{Agentic AI}}}, author = {Sapkota, Ranjan and Roumeliotis, Konstantinos I. and Karkee, Manoj}, date = {2025-05-20}, eprint = {2505.10468}, eprinttype = {arXiv}, eprintclass = {cs}, doi = {10.48550/arXiv.2505.10468}, abstract = {This review critically distinguishes between AI Agents and Agentic AI, offering a structured conceptual taxonomy, application mapping, and challenge analysis to clarify their divergent design philosophies and capabilities. We begin by outlining the search strategy and foundational definitions, characterizing AI Agents as modular systems driven by LLMs and LIMs for narrow, task-specific automation. Generative AI is positioned as a precursor, with AI agents advancing through tool integration, prompt engineering, and reasoning enhancements. In contrast, agentic AI systems represent a paradigmatic shift marked by multi-agent collaboration, dynamic task decomposition, persistent memory, and orchestrated autonomy. Through a sequential evaluation of architectural evolution, operational mechanisms, interaction styles, and autonomy levels, we present a comparative analysis across both paradigms. Application domains such as customer support, scheduling, and data summarization are contrasted with Agentic AI deployments in research automation, robotic coordination, and medical decision support. We further examine unique challenges in each paradigm including hallucination, brittleness, emergent behavior, and coordination failure and propose targeted solutions such as ReAct loops, RAG, orchestration layers, and causal modeling. This work aims to provide a definitive roadmap for developing robust, scalable, and explainable AI-driven systems.}, langid = {english}, pubstate = {prepublished}, keywords = {Computer Science - Artificial Intelligence}, note = {Comment: 33 pages, 14 figures, 11 tables} } @inproceedings{Saracevic:1995:EEI:215206.215351, title = {Evaluation of Evaluation in Information Retrieval}, booktitle = {Proc. 18th {{Annu}}. {{Int}}. {{ACM SIGIR Conf}}. {{Res}}. {{Dev}}. {{Inf}}. {{Retr}}.}, author = {Saracevic, Tefko}, date = {1995}, series = {{{SIGIR}} '95}, pages = {138--146}, publisher = {ACM}, location = {New York, NY, USA}, url = {http://doi.acm.org/10.1145/215206.215351}, acmid = {215351}, isbn = {0-89791-714-6}, nodoi = {10.1145/215206.215351}, numpages = {9} } @article{sarkarAllYouNeed, title = {All You Need to Know about ‘{{Attention}}’ and ‘{{Transformers}}’ — {{In-depth Understanding}} — {{Part}} 1}, author = {Sarkar, Arjun}, pages = {17}, langid = {english} } @article{sartaj1stInternationalWorkshop, title = {1st {{International Workshop}} on {{Model-driven Engineering}} and {{Large Language Models}} ({{ModLLM}}’24)}, author = {Sartaj, Hassan and Ali, Shaukat and Saadatmand, Mehrdad and Klikovits, Stefan}, abstract = {Model-driven Engineering (MDE) and Large Language Models (LLMs) represent two transformative forces in software engineering and artificial intelligence. MDE enhances software systems’ productivity, quality, and maintainability by focusing on models as primary artifacts. On the other hand, LLMs, powered by machine learning algorithms, can understand and generate human-like text. Integrating LLMs into MDE can substantially improve tasks such as automating modeling, building modeling assistants, code generation from models, design automation, and verification. This synergy between MDE and LLMs could redefine the MDE process, making it more efficient and robust. This workshop aims to bring together researchers and industry professionals from diverse fields utilizing LLMs for different MDE tasks, promoting an environment of collaboration and knowledge exchange.}, langid = {english} } @inproceedings{Sarwar:2001:ICF:371920.372071, ids = {IB-CF-2011}, title = {Item-Based Collaborative Filtering Recommendation Algorithms}, booktitle = {10th {{Int}}. {{Conf}}. {{World Wide Web}}}, author = {Sarwar, Badrul and Karypis, George and Konstan, Joseph and Riedl, John}, date = {2001}, pages = {285--295}, publisher = {ACM}, location = {New York}, acmid = {372071}, isbn = {1-58113-348-0}, nodoi = {10.1145/371920.372071}, numpages = {11} } @article{sasPerilsPitfallsClassifying, title = {The {{Perils}} and {{Pitfalls}} of {{Classifying Software Systems}}}, author = {Sas, Cezar and Capiluppi, Andrea}, pages = {11}, abstract = {Empirical results in software engineering have long started to show that findings and evidence are unlikely to be applicable to all software systems, or any domain: results need to be evaluated in specified contexts, and limited to the type of systems that they were extracted from.}, langid = {english} } @book{SATToSE2017Postproceedings2017, ids = {SATToSE2017Postproceedings2017a}, title = {{{SATToSE}} 2017: {{The}} Post-Proceedings Editorial}, date = {2017}, journaltitle = {CEUR Workshop Proceedings}, volume = {2070}, publisher = {CEUR-WS} } @inproceedings{Sauer201749, title = {Use of deep learning on the locally resolved description of part properties [Einsatz von Deep Learning zur ortsaufgelösten Beschreibung von Bauteileigenschaften]}, author = {Sauer, C. and Köstner, C. and Schleich, B. and Wartzack, S.}, editor = {Krause D., Wartzack S., Paetzold K.}, date = {2017}, series = {DFX 2017: Proceedings of the 28th Symposium Design for X}, pages = {49--60}, publisher = {TuTech Innovation Verlag}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85035053178&partnerID=40&md5=ef20b0fdc9966435df7632d5dc9badb7}, abbrev_source_title = {DFX: Proc. Symp. Des. X}, affiliation = {Lehrstuhl fur Konstruktionstechnik (KTmfk), Friedrich-Alexander-Universität Erlangen-Nörnberg, Germany}, document_type = {Conference Paper}, isbn = {978-3-946094-20-3}, langid = {English; German}, source = {Scopus}, note = {cited By 4} } @inproceedings{Sauer20182999, title = {Deep Learning in Sheet-Bulk Metal Forming Part Design}, author = {Sauer, C. and Schleich, B. and Wartzack, S.}, editor = {Bojcetic N., Storga M., Skec S., Pavkovic N., Marjanovic D.}, date = {2018}, series = {Proceedings of {{International Design Conference}}, {{DESIGN}}}, volume = {6}, pages = {2999--3010}, publisher = {{Faculty of Mechanical Engineering and Naval Architecture}}, issn = {18479073}, doi = {10.21278/idc.2018.0147}, abbrev_source_title = {Proc. Int. Des. Conf., DESIGN}, affiliation = {Friedrich-Alexander Universität Erlangen-Nürnberg, Engineering Design (KTmfk), Martensstraße 9, Erlangen, 91058, Germany}, document_type = {Conference Paper}, isbn = {978-953-7738-59-4}, langid = {english}, source = {Scopus}, note = {cited By 9} } @article{savary-leblanc_software_2023, title = {Software Assistants in Software Engineering: {{A}} Systematic Mapping Study}, shorttitle = {Software Assistants in Software Engineering}, author = {Savary-Leblanc, Maxime and Burgueño, Lola and Cabot, Jordi and Le Pallec, Xavier and Gérard, Sébastien}, date = {2023}, journaltitle = {Softw. Pract. Exp.}, volume = {53}, number = {3}, pages = {856--892}, issn = {1097-024X}, doi = {10.1002/spe.3170}, abstract = {The increasing essential complexity of software systems makes current software engineering methods and practices fall short in many occasions. Software assistants have the ability to help humans achieve a variety of tasks, including the development of software. Such assistants, which show human-like competences such as autonomy and intelligence, help software engineers do their job by empowering them with new knowledge. This article investigates the research efforts that have been conducted on the creation of assistants for software design, construction and maintenance paying special attention to the user-assistant interactions. To this end, we followed the standard systematic mapping study method to identify and classify relevant works in the state of the art. Out of the 7580 articles resulting from the automatic search, we identified 112 primary studies that present works which qualify as software assistants. We provide all the resources needed to reproduce our study. We report on the trends and goals of the assistants, the tasks they perform, how they interact with users, the technologies and mechanisms they exploit to embed intelligence and provide knowledge, and their level of automation. We propose a classification of software assistants based on interactions and present an analysis of the different automation patterns. As outcomes of our study, we provide a classification of software assistants dealing with the design, construction and maintenance phases of software development, we discuss the results, identify open lines of work and challenges and call for new innovative and rigorous research efforts in this field.}, langid = {english}, keywords = {software assistants,software construction,software design,software maintenance,systematic mapping study}, note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/spe.3170 \par TL;DR \par This article investigates the research efforts that have been conducted on the creation of assistants for software design, construction and maintenance paying special attention to the user‐assistant interactions and proposes a classification of software assistants based on interactions and presents an analysis of the different automation patterns.} } @article{savolainenSELECTIONLOWCODEPLATFORMS, title = {{{SELECTION OF LOW-CODE PLATFORMS BASED ON ORGANIZATION AND APPLICATION TYPE}}}, author = {Savolainen, Paula}, pages = {86}, langid = {english}, keywords = {lowcode} } @article{sawant_fine-grape_2017, title = {Fine-{{GRAPE}}: Fine-Grained {{APi}} Usage Extractor – an Approach and Dataset to Investigate {{API}} Usage}, shorttitle = {Fine-{{GRAPE}}}, author = {Sawant, Anand Ashok and Bacchelli, Alberto}, date = {2017-06}, journaltitle = {Empir. Softw. Eng.}, volume = {22}, number = {3}, pages = {1348--1371}, issn = {1382-3256, 1573-7616}, doi = {10.1007/s10664-016-9444-6}, abstract = {An Application Programming Interface (API) provides a set of functionalities to a developer with the aim of enabling reuse. APIs have been investigated from different angles such as popularity usage and evolution to get a better understanding of their various characteristics. For such studies, software repositories are mined for API usage examples. However, many of the mining algorithms used for such purposes do not take type information into account. Thus making the results unreliable. In this paper, we aim to rectify this by introducing fine-GRAPE, an approach that produces fine-grained API usage information by taking advantage of type information while mining API method invocations and annotation. By means of fine-GRAPE, we investigate API usages from Java projects hosted on GitHub. We select five of the most popular APIs across GitHub Java projects and collect historical API usage information by mining both the release history of these APIs and the code history of every project that uses them. We perform two case studies on the resulting dataset. The first measures the lag time of each client. The second investigates the percentage of used API features. In the first case we find that for APIs that release more frequently clients are far less likely to upgrade to a more recent version of the API as opposed to clients of APIs that release infrequently. The second case study shows us that for most APIs there is a small number of features that is actually used and most of these features relate to those that have been introduced early in the APIs lifecycle.}, langid = {english}, note = {TL;DR \par fine-GRAPE is introduced, an approach that produces fine-grained API usage information by taking advantage of type information while mining API method invocations and annotation, and investigates API usages from Java projects hosted on GitHub.} } @inproceedings{scavuzzoInteroperableDataMigration2014, title = {Interoperable Data Migration between NoSQL Columnar Databases}, author = {Scavuzzo, Marco and Nitto, Elisabetta Di and Ceri, Stefano}, date = {2014-09}, pages = {154--162}, publisher = {IEEE}, doi = {10.1109/EDOCW.2014.32}, isbn = {978-1-4799-5467-4}, langid = {italian}, note = {TL;DR \par This paper proposes an interoperable migration system for columnar NoSQL databases based on an orginal Metamodel, capable of preserving both strong and weak consistency between data updates, secondary indexes and various data types.} } @article{schaarschmidtAutomatedPolyglotPersistence, title = {Towards {{Automated Polyglot Persistence}}}, author = {Schaarschmidt, Michael and Gessert, Felix and Ritter, Norbert}, pages = {10}, abstract = {In this paper, we present an innovative solution for providing automated polyglot persistence based on service level agreements defined over functional and non-functional requirements of database systems. Complex applications require polyglot persistence to deal with a wide range of database related needs. Until now, the overhead and the required know-how to manage multiple database systems prevents many applications from employing efficient polyglot persistence solutions. Instead, developers are often forced to implement one-size-fits-all solutions that do not scale well and cannot easily be upgraded. Therefore, we introduce the concept for a Polyglot Persistence Mediator (PPM), which allows for runtime decisions on routing data to different backends according to schema-based annotations. This enables applications to either employ polyglot persistence right from the beginning or employ new systems at any point with minimal overhead. We have implemented and evaluated the concept of automated polyglot persistence for a REST-based Database-as-a-Service setting. Evaluations were performed on various EC2 setups, showing a scalable writeperformance increase of 50-100\% for a typical polyglot persistence scenario as well as drastically reduced latencies for reads and queries.}, langid = {english}, note = {TL;DR \par This paper introduces the concept for a Polyglot Persistence Mediator (PPM), which allows for runtime decisions on routing data to different backends according to schema-based annotations and enables applications to either employ polyglot persistence right from the beginning or employ new systems at any point with minimal overhead.} } @article{schaefferSurveyGraphClustering2007, title = {Survey: {{Graph}} Clustering}, author = {Schaeffer, Satu Elisa}, date = {2007-08}, journaltitle = {Comput. Sci. Rev.}, volume = {1}, number = {1}, pages = {27--64}, publisher = {Elsevier Science Publishers B. V.}, address = {Amsterdam, The Netherlands, The Netherlands}, issn = {1574-0137}, url = {http://dx.doi.org/10.1016/j.cosrev.2007.05.001}, acmid = {2296057}, issue_date = {August, 2007}, nodoi = {10.1016/j.cosrev.2007.05.001}, numpages = {38}, note = {TL;DR \par This survey overviews the definitions and methods for graph clustering, that is, finding sets of “related” vertices in graphs, and presents global algorithms for producing a clustering for the entire vertex set of an input graph.} } @inproceedings{schafer_mining_2008, title = {Mining Framework Usage Changes from Instantiation Code}, booktitle = {Procs 13th {{Int}}. {{Conf}}. {{Softw}}. {{Eng}}. - {{ICSE}} 08}, author = {Schäfer, Thorsten and Jonas, Jan and Mezini, Mira}, date = {2008}, pages = {471}, location = {Leipzig, Germany}, doi = {10.1145/1368088.1368153}, abstract = {Framework evolution may break existing users, which need to be migrated to the new framework version. This is a tedious and error-prone process that benefits from automation. Existing approaches compare two versions of the framework code in order to find changes caused by refactorings. However, other kinds of changes exist, which are relevant for the migration. In this paper, we propose to mine framework usage change rules from already ported instantiations, the latter being applications build on top of the framework, or test cases maintained by the framework developers. Our evaluation shows that our approach finds usage changes not only caused by refactorings, but also by conceptual changes within the framework. Further, it copes well with some issues that plague tools focusing on finding refactorings such as deprecated program elements or multiple changes applied to a single program element.}, isbn = {978-1-60558-079-1}, langid = {english}, nopublisher = {ACM Press}, nourl = {http://portal.acm.org/citation.cfm?doid=1368088.1368153}, note = {TL;DR \par This paper proposes to mine framework usage change rules from already ported instantiations, the latter being applications build on top of the framework, or test cases maintained by the framework developers, to find usage changes not only caused by refactorings, but also by conceptual changes within the framework.} } @incollection{Schafer:2007:CFR:1768197.1768208, title = {Collaborative Filtering Recommender Systems}, booktitle = {The Adaptive Web: {{Methods}} and Strategies of Web Personalization}, author = {Schafer, J. Ben and Frankowski, Dan and Herlocker, Jon and Sen, Shilad}, date = {2007}, pages = {291--324}, publisher = {Springer Berlin Heidelberg}, location = {Berlin, Heidelberg}, doi = {10.1007/978-3-540-72079-9_9}, isbn = {978-3-540-72079-9} } @incollection{schaferAdaptiveWeb2007, title = {The Adaptive Web}, author = {Schafer, J. Ben and Frankowski, Dan and Herlocker, Jon and Sen, Shilad}, editor = {Brusilovsky, Peter and Kobsa, Alfred and Nejdl, Wolfgang}, date = {2007}, pages = {291--324}, publisher = {Springer-Verlag}, location = {Berlin, Heidelberg}, acmid = {1768208}, chapter = {Collaborative Filtering Recommender Systems}, isbn = {978-3-540-72078-2}, numpages = {34} } @article{schaferDyadRankingUsing2018, title = {Dyad Ranking Using {{Plackett}}–{{Luce}} Models Based on Joint Feature Representations}, author = {Schäfer, Dirk and Hüllermeier, Eyke}, date = {2018-05}, journaltitle = {Mach Learn}, volume = {107}, number = {5}, pages = {903--941}, issn = {0885-6125, 1573-0565}, doi = {10.1007/s10994-017-5694-9}, langid = {english} } @inproceedings{SchaferG20, title = {Enhancing Development and Consistency of {{UML}} Models and Model Executions with {{USE}} Studio}, booktitle = {{{MODELS}} 20 {{ACMIEEE}} 23rd {{Int}}. {{Conf}}. {{Model Driven Eng}}. {{Lang}}. {{Syst}}. {{Virtual Event Can}}. 18-23 {{Oct}}. 2020 {{Companion Proc}}.}, author = {Schäfer, Marcel and Gogolla, Martin}, editor = {Guerra, Esther and Iovino, Ludovico}, date = {2020}, pages = {14:1--14:5}, publisher = {ACM}, doi = {10.1145/3417990.3422011}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Wed, 23 Feb 2022 12:16:52 +0100} } @article{Schatten2017359, title = {Automated {{MMORPG}} Testing – {{An}} Agent-Based Approach}, author = {Schatten, M. and Okreaša Ðurić, B. and Tomičič, I. and Ivkovič, N.}, editor = {Demazeau Y., Davidsson P., Bajo J., Vale Z.}, date = {2017}, journaltitle = {Lect. Notes Comput. Sci. Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinforma.}, volume = {10349 LNCS}, pages = {359--363}, publisher = {Springer Verlag}, issn = {03029743}, doi = {10.1007/978-3-319-59930-4_38}, abbrev_source_title = {Lect. Notes Comput. Sci.}, affiliation = {Artificial Intelligence Laboratory, Faculty of Organization and Informatics, University of Zagreb, Zagreb, Croatia}, correspondence_address1 = {Schatten, M.; Artificial Intelligence Laboratory, Croatia; email: markus.schatten@foi.hr}, document_type = {Conference Paper}, isbn = {9783319599298}, langid = {english}, source = {Scopus}, note = {cited By 1} } @article{schatzDesignSpaceExplorationConstraintBased2010, title = {Design-{{Space Exploration}} through {{Constraint-Based Model-Transformation}}}, author = {Schätz, Bernhard and Hölzl, Florian and Lundkvist, Torbjörn}, date = {2010}, journaltitle = {2010 17th IEEE Int. Conf. Workshop Eng. Comput. Based Syst.}, pages = {173--182}, doi = {10.1109/ECBS.2010.25}, note = {TL;DR \par Model transformations based on a declarative, relational approach can be formalized as transformation rules guiding a mechanized exploration of possible design alternatives for the (semi-)automatic, incremental deployment of logical architectures to hardware platforms.} } @article{schelterDeequDataQuality, title = {Deequ - {{Data Quality Validation}} for {{Machine Learning Pipelines}}}, author = {Schelter, Sebastian and Grafberger, Stefan and Schmidt, Philipp and Rukat, Tammo and Kiessling, Mario and Taptunov, Andrey and Biessmann, Felix and Lange, Dustin}, pages = {3}, abstract = {Modern machine learning (ML) systems are comprised of complex ML pipelines which typically have many implicit assumptions about the data they consume (e.g., about the scales of variables, the presence of missing values or the dictionary of categorical values). Violations of these assumptions can result in crashes or wrong predictions. We therefore present Deequ, a library that allows users to explicitly specify their assumptions about the data in a declarative way. Deequ enables the efficient automatic validation of these assumptions on large datasets. It is an open source library based on Apache Spark and meets the requirements of production use cases at Amazon.}, langid = {english}, note = {TL;DR \par Deequ is a library that allows users to explicitly specify their assumptions about the data in a declarative way and enables the efficient automatic validation of these assumptions on large datasets, and is designed to scale to datasets with billions of rows.} } @inproceedings{schlegelDesignAbstractionProcesses2010, title = {Design Abstraction and Processes in Robotics: From Code-Driven to Model-Driven Engineering}, shorttitle = {Design Abstraction and Processes in Robotics}, booktitle = {Int. {{Conf}}. {{Simul}}. {{Model}}. {{Program}}. {{Auton}}. {{Robots}}}, author = {Schlegel, Christian and Steck, Andreas and Brugali, Davide and Knoll, Alois}, date = {2010}, pages = {324--335}, publisher = {Springer}, url = {http://link.springer.com/content/pdf/10.1007/978-3-642-17319-6_31.pdf}, urldate = {2016-08-21} } @article{schmedesAutomatedIntegrationSafety, title = {Automated {{Integration}} of {{Safety Mechanisms}} into {{Functional Software}} for {{Safety-relevant Systems}}}, author = {Schmedes, Rolf and Nitsche, Gregor and Grüttner, Kim}, abstract = {Embedded systems have become indispensable in areas such as aerospace, the automotive industry, medical technology, and industrial control. Ensuring their reliable and safe operation is crucial for safeguarding human life and valuable assets. Software safety mechanisms play a key role in ensuring safety in these safety-relevant systems. International standards, such as IEC 61508, define necessary safety mechanisms to mitigate risks and achieve certification. This circumstance leads to an unchanging and recurring set of safety mechanisms that are manually re-implemented again and again from project to project. To make this software development process more efficient, this paper presents a model-based approach that utilizes a strict separation between functional and safety software to integrate safety mechanisms automatically into functional software for safety-related systems. An automated analysis of existing functional source code and the targeted hardware platform identifies possible integration points for safety mechanisms. A configuration a safety engineer creates is then used to integrate safety mechanisms from existing libraries through code generation. In addition to this approach, a prototypical implementation and its exemplary application are also described in this paper.}, langid = {english}, keywords = {LOGSEQ}, note = {\section{Annotazioni\\ (22/6/2024, 10:19:57)} \par - “Ensuring their reliable and safe operation is crucial for safeguarding human life and valuable asset” (Schmedes et al., p. 1) \#5fb236\\ \mkbibemph{ } \par - “safety mechanisms that are manually re-implemented again and again from project to project” (Schmedes et al., p. 1) \#a28ae5\\ \mkbibemph{ } \par - “model-based approach that utilizes a strict separation between functional and safety software to integrate safety mechanisms automatically into functional software for safety-related systems” (Schmedes et al., p. 1) \#a28ae5\\ \mkbibemph{ } \par - “identifies possible integration points for safety mechanisms.” (Schmedes et al., p. 1) \#ffd400\\ \mkbibemph{Interesting to see how!!! } \par - “software that is designed and implemented to ensure that it operates correctly and reliably, particularly in critical or hazardous situations where failures could lead to harm, injury, or damag” (Schmedes et al., p. 1) \#5fb236\\ \mkbibemph{ } \par - “Functionally safe software is the combination of functional software and safety software” (Schmedes et al., p. 1) \#a28ae5\\ \mkbibemph{ } \par - “systematic and rigorous process that considers the software’s functional and safety-related aspects.” (Schmedes et al., p. 1) \#a28ae5\\ \mkbibemph{ } \par - “Following those standards, the software design includes both the design of the functional software and the design of the safety software” (Schmedes et al., p. 1) \#a28ae5\\ \mkbibemph{ } \par - “integration of safety mechanisms increases the complexity of the overall code base” (Schmedes et al., p. 1) \#5fb236\\ \mkbibemph{ } \par - “no clear separation between functional and safety software.” (Schmedes et al., p. 1) \#a28ae5\\ \mkbibemph{ } \par - “Modifications to functional code may impact safety mechanisms and vice versa” (Schmedes et al., p. 1) \#a28ae5\\ \mkbibemph{ } \par - “Changes must be carefully analyzed to ensure they do not compromise safety requirements or introduce new risks.” (Schmedes et al., p. 1) \#5fb236\\ \mkbibemph{ } \par - “This tight coupling makes it more challenging to implement changes without affecting other aspects of the system.” (Schmedes et al., p. 1) \#a28ae5\\ \mkbibemph{ } \par - “assumption that a set of unchanging safety mechanisms must be implemented for the software system of a safety-relevant product.” (Schmedes et al., p. 1) \#ffd400\\ \mkbibemph{How strong/realistic is this assumption? IN the previous paragraphs authors have stressed the fact that software systems evolve and the coupled evolution of functionalities and software mechanisms occurs and it has to be properly managed. } \par - “his work presents a semi-automated process for integrating safety software mechanisms for safety-relevant systems.” (Schmedes et al., p. 1) \#a28ae5\\ \mkbibemph{ } \par - “weaving that is as automated as possible.” (Schmedes et al., p. 1) \#a28ae5\\ \mkbibemph{This reminds me generative programming and aspect oriented programming...... } \par - “The subsequent demonstration section shows the results using the example of an adaptive cruise controller.” (Schmedes et al., p. 2) \#5fb236\\ \mkbibemph{ } \par - “chapter” (Schmedes et al., p. 2) \#ff6666\\ \mkbibemph{SECTION } \par - “unctionally safe software” (Schmedes et al., p. 2) \#5fb236\\ \mkbibemph{ } \par - “software safety mechanisms required by common standard” (Schmedes et al., p. 2) \#5fb236\\ \mkbibemph{ } \par - “guidelines for designing, implementing, and maintaining systems intended for safety-relevant applications” (Schmedes et al., p. 2) \#5fb236\\ \mkbibemph{ } \par - “in [8]” (Schmedes et al., p. 2) \#ffd400\\ \mkbibemph{What's the novelty with respect to this work? } \par - “IEC 61508 is based on the principle that any safety-related system should either function correctly or fail predictably and safely under all possible stated conditions” (Schmedes et al., p. 2) \#a28ae5\\ \mkbibemph{ } \par - “the safety life cycle, consisting of 16 phases to achieve this objective.” (Schmedes et al., p. 2) \#5fb236\\ \mkbibemph{ } \par - “probabilistic failure approach to categorize the safety implications of a component’s failure” (Schmedes et al., p. 2) \#5fb236\\ \mkbibemph{ } \par - “hazard identification, analysis, and risk assessment” (Schmedes et al., p. 2) \#5fb236\\ \mkbibemph{ } \par - “Quantification of risk can be achieved through either qualitative or quantitative analysis techniques” (Schmedes et al., p. 2) \#a28ae5\\ \mkbibemph{ } \par - “safety integrity level (SIL)” (Schmedes et al., p. 2) \#5fb236\\ \mkbibemph{ } \par - “Safety integrity is defined as the likelihood that the safety-related system will effectively execute the necessary safety functions under all specified conditions” (Schmedes et al., p. 2) \#5fb236\\ \mkbibemph{ } \par - “Four distinct safety integrity levels outline the requirements for a particular function” (Schmedes et al., p. 2) \#a28ae5\\ \mkbibemph{ } \par - “to achieve a higher risk reduction, the safetyrelated system must have a higher reliability, which requires a correspondingly higher target SIL” (Schmedes et al., p. 2) \#a28ae5\\ \mkbibemph{ } \par - “The compatibility of those mechanisms with the presented approach will be discussed in the concluding section of this paper.” (Schmedes et al., p. 2) \#ffd400\\ \mkbibemph{Their compatibility with the proposed approach has been something consedered by design, or it happend "by accidence"? } \par - “Safety Mechanisms” (Schmedes et al., p. 2) \#5fb236\\ \mkbibemph{ } \par - “Contracts” (Schmedes et al., p. 2) \#5fb236\\ \mkbibemph{ } \par - “Dual Modular Redundancy” (Schmedes et al., p. 3) \#5fb236\\ \mkbibemph{ } \par - “safeguarded function is initially executed redundantly” (Schmedes et al., p. 3) \#5fb236\\ \mkbibemph{ } \par - “If the results match, there is no error.” (Schmedes et al., p. 3) \#a28ae5\\ \mkbibemph{ } \par - “even an external monitoring facility, depending on the actual implementation of the mechanism.” (Schmedes et al., p. 3) \#5fb236\\ \mkbibemph{ } \par - “Time Measurement and Control Blocks” (Schmedes et al., p. 3) \#5fb236\\ \mkbibemph{ } \par - “However, the complexity compared to AOP is reduced by limiting it to the two basic types of safety mechanisms,” (Schmedes et al., p. 6) \#5fb236\\ \mkbibemph{ } \par - “better compromise between complexity and effectiveness.” (Schmedes et al., p. 7) \#5fb236\\ \mkbibemph{ } \par - “However, the main distinction from AOP is the combined, holistic view of functional source code, safety mechanisms, and the targeted hardware platform.” (Schmedes et al., p. 7) \#5fb236\\ \mkbibemph{ }} } @article{schmidt2006model, title = {Model-Driven Engineering}, author = {Schmidt, Douglas C}, date = {2006}, journaltitle = {IEEE Comput.}, volume = {39}, number = {2}, pages = {25--31}, publisher = {IEEE} } @inproceedings{schmidtConstructingDifferenceTools2008, title = {Constructing Difference Tools for Models Using the {{SiDiff}} Framework}, booktitle = {Companion 13th {{Int}}. {{Conf}}. {{Softw}}. {{Eng}}. - {{ICSE Companion}} 08}, author = {Schmidt, Maik and Gloetzner, Tilman}, date = {2008}, pages = {947}, publisher = {ACM Press}, location = {Leipzig, Germany}, doi = {10.1145/1370175.1370201}, abstract = {Model-driven development requires a full set of development tools. While technologies for constructing graphical editors, compilers etc. are readily available, there is a lack of approaches for constructing version management tools which compare models and show their difference. The general problem is aggravated by the fact that such tools must consider the semantics of each particular model (or diagram) type, i.e. a whole family of tools needs to be constructed. This research demonstration shows how such families of difference tools can be constructed using the SiDiff framework.}, eventtitle = {Companion of the 13th International Conference}, isbn = {978-1-60558-079-1}, langid = {english} } @incollection{schneiderSymbolicModelGeneration2017, title = {Symbolic Model Generation for Graph Properties}, booktitle = {Fundamental {{Approaches}} to {{Software Engineering}}}, author = {Schneider, Sven and Lambers, Leen and Orejas, Fernando}, editor = {Huisman, Marieke and Rubin, Julia}, date = {2017}, volume = {10202}, pages = {226--243}, publisher = {Springer Berlin Heidelberg}, location = {Berlin, Heidelberg}, doi = {10.1007/978-3-662-54494-5_13}, isbn = {978-3-662-54493-8 978-3-662-54494-5}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {TL;DR \par It is shown that the tableau-based reasoning method for graph properties as introduced by Lambers and Orejas paves the way for a symbolic model generation algorithm for graph Properties, which gradually generates a finite set of so-called symbolic models, where each symbolic model describes a set of finite graphs satisfying the graph property.} } @inproceedings{schonbockModelDrivenCoevolutionAgile2015, title = {Model-{{Driven Co-evolution}} for {{Agile Development}}}, author = {Schonbock, J. and Etzlstorfer, J. and Kapsammer, E. and Kusel, A. and Retschitzegger, W. and Schwinger, W.}, date = {2015-01}, pages = {5094--5103}, publisher = {IEEE}, doi = {10.1109/HICSS.2015.603}, isbn = {978-1-4799-7367-5}, note = {TL;DR \par This paper provides an extensive survey evaluating various co-evolution approaches also from areas in software engineering like data, ontology, and grammar engineering on basis of a detailed set of criteria serving as a research roadmap for further developments in the area of co-Evolution for agile MDE.} } @misc{SCICOD2400068pdf, title = {{{SCICO-D-24-00068}}.Pdf}, langid = {english}, keywords = {⛔ No INSPIRE recid found,LOGSEQ} } @misc{SCICOD2400161_reviewer, title = {{{SCICO-D-24-00161}}\_reviewer}, keywords = {LOGSEQ}, note = {\section{Annotazioni\\ (30/8/2024, 02:27:49)} \par - “Although both standards are closely related and share essential concepts, it is important to note that they serve different purposes. While 42010 sets out general principles for describing architectures, 42020 provides” (“SCICO-D-24-00161\_reviewer”, pp. -) \#00b036 \par - “While the latter specifies the content and expression of an Architecture Description, the former focuses on its development and management.” (“SCICO-D-24-00161\_reviewer”, p. 1) \#00b036 \par - “The ISO/IEC/IEEE 42020 (42020 from here) is an international standard that provides guidelines for the development of software architectures and systems. The 42020 describes a set of processes for governance and management of a software architecture or a collection of software architectures, as well as for architecting their entities [1].” (“SCICO-D-24-00161\_reviewer”, p. 2) \#00b036 \par - “Standards 42020 provides guidelines for developing software architectures using Architectural Description. However, 42010 does not address how an Architectural Description should be developed and managed throughout the lifecycle of an entity, which is the case of 42020.” (“SCICO-D-24-00161\_reviewer”, p. 3) \#f0ff00\\ \mkbibemph{Verbose and repetitive intoduction} \par - “this article aims to combine this knowledge, identifying and presenting new approaches to designing software architectures that impact the practices related to this activity, disseminating the results of an exploratory study based on bibliographic analysis.” (“SCICO-D-24-00161\_reviewer”, p. 3) \#f0ff00\\ \mkbibemph{Why is this study needed? What are the challenges to be addressed?} \par - “both standards” (“SCICO-D-24-00161\_reviewer”, p. 6) \#5fb236 \par - “limits” (“SCICO-D-24-00161\_reviewer”, p. 6) \#5fb236 \par - “critical analysis” (“SCICO-D-24-00161\_reviewer”, p. 6) \#5fb236 \par - “confusion between architecture design” (“SCICO-D-24-00161\_reviewer”, p. 7) \#5fb236 \par - “software or sys-tems design” (“SCICO-D-24-00161\_reviewer”, p. 7) \#5fb236 \par - “software architecture design” concerns the conception and definitionof the fundamental structure of a software system, encompassing decision-making across various aspects of the system or software lifecycle” (“SCICO-D-24-00161\_reviewer”, p. 7) \#5fb236 \par - “components” (“SCICO-D-24-00161\_reviewer”, p. 7) \#5fb236 \par - “selection of architec-tural patterns and styles” (“SCICO-D-24-00161\_reviewer”, p. 7) \#5fb236 \par - “software architecture design” (“SCICO-D-24-00161\_reviewer”, p. 8) \#5fb236 \par - “software architecturedescriptions” (“SCICO-D-24-00161\_reviewer”, p. 8) \#5fb236 \par - “discussion about architecture and design” (“SCICO-D-24-00161\_reviewer”, p. 8) \#ffd400\\ \mkbibemph{This is the main point of discussion, isn’t it ?} \par - “These standards were developed to address the need for standardizedterminology, methodology, and tools for designing and managing Software Architectures” (“SCICO-D-24-00161\_reviewer”, p. 8) \#a28ae5 \par - “conceptual framework” (“SCICO-D-24-00161\_reviewer”, p. 8) \#a28ae5 \par - “managing architectures” (“SCICO-D-24-00161\_reviewer”, p. 8) \#a28ae5 \par - “Figure” (“SCICO-D-24-00161\_reviewer”, p. 10) \#ff6666\\ \mkbibemph{Some words in figure 2 are in Spanish.} \par - “The ISO/IEC/IEEE 42020and ISO/IEC/IEEE 42030 packages conform to the Architecture package by defining system or software architecture principles.” (“SCICO-D-24-00161\_reviewer”, p. 10) \#a28ae5 \par - “By addressing these aspects, this article aims toprovide a comprehensive analysis of standards 42010 and 42020, enriching the understanding of software architecture practices” (“SCICO-D-24-00161\_reviewer”, p. 10) \#5fb236 \par - “Architecture – a concept – what the act of architecting produces – a setof documents – and how to describe these documents – a standard – are notthe same thing” (“SCICO-D-24-00161\_reviewer”, p. 11) \#5fb236 \par - “42020, which establishes afoundation for sound architectural practices and terminology” (“SCICO-D-24-00161\_reviewer”, p. 11) \#5fb236 \par - “Standard 42010 is more focused on standardizing the structure and content of an Architectural Description document than on standardizing the process or methods for describing or documenting the architecture itself” (“SCICO-D-24-00161\_reviewer”, p. 12) \#ffd400\\ \mkbibemph{Said already many times.} \par - “Emphasis on Document Structures” (“SCICO-D-24-00161\_reviewer”, p. 12) \#5fb236 \par - “Non-Standardization of Description Process” (“SCICO-D-24-00161\_reviewer”, p. 12) \#5fb236 \par - “architecture description document” (“SCICO-D-24-00161\_reviewer”, p. 12) \#5fb236 \par - “The 42020 expands the scope of42010 and addresses the processes and practices involved in creating andmanaging systems or software architectures” (“SCICO-D-24-00161\_reviewer”, p. 13) \#a28ae5 \par - “Standard definition” (“SCICO-D-24-00161\_reviewer”, p. 13) \#5fb236 \par - “Scope” (“SCICO-D-24-00161\_reviewer”, p. 13) \#ffd400\\ \mkbibemph{Why not processes?} \par - “governance, management, conceptualization,evaluation, elaboration, and enablement” (“SCICO-D-24-00161\_reviewer”, p. 13) \#a28ae5 \par - “Purpose” (“SCICO-D-24-00161\_reviewer”, p. 13) \#5fb236 \par - “Interconnection with other standards” (“SCICO-D-24-00161\_reviewer”, p. 13) \#5fb236 \par - “Expanding the Role of Architecture:” (“SCICO-D-24-00161\_reviewer”, p. 14) \#5fb236 \par - “Impact on the occupation and education” (“SCICO-D-24-00161\_reviewer”, p. 14) \#5fb236 \par - “Role in standardization” (“SCICO-D-24-00161\_reviewer”, p. 14) \#5fb236 \par - “Elaboration” (“SCICO-D-24-00161\_reviewer”, p. 14) \#a28ae5 \par - “processes” (“SCICO-D-24-00161\_reviewer”, p. 14) \#ff6666\\ \mkbibemph{Among the scope isn’t it?} \par - “The Process of Describing an Architecture” (“SCICO-D-24-00161\_reviewer”, p. 14) \#ffd400\\ \mkbibemph{Only this section seems to be not enough to say that tha paper focuses on one specific standard because the whole paper presents and links together different standards with the same level of detail.} \par - “Architecture Elaboration” (“SCICO-D-24-00161\_reviewer”, p. 15) \#ffd400\\ \mkbibemph{What are the inputs/outputs from/to the other processes mentioned in the scope above?} \par - “This includes capturing the funda-mental concepts and properties of the architecture, aligning it with relevant requirements and design characteristics, and ensuring that it can guide the development and evolution of the entity being architected” (“SCICO-D-24-00161\_reviewer”, p. 15) \#a28ae5 \par - “The specific objectives of this process in-clude [2]:” (“SCICO-D-24-00161\_reviewer”, p. 15) \#ffd400\\ \mkbibemph{So far the paper seems to be an annotated bibliography. It’s an overview of a specific area, which has been done by considering several seminal papers. At this stage of the paper (more than half) I’m still missing the motivation of the paper, its novelty, and it’s scientific} \par - “The standards 42010 and 42020, which are involved with software orsystem architecture, are closely related but serve different purposes in thecontext of developing and documenting a software architecture” (“SCICO-D-24-00161\_reviewer”, p. 17) \#ffd400\\ \mkbibemph{This is by design, isn’t it?} \par - “Table 2: Characteristic/Purpose of each standard” (“SCICO-D-24-00161\_reviewer”, p. 18) \#ffd400\\ \mkbibemph{This table could complement fig 2 as part of the overview content summarising the different standards and their relationships.} \par - “One of the main strengths lies in the complementarity between the stan-dards. Together, 42010 and 42020 provide a robust conceptual framework (42010) and a practical guide (42020) for creating and documenting systemarchitectures [3, 2]. Both standards are designed to be flexible and adaptable to different contexts and types of architecture [20]. This allows organizationsto choose relevant parts of each standard and adjust them according to theirspecific needs.” (“SCICO-D-24-00161\_reviewer”, p. 19) \#ffd400\\ \mkbibemph{This is also a repetition.} \par - “Making the most of these standards requires an investment of time in understanding and implementing the guidelines. This may result in a steep learning curve, especially for teams inexperienced in system architecture.Proper training can help mitigate this challenge” (“SCICO-D-24-00161\_reviewer”, p. 19) \#ffd400\\ \mkbibemph{Can you give some evidence supporting such a claim? There are many sentences like this that are missing factual data supporting the given messages from the authors.} \par - “Lack of coordinationcan lead to inconsistency and redundancy in architecture documentation” (“SCICO-D-24-00161\_reviewer”, p. 19) \#ffd400\\ \mkbibemph{Same comment like the previous one.} \par - “document? We have an approach, but it raises questions” (“SCICO-D-24-00161\_reviewer”, p. 20) \#ffd400\\ \mkbibemph{No tools are mentioned in the paper.discussing them as possibly supporting means of the different standards can make the paper concrete and more interesting even for practitioners.} \par - “Another point is the flexibility in selecting different types of architectures,such as systems, software, and enterprise” (“SCICO-D-24-00161\_reviewer”, p. 20) \#ffd400\\ \mkbibemph{Can some supporting tools be of any help for this phase and also for other ones?} \par - “While 42010 setsout general principles for describing architectures, 42020 provides detailedguidance on implementing these principles in practice” (“SCICO-D-24-00161\_reviewer”, p. 21) \#ffd400\\ \mkbibemph{What are reference tools that can be used to,support such activities?}} } @article{scikit-learn, title = {Scikit-Learn: {{Machine}} Learning in {{Python}}}, author = {Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.}, date = {2011}, journaltitle = {J. Mach. Learn. Res.}, volume = {12}, pages = {2825--2830}, note = {TL;DR \par Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems, focusing on bringing machine learning to non-specialists using a general-purpose high-level language.} } @article{sculleyHiddenTechnicalDebt, title = {Hidden {{Technical Debt}} in {{Machine Learning Systems}}}, author = {Sculley, D and Holt, Gary and Golovin, Daniel and Davydov, Eugene and Phillips, Todd and Ebner, Dietmar and Chaudhary, Vinay and Young, Michael and Crespo, Jean-François and Dennison, Dan}, pages = {9}, abstract = {Machine learning offers a fantastically powerful toolkit for building useful complex prediction systems quickly. This paper argues it is dangerous to think of these quick wins as coming for free. Using the software engineering framework of technical debt, we find it is common to incur massive ongoing maintenance costs in real-world ML systems. We explore several ML-specific risk factors to account for in system design. These include boundary erosion, entanglement, hidden feedback loops, undeclared consumers, data dependencies, configuration issues, changes in the external world, and a variety of system-level anti-patterns.}, langid = {english}, keywords = {DONE,machine learning}, note = {\textbf{Green Annotations (18/12/2020, 18:46:01)} \par "Undeclared Consumers. Oftentimes, a prediction from a machine learning model ma is made widely accessible, either at runtime or by writing to files or logs that may later be consumed by other systems. Without access controls, some of these consumers may be undeclared, silently using the output of a given model as an input to another system. In more classical software engineering, these issues are referred to as visibility debt [13]." (\href{zotero://open-pdf/library/items/TJI3FLRY?page=2}{Sculley et al :10}) \par "It may be surprising to the academic community to know that only a tiny fraction of the code in many ML systems is actually devoted to learning or prediction - see Figure 1. In the language of Lin and Ryaboy, much of the remainder may be described as "plumbing" [11]." (\href{zotero://open-pdf/library/items/TJI3FLRY?page=4}{Sculley et al :12}) \par "Using generic packages often results in a glue code system design pattern, in which a massive amount of supporting code is written to get data into and out of general-purpose packages." (\href{zotero://open-pdf/library/items/TJI3FLRY?page=5}{Sculley et al :13}) \par "Because a mature system might end up being (at most) 5\% machine learning code and (at least) 95\% glue code, it may be less costly to create a clean native solution rather than re-use a generic package." (\href{zotero://open-pdf/library/items/TJI3FLRY?page=5}{Sculley et al :13}) \par "An important strategy for combating glue-code is to wrap black-box packages into common API's. This allows supporting infrastructure to be more reusable and reduces the cost of changing packages." (\href{zotero://open-pdf/library/items/TJI3FLRY?page=5}{Sculley et al :13}) \par "he resulting system for preparing data in an ML-friendly format may become a jungle of scrapes, joins, and sampling steps, often with intermediate files output." (\href{zotero://open-pdf/library/items/TJI3FLRY?page=5}{Sculley et al :13}) \par "Managing these pipelines, detecting errors and recovering from failures are all difficult and costly [1]." (\href{zotero://open-pdf/library/items/TJI3FLRY?page=5}{Sculley et al :13}) \par "Abstraction Debt." (\href{zotero://open-pdf/library/items/TJI3FLRY?page=5}{Sculley et al :13}) \par "The above issues highlight the fact that there is a distinct lack of strong abstractions to support ML systems." (\href{zotero://open-pdf/library/items/TJI3FLRY?page=5}{Sculley et al :13}) \par "What is the right interface to describe a stream of data, or a model, or a prediction?" (\href{zotero://open-pdf/library/items/TJI3FLRY?page=5}{Sculley et al :13}) \par \mkbibemph{THAT'S THE KEY!!!! (\href{zotero://open-pdf/library/items/TJI3FLRY?page=5}{note on p.13})} \par "distributed learning in particular, there remains a lack of widely accepted abstractions." (\href{zotero://open-pdf/library/items/TJI3FLRY?page=5}{Sculley et al :13}) \par \mkbibemph{DISTRIBUTED LEARNING (\href{zotero://open-pdf/library/items/TJI3FLRY?page=5}{note on p.13})} \par "The lack of standard abstractions makes it all too easy to blur the lines between components." (\href{zotero://open-pdf/library/items/TJI3FLRY?page=6}{Sculley et al :14}) \par "using multiple languages often increases the cost of effective testing and can increase the difficulty of transferring ownership to other individuals." (\href{zotero://open-pdf/library/items/TJI3FLRY?page=6}{Sculley et al :14}) \par \mkbibemph{THAT SUPPORTS THE NEED FOR DOMAIN-SPECIFIC LANGUAGES (\href{zotero://open-pdf/library/items/TJI3FLRY?page=6}{note on p.14})} \par "Another potentially surprising area where debt can accumulate is in the configuration of machine learning systems. Any large system has a wide range of configurable options, including which features are used, how data is selected, a wide variety of algorithm-specific learning settings, potential preor post-processing, verification methods, etc." (\href{zotero://open-pdf/library/items/TJI3FLRY?page=6}{Sculley et al :14}) \par "n a mature system which is being actively developed, the number of lines of configuration can far exceed the number of lines of the traditional code. Each configuration line has a potential for mistakes." (\href{zotero://open-pdf/library/items/TJI3FLRY?page=6}{Sculley et al :14}) \par "It should be easy to specify a configuration as a small change from a previous configuration. • It should be hard to make manual errors, omissions, or oversights. • It should be easy to see, visually, the difference in configuration between two models. • It should be easy to automatically assert and verify basic facts about the configuration: number of features used, transitive closure of data dependencies, etc. • It should be possible to detect unused or redundant settings. • Configurations should undergo a full code review and be checked into a repository." (\href{zotero://open-pdf/library/items/TJI3FLRY?page=6}{Sculley et al :14}) \par \mkbibemph{Support for domain-specific language!!!! (\href{zotero://open-pdf/library/items/TJI3FLRY?page=6}{note on p.14})} \par "Thus if a model updates on new data, the old manually set threshold may be invalid. Manually updating many thresholds across many models is time-consuming and brittle. One mitigation strategy for this kind of problem appears in [14], in which thresholds are learned via simple evaluation on heldout validation data." (\href{zotero://open-pdf/library/items/TJI3FLRY?page=7}{Sculley et al :15}) \par "Unit testing of individual components and end-to-end tests of running systems are valuable, but in the face of a changing world such tests are not sufficient to provide evidence that a system is working as intended." (\href{zotero://open-pdf/library/items/TJI3FLRY?page=7}{Sculley et al :15}) \par "data replaces code in ML systems" (\href{zotero://open-pdf/library/items/TJI3FLRY?page=7}{Sculley et al :15}) \par "code should be tested, then it seems clear that some amount of testing of input data is critical to a well-functioning system" (\href{zotero://open-pdf/library/items/TJI3FLRY?page=7}{Sculley et al :15}) \par \mkbibemph{Adversial Machine Learning??? (\href{zotero://open-pdf/library/items/TJI3FLRY?page=7}{note on p.15})} \par "non-determinism inherent in parallel learning" (\href{zotero://open-pdf/library/items/TJI3FLRY?page=8}{Sculley et al :16}) \par "Most of the use cases described in this paper have talked about the cost of maintaining a single model, but mature systems may have dozens or hundreds of models running simultaneously [14, 6]." (\href{zotero://open-pdf/library/items/TJI3FLRY?page=8}{Sculley et al :16}) \par \mkbibemph{THIS IS IMPORTANT ASPECTS TO BE MENTIONED AS MOTIVATION/CHALLENGE (\href{zotero://open-pdf/library/items/TJI3FLRY?page=8}{note on p.16})} \par "How easily can an entirely new algorithmic approach be tested at full scale?" (\href{zotero://open-pdf/library/items/TJI3FLRY?page=8}{Sculley et al :16}) \par "maintainable ML" (\href{zotero://open-pdf/library/items/TJI3FLRY?page=8}{Sculley et al :16}) \par "better abstractions" (\href{zotero://open-pdf/library/items/TJI3FLRY?page=8}{Sculley et al :16}) \par "testing methodologies" (\href{zotero://open-pdf/library/items/TJI3FLRY?page=8}{Sculley et al :16}) \par "design patterns" (\href{zotero://open-pdf/library/items/TJI3FLRY?page=8}{Sculley et al :16})} } @article{SEfSAS3challenges, title = {{{SEfSAS3-challenges}}} } @online{seGuestPostAdvanced2024, type = {Substack newsletter}, title = {📝 Guest Post: Advanced {{RAG}} Techniques: Bridging Text and Visuals for More Accurate Responses*}, shorttitle = {📝 Guest Post}, author = {Se, Ksenia}, date = {2024-12-09}, url = {https://thesequence.substack.com/p/guest-post-advanced-rag-techniques?publication_id=54309&utm_campaign=email-post-title&r=z2w67&utm_medium=email}, urldate = {2024-12-11}, abstract = {In this guest post, Fendy Feng from ZIlliz explores how RAG works, RAG challenges, and advanced RAG techniques like Small to Slide RAG and ColPali.}, langid = {english}, organization = {TheSequence}, keywords = {/unread} } @article{Segundo2017301, title = {Improvement of Newborn Screening Using a Fuzzy Inference System}, author = {Segundo, U. and Aldámiz-Echevarría, L. and López-Cuadrado, J. and Buenestado, D. and Andrade, F. and Pérez, T.A. and Barrena, R. and Pérez-Yarza, E.G. and Pikatza, J.M.}, date = {2017}, journaltitle = {Expert Syst. Appl.}, volume = {78}, pages = {301--318}, publisher = {Elsevier Ltd}, issn = {09574174}, doi = {10.1016/j.eswa.2017.02.022}, abstract = {This paper presents a decision support system (DSS) called DSScreening to rapidly detect inborn errors of metabolism (IEMs) in newborn screening (NS). The system has been created using the Aide-DS framework, which uses techniques imported from model-driven software engineering (MDSE) and soft computing, and it is available through eGuider, a web portal for the enactment of computerised clinical practice guidelines and protocols. MDSE provides the context and techniques to build new software artefacts based on models which conform to a specific metamodel. It also offers separation of concern, to disassociate medical from technological knowledge, thus allowing changes in one domain without affecting the other. The changes might include, for instance, the addition of new disorders to the DSS or new measures to the computation related to a disorder. Artificial intelligence and soft computing provide fuzzy logic to manage uncertainty and ambiguous situations. Fuzzy logic is embedded in an inference system to build a fuzzy inference system (FIS); specifically, a single-input rule modules connected zero-order Takagi-Sugeno FIS. The automatic creation of FISs is performed by the Aide-DS framework, which is capable of embedding the generated FISs in computerized clinical guidelines. It can also create a desktop application to execute the FIS. Technologically, it supports the addition of new target languages for the desktop applications and the inclusion of new ways of acquiring data. DSScreening has been tested by comparing its predictions with the results of 152 real analyses from two groups: (1) NS samples and (2) clinical samples belonging to individuals of all ages with symptoms that do not necessarily correspond to an IEM. The system has reduced the time needed by 98.7\% when compared to the interpretation time spent by laboratory professionals. Besides, it has correctly classified 100\% of the NS samples and obtained an accuracy of 70\% for samples belonging to individuals with clinical symptoms. © 2017 Elsevier Ltd}, coden = {ESAPE}, document_type = {Article}, source = {Scopus}, note = {cited By 10} } @article{sehrawatDataMiningIoT2018, title = {Data {{Mining}} in {{IoT}} and Its {{Challenges}}}, author = {Sehrawat, Deepti and {Department of Computer Science and Applications, Maharshi Dayanand University, Rohtak, Haryana, India} and Gill, Nasib Singh and {Department of Computer Science and Applications, Maharshi Dayanand University, Rohtak, Haryana, India}}, date = {2018-04-30}, journaltitle = {ijcse}, volume = {6}, number = {4}, pages = {289--295}, issn = {23472693}, doi = {10.26438/ijcse/v6i4.289295}, abstract = {Internet of Things (IoT) has provided enormous opportunities to make prevailing smart environment by influencing the increasing ubiquity of Radio Frequency Identification Devices (RFID), wireless network, and sensor devices. Recently, a large number of industrial IoT applications have embarked their presence. Rapid technological growth introduces tremendous information on the network. Big Data is an idea to assemble huge amount of data from IoT enabled devices like sensors, actuators in IoT smart environment to help monitor specific conditions, procedures, and system performance. In this new generation, it becomes more challenging to extract most relevant information quickly and efficiently. To solve this problem, a data mining technique widely known as automatic text summarization may also prove to be fruitful. Text summarization creates summarized information from a large text corpus. Various latest techniques used for text summarization viz. Classification, Particle Swarm Optimization, Genetic Algorithms, clustering, neural network and various hybridized approaches are presented in this paper. The latest and relevant algorithms may be customized in the context of IoT applications. This paper is aimed at reviewing these techniques and also discusses the challenges as well as other related research issues.}, langid = {english}, note = {\textbf{Blue Annotations (3/2/2022, 15:47:41)} \par "Big Data collects, process and analyse huge relevant data from internet enabled devices like sensors attached to monitor specific conditions, usage and system performance." (\href{zotero://open-pdf/library/items/3FC8SWRG?page=5}{Sehrawat et al 2018:293}) \par "Parallel programming model" (\href{zotero://open-pdf/library/items/3FC8SWRG?page=6}{Sehrawat et al 2018:294}) \par "find correlation among multi-faced data" (\href{zotero://open-pdf/library/items/3FC8SWRG?page=6}{Sehrawat et al 2018:294}) \par "text summarization techniques," (\href{zotero://open-pdf/library/items/3FC8SWRG?page=6}{Sehrawat et al 2018:294}) \par "extracting useful information from huge text corpus by protecting data and providing privacy and security to the secret and private data." (\href{zotero://open-pdf/library/items/3FC8SWRG?page=6}{Sehrawat et al 2018:294}) \par "Fast and effective data mining approaches for huge data corpus to read and write." (\href{zotero://open-pdf/library/items/3FC8SWRG?page=6}{Sehrawat et al 2018:294}) \par "uncertain" (\href{zotero://open-pdf/library/items/3FC8SWRG?page=6}{Sehrawat et al 2018:294}) \par "Integration of heterogeneous, noisy, incomplete, fuzzy, structured and semi-structured unstructured data sources and data types." (\href{zotero://open-pdf/library/items/3FC8SWRG?page=6}{Sehrawat et al 2018:294}) \par "and" (\href{zotero://open-pdf/library/items/3FC8SWRG?page=6}{Sehrawat et al 2018:294}) \par "Communication via diverse device types and systems and furthermore need to mine webpage data." (\href{zotero://open-pdf/library/items/3FC8SWRG?page=6}{Sehrawat et al 2018:294}) \par "Need to extract complex knowledge after analyzing properties of the data and finding association among them." (\href{zotero://open-pdf/library/items/3FC8SWRG?page=6}{Sehrawat et al 2018:294}) \par "Need to develop efficient and well-defined data mining framework which considers data security, data privacy, big data, and data sharing of prime importance" (\href{zotero://open-pdf/library/items/3FC8SWRG?page=6}{Sehrawat et al 2018:294}) \par "Need of enhanced parallel programming model by designing a dynamic multisource data mining model." (\href{zotero://open-pdf/library/items/3FC8SWRG?page=6}{Sehrawat et al 2018:294}) \par "data mining algorithms are not adapted to parallel platform and requires attention of researchers in this area." (\href{zotero://open-pdf/library/items/3FC8SWRG?page=6}{Sehrawat et al 2018:294})} } @incollection{seibelDedicatedLanguageContext2012, title = {A {{Dedicated Language}} for {{Context Composition}} and {{Execution}} of {{True Black-Box Model Transformations}}}, booktitle = {Software {{Language Engineering}}}, author = {Seibel, Andreas and Hebig, Regina and Neumann, Stefan and Giese, Holger}, editor = {Sloane, Anthony and Aßmann, Uwe}, date = {2012}, series = {Lecture {{Notes}} in {{Computer Science}}}, number = {6940}, pages = {19--39}, publisher = {Springer Berlin Heidelberg}, url = {http://link.springer.com/chapter/10.1007/978-3-642-28830-2_2}, urldate = {2015-03-24}, abstract = {Model-Driven Engineering (MDE) automates development activities by employing model transformations. Thereby, a plethora of model transformation approaches with individual capabilities have been developed. In certain cases, complex and automated MDE activities require the interaction of various, potentially heterogeneous, model transformations. This can be achieved by a loosely coupled and highly cohesive composition of model transformations implemented in different model transformation languages. However, existing approaches either do not support context composition, using other model transformations as additional context, or they violate the important black-box principle because they require adapting model transformations for context composition. In this paper, we present a dedicated model transformation composition framework (MoTCoF) that does not require the adaptation of model transformations and, thus, treats model transformations as true black-boxes. We illustrate our approach with an application example taken from an industrial case study.}, isbn = {978-3-642-28829-6 978-3-642-28830-2}, langid = {english}, keywords = {software engineering} } @article{SelectingThirdpartyLibraries, entrysubtype = {newspaper}, title = {Selecting {{Third-party Libraries}}: {{The Data Scientist}}’s {{Perspective}}} } @online{SelfmanagingInformationSystems, title = {Self-Managing Information Systems}, url = {http://www.inf.u-szeged.hu/~jelasity/selfstar05.html}, urldate = {2016-09-24} } @article{selimAutomatedVerificationModel2013, title = {Automated {{Verification}} of {{Model Transformations}} in the {{Automotive Industry}}}, author = {Selim, Gehan M. K. and Büttner, Fabian and Cordy, James R. and Dingel, Juergen and Wang, Shige}, date = {2013}, journaltitle = {Model-Driven Eng. Lang. Syst.}, volume = {8107}, pages = {690--706}, doi = {10.1007/978-3-642-41533-3_42}, note = {TL;DR \par This study reports on applying an automated verification approach to the GM-to-AUTOSAR transformation that is based on checking the satisfiability of a relational transformation representation, or a transformation model, with respect to well-formedness OCL constraints.} } @article{selimModelTransformationsMigrating2012, title = {Model {{Transformations}} for {{Migrating Legacy Models}}: {{An Industrial Case Study}}}, author = {Selim, Gehan M. K. and Wang, Shige and Cordy, James R. and Dingel, Juergen}, date = {2012}, journaltitle = {Model. Found. Appl.}, volume = {7349}, pages = {90--101}, doi = {10.1007/978-3-642-31491-9_9} } @inproceedings{SemerathBLMV20, title = {Automated Generation of Consistent Models with Structural and Attribute Constraints}, booktitle = {{{MoDELS}} 20 {{ACMIEEE}} 23rd {{Int}}. {{Conf}}. {{Model Driven Eng}}. {{Lang}}. {{Syst}}. {{Virtual Event Can}}. 18-23 {{Oct}}. 2020}, author = {Semeráth, Oszkár and Babikian, Aren A. and Li, Anqi and Marussy, Kristóf and Varró, Dániel}, editor = {Syriani, Eugene and Sahraoui, Houari A. and family=Lara, given=Juan, prefix=de, useprefix=true and Abrahão, Silvia}, date = {2020}, pages = {187--199}, publisher = {ACM}, doi = {10.1145/3365438.3410962}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {TL;DR \par A structural graph solver that uses partial models with an SMT-solver to automatically derive models which simultaneously fulfill structural and attribute constraints while key theoretical properties of model generation like completeness or diversity are still ensured are ensured.}, timestamp = {Sat, 09 Apr 2022 12:34:15 +0200} } @article{Sen2013236, title = {A Modified Differential Evolution for Symbol Detection in {{MIMO-OFDM}} System}, author = {Sen, A. and Roy, S. and Das, S.}, date = {2013}, journaltitle = {Lect. Notes Comput. Sci. Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinforma.}, volume = {8297 LNCS}, pages = {236--247}, issn = {03029743}, doi = {10.1007/978-3-319-03753-0_22}, abstract = {It is essential to estimate the Channel and detect symbol in multiple-input and multiple-output (MIMO)-orthogonal frequency division multiplexing (OFDM) systems. Symbol detection by applying the maximum likelihood (ML) detector gives excellent performance but in systems with higher number of antennas and greater constellation size, the computational complexity of this algorithm becomes quite high. In this paper we apply a recently developed modified Differential Evolution (DE) algorithm with novel mutation, crossover as well as parameter adaptation strategies (MDE-pBX) for reducing the search space of the ML detector and the computational complexity of symbol detection in MIMO-OFDM systems. The performance of MDE-pBX have been compared with two classical symbol detectors namely ML and ZF and two famous evolutionary algorithm namely SaDE and CLPSO. © 2013 Springer International Publishing.}, document_type = {Conference Paper}, isbn = {9783319037523}, issue = {PART 1}, source = {Scopus}, note = {cited By 0 \par TL;DR \par A recently developed modified Differential Evolution DE algorithm with novel mutation, crossover as well as parameter adaptation strategies MDE\_pBX is applied for reducing the search space of the ML detector and the computational complexity of symbol detection in MIMO-OFDM systems.} } @article{SenBV10, title = {Towards Domain-Specific Model Editors with Automatic Model Completion}, author = {Sen, Sagar and Baudry, Benoit and Vangheluwe, Hans}, date = {2010}, journaltitle = {Simul,}, volume = {86}, number = {2}, pages = {109--126}, doi = {10.1177/0037549709340530}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {TL;DR \par This paper presents a methodology to synthesize model editors equipped with automatic completion from a modeling language’s declarative specification consisting of a meta-model with a visual syntax, powered by a first-order relational logic engine implemented in ALLOY.}, timestamp = {Mon, 26 Oct 2020 08:24:52 +0100} } @article{SendallK03, title = {Model Transformation: The Heart and Soul of Model-Driven Software Development}, author = {Sendall, Shane and Kozaczynski, Wojtek}, date = {2003}, journaltitle = {IEEE Softw,}, volume = {20}, number = {5}, pages = {42--45}, doi = {10.1109/MS.2003.1231150}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Mon, 08 Jun 2020 22:31:27 +0200} } @inproceedings{serbanAdoptionEffectsSoftware2020, ids = {serbanAdoptionEffectsSoftware2020a}, title = {Adoption and {{Effects}} of {{Software Engineering Best Practices}} in {{Machine Learning}}}, booktitle = {Proc. 14th {{ACM IEEE Int}}. {{Symp}}. {{Empir}}. {{Softw}}. {{Eng}}. {{Meas}}. {{ESEM}}}, author = {Serban, Alex and family=Blom, given=Koen, prefix=van der, useprefix=true and Hoos, Holger and Visser, Joost}, date = {2020-10-05}, pages = {1--12}, publisher = {ACM}, location = {Bari Italy}, doi = {10.1145/3382494.3410681}, abstract = {Background. The increasing reliance on applications with machine learning (ML) components calls for mature engineering techniques that ensure these are built in a robust and future-proof manner. Aim. We aim to empirically determine the state of the art in how teams develop, deploy and maintain software with ML components. Method. We mined both academic and grey literature and identified 29 engineering best practices for ML applications. We conducted a survey among 313 practitioners to determine the degree of adoption for these practices and to validate their perceived effects. Using the survey responses, we quantified practice adoption, differentiated along demographic characteristics, such as geography or team size. We also tested correlations and investigated linear and non-linear relationships between practices and their perceived effect using various statistical models. Results. Our findings indicate, for example, that larger teams tend to adopt more practices, and that traditional software engineering practices tend to have lower adoption than ML specific practices. Also, the statistical models can accurately predict perceived effects such as agility, software quality and traceability, from the degree of adoption for specific sets of practices. Combining practice adoption rates with practice importance, as revealed by statistical models, we identify practices that are important but have low adoption, as well as practices that are widely adopted but are less important for the effects we studied. Conclusion. Overall, our survey and the analysis of responses received provide a quantitative basis for assessment and step-wise improvement of practice adoption by ML teams.}, eventtitle = {{{ESEM}} '20: {{ACM}} / {{IEEE International Symposium}} on {{Empirical Software Engineering}} and {{Measurement}}}, isbn = {978-1-4503-7580-1}, langid = {english}, keywords = {best practices,machine learning,software engineering}, note = {TL;DR \par The findings indicate, for example, that larger teams tend to adopt more practices, and that traditional software engineering practices tend to have lower adoption than ML specific practices.} } @article{serbanSurveyIntelligentAssistants2013, title = {A Survey of Intelligent Assistants for Data Analysis}, author = {Serban, Floarea and Vanschoren, Joaquin and Kietz, Jörg-Uwe and Bernstein, Abraham}, date = {2013-06}, journaltitle = {ACM Comput. Surv.}, volume = {45}, number = {3}, pages = {1--35}, issn = {0360-0300, 1557-7341}, doi = {10.1145/2480741.2480748}, abstract = {Research and industry increasingly make use of large amounts of data to guide decision-making. To do this, however, data needs to be analyzed in typically nontrivial refinement processes, which require technical expertise about methods and algorithms, experience with how a precise analysis should proceed, and knowledge about an exploding number of analytic approaches. To alleviate these problems, a plethora of different systems have been proposed that “intelligently” help users to analyze their data. This article provides a first survey to almost 30 years of research on intelligent discovery assistants (IDAs). It explicates the types of help IDAs can provide to users and the kinds of (background) knowledge they leverage to provide this help. Furthermore, it provides an overview of the systems developed over the past years, identifies their most important features, and sketches an ideal future IDA as well as the challenges on the road ahead.}, langid = {english}, note = {TL;DR \par The types of help IDAs can provide to users and the kinds of (background) knowledge they leverage to provide this help are explicated.} } @online{ServerlessApplicationsWhy, title = {Serverless {{Applications}}: {{Why}}, {{When}}, and {{How}}?}, url = {https://www.computer.org/csdl/magazine/so/2021/01/09190031/1mYZaiUIVhu}, urldate = {2021-01-17}, note = {TL;DR \par This work analyzes 89 serverless applications from open source projects, industrial sources, academic literature, and scientific computing-presenting the most extensive study to date.} } @incollection{sevillaruizInferringVersionedSchemas2015, title = {Inferring {{Versioned Schemas}} from {{NoSQL Databases}} and {{Its Applications}}}, booktitle = {Conceptual {{Modeling}}}, author = {Sevilla Ruiz, Diego and Morales, Severino Feliciano and García Molina, Jesús}, editor = {Johannesson, Paul and Lee, Mong Li and Liddle, Stephen W. and Opdahl, Andreas L. and Pastor López, Óscar}, date = {2015}, volume = {9381}, pages = {467--480}, publisher = {Springer International Publishing}, location = {Cham}, doi = {10.1007/978-3-319-25264-3_35}, abstract = {While the concept of database schema plays a central role in relational database systems, most NoSQL systems are schemaless: these databases are created without having to formally define its schema. Instead, it is implicit in the stored data. This lack of schema definition offers a greater flexibility; more specifically, the schemaless databases ease both the recording of non-uniform data and data evolution. However, this comes at the cost of losing some of the benefits provided by schemas. In this article, a MDE-based reverse engineering approach for inferring the schema of aggregate-oriented NoSQL databases is presented. We show how the obtained schemas can be used to build database utilities that tackle some of the problems encountered using implicit schemas: a schema diagram viewer and a data validator generator are presented.}, isbn = {978-3-319-25263-6 978-3-319-25264-3}, langid = {english}, note = {TL;DR \par A MDE-based reverse engineering approach for inferring the schema of aggregate-oriented NoSQL databases is presented and it is shown how the obtained schemas can be used to build database utilities that tackle some of the problems encountered using implicit schemas.} } @article{shafiqMachineLearningSoftware2020, ids = {Shafiq2020MachineLF}, title = {Machine {{Learning}} for {{Software Engineering}}: {{A Systematic Mapping}}}, shorttitle = {Machine {{Learning}} for {{Software Engineering}}}, author = {Shafiq, Saad and Mashkoor, Atif and Mayr-Dorn, Christoph and Egyed, Alexander}, date = {2020-05-27}, journaltitle = {ArXiv200513299 Cs}, eprint = {2005.13299}, eprinttype = {arXiv}, eprintclass = {cs}, url = {http://arxiv.org/abs/2005.13299}, urldate = {2020-10-26}, abstract = {Objective: This article addresses the aforementioned problem and aims to present a state-of-the-art on the growing number of uses of machine learning in software engineering. Method: We conduct a systematic mapping study on applications of machine learning to software engineering following the standard guidelines and principles of empirical software engineering. Results: This study introduces a machine learning for software engineering (MLSE) taxonomy classifying the state-of-the-art machine learning techniques according to their applicability to various software engineering life cycle stages. Overall, 227 articles were rigorously selected and analyzed as a result of this study. Conclusion: From the selected articles, we explore a variety of aspects that should be helpful to academics and practitioners alike in understanding the potential of adopting machine learning techniques during software engineering projects.}, langid = {english}, keywords = {machine learning} } @article{shahrivarBusinessModelCommercial2018, title = {A Business Model for Commercial Open Source Software: {{A}} Systematic Literature Review}, shorttitle = {A Business Model for Commercial Open Source Software}, author = {Shahrivar, Shahrokh and Elahi, Shaban and Hassanzadeh, Alireza and Montazer, Gholamali}, date = {2018-11-01}, journaltitle = {Information and Software Technology}, volume = {103}, pages = {202--214}, issn = {0950-5849}, doi = {10.1016/j.infsof.2018.06.018}, abstract = {Context Commercial open source software (COSS) and community open source software (OSS) are two types of open source software. The former is the newer concept with the grounds for research such as business model. However, in the literature of open source software, the revenue model has been studied as a business model, which is one component of the business model. Therefore, there is a need for a more complete review of the COSS business model with all components. Objective The purpose of this research is to describe and present the COSS business model with all its components. Method A systematic literature review of the COSS business model was conducted and 1157 studies were retrieved through search in six academic databases. The result of the process of selecting the primary studies was 21 studies. By backward snowballing, we discovered 10 other studies, and thus a total of 31 studies were found. Then, the grounded theory coding procedures were used to determine the characteristics and components of the COSS business model. Results The COSS business model was presented with value proposition, value creation \& delivery, and value capture. This business model includes eight components: COSS products and complementarities, COSS clients and users, COSS competitive strategies, organizational aspects of COSS, position of COSS producers in the value network, resources and capabilities of COSS business, COSS revenue sources, and COSS cost-benefit. Conclusion This study provides a complete illustration of the COSS business model. Identifies COSS generic competitive strategies. By cost-benefit component, we have considered both tangible and intangible components. This business model is especially effective in developing countries. In future research, it is necessary to review the management of the COSS community, the organization, the new revenue models for disruptive ability of open source software, and the localization of open source software.} } @article{shakyaSupportModelbasedData, title = {Support for Model-Based Data Sovereignty Analysis}, author = {Shakya, Sanjeev Sun and Ramadan, Qusai and Flake, Julian and Peikert, Alexander}, abstract = {The rapid growth of data as a commodity has led to the rise of data space ecosystems, emphasizing secure and sovereign data exchange across company borders. The International Data Spaces Reference Architecture Model (IDS-RAM) and Dataspace Protocol represent frameworks designed to enable secure and standardized data sharing across organizations and sectors. As with any traditional system, to ensure compliance with data security and sovereignty requirements, adherence to IDS-RAM and data space protocols must be integrated from the design phase of data space systems. For this, our contributions in this paper are threefold: (i) a mapping from requirements of IDS-RAM and Dataspace Protocol specifications to model-based analysis checks of UMLsec and existing extensions, (ii) two data sovereignty-oriented checks as extensions to UMLsec, namely Usage Control and Transfer Process Protocol, and (iii) an applicability evaluation based on a case study of the European Health Data Space (EHDS).}, langid = {english}, keywords = {⛔ No DOI found}, note = {All these requirements are high level and it is not clear who is supposed to use it and how they are implemented/met. \par - “Requirement” (Shakya et al., p. 8) \#ffd400 \par \section{Annotazioni\\ (15/1/2025, 23:21:06)} \par - “Data Sovereignty Analysis” (Shakya et al., p. 1) \#5fb236 \par - “The rapid growth of data as a commodity has led to the rise of data space ecosystems, emphasizing secure and sovereign data exchange across company borders” (Shakya et al., p. 1) \#5fb236 \par - “data security and sovereignty requirements” (Shakya et al., p. 1) \#5fb236 \par - “design phase of data space system” (Shakya et al., p. 1) \#5fb236 \par - “mapping from requirements of IDS-RAM and Dataspace Protocol specifications to model-based analysis checks of UMLsec and existing extensions” (Shakya et al., p. 1) \#2ea8e5 \par - “two data sovereignty-oriented checks as extensions to UMLsec,” (Shakya et al., p. 1) \#2ea8e5 \par - “applicability evaluation based on a case study of the European Health Data Space (EHDS).” (Shakya et al., p. 1) \#2ea8e5 \par - “Recently, many businesses and corporations have started to see data as a product that can be shared and monetized.” (Shakya et al., p. 1) \#5fb236 \par - “data spaces” (Shakya et al., p. 1) \#a28ae5 \par - “data sovereignty is the ability to create self-defined data usage rules, influence, and track data flows, and freely share and migrate data as desired.” (Shakya et al., p. 1) \#a28ae5 \par - “an” (Shakya et al., p. 1) \#ff6666 \par - “International Data Spaces Association” (Shakya et al., p. 1) \#2ea8e5 \par - “International Data Space Reference Architecture Model” (Shakya et al., p. 1) \#2ea8e5 \par - “To mitigate challenges in detecting violations of data security and sovereignty, it is crucial to incorporate the IDS-RAM and Dataspace Protocol specifications from the initial stages of data space design.” (Shakya et al., p. 1) \#e56eee \par - “design of data space system architectures” (Shakya et al., p. 1) \#a28ae5 \par - “UMLsec” (Shakya et al., p. 2) \#a28ae5 \par - “their applicability to the domain of data spaces remains insufficiently explored.” (Shakya et al., p. 2) \#a28ae5 \par - “lack of understanding of the extent to which existing UML-based security extensions can be leveraged to address the security specifications of IDS-RAM and Dataspace Protocol.” (Shakya et al., p. 2) \#ffd400\\ \mkbibemph{Why considering UML? It is a modeling language that was not intended to be used for dataspace modeling, isn't it?} \par - “we map the data security, privacy, and sovereignty aspects of IDS-RAM and Dataspace Protocol to an existing UML-based analysis method called UMLsec.” (Shakya et al., p. 2) \#2ea8e5 \par - “identify which IDSRAM and Dataspace Protocol specifications currently have at least partial reasoning support during the design phase.” (Shakya et al., p. 2) \#2ea8e5 \par - “UMLsec as the basis for our mapping due to its continuous development since its introduction in 2005, providing support for various security and privacy aspects across different levels of abstraction.” (Shakya et al., p. 2) \#a28ae5 \par - “(i) Usage Control Check” (Shakya et al., p. 2) \#2ea8e5 \par - “(ii) Transfer Process Protocol Check” (Shakya et al., p. 2) \#2ea8e5 \par - “The goal of this check is to ensure compliance with the expected flow of data transfer instructions.” (Shakya et al., p. 2) \#5fb236 \par - “European Health Data Space (EHDS)” (Shakya et al., p. 2) \#2ea8e5 \par - “UMLsec, IDS-RAM and Dataspace protocol” (Shakya et al., p. 2) \#5fb236 \par - “secure systems development.” (Shakya et al., p. 2) \#e56eee \par - “Verifying UMLsec diagrams” (Shakya et al., p. 2) \#ffd400\\ \mkbibemph{What kinds of verification can be done on UMLsec diagrams?} \par - “UMLsec extension to enable reasoning on privacy-specific aspects like purpose control and data retention” (Shakya et al., p. 2) \#5fb236 \par - “UMLsec4IDS, a profile designed to enable reasoning about security-specific concepts introduced in IDS-RAM 3.0, focusing on security-specific aspects such as identity management and access control” (Shakya et al., p. 2) \#5fb236 \par - “International Data Spaces Reference Architecture Model” (Shakya et al., p. 2) \#5fb236 \par - “IDS-RAM” (Shakya et al., p. 2) \#5fb236\\ \mkbibemph{Data space reference architecture model.} \par - “enhances data sovereignty through dynamic data usage control for real-time policy enforcement” (Shakya et al., p. 2) \#5fb236 \par - “including security” (Shakya et al., p. 2) \#5fb236 \par - “trust management,” (Shakya et al., p. 2) \#5fb236 \par - “usage control” (Shakya et al., p. 2) \#5fb236 \par - “data provenance” (Shakya et al., p. 2) \#5fb236 \par - “Dataspace Protocol.” (Shakya et al., p. 2) \#a28ae5 \par - “Table 1 The Relevant UMLsec Checks” (Shakya et al., p. 3) \#ffd400\\ \mkbibemph{All of these are based on architectural models, isn't it?} \par - “The protocols outline the structures and procedures necessary for entities to publish data, negotiate agreements, and access data as components of a data space” (Shakya et al., p. 3) \#5fb236 \par - “promoting interoperability and maintaining data sovereignty within data spaces” (Shakya et al., p. 3) \#5fb236 \par - “extracting the requirements is a challenging task and it is not possible to automatically specify all the architectural details of the system and verify them against IDS requirements based solely on the documents of IDS-RAM and Dataspace Protocol.” (Shakya et al., p. 4) \#5fb236 \par - “IDS-RAM and protocols requirements to model-based analysis checks is not always possible.” (Shakya et al., p. 4) \#ffd400\\ \mkbibemph{If this is the limitation that the authors want to address, then a better introduction is needed. In other words what are the research goals of this paper?} \par - “security-, privacy-, and sovereignty-relevant requirements.” (Shakya et al., p. 4) \#a28ae5 \par - “partial analysis support for them” (Shakya et al., p. 4) \#5fb236 \par - “The output of this phase is a list of requirements and a document that describes the mapping results.” (Shakya et al., p. 4) \#2ea8e5 \par - “Therefore, in this phase, software developers can refine existing UML-based analysis checks and/or develop new ones to better align with the extracted requirements” (Shakya et al., p. 4) \#5fb236 \par - “"Participants must adhere to the Transfer Process Protocol (TPP) during data transfer operations” (Shakya et al., p. 4) \#5fb236 \par - “This phase provides the evaluation based on a case study that describes the European Health Data Space” (Shakya et al., p. 4) \#5fb236 \par - “The model can then be automatically verified against these requirements. In our work, we use the UMLsec profile to annotate the UML model with the requirements and CARiSMA for verifying the models against them.” (Shakya et al., p. 4) \#ffd400\\ \mkbibemph{This is done with what was already existing right? It's not done with some novel means defined in this paper.} \par - “If the architectural model does not satisfy all the requirements, corrections are necessary” (Shakya et al., p. 4) \#a28ae5 \par - “data space model meets the security and data sovereignty requirements specified in IDS-RAM and Dataspace Protocol.” (Shakya et al., p. 4) \#a28ae5 \par - “conceptual level based on identified textual requirements and the corresponding textual descriptions of existing UMLsec checks from (Jürjens 2002), UMLsec4IDS checks from (Peikert 2023) and UMLsec checks for privacy from (Ahmadian, Strüber, et al. 2017).” (Shakya et al., p. 4) \#ffd400 \par - “each requirement” (Shakya et al., p. 4) \#2ea8e5 \par - “existing UMLsec check” (Shakya et al., p. 4) \#2ea8e5 \par - “different system design views” (Shakya et al., p. 4) \#2ea8e5 \par - “36 security-, privacy-, and sovereignty-specific requirements.” (Shakya et al., p. 4) \#ffd400\\ \mkbibemph{We are at page 4 and we are still missing concrete examples of concrete models or checks that are in the focus of the work.} \par - “For each group, we provide a table that lists the requirements under that group alongside our mappings to the corresponding UMLsec checks. The label "N/A" in the tables indicate that no UMLsec check was identified that corresponds to the requirement.” (Shakya et al., p. 4) \#ffd400\\ \mkbibemph{It's difficult to grasp the details here without concrete and explanatory examples.} \par - “G1. Secure Communication.” (Shakya et al., p. 4) \#2ea8e5 \par - “Table 2 lists the IDS-RAM requirements that need to be realized to establish a secure communication channel between IDS components. Also the table shows the result of our mappings to UMLsec checks” (Shakya et al., p. 4) \#a28ae5 \par - “G2. Secure Platform.” (Shakya et al., p. 4) \#2ea8e5 \par - “security mechanisms” (Shakya et al., p. 4) \#5fb236 \par - “Figure 1 Methodology to achieve our research goals.” (Shakya et al., p. 5) \#ffd400 \par - “(G1) Secure Communication” (Shakya et al., p. 5) \#a28ae5 \par - “UMLsec checks” (Shakya et al., p. 5) \#ffd400\\ \mkbibemph{There is only one of them in Table 3.} \par - “G3. Identity and Trust Management.” (Shakya et al., p. 5) \#2ea8e5 \par - “IDS-RAM provides support for identity management, ensuring that all participants in a data space are authenticated and authorized” (Shakya et al., p. 5) \#ffd400\\ \mkbibemph{how is such a support provided?} \par - “G4. Data Usage Control.” (Shakya et al., p. 5) \#2ea8e5 \par - “mechanisms and policies that govern how data can be used after it has been accessed or shared” (Shakya et al., p. 5) \#a28ae5 \par - “access restrictions, privacy constraints, or limitations on redistribution” (Shakya et al., p. 5) \#e56eee \par - “UMLsec check.” (Shakya et al., p. 6) \#ffd400\\ \mkbibemph{Where are they defined? who define them? Where and how are they executed?} \par - “Our mapping results show that many requirements have partial support, while some requirements currently lack any model-based analysis support. This indicates a gap at the modelbased development level that may require further development to enable a comprehensive analysis for the requirements of IDS-RAM and Dataspace Protocol.” (Shakya et al., p. 6) \#ffd400\\ \mkbibemph{Is this the goal of the paper?} \par - “UMLsec” (Shakya et al., p. 6) \#ffd400\\ \mkbibemph{Some preliminar overview is needed to understand the proposed extensions.} \par - “Usage Control Check” (Shakya et al., p. 6) \#2ea8e5 \par - “Transfer Process Protocol” (Shakya et al., p. 6) \#2ea8e5 \par - “Extension4IDS and uses the UML profile mechanism, a lightweight extension mechanism of the UML 2.5 metamodel” (Shakya et al., p. 6) \#5fb236 \par - “Data Usage Control is a critical concept within the IDS framework, ensuring that data shared between entities is used according to predefined policies” (Shakya et al., p. 6) \#5fb236 \par - “Usage control” (Shakya et al., p. 6) \#2ea8e5 \par - “connectors serve as secure gateways that facilitate data exchange while measuring the enforced usage policies” (Shakya et al., p. 6) \#2ea8e5 \par - “Profile Details.” (Shakya et al., p. 6) \#2ea8e5 \par - “«UsageControl»” (Shakya et al., p. 6) \#5fb236 \par - “«IDSconnector»:” (Shakya et al., p. 6) \#5fb236 \par - “Constraint of UsageControl Check” (Shakya et al., p. 6) \#2ea8e5 \par - “The deployment diagram presented in Figure 3 illustrates that the dependency link between the artifacts deployed in the "Router A" and "Router B" has «UsageControl» as the requirement, but only the artifact deployed in "Router A" is annotated with «IDSconnector» stereotype, suggesting that "Router B" doesn’t use IDS connector for data exchange. This indicates that the participants involved in the data exchange cannot guarantee usage control, or a malicious participant can simply ignore the usage control policies associated with the exchanged data set. So, if the Usage Control Check is executed in this model, the check fails. To fix the model in Figure 3, the software artifact deployed on "Route” (Shakya et al., p. 6) \#ffd400\\ \mkbibemph{How and where the semantics of the stereotypes used in the model in Fig. 3 are given.} \par - “Transfer Process Protocol i” (Shakya et al., p. 7) \#5fb236 \par - “extension4ids” (Shakya et al., p. 7) \#ffd400 \par - “«ProviderConnector»” (Shakya et al., p. 7) \#2ea8e5 \par - “Lifeline that represents the Provider’s connector” (Shakya et al., p. 7) \#ffd400 \par - “«ConsumerConnector»” (Shakya et al., p. 7) \#2ea8e5 \par - “Requirement” (Shakya et al., p. 8) \#ffd400\\ \mkbibemph{All these requirements are high level and it is not clear who is supposed to use it and how they are implemented/met.} \par - “«TransferProcessProtocol»” (Shakya et al., p. 9) \#2ea8e5 \par - “1. The data transfer process must begin with a "transfer request" message, which is sent by the Consumer (B, annotated as «ConsumerConnector») to the Provider (A, annotated as «ProviderConnector»). 2. The Provider (A) must respond with a "transfer start" message. 3. Depending on the type of transfer: – For a push transfer, the next message must be sent by the Provider (A) to the Consumer (B). – For a pull transfer, the next message must be sent by the Consumer (B) to the Provider (A). 4. After a successful transfer: – For a push transfer, the Provider (A) must send a "transfer complete" message. – For a pull transfer, the "transfer complete" message can be sent by either the Provider (A) or the Consumer (B). 5. If "transfer suspend" message is sent, then there should be either "transfer start" or "transfer terminate" message. 6. Mutual Exclusion: If a "transfer complete" message is sent, neither a "terminate" nor a "suspend" message can be sent afterward, and vice versa.” (Shakya et al., p. 9) \#ffd400\\ \mkbibemph{This description should be better aligned with~ Fig. 4, which seems that does not represent all the details given in the text.} \par - “figure” (Shakya et al., p. 9) \#ffd400\\ \mkbibemph{which one?} \par - “Pull message should be sent from the Consumer to the Provider, but in this case, the Pull message is sent by the Provider to the Consumer” (Shakya et al., p. 9) \#a28ae5 \par - “the check result is shown in Figure 7. The report clearly states the issue with the model. The check generates similar results for all other possible order violations that might occur.” (Shakya et al., p. 9) \#ff6666 \par - “new checks” (Shakya et al., p. 9) \#ffd400\\ \mkbibemph{Honestly they are not properly presented/introduced!} \par - “Scenario 1” (Shakya et al., p. 10) \#2ea8e5 \par - “Scenario 2” (Shakya et al., p. 10) \#2ea8e5 \par - “all model-based analysis checks” (Shakya et al., p. 10) \#ffd400\\ \mkbibemph{It's not clear how they have been implemented and checked.} \par - “Based on the check results, if a check fail, the corresponding UML diagram was corrected and analyzed until the check was successful.” (Shakya et al., p. 10) \#ffd400\\ \mkbibemph{The applied process is not clearly defined.} \par - “As part of our evaluation,” (Shakya et al., p. 10) \#ffd400\\ \mkbibemph{WHat are the research questions that you aimed at answering with the performed evaluation?} \par - “For Usage Control check, we conducted 5 different tests, which provided a code coverage of 89.9\%, calculated using EclEmma (Hoffmann et al. 2024).” (Shakya et al., p. 10) \#ffd400\\ \mkbibemph{How should read such a number?} \par - “: (i) it provides a mapping from the requirements of IDS-RAM and Dataspace Protocol specifications to existing model-based analysis checks in UMLsec,” (Shakya et al., p. 10) \#2ea8e5 \par - “(ii) it introduces two data sovereignty checks, namely UsageControl and TransferProcessProtocol,” (Shakya et al., p. 10) \#ffd400\\ \mkbibemph{Can you motivate why you decided to implement such checks?} \par - “(iii) it conducts an applicability evaluation through a case study based on the European Health Data Space (EHDS).” (Shakya et al., p. 10) \#ffd400 \par - “the study did not encompass the full spectrum of potential requirements” (Shakya et al., p. 10) \#5fb236 \par - “new profiles and checks could address these granular requirements to better address security and sovereignty concerns of data spaces” (Shakya et al., p. 10) \#ffd400\\ \mkbibemph{WHat are the requirements that need to be addressed and that will underpin the definition of new profiles and checks as claimed this discussion section?} \par If this is the limitation that the authors want to address, then a better introduction is needed. \par - “IDS-RAM and protocols requirements to model-based analysis checks is not always possible.” (Shakya et al., p. 4) \#ffd400} } @inproceedings{shaoQuantifyMusicArtist2008, title = {Quantify Music Artist Similarity Based on Style and Mood}, booktitle = {Proc. 10th {{ACM Workshop Web Inf}}. {{Data Manag}}.}, author = {Shao, Bo and Li, Tao and Ogihara, Mitsunori}, date = {2008}, series = {{{WIDM}} '08}, pages = {119--124}, publisher = {ACM}, location = {New York, NY, USA}, url = {http://doi.acm.org/10.1145/1458502.1458522}, acmid = {1458522}, isbn = {978-1-60558-260-3}, nodoi = {10.1145/1458502.1458522}, numpages = {6}, keywords = {hierarchical co-clustering,music artist similarity,similarity quantification} } @software{shapiroDaveshapOpenAI_Agent_Swarm2025, title = {Daveshap/{{OpenAI}}\_{{Agent}}\_{{Swarm}}}, author = {Shapiro, David}, date = {2025-05-07T23:48:18Z}, origdate = {2023-11-07T23:12:05Z}, url = {https://github.com/daveshap/OpenAI_Agent_Swarm}, urldate = {2025-05-08}, abstract = {HAAS = Hierarchical Autonomous Agent Swarm - "Resistance is futile!"}, keywords = {agent,autonomous,swarm} } @article{Sharifnia2021, title = {Robust Simulation Optimization for Supply Chain Problem under Uncertainty via Neural Network Metamodeling}, author = {Sharifnia, S.M.E. and Amrollahi Biyouki, S. and Sawhney, R. and Hwangbo, H.}, date = {2021}, journaltitle = {Comput. Ind. Eng.}, volume = {162}, publisher = {Elsevier Ltd}, issn = {03608352}, doi = {10.1016/j.cie.2021.107693}, abbrev_source_title = {Comput Ind Eng}, affiliation = {Department of Industrial and Systems Engineering, The University of Tennessee, Knoxville, United States}, art_number = {107693}, coden = {CINDD}, correspondence_address1 = {Sharifnia, S.M.E.; Department of Industrial and Systems Engineering, United States; email: ssharifn@vols.utk.edu}, document_type = {Article}, langid = {english}, source = {Scopus}, note = {cited By 2} } @inproceedings{Sharma:2017:CGR:3084226.3084287, title = {Cataloging {{GitHub}} Repositories}, booktitle = {Proc. 21st {{Int}}. {{Conf}}. {{Eval}}. {{Assess}}. {{Softw}}. {{Eng}}.}, author = {Sharma, Abhishek and Thung, Ferdian and Kochhar, Pavneet Singh and Sulistya, Agus and Lo, David}, date = {2017}, series = {{{EASE}}'17}, pages = {314--319}, publisher = {ACM}, location = {New York, NY, USA}, url = {http://doi.acm.org.univaq.clas.cineca.it/10.1145/3084226.3084287}, acmid = {3084287}, isbn = {978-1-4503-4804-1}, nodoi = {10.1145/3084226.3084287}, numpages = {6}, keywords = {Genetic Algorithm,GitHub,Latent Dirichlet Allocation}, note = {TL;DR \par This work explores the possibility of developing automatic cataloging system for GitHub by automatically extracting functionality descriptive text segments from readme files of GitHub repositories and demonstrates that additional meaningful categories which complement existing GitHub categories can be inferred.} } @article{shevtsovControlTheoreticalSoftwareAdaptation2018, title = {Control-{{Theoretical Software Adaptation}}: {{A Systematic Literature Review}}}, shorttitle = {Control-{{Theoretical Software Adaptation}}}, author = {Shevtsov, Stepan and Berekmeri, Mihaly and Weyns, Danny and Maggio, Martina}, date = {2018-08-01}, journaltitle = {IIEEE Trans. Software Eng.}, volume = {44}, number = {8}, pages = {784--810}, issn = {0098-5589, 1939-3520, 2326-3881}, doi = {10.1109/TSE.2017.2704579}, abstract = {Modern software applications are subject to uncertain operating conditions, such as dynamics in the availability of services and variations of system goals. Consequently, runtime changes cannot be ignored, but often cannot be predicted at design time. Control theory has been identified as a principled way of addressing runtime changes and it has been applied successfully to modify the structure and behavior of software applications. Most of the times, however, the adaptation targeted the resources that the software has available for execution (CPU, storage, etc.) more than the software application itself. This paper investigates the research efforts that have been conducted to make software adaptable by modifying the software rather than the resource allocated to its execution. This paper aims to identify: the focus of research on control-theoretical software adaptation; how software is modeled and what control mechanisms are used to adapt software; what software qualities and controller guarantees are considered. To that end, we performed a systematic literature review in which we extracted data from 42 primary studies selected from 1,512 papers that resulted from an automatic search. The results of our investigation show that even though the behavior of software is considered non-linear, research efforts use linear models to represent it, with some success. Also, the control strategies that are most often considered are classic control, mostly in the form of Proportional and Integral controllers, and Model Predictive Control. The paper also discusses sensing and actuating strategies that are prominent for software adaptation and the (often neglected) proof of formal properties. Finally, we distill open challenges for control-theoretical software adaptation.}, langid = {english} } @article{Shi:2014:CFB:2620784.2556270, title = {Collaborative Filtering beyond the User-Item Matrix: {{A}} Survey of the State of the Art and Future Challenges}, author = {Shi, Yue and Larson, Martha and Hanjalic, Alan}, date = {2014-05}, journaltitle = {ACM Comput. Surv.}, volume = {47}, number = {1}, pages = {3:1-3:45}, publisher = {ACM}, address = {New York, NY, USA}, issn = {0360-0300}, url = {http://doi.acm.org/10.1145/2556270}, acmid = {2556270}, articleno = {3}, issue_date = {July 2014}, nodoi = {10.1145/2556270}, numpages = {45}, keywords = {Algorithms,applications,challenges,collaborative filtering,recommender systems,social networks,survey} } @article{Shi2021, title = {A Physics-Informed Deep Learning Paradigm for Traffic State and Fundamental Diagram Estimation}, author = {Shi, R. and Mo, Z. and Huang, K. and Di, X. and Du, Q.}, date = {2021}, journaltitle = {IEEE Trans. Intell. Transp. Syst.}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {15249050}, doi = {10.1109/TITS.2021.3106259}, abstract = {Traffic state estimation (TSE) bifurcates into two main categories, model-driven and data-driven (e.g., machine learning, ML) approaches, while each suffers from either deficient physics or small data. To mitigate these limitations, recent studies introduced hybrid methods, such as physics-informed deep learning (PIDL), which contains both model-driven and data-driven components. This paper contributes an improved paradigm, called physics-informed deep learning with a fundamental diagram learner (PIDL + FDL), which integrates ML terms into the model-driven component to learn a functional form of a fundamental diagram (FD), i.e., a mapping from traffic density to flow or velocity. The proposed PIDL + FDL has the advantages of performing the TSE learning, model parameter identification, and FD estimation simultaneously. This paper focuses on highway TSE with observed data from loop detectors, using traffic density or velocity as traffic variables. We demonstrate the use of PIDL + FDL to solve popular first-order and second-order traffic flow models and reconstruct the FD relation as well as model parameters that are outside the FD term. We then evaluate the PIDL + FDL-based TSE using the Next Generation SIMulation (NGSIM) dataset. The experimental results show the superiority of the PIDL + FDL in terms of improved estimation accuracy and data efficiency over advanced baseline TSE methods, and additionally, the capacity to properly learn the unknown underlying FD relation. IEEE}, document_type = {Article}, source = {Scopus}, note = {cited By 0} } @online{shiEfficientGreenLarge2024, title = {Efficient and Green Large Language Models for Software Engineering: Vision and the Road Ahead}, shorttitle = {Efficient and {{Green Large Language Models}} for {{Software Engineering}}}, author = {Shi, Jieke and Yang, Zhou and Lo, David}, date = {2024-04-06}, eprint = {2404.04566}, eprinttype = {arXiv}, eprintclass = {cs}, url = {http://arxiv.org/abs/2404.04566}, urldate = {2024-04-12}, abstract = {Large Language Models (LLMs) have recently shown remarkable capabilities in various software engineering tasks, spurring the rapid development of the Large Language Models for Software Engineering (LLM4SE) area. However, limited attention has been paid to crafting efficient LLM4SE solutions that demand minimal time and memory resources, as well as green LLM4SE solutions that reduce energy consumption and carbon emissions. This 2030 Software Engineering position paper aims to redirect the focus of the research community towards the efficiency and greenness of LLM4SE, while also sharing potential research directions to achieve this goal. It commences with a brief overview of the significance of LLM4SE and highlights the need for efficient and green LLM4SE solutions. Subsequently, the paper presents a vision for a future where efficient and green LLM4SE revolutionizes the software engineering tool landscape, benefiting various stakeholders, including industry, individual practitioners, and society. The paper then delineates a roadmap for future research, outlining specific research paths and potential solutions for the research community to pursue. While not intended to be a definitive guide, the paper aims to inspire further progress, with the ultimate goal of establishing efficient and green LLM4SE as a central element in the future of software engineering.}, langid = {english}, pubstate = {prepublished}, keywords = {Computer Science - Software Engineering} } @inproceedings{shinDynamicAdaptationSoftwaredefined2020, title = {Dynamic Adaptation of Software-Defined Networks for {{IoT}} Systems: A Search-Based Approach}, shorttitle = {Dynamic Adaptation of Software-Defined Networks for {{IoT}} Systems}, booktitle = {Proc. {{IEEEACM}} 15th {{Int}}. {{Symp}}. {{Softw}}. {{Eng}}. {{Adapt}}. {{Self-Manag}}. {{Syst}}.}, author = {Shin, Seung Yeob and Nejati, Shiva and Sabetzadeh, Mehrdad and Briand, Lionel C. and Arora, Chetan and Zimmer, Frank}, date = {2020-06-29}, pages = {137--148}, publisher = {ACM}, location = {Seoul Republic of Korea}, doi = {10.1145/3387939.3391603}, eventtitle = {{{SEAMS}} '20: {{IEEE}}/{{ACM}} 15th {{International Symposium}} on {{Software Engineering}} for {{Adaptive}} and {{Self-Managing Systems}}}, isbn = {978-1-4503-7962-5}, langid = {english}, keywords = {DONE} } @article{shinNoSQLDatabaseDesign2017, ids = {shinNoSQLDatabaseDesign2017a}, title = {{{NoSQL Database Design Using UML Conceptual Data Model Based}} on {{Peter Chen}}’s {{Framework}}}, author = {Shin, Kwangchul and Hwang, Chulhyun and Jung, Hoekyung}, date = {2017}, volume = {12}, number = {5}, pages = {5}, abstract = {In the Big Data era, relational databases and NoSQL databases coexist in Polyglot Persistence environment. Although data management is more essential in an environment where a variety of databases are, NoSQL databases only concentrate on solving non-functional requirements to run well on large clusters. This situation makes consistent data management standards difficult. To solve this problem, this study proposes NoSQL database design method using conceptual data model based on Peter Chen’s framework. The proposed design method is applied to the e-commerce business area in order to examine the applicability of it.}, langid = {english}, note = {TL;DR \par The proposed NoSQL database design method using conceptual data model based on Peter Chen’s framework is applied to the e-commerce business area in order to examine the applicability of it.} } @inproceedings{shresthaAutomaticGenerationSimulink2020, title = {Automatic {{Generation}} of {{Simulink Models}} to {{Find Bugs}} in a {{Cyber-Physical System Tool Chain}} Using {{Deep Learning}}}, booktitle = {Proc. - 2020 {{ACMIEEE}} 42nd {{Int}}. {{Conf}}. {{Softw}}. {{Eng}}. {{Companion ICSE-Companion}} 2020}, author = {Shrestha, S.L.}, date = {2020}, pages = {110--112}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1145/3377812.3382163}, abstract = {Testing cyber-physical system (CPS) development tools such as MathWorks' Simulink is very important as they are widely used in design, simulation, and verification of CPS data-flow models. Existing randomized differential testing frameworks such as SLforge leverages semi-formal Simulink specifications to guide random model generation which requires significant research and engineering investment along with the need to manually update the tool, whenever MathWorks updates model validity rules. To address the limitations, we propose to learn validity rules automatically by learning a language model using our framework DeepFuzzSL from existing corpus of Simulink models. In our experiments, DeepFuz-zSL consistently generate over 90\% valid Simulink models and also found 2 confirmed bugs by MathWorks Support. © 2020 ACM.}, isbn = {978-1-4503-7122-3}, keywords = {Automatic Generation,Cyber Physical System,Cyber-physical systems (CPS),Data flow analysis,Dataflow model,Deep learning,Development tools,Differential testing,Embedded systems,Language model,Learning systems,Model validity,Program debugging,Simulink models,Software engineering}, note = {cited By 0 \par TL;DR \par This work proposes to learn validity rules automatically by learning a language model using the authors' framework DeepFuzzSL from existing corpus of Simulink models, which consistently generate over 90\% valid Simulinking models and also found 2 confirmed bugs by MathWorks Support.} } @inproceedings{Shriver:ICSE:2021, title = {Reducing {{DNN}} Properties to Enable Falsification with Adversarial Attacks}, booktitle = {43rd {{Int}}. {{Conf}}. {{Softw}}. {{Eng}}.}, author = {Shriver, David and Elbaum, Sebastian and Dwyer, Matthew B.}, date = {2021}, publisher = {IEEE}, note = {TL;DR \par This paper introduces a semantics-preserving reduction of multiple safety property types into a set of equivalid correctness problems amenable to adversarial attacks, and evaluates the reduction approach as an enabler of falsification on a range of DNN correctness problems and shows its cost-effectiveness and scalability.} } @article{Sidhu2022166, title = {A Machine Learning Approach to Software Model Refactoring}, author = {Sidhu, B.K. and Singh, K. and Sharma, N.}, date = {2022}, journaltitle = {Int. J. Comput. Appl.}, volume = {44}, number = {2}, pages = {166--177}, publisher = {{Taylor and Francis Ltd.}}, issn = {1206212X}, doi = {10.1080/1206212X.2020.1711616}, abbrev_source_title = {Int J Comput Appl}, affiliation = {Department of Computer Science and Engineering, Punjabi University, Patiala, India; University Computer Centre, Punjabi University, Patiala, India; Department of Computer Science, Punjabi University, Patiala, India}, coden = {IJCAF}, correspondence_address1 = {Sidhu, B.K.; Department of Computer Science and Engineering, Punjab, India; email: brahmaleen.ce@pbi.ac.in}, document_type = {Article}, langid = {english}, source = {Scopus}, keywords = {GOAL\_Model-Refactoring,notion,TECHNIQUE\_DNN}, note = {cited By 5 \par TL;DR \par It is proposed that identifying design flaws at a higher level of granularity will save from the vicious cycle of small refactoring operations and their cascaded side-effects.} } @book{siegwartIntroductionAutonomousMobile2004, title = {Introduction to Autonomous Mobile Robots}, author = {Siegwart, Roland and Nourbakhsh, Illah Reza}, date = {2004}, series = {Intelligent Robots and Autonomous Agents}, publisher = {MIT Press}, location = {Cambridge, Mass}, isbn = {978-0-262-19502-7}, pagetotal = {321}, note = {"A Bradford book." \par TL;DR \par Bringing together all aspects of mobile robotics into one volume, Introduction to Autonomous Mobile Robots can serve as a textbook or a working tool for beginning practitioners.} } @article{sierraSurveySelfadmittedTechnical2019, title = {A Survey of Self-Admitted Technical Debt}, author = {Sierra, Giancarlo and Shihab, Emad and Kamei, Yasutaka}, date = {2019-06}, journaltitle = {Journal of Systems and Software}, volume = {152}, pages = {70--82}, issn = {01641212}, doi = {10.1016/j.jss.2019.02.056}, langid = {english} } @incollection{sifakisAutonomousSystemsArchitectural2019, title = {Autonomous {{Systems}} – {{An Architectural Characterization}}}, booktitle = {Models, {{Languages}}, and {{Tools}} for {{Concurrent}} and {{Distributed Programming}}}, author = {Sifakis, Joseph}, editor = {Boreale, Michele and Corradini, Flavio and Loreti, Michele and Pugliese, Rosario}, date = {2019}, volume = {11665}, pages = {388--410}, publisher = {Springer International Publishing}, location = {Cham}, doi = {10.1007/978-3-030-21485-2_21}, isbn = {978-3-030-21484-5 978-3-030-21485-2}, langid = {english}, note = {TL;DR \par It is concluded that autonomy is a kind of broad intelligence that should be associated with functionality and not with specific techniques, and a general computational model combining a system architecture model and an agent model is proposed.} } @article{Sikdar2014225, title = {Modified Differential Evolution for Biochemical Name Recognizer}, author = {Sikdar, U.K. and Ekbal, A. and Saha, S.}, date = {2014}, journaltitle = {Lect. Notes Comput. Sci. Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinforma.}, volume = {8403 LNCS}, pages = {225--236}, publisher = {Springer Verlag}, issn = {03029743}, doi = {10.1007/978-3-642-54906-9_18}, abstract = {In this paper we propose a modified differential evolution (MDE) based feature selection and ensemble learning algorithms for biochemical entity recognizer. Identification and classification of chemical entities are relatively more complex and challenging compared to the other related tasks. As chemical entities we focus on IUPAC and IUPAC related entities. The algorithm performs feature selection within the framework of a robust machine learning algorithm, namely Conditional Random Field. Features are identified and implemented mostly without using any domain specific knowledge and/or resources. In this paper we modify traditional differential evolution to perform two tasks, viz. determining relevant set of features as well as determining proper voting weights for constructing an ensemble. The feature selection technique produces a set of potential solutions on the final population. We develop many models of CRF using these feature combinations. In order to further improve the performance the outputs of these classifiers are combined together using a classifier ensemble technique based on modified DE. Our experiments with the benchmark datasets yield the recall, precision and F-measure values of 82.34\%, 88.26\% and 85.20\%, respectively. © 2014 Springer-Verlag Berlin Heidelberg.}, document_type = {Conference Paper}, isbn = {9783642549052}, issue = {PART 1}, source = {Scopus}, note = {cited By 2} } @inproceedings{silvaSUAVEExemplarSelfAdaptive2023, ids = {silvaSUAVEExemplarSelfadaptive2023}, title = {{{SUAVE}}: {{An Exemplar}} for {{Self-Adaptive Underwater Vehicles}}}, shorttitle = {{{SUAVE}}}, booktitle = {2023 {{IEEEACM}} 18th {{Symp}}. {{Softw}}. {{Eng}}. {{Adapt}}. {{Self-Manag}}. {{Syst}}. {{SEAMS}}}, author = {Silva, Gustavo Rezende and Päßler, Juliane and Zwanepol, Jeroen and Alberts, Elvin and Tarifa, S. Lizeth Tapia and Gerostathopoulos, Ilias and Johnsen, Einar Broch and Corbato, Carlos Hernández}, date = {2023-05}, pages = {181--187}, publisher = {IEEE}, location = {Melbourne, Australia}, doi = {10.1109/SEAMS59076.2023.00031}, abstract = {Word count: 336 Autonomous robots must operate in diverse environments and handle multiple tasks despite uncertainties. This creates challenges in designing software architectures and task decisionmaking algorithms, as different contexts may require distinct task logic and architectural configurations. To address this, robotic systems can be designed as self-adaptive systems capable of adapting their task execution and software architecture at runtime based on their context. This paper introduces ROSA, a novel knowledge-based framework for RObot SelfAdaptation, which enables task-and-architecture co-adaptation (TACA) in robotic systems. ROSA achieves this by providing a knowledge model that captures all application-specific knowledge required for adaptation and by reasoning over this knowledge at runtime to determine when and how adaptation should occur. In addition to a conceptual framework, this work provides an open-source ROS 2-based reference implementation of ROSA and evaluates its feasibility and performance in an underwater robotics application. Experimental results highlight ROSA's advantages in reusability and development effort for designing self-adaptive robotic systems.Implementing task logic and architectural adaptation for autonomous robots to cope with multiple tasks in diverse environments is a challenge. Methods from self-adaptive systems can be used to reduce development effort and improve maintainability by providing separation of concerns. However, only a few works have explored both task and architectural adaptation, and they do not address extensibility to new tasks or reuse across robotic applications. This paper proposes a novel knowledge-based solution for RObot Self-Adaptation (ROSA) that addresses the previous limitations and simplifies the design of task-and-architecture co-adaptation (TACA) in robotic applications. ROSA is a modular knowledgebased managing subsystem that solves TACA by providing a knowledge model that can capture all the application-specific knowledge required for the adaptation logic, and by reasoning uniquely about the captured knowledge at runtime to decide when and how the robotic system should adapt. This work provides a ROS 2-based open-source reference implementation of ROSA and demonstrate its feasibility and performance in an underwater robotics application. The experimental evaluation highlights the advantages of using ROSA to develop selfadaptive robotic systems in respect to reusability and development effort.}, eventtitle = {2023 {{IEEE}}/{{ACM}} 18th {{Symposium}} on {{Software Engineering}} for {{Adaptive}} and {{Self-Managing Systems}} ({{SEAMS}})}, isbn = {979-8-3503-1192-1}, langid = {english}, note = {\section{Annotazioni\\ (16/12/2024, 22:55:42)} \par - “ask-and-architecture co-adaptation” (Silva et al., 2023, p. 1) \#a28ae5 \par - “reasoning uniquely about the captured knowledge at runtime 13 to decide when and how the robotic system should adapt. T” (Silva et al., 2023, p. 1) \#5fb236 \par - “enable robots to autonomously accomplish multiple tasks in diverse environments despite 21 internal and environmental uncertainties.” (Silva et al., 2023, p. 1) \#5fb236 \par - “navigation action that relies on vision-based localization cannot be executed in environments without lights, 24 but could potentially be executed with an alternative localization strategy based on lidar” (Silva et al., 2023, p. 1) \#a28ae5 \par - “y during runtime.” (Silva et al., 2023, p. 1) \#5fb236 \par - “TACA” (Silva et al., 2023, p. 1) \#5fb236\\ \mkbibemph{Task and architecture co-adaptation.} \par - “structural adaptation (i.e., activating or deactivating software components in the architecture) and parameter 30 adaptation.” (Silva et al., 2023, p. 2) \#5fb236 \par - “reusability, composability, and extensibility” (Silva et al., 2023, p. 2) \#5fb236 \par - “a modular architecture for self-adaptive robotic systems” (Silva et al., 2023, p. 2) \#ffd400\\ \mkbibemph{What's the novelty with respect to existing work?} \par - “a reusable knowledge model” (Silva et al., 2023, p. 2) \#ffd400\\ \mkbibemph{WHat are the characteristics of such a knowledge? How is it maintained? What feeds it?} \par - “underwater pipeline inspection.” (Silva et al., 2023, p. 3) \#a28ae5 \par - “searching for the 80 pipeline” (Silva et al., 2023, p. 3) \#5fb236 \par - “simultaneously following and inspecting the pipeline” (Silva et al., 2023, p. 3) \#5fb236 \par - “its task execution and architecture to perform A3” (Silva et al., 2023, p. 3) \#ffd400\\ \mkbibemph{Why do you need to TASK and ARCHITECTURE CO-ADAPTATION? What kind architecture adaptation is required to recharge the battery?} \par - “new reaction time metric that represents the time a managing 93 system takes to react to uncertainties and adapt the managed subsystem.” (Silva et al., 2023, p. 3) \#5fb236 \par - “To the best of our knowledge, none of these works propose a solution for TACA that can be reused 99 for different applications without requiring the adaptation logic to be at least partially re-programmed 100 (see Table 1).” (Silva et al., 2023, p. 3) \#ffd400\\ \mkbibemph{It is necessary to be more clear here, maybe by referring to some explanatory examples to better discuss the claims.} \par - “adapt the robot’s architecture 116 given its task plan.” (Silva et al., 2023, p. 4) \#5fb236 \par - “Previous works have already proposed using knowledge models to capture how a robot is architected in 141 order to represent the available capabilities of the robot and using this knowledge at runtime for either task 142 or configuration decision-making” (Silva et al., 2023, p. 5) \#5fb236 \par - “do not capture all knowledge required for TACA” (Silva et al., 2023, p. 5) \#ffd400\\ \mkbibemph{What does it mean? It's vague how it's written.} \par - “lack” (Silva et al., 2023, p. 5) \#a28ae5 \par - “relation between the robot’s actions and capabilities” (Silva et al., 2023, p. 5) \#a28ae5 \par - “configuration alternatives” (Silva et al., 2023, p. 5) \#a28ae5 \par - “knowledge required to decide when and how the robotic system should 147 adapt” (Silva et al., 2023, p. 5) \#a28ae5 \par - “ROSA’s architecture” (Silva et al., 2023, p. 5) \#5fb236 \par - “This work assumes that the robotics architecture is layered, containing a bottom functional layer, 154 an upper task decision layer, and an arbitrary number of layers in between, as common in robotics 155 architectures (Barnett et al., 2022).” (Silva et al., 2023, p. 5) \#5fb236 \par - “To enable architectural adaptation, the robotic 160 architecture must be component-based, its components must be able to be activated and deactivated at 161 runtime, and its components’ parameters must be able to be adapted at runtime.” (Silva et al., 2023, p. 5) \#ffd400\\ \mkbibemph{Check if later there is some mention about how states are synched} \par - “: (1) all knowledge required for the adaptation logic is captured in the central KB” (Silva et al., 2023, p. 6) \#5fb236 \par - “no inter-component communication between the MAPE components” (Silva et al., 2023, p. 6) \#a28ae5 \par - “he MAPE 169 components insert and read data from or to the KB via standardized interfaces” (Silva et al., 2023, p. 6) \#a28ae5 \par - “no explicit 170 coordination between the MAPE-K components.” (Silva et al., 2023, p. 6) \#a28ae5 \par - “Figure” (Silva et al., 2023, p. 6) \#ffd400\\ \mkbibemph{Is it only for presentation purposes? What's the semantics of the proposed models? What is supposed to read/execute the elements that are shown in the models in Fig. 2?} \par - “actions the robot can accomplish” (Silva et al., 2023, p. 6) \#2ea8e5 \par - “set of 184 functionalities the robot needs to realize an action” (Silva et al., 2023, p. 6) \#2ea8e5 \par - “set of components required to realize a functionality” (Silva et al., 2023, p. 6) \#2ea8e5 \par - “possible parameters for a component” (Silva et al., 2023, p. 6) \#2ea8e5 \par - “At runtime, ROSA performs parameter adaptation by switching the selected component 194 configurations. It performs structural adaptation by changing the selected function designs 195 and consequently the active Components. The task decision layer in combination with ROSA performs 196 TACA by selecting suitable function designs and component configurations for each Action 197 the robot needs to perform, and with the task decision layer selecting different Actions to perform 198 according to the feasible configurations.” (Silva et al., 2023, p. 6) \#ffd400\\ \mkbibemph{WHat is the execution environment? Up to this point, concepts are presented at a~ higher level of abstractions and they are missing details conveing information on why thins actually work in practice.} \par - “Architectural” (Silva et al., 2023, p. 7) \#ffd400\\ \mkbibemph{According to the text this should be Architectural Knowledge} \par - “The adaptation heuristic knowledge enables adaptation due to changes in the environment or the 204 system’s QAs with the constraint relationship by capturing constraints on the selection of Actions, 205 Components, function designs, or component configurations in terms of measured values of 206 Measures. It enables adaptation due to component failures by capturing a Component’s status as an 207 attribute and adaptation due to changes in the robot’s task execution by capturing what Actions need 208 to be performed with the required action relationship. At runtime, adaptation is triggered when a 209 measurement violates a constraint, when a component has a failure status, or when required actions change.” (Silva et al., 2023, p. 7) \#ffd400\\ \mkbibemph{This is high level and it does not highlight novel concepts with respect to what is existing in the control systems, the seams community.} \par - “To capture the decision criteria on how to select an adaptation, the priority attribute can be used to 211 express the order of priority for selecting each function design or component configuration. 212 For more complex criteria, the estimation relationship can be used to capture the estimated impact of 213 selecting a function design, Component, or component configuration on the measured values 214 of Measures. Furthermore, a required action can relate to a Measure to indicate the preferred 215 estimation when selecting a configuration for that action. At runtime, the configuration planner 216 component exploits this knowledge to decide which configuration to select.” (Silva et al., 2023, p. 7) \#ffd400\\ \mkbibemph{Some concrete examples need to be shown to give the possibility to appreciate novel contributions.} \par - “The proposed knowledge model was presented as a conceptual data model (CDM) in a non-machine240 readable format.” (Silva et al., 2023, p. 10) \#5fb236 \par - “Figure 3.” (Silva et al., 2023, p. 11) \#ffd400\\ \mkbibemph{EVen in this case, what is the formalism used to specify and execute the given rules?} \par - “TypeDB” (Silva et al., 2023, p. 12) \#ffd400\\ \mkbibemph{Some introductory paragraphs are need to present the technology and give preliminary notions that are needed to understand their usage later in the paper.} \par - “The component-status-confi” (Silva et al., 2023, p. 12) \#ff6666\\ \mkbibemph{Mind the format of the paper, this goes outside the page margin.} \par - “ROS DiagnosticArray” (Silva et al., 2023, p. 13) \#ffd400\\ \mkbibemph{Is there any specific format to use for created diagnostic messages?} \par - “To enable ROSA to leverage ROS 2 327 mechanisms to adapt the system, the knowledge model was extended to capture knowledge about ROS 2 328 components as depicted in Figure 4.” (Silva et al., 2023, p. 13) \#ffd400\\ \mkbibemph{It is important to highlight/stress earlier in the paper when presenting the Component concept, that this will get instantiated when implementing the approach by using ROS technologies.} \par - “PDDL problem formulation with information” (Silva et al., 2023, p. 14) \#5fb236 \par - “on whether the ROSA 355 actions are feasible or not using the aforementioned “action feasible ?action” PDDL predicate” (Silva et al., 2023, p. 14) \#5fb236 \par - “ROSA’s development” (Silva et al., 2023, p. 16) \#ffd400\\ \mkbibemph{It is important to highlight in this paper what is the novel contributions with respect to the work~available at~https://github.com/kas-lab/suave} \par - “To solve the adaptation scenarios of the SUAVE exemplar with ROSA, the model depicted in Figure 6 410 was created.” (Silva et al., 2023, p. 16) \#ffd400\\ \mkbibemph{It is necessary to clarify what is the execution environment and all the technical details that are needed to read and understand the model given in Fig. 6.} \par - “ROSA model for SUAVE.” (Silva et al., 2023, p. 16) \#ffd400\\ \mkbibemph{What is the process to create ROSA models like the one shown in Fig. 6?} \par - “Critical battery level (U3), occurring only in the extended SUAVE use case, was solved with TACA by 426 extending the knowledge model with a recharge action, and a battery level constraint to the search 427 pipeline and inspect pipeline actions, representing the minimum battery level at which they can be selected. 428 Runtime behavior: when the battery level drops below 0.25, the status of both the search pipeline and 429 inspect pipeline actions is set to unfeasible, and the task decision layer cannot select them anymore. 430 Therefore, the task decision layer selects the recharge action which also triggers structural adaptation” (Silva et al., 2023, p. 17) \#ffd400\\ \mkbibemph{What is the advantage of this formalism with respect to what already exist? It is necessary to provide a comparison and explicitely mention the strenghts of the proposed approach.} \par \href{zotero://note/u/2FXMQ36P/}{Welcome to Better Notes}} } @article{simGettingWholeStory2011, title = {Getting the Whole Story: An Experience Report on Analyzing Data Elicited Using the War Stories Procedure}, shorttitle = {Getting the Whole Story}, author = {Sim, Susan Elliott and Alspaugh, Thomas A.}, date = {2011-08}, journaltitle = {Empir Software Eng}, volume = {16}, number = {4}, pages = {460--486}, issn = {1382-3256, 1573-7616}, doi = {10.1007/s10664-011-9157-9}, abstract = {When analyzing data elicited using the “war stories” technique, previously introduced by Lutters and Seaman (Inf Softw Technol 49(6):576–587, 2007), we encountered unexpected challenges in applying standard qualitative analysis techniques. After reviewing the literature on stories and storytelling, we realized that a richer analysis would be possible if we accorded more respect to the data’s structure and nature as stories, rather than treating our participants’ utterances simply as textual data. We report on five lessons learned regarding how we can better analyze war stories as stories: 1) war stories tend to be about exceptional situations; 2) war stories tend to be diverse and resistant to being combined into a single grand narrative; 3) the humanities can be a valuable resource for analyzing war stories; 4) war stories are not just text, they are also performances; and 5) war stories are not just data, they are also instructive and evocative.}, langid = {english} } @online{SimilarityMatrix, title = {Similarity Matrix}, url = {https://docs.google.com/spreadsheets/d/1jJ7FGuN1I7cWJZw4J6dO-KYaU118AFtpbvNzC01re0c/edit?usp=sharing&usp=embed_facebook}, urldate = {2020-02-11}, abstract = {Petrinet Subject/dataset,petrinet2.ecore,PetriNet.ecore,petrinet\_extendable.ecore,PetriNets.ecore,petri\_nets.ecore,petrinet\_tgg\_rule.ecore,PetrinetDsl.ecore,PetriNet\_extended.ecore,PetriNetModel.ecore,petri.ecore petrinet2.ecore,100,33,33,66,33,0,25,33,33,50 PetriNet.ecore,20,100,64,20,37,0,20,4...}, langid = {british}, organization = {Google Docs} } @online{simonMethodologyEvaluatingRAG2024, title = {A {{Methodology}} for {{Evaluating RAG Systems}}: {{A Case Study On Configuration Dependency Validation}}}, shorttitle = {A {{Methodology}} for {{Evaluating RAG Systems}}}, author = {Simon, Sebastian and Mailach, Alina and Dorn, Johannes and Siegmund, Norbert}, date = {2024-10-11}, eprint = {2410.08801}, eprinttype = {arXiv}, eprintclass = {cs}, doi = {10.48550/arXiv.2410.08801}, abstract = {Retrieval-augmented generation (RAG) is an umbrella of different components, design decisions, and domain-specific adaptations to enhance the capabilities of large language models and counter their limitations regarding hallucination and outdated and missing knowledge. Since it is unclear which design decisions lead to a satisfactory performance, developing RAG systems is often experimental and needs to follow a systematic and sound methodology to gain sound and reliable results. However, there is currently no generally accepted methodology for RAG evaluation despite a growing interest in this technology. In this paper, we propose a first blueprint of a methodology for a sound and reliable evaluation of RAG systems and demonstrate its applicability on a real-world software engineering research task: the validation of configuration dependencies across software technologies. In summary, we make two novel contributions: (i) A novel, reusable methodological design for evaluating RAG systems, including a demonstration that represents a guideline, and (ii) a RAG system, which has been developed following this methodology, that achieves the highest accuracy in the field of dependency validation. For the blueprint's demonstration, the key insights are the crucial role of choosing appropriate baselines and metrics, the necessity for systematic RAG refinements derived from qualitative failure analysis, as well as the reporting practices of key design decision to foster replication and evaluation.}, pubstate = {prepublished}, keywords = {Computer Science - Information Retrieval,Computer Science - Software Engineering} } @online{SimplifyingModelTransformation, title = {Simplifying {{Model Transformation Chains}} by {{Rule Composition}} - {{Springer}}}, url = {http://link.springer.com/chapter/10.1007%2F978-3-642-21210-9_28}, urldate = {2015-03-24} } @inproceedings{Singaravel20171497, title = {Component-Based Machine Learning Modelling Approach for Design Stage Building Energy Prediction: {{Weather}} Conditions and Size}, author = {Singaravel, S. and Geyer, P. and Suykens, J.}, editor = {Barnaby C.S., Wetter M.}, date = {2017}, series = {Building {{Simulation Conference Proceedings}}}, volume = {3}, pages = {1497--1506}, publisher = {International Building Performance Simulation Association}, issn = {25222708}, doi = {10.26868/25222708.2017.059}, abbrev_source_title = {Build. Simul. Conf. Proc.}, affiliation = {Architectural Engineering Division, KU Leuven, Belgium; ESAT-STADIUS, KU Leuven, Belgium}, correspondence_address1 = {Singaravel, S.; Architectural Engineering Division, Belgium; email: sundar.singaravel@kuleuven.be}, document_type = {Conference Paper}, isbn = {978-1-5108-7067-3}, langid = {english}, source = {Scopus}, note = {cited By 1} } @article{singh_detection_2020, title = {Detection of Malicious Software by Analyzing the Behavioral Artifacts Using Machine Learning Algorithms}, author = {Singh, Jagsir and Singh, Jaswinder}, date = {2020-05}, journaltitle = {Inf. Softw. Technol.}, volume = {121}, pages = {106273}, issn = {0950-5849}, doi = {10.1016/j.infsof.2020.106273}, abstract = {Malicious software deliberately affects the computer systems. Malware are analyzed using static or dynamic analysis techniques. Using these techniques, unique patterns are extracted to detect malware correctly. In this paper, a behavior-based malware detection technique is proposed. Various runtime features are extracted by setting up a dynamic analysis environment using the Cuckoo sandbox. Three primary features are processed for developing malware classifier. Firstly, printable strings are processed word by word using text mining techniques which produced a very high dimension matrix of the string features. Then we apply the singular value decomposition technique for reducing dimensions of string features. Secondly, Shannon entropy is computed over the printable strings and API calls to consider the randomness of API and PSI features. In addition to these features, behavioral features regarding file operations, registry key modification and network activities are used in malware detection. Finally, all features are integrated in the training feature set to develop the malware classifiers using the machine learning algorithms. The proposed technique is validated with 16489 malware and 8422 benign files. Our experimental results show the accuracy of 99.54\% in malware detection using ensemble machine learning algorithms. Moreover, it aims to develop a behavior-based malware detection technique of high accuracy by processing the runtime features in a new way.}, langid = {english}, keywords = {Dynamic analysis,Machine learning algorithms,Malware,Random Forest,Static analysis} } @article{sinnapoluIntegratingWearablesCloudbased2018, title = {Integrating Wearables with Cloud-Based Communication for Health Monitoring and Emergency Assistance}, author = {Sinnapolu, GiriBabu and Alawneh, Shadi}, date = {2018-09}, journaltitle = {Internet Things}, volume = {1--2}, pages = {40--54}, issn = {25426605}, doi = {10.1016/j.iot.2018.08.004}, abstract = {Researchers and physicians have come a long way in inventing various types of wearable devices for health monitoring which makes it easier for medical professionals to monitor patients. Considering a situation, when a patient is driving, his/her health cannot be monitored or assisted immediately in case of emergency due to enormous drawbacks in the communication or the reporting system which is of today’s prime issue. The cloud-based communication helps solving the issue to some extent but inventing an application to integrate any wearable device to the Internet of things (IoT) and the cloud, considering portability and robustness will solve the prime issue. In this paper, we demonstrate a prototype working model along with the healthdetect iOS app for monitoring health data (heart rate) using wearables, if a serious heart rate data is detected by this app, from proximity sensor on the wearables, the microcontroller in the vehicle enables the healthlocateapp to locate and route to the nearest hospitals for the driver to drive. If the condition is critical and he/she is not responding for in-vehicle button press or driver related activity, then the microcontroller sends CAN message to activate the auto pilot to pull over for assistance.}, langid = {english} } @inproceedings{Šircelj20206290, title = {Accuracy-Perturbation Curves for Evaluation of Adversarial Attack and Defence Methods}, author = {Šircelj, J. and Skočaj, D.}, date = {2020}, series = {Proceedings - {{International Conference}} on {{Pattern Recognition}}}, pages = {6290--6297}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {10514651}, doi = {10.1109/ICPR48806.2021.9413143}, abstract = {With more research published on adversarial examples, we face a growing need for strong and insightful methods for evaluating the robustness of machine learning solutions against their adversarial threats. Previous work contains problematic and overly simplified evaluation methods, where different methods for generating adversarial examples are compared, even though they produce adversarial examples of differing perturbation magnitudes. This creates a biased evaluation environment, as higher perturbations yield naturally stronger adversarial examples. We propose a novel “accuracy-perturbation curve” that visualizes a classifiers classification accuracy response to adversarial examples of different perturbations. To demonstrate the utility of the curve we perform evaluation of responses of different image classifier architectures to four popular adversarial example methods. We also show how adversarial training improves the robustness of a classifier using the “accuracy-perturbation curve”. © 2020 IEEE}, art_number = {9413143}, coden = {PICRE}, document_type = {Conference Paper}, isbn = {978-1-7281-8808-9}, source = {Scopus}, keywords = {Classification accuracy,Image Classifiers,Pattern recognition,Simplified evaluations,Software engineering}, note = {cited By 1 \par TL;DR \par This work proposes a novel “accuracy-perturbation curve” that visualizes a classifiers classification accuracy response to adversarial examples of different perturbations, and shows how adversarial training improves the robustness of a classifier using the “ Accumulation curve’.} } @article{sirresAugmentingStructuringUser2018, title = {Augmenting and Structuring User Queries to Support Efficient Free-Form Code Search}, author = {Sirres, Raphael and Bissyandé, Tegawendé F. and Kim, Dongsun and Lo, David and Klein, Jacques and Kim, Kisub and Traon, Yves Le}, date = {2018-10-01}, journaltitle = {Proc. 40th Int. Conf. Softw. Eng.}, series = {{{ICSE}} '18}, volume = {23}, number = {5}, pages = {2622--2654}, location = {Gothenburg, Sweden}, issn = {1573-7616}, doi = {10.1007/s10664-017-9544-y}, abstract = {Source code terms such as method names and variable types are often different from conceptual words mentioned in a search query. This vocabulary mismatch problem can make code search inefficient. In this paper, we present COde voCABUlary (CoCaBu), an approach to resolving the vocabulary mismatch problem when dealing with free-form code search queries. Our approach leverages common developer questions and the associated expert answers to augment user queries with the relevant, but missing, structural code entities in order to improve the performance of matching relevant code examples within large code repositories. To instantiate this approach, we build GitSearch, a code search engine, on top of GitHub and Stack Overflow Q\&A data. We evaluate GitSearch in several dimensions to demonstrate that (1) its code search results are correct with respect to user-accepted answers; (2) the results are qualitatively better than those of existing Internet-scale code search engines; (3) our engine is competitive against web search engines, such as Google, in helping users solve programming tasks; and (4) GitSearch provides code examples that are acceptable or interesting to the community as answers for Stack Overflow questions.}, acmid = {3182513}, nodoi = {10.1145/3180155.3182513}, numpages = {1}, note = {TL;DR \par This paper builds GitSearch, a code search engine on top of GitHub and Stack Overflow Q\&A data that leverages common developer questions and the associated expert answers to augment user queries with the relevant, but missing, structural code entities in order to improve the performance of matching relevant code examples within large code repositories.} } @article{sivieriBuildingInternetThings2016, title = {Building {{Internet}} of {{Things}} Software with {{ELIoT}}}, author = {Sivieri, Alessandro and Mottola, Luca and Cugola, Gianpaolo}, date = {2016-02}, journaltitle = {Comput. Commun.}, issn = {01403664}, doi = {10.1016/j.comcom.2016.02.004}, langid = {english} } @online{SmallLanguageModels, title = {Small Language Models Instad {{ChatGPT-сlass}} Models | towards Data Science}, url = {https://towardsdatascience.com/your-company-needs-small-language-models-d0a223e0b6d9}, urldate = {2024-12-30}, langid = {english}, keywords = {source\_mobile} } @online{SmartAnythingEverywhere, title = {Smart {{Anything Everywhere}} | {{EU H2020}}}, url = {https://smartanythingeverywhere.eu/}, urldate = {2015-04-08} } @article{SMR:SMR567, title = {Feature Location in Source Code: A Taxonomy and Survey}, author = {Dit, Bogdan and Revelle, Meghan and Gethers, Malcom and Poshyvanyk, Denys}, date = {2013}, journaltitle = {J. Softw. Evol. Process}, volume = {25}, number = {1}, pages = {53--95}, publisher = {John Wiley \& Sons, Ltd}, issn = {2047-7481}, url = {http://dx.doi.org/10.1002/smr.567}, nodoi = {10.1002/smr.567}, keywords = {concept location,Feature location,program comprehension,software maintenance and evolution}, note = {TL;DR \par A systematic literature survey of feature location techniques is presented and eighty‐nine articles from 25 venues have been reviewed and classified within the taxonomy in order to organize and structure existing work in the field of feature locations.} } @misc{sobania2023analysis, title = {An Analysis of the Automatic Bug Fixing Performance of {{ChatGPT}}}, author = {Sobania, Dominik and Briesch, Martin and Hanna, Carol and Petke, Justyna}, date = {2023}, eprint = {2301.08653}, eprinttype = {arXiv}, eprintclass = {cs.SE} } @inproceedings{soeken2010verifying, title = {Verifying {{UML}}/{{OCL}} Models Using Boolean Satisfiability}, booktitle = {2010 {{Des}}. {{Autom}}. {{Test Eur}}. {{Conf}}. {{Exhib}}. {{DATE}} 2010}, author = {Soeken, Mathias and Wille, Robert and Kuhlmann, Mirco and Gogolla, Martin and Drechsler, Rolf}, date = {2010}, pages = {1341--1344}, publisher = {IEEE}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {TL;DR \par This paper describes how the respective components of a verification problem, namely system states of a UML model, OCL constraints, and the actual verification task, can be encoded and afterwards automatically solved using an off-the-shelf SAT solver.} } @online{SoftwareEngineeringSelfAdaptive, title = {Software {{Engineering}} for {{Self-Adaptive Systems}}: {{A Second Research Roadmap}} - {{Springer}}}, url = {http://link.springer.com/chapter/10.1007/978-3-642-35813-5_1}, urldate = {2016-02-17} } @incollection{soldatosBuildingBlocksIoT2016, title = {Building {{Blocks}} for {{IoT Analytics}}}, author = {Soldatos, John}, date = {2016}, pages = {1--294}, doi = {10.13052/rp-9788793519046}, langid = {english}, keywords = {Data analysis,DONE,internet of things}, note = {TL;DR \par IoT analytics will be a major contributor to IoT business value in the coming years, as it will enable organizations to process and fully leverage large amounts of IoT data, which are nowadays largely underutilized.} } @incollection{soltenbornTestDrivenSemanticsSpecification2009, title = {Towards {{Test-Driven Semantics Specification}}}, booktitle = {Model {{Driven Engineering Languages}} and {{Systems}}}, author = {Soltenborn, Christian and Engels, Gregor}, editor = {Schürr, Andy and Selic, Bran}, date = {2009}, volume = {5795}, pages = {378--392}, publisher = {Springer Berlin Heidelberg}, location = {Berlin, Heidelberg}, doi = {10.1007/978-3-642-04425-0_30}, abstract = {Behavioral models are getting more and more important within the software development cycle. To get the most use out of them, their behavior should be defined formally. As a result, many approaches exist which aim at specifying formal semantics for behavioral languages (e.g., Dynamic Meta Modeling (DMM), Semantic Anchoring). Most of these approaches give rise to a formal semantics which can e.g. be used to check the quality of a particular language instance, for instance using model checking techniques.}, isbn = {978-3-642-04424-3 978-3-642-04425-0}, langid = {english} } @article{song2016machine, title = {A Machine Learning Based Software Process Model Recommendation Method}, author = {Song, Qinbao and Zhu, Xiaoyan and Wang, Guangtao and Sun, Heli and Jiang, He and Xue, Chenhao and Xu, Baowen and Song, Wei}, date = {2016}, journaltitle = {J. Syst. Softw.}, volume = {118}, pages = {85--100}, publisher = {Elsevier}, note = {TL;DR \par A software process model automatically recommendation framework is proposed and different machine learning technologies are used to construct the recommendation models.} } @online{SostegnoProcessiDi, title = {Sostegno a Processi Di Ricerca e Innovazione e Incentivi All’assunzione Di Ricercatori | {{Coesione Abruzzo}}}, url = {https://coesione.regione.abruzzo.it/avvisi-pubblici/fesr/sostegno-processi-di-ricerca-e-innovazione-e-incentivi-all%E2%80%99assunzione-di}, urldate = {2025-05-12} } @misc{SOSYM2400004734_Proof_hi, title = {{{SOSYM-24-00004734}}\_{{Proof}}\_hi}, langid = {english}, keywords = {LOGSEQ}, note = {\section{Annotazioni\\ (10/10/2024, 14:54:36)} \subsubsection{SOSYM-24-00004734\_Proof\_hi} \par - “chemaless graph datasets” \#5fb236 \par - “Design Science effort” \#5fb236 \par - “KG development and integration method” \#5fb236 \par - “students who tend to perceive symbolic AI as a niche of questionable relevance” \#5fb236 \par - “"citizen knowledge acquisition".” \#a28ae5 \par - ““low-code knowledge graph building”” \#a28ae5 \par - “reate KG structures” \#a28ae5 \par - “RDF/OWL jargon or the ontology-first experience of a tool like Protégé” \#5fb236 \par - “This paper's proposal is a DSML (and associated modeling method deployed as an ADOxx modeling tool) that obscures as much as possible the formal details of knowledge representation standards such as RDF/S and OWL” \#5fb236 \par - “but the focus is still on the schema layer and not on the creation of arbitrary graph structure” \#5fb236 \par - “Section 2 provides extensive information about the problem identification objectives definition. Section 3 discusses design decisions and implementation details. Section 4 presents two application use-cases, Section 5 discusses a series of evaluations. Section 6 comments on related works, and Section 7 concludes our work with a SWOT analysis and outlook” \#5fb236 \par - “Moreover, the current visual tools are limited to saving the created schemas in some basic format and do not care about specialized usage scenarios such as those mentioned above, that may require interoperability.” \#5fb236 \par - “Exposing such exemplars to immediate consumption (the SEO and the GraphRAG use cases described above, but also the cognitive acquisition for novice students) is therefore prioritized.” \#5fb236 \par - “roblem Statement and Objectives Definition” \#ffd400\\ \mkbibemph{The section needs improvements. It does not clearly present the problem, it is verbose and should be more focused by clearly presenting the problem, e.g., by discussing explanatory cases and examples.} \par - “We aim for a Pareto-driven approach where 80\% of the user needs are served by 20\% of the RDF-based specifications captured in a DSML-based modeling method” \#ffd400\\ \mkbibemph{What do you mean with "served"?} \par - “eveloped here as a Design Science artifact” \#ffd400 \par - “weakly structured interviews were used to collect pain points about the tooling they can offer to students and the learning objective affordances of such tooling.” \#5fb236 \par - “GraphRAG scenario was adopted from recent technical propositions available in the literature” \#5fb236 \par - “nd also from KG platform vendors” \#5fb236 \par - “graphical note-taking tools have established this as a popular practice of structuring ideas and was the originating motivation for repurposing here the practice of DSML engineering;” \#ffd400\\ \mkbibemph{Why are they not enough to support the specification of concepts and relationships among them (thus box and lines?)} \par - “KG building is done by semantic technology experts in specialized tools that first require familiarity with standards such as OWL and RDF, if not with underlying formal logic” \#a28ae5 \par - “TBOX” \#ffd400\\ \mkbibemph{Still on the readability of the paper. The work mentions acronyms or concepts, assuming that the reader is aware of them. The authors should revise the paper by ensuring that all the mentioned concepts / technologies are properly introduced especially for the readers that are not experts of the considered domain. A related minor issue is that references re not always given the first time when a given technology is mentioned (e.g. see Langchain, it is mentioned at page 10 the first time, whereas the reference is given at page 17).} \par - “usability requirements” \#ffd400\\ \mkbibemph{How have you elicited such requirements? It is important to discuss how you have elicited them, e.g., if they come from the interview you have done with stakeholders, or if they come only by the experience of the authors.} \par - “schema can be postpone” \#5fb236 \par - “different visual loads:” \#5fb236 \par - “RDF/OWL jargon should be avoided wherever possible,” \#5fb236 \par - “LLM services” \#ffd400\\ \mkbibemph{What do you mean?} \par - “model-driven editing layer over a standards-compliant KG platform” \#5fb236 \par - “Langchain” \#ffd400 \par - “We decided to add less abstract classes because ADOxx as a metamodeling environment allows us to enforce a series of relation constraints” \#5fb236 \par - “Prefix changing” \#ffd400\\ \mkbibemph{It's not explicit the connection between "Prefix changing" and "URI generation" with the requirements given in Section 2.} \par - “coupling RAG with RDF” \#5fb236 \par - “AdoScripts in the modeling tool take care of collecting the natural language query and passing it to a Python proxy script that performs the actual KG and LLM access” \#5fb236 \par - “This serves to obtain a subgraph of entities and relations connected to the subject of the user query.” \#5fb236 \par - “Figure 6 presents the flow of the LLM integration through LangChain, emphasizing on the interactions with the KG and the extraction of a subgraph by navigating radial relationships starting from the subject node;” \#5fb236 \par - “the” \#ff6666 \par - “Step 5.” \#ffd400\\ \mkbibemph{A concrete example with the variable prompt and results actualized is necessary.} \par - “The modeling tool can serve to both building ontologies and graph data as shown in Figure 8.” \#5fb236 \par - “GRAPHxx capabilities were employed in this demo case for designing and querying a KG that can be rolled out on a real world scenario of analyzing interactions between business actors, employees and other constituting elements.” \#5fb236 \par - “We used ADOxx's scripting language to automatically populate diagrams in GRAPHxx.” \#5fb236 \par - “4500 concepts per diagram, also included only prefixes, URIs and labels for ThingIdentifier nodes and RelatedTo connectors” \#5fb236 \par - “conceptual schemas expressed as UML diagrams to graph equivalents.” \#5fb236 \par - “RDF schema generations directly from an intuitive diagramming environment that empowers users to think in free networks of associations close to the RDF data model” \#5fb236 \par - “BPMN2KG acts in a similar way, but does not rely on the BPMN standard, since it provides a visual layer bridging the RDF formalisms with ADOxx meta-metamodeling capabilities.” \#ffd400\\ \mkbibemph{WHat are the limitations of tools like BPMN2KG with respect to the requirements presented in Sec. 2? It is necessary to discuss existing works with respect to a set of elicited requirements and then show what are the challenges that still need to be addressed.} \par - “SWOT” \#5fb236 \par - “Strengths” \#2ea8e5 \par - “Opportunities” \#2ea8e5 \par - “Weaknesses” \#2ea8e5 \par - “Threats” \#2ea8e5} } @misc{SOSYM2500005088_Proof_hi, title = {{{SOSYM-25-00005088}}\_{{Proof}}\_hi}, langid = {english} } @misc{SOSYM2500005106_Proof_hi, title = {{{SOSYM-25-00005106}}\_{{Proof}}\_hi}, langid = {english}, keywords = {LOGSEQ}, note = {\section{Annotazioni\\ (3/4/2025, 15:16:58)} \par - “IoT Design lang” (“SOSYM-25-00005106\_Proof\_hi”, p. 1) \#5fb236\\ \mkbibemph{ } \par - “DEVS as a Language for Design of Adaptive IoT Systems” (“SOSYM-25-00005106\_Proof\_hi”, p. 1) \#5fb236\\ \mkbibemph{ } \par - “Complex adaptive concepts implemented in IoT-enabled systems are still emerging” (“SOSYM-25-00005106\_Proof\_hi”, p. 1) \#5fb236\\ \mkbibemph{ } \par - “applicability” (“SOSYM-25-00005106\_Proof\_hi”, p. 2) \#5fb236\\ \mkbibemph{ } \par - “Complex Adaptive System (CAS) concepts have existed for some time” (“SOSYM-25-00005106\_Proof\_hi”, p. 2) \#5fb236\\ \mkbibemph{ } \par - “dynamically adapt the behaviors of components in a dynamic execution environment.” (“SOSYM-25-00005106\_Proof\_hi”, p. 2) \#a28ae5\\ \mkbibemph{ } \par - “model expressiveness and model continuity for all stages of the process including design, simulation, and implementation” (“SOSYM-25-00005106\_Proof\_hi”, p. 2) \#a28ae5\\ \mkbibemph{ } \par - “transition the ”same” description from stage to stage in the development process, where by ”same”, we mean that little or no modification is needed in such transitions.” (“SOSYM-25-00005106\_Proof\_hi”, p. 2) \#a28ae5\\ \mkbibemph{ } \par - “IoT Design language” (“SOSYM-25-00005106\_Proof\_hi”, p. 2) \#5fb236\\ \mkbibemph{ } \par - “We will illustrate with several examples” (“SOSYM-25-00005106\_Proof\_hi”, p. 2) \#5fb236\\ \mkbibemph{ } \par - “(SES)” (“SOSYM-25-00005106\_Proof\_hi”, p. 2) \#ffd400\\ \mkbibemph{what is it? is it part of the contribution of this paper? } \par - “Fig. 1” (“SOSYM-25-00005106\_Proof\_hi”, p. 2) \#ffd400\\ \mkbibemph{The resolution of the figure needs to be improved. } \par - “DEVS model expressiveness is manifested in its support of hierarchical and modular model construction” (“SOSYM-25-00005106\_Proof\_hi”, p. 3) \#ffd400\\ \mkbibemph{Why are you focusing on these characteristincs when referring to "model expressiveness"? } \par - “DEVS supports model continuity in which only the simulator needs to be changed to run from abstract time to real-time [11, 12] while the model remains substantially the same” (“SOSYM-25-00005106\_Proof\_hi”, p. 3) \#5fb236\\ \mkbibemph{ } \par - “Closure under coupling of DEVS models” (“SOSYM-25-00005106\_Proof\_hi”, p. 4) \#ffd400\\ \mkbibemph{Concepts are suddenly mentioned without proper introduction or being motivated. } \par - “Aspect” (“SOSYM-25-00005106\_Proof\_hi”, p. 4) \#2ea8e5\\ \mkbibemph{ } \par - “This results in several Pruned Entity Structures (PES), which can then be automatically converted to simulatable DEVS models, thus enabling comparison of alternative architectures” (“SOSYM-25-00005106\_Proof\_hi”, p. 5) \#ffd400\\ \mkbibemph{What are the "Pruned Entity Structures"? Again, concepts/terms are used without proper introduction. } \par - “Fig. 3” (“SOSYM-25-00005106\_Proof\_hi”, p. 5) \#ffd400\\ \mkbibemph{It is necessary to describe the metaclasses/kinds of modeling constructs, which are used to define the shown model. } \par - “DEVS Universality” (“SOSYM-25-00005106\_Proof\_hi”, p. 5) \#2ea8e5\\ \mkbibemph{ } \par - “ECA (Event-Condition-Action)” (“SOSYM-25-00005106\_Proof\_hi”, p. 5) \#5fb236\\ \mkbibemph{ } \par - “Heterogeneity” (“SOSYM-25-00005106\_Proof\_hi”, p. 6) \#2ea8e5\\ \mkbibemph{ } \par - “The DEVS Universality simplify the specification of this specific description. Due to the DEVS universality these description formalisms can be reduced to DEVS.” (“SOSYM-25-00005106\_Proof\_hi”, p. 6) \#ffd400\\ \mkbibemph{Sentences like these need to be supported with a clear evidence! } \par - “identified traits” (“SOSYM-25-00005106\_Proof\_hi”, p. 6) \#ffd400\\ \mkbibemph{Where are identified? } \par - “Real-Time” (“SOSYM-25-00005106\_Proof\_hi”, p. 6) \#2ea8e5\\ \mkbibemph{ } \par - “Concurrency a” (“SOSYM-25-00005106\_Proof\_hi”, p. 6) \#2ea8e5\\ \mkbibemph{ } \par - “Adaptability” (“SOSYM-25-00005106\_Proof\_hi”, p. 6) \#2ea8e5\\ \mkbibemph{ } \par - “Scalability” (“SOSYM-25-00005106\_Proof\_hi”, p. 6) \#2ea8e5\\ \mkbibemph{ } \par - “Model-to-Execution Continuity” (“SOSYM-25-00005106\_Proof\_hi”, p. 6) \#ffd400\\ \mkbibemph{This has nothing to do with the characteristics of IoT systems as those previously listed. This is more related to the tools/environments~ supporting the development analysis and deployment of IoT systems! } \par - “they lack the comprehensive support for concurrency, event-driven behavior, and model continuity that DEVS offers. Thus, DEVS provides a robust and minimal set of traits that make it an ideal candidate for IoT system design and simulation” (“SOSYM-25-00005106\_Proof\_hi”, p. 7) \#ffd400\\ \mkbibemph{It is necessary to expand the discussion of such limitations and highlight their evidence. } \par - “Additionally, IoT systems must be adaptive to changing conditions, such as device failures or network disruptions, and DEVS supports dynamic model structures that allow for this flexibility.” (“SOSYM-25-00005106\_Proof\_hi”, p. 7) \#5fb236\\ \mkbibemph{ } \par - “DEVS” (“SOSYM-25-00005106\_Proof\_hi”, p. 7) \#ffd400\\ \mkbibemph{Mentioned so many tims but never presented properly. } \par - “his continuity is crucial for ensuring that IoT systems perform as intended when deploye” (“SOSYM-25-00005106\_Proof\_hi”, p. 7) \#5fb236\\ \mkbibemph{ } \par - “Practical Examples” (“SOSYM-25-00005106\_Proof\_hi”, p. 7) \#ffd400\\ \mkbibemph{Is showing such examples the goal of the paper? What's the goal? What are the criteria used to select such examples? There are many tools and approaches supporting the speficiation, development and analysis of IoT systems. How are they related to the works overviewed in this paper? } \par - “This is achieved via DEVS model continuity first using a DEVS simulator to verify the correctness of the models prior to their deployment and then transforming the models and the associated DEVS simulator to real-time implementation.” (“SOSYM-25-00005106\_Proof\_hi”, p. 7) \#5fb236\\ \mkbibemph{ } \par - “We note that all of these models had identical C++ code for both deployment on the device and simulation, except that the message broker model was replaced with an actual commercial off-the-shelf implementation and the client models were slightly modified to make them suitable for deployment on the hardware” (“SOSYM-25-00005106\_Proof\_hi”, p. 8) \#ffd400\\ \mkbibemph{This is possible also without using DEVS. If the system is properly designed, replacing a simulated MQTT client with a real MQTT compatible sensor or actual does not require changing the code of the system. } \par - “The key advantage of using DEVS lies in its flexibility to define multiple strategies through distinct DEVS model specifications.” (“SOSYM-25-00005106\_Proof\_hi”, p. 8) \#ffd400\\ \mkbibemph{Reviewers that are not aware of such formalism is supposed to read this kind of sentences without any supporting evidence. } \par - “typically involves” (“SOSYM-25-00005106\_Proof\_hi”, p. 8) \#ffd400\\ \mkbibemph{This is not always the case. As previously said, coupling specification can be just refining the configuration of the the IoT system at hand. } \par - “The system can manage multiple sensor farms and simultaneously improve predictive models in real-time by adapting and re-training models to keep forecasts accurate and updated.” (“SOSYM-25-00005106\_Proof\_hi”, p. 9) \#a28ae5\\ \mkbibemph{ } \par - “Model expressiveness:” (“SOSYM-25-00005106\_Proof\_hi”, p. 9) \#ffd400\\ \mkbibemph{Again, there are many approaches that provide such modeling constructs. What's the novelty? } \par - “IoT conflict” (“SOSYM-25-00005106\_Proof\_hi”, p. 10) \#ffd400\\ \mkbibemph{What do you mean? It is necessary to properly give a definition for "IoT conflict" } \par - “Challenges intensify as smart applications increasingly control shared IoT devices, particularly actuators that translate commands into physical effects.” (“SOSYM-25-00005106\_Proof\_hi”, p. 10) \#ffd400\\ \mkbibemph{ } \par - “M\&S approach” (“SOSYM-25-00005106\_Proof\_hi”, p. 10) \#ffd400\\ \mkbibemph{ } \par - “Logical ACM Design” (“SOSYM-25-00005106\_Proof\_hi”, p. 11) \#2ea8e5\\ \mkbibemph{ } \par - “Model Validation” (“SOSYM-25-00005106\_Proof\_hi”, p. 11) \#2ea8e5\\ \mkbibemph{ } \par - “Model Deployment:” (“SOSYM-25-00005106\_Proof\_hi”, p. 11) \#2ea8e5\\ \mkbibemph{ } \par - “The proposed approach” (“SOSYM-25-00005106\_Proof\_hi”, p. 11) \#ffd400\\ \mkbibemph{Where? In this paper? } \par - “The proposed simulation-based approach” (“SOSYM-25-00005106\_Proof\_hi”, p. 12) \#ffd400\\ \mkbibemph{This is another instance of the same problem occurring many times in the paper. It is not clearly defined the main contribution of this paper vs what is an overview of concepts presented alsewhere. In the end, it is not clear the technical contribution of this paper. } \par - “Fig. 12” (“SOSYM-25-00005106\_Proof\_hi”, p. 17) \#ffd400\\ \mkbibemph{The quality of this figure is very bad. The resolution is bad and it is not even readable some text. } \par - “for its validity as an IoT design language based on its model expressiveness and continuity.” (“SOSYM-25-00005106\_Proof\_hi”, p. 20) \#5fb236\\ \mkbibemph{ } \par - “Let’s explore how some of these popular formalisms compare to DEVS in terms of expressiveness” (“SOSYM-25-00005106\_Proof\_hi”, p. 23) \#ffd400\\ \mkbibemph{To support such claim it is necessary to perform a fair comparison by showing on a same system specification fragments or aspects that cannot be modeled or supported with existing technologies and that instead are possible with DEVS. }} } @article{spearmanProofMeasurementAssociation1904, title = {The Proof and Measurement of Association between Two Things}, author = {Spearman, Charles}, date = {1904}, journaltitle = {Am. J. Psychol.}, volume = {15}, number = {1}, pages = {72--101}, publisher = {JSTOR} } @book{spin, title = {The {{SPIN}} Model Checker - Primer and Reference Manual}, author = {Holzmann, Gerard J.}, date = {2004}, publisher = {Addison-Wesley}, bibsource = {dblp computer science bibliography, https://dblp.org}, isbn = {978-0-321-22862-8}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {TL;DR \par This is the most comprehensive reference guide to SPIN, written by the principal designer of the tool, and gives detailed advice on methods for tackling the most complex software verification problems.}, timestamp = {Thu, 14 Apr 2011 14:43:24 +0200} } @article{spinellisEngineeringDataProcessing2024, title = {Engineering {{Data Processing Workflows}}}, author = {Spinellis, Diomidis}, date = {2024-07}, journaltitle = {IEEE Softw.}, volume = {41}, number = {4}, pages = {25--29}, issn = {0740-7459, 1937-4194}, doi = {10.1109/MS.2024.3385665} } @article{spinellisSoftwareEngineeringInternetThings2017, title = {Software-{{Engineering}} the {{Internet}} of {{Things}}}, author = {Spinellis, Diomidis}, date = {2017}, journaltitle = {IEEE Softw.}, volume = {34}, number = {1}, pages = {4--6}, url = {http://ieeexplore.ieee.org/abstract/document/7819398/}, urldate = {2017-02-27}, keywords = {internet of things,iot}, note = {TL;DR \par New wiring transformed ENIAC into a versatile stored-program computer into a general-purpose computing fabric that changed how modern computation interfaces with the authors' environment.} } @article{spinellisSuccessHeavenlyMarriage2018, title = {The {{Success}} of a {{Heavenly Marriage}}}, author = {Spinellis, D.}, date = {2018-09}, journaltitle = {IEEE Softw.}, volume = {35}, number = {5}, pages = {3--6}, issn = {0740-7459}, doi = {10.1109/MS.2018.3571251}, abstract = {For a field that sprang out of a so-called software crisis, software engineering has done rather well over the past half-century. By riding on the coattails of Moore’s law, it has progressed phenomenally. The field’s achievements are visible through the large, complex, yet effective software systems that power our everyday lives. By looking at the drivers of the field’s progress and taking stock of its achievements, we can appreciate the challenges in front of us and confidently plan for the future. This article is part of a theme issue on software engineering’s 50th anniversary.}, note = {TL;DR \par For a field that sprang out of a so-called software crisis, software engineering has done rather well over the past half-century by riding on the coattails of Moore’s law.} } @inproceedings{spoletini_bias-aware_2018, title = {Bias-Aware Guidelines and Fairness-Preserving {{Taxonomy}} in Software Engineering Education}, booktitle = {2018 {{IEEE Front}}. {{Educ}}. {{Conf}}. {{FIE}}}, author = {Spoletini, Paola and Parizi, Reza M.}, date = {2018-10}, pages = {1--4}, doi = {10.1109/FIE.2018.8659178}, abstract = {This innovative practice work in progress paper tackles the problem of unfairness and bias in software, that recently has emerged in countless cases. This unfairness can be present in the way software makes its decision or can limit the software functionalities to work only with certain populations. Well-known examples of this problem are the Microsoft Kinect facial recognition algorithm, which does not work properly with darker skin players, and the software used in 2016 by Amazon.com to determine the parts of the United States to which offer free same-day delivery that made decisions that prevented minority neighborhoods from participating in the program. The reasons behind these phenomena have often roots in the fact that software is created by humans who are biased and live in biased and non-inclusive environments. Recent research from the software engineering community is starting to tackle this problem at many levels from requirements analysis to the new automatic fairness testing technique (proposed first at FSE 2017 conference). However, research in bias of software is still a very undervalued and rarely discussed problem as software is often seen as a product immune to bias and non-inclusivity. This problem will be not addressed unless software engineering educators start to include this notion as a first-class problem in their foundation courses to future generation of scholars. In this work, we propose a set of bias-aware guidelines and taxonomy on how to flesh out this problem and possible solutions to it in software engineering curricula.}, keywords = {Cultural differences,Ethics,Fairness,Software,Software engineering,Software engineering curricula,Software engineering education,Taxonomy,Testing,Tools}, note = {ISSN: 2377-634X \par TL;DR \par A set of bias-aware guidelines and taxonomy on how to flesh out this problem of unfairness and bias in software and possible solutions to it in software engineering curricula are proposed.} } @article{SprinkleK04, title = {A Domain-Specific Visual Language for Domain Model Evolution}, author = {Sprinkle, Jonathan and Karsai, Gabor}, date = {2004}, journaltitle = {J. Vis. Lang. Comput.}, volume = {15}, number = {3--4}, pages = {291--307}, doi = {10.1016/J.JVLC.2004.01.006}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Fri, 09 Apr 2021 18:34:20 +0200} } @inproceedings{Spruegel2018, title = {Methodology for Plausibility Checking of Structural Mechanics Simulations Using {{Deep Learning}} on Existing Simulation Data}, author = {Spruegel, T.C. and Rothfelder, R. and Bickel, S. and Grauf, A. and Sauer, C. and Schleich, B. and Wartzack, S.}, date = {2018}, series = {Proceedings of {{NordDesign}}: {{Design}} in the {{Era}} of {{Digitalization}}, {{NordDesign}} 2018}, publisher = {The Design Society}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85057150108&partnerID=40&md5=3fa21ccaf4f28ff72326348e85cbde09}, abbrev_source_title = {Proc. NordDesign: Des. Era Digitalization, NordDesign}, affiliation = {Friedrich-Alexander-Universität Erlangen-Nürnberg, Lehrstuhl für Konstruktionstechnik, Germany}, document_type = {Conference Paper}, isbn = {978-91-7685-185-2}, langid = {english}, source = {Scopus}, note = {cited By 6 \par TL;DR \par An approach to transfer different FE meshes, corresponding FE results and boundary conditions to an individual matrix of fixed size for very different structural mechanic FE simulation, using spherical detector surfaces to project three-dimensional information on its surface.} } @inproceedings{Sridhar2020351, title = {Model Governance: {{Reducing}} the Anarchy of Production {{ML}}}, author = {Sridhar, V. and Subramanian, S. and Arteaga, D. and Sundararaman, S. and Roselli, D. and Talagala, N.}, date = {2020}, series = {Proceedings of the 2018 {{USENIX Annual Technical Conference}}, {{USENIX ATC}} 2018}, pages = {351--357}, publisher = {USENIX Association}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075761598&partnerID=40&md5=393aa3aa505e90fb80de134aad651b4f}, abstract = {As the influence of machine learning grows over decisions in businesses and human life, so grows the need for Model Governance. In this paper, we motivate the need for, define the problem of, and propose a solution for Model Governance in production ML. We show that through our approach one can meaningfully track and understand the who, where, what, when, and how an ML prediction came to be. To the best of our knowledge, this is the first work providing a comprehensive framework for production Model Governance, building upon previous work in developer-focused Model Management. © Proceedings of the 2018 USENIX Annual Technical Conference, USENIX ATC 2018. All rights reserved.}, document_type = {Conference Paper}, isbn = {978-1-939133-02-1}, source = {Scopus}, note = {cited By 15} } @article{srinivasanWebAppSecurity2017, entrysubtype = {magazine}, title = {Web {{App Security}}: {{A Comparison}} and {{Categorization}} of {{Testing Frameworks}}}, shorttitle = {Web {{App Security}}}, author = {Srinivasan, Satish M. and Sangwan, Raghvinder S. and {undefined} and {undefined} and {undefined} and {undefined}}, date = {2017}, journaltitle = {IEEE Software}, volume = {34}, number = {1}, pages = {99--102}, issn = {0740-7459}, abstract = {Web app developers often face challenges in using the many available security-testing frameworks, owing to those frameworks' inherent complexity and the lack of proper documentation. No up-to-date criteria exist that can help practitioners and organizations select an appropriate framework. Consequently, numerous vulnerabilities go undetected in the final product, creating a potential for major attacks. To help practitioners select the right framework, researchers classified 26 frameworks, using 27 criteria.}, keywords = {software engineering}, note = {TL;DR \par To help practitioners select the right framework, researchers classified 26 frameworks, using 27 criteria, to help practitioners and organizations select an appropriate framework.} } @article{sriramInternetThingsPerspectives2015, title = {Internet of {{Things Perspectives}}}, author = {Sriram, Ram D. and Sheth, Amit}, date = {2015-05}, journaltitle = {IT Prof.}, volume = {17}, number = {3}, pages = {60--63}, issn = {1520-9202}, doi = {10.1109/MITP.2015.43} } @article{SS04, title = {How Is Open Source Affecting Software Development?}, author = {Spinellis, D. and Szyperski, C.}, date = {2004-01}, journaltitle = {IEEE Softw.}, volume = {21}, number = {1}, pages = {28--33}, issn = {0740-7459}, keywords = {Automation,Business communication,Law,Licenses,Open source software,Operating systems,Packaging,Programming,Software libraries,Software packages}, note = {TL;DR \par The software development process is gaining from the widespread availability and use of sophisticated open source development platforms and tools, and the adoption of corresponding development and coding practices by the programmer community.} } @incollection{staab2010model, title = {Model Driven Engineering with Ontology Technologies}, booktitle = {Reasoning Web International Summer School}, author = {Staab, Steffen and Walter, Tobias and Gröner, Gerd and Parreiras, Fernando Silva}, date = {2010}, pages = {62--98}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {TL;DR \par It will turn out that ontology-based metamodels constitute a core means for exploiting expressive ontology reasoning in the software modeling domain while remaining flexible enough to accommodate varying needs of software modelers.} } @misc{STAF_2025_paper_10, title = {{{STAF}}\_2025\_paper\_10}, langid = {english} } @book{stahlModeldrivenSoftwareDevelopment2006, title = {Model-Driven Software Development: Technology, Engineering, Management}, shorttitle = {Model-Driven Software Development}, author = {Stahl, Thomas and Völter, Markus}, date = {2006}, publisher = {John Wiley}, location = {Chichester, England ; Hoboken, NJ}, isbn = {978-0-470-02570-3}, langid = {english}, pagetotal = {428}, keywords = {Computer software,Development,Model-driven software architecture}, note = {TL;DR \par MDSD Tools: Roles, Architecture, SelectionCriteria, Selection Criteria, and Pointers, and MDSD Process Building Blocks and Best Practices.} } @online{StanfordCS229Machine, title = {Stanford {{CS229 I Machine Learning I Building Large Language Models}} ({{LLMs}})}, url = {https://youtu.be/9vM4p9NN0Ts?si=8g29foDKZpkAUxXq}, keywords = {LLMs,P1,source\_mobile} } @online{StanfordResearchersIntroduce, title = {Stanford {{Researchers Introduce OctoTools}}: {{A Training-Free Open-Source Agentic AI Framework Designed}} to {{Tackle Complex Reasoning Across Diverse Domains}} - {{MarkTechPost}}}, url = {https://www.marktechpost.com/2025/02/22/stanford-researchers-introduce-octotools-a-training-free-open-source-agentic-ai-framework-designed-to-tackle-complex-reasoning-across-diverse-domains/}, keywords = {source\_mobile} } @article{stankovicResearchDirectionsInternet2014, ids = {stankovicResearchDirectionsInternet2014a}, title = {Research {{Directions}} for the {{Internet}} of {{Things}}}, author = {Stankovic, John A.}, date = {2014-02}, journaltitle = {IEEE Internet Things J.}, volume = {1}, number = {1}, pages = {3--9}, issn = {2327-4662}, doi = {10.1109/JIOT.2014.2312291} } @article{stansburyGraduateProgramUnmanned2015, title = {A {{Graduate Program}} in {{Unmanned}} and {{Autonomous Systems Engineering}}}, author = {Stansbury, Richard S. and Moncayo, Hever and Currier, Patrick}, date = {2015}, url = {http://se.asee.org/proceedings/ASEE2015/papers2015/79.pdf}, urldate = {2016-08-21}, note = {TL;DR \par A detailed overview of the MSUASE program, its mission, objectives, and student outputs are provided including research projects used for capstone and thesis opportunities and issues related to the International Trade of Armaments Regulations (ITAR) will be discussed.} } @article{staronLawsEthicsFairness2025, title = {Laws, Ethics, and Fairness in Software Engineering}, author = {Staron, Miroslaw and Abraháo, Silvia and Serebrenik, Alexander and Penzenstadler, Birgit and Horkoff, Jennifer and Honnenahalli, Chetan}, date = {2025-01}, journaltitle = {IEEE Softw.}, volume = {42}, number = {1}, pages = {110--113}, issn = {1937-4194}, doi = {10.1109/MS.2024.3469488}, abstract = {Software engineering in the era of generative AI, large data sets and superfast pace of software development often tends to focus on technology, tools and methods, putting aside us, software engineers. In this column, we focus on softer aspects of software engineering and report from two conferences: 28th International Conference on Evaluation and Assessment in Software Engineering (EASE 2024) and 18th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM 2024). The selection of papers provides a glimpse on handling privacy, documenting ethical considerations in AI models and trustworthy AI.}, eventtitle = {{{IEEE Software}}}, langid = {english}, keywords = {/unread,Artificial intelligence,Ethics,Generative AI,Privacy,Software development management,Software engineering,Software measurement} } @online{StartBuildingThese, title = {Start {{Building These Projects}} to {{Become}} an {{LLM Engineer}} | by {{Harshit Tyagi}} | {{Medium}}}, url = {https://dswharshit.medium.com/start-building-these-projects-to-become-an-llm-engineer-0064e9e68d9d}, keywords = {source\_mobile} } @software{staticRegistroCronologiaDi, title = {Registro Della Cronologia Di Lettura}, shorttitle = {Chartero}, author = {{static}, volatile}, url = {https://github.com/volatile-static/Chartero}, abstract = {Dati della cronologia di lettura salvati da Chartero. NON modificare!}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {chartero\#33BF2A43\{"pages":\{"0":\{"p":\{"1712269361":1,"1712269362":1,"1712269363":1,"1712269364":1,"1712269365":1,"1712269366":1,"1712269367":1,"1712269368":1,"1712269369":1,"1712269370":1,"1712269371":1,"1712269372":1,"1712269373":1,"1712269374":1,"1712269375":1,"1712269376":1,"1712269377":1,"1712269378":1,"1712269379":1,"1712269380":1,"1712269386":1,"1712269387":1\}\},"3":\{"p":\{"1712269385":1\}\},"5":\{"p":\{"1712269357":1,"1712269359":1,"1712269360":1\}\}\},"numPages":10\} \par chartero\#EYEUBGCW\{"pages":\{"0":\{"p":\{"1712269395":1,"1712269396":1,"1712269397":1,"1712269398":1,"1712269399":1\}\},"5":\{"p":\{"1712269400":1,"1712269401":1,"1712269402":1,"1712269403":1\}\},"6":\{"p":\{"1712269404":1,"1712269405":1,"1712269406":1,"1712269407":1\}\}\},"numPages":26\} \par 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\par chartero\#I879APBF\{"pages":\{"0":\{"p":\{"1712269734":1,"1712269735":1,"1712269741":1\}\},"1":\{"p":\{"1712269736":1,"1712269737":1,"1712269740":1,"1712269742":1,"1712269743":1,"1712269744":1\}\},"20":\{"p":\{"1712269733":1\}\},"30":\{"p":\{"1712269730":1,"1712269732":1\}\}\},"numPages":32\} \par chartero\#IG9WPQ8Z\{"pages":\{"0":\{"p":\{"1712317131":1\}\}\},"numPages":22\} \par chartero\#ZH4DWQ5U\{"pages":\{"15":\{"p":\{"1712269725":1,"1712269726":1\}\}\},"numPages":16\} \par chartero\#ZL97YJGC\{"pages":\{"0":\{"p":\{"1712311103":1\}\}\},"numPages":20\}} } @inproceedings{steinbach00comparison, ids = {Steinbach00}, title = {A Comparison of Document Clustering Techniques}, booktitle = {{{KDD Workshop Text Min}}.}, author = {Steinbach, M. and Karypis, G. and Kumar, V.}, date = {2000}, url = {http://citeseer.ist.psu.edu/steinbach00comparison.html}, added-at = {2007-01-09T09:03:22.000+0100}, biburl = {https://www.bibsonomy.org/bibtex/210e5c1e3ff54d9dce505a231f8ae7b32/hotho}, description = {A Comparison of Document Clustering Techniques}, interhash = {3340fbf75ada2ccb45a50dd5832f5f07}, intrahash = {10e5c1e3ff54d9dce505a231f8ae7b32}, keywords = {imported,kmeans clustering bisec text ***** document hac}, note = {TL;DR \par This paper compares the two main approaches to document clustering, agglomerative hierarchical clustering and K-means, and indicates that the bisecting K-MEans technique is better than the standard K-Means approach and as good or better as the hierarchical approaches that were tested for a variety of cluster evaluation metrics.}, timestamp = {2007-01-09T09:03:22.000+0100} } @inproceedings{Stephan201921, title = {Towards a Cognizant Virtual Software Modeling Assistant Using Model Clones}, author = {Stephan, M.}, date = {2019}, series = {Proceedings - 2019 {{IEEE}}/{{ACM}} 41st {{International Conference}} on {{Software Engineering}}: {{New Ideas}} and {{Emerging Results}}, {{ICSE-NIER}} 2019}, pages = {21--24}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/ICSE-NIER.2019.00014}, abbrev_source_title = {Proc. - IEEE/ACM Int. Conf. Softw. Eng.: New Ideas Emerg. Results, ICSE-NIER}, affiliation = {Department of Computer Science and Software Engineering, Miami University, Oxford, OH, United States}, art_number = {8805738}, correspondence_address1 = {Stephan, M.; Department of Computer Science and Software Engineering, United States; email: stephamd@miamioh.edu}, document_type = {Conference Paper}, isbn = {978-1-7281-1758-4}, langid = {english}, source = {Scopus}, keywords = {GOAL\_Model-Assistance,notion}, note = {cited By 8 \par HIGH LEVEL. NO SPECIFIC TECHNIQUES ARE MENTIONED. \par TL;DR \par The vision in realizing a cognizant virtual software modeling assistant that provides suggestions to modelers performing model creation or extension in the form of complete models for insertion or guidance, and granular single-step operations is described.} } @inproceedings{stereotypes2004, title = {On the Classification of Uml’s Meta Model Extension Mechanism}, booktitle = {Int. {{Conf}}. {{Unified Model}}. {{Lang}}.}, author = {{Jiang} and Yanbing, Weizhong Shao, Zhiyi Ma, Xiangwen Meng, Lu Zhang and Ma., Haohai}, date = {2004}, pages = {54--68} } @inproceedings{Stevens201754, title = {On Ontologology}, author = {Stevens, P. and Gibbons, J.}, editor = {Johnson M., Eramo R.}, date = {2017}, series = {{{CEUR Workshop Proceedings}}}, volume = {1827}, pages = {54--58}, publisher = {CEUR-WS}, issn = {16130073}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85019246553&partnerID=40&md5=bf8dd48d63d77a58878a1fc67b9f12fe}, abbrev_source_title = {CEUR Workshop Proc.}, affiliation = {School of Informatics, University of Edinburgh, United Kingdom; Department of Computer Science, University of Oxford, United Kingdom}, document_type = {Conference Paper}, langid = {english}, source = {Scopus}, note = {cited By 0 \par TL;DR \par A high-level introduction to the work that has been done to relate the development of ontologies to the model-driven development of software, aiming to promote further study and perhaps collaboration between these communities.} } @incollection{stevensLandscapeBidirectionalModel2008, title = {A {{Landscape}} of {{Bidirectional Model Transformations}}}, booktitle = {Generative and {{Transformational Techniques}} in {{Software Engineering II}}}, author = {Stevens, Perdita}, editor = {Lämmel, Ralf and Visser, Joost and Saraiva, João}, date = {2008}, volume = {5235}, pages = {408--424}, publisher = {Springer Berlin Heidelberg}, location = {Berlin, Heidelberg}, doi = {10.1007/978-3-540-88643-3_10}, abstract = {Model transformations are a key element in the OMG’s Model Driven Development agenda. They did not begin here: the fundamental idea of transforming, automatically, one model into another is at least as old as the computer, provided that we take a sufficiently broad view of what a model is. In many contexts, people have encountered the need for bidirectional transformations. In this survey paper we discuss the various notions of bidirectional transformation, and their motivation from the needs of software engineering. We discuss the state of the art in work targeted specifically at the OMG’s MDD initiative, and also, briefly, related work from other communities. We point out some areas which are so far relatively under-researched, and propose research topics for the future.}, isbn = {978-3-540-88642-6 978-3-540-88643-3}, langid = {english} } @article{stilgoeMachineLearningSocial2018, title = {Machine Learning, Social Learning and the Governance of Self-Driving Cars}, author = {Stilgoe, Jack}, date = {2018}, journaltitle = {Soc. Stud. Sci.}, volume = {48}, number = {1}, pages = {25--56}, abstract = {Self-driving cars, a quintessentially ‘smart’ technology, are not born smart. The algorithms that control their movements are learning as the technology emerges. Self-driving cars represent a high-stakes test of the powers of machine learning, as well as a test case for social learning in technology governance. Society is learning about the technology while the technology learns about society. Understanding and governing the politics of this technology means asking ‘Who is learning, what are they learning and how are they learning?’ Focusing on the successes and failures of social learning around the much-publicized crash of a Tesla Model S in 2016, I argue that trajectories and rhetorics of machine learning in transport pose a substantial governance challenge. ‘Self-driving’ or ‘autonomous’ cars are misnamed. As with other technologies, they are shaped by assumptions about social needs, solvable problems, and economic opportunities. Governing these technologies in the public interest means improving social learning by constructively engaging with the contingencies of machine learning.}, nodoi = {10.1177/0306312717741687}, note = {PMID: 29160165 \par PMID: 29160165 \par TL;DR \par Focusing on the successes and failures of social learning around the much-publicized crash of a Tesla Model S in 2016, it is argued that trajectories and rhetorics of machine learning in transport pose a substantial governance challenge.} } @article{stolABCSoftwareEngineering2018, title = {The {{ABC}} of {{Software Engineering Research}}}, author = {Stol, Klaas-Jan and Fitzgerald, Brian}, date = {2018-09-17}, journaltitle = {ACM Trans. Softw. Eng. Methodol.}, volume = {27}, number = {3}, pages = {1--51}, issn = {1049331X}, doi = {10.1145/3241743}, langid = {english}, note = {TL;DR \par A taxonomy from the social sciences is adopted, termed here the ABC framework for SE research, which offers a holistic view of eight archetypal research strategies, and six ways in which the framework can advance SE research.} } @article{storzAnnotateTrainEvaluate2013, title = {Annotate. {{Train}}. {{Evaluate}}. {{A}} Unified Tool for the Analysis and Visualization of Workflows in Machine Learning Applied to Object Detection}, author = {Storz, M. and Ritter, M. and Manthey, R. and Lietz, H. and Eibl, M.}, date = {2013}, journaltitle = {Lect. Notes Comput. Sci. Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinforma.}, volume = {8008 LNCS}, pages = {196--205}, issn = {03029743}, doi = {10.1007/978-3-642-39342-6_22}, abstract = {The development of classifiers for object detection in images is a complex task that comprises the creation of representative and potentially large datasets from a target object by repetitive and time-consuming intellectual annotations, followed by a sequence of methods to train, evaluate and optimize the generated classifier. This is conventionally achieved by the usage and combination of many different tools. Here, we present a holistic approach to this scenario by providing a unified tool that covers the single development stages in one solution to facilitate the development process. We prove this concept by the example of creating a face detection classifier. © 2013 Springer-Verlag.}, isbn = {9783642393419}, issue = {PART 5}, keywords = {Development process,Development stages,Holistic approach,Human computer interaction,Image processing,Large datasets,Learning systems,Model-driven,Object Detection,Object recognition,Target object,Tools,Workflow analysis}, note = {cited By 3 \par TL;DR \par A holistic approach to the development of classifiers for object detection in images is presented by providing a unified tool that covers the single development stages in one solution to facilitate the development process.} } @article{strittmatter2016challenges, title = {Challenges for {{Addressing Quality Factors}} in {{Model Transformation}}}, author = {Syriani, Eugene and Gray, Jeff}, date = {2016}, journaltitle = {CEUR Workshop Proc.}, volume = {1706}, pages = {30--39}, doi = {10.1109/ICST.2012.198}, keywords = {duplicate-citation-key}, note = {TL;DR \par This paper proposes solution ideas to assist modelers in developing high quality transformation models by proposing to initiate a design pattern movement in the context of model transformation to satisfy quality attributes identified beforehand.} } @article{strittmatter2016challenges, title = {Challenges in the {{Evolution}} of {{Metamodels}}: {{Smells}} and {{Anti-Patterns}} of a {{Historically-Grown Metamodel}}}, author = {Strittmatter, Misha and Hinkel, Georg and Langhammer, Michael}, date = {2016}, journaltitle = {CEUR Workshop Proc.}, volume = {1706}, pages = {30--39}, abstract = {In model-driven engineering, modeling languages are developed to serve as basis for system design, simulation and code generation. Like any software artifact, modeling languages evolve over time. If, however, the metamodel that defines the language is badly designed, the effort needed for its maintenance is unnecessarily increased. In this paper, we present bad smells and anti-patterns that we discovered in a thorough metamodel review of the Palladio Component Model (PCM). The PCM is a good representative for big and old metamodels that have grown over time. Thus, these results are meaningful, as they reflect the types of smells that accumulate in such metamodels over time. Related work deals mainly with automatically detectable bad smells, anti-patterns and defects. However, there are smells and anti-patterns, which cannot be detected automatically. They should not be neglected. Thus, in this paper, we focus on both: automatically and non-automatically detectable smells.}, langid = {english}, keywords = {duplicate-citation-key}, note = {TL;DR \par This paper presents bad smells and anti-patterns that were discovered in a thorough metamodel review of the Palladio Component Model (PCM).} } @article{strittmatterChallengesEvolvingMetamodels, title = {Challenges in Evolving {{Metamodels}}}, author = {Strittmatter, Misha and Heinrich, Robert}, pages = {4}, abstract = {Like every other software artifact, metamodels are subject to change even in later phases of the software life cycle. In this problem description paper, we first classify metamodel changes. We then elaborate on the challenges of metamodel evolution. The main challenges are the tight coupling of code to metamodels and the pervasiveness of metamodel dependencies. As this is a problem description paper, we will only present a brief overview of possible solutions.}, langid = {english}, note = {TL;DR \par This problem description paper first classify metamodel changes, then elaborate on the challenges of metamodel evolution, and presents a brief overview of possible solutions.} } @online{studerCRISPMLQMachineLearning2021, title = {Towards {{CRISP-ML}}({{Q}}): {{A Machine Learning Process Model}} with {{Quality Assurance Methodology}}}, shorttitle = {Towards {{CRISP-ML}}({{Q}})}, author = {Studer, Stefan and Bui, Thanh Binh and Drescher, Christian and Hanuschkin, Alexander and Winkler, Ludwig and Peters, Steven and Mueller, Klaus-Robert}, date = {2021-02-24}, eprint = {2003.05155}, eprinttype = {arXiv}, eprintclass = {cs, stat}, url = {http://arxiv.org/abs/2003.05155}, urldate = {2022-08-04}, abstract = {Machine learning is an established and frequently used technique in industry and academia but a standard process model to improve success and efficiency of machine learning applications is still missing. Project organizations and machine learning practitioners have a need for guidance throughout the life cycle of a machine learning application to meet business expectations. We therefore propose a process model for the development of machine learning applications, that covers six phases from defining the scope to maintaining the deployed machine learning application. The first phase combines business and data understanding as data availability oftentimes affects the feasibility of the project. The sixth phase covers state-of-the-art approaches for monitoring and maintenance of a machine learning applications, as the risk of model degradation in a changing environment is eminent. With each task of the process, we propose quality assurance methodology that is suitable to adress challenges in machine learning development that we identify in form of risks. The methodology is drawn from practical experience and scientific literature and has proven to be general and stable. The process model expands on CRISP-DM, a data mining process model that enjoys strong industry support but lacks to address machine learning specific tasks. Our work proposes an industry and application neutral process model tailored for machine learning applications with focus on technical tasks for quality assurance.}, pubstate = {prepublished}, keywords = {Computer Science - Machine Learning,Computer Science - Software Engineering,Statistics - Machine Learning}, note = {Comment: Machine Learning Applications, Quality Assurance Methodology, Process Model, Best Practices for Machine Learning Applications, Automotive Industry and Academia, Best Practices, Guidelines} } @article{Subahi2020, title = {Cognification of Program Synthesis—a Systematic Feature-Oriented Analysis and Future Direction}, author = {Subahi, A.F.}, date = {2020}, journaltitle = {Computers}, volume = {9}, number = {2}, publisher = {MDPI AG}, issn = {2073431X}, doi = {10.3390/computers9020027}, abbrev_source_title = {Comput.}, affiliation = {Department of Computer Science, University College of Al Jamoum, Umm Al Qura University, P.O.Box 715, Mecca, Saudi Arabia}, art_number = {27}, correspondence_address1 = {Subahi, A.F.; Department of Computer Science, P.O.Box 715, Saudi Arabia; email: AFSubahi@uqu.edu.sa}, document_type = {Article}, langid = {english}, source = {Scopus}, keywords = {notion}, note = {cited By 0} } @article{subramaniyaswamy_adaptive_2017, title = {Adaptive {{KNN}} Based {{Recommender System}} through {{Mining}} of {{User Preferences}}}, author = {Subramaniyaswamy, V. and Logesh, R.}, date = {2017-11}, journaltitle = {Wirel. Pers. Commun.}, volume = {97}, number = {2}, pages = {2229--2247}, issn = {0929-6212, 1572-834X}, doi = {10.1007/s11277-017-4605-5}, abstract = {Research for the generation of reliable recommendations has been the main goal focused by many researchers in recent years. Though many recommendation approaches have been developed to assist users in the selection of their interesting items in the online world, still the personalization problem exists. In this paper, we present a new recommendation approach to address the problems such as scalability, sparsity, and cold-start in a collective way. We have developed a knowledge-based domain specific ontology for the generation of personalized recommendations. We have also introduced two different ontology-based predictive models as minion representation model and prominent representation model for the effective generation of recommendations to all types of users. The prediction models are induced by data mining algorithms by correlating the user preferences and features of items for user modeling. We have proposed a new variant of KNN algorithm as Adaptive KNN for the collaborative filtering based recommender system. The proposed recommendation approach is validated with standard MovieLens dataset and obtained results are evaluated with Precision, Recall, F-Measure, and Accuracy. The experimental results had proved the better performance of our proposed AKNN algorithm over other algorithms with the highly sparse data taken for the recommendation generation.}, langid = {english} } @inproceedings{Suchänek202074, title = {Bi-Directional Transformation between Normalized Systems Elements and Domain Ontologies in {{OWL}}}, author = {Suchänek, M. and Mannaert, H. and Uhnäk, P. and Pergl, R.}, editor = {Ali R., Kaindl H., Maciaszek L., Maciaszek L.}, date = {2020}, series = {{{ENASE}} 2020 - {{Proceedings}} of the 15th {{International Conference}} on {{Evaluation}} of {{Novel Approaches}} to {{Software Engineering}}}, pages = {74--85}, publisher = {SciTePress}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088385688&partnerID=40&md5=df36b44338dd85b1c2007e585a204b4d}, abbrev_source_title = {ENASE - Proc. Int. Conf. Eval. Novel Approaches Softw. Eng.}, affiliation = {Faculty of Information Technology, Czech Technical University in Prague, Thäkurova 9, Prague, Czech Republic; Normalized Systems Institute, University of Antwerp, Prinsstraat 13, Antwerp, Belgium; NSX Bvba, Wetenschapspark Universiteit Antwerpen, Galileilaan 15, Niel, 2845, Belgium}, document_type = {Conference Paper}, isbn = {978-989-758-421-3}, langid = {english}, source = {Scopus}, note = {cited By 3 \par TL;DR \par The potential advantages of having OWL representation of the NS model, the design of a bi-directional transformation between NS models and domain ontologies in OWL, and how the resulting ontology enables further work on the analytical level and leverages the system design are described.} } @online{SuggestionsPhDStudents, title = {Suggestions for {{PhD Students}}}, url = {https://www.linkedin.com/posts/dr-faheem-ullah_phd-research-supervision-activity-7279088776570654720-nW7a?utm_source=share&utm_medium=member_android}, keywords = {source\_mobile}, note = {PhD Students - How can you help your supervisors to give you the best supervision for your PhD? In a PhD program, supervisor-supervisee relationship is very important. Here are 10 things you can do on your part to get the best out of your PhD supervisors. 𝟏. 𝐒𝐡𝐨𝐰, 𝐝𝐨𝐧'𝐭 𝐭𝐞𝐥𝐥: Instead of telling your supervisor what you have worked on, show him/her the progress. For example, instead of saying that I have read 5 papers, you should say here are the critical summaries and 5 takeaways from these 5 papers that I think are useful for my research in these ways. 𝟐. 𝐓𝐚𝐤𝐞 𝐟𝐞𝐞𝐝𝐛𝐚𝐜𝐤 𝐩𝐨𝐬𝐢𝐭𝐢𝐯𝐞𝐥𝐲: If your supervisor criticizes you, actually he/she is not criticizing you but your work. This criticism is only for the purpose of improving your research and helping you to succeed. 𝟑. 𝐒𝐡𝐚𝐫𝐞 𝐦𝐞𝐞𝐭𝐢𝐧𝐠 𝐚𝐠𝐞𝐧𝐝𝐚 𝐚𝐧𝐝 𝐦𝐢𝐧𝐮𝐭𝐞𝐬: Send upfront the points about which you want to talk about in the meeting. Similarly, share what has been discussed in the meeting. It will only take around 10 minutes of your time. 𝟒. 𝐀𝐬𝐤, 𝐝𝐨𝐧'𝐭 𝐚𝐬𝐬𝐮𝐦𝐞: Do not assume outcomes from your supervisors. Instead, ask in a polite way. For example, do not assume that your supervisors will not allow you to apply for a research internship. Ask them. 𝟓. 𝐎𝐰𝐧 𝐲𝐨𝐮𝐫 𝐏𝐡𝐃: Your PhD is your project. After around 6 months, you are likely more expert on the topic than your supervisors. They are there to guide you through the process. 𝟔. 𝐑𝐞𝐦𝐢𝐧𝐝 𝐭𝐡𝐞𝐦: Supervisors have many things on their plates. It's possible that their minds skip something. Give them a software reminder if they need to get back to you on something such as feedback on a paper. 𝟕. 𝐒𝐞𝐞𝐤 𝐭𝐢𝐦𝐞𝐥𝐲 𝐟𝐞𝐞𝐝𝐛𝐚𝐜𝐤: Sending a paper draft to a supervisor one day before a submission deadline is a mistake on your end. Even if he/she provides the feedback, it might be shallow. Give them time. 𝟖. 𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐜𝐚𝐭𝐞 𝐜𝐥𝐞𝐚𝐫𝐥𝐲: Communicate clearly to your supervisors about any obstacles you are facing and any expectations you have. They have the experience to guide you accordingly but first, they need to know. 𝟗. 𝐃𝐨𝐧'𝐭 𝐫𝐞𝐩𝐞𝐚𝐭 𝐭𝐡𝐞 𝐬𝐚𝐦𝐞 𝐦𝐢𝐬𝐭𝐚𝐤𝐞𝐬: If your supervisors have corrected you about something, try not to repeat it. It will give them the impression that you did not value their feedback. If you don't agree, discuss it with them. 𝟏𝟎. 𝐑𝐞𝐬𝐩𝐞𝐜𝐭, 𝐫𝐞𝐬𝐩𝐞𝐜𝐭, 𝐚𝐧𝐝 𝐫𝐞𝐬𝐩𝐞𝐜𝐭: Doesn't matter how good or bad your relationship is with your supervisor, always respect them. I have hardly seen anyone successful who does not respect his/her teachers and supervisors. For more content like this, please subscribe my newsletter. Newsletter link:\texttt{ }\href{http://smartresearcher.me/}{http://smartresearcher.me} \href{https://www.linkedin.com/feed/hashtag/?keywords=phd&highlightedUpdateUrns=urn\%3Ali\%3Aactivity\%3A7279088776570654720}{hashtag\#phd}\texttt{ }\href{https://www.linkedin.com/feed/hashtag/?keywords=research&highlightedUpdateUrns=urn\%3Ali\%3Aactivity\%3A7279088776570654720}{hashtag\#research}\texttt{ }\href{https://www.linkedin.com/feed/hashtag/?keywords=supervision&highlightedUpdateUrns=urn\%3Ali\%3Aactivity\%3A7279088776570654720}{hashtag\#supervision} \par Activate to view larger image,} } @inproceedings{Sun:2014:ESN:2627508.2627514, title = {Empirical Studies on the {{NLP}} Techniques for Source Code Data Preprocessing}, booktitle = {Proc. 2014 3rd {{Int Workshop Evidential Assess}}. {{Soft Tech}}}, author = {Sun, Xiaobing and Liu, Xiangyue and Hu, Jiajun and Zhu, Junwu}, date = {2014}, series = {{{EAST}} 2014}, pages = {32--39}, publisher = {ACM}, location = {New York, NY, USA}, acmid = {2627514}, isbn = {978-1-4503-2965-1}, nodoi = {10.1145/2627508.2627514}, numpages = {8}, keywords = {empirical studies,NLP techniques,Program comprehension,source code preprocessing}, note = {TL;DR \par This paper conducts some empirical studies to show what are the differences before and after preprocessing the unstructured source code, and shows some interesting phenomena based on using or not using these preprocessing operations.} } @online{sun$texttransformer^2$SelfadaptiveLLMs2025, title = {\$\textbackslash text\{transformer\}\textasciicircum 2\$: Self-Adaptive {{LLMs}}}, shorttitle = {\$\textbackslash text\{transformer\}\textasciicircum 2\$}, author = {Sun, Qi and Cetin, Edoardo and Tang, Yujin}, date = {2025-01-14}, eprint = {2501.06252}, eprinttype = {arXiv}, eprintclass = {cs}, doi = {10.48550/arXiv.2501.06252}, abstract = {Self-adaptive large language models (LLMs) aim to solve the challenges posed by traditional fine-tuning methods, which are often computationally intensive and static in their ability to handle diverse tasks. We introduce \$\textbackslash text\{Transformer\}\textasciicircum 2\$, a novel self-adaptation framework that adapts LLMs for unseen tasks in real-time by selectively adjusting only the singular components of their weight matrices. During inference, \$\textbackslash text\{Transformer\}\textasciicircum 2\$ employs a two-pass mechanism: first, a dispatch system identifies the task properties, and then task-specific "expert" vectors, trained using reinforcement learning, are dynamically mixed to obtain targeted behavior for the incoming prompt. Our method outperforms ubiquitous approaches such as LoRA, with fewer parameters and greater efficiency. \$\textbackslash text\{Transformer\}\textasciicircum 2\$ demonstrates versatility across different LLM architectures and modalities, including vision-language tasks. \$\textbackslash text\{Transformer\}\textasciicircum 2\$ represents a significant leap forward, offering a scalable, efficient solution for enhancing the adaptability and task-specific performance of LLMs, paving the way for truly dynamic, self-organizing AI systems.}, langid = {english}, pubstate = {prepublished}, version = {2}, keywords = {Computer Science - Artificial Intelligence,Computer Science - Computation and Language,Computer Science - Machine Learning}, note = {Comment: 18 panges, 11 figures, 9 tables} } @article{sunAIEnhancedOffloadingEdge2019, title = {{{AI-Enhanced Offloading}} in {{Edge Computing}}: {{When Machine Learning Meets Industrial IoT}}}, shorttitle = {{{AI-Enhanced Offloading}} in {{Edge Computing}}}, author = {Sun, Wen and Liu, Jiajia and Yue, Yanlin}, date = {2019-09}, journaltitle = {IEEE Network}, volume = {33}, number = {5}, pages = {68--74}, issn = {0890-8044, 1558-156X}, doi = {10.1109/MNET.001.1800510}, abstract = {The Industrial Internet of Things (IIoT) enables intelligent industrial operations by incorporating artificial intelligence (AI) and big data technologies. An AI-enabled framework typically requires prompt and private cloud-based service to process and aggregate manufacturing data. Thus, integrating intelligence into edge computing is without doubt a promising development trend. Nevertheless, edge intelligence brings heterogeneity to the edge servers, in terms of not only computing capability, but also service accuracy. Most works on offloading in edge computing focus on finding the power-delay trade-off, ignoring service accuracy provided by edge servers as well as the accuracy required by IIoT devices. In this vein, in this article we introduce an intelligent computing architecture with cooperative edge and cloud computing for IIoT. Based on the computing architecture, an AI enhanced offloading framework is proposed for service accuracy maximization, which considers service accuracy as a new metric besides delay, and intelligently disseminates the traffic to edge servers or through an appropriate path to remote cloud. A case study is performed on transfer learning to show the performance gain of the proposed framework.}, langid = {english}, keywords = {DONE,machine learning}, note = {\textbf{Gray Annotations (17/12/2020, 23:14:22)} \par "According to GE Digital, IIoT is estimated to unlock manufacturing savings and benefit 46 percent of the global economy [1]" (\href{zotero://open-pdf/library/items/WXTJVNM8?page=1}{Sun et al 2019:68}) \par "Table 1 compares the existing works on AI applications in networks." (\href{zotero://open-pdf/library/items/WXTJVNM8?page=2}{Sun et al 2019:69}) \par "Bisio et al. [10] studied the role of context awareness in IIoT applications such as smart health, smart factory, and smart home scenarios." (\href{zotero://open-pdf/library/items/WXTJVNM8?page=2}{Sun et al 2019:69}) \par "Wang et al. [11] explored the trade-off between energy consumption and service latency in IIoT" (\href{zotero://open-pdf/library/items/WXTJVNM8?page=2}{Sun et al 2019:69}) \par "Li et al. [13] applied deep learning for IoT in the edge computing environment. They designed a scheduling algorithm to maximize the number of tasks in edge computing with guaranteed quality of service (QoS) requirements." (\href{zotero://open-pdf/library/items/WXTJVNM8?page=3}{Sun et al 2019:70}) \par "In [3], edge servers learn model parameters from data distributed at the edge nodes, using the gradient-descent method based on distributed learning, instead of sending data to the centralized cloud. They proposed a control algorithm for the trade-off between local update and global parameter aggregation to minimize loss function and under a given resource budget." (\href{zotero://open-pdf/library/items/WXTJVNM8?page=3}{Sun et al 2019:70}) \par "[1] GE Digital Report, "Everything You Need to Know about the Industrial Internet of Things," 2017; https://www.ge.com/ digital/blog/everything-you-needknow-about-industrial-internet-things" (\href{zotero://open-pdf/library/items/WXTJVNM8?page=7}{Sun et al 2019:74}) \par \textbf{Green Annotations (17/12/2020, 23:14:22)} \par "The Industrial Internet of Things (IIoT) enables intelligent industrial operations by incorporating artificial intelligence (AI) and big data technologies." (\href{zotero://open-pdf/library/items/WXTJVNM8?page=1}{Sun et al 2019:68}) \par "An AI-enabled framework typically requires prompt and private cloud-based service to process and aggregate manufacturing data" (\href{zotero://open-pdf/library/items/WXTJVNM8?page=1}{Sun et al 2019:68}) \par "integrating intelligence into edge computing is without doubt a promising development trend" (\href{zotero://open-pdf/library/items/WXTJVNM8?page=1}{Sun et al 2019:68}) \par "edge intelligence brings heterogeneity to the edge servers, in terms of not only computing capability, but also service accuracy" (\href{zotero://open-pdf/library/items/WXTJVNM8?page=1}{Sun et al 2019:68}) \par "this article we introduce an intelligent computing architecture with cooperative edge and cloud computing for IIoT" (\href{zotero://open-pdf/library/items/WXTJVNM8?page=1}{Sun et al 2019:68}) \par "AI enhanced offloading framework is proposed for service accuracy maximization, which considers service accuracy as a new metric besides delay, and intelligently disseminates the traffic to edge servers or through an appropriate path to remote cloud." (\href{zotero://open-pdf/library/items/WXTJVNM8?page=1}{Sun et al 2019:68}) \par "performance gain of the proposed framework" (\href{zotero://open-pdf/library/items/WXTJVNM8?page=1}{Sun et al 2019:68}) \par "interconnects a multitude of industrial devices, actuators, and people at work" (\href{zotero://open-pdf/library/items/WXTJVNM8?page=1}{Sun et al 2019:68}) \par "IIoT incorporates artificial intelligence (AI) technologies to process and analyze data from various sources and make advanced predictive analytics, such as fault class prediction, predictive maintenance, demand forecasting." (\href{zotero://open-pdf/library/items/WXTJVNM8?page=1}{Sun et al 2019:68}) \par "smart manufacturing is a large connected and complex industrial process, which produces a large amount of multi-feature data, it is difficult to construct its operation process with an accurate mathematical model" (\href{zotero://open-pdf/library/items/WXTJVNM8?page=1}{Sun et al 2019:68}) \par "AI algorithms are able to extract critical features without in-depth physical understanding of the concerned system" (\href{zotero://open-pdf/library/items/WXTJVNM8?page=1}{Sun et al 2019:68}) \par "in IIoT, predictive maintenance relies on machine learning to detect anomalies in systems and then predict the failure of devices by correlating and analyzing the change in the pattern" (\href{zotero://open-pdf/library/items/WXTJVNM8?page=1}{Sun et al 2019:68}) \par "IIoT typically requires prompt and private computing service to process and aggregate the manufacturing data" (\href{zotero://open-pdf/library/items/WXTJVNM8?page=1}{Sun et al 2019:68}) \par "integrating intelligence into the edge is without doubt a promising development trend [2]" (\href{zotero://open-pdf/library/items/WXTJVNM8?page=1}{Sun et al 2019:68}) \par "distributed computing service through small-scale data centers near the edge of the network." (\href{zotero://open-pdf/library/items/WXTJVNM8?page=1}{Sun et al 2019:68}) \par "edge computing provides real-time data analytics with privacy preserving, increases network capabilities, and avoids congestion of backbone networks and the Internet core" (\href{zotero://open-pdf/library/items/WXTJVNM8?page=1}{Sun et al 2019:68}) \par "Personalization: Customized AI models can be developed at the edge servers, which are tailored to individual users' behaviors and requirements to deliver accurate results to the users." (\href{zotero://open-pdf/library/items/WXTJVNM8?page=1}{Sun et al 2019:68}) \par \mkbibemph{This might be related to the citizen developer / lowcode (\href{zotero://open-pdf/library/items/WXTJVNM8?page=1}{note on p.68})} \par "Responsiveness: While the industrial process is time-varying and unpredictable, the computing service must be prompt and more adaptive and feasible to the new situation." (\href{zotero://open-pdf/library/items/WXTJVNM8?page=1}{Sun et al 2019:68}) \par \mkbibemph{This might be related to the federated learning aspect mentioned by Luciano (\href{zotero://open-pdf/library/items/WXTJVNM8?page=1}{note on p.68})} \par "Privacy Preserving: Especially for IIoT, the processing information owned by industrial companies may not be willing to transmit to the remote cloud for privacy issues; thus, the edge server provides private service." (\href{zotero://open-pdf/library/items/WXTJVNM8?page=1}{Sun et al 2019:68}) \par "The AI service deployed on edge servers exhibits heterogeneity in terms of service accuracy due to the limited and heterogeneous computing capability of edge servers." (\href{zotero://open-pdf/library/items/WXTJVNM8?page=1}{Sun et al 2019:68}) \par "The impacts of edge intelligence on computing offloading remains untouched." (\href{zotero://open-pdf/library/items/WXTJVNM8?page=1}{Sun et al 2019:68}) \par "intelligent computing architecture with cooper-" (\href{zotero://open-pdf/library/items/WXTJVNM8?page=1}{Sun et al 2019:68}) \par "ative edge and cloud computing for IIoT. Then" (\href{zotero://open-pdf/library/items/WXTJVNM8?page=2}{Sun et al 2019:69}) \par "AI enhanced offloading framework for service accuracy maximization is developed" (\href{zotero://open-pdf/library/items/WXTJVNM8?page=2}{Sun et al 2019:69}) \par "service accuracy as a new metric besides delay," (\href{zotero://open-pdf/library/items/WXTJVNM8?page=2}{Sun et al 2019:69}) \par "intelligently disseminates the traffic to edge servers or through appropriate paths to remote cloud." (\href{zotero://open-pdf/library/items/WXTJVNM8?page=2}{Sun et al 2019:69}) \par "Machine learning, as an application of AI, gives devices or computer systems the ability to "learn" with data without being explicitly programmed [2]." (\href{zotero://open-pdf/library/items/WXTJVNM8?page=2}{Sun et al 2019:69}) \par "supervised learning conducts classification or regression tasks from labeled data" (\href{zotero://open-pdf/library/items/WXTJVNM8?page=2}{Sun et al 2019:69}) \par "unsupervised learning categorizes the unlabeled data into clusters" (\href{zotero://open-pdf/library/items/WXTJVNM8?page=2}{Sun et al 2019:69}) \par "Reinforcement learning indicates agents to take actions so as to maximize the cumulative reward" (\href{zotero://open-pdf/library/items/WXTJVNM8?page=2}{Sun et al 2019:69}) \par "In IIoT, machine learning algorithms are leveraged to analyze the complex manufacturing data and believer insights about predictive maintenance, industrial prognostics, and demand forecasting." (\href{zotero://open-pdf/library/items/WXTJVNM8?page=2}{Sun et al 2019:69}) \par "Generally, cloud computing is employed for data processing. However, it is difficult to transmit huge amounts of data to the remote cloud; thus, approximate and distributed computing service becomes necessary." (\href{zotero://open-pdf/library/items/WXTJVNM8?page=2}{Sun et al 2019:69}) \par \mkbibemph{THIS IS A RELEVANT CASE, I.E., WHEN YOU CANNOT SEND HUDGE AMOUNT OF DATA TO THE CLOUD FOR PERFORMING DATA ANALYTICS TASKS!!! (\href{zotero://open-pdf/library/items/WXTJVNM8?page=2}{note on p.69})} \par "There are edge servers that are actually designed for AI-enabled computing tasks such as the NVIDIA DGX workstation" (\href{zotero://open-pdf/library/items/WXTJVNM8?page=2}{Sun et al 2019:69}) \par "analyzed video streams recorded on a number of surveillance cameras" (\href{zotero://open-pdf/library/items/WXTJVNM8?page=2}{Sun et al 2019:69}) \par \mkbibemph{THIS MAKES SENSE FOR THE THE SCENARIO OF LUCIANO ON SMART BUILDING. IN SUCH CASES DATA NEED TO BE ANALYSED ON THE EDGE. (\href{zotero://open-pdf/library/items/WXTJVNM8?page=2}{note on p.69})} \par "The MEC application examined the video streams, classified what were normal and abnormal patterns, and then only needed to send the stream to the backbone when a potential security issue was identified." (\href{zotero://open-pdf/library/items/WXTJVNM8?page=3}{Sun et al 2019:70}) \par \mkbibemph{YES! VERY IMPORTANT (\href{zotero://open-pdf/library/items/WXTJVNM8?page=3}{note on p.70})} \par "AI tasks laid a heavy burden on edge servers with limited resources" (\href{zotero://open-pdf/library/items/WXTJVNM8?page=3}{Sun et al 2019:70}) \par "Industrial MDs monitor the industrial parameters, and deliver the collected data to the data center for aggregation" (\href{zotero://open-pdf/library/items/WXTJVNM8?page=3}{Sun et al 2019:70}) \par "The AI-enabled IIoT service includes self-monitoring, demand forecasting, fault detection, and workforce management." (\href{zotero://open-pdf/library/items/WXTJVNM8?page=3}{Sun et al 2019:70}) \par "The decision is fed back to the IIoT devices and executed automatically." (\href{zotero://open-pdf/library/items/WXTJVNM8?page=3}{Sun et al 2019:70}) \par "he intelligent computational architecture needs to be reshaped." (\href{zotero://open-pdf/library/items/WXTJVNM8?page=3}{Sun et al 2019:70}) \par "two-layer intelligent data center, that is, edge layer and cloud layer" (\href{zotero://open-pdf/library/items/WXTJVNM8?page=3}{Sun et al 2019:70}) \par \mkbibemph{THAT'S THE MAIN ARCHITECTURAL INNOVATION THAT THEY PROPOSE. (\href{zotero://open-pdf/library/items/WXTJVNM8?page=3}{note on p.70})} \par "Edge Layer: It accommodates lightweight intelligent computing service for IIoT," (\href{zotero://open-pdf/library/items/WXTJVNM8?page=3}{Sun et al 2019:70}) \par "Cloud Layer: It provides powerful and comprehensive computing service for IIoT at the cost of latency and communication burden." (\href{zotero://open-pdf/library/items/WXTJVNM8?page=3}{Sun et al 2019:70}) \par "The interaction between the edge layer and the cloud layer is at the cost of additional communication on the backbone network." (\href{zotero://open-pdf/library/items/WXTJVNM8?page=3}{Sun et al 2019:70}) \par "how to deploy the computing service between the edge layer and cloud layer, and afterward assign the computing tasks of IIoT devices according to their requirements as well as the characteristics of heterogeneous edge servers and remote cloud, needs serious consideration." (\href{zotero://open-pdf/library/items/WXTJVNM8?page=3}{Sun et al 2019:70}) \par \mkbibemph{THAT'S ANOTHER IMPORTANT *CHALLENGES* (\href{zotero://open-pdf/library/items/WXTJVNM8?page=3}{note on p.70})} \par "priority" (\href{zotero://open-pdf/library/items/WXTJVNM8?page=4}{Sun et al 2019:71}) \par "accuracy" (\href{zotero://open-pdf/library/items/WXTJVNM8?page=4}{Sun et al 2019:71}) \par "delay" (\href{zotero://open-pdf/library/items/WXTJVNM8?page=4}{Sun et al 2019:71}) \par "(ui, ai, di), where ui is the degree of urgency of MD i, that is, priority (a scalar value within (0, 1)), and ai is the acceptable accuracy of MD i, and di is the acceptable delay of MD i" (\href{zotero://open-pdf/library/items/WXTJVNM8?page=4}{Sun et al 2019:71}) \par "o choose an optimal jD i, it is imperative to estimate the delay and accuracy of offloading to available edge servers, and determine the optimal offloading option according to its requirement." (\href{zotero://open-pdf/library/items/WXTJVNM8?page=4}{Sun et al 2019:71}) \par "Different from the previous research, the offloading decision depends not only on the estimated access delay but also on the accuracy the edge server can provide." (\href{zotero://open-pdf/library/items/WXTJVNM8?page=4}{Sun et al 2019:71}) \par \mkbibemph{THAT'S IMPORTANT, IT IS RELATED TO THE WAY TASKS ARE DISTRIBUTED AND ASSIGNED TO THE DIFFERENT EDGE SERVERS. (\href{zotero://open-pdf/library/items/WXTJVNM8?page=4}{note on p.71})} \par "near-optimal offloading framework for accuracy maximization offloading with latency constraints" (\href{zotero://open-pdf/library/items/WXTJVNM8?page=4}{Sun et al 2019:71}) \par "Step 1: (Estimate the accuracy of computing task from IIoT MD i.)" (\href{zotero://open-pdf/library/items/WXTJVNM8?page=4}{Sun et al 2019:71}) \par "Step 2: (Estimate the access delay of computing tasks from MD i.)" (\href{zotero://open-pdf/library/items/WXTJVNM8?page=4}{Sun et al 2019:71}) \par "Step 3: (Offload to the appropriate edge servers.) According to the estimated accuracy and dela" (\href{zotero://open-pdf/library/items/WXTJVNM8?page=4}{Sun et al 2019:71}) \par "For those MDs that did not find an appropriate edge server or remote server, it can be accomplished locally at the CPU of MDs. For those computing tasks with predicted delay to the remote cloud lower than its delay requirement, we tend to route the computing tasks to the remote cloud, since the remote cloud is most powerful and can provide the highest accuracy. Thus, through Step 3, the accuracy of MDs is deter" (\href{zotero://open-pdf/library/items/WXTJVNM8?page=4}{Sun et al 2019:71}) \par "By the proposed offloading framework, traffic will be disseminated intelligently, according to its requirement, to the optimal edge servers or to the remote cloud through an appropriate path so that the pressure on the backbone network will be effectively alleviated." (\href{zotero://open-pdf/library/items/WXTJVNM8?page=4}{Sun et al 2019:71}) \par "y edge servers and therefore does not consume much bandwidth. Unlike large-scale training in remote cloud, transfer training requires only a small amount of targeted training data to achieve high accuracy of the network." (\href{zotero://open-pdf/library/items/WXTJVNM8?page=5}{Sun et al 2019:72}) \par "trAnsfEr lEArnIn" (\href{zotero://open-pdf/library/items/WXTJVNM8?page=5}{Sun et al 2019:72}) \par "IoT MDs turn to the edge servers for image processing to monitor the industrial process." (\href{zotero://open-pdf/library/items/WXTJVNM8?page=5}{Sun et al 2019:72}) \par \mkbibemph{SIMILE SCENARIO PER NOI??? (\href{zotero://open-pdf/library/items/WXTJVNM8?page=5}{note on p.72})} \par "The transfer-learning-based computing and offloading framework is done following five phases, that is, training the source neural network with large-scale data in the remote cloud, loading the pretrained neural network, customizing the predictive model, training the predictive model with small-scale data in the edge, and offloading tasks to appropriate edge servers." (\href{zotero://open-pdf/library/items/WXTJVNM8?page=5}{Sun et al 2019:72}) \par "First, the source neural networks are trained in large-scale data in remote cloud" (\href{zotero://open-pdf/library/items/WXTJVNM8?page=5}{Sun et al 2019:72}) \par "Then an edge server loads the pretrained neural network from remote cloud" (\href{zotero://open-pdf/library/items/WXTJVNM8?page=5}{Sun et al 2019:72}) \par "he pretrained network then transforms to a customized predictive model" (\href{zotero://open-pdf/library/items/WXTJVNM8?page=5}{Sun et al 2019:72}) \par "Finally, we assess the service accuracy of the predictive model and offload the computing tasks of IIoT devices according to AMLC." (\href{zotero://open-pdf/library/items/WXTJVNM8?page=5}{Sun et al 2019:72}) \par "In order to develop lightweight machine learning technologies on edge servers in IIoT, transfer learning is adopted." (\href{zotero://open-pdf/library/items/WXTJVNM8?page=5}{Sun et al 2019:72}) \par "Transfer learning is a popular approach in deep learning where pre-trained models are used as the starting point to learn a new task" (\href{zotero://open-pdf/library/items/WXTJVNM8?page=5}{Sun et al 2019:72}) \par \mkbibemph{IMPORTANT FOR THE THINGS ABOUT FEDERATED VS DISTRIBUTED LEARNING MENTIONED BY LUCIANO (\href{zotero://open-pdf/library/items/WXTJVNM8?page=5}{note on p.72})} \par "This shows that at the edge layer, the customized predictive model on edge servers differs from each other due to different local data, even when the pretrained networks are the same." (\href{zotero://open-pdf/library/items/WXTJVNM8?page=6}{Sun et al 2019:73}) \par "we find it feasible to deploy machine learning applications to edge servers and employ service accuracy as a metric in the traffic offloading of MEC, while it also faces many challenges, such as the storage of training and test data, model training, and parameter updates" (\href{zotero://open-pdf/library/items/WXTJVNM8?page=6}{Sun et al 2019:73}) \par \mkbibemph{IMPORTANT CHALLENGES!!! (\href{zotero://open-pdf/library/items/WXTJVNM8?page=6}{note on p.73})} \par "in AI-enabled edge computing, it is also challenging to appropriately tailor the AI-based computing service to trade off between accuracy and the constrained computing resources." (\href{zotero://open-pdf/library/items/WXTJVNM8?page=6}{Sun et al 2019:73}) \par \mkbibemph{CHALLENGE (\href{zotero://open-pdf/library/items/WXTJVNM8?page=6}{note on p.73})} \par "proposed an intelligent computing architecture in IIoT with cooperation between edge servers and remote cloud." (\href{zotero://open-pdf/library/items/WXTJVNM8?page=7}{Sun et al 2019:74}) \par \mkbibemph{THAT'S THE IDEA OF THE PROPOSED APPROACH (\href{zotero://open-pdf/library/items/WXTJVNM8?page=7}{note on p.74})} \par "AI-driven offloading framework considering service accuracy as a new metric, intelligently disseminating traffic to edge servers or remote cloud" (\href{zotero://open-pdf/library/items/WXTJVNM8?page=7}{Sun et al 2019:74}) \par "AI-based computing service to trade off between accuracy and the constrained computing resources." (\href{zotero://open-pdf/library/items/WXTJVNM8?page=7}{Sun et al 2019:74}) \par \mkbibemph{THAT'S A POSSIBLE FUTURE WORK (\href{zotero://open-pdf/library/items/WXTJVNM8?page=7}{note on p.74})} \par TL;DR \par An AI enhanced offloading framework is proposed for service accuracy maximization, which considers service accuracy as a new metric besides delay, and intelligently disseminates the traffic to edge servers or through an appropriate path to remote cloud.} } @article{sunConvergenceRecommenderSystems2020, title = {Convergence of {{Recommender Systems}} and {{Edge Computing}}: {{A Comprehensive Survey}}}, shorttitle = {Convergence of {{Recommender Systems}} and {{Edge Computing}}}, author = {Sun, Chuan and Li, Hui and Li, Xiuhua and Wen, Junhao and Xiong, Qingyu and Zhou, Wei}, date = {2020}, journaltitle = {IEEE Access}, volume = {8}, pages = {47118--47132}, issn = {2169-3536}, doi = {10.1109/ACCESS.2020.2978896}, keywords = {internet of things,recommendation systems}, note = {\textbf{Gray Annotations (18/12/2020, 15:41:24)} \par "According to Cisco reports, nearly 850 ZB data will be generated each year by 2021, but the data center is only 20.6 ZB [10]." (\href{zotero://open-pdf/library/items/YWLPYTI3?page=2}{Sun et al 2020:47119}) \par \mkbibemph{QUESTO E' FONDAMENTALE PER MOTIVARE LA QUESTIONE EDGE E FEDERATED LEARNING (\href{zotero://open-pdf/library/items/YWLPYTI3?page=2}{note on p.47119})} \par "It indicates that the location distribution of data sources is undergoing a transformation from data centers to an expanding number of edge devices" (\href{zotero://open-pdf/library/items/YWLPYTI3?page=2}{Sun et al 2020:47119}) \par "Nowadays, various machine learning-based intelligent services have been deployed at edge servers to meet the critical requirements (e.g., agility, heterogeneous data analysis, and privacy-policy strategy) of computation tasks [16][18]." (\href{zotero://open-pdf/library/items/YWLPYTI3?page=2}{Sun et al 2020:47119}) \par \mkbibemph{SUPPORTO MOTIVAZIONE (\href{zotero://open-pdf/library/items/YWLPYTI3?page=2}{note on p.47119})} \par \textbf{Green Annotations (18/12/2020, 15:41:24)} \par "recommender systems have been used as an effective technology to lter useless information and attempt to recommend the most useful items" (\href{zotero://open-pdf/library/items/YWLPYTI3?page=1}{Sun et al 2020:47118}) \par \mkbibemph{USE OF RECSYS (\href{zotero://open-pdf/library/items/YWLPYTI3?page=1}{note on p.47118})} \par "Mobile edge computing is a novel computing paradigm via pushing computation/storage resource from the remote cloud servers to the network edge servers to provide more intelligent and personalized service." (\href{zotero://open-pdf/library/items/YWLPYTI3?page=1}{Sun et al 2020:47118}) \par "collaborative ltering (CF)" (\href{zotero://open-pdf/library/items/YWLPYTI3?page=1}{Sun et al 2020:47118}) \par "content-based recommendation (CB)" (\href{zotero://open-pdf/library/items/YWLPYTI3?page=1}{Sun et al 2020:47118}) \par "nowledge-based recommendation (KB)" (\href{zotero://open-pdf/library/items/YWLPYTI3?page=1}{Sun et al 2020:47118}) \par "hybrid recommendation (HR)" (\href{zotero://open-pdf/library/items/YWLPYTI3?page=1}{Sun et al 2020:47118}) \par "CF achieves the best accuracy of predictions about how much someone is going to enjoy a movie in Netix Prize, however, it has sparseness and cold-start problems [6]" (\href{zotero://open-pdf/library/items/YWLPYTI3?page=1}{Sun et al 2020:47118}) \par "data sources contain the following key characteristics." (\href{zotero://open-pdf/library/items/YWLPYTI3?page=2}{Sun et al 2020:47119}) \par "Sparsity: in the edge environment, the historical data sources stored in edge server comes from a small amount or even one user's proles" (\href{zotero://open-pdf/library/items/YWLPYTI3?page=2}{Sun et al 2020:47119}) \par "Heterogeneity: edge devices are produced by companies around the world" (\href{zotero://open-pdf/library/items/YWLPYTI3?page=2}{Sun et al 2020:47119}) \par "Mobility: the mobility is an inherent characteristic of users in the mobile networks" (\href{zotero://open-pdf/library/items/YWLPYTI3?page=2}{Sun et al 2020:47119}) \par "Volatility: the state of the mobile edge network is volatility, when one user invokes a service many times, the QoS data may be different each time." (\href{zotero://open-pdf/library/items/YWLPYTI3?page=2}{Sun et al 2020:47119}) \par "recommender systems based on cloud computing have been proposed in traditional Internet environments, they are gradually unable to deal with these novel emerging services and massively distributed data in mobile edge network, they may fail to predict what users' interests and demands are." (\href{zotero://open-pdf/library/items/YWLPYTI3?page=2}{Sun et al 2020:47119}) \par \mkbibemph{THIS IS A MOTIVATION STATEMENT! CHALLENGE (\href{zotero://open-pdf/library/items/YWLPYTI3?page=2}{note on p.47119})} \par "1) cold-start problem, as data sources of active users are usually very sparse, even new or inactive users lack relevant proles, the cold-start problem occurs" (\href{zotero://open-pdf/library/items/YWLPYTI3?page=2}{Sun et al 2020:47119}) \par "2) exploration and exploitation problem, for example, in online shopping, exploration implies recommending new goods and exploitation entails reusing existing goods. How to nd an optimal trade-off between exploration and exploitation is crucial issu" (\href{zotero://open-pdf/library/items/YWLPYTI3?page=2}{Sun et al 2020:47119}) \par ") security and privacy problem, the data sources are produced by various IoT devices and distributed at different edge platforms, resulting in potential leakage of user data security problem" (\href{zotero://open-pdf/library/items/YWLPYTI3?page=2}{Sun et al 2020:47119}) \par "Mobile edge computing (MEC)" (\href{zotero://open-pdf/library/items/YWLPYTI3?page=2}{Sun et al 2020:47119}) \par "four enabling technologies for building recommender systems and Edge computing:" (\href{zotero://open-pdf/library/items/YWLPYTI3?page=3}{Sun et al 2020:47120}) \par ") Recommender systems on Edge" (\href{zotero://open-pdf/library/items/YWLPYTI3?page=3}{Sun et al 2020:47120}) \par "2) Recommender systems in Edge" (\href{zotero://open-pdf/library/items/YWLPYTI3?page=3}{Sun et al 2020:47120}) \par "3) Edge computing for recommender systems" (\href{zotero://open-pdf/library/items/YWLPYTI3?page=3}{Sun et al 2020:47120}) \par "4) Recommender systems for Edge computing" (\href{zotero://open-pdf/library/items/YWLPYTI3?page=3}{Sun et al 2020:47120}) \par "However, conventional recommender systems are gradually unable to meet the requirements of IoT services. Recently, a novel computing paradigm has been proposed by pushing computation and storage resources from the central cloud servers to network edges. Hence, deploying recommender systems applications at the edge servers can perform some lightweight processing to improve QoS." (\href{zotero://open-pdf/library/items/YWLPYTI3?page=8}{Sun et al 2020:47125})} } @article{sundbergDemocratizingArtificialIntelligence2023, title = {Democratizing Artificial Intelligence: {{How}} No-Code {{AI}} Can Leverage Machine Learning Operations}, shorttitle = {Democratizing Artificial Intelligence}, author = {Sundberg, Leif and Holmström, Jonny}, date = {2023-04}, journaltitle = {Business Horizons}, pages = {S0007681323000502}, issn = {00076813}, doi = {10.1016/j.bushor.2023.04.003}, abstract = {Organizations are increasingly seeking to generate value and insights from their data by integrating advances in artificial intelligence (AI) such as machine learning (ML) systems into their operations. However, there are several managerial challenges associated with ML operations (MLOps). In this article we outline three key challenges and discuss how an emerging form of AI platforms – ‘no-code AI’ – may help organizations to address and overcome them. We outline how no-code AI can leverage MLOps by closing the gap between business and technology experts, enabling faster iterations between problems and solutions, and aiding infrastructure management. After outlining important remaining challenges associated with no-code AI and MLOps we propose three managerial Journal Pre-proof recommendations. By doing so, we provide insights into an important novel, emerging phenomenon in AI software and set the stage for further research in the area.}, langid = {english}, keywords = {LOGSEQ} } @article{sunhareInternetThingsData2020, title = {Internet of Things and Data Mining: {{An}} Application Oriented Survey}, shorttitle = {Internet of Things and Data Mining}, author = {Sunhare, Priyank and Chowdhary, Rameez R. and Chattopadhyay, Manju K.}, date = {2020-07}, journaltitle = {Journal of King Saud University - Computer and Information Sciences}, pages = {S131915782030416X}, issn = {13191578}, doi = {10.1016/j.jksuci.2020.07.002}, abstract = {Advancement in the fields of electronic communication, data processing, and internet technologies enable easy access to and interaction with a variety of physical devices throughout the globe. Our whole world is enveloped by a blanket of innumerable smart devices equipped with the sensors and actuators. Extensive research on the Internet of things (IoT) with cloud technologies, make it possible to accumulate tremendous data created from this heterogeneous environment and transform it into precious knowledge by utilizing data mining technologies. Furthermore, this generated knowledge will play a key role in intelligent decision making, system performance boosting, and optimum management of resources and services. With this background, this paper presents a systematic and detailed review of various data mining techniques employed in the large and small scale IoT applications to formulate an intelligent environment. It also presents an overview of cloud-assisted IoT Big data mining system to better understand the importance of data mining for an IoT environment.}, langid = {english} } @article{sunkleAIdrivenStreamlinedModeling2022, title = {{{AI-driven}} Streamlined Modeling: Experiences and Lessons Learned from Multiple Domains}, shorttitle = {{{AI-driven}} Streamlined Modeling}, author = {Sunkle, Sagar and Saxena, Krati and Patil, Ashwini and Kulkarni, Vinay}, date = {2022-06}, journaltitle = {Softw Syst Model}, volume = {21}, number = {3}, pages = {1--23}, issn = {1619-1366, 1619-1374}, doi = {10.1007/s10270-022-00982-6}, langid = {english}, keywords = {notion}, note = {TL;DR \par Five industrial case studies where AI techniques are used in different modeling activities to help the researchers and practitioners make sense of various artifacts and data available to them and use applicable AI techniques to enhance suitable modeling activities.} } @inproceedings{SunyePTJ01, title = {Refactoring {{UML}} Models}, booktitle = {«{{UML}}» 2001 - {{Unified Model}}. {{Lang}}. {{Model}}. {{Lang}}. {{Concepts Tools}} 4th {{Int}}. {{Conf}}. {{Tor}}. {{Can}}. {{Oct}}. 1-5 2001 {{Proc}}.}, author = {Sunyé, Gerson and Pollet, Damien and Traon, Yves Le and Jézéquel, Jean-Marc}, editor = {Gogolla, Martin and Kobryn, Cris}, date = {2001}, series = {Lecture Notes in Computer Science}, volume = {2185}, pages = {134--148}, publisher = {Springer}, doi = {10.1007/3-540-45441-1\_11}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {TL;DR \par A set of refactorings is presented and it is explained how they can be designed so as to preserve the behavior of a UML model.}, timestamp = {Tue, 14 May 2019 10:00:55 +0200} } @inproceedings{suriModelbasedDevelopmentModular2017, title = {Model-Based {{Development}} of {{Modular Complex Systems}} for {{Accomplishing System Integration}} for {{Industry}} 4.0:}, shorttitle = {Model-Based {{Development}} of {{Modular Complex Systems}} for {{Accomplishing System Integration}} for {{Industry}} 4.0}, author = {Suri, Kunal and Cuccuru, Arnaud and Cadavid, Juan and Gerard, Sebastien and Gaaloul, Walid and Tata, Samir}, date = {2017}, pages = {487--495}, publisher = {{SCITEPRESS - Science and Technology Publications}}, doi = {10.5220/0006210504870495}, isbn = {978-989-758-210-3} } @online{SurveyClusteringData, title = {A {{Survey}} of {{Clustering Data Mining Techniques}} - {{Springer}}}, url = {http://link.springer.com/chapter/10.1007%2F3-540-28349-8_2}, urldate = {2015-04-16} } @online{SurveyNoSQLDatabase, title = {Survey on {{NoSQL}} Database}, url = {http://ieeexplore.ieee.org/abstract/document/6106531/?casa_token=skk-O-EQilsAAAAA:E0LtNJ8JtgHBiTRq54qaAudBrRo6Iz4BFciGElfCEkBSW7ZVSzK8lyjhT-MGt35cwpStASMZ}, urldate = {2021-03-22}, abstract = {With the development of the Internet and cloud computing, there need databases to be able to store and process big data effectively, demand for high-performance when reading and writing, so the traditional relational database is facing many new challenges. Especially in large scale and high-concurrency applications, such as search engines and SNS, using the relational database to store and query dynamic user data has appeared to be inadequate. In this case, NoSQL database created. This paper describes the background, basic characteristics, data model of NoSQL. In addition, this paper classifies NoSQL databases according to the CAP theorem. Finally, the mainstream NoSQL databases are separately described in detail, and extract some properties to help enterprises to choose NoSQL.}, langid = {american}, note = {TL;DR \par The background, basic characteristics, data model of NoSQL, and the mainstream NoSQL databases are separately described in detail, and some properties are extracted to help enterprises to choose NoSQL.} } @article{sutskeverSequenceSequenceLearning2014, title = {Sequence to {{Sequence Learning}} with {{Neural Networks}}}, author = {Sutskever, Ilya and Vinyals, Oriol and Le, Quoc V.}, date = {2014-12-14}, journaltitle = {ArXiv14093215 Cs}, eprint = {1409.3215}, eprinttype = {arXiv}, eprintclass = {cs}, url = {http://arxiv.org/abs/1409.3215}, urldate = {2021-03-31}, abstract = {Deep Neural Networks (DNNs) are powerful models that have achieved excellent performance on difficult learning tasks. Although DNNs work well whenever large labeled training sets are available, they cannot be used to map sequences to sequences. In this paper, we present a general end-to-end approach to sequence learning that makes minimal assumptions on the sequence structure. Our method uses a multilayered Long Short-Term Memory (LSTM) to map the input sequence to a vector of a fixed dimensionality, and then another deep LSTM to decode the target sequence from the vector. Our main result is that on an English to French translation task from the WMT'14 dataset, the translations produced by the LSTM achieve a BLEU score of 34.8 on the entire test set, where the LSTM's BLEU score was penalized on out-of-vocabulary words. Additionally, the LSTM did not have difficulty on long sentences. For comparison, a phrase-based SMT system achieves a BLEU score of 33.3 on the same dataset. When we used the LSTM to rerank the 1000 hypotheses produced by the aforementioned SMT system, its BLEU score increases to 36.5, which is close to the previous best result on this task. The LSTM also learned sensible phrase and sentence representations that are sensitive to word order and are relatively invariant to the active and the passive voice. Finally, we found that reversing the order of the words in all source sentences (but not target sentences) improved the LSTM's performance markedly, because doing so introduced many short term dependencies between the source and the target sentence which made the optimization problem easier.}, keywords = {Computer Science - Computation and Language,Computer Science - Machine Learning}, note = {Comment: 9 pages} } @article{Svozil1997, title = {Introduction to Multi-Layer Feed-Forward Neural Networks}, author = {Svozil, Daniel and Kvasnicka, Vladimir and Pospíchal, Jiří}, date = {1997-11}, journaltitle = {Chemom. Intell. Lab. Syst.}, volume = {39}, pages = {43--62}, nodoi = {10.1016/S0169-7439(97)00061-0} } @article{svyatkovskiyPythiaAIassistedCode2019, title = {Pythia: {{AI-assisted Code Completion System}}}, shorttitle = {Pythia}, author = {Svyatkovskiy, Alexey and Zhao, Ying and Fu, Shengyu and Sundaresan, Neel}, date = {2019-07-25}, journaltitle = {Proc. 25th ACM SIGKDD Int. Conf. Knowl. Discov. Data Min.}, eprint = {1912.00742}, eprinttype = {arXiv}, pages = {2727--2735}, doi = {10.1145/3292500.3330699}, abstract = {In this paper, we propose a novel end-to-end approach for AI-assisted code completion called Pythia. It generates ranked lists of method and API recommendations which can be used by software developers at edit time. The system is currently deployed as part of Intellicode extension in Visual Studio Code IDE. Pythia exploits state-of-the-art large-scale deep learning models trained on code contexts extracted from abstract syntax trees. It is designed to work at a high throughput predicting the best matching code completions on the order of 100 ms. We describe the architecture of the system, perform comparisons to frequency-based approach and invocation-based Markov Chain language model, and discuss challenges serving Pythia models on lightweight client devices. The offline evaluation results obtained on 2700 Python open source software GitHub repositories show a top-5 accuracy of 92\%, surpassing the baseline models by 20\% averaged over classes, for both intra and cross-project settings.}, langid = {english}, keywords = {Computer Science - Machine Learning,Computer Science - Software Engineering}, note = {Comment: Published in Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining (KDD '19)} } @online{SwarmRobotsCan, title = {Swarm Robots Can Learn by Simply Observing -- {{ScienceDaily}}}, url = {https://www.sciencedaily.com/releases/2016/08/160830083653.htm}, urldate = {2016-08-30} } @online{SWEbench, title = {{{SWE-bench}}}, url = {https://www.swebench.com/}, urldate = {2025-01-22}, abstract = {SWE-bench: Evaluate Language Models on Open Source Software Tasks}, langid = {english}, keywords = {/unread} } @article{Symonds2016606, title = {Development of an {{England-wide}} Indoor Overheating and Air Pollution Model Using Artificial Neural Networks}, author = {Symonds, P. and Taylor, J. and Chalabi, Z. and Mavrogianni, A. and Davies, M. and Hamilton, I. and Vardoulakis, S. and Heaviside, C. and Macintyre, H.}, date = {2016}, journaltitle = {J. Build. Perform. Simul.}, volume = {9}, number = {6}, pages = {606--619}, publisher = {{Taylor and Francis Ltd.}}, issn = {19401493}, doi = {10.1080/19401493.2016.1166265}, abbrev_source_title = {J. Build. Perform. Simul.}, affiliation = {Institute of Environmental Design and Engineering, University College London, Central House, 14 Woburn Place, London, WC1H 0NN, United Kingdom; London School of Hygiene and Tropical Medicine, 15–17 Tavistock Place, London, WC1H 9SH, United Kingdom; Centre for Radiation, Chemical and Environmental Hazards, Public Health England, Didcot, United Kingdom}, correspondence_address1 = {Symonds, P.; Institute of Environmental Design and Engineering, Central House, 14 Woburn Place, United Kingdom; email: p.symonds@ucl.ac.uk}, document_type = {Article}, langid = {english}, source = {Scopus}, note = {cited By 28} } @article{syrianiGuestEditorialTheme2021, title = {Guest Editorial to the Theme Section on {{Multi-Paradigm Modeling}} for {{Cyber-Physical Systems}}}, author = {Syriani, Eugene and Wimmer, Manuel}, date = {2021-06}, journaltitle = {Softw Syst Model}, volume = {20}, number = {3}, pages = {607--609}, issn = {1619-1366, 1619-1374}, doi = {10.1007/s10270-021-00882-1}, abstract = {The networked combination of multi-physics systems (mechanical, electrical, hydraulic, biochemical, among others) with computational systems (control systems, signal processing, logical inference, planning, among others), often interacting with human actors, in uncertain environments, in a socio-economic context, has led to so-called Cyber-Physical Systems (CPS). The CPS that are engineered today are reaching a previously unseen level of complexity. To date, no unifying theory nor systematic design methods, techniques and tools exist for such systems. Individual (mechanical, electrical, network or software) engineering disciplines only offer partial solutions. Multi-Paradigm Modeling (MPM) proposes to model every part and aspect of such complex systems explicitly, at the most appropriate level(s) of abstraction, using the most appropriate modeling language(s)/formalism(s). This includes the explicit modeling of the often complex engineering workflows. Modular modeling language engineering, including model transformation and the study of modeling language semantics, are used to realize MPM, which has the potential to be an effective answer to the challenges of designing CPS. This sixth edition will be organised as a highly interactive workshop, promoting discussions and collaboration around a specific theme: we want to explore the notion of ”appropriateness” (of formalisms, of processes, of abstraction levels, of views, of architectures, etc.) that is at the core of the MPM approach. The Workshop will host paper presentations in the morning, and discussions and collaborative work in the afternoon, by bringing together international experts in the field for an intense one-day workshop.}, langid = {english}, note = {TL;DR \par This theme section covers papers on the foundations and applications of MPM for CPS, a continuation of the successful MPM workshop series with a stronger focus on CPS as especially these systems pose several new challenges on the engineering process and beyond.} } @article{syrianiModelingModelTransformation2013, title = {Modeling a {{Model Transformation Language}}}, author = {Syriani, Eugene and Gray, Jeff and Vangheluwe, Hans}, date = {2013}, journaltitle = {Domain Eng.}, pages = {211--237}, doi = {10.1007/978-3-642-36654-3_9}, note = {TL;DR \par This chapter introduces a language engineering technique for building MTLs that is based on treating each MTL as a domain-specific language, more specifically, as languages for describing specific classes of transformations.} } @book{syrianiProceedings23rdACM2020, title = {Proceedings of the 23rd {{ACM}}/{{IEEE International Conference}} on {{Model Driven Engineering Languages}} and {{Systems}}.}, author = {Syriani, Eugene and {Association for Computing Machinery} and {Special Interest Group on Software Engineering}}, date = {2020}, url = {https://dl.acm.org/action/showBook?doi=10.1145/3365438}, urldate = {2021-01-07}, isbn = {978-1-4503-7019-6}, langid = {english} } @article{syrianiTCoreFrameworkCustombuilt2013, title = {T-{{Core}}: A Framework for Custom-Built Model Transformation Engines}, author = {Syriani, Eugene and Vangheluwe, Hans and LaShomb, Brian}, date = {2013}, journaltitle = {Softw. Syst. Model.}, doi = {10.1007/s10270-013-0370-4}, note = {TL;DR \par T-Core is proposed, a framework where primitive transformation constructs can be combined to define and encapsulate reusable model transformation idioms and allows the transformation engineer to design transformations with the most appropriate constructs for the task at hand.} } @inproceedings{SyrianiVMHME13, title = {{{AToMPM}}: {{A Web-based Modeling Environment}}.}, shorttitle = {{{AToMPM}}}, booktitle = {{{DemosPostersStudentResearch MoDELS}}}, author = {Syriani, Eugene and Vangheluwe, Hans and Mannadiar, Raphael and Hansen, Conner and Van Mierlo, Simon and Ergin, Hüseyin}, date = {2013}, series = {{{CEUR}} Workshop Proceedings}, volume = {1115}, pages = {21--25}, publisher = {Citeseer}, url = {http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.407.6965&rep=rep1&type=pdf}, urldate = {2015-06-24}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {TL;DR \par This work introduces AToMPM, an open-source framework for designing domain-specific modeling environments, performing model transformations, manipulating and managing models, which is independent from any operating system, platform, or device it may execute on.}, timestamp = {Fri, 10 Mar 2023 16:22:20 +0100} } @misc{SyrianiWimmer2021, title = {Syriani e Wimmer - 2021 - Guest Editorial to the Theme Section on Multi-Para}, langid = {english} } @article{SYS24108, title = {{{SYS-24-108}}}, url = {https://mc.manuscriptcentral.com/sys?DOWNLOAD=TRUE&PARAMS=xik_8k2MFRtZDdiyYMdehjAJtgjtZGPEFtaZFVWayLPWCubjU2PiX3YGVRMy3mX7XVWTB6et2iNHtDzZUMEsNuoF93kuZu8rs21ziGWAMAZmYY6nrfNEQNDbtKBr7ZBHA5fLsKP82KL9n8ZBPK2Gt7a7tuxhEL1YdmX6W1mCPfAxxhNcgZJTjvVnUEsNu9HKfPGj2TZcH}, urldate = {2024-05-05}, keywords = {⛔ No INSPIRE recid found,LOGSEQ}, note = {\section{Annotazioni\\ (7/5/2024, 09:45:51)} \par - “whether the actual system is an engineered or a non‐engineered” (“SYS-24-108”, p. 4) \#f0ff00\\ \mkbibemph{What do you mean? Too vague. } \par - “the logical” (“SYS-24-108”, p. 5) \#f0ff00\\ \mkbibemph{? } \par - “complex systems (including biological and socio‐technicalsystems), the CoSMoS approach 29 suggests” (“SYS-24-108”, p. 6) \#f0ff00\\ \mkbibemph{Cosmos come out of the blue. I suggest to properly introduce it. } \par - “the design models are” (“SYS-24-108”, p. 7) \#f0ff00\\ \mkbibemph{This is a bit obscure as it is not clear ow such a generative step can work in practice. } \par - “we are focusing on” (“SYS-24-108”, p. 7) \#f0ff00\\ \mkbibemph{Where? } \par - “size 1 but” (“SYS-24-108”, p. 7) \#f0ff00\\ \mkbibemph{What do you mean? } \par - “digital‐twin challenges” (“SYS-24-108”, p. 8) \#f0ff00\\ \mkbibemph{I would clearly list them before ending the previous section. }} } @misc{SYS24108R1_Proof_hi, title = {{{SYS-24-108}}.{{R1}}\_{{Proof}}\_hi}, langid = {english} } @article{szvetitsSystematicLiteratureReview2013, title = {Systematic Literature Review of the Objectives, Techniques, Kinds, and Architectures of Models at Runtime}, author = {Szvetits, Michael and Zdun, Uwe}, date = {2013-12-17}, journaltitle = {Softw. Syst. Model.}, issn = {1619-1366, 1619-1374}, doi = {10.1007/s10270-013-0394-9}, langid = {english}, note = {TL;DR \par This article analyzes the usage of models at runtime in the existing research literature using the Systematic Literature Review (SLR) research method to provide an overview and classification of current research approaches using models at Runtime and to identify research areas not covered by models atruntime so far.} } @article{TableContents2017, title = {Table of Contents}, date = {2017-01}, journaltitle = {IEEE Softw.}, volume = {34}, number = {1}, pages = {2--3}, issn = {0740-7459}, doi = {10.1109/MS.2017.23}, langid = {english} } @online{TableContentsIEEE, title = {Table of {{Contents}} - {{IEEE Software}} | {{IEEE Computer Society Digital Library}}}, url = {https://www.computer.org/csdl/magazine/so/2022/05}, urldate = {2022-09-09} } @article{Tadejko2020169, title = {Cloud Cognitive Services Based on Machine Learning Methods in Architecture of Modern Knowledge Management Solutions}, author = {Tadejko, P.}, date = {2020}, journaltitle = {Lect. Notes Data Eng. Commun. Technol.}, volume = {40}, pages = {169--190}, publisher = {{Springer Science and Business Media Deutschland GmbH}}, issn = {23674512}, doi = {10.1007/978-3-030-34706-2_9}, abstract = {Cognitive Services are cloud computing services available to help developers build intelligent applications based on Machine Learning (ML) methods with pre-trained models as a service. Machine Learning platforms are one of the fastest growing services of the cloud because ML and Artificial Intelligence (AI) platforms are available through diverse delivery models such as cognitive computing, automated machine learning, model management. Cognitive Computing is delivered as a set of APIs. Due to the nature of the technologies involved in ML ecosystems and Knowledge Hierarchy—Data, Information, Knowledge, Wisdom (DIKW) Pyramid, there is a natural overlap of a technologies and Knowledge Management (KM) processes. The modern architecture of software solutions can be developed with the use of a wide technology stack, including cloud computing technologies and Cognitive Services (CS). We can use a wide range of ML tools at all levels of the DIKW pyramid. In this paper, we propose a new CS based approach to build an architecture of Knowledge Management system. We have analyzed the possibilities of using CS at all levels of the DIKW pyramid. We discussed some of the relevant aspects of Cloud CS and ML in Knowledge Management context and possibilities implementation of Cognitive Services on knowledge processing. © Springer Nature Switzerland AG 2020.}, document_type = {Book Chapter}, source = {Scopus}, note = {cited By 3 \par TL;DR \par A new CS based approach to build an architecture of Knowledge Management system is proposed and the possibilities of using CS at all levels of the DIKW pyramid are analyzed.} } @incollection{taentzerCotransformationGraphsType2012, ids = {Taentzer_Mantz_Lamo_2012}, title = {Co-Transformation of {{Graphs}} and {{Type Graphs}} with {{Application}} to {{Model Co-evolution}}}, booktitle = {Graph {{Transformations}}}, author = {Taentzer, Gabriele and Mantz, Florian and Lamo, Yngve}, editor = {Ehrig, Hartmut and Engels, Gregor and Kreowski, Hans-Jörg and Rozenberg, Grzegorz}, editora = {Hutchison, David and Kanade, Takeo and Kittler, Josef and Kleinberg, Jon M. and Mattern, Friedemann and Mitchell, John C. and Naor, Moni and Nierstrasz, Oscar and Pandu Rangan, C. and Steffen, Bernhard and Sudan, Madhu and Terzopoulos, Demetri and Tygar, Doug and Vardi, Moshe Y. and Weikum, Gerhard}, editoratype = {redactor}, date = {2012}, series = {Lecture Notes in Computer Science}, volume = {7562}, pages = {326--340}, publisher = {Springer Berlin Heidelberg}, location = {Berlin, Heidelberg}, doi = {10.1007/978-3-642-33654-6_22}, isbn = {978-3-642-33653-9 978-3-642-33654-6}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found} } @article{TaentzerELW14, title = {A Fundamental Approach to Model Versioning Based on Graph Modifications: From Theory to Implementation}, author = {Taentzer, Gabriele and Ermel, Claudia and Langer, Philip and Wimmer, Manuel}, date = {2014}, journaltitle = {Software Syst. Model.}, volume = {13}, number = {1}, pages = {239--272}, doi = {10.1007/S10270-012-0248-X}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Thu, 14 Oct 2021 09:25:04 +0200} } @article{taghaviNewInsightsDeveloping2018, title = {New {{Insights Towards Developing Recommender Systems}}}, author = {Taghavi, Mona and Bentahar, Jamal and Bakhtiyari, Kaveh and Hanachi, Chihab}, date = {2018-03-01}, journaltitle = {Comput. J.}, volume = {61}, number = {3}, pages = {319--348}, issn = {0010-4620, 1460-2067}, doi = {10.1093/comjnl/bxx056}, langid = {english} } @article{tahaModelingBasicAspects2013, title = {Modeling Basic Aspects of Cyber-Physical Systems}, author = {Taha, Walid and Philippsen, Roland}, date = {2013}, journaltitle = {ArXiv Prepr. ArXiv13032792}, eprint = {1303.2792}, eprinttype = {arXiv}, url = {http://arxiv.org/abs/1303.2792}, urldate = {2016-02-05} } @article{tairasCorpusbasedAnalysisDomainspecific2015, title = {Corpus-Based Analysis of Domain-Specific Languages}, author = {Tairas, Robert and Cabot, Jordi}, date = {2015-05}, journaltitle = {Softw. Syst. Model.}, volume = {14}, number = {2}, pages = {889--904}, issn = {1619-1366, 1619-1374}, doi = {10.1007/s10270-013-0352-6}, langid = {english}, note = {TL;DR \par This paper describes the utilization of corpus-based analysis techniques and exemplify them on the evaluation of the Puppet and ATL DSLs and outlines an Eclipse plug-in, which is a generic corpus- based DSL analysis tool that can accommodate the evaluationof different DSLs.} } @article{taivalsaariRoadmapProgrammableWorld2017, entrysubtype = {magazine}, title = {A {{Roadmap}} to the {{Programmable World}}: {{Software Challenges}} in the {{IoT Era}}}, shorttitle = {A {{Roadmap}} to the {{Programmable World}}}, author = {Taivalsaari, Antero and Mikkonen, Tommi and {undefined} and {undefined} and {undefined} and {undefined}}, date = {2017}, journaltitle = {IEEE Software}, volume = {34}, number = {1}, pages = {72--80}, issn = {0740-7459}, abstract = {The Internet of Things (IoT) represents the next significant step in the evolution of the Internet and software development. Although most IoT research focuses on data acquisition, analytics, and visualization, a subtler but equally important transition is underway. Hardware advances are making it possible to embed fully fledged virtual machines and dynamic language runtimes virtually everywhere, leading to a Programmable World in which all our everyday things are connected and programmable dynamically. The emergence of millions of remotely programmable devices in our surroundings will pose significant software development challenges. A roadmap from today's cloud-centric, data-centric IoT systems to the Programmable World highlights the technical challenges that deserve to be part of developer education and deserve deeper investigation beyond those IoT topics that receive the most attention today.}, keywords = {internet of things,software engineering}, note = {TL;DR \par A roadmap from today's cloud-centric, data-centric IoT systems to the Programmable World highlights the technical challenges that deserve to be part of developer education and deserve deeper investigation beyond those IoT topics that receive the most attention today.} } @online{tamiettoContrattiDiRicerca2025, title = {Contratti Di Ricerca: Una Riforma Che Non Funziona}, shorttitle = {Contratti Di Ricerca}, author = {Tamietto, Guido Boella Michele Graziadei Marco}, date = {2025-04-24T08:48:07+02:00}, url = {https://www.roars.it/contratti-di-ricerca-una-riforma-che-non-funziona/}, urldate = {2025-04-24}, abstract = {Le università sono organismi complessi, eterogenei al loro interno e fra di loro, impiegano migliaia di persone a tempo indeterminato con ruoli diversissimi e complementari, e quasi altrettante figure in una situazione di precariato contrattuale. E’ doveroso assicurare loro forme contrattuali che of} } @article{Tan2020566, title = {Information Model for Slope Construction in Hydropower Projects [水电工程边坡施工全过程信息模型研究与应用]}, author = {Tan, Y. and Chen, W. and Guo, Z. and Lin, E. and Lin, P. and Zhou, M. and Li, J.}, date = {2020}, journaltitle = {Qinghua Daxue XuebaoJournal Tsinghua Univ.}, volume = {60}, number = {7}, pages = {566--574}, publisher = {Press of Tsinghua University}, issn = {10000054}, doi = {10.16511/j.cnki.qhdxxb.2020.26.004}, abstract = {Big data and artificial intelligence methods are combined with information technology methods for engineering construction to develop an information model for slope design during construction of hydropower stations using the BIM technique and an information model. An information model management platform for slope construction was developed for hydropower projects based on intelligent construction theory for sense, analysis, and control with integrated scheduling, quality control and safety management. Results for the construction of the Baihetan Hydropower Project as an example show that the platform provides comprehensive digital management for design results, construction processes and slope construction for large hydropower projects. The system more effectively controls the construction progress, reduces safety risks and provides a comprehensive data archive for the entire slope construction process to improve the construction efficiency and economics. © 2020, Tsinghua University Press. All right reserved.}, coden = {QDXKE}, document_type = {Article}, source = {Scopus}, note = {cited By 5} } @inproceedings{Tang2019385, title = {Improving Code Generation from Descriptive Text by Combining Deep Learning and Syntax Rules}, author = {Tang, X. and Wang, Z. and Qi, J. and Li, Z.}, date = {2019}, series = {Proceedings of the {{International Conference}} on {{Software Engineering}} and {{Knowledge Engineering}}, {{SEKE}}}, volume = {2019-July}, pages = {385--390}, publisher = {Knowledge Systems Institute Graduate School}, issn = {23259000}, doi = {10.18293/SEKE2019-170}, abbrev_source_title = {Proc. Int. Conf. Softw. Eng. Knowl. Eng., SEKE}, affiliation = {School of Computer Science, Central China Normal University, Wuhan, China; Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan, China; School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China}, correspondence_address1 = {Li, Z.; School of Computer Science, China; email: zengyangli@mail.ccnu.edu.cn}, document_type = {Conference Paper}, isbn = {1-891706-48-9}, langid = {english}, source = {Scopus}, keywords = {GOAL\_Code-generation,notion,TECHNIQUE\_CNN,TECHNIQUE\_SelfAttentionNetwork}, note = {cited By 1 \par CNN and SelfAttention Network} } @inproceedings{tangBridgingGapRequirement2015, title = {Bridging the Gap between Requirement Analysis and Architecture Design of Self-Adaptive Systems}, booktitle = {Softw. {{Eng}}. {{Serv}}. {{Sci}}. {{ICSESS}} 2015 6th {{IEEE Int}}. {{Conf}}. {{On}}}, author = {Tang, Shan and Li, Liping and Cao, Xiaoxia and Tan, Wenan}, date = {2015}, pages = {1102--1105}, publisher = {IEEE}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7339244}, urldate = {2016-08-21} } @online{tangCodeAgentCollaborativeAgents2024, title = {{{CodeAgent}}: Collaborative Agents for Software Engineering}, shorttitle = {{{CodeAgent}}}, author = {Tang, Daniel and Chen, Zhenghan and Kim, Kisub and Song, Yewei and Tian, Haoye and Ezzini, Saad and Huang, Yongfeng and Klein, Jacques and Bissyande, Tegawende F.}, date = {2024-02-15}, eprint = {2402.02172}, eprinttype = {arXiv}, eprintclass = {cs}, doi = {10.48550/arXiv.2402.02172}, abstract = {Code review is a heavily collaborative process, which aims at ensuring the overall quality and reliability of software. While it provides massive benefits, the implementation of code review in an organization faces several challenges that make its automation appealing. Automated code review tools have been around for a while and are now improving thanks to the adoption of novel AI models, which help can learn about standard practices and systematically check that the reviewed code adheres to them. Unfortunately, existing methods fall short: they often target a single input-output generative model, which cannot simulate the collaboration interactions in code review to account for various perspectives; they are also sub-performing on various critical code review sub-tasks. In this paper, we advance the state of the art in code review automation by introducing CodeAgent, a novel multi-agent-based system for code review. Fundamentally, CodeAgent is steered by QA-Checker (short for "Question-Answer Checking"), a supervision agent, designed specifically to ensure that all agents' contributions remain relevant to the initial review question. CodeAgent is autonomous, multi-agent, and Large language model-driven. To demonstrate the effectiveness of CodeAgent, we performed experiments to assess its capabilities in various tasks including 1) detection of inconsistencies between code changes and commit messages, 2) detection of vulnerability introduction by commits, and 3) validation of adherence to code style. Our website is accessed in \textbackslash url\{https://code-agent-new.vercel.app/index.html\}.}, langid = {english}, pubstate = {prepublished}, keywords = {Computer Science - Software Engineering} } @online{tangCollaborativeAgentsSoftware2024, title = {Collaborative {{Agents}} for {{Software Engineering}}}, author = {Tang, Daniel and Chen, Zhenghan and Kim, Kisub and Song, Yewei and Tian, Haoye and Ezzini, Saad and Huang, Yongfeng and Klein, Jacques and Bissyande, Tegawende F.}, date = {2024-02-06}, eprint = {2402.02172}, eprinttype = {arXiv}, eprintclass = {cs}, url = {http://arxiv.org/abs/2402.02172}, urldate = {2024-02-14}, abstract = {Code review is a heavily collaborative process, which aims at ensuring the overall quality and reliability of software. While it provides massive benefits, the implementation of code review in an organization faces several challenges that make its automation appealing. Automated code review tools have been around for a while and are now improving thanks to the adoption of novel AI models, which help can learn about standard practices and systematically check that the reviewed code adheres to them. Unfortunately, existing methods fall short: they often target a single input-output generative model, which cannot simulate the collaboration interactions in code review to account for various perspectives; they are also sub-performing on various critical code review sub-tasks. In this paper, we advance the state of the art in code review automation by introducing CodeAgent, a novel multi-agent-based system for code review. Fundamentally, CodeAgent is steered by QA-Checker (short for "Question-Answer Checking"), a supervision agent, designed specifically to ensure that all agents' contributions remain relevant to the initial review question. CodeAgent is autonomous, multi-agent, and Large language model-driven. To demonstrate the effectiveness of CodeAgent, we performed experiments to assess its capabilities in various tasks including 1) detection of inconsistencies between code changes and commit messages, 2) detection of vulnerability introduction by commits, and 3) validation of adherence to code style. Our website is accessed in \textbackslash url\{https://code-agent-new.vercel.app/index.html\}.}, pubstate = {prepublished}, keywords = {Computer Science - Software Engineering}, note = {TL;DR \par The state of the art in code review automation is advanced by introducing CodeAgent, a novel multi-agent-based system for code review, steered by QA-Checker (short for"Question-Answer Checking"), a supervision agent designed specifically to ensure that all agents' contributions remain relevant to the initial review question.} } @inproceedings{Tao201910777, title = {Variational Annealing of {{GANs}}: {{A}} Langevin Perspective}, author = {Tao, C. and Dai, S. and Chen, L. and Bai, K. and Chen, J. and Liu, C. and Zhang, R. and Bobashev, G. and Carin, L.}, date = {2019}, series = {36th {{International Conference}} on {{Machine Learning}}, {{ICML}} 2019}, volume = {2019-June}, pages = {10777--10786}, publisher = {International Machine Learning Society (IMLS)}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078013392&partnerID=40&md5=4667134c5dc17d8a8f9e4b830ed65f02}, abstract = {The generative adversarial network (GAN) has received considerable attention recently as a model for data synthesis, without an explicit specification of a likelihood function. There has been commensurate interest in leveraging likelihood estimates to improve GAN training. To enrich the understanding of this fast-growing yet almost exclusively heuristic-driven subject, we elucidate the theoretical roots of some of the empirical attempts to stabilize and improve GAN training with the introduction of likelihoods. We highlight new insights from variational theory of diffusion processes to derive a likelihood-based regularizing scheme for GAN training, and present a novel approach to train GANs with an unnormalized distribution instead of empirical samples. To substantiate our claims, we provide experimental evidence on how our theoretically-inspired new algorithms improve upon current practice. © 36th International Conference on Machine Learning, ICML 2019. All rights reserved.}, document_type = {Conference Paper}, isbn = {978-1-5108-8698-8}, source = {Scopus}, keywords = {Adversarial networks,Artificial intelligence,Current practices,Data synthesis,Diffusion process,Experimental evidence,Likelihood estimate,Likelihood functions,Machine learning,Software engineering,Variational theory}, note = {cited By 1} } @article{Tayarani-Najaran2022, title = {A Novel Ensemble Machine Learning and an Evolutionary Algorithm in Modeling the {{COVID-19}} Epidemic and Optimizing Government Policies}, author = {Tayarani-Najaran, M.}, date = {2022}, journaltitle = {IEEE Trans. Syst. Man Cybern. Syst.}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {21682216}, doi = {10.1109/TSMC.2022.3143955}, abbrev_source_title = {IEEE Trans. Syst. Man Cybern. Syst.}, affiliation = {School of Computer Science, University of Hertfordshire, Hatfield AL10 9AB, U.K. (e-mail: tayaranm@herts.ac.uk)}, document_type = {Article}, langid = {english}, source = {Scopus}, note = {cited By 0 \par TL;DR \par A population-based multiobjective optimization algorithm is developed, which uses the model to search through the policy space and finds a set of policies that minimize the cost induced to the society due to the policies and the growth rate of the pandemic.} } @article{Teh:EtAl:06, title = {Hierarchical Dirichlet Processes}, author = {Teh, Yee Whye and Jordan, Michael I. and Beal, Matthew J. and Blei, David M.}, date = {2006}, journaltitle = {J. Am. Stat. Assoc.}, volume = {101}, number = {476}, pages = {1566--1581}, url = {http://www.gatsby.ucl.ac.uk/~ywteh/research/npbayes/jasa2006.pdf}, abstract = {We consider problems involving groups of data, where each observation within a group is a draw from a mixture model, and where it is desirable to share mixture components between groups. We assume that the number of mixture components is unknown a priori and is to be inferred from the data. In this setting it is natural to consider sets of Dirichlet processes, one for each group, where the well-known clustering property of the Dirichlet process provides a nonparametric prior for the number of mixture componentswithin each group. Given our desire to tie the mixture models in the various groups, we consider a hierarchical model, specifically one in which the base measure for the child Dirichlet processes is itself distributed according to a Dirichlet process. Such a base measure being discrete, the child Dirichlet processes necessarily share atoms. Thus, as desired, the mixture models in the different groups necessarily share mixture components. We discuss representations of hierarchical Dirichlet processes in terms of a stick-breaking process, and a generalization of the Chinese restaurant process that we refer to as the "Chinese restaurant franchise". We present Markov chain Monte Carlo algorithms for posterior inference in hierarchical Dirichlet process mixtures, and describe applications to problems in information retrieval and text modelling.}, added-at = {2007-03-02T00:36:19.000+0100}, biburl = {https://www.bibsonomy.org/bibtex/2306a104860208c7a3c9be306ef709008/seandalai}, interhash = {34e30f6d1538ed136344f6a9cf8a791b}, intrahash = {306a104860208c7a3c9be306ef709008}, keywords = {2006 dirichlet bayesian}, note = {TL;DR \par This work considers problems involving groups of data where each observation within a group is a draw from a mixture model and where it is desirable to share mixture components between groups, and considers a hierarchical model, specifically one in which the base measure for the childDirichlet processes is itself distributed according to a Dirichlet process.}, timestamp = {2007-03-02T00:36:19.000+0100} } @online{TemporalEMFTemporalMeta, title = {{{TemporalEMF}}: {{A Temporal}} (Meta) Modeling {{Framework}}}, url = {https://modeling-languages.com/temporal-modeling-framework-emf/}, urldate = {2018-08-10} } @inproceedings{teyton_mining_2012, title = {Mining {{Library Migration Graphs}}}, booktitle = {2012 19th {{Work}}. {{Conf Reverse Eng}}.}, author = {Teyton, Cédric and Falleri, Jean-Rémy and Blanc, Xavier}, date = {2012-10}, pages = {289--298}, doi = {10.1109/WCRE.2012.38}, abstract = {Software systems intensively depend on external libraries, chosen at conception time. However, relevance of any library irremediably changes during projects and/or library life cycle. As a consequence, projects developers must periodically reconsider the libraries they depend on, and must think about library migration. When they want to migrate their libraries, they then have to identify candidate libraries that offer similar facilities and thus can substitute to each other. They also have to compare candidates to choose the one that best fits their needs. Finding a relevant library replacement is a well known tedious and time-consuming task. In this paper, we propose an approach that identifies sets of similar libraries and that produces what we call library migration graphs that show how existing projects have performed migrations among them. These graphs, constructed from the observation of a large number of software projects, ease the discovery and selection of library replacements.}, keywords = {data mining,Data mining,dependencies management,external software library,Google,graph theory,Libraries,library life cycle,library migration graph mining,library replacement,Manuals,project life cycle,Search engines,Software,Software algorithms,software evolution,software libraries,software maintenance,software project,software system} } @article{thum2014featureide, title = {{{FeatureIDE}}: {{An}} Extensible Framework for Feature-Oriented Software Development}, author = {Thüm, Thomas and Kästner, Christian and Benduhn, Fabian and Meinicke, Jens and Saake, Gunter and Leich, Thomas}, date = {2014}, journaltitle = {Sci. Comput. Program.}, volume = {79}, pages = {70--85}, publisher = {Elsevier} } @inproceedings{thummalapentaParsewebProgrammerAssistant2007, title = {Parseweb: {{A}} Programmer Assistant for Reusing Open Source Code on the Web}, booktitle = {Proc. {{Twenty-Second IEEEACM Int}}. {{Conf}}. {{Autom}}. {{Softw}}. {{Eng}}.}, author = {Thummalapenta, Suresh and Xie, Tao}, date = {2007}, series = {{{ASE}} '07}, pages = {204--213}, publisher = {ACM}, location = {New York, NY, USA}, url = {http://doi.acm.org/10.1145/1321631.1321663}, acmid = {1321663}, isbn = {978-1-59593-882-4}, nodoi = {10.1145/1321631.1321663}, numpages = {10}, keywords = {code examples,code reuse,code search engine,ranking code samples}, note = {TL;DR \par An approach that takes queries of the form "Source object type → Destination object type" as input, and suggests relevant method-invocation sequences that can serve as solutions that yield the destination object from the source object given in the query is developed.} } @article{Thung2013Automated, title = {Thung et al. - 2013 - {{Automated}} Library Recommendation} } @inproceedings{thungAPIRecommendationSystem2016, title = {{{API}} Recommendation System for Software Development}, booktitle = {Autom. {{Softw}}. {{Eng}}. {{ASE}} 2016 31st {{IEEEACM Int}}. {{Conf}}. {{On}}}, author = {Thung, Ferdian}, date = {2016}, pages = {896--899}, publisher = {IEEE}, url = {http://ieeexplore.ieee.org/abstract/document/7582836/}, urldate = {2017-06-19}, note = {TL;DR \par This work plans to automatically recommend relevant APIs to developers and is working on another system which can recommend web APIs (i.e., libraries) given a description of the task and a system that recommends correct parameters given an API method.} } @inproceedings{thungAutomaticRecommendationAPI2013, ids = {Thung:2013:ARA:3107656.3107694}, title = {Automatic Recommendation of {{API}} Methods from Feature Requests}, booktitle = {Proc. 28th {{IEEEACM Int}}. {{Conf}}. {{Autom}}. {{Softw}}. {{Eng}}.}, author = {Thung, Ferdian and Wang, Shaowei and Lo, David and Lawall, Julia}, date = {2013}, pages = {290--300}, publisher = {IEEE Press}, location = {Silicon Valley, CA, USA}, acmid = {3107694}, nodoi = {10.1109/ASE.2013.6693088}, numpages = {11}, note = {TL;DR \par This work proposes an automated approach that takes as input a textual description of a feature request, and recommends methods in library APIs that developers can use to implement the feature.} } @inproceedings{thungDetectingSimilarApplications2012, ids = {Lo:2012:DSA:2473496.2473616}, title = {Detecting Similar Applications with Collaborative Tagging}, booktitle = {Softw. {{Maint}}. {{ICSM}} 2012 28th {{IEEE Int}}. {{Conf}}. {{On}}}, author = {Thung, Ferdian and Lo, David and Jiang, Lingxiao}, date = {2012}, pages = {600--603}, publisher = {IEEE}, url = {http://ieeexplore.ieee.org/abstract/document/6405331/}, urldate = {2017-03-14}, acmid = {2473616}, nodoi = {10.1109/ICSM.2012.6405331}, numpages = {4}, keywords = {Cloning,Collaboration,Java,Search engines,Software systems,Tagging}, note = {TL;DR \par This study complements the study by McMillan et al. by leveraging another source of information aside from API usage patterns, namely software tags, and shows that collaborative tagging is a promising source of Information useful for detecting similar software applications.} } @article{thurimellaGuidelinesManagingRequirements2017, entrysubtype = {magazine}, title = {Guidelines for {{Managing Requirements Rationales}}}, author = {Thurimella, Anil Kumar and Schubanz, Mathias and Pleuss, Andreas and Botterweck, Goetz and {undefined} and {undefined} and {undefined} and {undefined}}, date = {2017}, journaltitle = {IEEE Software}, volume = {34}, number = {1}, pages = {82--90}, issn = {0740-7459}, abstract = {Requirements are identified and elaborated on the basis of stakeholders' decisions. The reasoning behind those decisions can be expressed as rationales. Systematic rationale management offers both short-term benefits, such as clearer requirements leading to fewer defects, and long-term benefits, such as simplified requirements evolution. However, little guidance exists for managing requirements rationales. This article presents guidelines to pragmatically capture, trace, maintain, and reuse such rationales. A list of questions augments the guidelines, improving their usability.}, keywords = {software engineering}, note = {TL;DR \par New guidelines to pragmatically capture, trace, maintain, and reuse requirements rationales are presented, improving their usability.} } @article{Tiarks2011, title = {An Extended Assessment of Type-3 Clones as Detected by State-of-the-Art Tools}, author = {Tiarks, Rebecca and Koschke, Rainer and Falke, Raimar}, date = {2011-06-01}, journaltitle = {Softw. Qual. J.}, volume = {19}, number = {2}, pages = {295--331}, issn = {1573-1367}, doi = {10.1007/s11219-010-9115-6}, abstract = {Code reuse through copying and pasting leads to so-called software clones. These clones can be roughly categorized into identical fragments (type-1 clones), fragments with parameter substitution (type-2 clones), and similar fragments that differ through modified, deleted, or added statements (type-3 clones). Although there has been extensive research on detecting clones, detection of type-3 clones is still an open research issue due to the inherent vagueness in their definition. In this paper, we analyze type-3 clones detected by state-of-the-art tools and investigate type-3 clones in terms of their syntactic differences. Then, we derive their underlying semantic abstractions from their syntactic differences. Finally, we investigate whether there are code characteristics that indicate that a tool-suggested clone candidate is a real type-3 clone from a human's perspective. Our findings can help developers of clone detectors and clone refactoring tools to improve their tools.}, note = {TL;DR \par This paper analyzes type-3 clones detected by state-of-the-art tools and investigates type- 3 clones in terms of their syntactic differences, and derives their underlying semantic abstractions from their Syntactic differences.} } @article{tichyEmpiricalSoftwareResearch2011, title = {Empirical Software Research: An Interview with {{Dag Sjøberg}}, {{University}} of {{Oslo}}, {{Norway}}}, shorttitle = {Empirical Software Research}, author = {Tichy, Walter}, date = {2011}, journaltitle = {Ubiquity}, volume = {2011}, pages = {2}, url = {http://dl.acm.org/citation.cfm?id=1998374}, urldate = {2017-02-25}, issue = {June}, note = {TL;DR \par When I began my studies at the Technical University of Munich in 1971, the authors had the luxury of an interactive, line-oriented editor for typing their programs, and switching from Assembler to Fortran, Algol, or Pascal was a no-brainer.} } @article{Tijskens202120, title = {The Impact of a Reduced Training Subspace on the Prediction Accuracy of Neural Networks for Hygrothermal Predictions}, author = {Tijskens, A. and Janssen, H. and Roels, S.}, date = {2021}, journaltitle = {J. Build. Perform. Simul.}, volume = {14}, number = {1}, pages = {20--37}, publisher = {{Taylor and Francis Ltd.}}, issn = {19401493}, doi = {10.1080/19401493.2020.1832148}, abbrev_source_title = {J. Build. Perform. Simul.}, affiliation = {Department of Civil Engineering, Building Physics Section, KU Leuven, Kasteelpark Arenberg 40 Bus 2447, Heverlee, 3001, Belgium}, correspondence_address1 = {Tijskens, A.; Department of Civil Engineering, Kasteelpark Arenberg 40 Bus 2447, Belgium; email: astrid.tijskens@kuleuven.be}, document_type = {Article}, langid = {english}, source = {Scopus}, note = {cited By 2} } @inproceedings{TisiPC13, title = {Parallel Execution of {{ATL}} Transformation Rules}, booktitle = {Model-{{Driven Eng}}. {{Lang}}. {{Syst}}. - 16th {{Int}}. {{Conf}}. {{MODELS}} 2013 {{Miami FL USA Sept}}. 29 - {{Oct}}. 4 2013 {{Proc}}.}, author = {Tisi, Massimo and Perez, Salvador Martínez and Choura, Hassene}, editor = {Moreira, Ana and Schätz, Bernhard and Gray, Jeff and Vallecillo, Antonio and Clarke, Peter J.}, date = {2013}, series = {Lecture Notes in Computer Science}, volume = {8107}, pages = {656--672}, publisher = {Springer}, doi = {10.1007/978-3-642-41533-3\_40}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {TL;DR \par It is shown that rule-based languages like ATL have strong parallelization properties, and a transformation engine can automatically parallelize execution when developing parallel model transformations in a general-purpose language.}, timestamp = {Mon, 21 Jun 2021 12:26:18 +0200} } @article{tisiTwinDrivenEngineeringOverview2021, ids = {tisiTwinDrivenEngineeringOverview2021a,tisiTwinDrivenEngineeringOverview2021b,tisiTwinDrivenEngineeringOverview2021c}, title = {Towards {{Twin-Driven Engineering}}: {{Overview}} of the {{State-of-The-Art}} and {{Research Directions}}}, author = {Tisi, M. and Bruneliere, H. and family=Lara, given=J., prefix=de, useprefix=true and Di Ruscio, D. and Kolovos, D.}, editor = {Dolgui A., Bernard A., Lemoine D., von Cieminski G., Romero D.}, date = {2021}, journaltitle = {IFIP Adv. Inf. Commun. Technol.}, volume = {630 IFIP}, pages = {351--359}, publisher = {{Springer Science and Business Media Deutschland GmbH}}, issn = {18684238}, doi = {10.1007/978-3-030-85874-2_37}, isbn = {9783030858735}, langid = {english}, keywords = {Analysis and verifications,Artificial intelligence,Complex physical systems,Control and management,Cyber Physical System,Cyber physical systems (CPSs),Cyber-physical systems (CPS),Digital twin,Embedded systems,Engineering activities,Engineering research,Industrial companies,Industrial management,Manufacture,Software engineering,System of systems,Virtual representations}, note = {cited By 0 \par cited By 0 \par cited By 0} } @incollection{tisiUseHigherOrderModel2009, title = {On the {{Use}} of {{Higher-Order Model Transformations}}}, booktitle = {Model {{Driven Architecture}} - {{Foundations}} and {{Applications}}}, author = {Tisi, Massimo and Jouault, Frédéric and Fraternali, Piero and Ceri, Stefano and Bézivin, Jean}, editor = {Paige, Richard F. and Hartman, Alan and Rensink, Arend}, date = {2009}, volume = {5562}, pages = {18--33}, publisher = {Springer Berlin Heidelberg}, location = {Berlin, Heidelberg}, doi = {10.1007/978-3-642-02674-4_3}, abstract = {The level of maturity that has been reached by model transformation technologies is proved by the growing literature on transformation libraries that address an increasingly wide spectrum of applications. With the success of the modeling and transformation paradigm, the need arises to address more complex applications that require a direct manipulation of model transformations.}, isbn = {978-3-642-02673-7 978-3-642-02674-4}, langid = {english} } @article{Tong2021298, title = {Design of Hotel Marketing Information Management Model Based on Deep Learning}, author = {Tong, L. and Wang, F.}, editor = {Fu W., Xu Y., Zhang Y., Wang S.}, date = {2021}, journaltitle = {Lect. Notes Inst. Comput. Sci. Soc.-Inform. Telecommun. Eng. LNICST}, volume = {387}, pages = {298--310}, publisher = {{Springer Science and Business Media Deutschland GmbH}}, issn = {18678211}, doi = {10.1007/978-3-030-82562-1_28}, abstract = {The traditional information management model has poor data transmission efficiency in the process of pushing information services. To solve this problem, this paper designs a hotel marketing information management model based on deep learning. Using Oracle relational database and MVC architecture to build a marketing information database, then use deep learning to extract information features, and classify marketing information of different service categories, connect hotel management and client, and integrate model management functions to provide information services for hotel managers and customers. The experimental results show that the data throughput and transmission rate of the above model are higher than those of the traditional model, and the information transmission efficiency is improved. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.}, document_type = {Conference Paper}, isbn = {9783030825614}, source = {Scopus}, note = {cited By 0} } @inproceedings{Toscano20164358, title = {Study of the Approximation of the Fitness Landscape and the Ranking Process of Scalarizing Functions for Many-Objective Problems}, author = {Toscano, G. and Deb, K.}, date = {2016}, series = {2016 {{IEEE Congress}} on {{Evolutionary Computation}}, {{CEC}} 2016}, pages = {4358--4365}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/CEC.2016.7744344}, abbrev_source_title = {IEEE Congr. Evol. Comput., CEC}, affiliation = {CINVESTAV-Tamaulipas, Cd. Victoria, Tamaulipas, 87130, Mexico; Michigan State University, East LansingMI, United States}, art_number = {7744344}, document_type = {Conference Paper}, isbn = {978-1-5090-0622-9}, langid = {english}, source = {Scopus}, note = {cited By 2 \par TL;DR \par Besides finding that Tchebycheff scalarizing function and Gaussian processes for machine learning are accurate methods to handle many-objective problems, one of the most important findings involves the capabilities of metamodeling techniques to approximate the ranking procedure from the information gathered from the parameter space.} } @article{TOSEM20230391R1_Proof_hipdf, title = {{{TOSEM-2023-0391}}.{{R1}}\_{{Proof}}\_hi.Pdf}, langid = {english}, keywords = {⛔ No INSPIRE recid found,LOGSEQ} } @online{TOSEM20230432R1pdf, title = {{{TOSEM-2023-0432}}.{{R1}}.Pdf}, url = {https://mc.manuscriptcentral.com/tosem?DOWNLOAD=TRUE&PARAMS=xik_F7gCBUZMPaGhR9n3QV85r9TsvDWJivdXD1ANZaNdGsG8CbpzG8QpfkWnxT8GgFQu1KRYJngUGSgsUmRkM9mBbYNTjFjssiSqdHyej2DTzGvRwSGcQPUX5QLUUTUkSbU8FSgUtq3Z7yAqUnQd4ywFfGhrtExEGRr3Vfqkn4ftVNBgRYfwhwetSsDMsn8Uhg7qDhhHh}, urldate = {2025-01-20}, langid = {english} } @online{TOSEM20240086R1pdf, title = {{{TOSEM-2024-0086}}.{{R1}}.Pdf}, url = {https://mc.manuscriptcentral.com/tosem?DOWNLOAD=TRUE&PARAMS=xik_dHzQoe3LoY6VySKgRqHS4LHt2pierytaPsVuBW1twgBwj9X9qo2kLNAbfbKe4cz1mLsThhmaCsTRaSFdaduPV3HLyifw7DKuEP8e3bhxPiBRhothRS9dc4enYAy5mrZ5BsH8bC793Bs3r9exuysypxdDLA8hiAz8M52cErupofcYhP3FsxLt9GCmN3s3BKM9qLey}, urldate = {2024-07-15}, keywords = {LOGSEQ} } @misc{TOSEM20240271R1_Proof_hi, title = {{{TOSEM-2024-0271}}.{{R1}}\_{{Proof}}\_hi}, langid = {english}, note = {\section{Annotazioni\\ (12/10/2024, 16:29:38)} \par - “FM requirement analysis” (“TOSEM-2024-0271.R1\_Proof\_hi”, p. 1) \#5fb236 \par - “application scenarios for Foundation Models (FMs)” (“TOSEM-2024-0271.R1\_Proof\_hi”, p. 1) \#5fb236 \par - “research opportunities for the requirement engineering of FM” (“TOSEM-2024-0271.R1\_Proof\_hi”, p. 1) \#5fb236 \par - “requirement analysis” (“TOSEM-2024-0271.R1\_Proof\_hi”, p. 1) \#5fb236 \par - “I agree with the author on the overall framework of foundation model development” (“TOSEM-2024-0271.R1\_Proof\_hi”, p. 2) \#5fb236 \par - “For example, model training and fine-tuning require substantial GPU computational resources, which are often not affordable for a single person or a small team. It would be beneficial to discuss the impact of these infrastructure-level differences on development.” (“TOSEM-2024-0271.R1\_Proof\_hi”, p. 2) \#a28ae5 \par - “subsection discussing the application scenarios for FMs In Section 3 (State of the Practice)” (“TOSEM-2024-0271.R1\_Proof\_hi”, p. 2) \#2ea8e5 \par - “two sections In Section 4 (Research Opportunities)” (“TOSEM-2024-0271.R1\_Proof\_hi”, p. 2) \#2ea8e5 \par - “data management such as data cleaning and labeling” (“TOSEM-2024-0271.R1\_Proof\_hi”, p. 3) \#a28ae5 \par - “developers often manually curate and process data, which can be labor-intensive and prone to errors.” (“TOSEM-2024-0271.R1\_Proof\_hi”, p. 3) \#a28ae5 \par - “Difference between FME and traditional deep-learning model development” (“TOSEM-2024-0271.R1\_Proof\_hi”, p. 3) \#5fb236 \par - “FME requires continuously evolving a model for different purposes of downstream tasks” (“TOSEM-2024-0271.R1\_Proof\_hi”, p. 3) \#5fb236 \par - “We have added two dedicated paragraphs in Section 4 (Research Opportunities) to discuss the research opportunities for secured data and model engineering.” (“TOSEM-2024-0271.R1\_Proof\_hi”, p. 3) \#a28ae5 \par - “Through the establishment of FM engineering, we aim to provide a robust, automated, and extensible framework that addresses the imminent challenges, and discovering new research opportunities for the software engineering field” (“TOSEM-2024-0271.R1\_Proof\_hi”, p. 4) \#a28ae5 \par - “Foundation Model Engineering: Engineering Foundation Models Just as Engineering Software” (“TOSEM-2024-0271.R1\_Proof\_hi”, p. 4) \#a28ae5\\ \mkbibemph{This paper is very much related to [[PROJECTS/MOSAICO]]} \par - “254,871 models with Transformer architecture” (“TOSEM-2024-0271.R1\_Proof\_hi”, p. 5) \#a28ae5 \par - “127,462 datasets” (“TOSEM-2024-0271.R1\_Proof\_hi”, p. 5) \#a28ae5 \par - “170,000 AI app demos,” (“TOSEM-2024-0271.R1\_Proof\_hi”, p. 5) \#a28ae5 \par - “Innovative techniques are needed for minimizing the barriers posed by interdisciplinary work and effective collaboration.” (“TOSEM-2024-0271.R1\_Proof\_hi”, p. 6) \#a28ae5 \par - “When the base FM updates, particularly security-related fixes, it necessitates a corresponding update to the finetuned FMs. It is challenging to efficiently and cost-effectively manage the evolution of these finetuned FMs, catching up with the base model updates while maintaining their domain-specific enhancements.” (“TOSEM-2024-0271.R1\_Proof\_hi”, p. 6) \#a28ae5 \par - “This diverse ecosystem of models, datasets, and applications is a clear indication that the future of LLMs lies in their ability to be customized.” (“TOSEM-2024-0271.R1\_Proof\_hi”, p. 6) \#a28ae5 \par - “ata management and model management” (“TOSEM-2024-0271.R1\_Proof\_hi”, p. 6) \#a28ae5 \par - “Data management with weak supervision.” (“TOSEM-2024-0271.R1\_Proof\_hi”, p. 6) \#2ea8e5 \par - “combining limited amounts of labeled data with large quantities of unlabeled data, using algorithms to infer labels and improve data quality. This method significantly reduces the time and resources required for data preparation.” (“TOSEM-2024-0271.R1\_Proof\_hi”, p. 6) \#5fb236 \par - “Model management with workflows and continuous integration” (“TOSEM-2024-0271.R1\_Proof\_hi”, p. 6) \#2ea8e5 \par - “We also envision a distributed version control system like Git [41] to track the update of FMs, manage model branches, and resolve conflicts of model updates.” (“TOSEM-2024-0271.R1\_Proof\_hi”, p. 6) \#a28ae5 \par - “Programmatic FM development with declarative specifications” (“TOSEM-2024-0271.R1\_Proof\_hi”, p. 7) \#2ea8e5 \par - “unified and declarative APIs that abstract away the underlying complexities of model and data management.” (“TOSEM-2024-0271.R1\_Proof\_hi”, p. 7) \#5fb236 \par - “This not only makes the process more accessible to a wider range of users, including those with less technical expertise but also accelerates the development cycle by enabling quicker iterations and refinements of models.” (“TOSEM-2024-0271.R1\_Proof\_hi”, p. 7) \#5fb236\\ \mkbibemph{Kind of [[Lowcode]] for engineering and use of [[Foundation Models]]} \par - “discover research opportunities for the software engineering community in the FM er” (“TOSEM-2024-0271.R1\_Proof\_hi”, p. 7) \#5fb236 \par - ““FM crisis"” (“TOSEM-2024-0271.R1\_Proof\_hi”, p. 7) \#5fb236 \par - “This modular approach allows for targeted fine-tuning of each expert model, ensuring that the collective output is both accurate and efficient in handling the task at hand.” (“TOSEM-2024-0271.R1\_Proof\_hi”, p. 12) \#5fb236 \par - “integrating new data,” (“TOSEM-2024-0271.R1\_Proof\_hi”, p. 12) \#5fb236 \par - “enabling users to tailor Foundation Models for diverse needs with ease” (“TOSEM-2024-0271.R1\_Proof\_hi”, p. 12) \#5fb236 \par - “Each new user or task traditionally necessitates a new pod and GPU, leading to potentially excessive resource consumption” (“TOSEM-2024-0271.R1\_Proof\_hi”, p. 12) \#5fb236 \par - “This realization opens up possibilities for sharing the base model across multiple adapters, thereby optimizing resource use.” (“TOSEM-2024-0271.R1\_Proof\_hi”, p. 12) \#5fb236} } @misc{TOSEM20240332_Proof_hi, title = {{{TOSEM-2024-0332}}\_{{Proof}}\_hi}, keywords = {LOGSEQ}, note = {\section{Annotazioni\\ (17/7/2024, 15:09:35)} \par - “Domain Modeling using automatically generated DomainSpecific Modeling Lan” (“TOSEM-2024-0332\_Proof\_hi”, p. 1) \#5fb236 \par - ““Intino” (“TOSEM-2024-0332\_Proof\_hi”, p. 1) \#5fb236 \par - “having three separate models.” (“TOSEM-2024-0332\_Proof\_hi”, p. 2) \#f0ff00\\ \mkbibemph{Why three? What are you referring to?} \par - “The final outcome is a unified model that represents the domain across multiple levels of abstraction despite being expressed with three different DSMLs. However, this model not only facilitates the automatic generation of the necessary DSMLs to build the model itself but also enables” (“TOSEM-2024-0332\_Proof\_hi”, p. 2) \#f0ff00\\ \mkbibemph{Thre motivation for having three different languages is not clear.} \par - “[32],expressed” (“TOSEM-2024-0332\_Proof\_hi”, p. 3) \#ff4400 \par - “• Model. A model is an abstraction that defines specific aspects of a system. Models are used to capture and convey domain knowledge, requirements, structure, behavior, and other characteristics of a system, facilitating a deeper understanding before moving to the implementation phase. • Metamodel.” (“TOSEM-2024-0332\_Proof\_hi”, p. 4) \#00b036 \par - “metamodels. • Metametamodel.” (“TOSEM-2024-0332\_Proof\_hi”, p. 4) \#00b036 \par - “metam” (“TOSEM-2024-0332\_Proof\_hi”, p. 5) \#ffd400\\ \mkbibemph{Ref [40] should have been used instead to metadepth, which is an approach that is related to the one presented by this paper.} \par - “Three fundamental features distinguish Intino from current MDE approaches. Firstly, it proposes the implementation of a single and unified model that integrates the three levels of abstraction, diverging from the traditional MDE practice of utilizing thre” (“TOSEM-2024-0332\_Proof\_hi”, p. 6) \#ffd400\\ \mkbibemph{Why is this needed? It is important to motivate it.} \par - “Ψ : W → D,” (“TOSEM-2024-0332\_Proof\_hi”, p. 6) \#f0ff00 \par - “Single Domain Model” (“TOSEM-2024-0332\_Proof\_hi”, p. 7) \#ffd400\\ \mkbibemph{How the proposed approach related to multimodeling. WHy is it relevant to have only one model, instead of defining different models at different layers (see metadepth)} \par - “elemental unit of modeling” (“TOSEM-2024-0332\_Proof\_hi”, p. 7) \#ffd400\\ \mkbibemph{The used terminology is not always aligned with that from the modeling community. For intance, mograms, element unit of modelings are not used by the MDE community.} \par - “This transformation relies on a novel construct: every DSML is an extension of a mother language, analogous to the inheritance [7] in object-oriented programming, where a general class serves a the basis for deriving specialized classes. Therefore, the transformation involves specializing the mother language to create a DSML. This construct of mother language will be examined in detail in subsection 3.5” (“TOSEM-2024-0332\_Proof\_hi”, p. 8) \#ffd400\\ \mkbibemph{we need some examples, sentences like this are not very clear to me.} \par - “three levels” (“TOSEM-2024-0332\_Proof\_hi”, p. 8) \#f0ff00\\ \mkbibemph{why three\_ It can be even more, isn't it\\ again, the link with multimodeling is missing here.} \par - “lexicon” (“TOSEM-2024-0332\_Proof\_hi”, p. 8) \#5fb236 \par - “syntax” (“TOSEM-2024-0332\_Proof\_hi”, p. 8) \#5fb236 \par - “semantic” (“TOSEM-2024-0332\_Proof\_hi”, p. 8) \#5fb236 \par - “This means that M2 mograms can be represented with either λ2 or λ3” (“TOSEM-2024-0332\_Proof\_hi”, p. 8) \#ffd400\\ \mkbibemph{Why do we need such a complication?} \par - “flexibility” (“TOSEM-2024-0332\_Proof\_hi”, p. 8) \#ffd400\\ \mkbibemph{This can overcomplicate the modeling phases, isn't it?} \par - “Definition.” (“TOSEM-2024-0332\_Proof\_hi”, p. 8) \#ffd400\\ \mkbibemph{I think we need some explanatory examples to make such definitions concrete and related to real cases.} \par - “each step in this progression contributes to the completion of the domain model.” (“TOSEM-2024-0332\_Proof\_hi”, p. 8) \#ffd400\\ \mkbibemph{What do you mean? Examples, please!} \par - “Stepwise modeling process in Intino.” (“TOSEM-2024-0332\_Proof\_hi”, p. 9) \#ffd400\\ \mkbibemph{Where is the mother language here?} \par - “the automatic DSML generation involves” (“TOSEM-2024-0332\_Proof\_hi”, p. 9) \#5fb236 \par - “It defines an universal syntax, and provides a lexicon and a semantics that can be extended.” (“TOSEM-2024-0332\_Proof\_hi”, p. 9) \#5fb236 \par - “is a mother” (“TOSEM-2024-0332\_Proof\_hi”, p. 10) \#ffd400\\ \mkbibemph{Why do we need such a definition with the "if" statement? Is it possible to have several mother languages?} \par - “λˆ is domain-agnostic” (“TOSEM-2024-0332\_Proof\_hi”, p. 10) \#ffd400 \par - “determine the domain (D).” (“TOSEM-2024-0332\_Proof\_hi”, p. 10) \#ffd400\\ \mkbibemph{How can this analysis be performed automatically? Is it tool supported?} \par - “automatic creation of DSML” (“TOSEM-2024-0332\_Proof\_hi”, p. 10) \#ffd400\\ \mkbibemph{AUTOMATIC? We really need running examples to help the reader understand the different steps of the proposed process.} \par - “if multiple domains share the same analytical constructs, it is possible to create a mother language capable of universally expressing th” (“TOSEM-2024-0332\_Proof\_hi”, p. 11) \#5fb236 \par - “the potential to use universal analytical constructs that possesses the versatility to be applied across heterogeneous domains; and a language whose syntactic structures support these constructs, thereby suggesting its applicability as a universal syntax across multiple DSMLs.” (“TOSEM-2024-0332\_Proof\_hi”, p. 11) \#ffd400\\ \mkbibemph{This is vague ...} \par - “different domains can be analyzed through a set analytical” (“TOSEM-2024-0332\_Proof\_hi”, p. 11) \#5fb236 \par - “Tara” (“TOSEM-2024-0332\_Proof\_hi”, p. 11) \#5fb236 \par - “analytical construct” (“TOSEM-2024-0332\_Proof\_hi”, p. 11) \#5fb236 \par - “properties, relationships, and semantic constraints” (“TOSEM-2024-0332\_Proof\_hi”, p. 11) \#5fb236 \par - “e.T” (“TOSEM-2024-0332\_Proof\_hi”, p. 11) \#ff6666 \par - “In the ontological approach, entities and concepts are analyzed differently [60]. In contrast, the ontical approach treats concepts as entities, aiming to ensure uniform analysis across diverse levels of abstraction [59].” (“TOSEM-2024-0332\_Proof\_hi”, p. 11) \#ffd400\\ \mkbibemph{Without concrete examples, it's not easy to appreciate the sentences like this one. There are many sentences like it.} \par - “Properties” (“TOSEM-2024-0332\_Proof\_hi”, p. 12) \#ffd400\\ \mkbibemph{Until section 5 included, the paper is not easy to follow. It's plenty of definitions without referring to concrete examples or links to existing technologies like EMF to make the discussion more concrete and help the reader to grasp the introduced concepts that are introduced only in an abstract manner.} \par - “descriptive,” (“TOSEM-2024-0332\_Proof\_hi”, p. 12) \#5fb236 \par - “Descriptive properties” (“TOSEM-2024-0332\_Proof\_hi”, p. 12) \#5fb236 \par - “Prescriptive properties” (“TOSEM-2024-0332\_Proof\_hi”, p. 12) \#5fb236 \par - “Relational properties” (“TOSEM-2024-0332\_Proof\_hi”, p. 12) \#5fb236 \par - “While descriptive properties allow for the detailed representation of each entity’s unique features, prescriptive properties define the concept itself, ensuring that all entities classified under a particular concept share certain fundamental characteristics.” (“TOSEM-2024-0332\_Proof\_hi”, p. 12) \#5fb236 \par - “Fig. 5. Framework for model properties” (“TOSEM-2024-0332\_Proof\_hi”, p. 13) \#ffd400\\ \mkbibemph{Complementing FIg. 5 with some concrete examples is necessary} \par - “TARA LANGUAGE” (“TOSEM-2024-0332\_Proof\_hi”, p. 19) \#ffd400\\ \mkbibemph{Have you considered "metaDepth: A framework for deep meta-modelling" ???} \par - “NL (New Line).” (“TOSEM-2024-0332\_Proof\_hi”, p. 19) \#ffd400\\ \mkbibemph{Is this really necessary? It's the grammar then, why not using EBNF or alike?} \par - “Syntax” (“TOSEM-2024-0332\_Proof\_hi”, p. 20) \#ffd400\\ \mkbibemph{Why do you need to distinguish lexicon + syntax, when it is possible to present the EBNF specification of the language? Sec 6.1 and 6.2 can be merged in my opinion.} \par - “relations with other mograms” (“TOSEM-2024-0332\_Proof\_hi”, p. 20) \#ffd400\\ \mkbibemph{examples?} \par - “Fig. 8. Model Transformations.” (“TOSEM-2024-0332\_Proof\_hi”, p. 23) \#ffd400\\ \mkbibemph{How the code generated at different stages of the process is glued together? What is the underlying execution environment?} \par - “7 SPECIALIZATION OF TARA” (“TOSEM-2024-0332\_Proof\_hi”, p. 23) \#ffd400\\ \mkbibemph{Also this section is affected by the same my problem affecting the other sections. Too high level presentation, verbose, and difficult to follow. They are not strict to the point and they are missing discussion on concrete examples and cases.} \par - “specialization is achieved through automatic model transformation” (“TOSEM-2024-0332\_Proof\_hi”, p. 23) \#ffd400\\ \mkbibemph{This is not clear how it is performed.} \par - “By adopting this approach, we leverage the power of MDE to streamline and optimize the translation of abstract models into executable code. This ensures that the generated code is not only accurate and efficient but also adaptable to the evolving needs of software development in different programming languages.” (“TOSEM-2024-0332\_Proof\_hi”, p. 23) \#ffd400\\ \mkbibemph{There are many sentences like this one that are vague and do not convey any technical message.} \par - “It is important to note that this paper does not directly address the process of code generation. This aspect remains an area for future research, where we plan to explore extending our method to include the automated generation of code.” (“TOSEM-2024-0332\_Proof\_hi”, p. 23) \#ffd400\\ \mkbibemph{Does it mean that the process shown in Fig. 8 has not been fully realized?} \par - “Currently, our focus is solely on demonstrating the process of generating lexicon and semantic rules.” (“TOSEM-2024-0332\_Proof\_hi”, p. 23) \#ffd400\\ \mkbibemph{Even in this case, my previous point applies!} \par - “Fig. 9. Aviation model by λ” (“TOSEM-2024-0332\_Proof\_hi”, p. 25) \#ffd400\\ \mkbibemph{what is the technology to develop such a language? How the used types (i.e., Manufacturer, Airline, etc. defined in Fig. 10) are imported?} \par - “Fig. 10. Aviation model by” (“TOSEM-2024-0332\_Proof\_hi”, p. 27) \#ffd400\\ \mkbibemph{Same here, what the technology used to develop this?} \par - “VALIDATION” (“TOSEM-2024-0332\_Proof\_hi”, p. 29) \#ffd400\\ \mkbibemph{The validation should be about the provided framework, which needs to be discussed with respect to precise requirements. Expressiveness or domain independence are example of possible requirements. Then the validation is missing a direct comparison with existing technologies, like matadepth.}} } @misc{TOSEM20240474_Proof_hi, title = {{{TOSEM-2024-0474}}\_{{Proof}}\_hi}, keywords = {LOGSEQ}, note = {\section{Annotazioni\\ (14/10/2024, 15:56:19)} \par - “Why Personalizing Deep Learning-Based Code Completion Tools Matters” (“TOSEM-2024-0474\_Proof\_hi”, pp. -) \#5fb236 \par - “providing unprecedented code generation capabiliti” (“TOSEM-2024-0474\_Proof\_hi”, p. 2) \#5fb236 \par - “learning natural coding patterns observed across many training instances” (“TOSEM-2024-0474\_Proof\_hi”, p. 2) \#5fb236 \par - “extent to which additional training effort (fine-tuning) aimed at specializing the models towards the code base of a given organization/developer further benefits their code completion capabilities” (“TOSEM-2024-0474\_Proof\_hi”, p. 2) \#5fb236 \par - “training a “generic” DL model on a code completion dataset featuring instances from 2,098 open source projects” (“TOSEM-2024-0474\_Proof\_hi”, p. 2) \#2ea8e5 \par - “further fine-tuned” (“TOSEM-2024-0474\_Proof\_hi”, p. 2) \#2ea8e5 \par - “organization-specific” (“TOSEM-2024-0474\_Proof\_hi”, p. 2) \#2ea8e5 \par - “loper-specific dataset” (“TOSEM-2024-0474\_Proof\_hi”, p. 2) \#2ea8e5 \par - “100 different developers” (“TOSEM-2024-0474\_Proof\_hi”, p. 2) \#2ea8e5 \par - “242 DL models” (“TOSEM-2024-0474\_Proof\_hi”, p. 2) \#2ea8e5 \par - “predicting the next token the developer is likely to type” (“TOSEM-2024-0474\_Proof\_hi”, p. 2) \#5fb236 \par - “generation of co” (“TOSEM-2024-0474\_Proof\_hi”, p. 2) \#5fb236 \par - “set includes 159 GB of code” (“TOSEM-2024-0474\_Proof\_hi”, p. 2) \#5fb236 \par - “54M public GitHub” (“TOSEM-2024-0474\_Proof\_hi”, p. 2) \#5fb236 \par - “generation of meaningful recommendations” (“TOSEM-2024-0474\_Proof\_hi”, p. 2) \#5fb236 \par - “DL model Mb that has been trained on a large and generic code corpus which does not feature an” (“TOSEM-2024-0474\_Proof\_hi”, p. 3) \#5fb236 \par - “Also in this case the performance has been assessed on the developer-specific test sets, representing future changes that org’s developers will implement.” (“TOSEM-2024-0474\_Proof\_hi”, p. 3) \#5fb236 \par - “While the usefulness of these code recommenders is backed-up by empirical evidence [46], there is still room for improvement when it comes to their performance” (“TOSEM-2024-0474\_Proof\_hi”, p. 3) \#2ea8e5 \par - “One of the open questions when it comes to the adoption of DL-based code completion tools is whether their fine-tuning to the specific organization/developer using them may help in boosting performance.” (“TOSEM-2024-0474\_Proof\_hi”, p. 3) \#a28ae5 \par - “perform a further training step after the “generic fine-tuning”” (“TOSEM-2024-0474\_Proof\_hi”, p. 3) \#a28ae5 \par - “specializing the DL model to a given code base” (“TOSEM-2024-0474\_Proof\_hi”, p. 3) \#5fb236 \par - “fine-tune one of these models on their code base with the goal of deploying an in-house code r” (“TOSEM-2024-0474\_Proof\_hi”, p. 3) \#5fb236 \par - “two different levels of personalization” (“TOSEM-2024-0474\_Proof\_hi”, p. 3) \#a28ae5 \par - ““generic” DL-based” (“TOSEM-2024-0474\_Proof\_hi”, p. 3) \#5fb236 \par - “changes performed over time by developers who contributed to projects run by org” (“TOSEM-2024-0474\_Proof\_hi”, p. 3) \#5fb236 \par - “training, evaluation and test set” (“TOSEM-2024-0474\_Proof\_hi”, p. 3) \#a28ae5 \par - “D-specific training se” (“TOSEM-2024-0474\_Proof\_hi”, p. 3) \#5fb236 \par - “Finally, we put together all previously built developer-specific training sets, thus creating an organization-specific training set.” (“TOSEM-2024-0474\_Proof\_hi”, p. 3) \#a28ae5 \par - “Text-To-Text-Transfer-Transformer (T5)” (“TOSEM-2024-0474\_Proof\_hi”, p. 3) \#5fb236 \par - “∼2.3M instances” (“TOSEM-2024-0474\_Proof\_hi”, p. 3) \#5fb236 \par - “verify whether our findings hold for models of different size” (“TOSEM-2024-0474\_Proof\_hi”, p. 3) \#a28ae5\\ \mkbibemph{Very good.} \par - “since we must ensure that the training data used for the baselines does not feature code from the organization (org) we use as a case study” (“TOSEM-2024-0474\_Proof\_hi”, p. 3) \#a28ae5\\ \mkbibemph{Exactly!!!!} \par - “A very cheap fine-tuning performed on a developer-specific dataset significantly boosts the performance of DL-based code completion tools.” (“TOSEM-2024-0474\_Proof\_hi”, p. 4) \#a28ae5 \par - “An organization-specific fine-tuning works better than a developer-specific training, and should be the obvious choice in most of cases” (“TOSEM-2024-0474\_Proof\_hi”, p. 4) \#a28ae5 \par - “control on both the pre-training and the fine-tuning datasets, which is the reason why we train the T5 models from scratch.” (“TOSEM-2024-0474\_Proof\_hi”, p. 4) \#5fb236 \par - “the 100 developers responsible for the highest number of contributions (in our context, code additions) across all projects.” (“TOSEM-2024-0474\_Proof\_hi”, p. 4) \#5fb236 \par - “100 developer-specific training, evaluation and test sets” (“TOSEM-2024-0474\_Proof\_hi”, p. 4) \#5fb236 \par - “100 developer-specialized DL-based code completion models adopting the T5small architecture.” (“TOSEM-2024-0474\_Proof\_hi”, p. 4) \#5fb236 \par - “T5large architecture for the top-10 developers” (“TOSEM-2024-0474\_Proof\_hi”, p. 4) \#5fb236 \par - “To what extent personalizing a DL-based code completion tool to the specific developer using it boosts its performance?” (“TOSEM-2024-0474\_Proof\_hi”, p. 4) \#a28ae5\\ \mkbibemph{That's is very interesting!} \par - “leading to 100 new models based on the T5small architecture (plus 10 for the T5large architecture).” (“TOSEM-2024-0474\_Proof\_hi”, p. 4) \#ffd400\\ \mkbibemph{Why 100? I was expecting one T5\_small and one T5\_large model organization specific. Maybe, this will be clarified in Section 2. Anyhow, maybe here the paper needs to be clarified.} \par - “To what extent personalizing a DL-based code completion tool to a software organization boosts its performance for individual developers of the orga” (“TOSEM-2024-0474\_Proof\_hi”, p. 4) \#a28ae5\\ \mkbibemph{This also is very interesting.} \par - “+5.29\%.” (“TOSEM-2024-0474\_Proof\_hi”, p. 4) \#a28ae5 \par - “7.87\%.” (“TOSEM-2024-0474\_Proof\_hi”, p. 4) \#a28ae5 \par - “To what extent personalizing a DL-based code completion tool can boost its performance?” (“TOSEM-2024-0474\_Proof\_hi”, p. 5) \#a28ae5 \par - “We did not observe an increase in performance comparable to the one provided by the two original specializations” (“TOSEM-2024-0474\_Proof\_hi”, p. 5) \#a28ae5 \par - “(e.g., performing better than GitHub Copilot),” (“TOSEM-2024-0474\_Proof\_hi”, p. 5) \#a28ae5\\ \mkbibemph{This is important by the way. Improve Copilot by considering the particular user or organization.} \par - “to show the potential of personalization for the code completion task, especially for smaller models (i.e., 60M and 770M parameters), which are easier to deploy on local machines and cheaper to train for an interested com” (“TOSEM-2024-0474\_Proof\_hi”, p. 5) \#a28ae5 \par - “developer-specific” (“TOSEM-2024-0474\_Proof\_hi”, p. 5) \#2ea8e5 \par - “organization-specific.” (“TOSEM-2024-0474\_Proof\_hi”, p. 5) \#2ea8e5 \par - “contiguous tokens masked” (“TOSEM-2024-0474\_Proof\_hi”, p. 5) \#5fb236 \par - “we exclude those being outliers in terms of number of modified files, i.e., impacting more than Q3 + 1.5 × IQR files, where Q3 is the third quartile and IQR is the interquartile range of the distribution of impacted files across all commits. This process narrowed down the initial set of 1,272,556 commits to 1,114,142 relevant commits.” (“TOSEM-2024-0474\_Proof\_hi”, p. 6) \#ffd400\\ \mkbibemph{This step is not clear.} \par - “Extracting Java methods featuring new code” (“TOSEM-2024-0474\_Proof\_hi”, p. 6) \#2ea8e5 \par - “We only focus on added lines and ignore the modified ones since our idea is to exploit these methods to generate training instances in which the code written by a specific developer (i.e., the added lines) is masked, and the model is in charge of predicting it.” (“TOSEM-2024-0474\_Proof\_hi”, p. 6) \#a28ae5 \par - “developer-specific (Section 2.2.1)” (“TOSEM-2024-0474\_Proof\_hi”, p. 6) \#2ea8e5 \par - “organization-specific (Section 2.2.2” (“TOSEM-2024-0474\_Proof\_hi”, p. 6) \#2ea8e5 \par - “generic pre-training and fine-tuning datasets (Section 2.3).” (“TOSEM-2024-0474\_Proof\_hi”, p. 6) \#2ea8e5 \par - “Commits mining” (“TOSEM-2024-0474\_Proof\_hi”, p. 6) \#2ea8e5 \par - “686 valid” (“TOSEM-2024-0474\_Proof\_hi”, p. 6) \#5fb236 \par - “1,148,324 Java methods with at least one new line implemented in a given commit.” (“TOSEM-2024-0474\_Proof\_hi”, p. 7) \#a28ae5 \par - “training/testing instances from each method extracted in the previous steps.” (“TOSEM-2024-0474\_Proof\_hi”, p. 7) \#5fb236 \par - “code completion instances derived from the most recent 500 commits are taken as test set,” (“TOSEM-2024-0474\_Proof\_hi”, p. 7) \#5fb236 \par - “while the rest is split into 90\% training and 10\% validation” (“TOSEM-2024-0474\_Proof\_hi”, p. 7) \#5fb236 \par - “early stopping, with checkpoints saved every epoch, a delta of 0.005, and a patience of 5” (“TOSEM-2024-0474\_Proof\_hi”, p. 9) \#5fb236\\ \mkbibemph{[[pretrainedmodels]]} \par - “a ▼ indicates statistically significant decreases in performance.” (“TOSEM-2024-0474\_Proof\_hi”, p. 10) \#ffd400\\ \mkbibemph{Is there any motivation explaining such a decrease in performance?} \par - “EM predictions with respect to the baseline (p-value {$<$}0.05);” (“TOSEM-2024-0474\_Proof\_hi”, p. 10) \#5fb236 \par - “developer-specific training tends to improve the model’s code completion capabilities. The average improvement is +5.29\% in EM predictions” (“TOSEM-2024-0474\_Proof\_hi”, p. 12) \#ffd400\\ \mkbibemph{Even though this is an interesting result, from a software engineering perspective, what it the tradeoff analysis? Does it make sense to perform such training process for only 5.29\% of performance increase?} \par - “he average increase in EM predictions is +7.87\% over the baseline, with a mean OR of 7.86.” (“TOSEM-2024-0474\_Proof\_hi”, p. 13) \#ffd400\\ \mkbibemph{See my previous comment. The tradeoff analysis is even more important in this case, considering the effort needed to train organization-specific models.} \par - “Domain Coverage” (“TOSEM-2024-0474\_Proof\_hi”, p. 14) \#2ea8e5 \par - “Vocabulary Coverage.” (“TOSEM-2024-0474\_Proof\_hi”, p. 14) \#2ea8e5 \par - “Relevance of the Training Data.” (“TOSEM-2024-0474\_Proof\_hi”, p. 15) \#2ea8e5} } @article{TOSEM20240474R1, title = {{{TOSEM-2024-0474}}.{{R1}}}, url = {https://mc.manuscriptcentral.com/tosem?DOWNLOAD=TRUE&PARAMS=xik_BttvgDteoMH9z3S9cRxSu2B9u2RWmWM8JKh366R4x4NXg8FsCsP7aMtSPpiZriasSsJqb2Bk4aHodqzNHiUmbrBcc4ga8LVnHUQuoVm9ZJKz7NDRZKpxDfRGG3vfAQMLRzoS87mLNxpwRFz2S1faJsTVFE9B6gp3YHhr6LoJKyrE9Kenn1qdJufLfbvkf6FPfJqKS}, urldate = {2024-12-14}, langid = {english} } @misc{TOSEM20240648_Proof_hi, title = {{{TOSEM-2024-0648}}\_{{Proof}}\_hi}, langid = {english}, keywords = {LOGSEQ}, note = {\section{Annotazioni\\ (16/12/2024, 12:55:04)} \par - “Towards Understanding Refactoring Engine Bug” (“TOSEM-2024-0648\_Proof\_hi”, p. 1) \#a28ae5 \par - “refactoring engines can generate incorrect refactored programs, resulting in unexpected behaviors or even crashes.” (“TOSEM-2024-0648\_Proof\_hi”, p. 2) \#5fb236 \par - “Refactoring is a critical process in software development, aiming at improving the internal structure of code while preserving its external behavior.” (“TOSEM-2024-0648\_Proof\_hi”, p. 2) \#5fb236 \par - “first systematic study of refactoring engine bugs” (“TOSEM-2024-0648\_Proof\_hi”, p. 2) \#a28ae5 \par - “12 findings and provided a series of valuable guidelines for future work” (“TOSEM-2024-0648\_Proof\_hi”, p. 2) \#5fb236 \par - “developers often experience unexpected results due to the flaws in the refactoring engines.” (“TOSEM-2024-0648\_Proof\_hi”, p. 3) \#a28ae5 \par - “hose bugs could silently change the program behavior or induce inconsistencies in the code base, thus producing very serious effects on real-world applications or improving the complexity of maintenance.” (“TOSEM-2024-0648\_Proof\_hi”, p. 3) \#e56eee \par - “how refactoring engine bugs are induced (i.e., root causes)” (“TOSEM-2024-0648\_Proof\_hi”, p. 3) \#5fb236 \par - “how these bugs affect the software” (“TOSEM-2024-0648\_Proof\_hi”, p. 3) \#5fb236 \par - “how these bugs can be found” (“TOSEM-2024-0648\_Proof\_hi”, p. 3) \#5fb236 \par - “analyze bugs in refactoring engines” (“TOSEM-2024-0648\_Proof\_hi”, p. 3) \#a28ae5 \par - “more likely to trigger refactoring engine bugs” (“TOSEM-2024-0648\_Proof\_hi”, p. 3) \#5fb236 \par - “ug Symptoms” (“TOSEM-2024-0648\_Proof\_hi”, p. 3) \#a28ae5 \par - “Root Causes” (“TOSEM-2024-0648\_Proof\_hi”, p. 3) \#a28ae5 \par - “Symptoms \& Root Cause” (“TOSEM-2024-0648\_Proof\_hi”, p. 3) \#a28ae5 \par - “Triggering Condition” (“TOSEM-2024-0648\_Proof\_hi”, p. 3) \#a28ae5 \par - “The input includes the input program and configuration for a refactoring, the output is the refactored program.” (“TOSEM-2024-0648\_Proof\_hi”, p. 4) \#ffd400\\ \mkbibemph{It is important to consider also the goal or motivations for the refactoring. Is it for updating used third-party libraries? Apply some design-patterns? I think the Configuration box in Fig. 1 should be expanded to cover such aspects.} \par - “518 bugs” (“TOSEM-2024-0648\_Proof\_hi”, p. 4) \#a28ae5 \par - “six root causes” (“TOSEM-2024-0648\_Proof\_hi”, p. 4) \#e56eee \par - “nine bug symptoms” (“TOSEM-2024-0648\_Proof\_hi”, p. 4) \#e56eee \par - “eight main input program characteristics” (“TOSEM-2024-0648\_Proof\_hi”, p. 4) \#e56eee \par - “better understanding” (“TOSEM-2024-0648\_Proof\_hi”, p. 4) \#e56eee \par - “RefactorBench” (“TOSEM-2024-0648\_Proof\_hi”, p. 4) \#a28ae5 \par - “Initialization” (“TOSEM-2024-0648\_Proof\_hi”, p. 4) \#2ea8e5 \par - “Initial conditions checking” (“TOSEM-2024-0648\_Proof\_hi”, p. 4) \#2ea8e5 \par - “Additional information” (“TOSEM-2024-0648\_Proof\_hi”, p. 4) \#2ea8e5 \par - “Our study found that “Incorrect Transformations” (165) is the most common root cause for both Eclipse and IntelliJ IDEA, accounting for 62 and 1” (“TOSEM-2024-0648\_Proof\_hi”, p. 5) \#5fb236 \par - “workflow” (“TOSEM-2024-0648\_Proof\_hi”, p. 6) \#ffd400\\ \mkbibemph{To make the figure consistent with the text, "Final preconditions" should be "Final conditions checking", isn't it?} \par - “Final conditions checking” (“TOSEM-2024-0648\_Proof\_hi”, p. 6) \#2ea8e5 \par - “For example, the Make Static refactoring cannot be executed if the refactored method is overridden in a child class because a static method cannot be overridden” (“TOSEM-2024-0648\_Proof\_hi”, p. 6) \#ffd400\\ \mkbibemph{For this specific example, why such a check cannot be verified during the "Initial preconditions" phase? The differences between Initial and final condition checks are not very clear.} \par - “Create change” (“TOSEM-2024-0648\_Proof\_hi”, p. 6) \#2ea8e5 \par - “refactoring engine testing tools fail to trigger the bug” (“TOSEM-2024-0648\_Proof\_hi”, p. 6) \#a28ae5 \par - “insights we could get by analyzing existing bug reports.” (“TOSEM-2024-0648\_Proof\_hi”, p. 6) \#a28ae5 \par - “Running the initial input program in Figure 2 would produce “Counter: 15”. However, after performing Make Static refactoring (RQ1: refactoring type is Make Static refactoring) using the refactoring engine of Eclipse on the method “toBeRefactored()”, the output becomes “Counter: 5”. The refactored program does not contain any syntax error and Eclipse just silently changes the program behavior without any warning (RQ2: the symptom is “Behavior Change”). This happens because Eclipse erroneously interprets a method call within an anonymous class inside the refactored method as a call on “this” and thus changes the method invocation to be performed on the added input parameter of the method (RQ3). However, the call to the method “toCall()” was made on an instance of” (“TOSEM-2024-0648\_Proof\_hi”, p. 6) \#ffd400\\ \mkbibemph{The description of the bug should be improved. The "Counter" references are not clear and the RQx references should be better described.} \par - “producing unexpected behavior on real-world applications or making it difficult to maintain.” (“TOSEM-2024-0648\_Proof\_hi”, p. 7) \#5fb236 \par - “study the refactoring engine bugs, paves the road for related future research, and provides guidelines for engine developers and researchers to test and improve the reliability of the refactoring engines.” (“TOSEM-2024-0648\_Proof\_hi”, p. 7) \#2ea8e5 \par - “(1) Java Development Tools (JDT) from Eclipse” (“TOSEM-2024-0648\_Proof\_hi”, p. 7) \#5fb236 \par - “(2) the Java refactoring component of IntelliJ IDEA” (“TOSEM-2024-0648\_Proof\_hi”, p. 7) \#5fb236 \par - “(3) Netbeans refactoring engine” (“TOSEM-2024-0648\_Proof\_hi”, p. 7) \#5fb236 \par - “Table 2. Statistical Information of Datasets” (“TOSEM-2024-0648\_Proof\_hi”, p. 8) \#5fb236 \par - “we need to identify the bug reports that aim to fix refactoring engine bugs only” (“TOSEM-2024-0648\_Proof\_hi”, p. 8) \#a28ae5 \par - “(i.e., “refactoring” and “refactor”)” (“TOSEM-2024-0648\_Proof\_hi”, p. 8) \#ffd400\\ \mkbibemph{It is not clear how authors manage to retrieve refactoring engine bugs starting from keywords like these two.} \par - “collected bug reports” (“TOSEM-2024-0648\_Proof\_hi”, p. 8) \#2ea8e5 \par - “we identified 637 bug reports related to refactoring engine bugs.” (“TOSEM-2024-0648\_Proof\_hi”, p. 8) \#ffd400\\ \mkbibemph{It is not clear how such a crucial phase has been performed.} \par - “Finally, we collected 326 bug reports containing both the input program and patch (IPP dataset).” (“TOSEM-2024-0648\_Proof\_hi”, p. 9) \#2ea8e5 \par - “IPP” (“TOSEM-2024-0648\_Proof\_hi”, p. 9) \#a28ae5 \par - “1) the refactoring type of the bug, 2) the root cause of the bug, 3) the symptom that the bug exhibits, and 4) the characteristics of the input program triggering the bug.” (“TOSEM-2024-0648\_Proof\_hi”, p. 9) \#ffd400\\ \mkbibemph{These four aspects are clear and I can understand their motivations. However, it is necessary to give explanatory examples and improve the discussion on how authors manage to recognize and retrieve bugs exibhiting such aspects that are crucial for the goal of the paper. Moreover, concerning four aspects, it is not clear if authors defined 4 set of labels to cover these four aspects.} \par - “ngine bugs” (“TOSEM-2024-0648\_Proof\_hi”, p. 9) \#ffd400\\ \mkbibemph{it is necessary to clarify if with "refactoring engine bugs" authors refer to bugs that are added by the employed refactoring engines, or to malfunctioning of the refactoring engines that do not necessarily manifest bugs in the generated code.} \par - “error-prone refactoring transformations,” (“TOSEM-2024-0648\_Proof\_hi”, p. 9) \#5fb236 \par - “Table 3. Statistics of bugs found across different refactoring types” (“TOSEM-2024-0648\_Proof\_hi”, p. 10) \#ffd400\\ \mkbibemph{It is necessary to clarify the focus of the work with respect to the considered technologies. In particular, in some parts of the paper it seems that authors intend to analyze Eclipse, IntelliJ Idea, and netbeans, whereas while discussing Table 3 in section 4, the discussion of these tools is mixed up with ASTGen, SAFEREFACTOR, and Gligoric. Please clarify how you have considered such tecnhologies together in your investigation.} \par - “more challenging for the refactoring engines to take into account all the complicated usage scenarios.” (“TOSEM-2024-0648\_Proof\_hi”, p. 11) \#a28ae5 \par - “Our analysis revealed that existing” (“TOSEM-2024-0648\_Proof\_hi”, p. 11) \#ffd400\\ \mkbibemph{How did you investigate that? How did you get this result?} \par - “The refactored program is syntactically incorrect, resulting in compile errors.” (“TOSEM-2024-0648\_Proof\_hi”, p. 12) \#ffd400\\ \mkbibemph{Here we are referring to bugs in the generated code.} \par - “The refactoring engine terminates unexpectedly during refactoring, which usually produces an error message.” (“TOSEM-2024-0648\_Proof\_hi”, p. 12) \#ffd400\\ \mkbibemph{Here, instead you are referring to bugs of the refactoring engine occurring during the refactoring process. This reinforces my previous comment when I say that it is not always clear if you are referring to bugs of the artifacts produced by the refactoring engines or to bugs of the refactoring engines itself. Finding 2 of the paper should be improved after having solved such an issue.} \par - ““Compile Error” (242), “Crash” (106), and “Behavior Change” (66) are the top three most common symptoms of refactoring engine bugs.” (“TOSEM-2024-0648\_Proof\_hi”, p. 13) \#ffd400 \par - “Bug Distribution by Root Causes” (“TOSEM-2024-0648\_Proof\_hi”, p. 16) \#ffd400\\ \mkbibemph{Can you give a qualitative discussion on why Netbeans is not considered in Fig. 6?} \par - “The result indicates that handling transformation issues in refactoring engines is very challenging and deserves more attention.” (“TOSEM-2024-0648\_Proof\_hi”, p. 16) \#5fb236 \par - “First, those input programs could be used to design unit tests to expose bugs in refactoring engines” (“TOSEM-2024-0648\_Proof\_hi”, p. 17) \#5fb236 \par - “The decision to focus on the top 10 is motivated by their higher likelihood of triggering bugs, as well as the fact that manually validating all CRTs would be non-trivial and time-consuming.” (“TOSEM-2024-0648\_Proof\_hi”, p. 24) \#5fb236 \par - “a partition, we first check the bug-tracking systems of the target IDEs to avoid duplicate reports before manual validation” (“TOSEM-2024-0648\_Proof\_hi”, p. 24) \#ffd400\\ \mkbibemph{Such a checking activity needs to be clarified. It is not clear how authors have done it.} \par - “Among the 21 bugs we submitted, 10 bugs are confirmed by their developers, and seven of them have already been fixed.” (“TOSEM-2024-0648\_Proof\_hi”, p. 28) \#a28ae5 \par - “quality of refactoring engines.” (“TOSEM-2024-0648\_Proof\_hi”, p. 28) \#a28ae5} } @misc{TOSEM20250054_Proof_hi, title = {{{TOSEM-2025-0054}}\_{{Proof}}\_hi}, langid = {english}, keywords = {/unread,LOGSEQ}, note = {\section{Annotazioni\\ (1/4/2025, 14:25:59)} \par - “Software Engineering and Methodology” (“TOSEM-2025-0054\_Proof\_hi”, p. 1) \#ffd400\\ \mkbibemph{ } \par - “Automated Software Engineering” (“TOSEM-2025-0054\_Proof\_hi”, p. 1) \#ffd400\\ \mkbibemph{ } \par - “Transactions on Software Engineering” (“TOSEM-2025-0054\_Proof\_hi”, p. 1) \#ffd400\\ \mkbibemph{ } \par - “we did not compare our method with the state-of-the-art TPL recommendation method” (“TOSEM-2025-0054\_Proof\_hi”, p. 1) \#5fb236\\ \mkbibemph{ } \par - “we did not evaluate the performance of our method in” (“TOSEM-2025-0054\_Proof\_hi”, p. 1) \#5fb236\\ \mkbibemph{ } \par - “function information of the app itself.” (“TOSEM-2025-0054\_Proof\_hi”, p. 2) \#a28ae5\\ \mkbibemph{ } \par - “each TPL in the TPL context equally” (“TOSEM-2025-0054\_Proof\_hi”, p. 2) \#a28ae5\\ \mkbibemph{ } \par - “ining the hidden relationships among candidate TPL” (“TOSEM-2025-0054\_Proof\_hi”, p. 2) \#a28ae5\\ \mkbibemph{ } \par - “the TPL context,” (“TOSEM-2025-0054\_Proof\_hi”, p. 2) \#a28ae5\\ \mkbibemph{ } \par - “LLM to mine app functions from description texts.” (“TOSEM-2025-0054\_Proof\_hi”, p. 2) \#a28ae5\\ \mkbibemph{ } \par - “Finding the synergy between different TPLs is another time-consuming process for developer” (“TOSEM-2025-0054\_Proof\_hi”, p. 3) \#5fb236\\ \mkbibemph{ } \par - “CF-based recommendation methods are often susceptible to the popularity bias [14, 28], that is, a small fraction of popular TPLs - those that are used by a large number of apps - dominate the prediction results and most other TPLs are ill-served.” (“TOSEM-2025-0054\_Proof\_hi”, p. 3) \#5fb236\\ \mkbibemph{ } \par - “mogenization of node” (“TOSEM-2025-0054\_Proof\_hi”, p. 3) \#a28ae5\\ \mkbibemph{ } \par - “ignore the function information of the app” (“TOSEM-2025-0054\_Proof\_hi”, p. 3) \#a28ae5\\ \mkbibemph{ } \par - “value that reflects the match degree between app product and candidate TPL.” (“TOSEM-2025-0054\_Proof\_hi”, p. 3) \#5fb236\\ \mkbibemph{ } \par - “Fig. 1. TPL usage for two different apps” (“TOSEM-2025-0054\_Proof\_hi”, p. 4) \#5fb236\\ \mkbibemph{ } \par - “we mine their description texts.” (“TOSEM-2025-0054\_Proof\_hi”, p. 4) \#a28ae5\\ \mkbibemph{ } \par - “app product” (“TOSEM-2025-0054\_Proof\_hi”, p. 4) \#5fb236\\ \mkbibemph{ } \par - “candidate TPL.” (“TOSEM-2025-0054\_Proof\_hi”, p. 4) \#5fb236\\ \mkbibemph{ } \par - “function information of app products” (“TOSEM-2025-0054\_Proof\_hi”, p. 4) \#5fb236\\ \mkbibemph{ } \par - “reliability and diversity” (“TOSEM-2025-0054\_Proof\_hi”, p. 4) \#5fb236\\ \mkbibemph{ } \par - “Meanwhile, since T3 and T5 are as relevant to the development of video game as T2, we should pay more attention to T3 and T5 among T1, T3, and T5 to explore whether T2 can synergize with them to complete some video game-related development tasks.” (“TOSEM-2025-0054\_Proof\_hi”, p. 5) \#a28ae5\\ \mkbibemph{ } \par - “App. App refers to mobile application.” (“TOSEM-2025-0054\_Proof\_hi”, p. 6) \#ffd400\\ \mkbibemph{Is the proposed approach only for supporting the development of mobile applications? } \par - “by app product.” (“TOSEM-2025-0054\_Proof\_hi”, p. 6) \#ff6666\\ \mkbibemph{which one? } \par - “TPLs in the historical data.” (“TOSEM-2025-0054\_Proof\_hi”, p. 6) \#ffd400\\ \mkbibemph{what do you mean? } \par - “apps in the historical data” (“TOSEM-2025-0054\_Proof\_hi”, p. 6) \#ffd400\\ \mkbibemph{What do you mean? } \par - “we first mine the functions from its description text by utilizing LLM” (“TOSEM-2025-0054\_Proof\_hi”, p. 6) \#e56eee\\ \mkbibemph{ } \par - “app product and each candidate TPL in the TPL pooling, and thereby establishing a TPL recommendation list.” (“TOSEM-2025-0054\_Proof\_hi”, p. 6) \#ffd400\\ \mkbibemph{This is a potential and initial list, isn't it? } \par - “pp product and candidate TPL” (“TOSEM-2025-0054\_Proof\_hi”, p. 7) \#ffd400\\ \mkbibemph{only one candidate TPL is given? Or more than one recommended items are given? } \par - “provided” (“TOSEM-2025-0054\_Proof\_hi”, p. 7) \#ffd400\\ \mkbibemph{to be provided, isn't it? } \par - “To provide better support for TPL recommendation, we employ LLM to mine the functions from the description text of app product.” (“TOSEM-2025-0054\_Proof\_hi”, p. 7) \#ffd400\\ \mkbibemph{it's uncommon to have such a text before the release of the wanted app. } \par - “generating” (“TOSEM-2025-0054\_Proof\_hi”, p. 8) \#ffd400\\ \mkbibemph{generating a list of app functions can be a threat to validity. Check if it is stated later in the paper. I'm expecting a research question on this point. } \par - “First, Atten-TPL generates the m-dimensional vectors for candidate TPL and TPLs in the TPL context using One-hot Encoding [31], where m is the number of TPLs in the TPL pooling. Second, Atten-TPL multiplies the vector of candidate TPL and the ones of TPLs in the TPL context with two trainable weight matrixes W q ∈ Rm×dk and W k ∈ Rm×dk to obtain Q ∈ R1×dk and K ∈ Rn×dk , respectively (n is the number of TPLs in the TPL context).” (“TOSEM-2025-0054\_Proof\_hi”, p. 8) \#ffd400\\ \mkbibemph{A concrete example is necessary here. An illustrative project with the corresponding TPL and the corresponding represenation (even abstract) to help reader understand how the proposed encoding works. } \par - “Task” (“TOSEM-2025-0054\_Proof\_hi”, p. 8) \#2ea8e5\\ \mkbibemph{ } \par - “Function definition” (“TOSEM-2025-0054\_Proof\_hi”, p. 8) \#2ea8e5\\ \mkbibemph{ } \par - “Function examples” (“TOSEM-2025-0054\_Proof\_hi”, p. 8) \#2ea8e5\\ \mkbibemph{ } \par - “we provide five function examples, which are manually extracted from the description texts of existing app products” (“TOSEM-2025-0054\_Proof\_hi”, p. 8) \#5fb236\\ \mkbibemph{ } \par - “Non-functional content” (“TOSEM-2025-0054\_Proof\_hi”, p. 8) \#2ea8e5\\ \mkbibemph{ } \par - “Input and output” (“TOSEM-2025-0054\_Proof\_hi”, p. 8) \#2ea8e5\\ \mkbibemph{ } \par - “Atten-TPL” (“TOSEM-2025-0054\_Proof\_hi”, p. 8) \#e56eee\\ \mkbibemph{ } \par - “each candidate TPL” (“TOSEM-2025-0054\_Proof\_hi”, p. 8) \#5fb236\\ \mkbibemph{ } \par - “Generating the vector for TPL context.” (“TOSEM-2025-0054\_Proof\_hi”, p. 8) \#2ea8e5\\ \mkbibemph{ } \par - “app product” (“TOSEM-2025-0054\_Proof\_hi”, p. 8) \#5fb236\\ \mkbibemph{ } \par - “not every TPL in the TPL context can make a positive contribution.” (“TOSEM-2025-0054\_Proof\_hi”, p. 8) \#a28ae5\\ \mkbibemph{ } \par - “truly useful for the task” (“TOSEM-2025-0054\_Proof\_hi”, p. 8) \#e56eee\\ \mkbibemph{ } \par - “m-dimensional vectors” (“TOSEM-2025-0054\_Proof\_hi”, p. 8) \#5fb236\\ \mkbibemph{ } \par - “formed randomly from the vectors of all TPLs in the TPL context.” (“TOSEM-2025-0054\_Proof\_hi”, p. 8) \#ffd400\\ \mkbibemph{This is not clear! } \par - “After that, the model applies a softmax operation to normalize all attention scores so that they are added up to 1.” (“TOSEM-2025-0054\_Proof\_hi”, p. 8) \#5fb236\\ \mkbibemph{ } \par - “match degree between app product and candidate TPL.” (“TOSEM-2025-0054\_Proof\_hi”, p. 9) \#a28ae5\\ \mkbibemph{ } \par - “(3)” (“TOSEM-2025-0054\_Proof\_hi”, p. 9) \#5fb236\\ \mkbibemph{ } \par - “Evaluating the match degree between app prod” (“TOSEM-2025-0054\_Proof\_hi”, p. 9) \#2ea8e5\\ \mkbibemph{ } \par - “activations, bias, and model weights” (“TOSEM-2025-0054\_Proof\_hi”, p. 9) \#e56eee\\ \mkbibemph{ } \par - “sentence-level tasks” (“TOSEM-2025-0054\_Proof\_hi”, p. 10) \#5fb236\\ \mkbibemph{ } \par - “We create a dataset consisting of positive triples and negative triples based on the hold-one-out strategy [7], and each triple contains three parts, including: candidate TPL, TPL context, and function list.” (“TOSEM-2025-0054\_Proof\_hi”, p. 10) \#ffd400\\ \mkbibemph{The creation of this dataset is also a critical point of the approach, which can represent a threat to validity. } \par - “The function list is obtained by utilizing LLM to process the description text of app product.” (“TOSEM-2025-0054\_Proof\_hi”, p. 10) \#ffd400\\ \mkbibemph{As I said the fact that the function lists are obtained by means of LLMs represents a crucial threat to validity because creating a clean and correct dataset is of paramount importance in this case. } \par - “Meanwhile, we also establish multiple negative triples for each app, and the number of negative triples is ten times the number of positive triples” (“TOSEM-2025-0054\_Proof\_hi”, p. 10) \#5fb236\\ \mkbibemph{ } \par - “softmax classifier” (“TOSEM-2025-0054\_Proof\_hi”, p. 10) \#a28ae5\\ \mkbibemph{ } \par - “Second, Atten-TPL calculates the mean for the vectors of all functions and uses the result as the vector of function list.” (“TOSEM-2025-0054\_Proof\_hi”, p. 10) \#ffd400\\ \mkbibemph{Also here, can you give an illustrative example? } \par - “hold-one-out strategy” (“TOSEM-2025-0054\_Proof\_hi”, p. 10) \#a28ae5\\ \mkbibemph{ } \par - “each triple contains three parts, i” (“TOSEM-2025-0054\_Proof\_hi”, p. 10) \#a28ae5\\ \mkbibemph{ } \par - “In each operation, we remove a TPL from the TPL list of app product, the removed TPL is treated as candidate TPL, and the remaining TPLs are used as the TPL context. The function list is obtained by utilizing LLM to process the description text of app product” (“TOSEM-2025-0054\_Proof\_hi”, p. 10) \#ffd400\\ \mkbibemph{how many times has this been done? For how many product, how many candidate TPLs? } \par - “we remove a TPL from the TPL list of app product” (“TOSEM-2025-0054\_Proof\_hi”, p. 10) \#a28ae5\\ \mkbibemph{ } \par - “Positive triples and negative triples are labeled 1 and 0, respectively.” (“TOSEM-2025-0054\_Proof\_hi”, p. 10) \#a28ae5\\ \mkbibemph{ } \par - “How does Atten-TPL perform on the TPL recommendation?” (“TOSEM-2025-0054\_Proof\_hi”, p. 10) \#2ea8e5\\ \mkbibemph{ } \par - “function information of app product actually improve the performance of Atten-TPL?” (“TOSEM-2025-0054\_Proof\_hi”, p. 11) \#2ea8e5\\ \mkbibemph{ } \par - “attention mechanism positively affect” (“TOSEM-2025-0054\_Proof\_hi”, p. 11) \#2ea8e5\\ \mkbibemph{ } \par - “Can Atten-TPL provide help for app developers in software engineering practice?” (“TOSEM-2025-0054\_Proof\_hi”, p. 11) \#ffd400\\ \mkbibemph{This is a qualitative research question. Are used involved?Let's see what the authors have done for this. } \par - “MALib dataset [14], a public real-world dataset that contains 61,722 apps, 827 distinct TPLs, and 725,502 app-TPL usage records.” (“TOSEM-2025-0054\_Proof\_hi”, p. 11) \#a28ae5\\ \mkbibemph{ } \par - “Since the MALib dataset does not provide the description texts needed for our recommendation model, we wrote a web crawler to scrape the description texts for apps in it from app store or third-party website.” (“TOSEM-2025-0054\_Proof\_hi”, p. 11) \#ffd400\\ \mkbibemph{This can be a potential threat to validity. Have you double checked the crawled data and checked that they are linked to the projects correctly? } \par - “44,873 apps, and these apps were used as the dataset of our experiment.” (“TOSEM-2025-0054\_Proof\_hi”, p. 11) \#ffd400\\ \mkbibemph{Have you double checked that the crawled information is consistent with the corresponding apps? } \par - “TPL author, TPL Dependency” (“TOSEM-2025-0054\_Proof\_hi”, p. 11) \#5fb236\\ \mkbibemph{ } \par - “0.2,” (“TOSEM-2025-0054\_Proof\_hi”, p. 11) \#5fb236\\ \mkbibemph{ } \par - “The larger the value, the more random the generated text, while the smaller the value, the less random the generated text, resulting in the more organized and precise response.” (“TOSEM-2025-0054\_Proof\_hi”, p. 11) \#5fb236\\ \mkbibemph{ } \par - “2rm” (“TOSEM-2025-0054\_Proof\_hi”, p. 11) \#ffd400\\ \mkbibemph{what is it? } \par - “Atten-TPL mines app functions from the description text by employing ChatGPT” (“TOSEM-2025-0054\_Proof\_hi”, p. 15) \#ffd400\\ \mkbibemph{ } \par - “n addition to the TPL context, the input of our recommendation method includes the description text of app product. Considering that the description text must be completed by app developers before the application is” (“TOSEM-2025-0054\_Proof\_hi”, p. 19) \#5fb236\\ \mkbibemph{ } \par - “In this paper, we propose a novel TPL recommendation model called Atten-TPL. Atten-TPL can analyze the match degree between app product and each candidate TPL in the TPL pooling by utilizing a deep neural network to mine the hidden relationships among candidate TPL, the TPL context and the functions of app product. By employing the attention mechanism, Atten-TPL can pay more attention to TPLs in the TPL context that are truly useful for the evaluation task. Furthermore, to effectively gain the functions provided by apps, we use LLM to mine their description texts. Compared to existing state-of-the-art TPL recommendation methods, Atten-TPL can return more reliable recomemndation results and performs well in terms of diversity. In future work, we plan to propose a method to generate the explanatory texts for TPL recommendation lists, which will assist developers in understanding and utilizing the recommended TPLs. The robust text generation capability of LLM holds promise for achieving this goal.” (“TOSEM-2025-0054\_Proof\_hi”, p. 20) \#a28ae5\\ \mkbibemph{ } \par - “released, our recommendation model is highly practical in real-world development.” (“TOSEM-2025-0054\_Proof\_hi”, p. 20) \#5fb236\\ \mkbibemph{ } \par - “Meanwhile, even if the developers of app product do not provide the description text, our model can still rely on the TPL context to recommend the available TPLs for them with acceptable results (The experimental result of RQ2 can demonstrate this). Furthermore, since the TPL ecosystem is dynamic, the app and TPL poolings can be updated and the recommendation model can be retrained periodically, enabling the model to integrate new or updated TPLs.” (“TOSEM-2025-0054\_Proof\_hi”, p. 20) \#ffd400\\ \mkbibemph{ }} } @article{totterdaleCASESTUDYUTILIZATION2018, title = {{{CASE STUDY}}: {{THE UTILIZATION OF LOW-CODE DEVELOPMENT TECHNOLOGY TO SUPPORT RESEARCH DATA COLLECTION}}}, author = {Totterdale, Robert L}, date = {2018}, volume = {19}, number = {2}, pages = {8}, abstract = {Research data must be collected and maintained in compliance with a myriad of laws and regulations that protect the privacy of participant’s information, and should be captured in a manner that is cost effective and timely. This paper discusses research data collection, explores challenges and approaches for collecting data, and describes how low-code development technology can be utilized to facilitate the secure and efficient collection of research data in the healthcare industry. This paper is based on research being conducted in the healthcare industry but has broad applicability across other industries and research areas that collect personally identifiable information, or other sensitive data protected by U.S. or international laws and regulations.}, langid = {english} } @inproceedings{Toutiaee20201097, title = {Gaussian Function on Response Surface Estimation}, author = {Toutiaee, M. and Miller, J.A.}, editor = {Wu X., Jermaine C., Hu X.T., Kotevska O., Lu S., Xu W., Aluru S., Zhai C., Al-Masri E., Chen Z., Saltz J., Xiong L.}, date = {2020}, series = {Proceedings - 2020 {{IEEE International Conference}} on {{Big Data}}, {{Big Data}} 2020}, pages = {1097--1102}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/BigData50022.2020.9378132}, abbrev_source_title = {Proc. - IEEE Int. Conf. Big Data, Big Data}, affiliation = {University of Georgia, Computer Science Department, Athens, GA, United States}, art_number = {9378132}, document_type = {Conference Paper}, isbn = {978-1-7281-6251-5}, langid = {english}, source = {Scopus}, note = {cited By 0} } @online{TraditionalVsAgentic, title = {Traditional vs. {{Agentic RAG}}}, url = {https://www.linkedin.com/posts/akshay-pachaar_traditional-vs-agentic-rag-clearly-explained-activity-7278030989275144192-YSlF?utm_source=share&utm_medium=member_android}, keywords = {source\_mobile} } @article{trakadasArtificialIntelligenceBasedCollaboration2020, title = {An {{Artificial Intelligence-Based Collaboration Approach}} in {{Industrial IoT Manufacturing}}: {{Key Concepts}}, {{Architectural Extensions}} and {{Potential Applications}}}, shorttitle = {An {{Artificial Intelligence-Based Collaboration Approach}} in {{Industrial IoT Manufacturing}}}, author = {Trakadas, Panagiotis and Simoens, Pieter and Gkonis, Panagiotis and Sarakis, Lambros and Angelopoulos, Angelos and Ramallo-González, Alfonso P. and Skarmeta, Antonio and Trochoutsos, Christos and Calvο, Daniel and Pariente, Tomas and Chintamani, Keshav and Fernandez, Izaskun and Irigaray, Aitor Arnaiz and Parreira, Josiane Xavier and Petrali, Pierluigi and Leligou, Nelly and Karkazis, Panagiotis}, date = {2020-01}, journaltitle = {Sensors}, volume = {20}, number = {19}, pages = {5480}, publisher = {Multidisciplinary Digital Publishing Institute}, doi = {10.3390/s20195480}, abstract = {The digitization of manufacturing industry has led to leaner and more efficient production, under the Industry 4.0 concept. Nowadays, datasets collected from shop floor assets and information technology (IT) systems are used in data-driven analytics efforts to support more informed business intelligence decisions. However, these results are currently only used in isolated and dispersed parts of the production process. At the same time, full integration of artificial intelligence (AI) in all parts of manufacturing systems is currently lacking. In this context, the goal of this manuscript is to present a more holistic integration of AI by promoting collaboration. To this end, collaboration is understood as a multi-dimensional conceptual term that covers all important enablers for AI adoption in manufacturing contexts and is promoted in terms of business intelligence optimization, human-in-the-loop and secure federation across manufacturing sites. To address these challenges, the proposed architectural approach builds on three technical pillars: (1) components that extend the functionality of the existing layers in the Reference Architectural Model for Industry 4.0; (2) definition of new layers for collaboration by means of human-in-the-loop and federation; (3) security concerns with AI-powered mechanisms. In addition, system implementation aspects are discussed and potential applications in industrial environments, as well as business impacts, are presented.}, issue = {19}, langid = {english}, keywords = {DONE}, note = {\textbf{Gray Annotations (18/12/2020, 15:08:12)} \par "Industry 4.0 concepts are expected to significantly increase their footprint in industrial sectors by 20\% in the next five years, since they allow leaner and more ecient production [4,5]" (\href{zotero://open-pdf/library/items/K7JAGWW6?page=2}{Trakadas et al 2020:5481}) \par "In this context, many manufacturing companies are interested in accelerating the adoption and integration of secure, trustworthy artificial intelligence (AI) [6]" (\href{zotero://open-pdf/library/items/K7JAGWW6?page=2}{Trakadas et al 2020:5481}) \par "AI-based manufacturing has the potential to improve the business key performance indicators (KPIs) of manufacturing processes by leveraging heterogeneous industrial big data analysis, information modelling and federation [7-9]" (\href{zotero://open-pdf/library/items/K7JAGWW6?page=2}{Trakadas et al 2020:5481}) \par "interconnection of AI-based manufacturing processes with currently deployed wireless networks is a challenging research field, especially when central processing is performed outside industrial premises [10,11]" (\href{zotero://open-pdf/library/items/K7JAGWW6?page=2}{Trakadas et al 2020:5481}) \par "AI is critical to the cybersecurity aspect of an IIoT-enabled connected manufacturing environment, for accurately detecting and mitigating threats [16-19]" (\href{zotero://open-pdf/library/items/K7JAGWW6?page=2}{Trakadas et al 2020:5481}) \par "Industry 4.0 will make machines increasingly smarter by using AI models [28]" (\href{zotero://open-pdf/library/items/K7JAGWW6?page=3}{Trakadas et al 2020:5482}) \par \mkbibemph{REFERENCE IMPORTANTE CHE MOTIVA ASPETTI DI INTEROPERABILITA' (\href{zotero://open-pdf/library/items/K7JAGWW6?page=4}{note on p.5483})} \par "TensorFlow Federated provide support for decentralized AI models learning or computation over locally controlled data sources [39]." (\href{zotero://open-pdf/library/items/K7JAGWW6?page=7}{Trakadas et al 2020:5486}) \par "adopting this novel approach [40], this scheme heavily reduces the administrative eort for key sharing and management, while ensuring end-to-end information protection" (\href{zotero://open-pdf/library/items/K7JAGWW6?page=8}{Trakadas et al 2020:5487}) \par "adversarial attacks, which is the core element of trustworthy AI, has recently received much attention [41]." (\href{zotero://open-pdf/library/items/K7JAGWW6?page=8}{Trakadas et al 2020:5487}) \par "The creation of models of the state and behavior can be done in a semi-automatic manner, using a newly devised AutoML tool that takes as input vector representations of sequential input data [46]" (\href{zotero://open-pdf/library/items/K7JAGWW6?page=9}{Trakadas et al 2020:5488}) \par \mkbibemph{AUTOML (\href{zotero://open-pdf/library/items/K7JAGWW6?page=9}{note on p.5488})} \par "Currently, federated learning is being adopted in dierent scenarios such as banking [61] and healthcare [62]." (\href{zotero://open-pdf/library/items/K7JAGWW6?page=11}{Trakadas et al 2020:5490}) \par \mkbibemph{HERE WE HAVE COUPLE OF CONCRETE SCNEARIOS USING FEDERATED LEARNING (\href{zotero://open-pdf/library/items/K7JAGWW6?page=11}{note on p.5490})} \par "There are currently many research activities to develop faster or more resource ecient PSI protocols [38,64]." (\href{zotero://open-pdf/library/items/K7JAGWW6?page=11}{Trakadas et al 2020:5490}) \par "26. Sittón-Candanedo, I.; Alonso, R.C.; Rodríguez-González, S.; Alberto García Coria, J.; De La Prieta, F. Edge Computing Architectures in Industry 4.0: A General Survey and Comparison. In International Workshop on Soft Computing Models in Industrial and Environmental Applications; Springer: Cham, Switzerland, 2019; Volume 950." (\href{zotero://open-pdf/library/items/K7JAGWW6?page=18}{Trakadas et al 2020:5497}) \par "Comiter, M. Attacking Artificial Intelligence, AI's Security Vulnerability and What Policymakers about It. In Belfer Center Paper; Harvard Kenedy School: Cambridge, MA, USA, 2019." (\href{zotero://open-pdf/library/items/K7JAGWW6?page=19}{Trakadas et al 2020:5498}) \par \textbf{Green Annotations (18/12/2020, 15:08:12)} \par "important enablers for AI adoption in manufacturing contexts and is promoted in terms of business intelligence optimization, human-in-the-loop and secure federation across manufacturing sites" (\href{zotero://open-pdf/library/items/K7JAGWW6?page=1}{Trakadas et al 2020:5480}) \par "(1) components that extend the functionality of the existing layers in the Reference Architectural Model for Industry 4.0; (2) definition of new layers for collaboration by means of human-in-the-loop and federation; (3) security concerns with AI-powered mechanisms." (\href{zotero://open-pdf/library/items/K7JAGWW6?page=1}{Trakadas et al 2020:5480}) \par "most AI techniques are based on mathematical models that are dicult to understand by the general public, so most people use AI-based technology as a black box that they eventually start to trust based on their personal experience" (\href{zotero://open-pdf/library/items/K7JAGWW6?page=2}{Trakadas et al 2020:5481}) \par "The application of human-centric AI (HAI) in internet of things (IoT) systems, so that IoT systems cannot only learn from users but also provide easy-to-understand explanations about decisions or estimations is a new research field [12]." (\href{zotero://open-pdf/library/items/K7JAGWW6?page=2}{Trakadas et al 2020:5481}) \par \mkbibemph{ASPETTI MOTIVAZIONALI MOLTO IMPORTANTI. (\href{zotero://open-pdf/library/items/K7JAGWW6?page=2}{note on p.5481})} \par "At the same time, introducing AI will lead to a more productive and safer working space, relieving human workers from routine procedures and employing intelligent machines and robots to perform heavy tasks, thus allowing human workers to focus on creativity, reasoning and decision making [20,21]." (\href{zotero://open-pdf/library/items/K7JAGWW6?page=2}{Trakadas et al 2020:5481}) \par \mkbibemph{MOTIVATION (\href{zotero://open-pdf/library/items/K7JAGWW6?page=2}{note on p.5481})} \par "to provide an IIoT-based system that increases performance and safety in the manufacturing domain." (\href{zotero://open-pdf/library/items/K7JAGWW6?page=2}{Trakadas et al 2020:5481}) \par \mkbibemph{MAIN GOAL (\href{zotero://open-pdf/library/items/K7JAGWW6?page=2}{note on p.5481})} \par "AI solutions are implemented in dispersed and isolated components of manufacturing IT systems." (\href{zotero://open-pdf/library/items/K7JAGWW6?page=3}{Trakadas et al 2020:5482}) \par "Current Industry 4.0 reference architectures do not properly integrate the needed building blocks such as new deployment paradigms (e.g., edge-based learning to reduce bandwidth load on the enterprise network)" (\href{zotero://open-pdf/library/items/K7JAGWW6?page=3}{Trakadas et al 2020:5482}) \par "scalable data-processing pipelines and information models" (\href{zotero://open-pdf/library/items/K7JAGWW6?page=3}{Trakadas et al 2020:5482}) \par "AI-enabled digital twins used for monitoring and optimizing business intelligence [24-26]" (\href{zotero://open-pdf/library/items/K7JAGWW6?page=3}{Trakadas et al 2020:5482}) \par \mkbibemph{IMPORTANT MOTIVATIONS AND CONTEXT DESCRIPTION (\href{zotero://open-pdf/library/items/K7JAGWW6?page=3}{note on p.5482})} \par "availability of big data has been one of the most important enablers for the recent wave of AI innovations [27]" (\href{zotero://open-pdf/library/items/K7JAGWW6?page=3}{Trakadas et al 2020:5482}) \par "Moreover, every phase of AI algorithm design requires high-level skills (model selection, training, hyperparameter optimization). In the agile manufacturing of the future, these costs must be amortized over low-volume batches (even lot-size-one)." (\href{zotero://open-pdf/library/items/K7JAGWW6?page=3}{Trakadas et al 2020:5482}) \par \mkbibemph{IMPORTANT MOTIVATIONS AND CONTEXT DESCRIPTION (\href{zotero://open-pdf/library/items/K7JAGWW6?page=3}{note on p.5482})} \par "AI technologies should not only be used for data analytics in support of business intelligence, but also for automated decision making on manufacturing process parameters and configurations." (\href{zotero://open-pdf/library/items/K7JAGWW6?page=3}{Trakadas et al 2020:5482}) \par "AI algorithms are often black-box models (e.g., deep learning), while the inner workings of an algorithm fetched from a remote repository are not fully understood or the decisions of one algorithm create a conflict with other algorithmic decisions" (\href{zotero://open-pdf/library/items/K7JAGWW6?page=3}{Trakadas et al 2020:5482}) \par "Proper secure federation mechanisms and AI-based cyberattack risk analysis are crucial cross-cutting concerns in AI-based manufacturing systems." (\href{zotero://open-pdf/library/items/K7JAGWW6?page=3}{Trakadas et al 2020:5482}) \par "the performance of modern AI techniques requires large volumes of high-quality data which are often not available inside a single enterpris" (\href{zotero://open-pdf/library/items/K7JAGWW6?page=3}{Trakadas et al 2020:5482}) \par \mkbibemph{MOTIVA BENE LA QUESTIONE DI LUCIANO, PERCHE' SI HA BISOGNO DI FEDERATED LEARNING (\href{zotero://open-pdf/library/items/K7JAGWW6?page=3}{note on p.5482})} \par "AI techniques will be used to extend and improve the levels of communication and collaboration between computer systems and human workers." (\href{zotero://open-pdf/library/items/K7JAGWW6?page=3}{Trakadas et al 2020:5482}) \par "new intelligent design and decision-making tools must be developed to promote human agency and oversight, simplifying the understanding and usage of AI results and considering multiple collaboration schemes depending on the situation. Human-AI will work in tandem in any phase of the product construction process, from design, over intelligent manufacturing execution monitoring to predictive maintenance." (\href{zotero://open-pdf/library/items/K7JAGWW6?page=3}{Trakadas et al 2020:5482}) \par \mkbibemph{OBIETTIVI (\href{zotero://open-pdf/library/items/K7JAGWW6?page=3}{note on p.5482})} \par "collaborative and intelligent factory of the future." (\href{zotero://open-pdf/library/items/K7JAGWW6?page=5}{Trakadas et al 2020:5484}) \par "ration with other manufacturing sites, while security is aboration and for cross-cutting concern. In the following, we describe the functionality of each component as shown inis a cross-cutting Figure 2 concern" (\href{zotero://open-pdf/library/items/K7JAGWW6?page=5}{Trakadas et al 2020:5484}) \par \mkbibemph{LUCIANO (\href{zotero://open-pdf/library/items/K7JAGWW6?page=5}{note on p.5484})} \par "the factory-wide datasets so that the components of the upperyersistoperform layers can make decisions based on the outcome of AI algorithms running on top of such processedrlayerscanmake data streams or batch datasets. Raw (non-labeled) data generated by manufacturing devices ared data streams or processed alo batchdatasets" (\href{zotero://open-pdf/library/items/K7JAGWW6?page=5}{Trakadas et al 2020:5484}) \par "re functionality to deploy AI algorithms closer to the sensor (edgeer to the sensor computing) and to detect shifts in the dataset statistics, indicating a need to retrain algorithms. Inetrainalgorithms" (\href{zotero://open-pdf/library/items/K7JAGWW6?page=5}{Trakadas et al 2020:5484}) \par "AI-enabled data pipelines orchestrator component enables the creation and deployment ofnddeploymentof data processing pipelines, with two major objectivemajorobjectives:" (\href{zotero://open-pdf/library/items/K7JAGWW6?page=5}{Trakadas et al 2020:5484}) \par "(i) the component should allow the possibilitythepossibilityto to set up pipelines consisting of typical data processing tasks (feature conversion, feature reduction,rereduction,data data anonymization and fusion, data cleaning, labeling and annotation, etc.) and AI models (used inodels(usedin the the services of the upper latheupperlayers)" (\href{zotero://open-pdf/library/items/K7JAGWW6?page=5}{Trakadas et al 2020:5484}) \par "e factory; (ii) thei)thecomponent component should allow the deployment of the pipelines and the orchestration of the differentcomponents" (\href{zotero://open-pdf/library/items/K7JAGWW6?page=5}{Trakadas et al 2020:5484}) \par "automate the deployment process on distributed infrastructure (edge device, edge cloud, public cloud) and to orchestrate the dierent modules and exact frameworks needed to run the processes." (\href{zotero://open-pdf/library/items/K7JAGWW6?page=6}{Trakadas et al 2020:5485}) \par \mkbibemph{IMPORTANTE: RELATIVO AL AI PIPELINES (DESIGN AND DEPLOYMENT) (\href{zotero://open-pdf/library/items/K7JAGWW6?page=6}{note on p.5485})} \par "Edge-based learning is required for latency-sensitive situations and/or when upstream bandwidth is insucient, e.g., audio and video from an augmented reality (AR) headset or processing light detection and ranging (LIDAR) data on a mobile robot. The component will support novel neural network architectures that can be trained without requiring large amounts of labelled data and that are resource-ecient [36]." (\href{zotero://open-pdf/library/items/K7JAGWW6?page=6}{Trakadas et al 2020:5485}) \par \mkbibemph{IMPORTANTE LUCIANO (\href{zotero://open-pdf/library/items/K7JAGWW6?page=6}{note on p.5485})} \par "threat intelligence manager takes advantage of the collected and curated datasets and applies AI algorithms for executing threat analysis in order not only to predict potential cybersecurity incidents but most importantly to manage and mitigate such incidents in a timely manner." (\href{zotero://open-pdf/library/items/K7JAGWW6?page=6}{Trakadas et al 2020:5485}) \par "4.2. Functional and Business Intelligence" (\href{zotero://open-pdf/library/items/K7JAGWW6?page=6}{Trakadas et al 2020:5485}) \par "mirror the state of the production process in digital twins, including the logic that determines the transition to other production steps or states." (\href{zotero://open-pdf/library/items/K7JAGWW6?page=6}{Trakadas et al 2020:5485}) \par "This layer provides innovative tools that will facilitate intuitive and ecient collaboration between humans, machines and AI systems allowing them to take advantage of each other 's strengths for more eective cooperative and intuitive task execution and decision making" (\href{zotero://open-pdf/library/items/K7JAGWW6?page=7}{Trakadas et al 2020:5486}) \par "Federated Learning component aims at solving the problem of data collection for feeding or training AI models, while assuring the ownership and confidentiality of the data. In manufacturing, most (if not all) data and information are confidential because they relate directly to details of the production process, product characteristics, volumes, etc." (\href{zotero://open-pdf/library/items/K7JAGWW6?page=7}{Trakadas et al 2020:5486}) \par "The inter-manufacturing knowledge exchange serves as an interface for knowledge exchange across manufacturing sites or distinct manufacturing processes." (\href{zotero://open-pdf/library/items/K7JAGWW6?page=7}{Trakadas et al 2020:5486}) \par "ince there is a need to control what information is exposed and exchanged, rather than allowing open access to the local knowledge repository, this component contains a query engine to handle external requests. Such query engines also enable the realization of a federated query-processing mechanism over multiple sites." (\href{zotero://open-pdf/library/items/K7JAGWW6?page=7}{Trakadas et al 2020:5486}) \par \mkbibemph{FEDERATED QUERY-RPOCESSING METCHANISM OVER MULTIPLE SITES (\href{zotero://open-pdf/library/items/K7JAGWW6?page=7}{note on p.5486})} \par "4.5. Security and Authorization" (\href{zotero://open-pdf/library/items/K7JAGWW6?page=8}{Trakadas et al 2020:5487}) \par "security and authorization requirements on information and data sharing." (\href{zotero://open-pdf/library/items/K7JAGWW6?page=8}{Trakadas et al 2020:5487}) \par "Cybersecurity for Artificial Intelligence (AI)" (\href{zotero://open-pdf/library/items/K7JAGWW6?page=8}{Trakadas et al 2020:5487}) \par "Artificial intelligence attacks, i.e., attacks on the AI algorithm, can take two forms: input attacks and poisoning attacks. The former consists in manipulating the input to the AI system during the operation phase so that it delivers the wrong results. Input attacks are relatively easy to launch and succeed since they do not require a manipulated AI system. Poisoning attacks, on the other hand, have to do with the corruption of the process used to build the AI model. In this case, inaccurate or mislabeled data are provided to the model during the training phase to manipulate the learning process. This type of attack can also be launched against federated learning; in this case, manipulated data or an algorithm of a member of the federation can result in the corruption of the global model." (\href{zotero://open-pdf/library/items/K7JAGWW6?page=8}{Trakadas et al 2020:5487}) \par \mkbibemph{IMPORTANT! RELATED TO AML WORK!!! IT APPLIES ALSO IN THE CASE OF FEDERATED LEARNING (\href{zotero://open-pdf/library/items/K7JAGWW6?page=8}{note on p.5487})} \par "challenges regarding the implementation of the innovative AI-based components of the proposed system" (\href{zotero://open-pdf/library/items/K7JAGWW6?page=8}{Trakadas et al 2020:5487}) \par "AI-Driven Modelling of Manufacturing Assets" (\href{zotero://open-pdf/library/items/K7JAGWW6?page=8}{Trakadas et al 2020:5487}) \par "Digital twins are virtual, high-fidelity models of the current state and internal behavior of physical assets on the shop floor [25]." (\href{zotero://open-pdf/library/items/K7JAGWW6?page=8}{Trakadas et al 2020:5487}) \par "there is a lack of information models and process libraries that allow users to replicate and scale their digital twins." (\href{zotero://open-pdf/library/items/K7JAGWW6?page=8}{Trakadas et al 2020:5487}) \par "AutoML" (\href{zotero://open-pdf/library/items/K7JAGWW6?page=9}{Trakadas et al 2020:5488}) \par \mkbibemph{DA VEDERE AUTOML (\href{zotero://open-pdf/library/items/K7JAGWW6?page=9}{note on p.5488})} \par "For the IT resource aspect, processing all raw data on a public cloud infrastructure is an unscalable solution for many manufacturing companies, either because there is too much sensor data to upload, the latency to the cloud is prohibitive or because the sensor data is too sensitive and the company does not want to expose this. Therefore, edge computing has been proposed and several reference architectures for edge computing in Industry 4.0 have been proposed [26" (\href{zotero://open-pdf/library/items/K7JAGWW6?page=9}{Trakadas et al 2020:5488}) \par \mkbibemph{LACK OF RESOURCES FOR AI IN INDUSTRY (\href{zotero://open-pdf/library/items/K7JAGWW6?page=9}{note on p.5488})} \par "Techniques to make deep neural networks available for the latter form of edge computing start to find their way into production as more user-friendly tools become available. For instance, TensorFlow Lite allows converting a trained model for deployment on microcontrollers or embedded Graphics Processor Units (GPUs)." (\href{zotero://open-pdf/library/items/K7JAGWW6?page=9}{Trakadas et al 2020:5488}) \par \mkbibemph{TensorFlow Lite: Come diceva Luciano (\href{zotero://open-pdf/library/items/K7JAGWW6?page=9}{note on p.5488})} \par "5.2. Multi-Channel, Context-Aware Interaction on the Shop Floor" (\href{zotero://open-pdf/library/items/K7JAGWW6?page=9}{Trakadas et al 2020:5488}) \par \mkbibemph{Relativo alla parte di NLI di Mariani (\href{zotero://open-pdf/library/items/K7JAGWW6?page=9}{note on p.5488})} \par "The latest developments [48,49] allow humans to convey information with AI systems through multiple channels by integrating advanced human-machine interfaces (gestures, facial expressions). These interfaces, which can be obtained by using 2-D and/or 3-D cameras and other sensors, such as gyroscopes or accelerometers, oer information related to the context and the situation that is relevant to the interaction." (\href{zotero://open-pdf/library/items/K7JAGWW6?page=9}{Trakadas et al 2020:5488}) \par \mkbibemph{Gesture, facial expressions interaction etc (\href{zotero://open-pdf/library/items/K7JAGWW6?page=9}{note on p.5488})} \par "voice understanding" (\href{zotero://open-pdf/library/items/K7JAGWW6?page=9}{Trakadas et al 2020:5488}) \par "experts in simulations and modelling are not available in SMEs and large enterprises, and that data-driven digital twins are usually trained on data collected at system level. These limitations make it economically costly to develop novel digital twins, which is often needed in modern manufacturing with agile reconfigurations." (\href{zotero://open-pdf/library/items/K7JAGWW6?page=9}{Trakadas et al 2020:5488}) \par \mkbibemph{IMPORTANT - LACK OF EXPERTS (\href{zotero://open-pdf/library/items/K7JAGWW6?page=9}{note on p.5488})} \par "Data-driven digital twins are built using machine learning." (\href{zotero://open-pdf/library/items/K7JAGWW6?page=9}{Trakadas et al 2020:5488}) \par "DL comes with its own problems: it requires domain knowledge to select the appropriate machine learning pipeline and it is very resource hungry in terms of computing and storage resources [36,43,44]" (\href{zotero://open-pdf/library/items/K7JAGWW6?page=9}{Trakadas et al 2020:5488}) \par \mkbibemph{PROBLEMS ON DEEP LEARNING (\href{zotero://open-pdf/library/items/K7JAGWW6?page=9}{note on p.5488})} \par "Intelligent Decision Support" (\href{zotero://open-pdf/library/items/K7JAGWW6?page=10}{Trakadas et al 2020:5489}) \par "increase the eciency in the decision-making process" (\href{zotero://open-pdf/library/items/K7JAGWW6?page=10}{Trakadas et al 2020:5489}) \par "urrent approaches fail to learn from decisions taken by humans." (\href{zotero://open-pdf/library/items/K7JAGWW6?page=10}{Trakadas et al 2020:5489}) \par "ontinuous learning functionalities" (\href{zotero://open-pdf/library/items/K7JAGWW6?page=10}{Trakadas et al 2020:5489}) \par "Threat Intelligence Manager (TIM)" (\href{zotero://open-pdf/library/items/K7JAGWW6?page=10}{Trakadas et al 2020:5489}) \par "In the case of malicious activity, various AI algorithms, such as naïve Bayes, random forests and support vector machine (SVM) have been proposed [55]." (\href{zotero://open-pdf/library/items/K7JAGWW6?page=10}{Trakadas et al 2020:5489}) \par "By exploiting advanced AI methods, the threat intelligence manager (TIM) component intends to model the dynamic interactions of Industry 4.0 subsystems and discover known and unknown attacks, while surpassing existing signatureand anomaly-based methods." (\href{zotero://open-pdf/library/items/K7JAGWW6?page=10}{Trakadas et al 2020:5489}) \par "Federated AI across Manufacturing Sites" (\href{zotero://open-pdf/library/items/K7JAGWW6?page=10}{Trakadas et al 2020:5489}) \par "In this context, federated query processing [59] is an active research field dealing with techniques for proper delegation of the execution of parts of queries to specific sources" (\href{zotero://open-pdf/library/items/K7JAGWW6?page=11}{Trakadas et al 2020:5490}) \par \mkbibemph{IMPORTANT! FEDERATED QUERY PROCESSING (\href{zotero://open-pdf/library/items/K7JAGWW6?page=11}{note on p.5490})} \par "To improve the scalability of such federations, aggregation techniques could be used, where one or more independent aggregators would continuously crawl sources, and maintain data summaries [60]." (\href{zotero://open-pdf/library/items/K7JAGWW6?page=11}{Trakadas et al 2020:5490}) \par "Joining datasets held by dierent actors can address this issue. Oft" (\href{zotero://open-pdf/library/items/K7JAGWW6?page=11}{Trakadas et al 2020:5490}) \par \mkbibemph{THAT'S THE GOAL TO ADDRESS THE ISSUE THAT SINGLE PARTY DOES NOT HAVE SUFFICIENTLY LARGE DATA SETS FOR TRAINING (\href{zotero://open-pdf/library/items/K7JAGWW6?page=11}{note on p.5490})} \par "federated learning provides a solution enabling machine learning over distributed and decentralized datasets." (\href{zotero://open-pdf/library/items/K7JAGWW6?page=11}{Trakadas et al 2020:5490}) \par "federated learning opens up new business models (AI as a Service, AIaaS) to analyze data provided by a customer" (\href{zotero://open-pdf/library/items/K7JAGWW6?page=11}{Trakadas et al 2020:5490}) \par "federated learning represents a solution to this problem enabling both the service provider and its customer to achieve their objectives while preserving the business assets." (\href{zotero://open-pdf/library/items/K7JAGWW6?page=11}{Trakadas et al 2020:5490}) \par "t is possible to generate in an automatic manner intelligent services in parts of the building blocks or in the process as a whole." (\href{zotero://open-pdf/library/items/K7JAGWW6?page=11}{Trakadas et al 2020:5490}) \par "handling and labeling of data of very dierent types." (\href{zotero://open-pdf/library/items/K7JAGWW6?page=12}{Trakadas et al 2020:5491}) \par "dierent system components can be exploited for optimizing manufacturing logistics processes, and facilitating zero-defect manufacturing" (\href{zotero://open-pdf/library/items/K7JAGWW6?page=12}{Trakadas et al 2020:5491}) \par "The current AMR production logistics is supposed to be carried out by the system illustrated in Figure 3a" (\href{zotero://open-pdf/library/items/K7JAGWW6?page=12}{Trakadas et al 2020:5491}) \par "t is assumed that there are two issues that need to be addressed in an ecient manner: (a) unpredictable delivery times and downtime, and (b) vulnerability to network attacks." (\href{zotero://open-pdf/library/items/K7JAGWW6?page=12}{Trakadas et al 2020:5491}) \par "le and machines at high levels of safety and tenance services for their AMRs" (\href{zotero://open-pdf/library/items/K7JAGWW6?page=13}{Trakadas et al 2020:5492}) \par "reduced by avoiding heavy vehicles like forklifts or tuggers in fast-moving intralogistics areasorevencompromise during pe site safety." (\href{zotero://open-pdf/library/items/K7JAGWW6?page=13}{Trakadas et al 2020:5492}) \par "neously, AMR downtime is undesirable andAMRstothenetwork should be reduced as much as possible. entcybersecurityriskduetodynamicsoftwarevulnerabilitiesthataffect This scenario assumes that a company has implemented a flmunicationprotocols." (\href{zotero://open-pdf/library/items/K7JAGWW6?page=13}{Trakadas et al 2020:5492}) \par "Advances leveraging the proposed platform" (\href{zotero://open-pdf/library/items/K7JAGWW6?page=13}{Trakadas et al 2020:5492}) \par "learning from patterns of business process models and anomaly detection techniques" (\href{zotero://open-pdf/library/items/K7JAGWW6?page=13}{Trakadas et al 2020:5492}) \par "prediction of AMR downtime can be done using multivariate statistical models on data of key AMR system parameters" (\href{zotero://open-pdf/library/items/K7JAGWW6?page=13}{Trakadas et al 2020:5492}) \par "Edge-based (i.e., on-robot) learning can be used to reduce the amount of data uploaded via the customer network to AMR supplier 's cloud back-end and assure the confidentiality of production activity related data" (\href{zotero://open-pdf/library/items/K7JAGWW6?page=13}{Trakadas et al 2020:5492}) \par "run AI observers that collect data about typical movement patterns on the floor (e.g., edge-based learning on surveillance camera data) and about how fleet behaviors relate to production goals (e.g., from Enterprise Resource Planning—ERP and Manufacturing Execution System—MES systems)" (\href{zotero://open-pdf/library/items/K7JAGWW6?page=13}{Trakadas et al 2020:5492}) \par "As unauthorized access to machines and data might compromise the physical integrity of human workers, communication between AMR, the platform and AMR supplier 's cloud back-end can be secured by signcryption schemes provided by the system." (\href{zotero://open-pdf/library/items/K7JAGWW6?page=14}{Trakadas et al 2020:5493}) \par "The number and the diversity of characteristics of the orders do not allow for common process standardization." (\href{zotero://open-pdf/library/items/K7JAGWW6?page=15}{Trakadas et al 2020:5494}) \par \mkbibemph{DIVERSITY (\href{zotero://open-pdf/library/items/K7JAGWW6?page=15}{note on p.5494})} \par "The data and information collected across the departments through manual and automatic procedures are solely processed by each department, without considering factory-wide optimization metrics." (\href{zotero://open-pdf/library/items/K7JAGWW6?page=15}{Trakadas et al 2020:5494}) \par \mkbibemph{FACTORY-WIDE OPTIMIZATION METRICS (\href{zotero://open-pdf/library/items/K7JAGWW6?page=15}{note on p.5494})} \par "The deficiencies are observed, in the majority of the cases, during the quality control of the final product, thus leaving no space for corrective actions." (\href{zotero://open-pdf/library/items/K7JAGWW6?page=15}{Trakadas et al 2020:5494}) \par \mkbibemph{NEEDS FOR EARLY CORRECTIVE ACTIONS (\href{zotero://open-pdf/library/items/K7JAGWW6?page=15}{note on p.5494})} \par "letting personnel only make the final decisions." (\href{zotero://open-pdf/library/items/K7JAGWW6?page=15}{Trakadas et al 2020:5494}) \par \mkbibemph{LACK OF PERSONNEL KNOWLEDGE AND TRAINING ON THE USE OF INNOVATIVE TOOLS (\href{zotero://open-pdf/library/items/K7JAGWW6?page=15}{note on p.5494})} \par "The combination of data analytics and In testing and validation procedure, the goal is to define a set of KPIs per potential use case andhat can be used for validate the performance of our proposed AI-barootcause analyses" (\href{zotero://open-pdf/library/items/K7JAGWW6?page=15}{Trakadas et al 2020:5494}) \par \mkbibemph{WHAT-IF ANALYSIS, PROJECTIONS, AND ROOT CAUSE ANALYSIS (\href{zotero://open-pdf/library/items/K7JAGWW6?page=15}{note on p.5494})} \par "manufacturers will be capable of realizing agile production processes and improve the quality of products and processes" (\href{zotero://open-pdf/library/items/K7JAGWW6?page=16}{Trakadas et al 2020:5495}) \par \mkbibemph{BUSINESS IMPACTS (\href{zotero://open-pdf/library/items/K7JAGWW6?page=16}{note on p.5495})} \par "more competitive in the market and thus increase their market share." (\href{zotero://open-pdf/library/items/K7JAGWW6?page=16}{Trakadas et al 2020:5495}) \par "The federated intelligence layer introduced in our approach enables new business models (e.g., high-quality AIaaS), as well as the collaboration of dierent industries towards the creation of digital twins" (\href{zotero://open-pdf/library/items/K7JAGWW6?page=16}{Trakadas et al 2020:5495}) \par "rchitecture that facilitates the collaboration between manufacturing machinery, AI and humans" (\href{zotero://open-pdf/library/items/K7JAGWW6?page=16}{Trakadas et al 2020:5495}) \par \mkbibemph{FACILITATING ARCHITECTURE!!! (\href{zotero://open-pdf/library/items/K7JAGWW6?page=16}{note on p.5495})} \par "Last but not least, companies with expertise in AI for manufacturing can create significantly higher revenues by being capable of integrating their components with IoT and IT systems from dierent vendors/creators." (\href{zotero://open-pdf/library/items/K7JAGWW6?page=16}{Trakadas et al 2020:5495}) \par \mkbibemph{OPEN COLLABORATION AMONG COMPONENTS FROM DIFFERENT VENDORS/CREATIOS (\href{zotero://open-pdf/library/items/K7JAGWW6?page=16}{note on p.5495})} \par "components for timely data collection" (\href{zotero://open-pdf/library/items/K7JAGWW6?page=17}{Trakadas et al 2020:5496}) \par "processing and curation" (\href{zotero://open-pdf/library/items/K7JAGWW6?page=17}{Trakadas et al 2020:5496}) \par "elying on the dynamic instantiation of data pipelines" (\href{zotero://open-pdf/library/items/K7JAGWW6?page=17}{Trakadas et al 2020:5496}) \par "while addressing security, privacy and confidentiality concerns" (\href{zotero://open-pdf/library/items/K7JAGWW6?page=17}{Trakadas et al 2020:5496}) \par "cross the physical and virtual entities" (\href{zotero://open-pdf/library/items/K7JAGWW6?page=17}{Trakadas et al 2020:5496}) \par "The collected data and information models are transformed into AI-enabled functional intelligence, leading to business knowledge, actionable insights and informed decisions, while being capable of recognizing complex events and process deviations that cannot be captured easily and in a timely way through human judgment" (\href{zotero://open-pdf/library/items/K7JAGWW6?page=17}{Trakadas et al 2020:5496}) \par \mkbibemph{MAIN GOALS/BENEFITS (\href{zotero://open-pdf/library/items/K7JAGWW6?page=17}{note on p.5496})} \par "25. Tao, F.; Qi, Q.; Wang, L.; Nee, A.Y.C. Digital Twins and Cyber-Physical Systems toward Smart Manufacturing and Industry 4.0. Engineering 2019, 5, 653-661. [CrossRef]" (\href{zotero://open-pdf/library/items/K7JAGWW6?page=18}{Trakadas et al 2020:5497}) \par TL;DR \par The goal of this manuscript is to present a more holistic integration of AI by promoting collaboration as a multi-dimensional conceptual term that covers all important enablers for AI adoption in manufacturing contexts and is promoted in terms of business intelligence optimization, human-in-the-loop and secure federation across manufacturing sites.} } @article{tranDependableControlSystems2015, title = {Dependable Control Systems with {{Internet}} of {{Things}}}, author = {Tran, Tri and Ha, Q.P.}, date = {2015-11}, journaltitle = {ISA Trans.}, volume = {59}, pages = {303--313}, issn = {00190578}, doi = {10.1016/j.isatra.2015.08.008}, langid = {english} } @incollection{tranMultiBackEndsModel2013, title = {Multi {{Back-Ends}} for a {{Model Library Abstraction Layer}}}, booktitle = {Computational {{Science}} and {{Its Applications}}–{{ICCSA}} 2013}, author = {Tran, Ngoc Viet and Ganser, Andreas and Lichter, Horst}, date = {2013}, pages = {160--174}, publisher = {Springer}, url = {http://link.springer.com/chapter/10.1007/978-3-642-39646-5_12}, urldate = {2015-06-24} } @online{TransformersLaidOut, title = {Transformers {{Laid Out}}}, url = {https://goyalpramod.github.io/blogs/Transformers_laid_out/}, keywords = {source\_mobile} } @online{TransformersLargeLanguage, title = {Transformers \& {{Large Language Models}}.Pdf}, url = {https://media.licdn.com/dms/document/media/v2/D561FAQGlXw5uiv2DTQ/feedshare-document-pdf-analyzed/B56ZXiD.8yHoAY-/0/1743254455227?e=1744243200&v=beta&t=qk3yYU6fceEbMUbBhECqPSoGmcIrG7mU5d8OGlWyPmE}, urldate = {2025-03-31}, keywords = {/unread} } @inproceedings{Tsantalis:2018:AER:3180155.3180206, title = {Accurate and Efficient Refactoring Detection in Commit History}, booktitle = {Proc. 40th {{Int}}. {{Conf}}. {{Softw}}. {{Eng}}.}, author = {Tsantalis, Nikolaos and Mansouri, Matin and Eshkevari, Laleh M. and Mazinanian, Davood and Dig, Danny}, date = {2018}, series = {{{ICSE}} '18}, pages = {483--494}, publisher = {ACM}, location = {New York, NY, USA}, url = {http://doi.acm.org/10.1145/3180155.3180206}, acmid = {3180206}, isbn = {978-1-4503-5638-1}, nodoi = {10.1145/3180155.3180206}, numpages = {12}, keywords = {abstract syntax tree,accuracy,commit,Git,Oracle,refactoring} } @article{TSE2023110555R1, title = {{{TSE-2023-11-0555}}.{{R1}}}, url = {https://mc.manuscriptcentral.com/tse-cs?DOWNLOAD=TRUE&PARAMS=xik_5ixaSHQzZd5KCTLLff5UMEm4WgC87X7TToMK39q667TqkyTFecyPLLEx78tiKu8LNLxMizxGzVQXazFgQyb6mmkELuJz51CwUhkJWgaHK8b9oq1wA26WNLEGKFhF9byKYiCLU1K8tet6uLQUWVQrD4YERJAz1rBBPZp2wcoNXMuBorvpedXB1Fc1E1deQzdtZaiC9}, urldate = {2024-04-15}, langid = {english}, keywords = {LOGSEQ} } @misc{TSE2024030126_Proof_hipdf, title = {{{TSE-2024-03-0126}}\_{{Proof}}\_hi.Pdf}, keywords = {LOGSEQ} } @misc{TSE2024080441_Proof_hi, title = {{{TSE-2024-08-0441}}\_{{Proof}}\_hi}, keywords = {LOGSEQ}, note = {\section{Annotazioni\\ (27/10/2024, 11:15:25)} \par - “The inclusion of comprehensive setup instructions in ReadMe files is crucial for newcomers to effectively engage with these projects.” (“TSE-2024-08-0441\_Proof\_hi”, p. 1) \#5fb236\\ \mkbibemph{ } \par - “developers might overlook this aspect, posing challenges for newcomers in setting up JWAs.” (“TSE-2024-08-0441\_Proof\_hi”, p. 1) \#a28ae5\\ \mkbibemph{ } \par - “PAL4JavaWeb, a novel method designed to simplify the process of setting up JWA projects by automatically generating setup steps from the source files.” (“TSE-2024-08-0441\_Proof\_hi”, p. 1) \#a28ae5\\ \mkbibemph{By reading this, it does not seem to be a so disruptive contribution. } \par - “utilizing the obtained segments, we leverage the LLM to recognize key configuration steps and generate setup instructions accordingly.” (“TSE-2024-08-0441\_Proof\_hi”, p. 1) \#5fb236\\ \mkbibemph{ } \par - “baseline method by 48.57\%” (“TSE-2024-08-0441\_Proof\_hi”, p. 1) \#ffd400\\ \mkbibemph{Check what is the baseline method that has been considered. } \par - “This resulted in positive responses, with 10 PRs being merged, highlighting the practical usefulness of PAL4JavaWeb in simplifying the onboarding process for newcomers” (“TSE-2024-08-0441\_Proof\_hi”, p. 1) \#ffd400\\ \mkbibemph{Check what happened with the remaining 3. } \par - “online shopping, Internet banking” (“TSE-2024-08-0441\_Proof\_hi”, p. 1) \#5fb236\\ \mkbibemph{ } \par - “developers need to install and configure some requirements such as the framework, the dependency, the database, etc, both in front-end and back-end, as shown in the Fig. 1.” (“TSE-2024-08-0441\_Proof\_hi”, p. 1) \#5fb236\\ \mkbibemph{ } \par - “It is difficult for developers, especially newcomers to set up a project successfully with only the source code because they have to get the exact steps from the unfamiliar source code.” (“TSE-2024-08-0441\_Proof\_hi”, p. 1) \#5fb236\\ \mkbibemph{ } \par - “We selected 113,000 JWA projects from GitHub for research, among which 82.0\% of the projects contain issues related to setup steps, totally 140,900 issues. Among these issues, 31.2\% are still in open status, indicating they are not solved.” (“TSE-2024-08-0441\_Proof\_hi”, p. 2) \#5fb236\\ \mkbibemph{ } \par - “we randomly set up” (“TSE-2024-08-0441\_Proof\_hi”, p. 2) \#ffd400\\ \mkbibemph{what do you mean? } \par - “apply Machine Learning (ML) techniques [10] to evaluate the ReadMe file, then send evaluation results to project owners, reminding them to improve the setup section.” (“TSE-2024-08-0441\_Proof\_hi”, p. 2) \#5fb236\\ \mkbibemph{ } \par - “Some studies [11, 12] constructed relational databases containing information about all the requirements for the entire environment,” (“TSE-2024-08-0441\_Proof\_hi”, p. 2) \#5fb236\\ \mkbibemph{ } \par - “The LLMs trained on billions of samples from the Internet exhibit promising performance across various tasks and domains, including machine translation [13], information retrieval [14], bug detection [15], mathematical reasoning [16], and so o” (“TSE-2024-08-0441\_Proof\_hi”, p. 2) \#5fb236\\ \mkbibemph{ } \par - “natural language generation, and roleplaying, we designate the LLMs as expert developers capable of generating setup steps” (“TSE-2024-08-0441\_Proof\_hi”, p. 2) \#5fb236\\ \mkbibemph{ } \par - “1) How to extract setup-related information efficiently from diverse source codes to address LLMs token limitations” (“TSE-2024-08-0441\_Proof\_hi”, p. 2) \#5fb236\\ \mkbibemph{ } \par - “In this paper, we introduce PAL4JavaWeb (Program Analysis and LLM for JavaWeb), a method that automatically generates setup steps using program analysis and LLMs.” (“TSE-2024-08-0441\_Proof\_hi”, p. 2) \#a28ae5\\ \mkbibemph{ } \par - “first challenge,” (“TSE-2024-08-0441\_Proof\_hi”, p. 2) \#2ea8e5\\ \mkbibemph{ } \par - “next two challenges” (“TSE-2024-08-0441\_Proof\_hi”, p. 2) \#2ea8e5\\ \mkbibemph{ } \par - “we provide LLMs with the chain-of-thought reasoning from developers, guiding it in recognizing the frameworks, databases and configuration information contained in the data obtained in the first phase.” (“TSE-2024-08-0441\_Proof\_hi”, p. 2) \#5fb236\\ \mkbibemph{ } \par - “all recognized results are consolidated into a JSON object with key-value pairs.” (“TSE-2024-08-0441\_Proof\_hi”, p. 2) \#5fb236\\ \mkbibemph{ } \par - “35 JWA projects from GitHu” (“TSE-2024-08-0441\_Proof\_hi”, p. 2) \#5fb236\\ \mkbibemph{ } \par - “first work that combines program analysis and LLMs for the generation of JWA setup steps” (“TSE-2024-08-0441\_Proof\_hi”, p. 2) \#ffd400\\ \mkbibemph{What does it mean integration of program analysis with LLMs? To be checked. } \par - “PAL4JavaWeb, leveraging static program analysis and prompt engineering to harness LLM’s knowledge for setup steps generation.” (“TSE-2024-08-0441\_Proof\_hi”, p. 2) \#5fb236\\ \mkbibemph{ } \par - “METHOD” (“TSE-2024-08-0441\_Proof\_hi”, p. 2) \#ffd400\\ \mkbibemph{Before this section, it is necessary to motivate the work. It is necessary to convince the reader that the problem is worth exploring by giving evidence of the difficulties that currently developers encounter when developing and configuring JWA. What's the state of practice? What are existing technologies that are employed to support related challenges? } \par - “Extraction Module,” (“TSE-2024-08-0441\_Proof\_hi”, p. 2) \#2ea8e5\\ \mkbibemph{ } \par - “Recognition Module” (“TSE-2024-08-0441\_Proof\_hi”, p. 2) \#2ea8e5\\ \mkbibemph{ } \par - “Generation Module” (“TSE-2024-08-0441\_Proof\_hi”, p. 2) \#2ea8e5\\ \mkbibemph{ } \par - “front-end framework” (“TSE-2024-08-0441\_Proof\_hi”, p. 2) \#2ea8e5\\ \mkbibemph{ } \par - “dependency for the front-end” (“TSE-2024-08-0441\_Proof\_hi”, p. 2) \#2ea8e5\\ \mkbibemph{ } \par - “As shown in Table I.” (“TSE-2024-08-0441\_Proof\_hi”, p. 2) \#ff6666\\ \mkbibemph{should it be connected with the subsequent paragraph? } \par - “we need to extract dependency file to determine the dependencies the project uses.” (“TSE-2024-08-0441\_Proof\_hi”, p. 2) \#5fb236\\ \mkbibemph{ } \par - “TABLE I” (“TSE-2024-08-0441\_Proof\_hi”, p. 3) \#ffd400\\ \mkbibemph{The paper is missing the presentation of technical details on the execution environment of the proposed process. How users are supposed to interact with it? IS there any support for the specification of prompts that authors have devised? Is it only based on the diligence of the user the right usage of the defined prompt patterns? } \par - “we need to analyze the POM.XML according to the predefined rules.” (“TSE-2024-08-0441\_Proof\_hi”, p. 3) \#5fb236\\ \mkbibemph{ } \par - “Front-end prompt contains 5 representative examples and Back-end prompt contains 4 examples. Each example includes code snippets, Role with Task, Chain-of-Thought (CoT), and example task output.” (“TSE-2024-08-0441\_Proof\_hi”, p. 3) \#5fb236\\ \mkbibemph{ } \par - “as JSONformat data.” (“TSE-2024-08-0441\_Proof\_hi”, p. 3) \#5fb236\\ \mkbibemph{ } \par - “to determine which framework is used, we need to obtain characteristic files from the project.” (“TSE-2024-08-0441\_Proof\_hi”, p. 3) \#ffd400\\ \mkbibemph{Do you have a kind of catalogue of framework that you already support? } \par - “It is challenging to confirm the front-end framework by the characteristic files through rule enumeration for two main reasons.” (“TSE-2024-08-0441\_Proof\_hi”, p. 4) \#ffd400\\ \mkbibemph{Right, this can represent a bias, right? } \par - “utilize” (“TSE-2024-08-0441\_Proof\_hi”, p. 4) \#ff6666\\ \mkbibemph{ } \par - “or dependencies, since the location of these dependencies is not fixed, we need to match them throughout the folder. Firstly, we collected 4,387 common front-end dependencies from open source front-end dependency search sites, including BootCDN and Google Hosted Libraries, to match the dependencies used in JWA projects. Most of the dependencies in JWA projects come from this collection, which meets the matching requirements of existing JWA projects. Then, we extract files from the analyzed project that match those in the set of common front-end dependencies.” (“TSE-2024-08-0441\_Proof\_hi”, p. 4) \#ffd400\\ \mkbibemph{???? } \par - “2) Extraction of back-end-Related code snippets” (“TSE-2024-08-0441\_Proof\_hi”, p. 4) \#2ea8e5\\ \mkbibemph{ } \par - “back-end configuration information.” (“TSE-2024-08-0441\_Proof\_hi”, p. 4) \#5fb236\\ \mkbibemph{ } \par - “application.yaml. In this file, developers use a series of key-value pairs for configuration. While the key names sometimes vary for each project, their format is similar.” (“TSE-2024-08-0441\_Proof\_hi”, p. 4) \#5fb236\\ \mkbibemph{ } \par - “[’Vue.js’]” (“TSE-2024-08-0441\_Proof\_hi”, p. 5) \#ffd400\\ \mkbibemph{This example is not clear. In particular, it is not clear the usage that one should do with the string ['Vue.js'] } \par - “We design one prompt for the frontend and two prompts for the back-end.” (“TSE-2024-08-0441\_Proof\_hi”, p. 5) \#5fb236\\ \mkbibemph{ } \par - “chain-of-thought reasoning as inference” (“TSE-2024-08-0441\_Proof\_hi”, p. 5) \#5fb236\\ \mkbibemph{ } \par - “the example configuration information as output” (“TSE-2024-08-0441\_Proof\_hi”, p. 5) \#5fb236\\ \mkbibemph{ } \par - “In-context Learning” (“TSE-2024-08-0441\_Proof\_hi”, p. 5) \#2ea8e5\\ \mkbibemph{ } \par - “three” (“TSE-2024-08-0441\_Proof\_hi”, p. 5) \#a28ae5\\ \mkbibemph{ } \par - “65” (“TSE-2024-08-0441\_Proof\_hi”, p. 5) \#ffd400\\ \mkbibemph{Check if it has been said how these projects have been selected. } \par - “9 examples they collectively deemed representative, some of the examples is shown in Table II and III.” (“TSE-2024-08-0441\_Proof\_hi”, p. 5) \#ffd400\\ \mkbibemph{9 examples to cover all the possible cases that the LLM should manage/able to recognize? } \par - “Chain-of-thought reasoning” (“TSE-2024-08-0441\_Proof\_hi”, p. 5) \#2ea8e5\\ \mkbibemph{ } \par - “10 hours of experimentation” (“TSE-2024-08-0441\_Proof\_hi”, p. 5) \#ffd400\\ \mkbibemph{Which LLM has been considered? Have you experimented with more than one to investigate the generalizability of the proposed prompt? } \par - “1) Incontext-learning” (“TSE-2024-08-0441\_Proof\_hi”, p. 6) \#2ea8e5\\ \mkbibemph{ } \par - “The selection of examples also involved a meeting with the same three professional developers who examined our dataset of open-source projects as before” (“TSE-2024-08-0441\_Proof\_hi”, p. 6) \#5fb236\\ \mkbibemph{ } \par - “JWA setup steps?” (“TSE-2024-08-0441\_Proof\_hi”, p. 7) \#ffd400\\ \mkbibemph{Is it really difficult? See my comments on the early sections of the paper. } \par - “Data Preparation” (“TSE-2024-08-0441\_Proof\_hi”, p. 7) \#ffd400\\ \mkbibemph{Can you give more details on the dataset creation by referring to the typical train,test,validation phases? Currently, it is not clear if there existis some overlap between the selected JWA projects and those used as discussed in the previous section. } \par - “,” (“TSE-2024-08-0441\_Proof\_hi”, p. 7) \#ff6666\\ \mkbibemph{ } \par - “i.e.,C” (“TSE-2024-08-0441\_Proof\_hi”, p. 7) \#ff6666\\ \mkbibemph{many typos and grammatical mistakes } \par - “Front-end and back-end separation in an application indicates that the front-end and back-end components are independently developed [3].” (“TSE-2024-08-0441\_Proof\_hi”, p. 7) \#5fb236\\ \mkbibemph{ } \par - “KeyWord Search (KWS).” (“TSE-2024-08-0441\_Proof\_hi”, p. 7) \#ffd400\\ \mkbibemph{This is not clear. The ground true construction is critical and there are no discussions on the possible alternatives to construct it. Since the goal is generating the configuration specifications for the used framework, the authors could have compared what they are able to generate with the real files belonging to the considered projects. Again, the process for the ground true data needs to be clarified. } \par - “matche” (“TSE-2024-08-0441\_Proof\_hi”, p. 8) \#ff6666\\ \mkbibemph{ } \par - “From the Table V, it can be observed that PAL4JavaWeb achieves a success rate of 91.43\%, generating complete setup steps of 32 JWAs.” (“TSE-2024-08-0441\_Proof\_hi”, p. 8) \#ffd400\\ \mkbibemph{In the previous section, it seems that the authors considered 35 projects and not 32. Again, it is important to improve the presentation of the dataset creation. } \par - “58 projects with issues requesting for the setup step” (“TSE-2024-08-0441\_Proof\_hi”, p. 10) \#ffd400\\ \mkbibemph{Again, it is necessary to motivate the selection of the considered projects. The authors have not properly and clearly motivated the size of the considered data and the characteristics of the projects they used to perform the experiments. } \par - “use PAL4JavaWeb to generate setup steps, and then submit pull requests (PRs) for them, while also addressing issues raised by other developers, and then wait for feedback from the developers.” (“TSE-2024-08-0441\_Proof\_hi”, p. 10) \#ffd400\\ \mkbibemph{This is obscured. What does it mean that authors submitted pull requests to randomly selected projects? PAL4JavaWeb is supposed to generate configuration specifications and I don't understand how these can be the subject of PRs. }} } @misc{TSE2025010025_Proof_hi, title = {{{TSE-2025-01-0025}}\_{{Proof}}\_hi}, langid = {english}, keywords = {LOGSEQ}, note = {\section{Annotazioni\\ (11/4/2025, 16:52:40)} \par - “Software researchers and industry experts are increasingly focused on automating the task of requirement analysis.” (“TSE-2025-01-0025\_Proof\_hi”, p. 1) \#5fb236\\ \mkbibemph{ } \par - “While numerous methodologies and tools exist to automate the translation of user requirements into UML diagrams, practical usability remains challenging due to their inherent complexity” (“TSE-2025-01-0025\_Proof\_hi”, p. 1) \#5fb236\\ \mkbibemph{ } \par - “This research addresses the challenge of converting textual software requirements into UML class diagrams.” (“TSE-2025-01-0025\_Proof\_hi”, p. 1) \#a28ae5\\ \mkbibemph{ } \par - “An intermediate layer is introduced, representing diagram elements as statements in the PlantUML language” (“TSE-2025-01-0025\_Proof\_hi”, p. 1) \#a28ae5\\ \mkbibemph{ } \par - “The study employs the natural language processing Transformer (NLP Transformer) as a Neural Machine Translation (NMT) tool, exploring two approaches: sequence-to-sequence (Seq2Seq) and sequence-to-Abstract-Syntax-Tree (Seq2Ast).” (“TSE-2025-01-0025\_Proof\_hi”, p. 1) \#a28ae5\\ \mkbibemph{ } \par - “The Seq2Seq approach demonstrates gains in precision, recall, and over-specification at 74.16\%, 81.95\%, and 28.91\%, respectively” (“TSE-2025-01-0025\_Proof\_hi”, p. 1) \#a28ae5\\ \mkbibemph{ } \par - “Seq2Ast approach achieves higher values, with precision, recall, and over-specification at 86.19\%, 90.81\%, and 33.38\%.” (“TSE-2025-01-0025\_Proof\_hi”, p. 1) \#a28ae5\\ \mkbibemph{ } \par - “dramatic” (“TSE-2025-01-0025\_Proof\_hi”, p. 1) \#ff6666\\ \mkbibemph{ } \par - “Companies and organizations are investing heavily in AI research and development, with applications being developed in fields such as robotics, manufacturing, agriculture, and logistics.” (“TSE-2025-01-0025\_Proof\_hi”, p. 1) \#5fb236\\ \mkbibemph{ } \par - “Machine learning (ML) and deep learning (DL) methods can be employed during the requirements-gathering phase [7].” (“TSE-2025-01-0025\_Proof\_hi”, p. 1) \#5fb236\\ \mkbibemph{ } \par - “In the subsequent phase of the SDLC, the design stage, ML techniques prove beneficial by automating diagram creation, thus reducing time and effort consumption” (“TSE-2025-01-0025\_Proof\_hi”, p. 1) \#5fb236\\ \mkbibemph{ } \par - “This requirement analysis process is resource-intensive, demanding time and expertise to establish the foundational rules for software development.” (“TSE-2025-01-0025\_Proof\_hi”, p. 1) \#5fb236\\ \mkbibemph{ } \par - “The research’s general problem is to study the possibility of converting textual data written in natural language to the ontology domain.” (“TSE-2025-01-0025\_Proof\_hi”, p. 2) \#5fb236\\ \mkbibemph{ } \par - “Within software engineering, UML diagrams act as essential ontologies, in this research specifically addressing the extraction of UML class diagram from software requirements” (“TSE-2025-01-0025\_Proof\_hi”, p. 2) \#a28ae5\\ \mkbibemph{ } \par - “The study also discusses the possibility of applying the methods developed here to other types of UML diagrams.” (“TSE-2025-01-0025\_Proof\_hi”, p. 2) \#a28ae5\\ \mkbibemph{ } \par - “diagrams rely on a more constrained set of visual conventions for conveying information” (“TSE-2025-01-0025\_Proof\_hi”, p. 2) \#5fb236\\ \mkbibemph{ } \par - “this process of information distillation carries the inherent risk of data loss, potentially leading to unforeseen consequences downstream.” (“TSE-2025-01-0025\_Proof\_hi”, p. 2) \#a28ae5\\ \mkbibemph{ } \par - “Can textual data be transformed into diagrams, and if so, what factors determine the feasibility?” (“TSE-2025-01-0025\_Proof\_hi”, p. 2) \#2ea8e5\\ \mkbibemph{ } \par - “Can textual data be automatically converted into UML diagrams through existing models or established mapping techniques?” (“TSE-2025-01-0025\_Proof\_hi”, p. 2) \#ffd400\\ \mkbibemph{This research query is very similar to the previous one. How are they different? This needs to be clarified. } \par - “Linear text, composed of sentences and words, depicts situations in a sequential manner.” (“TSE-2025-01-0025\_Proof\_hi”, p. 2) \#2ea8e5\\ \mkbibemph{ } \par - “can we represent a diagram as a collection of distinct parts, independent of resorting to natural language descriptions?” (“TSE-2025-01-0025\_Proof\_hi”, p. 2) \#2ea8e5\\ \mkbibemph{ } \par - “Consequently, formulating a set of guidelines is pivotal in this approach, along with possessing preliminary training or knowledge.” (“TSE-2025-01-0025\_Proof\_hi”, p. 2) \#5fb236\\ \mkbibemph{ } \par - “discussing generalizing the approach to the rest of the UML diagrams.” (“TSE-2025-01-0025\_Proof\_hi”, p. 2) \#ffd400\\ \mkbibemph{ } \par - “The last critical question requires an intermediate layer between natural language and ontology. This layer must fit between the textual and ontology representation domains.” (“TSE-2025-01-0025\_Proof\_hi”, p. 2) \#ffd400\\ \mkbibemph{not clear } \par - “This framework aims to leverage textual software requirements as input to generate both the corresponding software code and project structures” (“TSE-2025-01-0025\_Proof\_hi”, p. 2) \#5fb236\\ \mkbibemph{ } \par - “the textual data into UML diagrams, establishing a software project structure, and generating code based on the structural or behavioral diagrams and the software design patterns” (“TSE-2025-01-0025\_Proof\_hi”, p. 2) \#5fb236\\ \mkbibemph{ } \par - “• Formulating the task of generating class diagrams from software requirements as a machine translation task.” (“TSE-2025-01-0025\_Proof\_hi”, p. 2) \#2ea8e5\\ \mkbibemph{ } \par - “troducing the GenClass model, a class diagram generation model proficient in producing class diagrams from natural language.” (“TSE-2025-01-0025\_Proof\_hi”, p. 2) \#2ea8e5\\ \mkbibemph{ } \par - “Exploring potential strategies to achieve this translation in the optimal manner.” (“TSE-2025-01-0025\_Proof\_hi”, p. 2) \#2ea8e5\\ \mkbibemph{ } \par - “Our suggested method is evaluated, and the results are reported in section IV.” (“TSE-2025-01-0025\_Proof\_hi”, p. 2) \#ffd400\\ \mkbibemph{ } \par - “This process aims to convert human natural language information into a format that machines can process, store, and analyze, making it more accessible for computational tasks.” (“TSE-2025-01-0025\_Proof\_hi”, p. 3) \#5fb236\\ \mkbibemph{ } \par - “Automatic knowledge representation often involves the creation of knowledge graphs, ontologies, databases, or other structured data models” (“TSE-2025-01-0025\_Proof\_hi”, p. 3) \#5fb236\\ \mkbibemph{ } \par - “Upadhyay and Fujii proposed a software system for knowledge support that extracts sentences and keywords from an extensive document database” (“TSE-2025-01-0025\_Proof\_hi”, p. 3) \#5fb236\\ \mkbibemph{ } \par - “Artequakt project [22] connects a knowledge extraction tool with an ontology to provide continuous knowledge assistance, guiding the information extraction process in a structured manner.” (“TSE-2025-01-0025\_Proof\_hi”, p. 3) \#5fb236\\ \mkbibemph{ } \par - “The semiautomated approach combines manual input and automated tools to transform textual requirements into UML diagrams.” (“TSE-2025-01-0025\_Proof\_hi”, p. 3) \#5fb236\\ \mkbibemph{ } \par - “Overall, MOVA heavily relies on human intervention due to its inability to identify OO concepts autonomously” (“TSE-2025-01-0025\_Proof\_hi”, p. 3) \#5fb236\\ \mkbibemph{ } \par - “The literature review highlights gaps in heuristic and pattern-based software modeling methods, which lack adaptability and semantic depth. Recent advancements suggest that treating the problem as a machine translation task can offer a promising solution. In our work, we try to employ the Transformer to solve the problem of converting textual requirements into precise class diagrams. Our approach attempts to address the limitations of heuristic methods, which are interpretable and fast but struggle in unstructured environments. While statistical models are powerful, they are often viewed as black boxes, lacking the transparency of heuristic methods.” (“TSE-2025-01-0025\_Proof\_hi”, p. 4) \#a28ae5\\ \mkbibemph{ } \par - “the input is text written in English, while the output is a set of PlantUML statements.” (“TSE-2025-01-0025\_Proof\_hi”, p. 4) \#5fb236\\ \mkbibemph{ } \par - “PlantUML syntax that defines an element, a relationship, or any component of a class diagram.” (“TSE-2025-01-0025\_Proof\_hi”, p. 4) \#5fb236\\ \mkbibemph{ } \par - “NLP, tokenization involves breaking a sentence into smaller units to simplify assigning the most appropriate meaning to each part.” (“TSE-2025-01-0025\_Proof\_hi”, p. 4) \#5fb236\\ \mkbibemph{ } \par - “with type” (“TSE-2025-01-0025\_Proof\_hi”, p. 5) \#ffd400\\ \mkbibemph{where is the type? Maybe you wanted to say Class with Attribute? } \par - “Conventionally, we will rely on data written in natural language, which will be input to the encoder, called source data, and data written in PlantUML, which is the output of the decoder, called target data” (“TSE-2025-01-0025\_Proof\_hi”, p. 5) \#a28ae5\\ \mkbibemph{ } \par - “Whitespace tokenization was used for the source data” (“TSE-2025-01-0025\_Proof\_hi”, p. 5) \#5fb236\\ \mkbibemph{ } \par - “Stop words removal to remove the most frequent words from the corpus records. Coreference resolution is a technique used to determine when two or more expressions in a text refer to the same entity. These expressions can be pronouns, noun phrases, or other references. It aims to link these expressions together so that we know which words or phrases in a text are talking about the same thing.” (“TSE-2025-01-0025\_Proof\_hi”, p. 5) \#5fb236\\ \mkbibemph{ } \par - “Byte-Pair Encoding (BPE)” (“TSE-2025-01-0025\_Proof\_hi”, p. 5) \#5fb236\\ \mkbibemph{ } \par - “Neural machine translation (NMT)” (“TSE-2025-01-0025\_Proof\_hi”, p. 5) \#a28ae5\\ \mkbibemph{ } \par - “It has outperformed traditional phrase-based systems and effectively overcome issues like the requirement for manually crafted features” (“TSE-2025-01-0025\_Proof\_hi”, p. 5) \#5fb236\\ \mkbibemph{ } \par - “Encoder and a Decoder: the former processes input text sequences, while the latter produces translated output sequences.” (“TSE-2025-01-0025\_Proof\_hi”, p. 5) \#5fb236\\ \mkbibemph{ } \par - “attention mechanism that aligns target tokens with their corresponding source tokens” (“TSE-2025-01-0025\_Proof\_hi”, p. 5) \#5fb236\\ \mkbibemph{ } \par - “we employ two prevalent methods, sequenceto-sequence (Seq2Seq) and Sequence-to-Abstract-Syntax-Tree (Seq2Ast), to assess their capacity for translation and determine their comparative effectiveness.” (“TSE-2025-01-0025\_Proof\_hi”, p. 5) \#a28ae5\\ \mkbibemph{ } \par - “These sublayers include multi-head self-attention, a fully connected feed-forward network, and decoder self-attention (as multihead attention in the case of decoders)” (“TSE-2025-01-0025\_Proof\_hi”, p. 6) \#5fb236\\ \mkbibemph{ } \par - “Encoder” (“TSE-2025-01-0025\_Proof\_hi”, p. 6) \#2ea8e5\\ \mkbibemph{ } \par - “The encoder’s role is to progress through the input time steps and transform the entire sequence into a constant-length vector known as a context vector.” (“TSE-2025-01-0025\_Proof\_hi”, p. 6) \#5fb236\\ \mkbibemph{ } \par - “The first sub-layer employs a multi-head self-attention mechanism, while the second sub-layer uses a straightforward, position-wise, fully connected feed-forward network” (“TSE-2025-01-0025\_Proof\_hi”, p. 6) \#5fb236\\ \mkbibemph{ } \par - “In other words, the output of each sub-layer is passed through a normalization layer.” (“TSE-2025-01-0025\_Proof\_hi”, p. 6) \#5fb236\\ \mkbibemph{ } \par - “Decoder” (“TSE-2025-01-0025\_Proof\_hi”, p. 6) \#2ea8e5\\ \mkbibemph{ } \par - “This additional sub-layer conducts multihead attention over the encoder stack’s output” (“TSE-2025-01-0025\_Proof\_hi”, p. 6) \#5fb236\\ \mkbibemph{ } \par - “Self-Attention mechanisms” (“TSE-2025-01-0025\_Proof\_hi”, p. 6) \#2ea8e5\\ \mkbibemph{ } \par - “Within the selfattention layer, an input represented as a vector, denoted as x, transforms another vector called z” (“TSE-2025-01-0025\_Proof\_hi”, p. 6) \#a28ae5\\ \mkbibemph{ } \par - “q (queries), k (keys), and v (values).” (“TSE-2025-01-0025\_Proof\_hi”, p. 6) \#a28ae5\\ \mkbibemph{ } \par - “These encodings are computed using sine and cosine functions” (“TSE-2025-01-0025\_Proof\_hi”, p. 6) \#5fb236\\ \mkbibemph{ } \par - “The selection of the sinusoidal approach was motivated by the potential to enable the model to make predictions for sequence lengths that extend beyond those encountered during its training.” (“TSE-2025-01-0025\_Proof\_hi”, p. 6) \#5fb236\\ \mkbibemph{ } \par - “The process followed in this research involves creating an intermediate representation using the PlantUML language, structured as a tree.” (“TSE-2025-01-0025\_Proof\_hi”, p. 6) \#5fb236\\ \mkbibemph{ } \par - “each child node corresponds to a distinct part of the class diagram,” (“TSE-2025-01-0025\_Proof\_hi”, p. 7) \#a28ae5\\ \mkbibemph{ } \par - “Each class node further branches into nodes that define its attributes, methods and relationships.” (“TSE-2025-01-0025\_Proof\_hi”, p. 7) \#5fb236\\ \mkbibemph{ } \par - “Relationships are depicted by relationship nodes that include child nodes representing the related classes, establishing connections and interactions between different classes within the diagram.” (“TSE-2025-01-0025\_Proof\_hi”, p. 7) \#a28ae5\\ \mkbibemph{ } \par - “This ensures that the most interconnected classes are established early, providing a strong foundation for the diagram” (“TSE-2025-01-0025\_Proof\_hi”, p. 7) \#5fb236\\ \mkbibemph{ } \par - “Out-of-vocabulary (OOV) pertains to words identified during testing or validation that do not exist in the training data.” (“TSE-2025-01-0025\_Proof\_hi”, p. 7) \#a28ae5\\ \mkbibemph{ } \par - “Out-of-vocabulary tokens are substituted with a distinct symbol denoted as 〈UNK〉” (“TSE-2025-01-0025\_Proof\_hi”, p. 7) \#5fb236\\ \mkbibemph{ } \par - “non-terminal nodes are characterized by their element type attribute” (“TSE-2025-01-0025\_Proof\_hi”, p. 7) \#5fb236\\ \mkbibemph{ } \par - “GenClass” (“TSE-2025-01-0025\_Proof\_hi”, p. 7) \#ffd400\\ \mkbibemph{Is it mentioned before? } \par - “,” (“TSE-2025-01-0025\_Proof\_hi”, p. 7) \#ff6666\\ \mkbibemph{ } \par - “sequance” (“TSE-2025-01-0025\_Proof\_hi”, p. 7) \#ff6666\\ \mkbibemph{sequence } \par - “E. Enhancing through Over-specification” (“TSE-2025-01-0025\_Proof\_hi”, p. 7) \#5fb236\\ \mkbibemph{ } \par - “In most cases, there are attributes, methods, and sometimes classes within the class diagrams that do not appear or are not mentioned explicitly within the textual requirements of the software.” (“TSE-2025-01-0025\_Proof\_hi”, p. 7) \#5fb236\\ \mkbibemph{ } \par - “In this section, some special processing operations in the ontology domain were considered, which would improve the resulting final diagrams by finding the missing attributes, methods, or relationships” (“TSE-2025-01-0025\_Proof\_hi”, p. 9) \#5fb236\\ \mkbibemph{ } \par - “FPgrowth algorithm can be applied by identifying frequent individual items in the dataset, i.e., items that appear above a specified minimum support threshold.” (“TSE-2025-01-0025\_Proof\_hi”, p. 9) \#5fb236\\ \mkbibemph{ } \par - “FP-Growth is particularly effective for analyzing and identifying associations or patterns among items.” (“TSE-2025-01-0025\_Proof\_hi”, p. 9) \#5fb236\\ \mkbibemph{ } \par - “This section covers an analysis of the proposed framework’s performance and results across various datasets.” (“TSE-2025-01-0025\_Proof\_hi”, p. 9) \#5fb236\\ \mkbibemph{ } \par - “top-tier” (“TSE-2025-01-0025\_Proof\_hi”, p. 10) \#ffd400\\ \mkbibemph{Not sure, they are toy models in the end. } \par - “These diagrams vary in size, ranging from a few to numerous classes, and are broken down into discrete classes and their relationships” (“TSE-2025-01-0025\_Proof\_hi”, p. 10) \#5fb236\\ \mkbibemph{ } \par - “subset contains a collection of 649 UML class components.” (“TSE-2025-01-0025\_Proof\_hi”, p. 10) \#5fb236\\ \mkbibemph{ } \par - “The PlantUCD (PlantUML Class Diagram) dataset was developed by aggregating diverse software problem statements and descriptions from various sources, including Stack Overflow, TutorialsPoint, and relevant literature” (“TSE-2025-01-0025\_Proof\_hi”, p. 10) \#5fb236\\ \mkbibemph{ } \par - “Over two months, 618 responses were collected and subsequently refined to extract 147 UML class diagrams.” (“TSE-2025-01-0025\_Proof\_hi”, p. 10) \#5fb236\\ \mkbibemph{ } \par - “data augmentation techniques were employed to expand the sample size of requirements and corresponding PlantUML statements.” (“TSE-2025-01-0025\_Proof\_hi”, p. 10) \#5fb236\\ \mkbibemph{ } \par - “client or buyer , while shop owner might be altered to shopkeeper or merchant. However, substituting synonyms alone was not sufficient; thus, we suggest rephrasing the software requirements to further increase the data volume, ensuring that the underlying system remains unchanged” (“TSE-2025-01-0025\_Proof\_hi”, p. 10) \#ffd400\\ \mkbibemph{But what about mutations about the structure of the model. I'm not convinced about this operated data augmentation process. } \par - “This augmentation encompassed entity names, attributes, and relationships, resulting in a 50\% increase in the overall data volume.” (“TSE-2025-01-0025\_Proof\_hi”, p. 10) \#ffd400\\ \mkbibemph{... and how those changes were reflected to the corresponding textual requirements? How about mutations that do not involve only renaming, but also structure changes! } \par - “It measures the similarity between the generated and reference sequences.” (“TSE-2025-01-0025\_Proof\_hi”, p. 10) \#5fb236\\ \mkbibemph{ } \par - “The training spans approximately 60 epochs” (“TSE-2025-01-0025\_Proof\_hi”, p. 11) \#5fb236\\ \mkbibemph{ } \par - “QUANTITATIVE, QUALITATIVE ANALYSIS AND DISCUSSIONS” (“TSE-2025-01-0025\_Proof\_hi”, p. 12) \#ffd400\\ \mkbibemph{The evaluation does not have explciti research questions. It is important to have them upfront! } \par - “The results gained in Section IV-D show the superiority of the Seq2Ast approach over the Seq2Seq approach in general.” (“TSE-2025-01-0025\_Proof\_hi”, p. 12) \#a28ae5\\ \mkbibemph{ } \par - “The superiority can be explained by the fact that Seq2Seq models generate output sequences solely based on input sequences without considering the syntax or structure of the PlantUML language.” (“TSE-2025-01-0025\_Proof\_hi”, p. 12) \#a28ae5\\ \mkbibemph{ } \par - “Seq2Ast models are designed to transform sequences into abstract syntax trees, effectively capturing the input text’s structural and semantic elements.” (“TSE-2025-01-0025\_Proof\_hi”, p. 12) \#a28ae5\\ \mkbibemph{ } \par - “Tables VI, VII, and VIII present a comparative analysis between our proposed model and alternative models gathered from diverse studies.” (“TSE-2025-01-0025\_Proof\_hi”, p. 12) \#5fb236\\ \mkbibemph{ } \par - “threats to validity” (“TSE-2025-01-0025\_Proof\_hi”, p. 13) \#ffd400\\ \mkbibemph{I would discuss this in a dedicated threats to validity section! } \par - “We know that UML diagrams can be categorized into structural and behavioral types. If we can express any diagram category using the PlantUML language, it opens the possibility of extending the methodology employed in this research to other diagram types.” (“TSE-2025-01-0025\_Proof\_hi”, p. 14) \#ffd400\\ \mkbibemph{The paper does not give any evidence to answer this question. } \par - “[49] Songyang-dev, “GitHub - songyang-dev/uml-classes-and-specs: Repository that contains the data used for ‘Extraction of UML Class Diagrams from Natural Language Specification’ (Yang et al. 2022),” GitHub, Jul. 21, 2022. Available: https://github.com/songyang-dev/uml-classes-and-specs. [Accessed: Oct. 22, 2024]” (“TSE-2025-01-0025\_Proof\_hi”, p. 15) \#5fb236\\ \mkbibemph{ }} } @misc{TSE2025010038_Proof_hi, title = {{{TSE-2025-01-0038}}\_{{Proof}}\_hi}, keywords = {LOGSEQ}, note = {\section{Annotazioni\\ (23/3/2025, 18:30:54)} \par - “A Study of Third-Party Libraries in GitHub Projects” (“TSE-2025-01-0038\_Proof\_hi”, p. 1) \#ffd400\\ \mkbibemph{The real focus of the paper is not broad, it supports Python projects. The title has to be refined and also the threats to validity section should include a discussion on this aspect.} \par - “Open-source licenses provide developers with considerable freedom to use, modify, and distribute source code, but they also impose obligations that can lead to legal or compliance risks.” (“TSE-2025-01-0038\_Proof\_hi”, p. 1) \#5fb236 \par - “Although license-related issues have received significant research attention, most existing work focuses on a limited subset of licenses and often overlooks copyright terms.” (“TSE-2025-01-0038\_Proof\_hi”, p. 1) \#a28ae5 \par - “Our approach systematically extracts licenses for both the project and its dependencies, employs large language models (LLMs) to interpret key copyright-related terms, and checks for potential noncompliance.” (“TSE-2025-01-0038\_Proof\_hi”, p. 1) \#e56eee \par - “These licenses are among the most critical nonexecutable components of software systems” (“TSE-2025-01-0038\_Proof\_hi”, p. 1) \#5fb236 \par - “Adherence to the terms and conditions outlined in these licenses is crucial.” (“TSE-2025-01-0038\_Proof\_hi”, p. 1) \#5fb236 \par - “legal disputes have arisen in cases where redistribution terms were not followed” (“TSE-2025-01-0038\_Proof\_hi”, p. 1) \#5fb236 \par - “By ensuring license adherence, developers can prevent legal disputes and sustain the collaborative ethos of the open-source community.” (“TSE-2025-01-0038\_Proof\_hi”, p. 1) \#5fb236 \par - “verifying compliance with copyright-related terms remains challenging for opensource projects.” (“TSE-2025-01-0038\_Proof\_hi”, p. 1) \#e56eee \par - “the widespread use of third-party libraries complicates the process.” (“TSE-2025-01-0038\_Proof\_hi”, p. 1) \#5fb236 \par - “28 third-party libraries, with one project relying on as many as 764” (“TSE-2025-01-0038\_Proof\_hi”, p. 1) \#a28ae5 \par - “Mismanagement of third-party dependencies can lead to inadvertent violations of license terms.” (“TSE-2025-01-0038\_Proof\_hi”, p. 1) \#5fb236 \par - “Python projects typically reference third-party packages in a requirements file.” (“TSE-2025-01-0038\_Proof\_hi”, p. 1) \#a28ae5 \par - “However, some Python projects embed third-party package files directly in their repositories or bundle them within distributed software packages” (“TSE-2025-01-0038\_Proof\_hi”, p. 1) \#a28ae5 \par - “distribution, and failure to comply with the relevant license terms under such circumstances may result in copyright violations” (“TSE-2025-01-0038\_Proof\_hi”, p. 1) \#e56eee \par - “Identifying and evaluating all applicable licenses is therefore critical to ensure compliance in open-source projects.” (“TSE-2025-01-0038\_Proof\_hi”, p. 1) \#e56eee \par - “The Open Source Initiative (OSI) has approved approximately 130 licenses [11], each with unique terms, and the list continues to grow as new licenses are developed to address specific needs [12]” (“TSE-2025-01-0038\_Proof\_hi”, p. 1) \#f19837 \par - “level of restriction they impose” (“TSE-2025-01-0038\_Proof\_hi”, p. 1) \#5fb236 \par - “strong copyleft” (“TSE-2025-01-0038\_Proof\_hi”, p. 2) \#2ea8e5 \par - “weak copyleft” (“TSE-2025-01-0038\_Proof\_hi”, p. 2) \#2ea8e5 \par - “permissive” (“TSE-2025-01-0038\_Proof\_hi”, p. 2) \#2ea8e5 \par - “Strong copyleft licenses, such as GPLv3 and OSLv3, require derivative works to remain under the same license and be open source” (“TSE-2025-01-0038\_Proof\_hi”, p. 2) \#2ea8e5 \par - “copyleft licenses, such as EPLv2 and MPLv2, allow derivative works to adopt different licenses while maintaining the original code as open source.” (“TSE-2025-01-0038\_Proof\_hi”, p. 2) \#2ea8e5 \par - “ermissive licenses, including BSD and Apachev2.0, provide significant flexibility, permitting redistribution under different licenses” (“TSE-2025-01-0038\_Proof\_hi”, p. 2) \#2ea8e5 \par - “Several studies have explored the general usage of licenses [14], [15], [16], [17], analyzing their evolution and popularity to identify trends within the OSS community.” (“TSE-2025-01-0038\_Proof\_hi”, p. 2) \#f19837 \par - “To address this knowledge gap, this paper introduces an automated approach for detecting copyright term violations in GitHub projects.” (“TSE-2025-01-0038\_Proof\_hi”, p. 2) \#e56eee \par - “identifying the third-party libraries used within a project and extracting their associated licenses” (“TSE-2025-01-0038\_Proof\_hi”, p. 2) \#2ea8e5 \par - “analyzed the key characteristics of the licenses and the copyright-related terms present in these projects.” (“TSE-2025-01-0038\_Proof\_hi”, p. 2) \#2ea8e5 \par - “Leveraging large language models (LLMs), we employ customized prompts to interpret these terms with high accuracy.” (“TSE-2025-01-0038\_Proof\_hi”, p. 2) \#2ea8e5 \par - “we assess whether the project adheres to the copyright requirements specified in the licenses of the relevant third-party libraries” (“TSE-2025-01-0038\_Proof\_hi”, p. 2) \#2ea8e5 \par - “Our approach successfully extracted tens of thousands of licenses and identified numerous instances of copyright term violations.” (“TSE-2025-01-0038\_Proof\_hi”, p. 2) \#ffd400\\ \mkbibemph{Interesting to see how these have been validated.} \par - “approximately 10\% of the projects were found to violate one or more copyright terms” (“TSE-2025-01-0038\_Proof\_hi”, p. 2) \#f19837 \par - “We address the issue of detecting violations of copyright-related terms in opensource projects” (“TSE-2025-01-0038\_Proof\_hi”, p. 2) \#2ea8e5 \par - “extracts licenses from projects and LLMs to interpret copyright terms” (“TSE-2025-01-0038\_Proof\_hi”, p. 2) \#2ea8e5 \par - “evaluated it on 500 popular Python projects, examining key aspects such as license extraction, term interpretation, and violation detection.” (“TSE-2025-01-0038\_Proof\_hi”, p. 2) \#2ea8e5 \par - “license comprehension” (“TSE-2025-01-0038\_Proof\_hi”, p. 2) \#2ea8e5 \par - “license violation” (“TSE-2025-01-0038\_Proof\_hi”, p. 2) \#2ea8e5 \par - “license compatibility.” (“TSE-2025-01-0038\_Proof\_hi”, p. 2) \#2ea8e5 \par - “manual extraction and are limited to a small subset of popular licenses.” (“TSE-2025-01-0038\_Proof\_hi”, p. 3) \#f19837 \par - “extract rights and obligations from license texts.” (“TSE-2025-01-0038\_Proof\_hi”, p. 3) \#a28ae5 \par - “text summarization and term classification” (“TSE-2025-01-0038\_Proof\_hi”, p. 3) \#5fb236 \par - “Our research focuses on extracting and interpreting copyright-related terms within licenses. Unlike previous studies, we leverage LLMs to achieve this goal. The use of LLMs offers several advantages, including the ability to handle a broader spectrum of license types and expressions with greater accuracy and flexibility.” (“TSE-2025-01-0038\_Proof\_hi”, p. 3) \#a28ae5 \par - “These methods often involve manually modeling the compatibility relationships of widely used licenses or those implemented in real-world projects, focusing on resolving non-compliance due to conflicting terms” (“TSE-2025-01-0038\_Proof\_hi”, p. 3) \#5fb236 \par - “Furthermore, these methods generally concentrate on a limited set of popular licenses and rely on manual modeling of violations, particularly those related to copyleft terms.” (“TSE-2025-01-0038\_Proof\_hi”, p. 3) \#a28ae5 \par - “Given that software released in binary form often includes third-party packages without respecting their licensing terms, Hemel et al. [7] introduced a tool to scan binary files and identify the third-party packages used in a project.” (“TSE-2025-01-0038\_Proof\_hi”, p. 3) \#5fb236 \par - “there is currently no comprehensive work on detecting violations across the full spectrum of copyright-related terms.” (“TSE-2025-01-0038\_Proof\_hi”, p. 3) \#a28ae5 \par - “learning-based techniques [2]” (“TSE-2025-01-0038\_Proof\_hi”, p. 4) \#f19837 \par - “Unlike these works, which focus on the compatibility of license terms, this paper examines whether open GitHub projects comply with the copyright-related terms in their licenses.” (“TSE-2025-01-0038\_Proof\_hi”, p. 4) \#ffd400\\ \mkbibemph{It's not clear to what extent this is different from the mentioned existing work on license compatibility!!! TO BE CHECKED} \par - “violations of copyright terms in licenses associated with GitHub projects” (“TSE-2025-01-0038\_Proof\_hi”, p. 4) \#e56eee \par - “license extraction, license understanding, and license violation detection.” (“TSE-2025-01-0038\_Proof\_hi”, p. 4) \#2ea8e5 \par - “The primary objective of this approach is to verify whether projects comply with copyright requirements when using third-party librarie” (“TSE-2025-01-0038\_Proof\_hi”, p. 4) \#e56eee \par - “identifying the third-party libraries utilized by an open-source GitHub project” (“TSE-2025-01-0038\_Proof\_hi”, p. 4) \#2ea8e5 \par - “extracting the licenses associated with these dependencies” (“TSE-2025-01-0038\_Proof\_hi”, p. 4) \#2ea8e5 \par - “Additionally, understanding the project’s own license is essential for detecting potential conflicts or violations involving third-party licenses” (“TSE-2025-01-0038\_Proof\_hi”, p. 4) \#5fb236 \par - “To address this, our approach extracts the licenses of both the project and its third-party libraries by analyzing the codebase and repository files.” (“TSE-2025-01-0038\_Proof\_hi”, p. 4) \#2ea8e5 \par - “analyze the copyright-related terms of the third-party licenses.” (“TSE-2025-01-0038\_Proof\_hi”, p. 4) \#2ea8e5 \par - “To ensure accurate interpretation of these terms, we leverage LLMs with customized prompts tailored to the specific features of each license, enhancing the precision of the analysis.” (“TSE-2025-01-0038\_Proof\_hi”, p. 4) \#5fb236 \par - “we pre-analyzed the copyright terms of OSI-certified licenses and developed a comprehensive license knowledge base” (“TSE-2025-01-0038\_Proof\_hi”, p. 4) \#5fb236 \par - “efficient identification of potential violations” (“TSE-2025-01-0038\_Proof\_hi”, p. 4) \#ffd400\\ \mkbibemph{TO BE CHECKED HOW THIS IDENTIFICATION IS DONE AND TO WHAT EXTENT SUCH A KNOWLEDGE BASE CONTRIBUTE THE IDENTIFICATION.} \par - “Licenses of thirdparty libraries often contain copyright-related terms that typically apply to actions such as distribution or the creation of derivative works.” (“TSE-2025-01-0038\_Proof\_hi”, p. 4) \#5fb236 \par - “For such licenses, even importing a library may require compliance with their copyright terms.” (“TSE-2025-01-0038\_Proof\_hi”, p. 4) \#5fb236 \par - “Subsequently, we focus on assessing the project’s compliance with the copyright requirements of its third-party dependencies.” (“TSE-2025-01-0038\_Proof\_hi”, p. 4) \#a28ae5 \par - “In this paper, we focus on Python projects, where dependencies and third-party packages are typically declared in a requirements.txt file.” (“TSE-2025-01-0038\_Proof\_hi”, p. 4) \#5fb236 \par - “However, previous research [24] has shown that the licenses for PyPi packages can evolve over time, with different versions of the same package potentially being governed by different licenses.” (“TSE-2025-01-0038\_Proof\_hi”, p. 5) \#5fb236 \par - “This file includes all third-party dependencies along with their corresponding versions.” (“TSE-2025-01-0038\_Proof\_hi”, p. 5) \#a28ae5 \par - “normalized\_licenses field” (“TSE-2025-01-0038\_Proof\_hi”, p. 5) \#5fb236 \par - “spdx\_expression” (“TSE-2025-01-0038\_Proof\_hi”, p. 5) \#ffd400\\ \mkbibemph{what's that?} \par - “While this API provides licensing information for dependencies, the data retrieved may not always be comprehensive.” (“TSE-2025-01-0038\_Proof\_hi”, p. 5) \#5fb236 \par - “This combined approach ensured that we acquired all necessary licensing information for the thirdparty packages used in the GitHub project.” (“TSE-2025-01-0038\_Proof\_hi”, p. 5) \#a28ae5 \par - “However, we limited our license extraction to the first level of dependencies.” (“TSE-2025-01-0038\_Proof\_hi”, p. 5) \#ffd400\\ \mkbibemph{Does this induce some limitations of the whole copryright violation approach?} \par - “Moreover, from the perspective of developers using third-party libraries, their responsibility typically extends only to complying with the licenses explicitly declared by the third-party packages they use.” (“TSE-2025-01-0038\_Proof\_hi”, p. 5) \#5fb236 \par - “They should not be held accountable for issues arising from transitive dependencies,” (“TSE-2025-01-0038\_Proof\_hi”, p. 5) \#e56eee \par - “Additionally, in cases where the license information for third-party packages is incomplete, our approach could be applied to those packages as well, addressing any gaps” (“TSE-2025-01-0038\_Proof\_hi”, p. 5) \#5fb236 \par - “Therefore, our analysis is confined to the first layer of dependencies.” (“TSE-2025-01-0038\_Proof\_hi”, p. 5) \#a28ae5 \par - “The final metadata for third-party dependencies includes pakName, pakLic, pakAuthor, pakURL, and pakLicenseURL.” (“TSE-2025-01-0038\_Proof\_hi”, p. 5) \#5fb236 \par - “pakLicenseURL refers to the original link to the license.” (“TSE-2025-01-0038\_Proof\_hi”, p. 5) \#a28ae5 \par - “Although our focus here is on Python projects, a similar approach can be adapted to projects written in other languages, with the appropriate tools or commands used to extract the relevant information.” (“TSE-2025-01-0038\_Proof\_hi”, p. 5) \#ffd400\\ \mkbibemph{Maybe. We are missing evidence on this. We do not have any supporting reference for this statement!} \par - “ScanCode constructs a search index of license texts and detection rules, enabling it to match text extracted from files with an accuracy rate of up to 95\%” (“TSE-2025-01-0038\_Proof\_hi”, p. 5) \#5fb236 \par - “It begins by cataloging code files and classifying them by type.” (“TSE-2025-01-0038\_Proof\_hi”, p. 5) \#5fb236 \par - “xtracts text from binary files.” (“TSE-2025-01-0038\_Proof\_hi”, p. 5) \#5fb236 \par - “The tool subsequently organizes the project code and gathers relevant metadata, ultimately generating a report in JSON or CSV format.” (“TSE-2025-01-0038\_Proof\_hi”, p. 5) \#a28ae5 \par - “license types” (“TSE-2025-01-0038\_Proof\_hi”, p. 5) \#2ea8e5 \par - “associated file” (“TSE-2025-01-0038\_Proof\_hi”, p. 5) \#2ea8e5 \par - “hierarchy” (“TSE-2025-01-0038\_Proof\_hi”, p. 5) \#2ea8e5 \par - “corresponding license texts.” (“TSE-2025-01-0038\_Proof\_hi”, p. 5) \#2ea8e5 \par - “SPDX format, a widely adopted standard for open-source licenses” (“TSE-2025-01-0038\_Proof\_hi”, p. 5) \#a28ae5 \par - “Through this process, we identified the third-party libraries used in the projects.” (“TSE-2025-01-0038\_Proof\_hi”, p. 5) \#e56eee \par - “a coarse-grained identification of license information, such as the license name and basic details, without delving into the specific terms of the licenses.” (“TSE-2025-01-0038\_Proof\_hi”, p. 5) \#a28ae5 \par - “identify and interpret these terms within the licenses.” (“TSE-2025-01-0038\_Proof\_hi”, p. 5) \#a28ae5 \par - “transformer architecture” (“TSE-2025-01-0038\_Proof\_hi”, p. 5) \#5fb236 \par - “"pakLic":"GPL-3.0",” (“TSE-2025-01-0038\_Proof\_hi”, p. 6) \#ffd400 \par - ““detected\_license\_expression\_spdx”:”GPL-3.0”,” (“TSE-2025-01-0038\_Proof\_hi”, p. 6) \#ffd400 \par - “reduced computational costs” (“TSE-2025-01-0038\_Proof\_hi”, p. 6) \#ffd400 \par - “ChatGPT stand out in natural language inference, significantly outperforming previous models like BERT on various datasets.” (“TSE-2025-01-0038\_Proof\_hi”, p. 6) \#a28ae5 \par - “ChatGPT-4.0 [” (“TSE-2025-01-0038\_Proof\_hi”, p. 6) \#2ea8e5 \par - “interpreting and classifying license terms, with a particular emphasis on copyright-related terms.” (“TSE-2025-01-0038\_Proof\_hi”, p. 6) \#a28ae5 \par - “The essence of this strategy lies in carefully structuring the prompts to guide the model in understanding complex legal language while also ensuring that the generated outputs are both precise and relevant.” (“TSE-2025-01-0038\_Proof\_hi”, p. 6) \#e56eee \par - “1) Provision of License Text” (“TSE-2025-01-0038\_Proof\_hi”, p. 6) \#2ea8e5 \par - “Dimension-Based Structuring” (“TSE-2025-01-0038\_Proof\_hi”, p. 6) \#2ea8e5 \par - “e.g.,” (“TSE-2025-01-0038\_Proof\_hi”, p. 6) \#ffd400\\ \mkbibemph{What else? It is necessary to show the full set of dimensions!} \par - “permissions” (“TSE-2025-01-0038\_Proof\_hi”, p. 6) \#5fb236 \par - “restrictions” (“TSE-2025-01-0038\_Proof\_hi”, p. 6) \#5fb236 \par - “obligations” (“TSE-2025-01-0038\_Proof\_hi”, p. 6) \#5fb236 \par - “Interpretation Guidance:” (“TSE-2025-01-0038\_Proof\_hi”, p. 6) \#2ea8e5 \par - “guiding” (“TSE-2025-01-0038\_Proof\_hi”, p. 6) \#ffd400\\ \mkbibemph{what else?} \par - “4) Clear Objectives:” (“TSE-2025-01-0038\_Proof\_hi”, p. 6) \#2ea8e5 \par - “5) Output Standardization:” (“TSE-2025-01-0038\_Proof\_hi”, p. 6) \#2ea8e5 \par - “three categories:” (“TSE-2025-01-0038\_Proof\_hi”, p. 6) \#f19837 \par - “permissions (actions the license permits developers to perform), restrictions (additional conditions developers must follow), and obligations (requirements developers must adhere to)” (“TSE-2025-01-0038\_Proof\_hi”, p. 6) \#ffd400\\ \mkbibemph{Previously, these were presented as example. Have you considered these three categories, or do you have some more? Moreover, here you are repeating what it is written for the "Dimension-Based Structuring" aspect.} \par - “These categories include terms related to distribution, modification, commercial use, liability, and more [21]. Each term is assigned one of four attitudes: “allowed”, indicating the license permits the activity; “prohibited”, meaning the license explicitly forbids it; “must”, specifying the license mandates the action; and “not mentioned”, meaning the license does not address the particular issue, enhancing ChatGPT’s ability to interpret licenses.” (“TSE-2025-01-0038\_Proof\_hi”, p. 6) \#ffd400\\ \mkbibemph{This is a repetition of what has been written for the "interpretation guidance" part.} \par - “We then narrow the focus to copyright-related terms, referencing previous research that has analyzed some licenses [20], [21], [64] to provide examples of such terms” (“TSE-2025-01-0038\_Proof\_hi”, p. 6) \#5fb236 \par - “Finally, we explicitly state our objective and provide an example output format to guide ChatGPT in generating the desired results.” (“TSE-2025-01-0038\_Proof\_hi”, p. 6) \#ffd400\\ \mkbibemph{Can you show an example?} \par - “134 licenses, focusing on extracting their copyright terms” (“TSE-2025-01-0038\_Proof\_hi”, p. 6) \#5fb236 \par - “Additionally, we analyzed the extracted copyright terms across these licenses and identified five common types of terms.” (“TSE-2025-01-0038\_Proof\_hi”, p. 6) \#5fb236 \par - “Include Copyright” (“TSE-2025-01-0038\_Proof\_hi”, p. 6) \#2ea8e5 \par - “indemnification” (“TSE-2025-01-0038\_Proof\_hi”, p. 7) \#a28ae5 \par - “copyright clauses:” (“TSE-2025-01-0038\_Proof\_hi”, p. 7) \#ffd400\\ \mkbibemph{What is the resulting clauses that you got by analyzing the different licenses?} \par - “Example of standardized JSON output format” (“TSE-2025-01-0038\_Proof\_hi”, p. 7) \#ffd400\\ \mkbibemph{Can you show an example/fragment for a real license?} \par - “Include License” (“TSE-2025-01-0038\_Proof\_hi”, p. 7) \#2ea8e5 \par - “Include Notice” (“TSE-2025-01-0038\_Proof\_hi”, p. 7) \#2ea8e5 \par - “Same License” (“TSE-2025-01-0038\_Proof\_hi”, p. 7) \#2ea8e5 \par - “Disclose Source” (“TSE-2025-01-0038\_Proof\_hi”, p. 7) \#2ea8e5 \par - “These terms were selected because they represent the most critical legal requirements in open-source licensing, addressing fundamental aspects of software usage, modification, and redistribution. The decision to concentrate on these terms, rather than others such as patent-related terms or specific attribution requirements, was guided by their broad applicability across diverse licenses and their significant impact on copyright compliance” (“TSE-2025-01-0038\_Proof\_hi”, p. 7) \#ffd400\\ \mkbibemph{Some statistics related to these terms can better support this claim. Moreover, it is not clear how humans have been in the loop during this phase of the process.} \par - “Although other terms are also important, our selection prioritizes those most likely to result in significant license violations if not properly followed, thereby providing a strong foundation for our license violation detection efforts.” (“TSE-2025-01-0038\_Proof\_hi”, p. 7) \#5fb236 \par - “The knowledge base is also available in the online supplementary materials1.” (“TSE-2025-01-0038\_Proof\_hi”, p. 7) \#ff6666\\ \mkbibemph{It's missing!} \par - “Here, we rely on the licenses extracted from the project and the interpreted copyright terms established in earlier stages” (“TSE-2025-01-0038\_Proof\_hi”, p. 7) \#5fb236 \par - “In most cases, copyright-related requirements apply only in situations of distribution or derivative works.” (“TSE-2025-01-0038\_Proof\_hi”, p. 8) \#ffd400\\ \mkbibemph{This is not clear, especially after the previous sentence.} \par - “we begin by determining whether the project’s use of these libraries qualifies as distribution or derivation (except for libraries under strong copyleft licenses).” (“TSE-2025-01-0038\_Proof\_hi”, p. 8) \#ffd400\\ \mkbibemph{This is important. However, an explanatory example is missing here!} \par - “We start by describing our approach for assessing distribution behavior and then discuss how we evaluate compliance with each of these terms” (“TSE-2025-01-0038\_Proof\_hi”, p. 8) \#5fb236 \par - “Sometimes, third-party libraries are bundled with the project for convenience or for offline usage.” (“TSE-2025-01-0038\_Proof\_hi”, p. 8) \#a28ae5 \par - “To determine whether a project is distributing a thirdparty library, we begin by scanning all project files for formats commonly used to package Python libraries, such as .tar.gz, .zip, .whl, and .egg.” (“TSE-2025-01-0038\_Proof\_hi”, p. 8) \#ffd400\\ \mkbibemph{This is very Python specific and it confirms my previous comments on the generalizability of the approach.} \par - “non-empty purl field that accurately identifies the package name and version.” (“TSE-2025-01-0038\_Proof\_hi”, p. 8) \#ffd400\\ \mkbibemph{Can this be a threat to constuct validity of the approach?} \par - “To avoid false positives, we validate the package name and version using the libraries.io API, ensuring the package originates from an official source.” (“TSE-2025-01-0038\_Proof\_hi”, p. 8) \#ffd400\\ \mkbibemph{This step is not clear. What are the given inputs?} \par - “Files passing these checks are classified as third-party libraries that have been distributed by the opensource project” (“TSE-2025-01-0038\_Proof\_hi”, p. 8) \#5fb236 \par - “Include Copyright.” (“TSE-2025-01-0038\_Proof\_hi”, p. 8) \#2ea8e5 \par - “If an Authori (1 ≤ i ≤ n) matching the third-party dependency is found in the project file, we consider the term requirements satisfied.” (“TSE-2025-01-0038\_Proof\_hi”, p. 8) \#ffd400\\ \mkbibemph{All these steps are not clear without an explanatory example.} \par - “If no matching author is found in the project files, then strictly speaking, the project fails to fulfill this term’s requirement.” (“TSE-2025-01-0038\_Proof\_hi”, p. 8) \#5fb236 \par - “As a result, we also check whether the distributed package includes a copyright notice. If neither the open-source project nor the package contains the necessary notice, we conclude that a license violation has occur” (“TSE-2025-01-0038\_Proof\_hi”, p. 8) \#ffd400\\ \mkbibemph{See my previous comment.} \par - “Include License.” (“TSE-2025-01-0038\_Proof\_hi”, p. 8) \#2ea8e5 \par - “However, we apply a relaxed criterion: if the third-party package itself includes the required license information, we treat the term as satisfied.” (“TSE-2025-01-0038\_Proof\_hi”, p. 8) \#5fb236 \par - “Include Notice.” (“TSE-2025-01-0038\_Proof\_hi”, p. 8) \#2ea8e5 \par - “license extraction, license understanding, and license violation detection, each of which is integral to ensuring compliance with opensource licenses, particularly concerning copyright terms.” (“TSE-2025-01-0038\_Proof\_hi”, p. 9) \#ffd400\\ \mkbibemph{You already said this.} \par - “license extraction component” (“TSE-2025-01-0038\_Proof\_hi”, p. 9) \#2ea8e5 \par - “What are the characteristics of these licenses, including their quantity, type, and distribution?” (“TSE-2025-01-0038\_Proof\_hi”, p. 9) \#ffd400\\ \mkbibemph{This part is not properly connected with the first part of the research question} \par - “accurately” (“TSE-2025-01-0038\_Proof\_hi”, p. 9) \#2ea8e5 \par - “interpret” (“TSE-2025-01-0038\_Proof\_hi”, p. 9) \#2ea8e5 \par - “What is the proportion and coverage of these terms across licenses in open-source projects?” (“TSE-2025-01-0038\_Proof\_hi”, p. 9) \#ffd400\\ \mkbibemph{Same as RQ1. This second part is not properly connected with the first part of RQ2.} \par - “effectively” (“TSE-2025-01-0038\_Proof\_hi”, p. 9) \#ffd400\\ \mkbibemph{How to measure that?} \par - “how accurate is its assessment of thirdparty library distribution behavior?” (“TSE-2025-01-0038\_Proof\_hi”, p. 9) \#ffd400\\ \mkbibemph{This is disconnected from the first part of RQ3.} \par - “RQ1 evaluates the effectiveness of our license extraction component, focusing on how accurately it identifies licenses in open-source projects.” (“TSE-2025-01-0038\_Proof\_hi”, p. 9) \#2ea8e5 \par - “RQ2 assesses our approach’s capability in understanding copyright terms within the extracted licenses” (“TSE-2025-01-0038\_Proof\_hi”, p. 9) \#2ea8e5 \par - “interpreted copyright terms” (“TSE-2025-01-0038\_Proof\_hi”, p. 9) \#2ea8e5 \par - “RQ3 investigates how effectively our approach detects violations of copyright-related terms.” (“TSE-2025-01-0038\_Proof\_hi”, p. 9) \#2ea8e5 \par - “We will measure the number of detected violations and, because many of these terms impose restrictions on distribution, we will also assess our approach’s accuracy in evaluating third-party library distribution behavior.” (“TSE-2025-01-0038\_Proof\_hi”, p. 9) \#5fb236 \par - “project metadat” (“TSE-2025-01-0038\_Proof\_hi”, p. 9) \#2ea8e5 \par - “epository content” (“TSE-2025-01-0038\_Proof\_hi”, p. 9) \#2ea8e5 \par - “commit histories.” (“TSE-2025-01-0038\_Proof\_hi”, p. 9) \#ffd400\\ \mkbibemph{Why do you need them? Is time also a dimension that you take into account?} \par - “Our focus is specifically on Python projects due to Python’s widespread use and the availability of numerous well-maintained open-source projects in this language. Python is also commonly used in various domains, making it a representative language for evaluating our approach” (“TSE-2025-01-0038\_Proof\_hi”, p. 9) \#ffd400\\ \mkbibemph{It's too late to say that. The work is Python specific and consequently this is both motivation and description of the paper's focus. I don't think the approach can be used on different technologies.} \par - “500 Python projects” (“TSE-2025-01-0038\_Proof\_hi”, p. 9) \#2ea8e5 \par - “128 small projects (0-5MB)” (“TSE-2025-01-0038\_Proof\_hi”, p. 9) \#2ea8e5 \par - “192 medium-sized projects (5MB-50MB)” (“TSE-2025-01-0038\_Proof\_hi”, p. 9) \#2ea8e5 \par - “180 large projects (over 50MB)” (“TSE-2025-01-0038\_Proof\_hi”, p. 9) \#2ea8e5 \par - “have a significant impact on the open-source community. By including projects from various fields, such as web development, data science, machine learning, and system utilities, we ensure that our dataset reflects the broad spectrum of Python applications, making our findings more generalizable and applicable across different types of software projects. We believe that this dataset provides substantial reference value for our research, as it mirrors the practices and challenges faced by developers working on prominent projects. The complete list of these p” (“TSE-2025-01-0038\_Proof\_hi”, p. 10) \#ffd400\\ \mkbibemph{A table presenting the peculiar characteristics (including the domain of each project) of the dataset would help here.} \par - “effectiveness of the license extraction component in our approach” (“TSE-2025-01-0038\_Proof\_hi”, p. 10) \#2ea8e5 \par - “Since we focus on Python projects, our approach for extracting licenses begins by using the pipreqs command to identify the third-party packages used in the project. We then leverage the libraries.io API to retrieve the corresponding license information for these packages. Both steps rely on official commands and APIs, which we consider reliable, so no further validation of these results was deemed necessary” (“TSE-2025-01-0038\_Proof\_hi”, p. 10) \#ffd400\\ \mkbibemph{this is already said, isn't it?} \par - “To extract this information, we utilized ScanCode as the primary tool” (“TSE-2025-01-0038\_Proof\_hi”, p. 10) \#ffd400\\ \mkbibemph{This is also said. It's a repetition. Essentially you rely on ScanCode, an existing approach. WHat's your contribution concerning the license enstraction task. What do you add to the work that ScanCode already does?} \par - “Given ScanCode’s established reliability, our focus for this part was on conducting additional validation to ensure the accuracy of our results.” (“TSE-2025-01-0038\_Proof\_hi”, p. 10) \#ffd400 \par - “RQ2 focuses on evaluating the effectiveness of our approach in understanding copyright terms within the extracted licenses.” (“TSE-2025-01-0038\_Proof\_hi”, p. 10) \#2ea8e5 \par - “Our approach leverages ChatGPT, enhanced with well-structured and context-specific prompts, to accurately interpret these copyright terms.” (“TSE-2025-01-0038\_Proof\_hi”, p. 10) \#ffd400\\ \mkbibemph{This is also said in the previous section.} \par - “Cohen’s kappa score” (“TSE-2025-01-0038\_Proof\_hi”, p. 10) \#5fb236 \par - “identify any cases where misinterpretations occurred” (“TSE-2025-01-0038\_Proof\_hi”, p. 10) \#5fb236 \par - “This analysis aimed to determine how prevalent these terms are within the licenses and to ide” (“TSE-2025-01-0038\_Proof\_hi”, p. 10) \#5fb236 \par - “RQ3 represents a crucial aspect of our study, as it examines the effectiveness of our approach in detecting violations of copyright-related terms in real-world opensource projects.” (“TSE-2025-01-0038\_Proof\_hi”, p. 10) \#2ea8e5 \par - “measuring the total number of identified violations.” (“TSE-2025-01-0038\_Proof\_hi”, p. 10) \#ffd400\\ \mkbibemph{How did you measure the detection accuracy? (e.g. false positive, false negative, etc.)} \par - “no existing work provides a comprehensive solution for detecting violations across all these specific terms, making our approach unique and leaving us without a direct baseline for comparison.” (“TSE-2025-01-0038\_Proof\_hi”, p. 10) \#5fb236 \par - “third-party library distribution across 104 projects” (“TSE-2025-01-0038\_Proof\_hi”, p. 10) \#a28ae5 \par - “sampled 100 of the 1,117 identified distribution instances and 50 of the 396 projects showing no distribution, then performed a manual review.” (“TSE-2025-01-0038\_Proof\_hi”, p. 10) \#ffd400\\ \mkbibemph{Ok this answered my previous comment concerning the accuracy of the violation detection.} \par - “For instance, incorporating numerous external packages can introduce oversights, such as inadvertently adding third-party files to the project’s repository or bundling them for distribution, thereby triggering stricter copyright constraints. Extracting the licenses of third-party packages is a prerequisite for checking copyright terms. Moreover, understanding the licenses used by these packages provides insight into broader licensing practices within open-source projects.” (“TSE-2025-01-0038\_Proof\_hi”, p. 10) \#ffd400\\ \mkbibemph{This is also a repetition.} \par - “To validate the accuracy of ScanCode’s extraction of each project’s own licenses, we performed a manual review of 200 randomly selected licenses.” (“TSE-2025-01-0038\_Proof\_hi”, p. 11) \#ffd400\\ \mkbibemph{THis is also said in sec 4.1.2} \par - “However, incorporating numerous third-party packages can create additional responsibilities for developers. If not managed properly, these packages may introduce copyright infringement risks, as each thirdparty package may come with its own license.” (“TSE-2025-01-0038\_Proof\_hi”, p. 11) \#ffd400\\ \mkbibemph{Repetition, in different words, but the concept is already said in the previous section.} \par - “they examined over 500 copyright terms” (“TSE-2025-01-0038\_Proof\_hi”, p. 11) \#5fb236 \par - “second” (“TSE-2025-01-0038\_Proof\_hi”, p. 11) \#ff6666\\ \mkbibemph{first row} \par - “The positive outcomes of this experiment provide us with the confidence to apply our approach to the automated understanding of a broader set of licenses in future work.” (“TSE-2025-01-0038\_Proof\_hi”, p. 12) \#5fb236 \par - “distribution or derivative work” (“TSE-2025-01-0038\_Proof\_hi”, p. 13) \#5fb236 \par - “However, some of these libraries used licenses that were not OSI-certified, and since our focus is on OSI-certified licenses, we excluded those cases. In the end, we retained 976 instances of third-party library distribution spread over 93 of the 500 projects, involving 305 distinct third-party libraries.” (“TSE-2025-01-0038\_Proof\_hi”, p. 13) \#5fb236 \par - “for detecting copyright term violations related to third-party libraries in GitHub projects.” (“TSE-2025-01-0038\_Proof\_hi”, p. 14) \#5fb236 \par - “uses LLMs to interpret copyright terms and pinpoints potential violations by evaluating how projects behave alongside the license requirements of third-party libraries.” (“TSE-2025-01-0038\_Proof\_hi”, p. 14) \#5fb236 \par - “10\% contained copyright term violations.” (“TSE-2025-01-0038\_Proof\_hi”, p. 14) \#5fb236} } @online{TSEFOCUSJournalPaper, title = {{{TSE-FOCUS Journal Paper}}}, url = {https://docs.google.com/document/d/1_40QPw-9Ddk7yZ2fQy1HRaPtK5I1dI_TxOSuOWcHKkU/edit?usp=embed_facebook}, urldate = {2020-02-11}, abstract = {Supporting software development with API function calls and usage patterns Link: https://github.com/MDEGroup/FOCUS/tree/master/TSE-FOCUS Journal: Transactions on Software Engineering (submission instruction) Introduction API function calls recommendation Issues (Redundancy, execution time) Liter...}, langid = {british}, organization = {Google Docs} } @inproceedings{Tun202113, title = {Goal-Centralized Metamodel Based Requirements Integration for Machine Learning Systems}, author = {Tun, H.T. and Husen, J.H. and Yoshioka, N. and Washizaki, H. and Fukazawa, Y.}, date = {2021}, series = {Proceedings - {{Asia-Pacific Software Engineering Conference}}, {{APSEC}}}, pages = {13--16}, publisher = {IEEE Computer Society}, issn = {15301362}, doi = {10.1109/APSECW53869.2021.00013}, abbrev_source_title = {Proc. Asia Pac. Softw. Eng. Conf. APSEC}, affiliation = {Waseda University, Tokyo, Japan}, document_type = {Conference Paper}, isbn = {978-1-6654-3813-1}, langid = {english}, source = {Scopus}, note = {cited By 1} } @article{turkiRecommendingScholarlyArticles, title = {Recommending Scholarly Articles to Monitor {{COVID-19}} Trends in Social Media Based on Low-Cost Topic Modeling}, author = {Turki, Houcemeddine and Taieb, Mohamed Ali Hadj and Aouicha, Mohamed Ben}, pages = {11}, abstract = {During the last years, many computer systems have been developed to track and monitor COVID-19 social network interactions. However, these systems have been mainly based on robust probabilistic approaches like Latent Dirichlet Allocation (LDA), Embeddings, and Deep Learning. Such approaches cannot be easily debugged and enhanced to achieve better accuracy rates and usually require advanced computer infrastructures that need lots of energy and money to be maintained. In this research paper, we propose to modify LDA by letting it be driven by knowledge resources and we demonstrate how we can apply our topic modeling method to local social network interactions about COVID-19 to generate precise topic clusters reflecting the social trends about the pandemic at a low cost. Then, we outline how terms in every topic cluster can be converted into a search query to generate scholarly publications from PubMed Central for adjusting COVID-19 trendy thoughts in a considered population.}, langid = {english}, keywords = {LOGSEQ} } @article{Turney:2010:FMV:1861751.1861756, title = {From Frequency to Meaning: {{Vector}} Space Models of Semantics}, author = {Turney, Peter D. and Pantel, Patrick}, date = {2010-01}, journaltitle = {J. Artif. Int. Res.}, volume = {37}, number = {1}, pages = {141--188}, publisher = {AI Access Foundation}, address = {USA}, issn = {1076-9757}, url = {http://dl.acm.org/citation.cfm?id=1861751.1861756}, acmid = {1861756}, issue_date = {January 2010}, numpages = {48}, note = {TL;DR \par The goal in this survey is to show the breadth of applications of VSMs for semantics, to provide a new perspective on VSMs, and to provide pointers into the literature for those who are less familiar with the field.} } @article{tversky1977features, title = {Features of Similarity}, author = {Tversky, Amos}, date = {1977}, journaltitle = {Psychol. Rev.}, volume = {84}, number = {4}, pages = {327--352}, publisher = {American Psychological Association}, address = {US}, issn = {19391471}, abstract = {Questions the metric and dimensional assumptions that underlie the geometric representation of similarity on both theoretical and empirical grounds. A new set-theoretical approach to similarity is developed in which objects are represented as collections of features and similarity is described as a feature-matching process. Specifically, a set of qualitative assumptions is shown to imply the contrast model, which expresses the similarity between objects as a linear combination of the measures of their common and distinctive features. Several predictions of the contrast model are tested in studies of similarity with both semantic and perceptual stimuli. The model is used to uncover, analyze, and explain a variety of empirical phenomena such as the role of common and distinctive features, the relations between judgments of similarity and difference, the presence of asymmetric similarities, and the effects of context on judgments of similarity. The contrast model generalizes standard representations of similarity data in terms of clusters and trees. It is also used to analyze the relations of prototypicality and family resemblance. (39 ref) (PsycINFO Database Record (c) 2012 APA, all rights reserved)}, added-at = {2014-02-04T10:50:37.000+0100}, biburl = {https://www.bibsonomy.org/bibtex/26213352d60fe7cf406596d8f1db71f8a/jaeschke}, interhash = {03a061a2e7ecca2b5e8d900655596144}, intrahash = {6213352d60fe7cf406596d8f1db71f8a}, nodoi = {10.1037/0033-295X.84.4.327}, refid = {1978-09287-001}, keywords = {psychology similarity toread}, timestamp = {2014-07-28T15:57:31.000+0200} } @online{TypesResearchGaps, title = {Types of {{Research Gaps}}}, url = {https://www.linkedin.com/posts/jason-thatcher-0329764_academicresearch-activity-7279662548377907200-E4Bh/?utm_source=share&utm_medium=member_android}, urldate = {2025-04-13}, langid = {english}, keywords = {/unread} } @inproceedings{ugurelWhatsCodeAutomatic2002, title = {What's the Code?: {{Automatic}} Classification of Source Code Archives}, booktitle = {Proc. {{Eighth ACM SIGKDD Int}}. {{Conf}}. {{Knowl}}. {{Discov}}. {{Data Min}}.}, author = {Ugurel, Secil and Krovetz, Robert and Giles, C. Lee}, date = {2002}, series = {{{KDD}} '02}, pages = {632--638}, publisher = {ACM}, location = {New York, NY, USA}, url = {http://doi.acm.org/10.1145/775047.775141}, acmid = {775141}, isbn = {1-58113-567-X}, nodoi = {10.1145/775047.775141}, numpages = {7}, note = {TL;DR \par It is shown that source code can be accurately and automatically classified into topical categories and can be identified to be in a specific programming language class.} } @article{undefinedDarkitectureRealitySkirted2017, entrysubtype = {magazine}, title = {Darkitecture: {{The Reality Skirted}} by {{Architecture}}}, shorttitle = {Darkitecture}, author = {{undefined} and {undefined} and {undefined} and {undefined}}, date = {2017}, journaltitle = {IEEE Software}, volume = {34}, number = {1}, pages = {103--105}, issn = {0740-7459}, abstract = {Just as physicists infer dark matter's presence on the basis of its gravitational effects on visible matter, we can conceptualize a "darkitecture" that outlines visible software architectures.} } @article{undefinedKeyAbstractionsIoTOriented2017, entrysubtype = {magazine}, title = {Key {{Abstractions}} for {{IoT-Oriented Software Engineering}}}, author = {{undefined} and {undefined} and {undefined} and {undefined}}, date = {2017}, journaltitle = {IEEE Software}, volume = {34}, number = {1}, pages = {38--45}, issn = {0740-7459}, abstract = {Despite the progress in Internet of Things (IoT) research, a general software engineering approach for systematic development of IoT systems and applications is still missing. A synthesis of the state of the art in the area can help frame the key abstractions related to such development. Such a framework could be the basis for guidelines for IoT-oriented software engineering.}, keywords = {internet of things,software development,software engineering} } @article{undefinedPracticesTechnologiesComputer2017, entrysubtype = {magazine}, title = {Practices and {{Technologies}} in {{Computer Game Software Engineering}}}, author = {{undefined} and {undefined} and {undefined} and {undefined}}, date = {2017}, journaltitle = {IEEE Software}, volume = {34}, number = {1}, pages = {110--116}, issn = {0740-7459}, abstract = {Computer games are rich, complex, and often large-scale software applications. They're a significant, interesting, and often compelling domain for innovative research in software engineering techniques and technologies. Computer games are progressively changing the everyday world in many positive ways. Game developers, whether focusing on entertainment market opportunities or game-based applications in nonentertainment domains such as education, healthcare, defense, or scientific research (that is, serious games), thus share a common interest in how best to engineer game software. This article examines techniques and technologies that inform contemporary computer game software engineering.}, keywords = {software engineering}, note = {TL;DR \par Techniques and technologies that inform contemporary computer game software engineering are examined.} } @article{undefinedSoftwareEngineeringInternetThings2017, entrysubtype = {magazine}, title = {Software-{{Engineering}} the {{Internet}} of {{Things}}}, author = {{undefined} and {undefined} and {undefined} and {undefined}}, date = {2017}, journaltitle = {IEEE Software}, volume = {34}, number = {1}, pages = {4--6}, issn = {0740-7459} } @article{undefinedValueDoubt2017, entrysubtype = {magazine}, title = {The {{Value}} of {{Doubt}}}, author = {{undefined} and {undefined} and {undefined} and {undefined}}, date = {2017}, journaltitle = {IEEE Software}, volume = {34}, number = {1}, pages = {106--109}, issn = {0740-7459}, abstract = {Doubt is key to becoming a good programmer. If you don't doubt the correctness of your work, you have no incentive to look for the hidden spoilers that are always there.}, note = {TL;DR \par Doubt is key to becoming a good programmer because if you don't doubt the correctness of your work, you have no incentive to look for the hidden spoilers that are always there.} } @book{ungerAutonomousSystemsDevelopments2012, title = {Autonomous {{Systems}}: {{Developments}} and {{Trends}}}, shorttitle = {Autonomous {{Systems}}}, editor = {Unger, Herwig and Kyamaky, Kyandoghere and Kacprzyk, Janusz}, date = {2012}, series = {Studies in {{Computational Intelligence}}}, volume = {391}, publisher = {Springer Berlin Heidelberg}, location = {Berlin, Heidelberg}, url = {http://link.springer.com/10.1007/978-3-642-24806-1}, urldate = {2016-08-21}, isbn = {978-3-642-24805-4 978-3-642-24806-1}, note = {TL;DR \par The Workshops on Autonomous Systems emanated from a gathering with the doctoral students of just three chairs at Fernuniversitt in Hagen, which has grown and matured in several respects and turned into a visible scientific event.} } @online{UniversitaDelittoPerfetto, title = {Università: Il Delitto Perfetto | {{ROARS}}}, url = {https://www.roars.it/universita-il-delitto-perfetto/}, urldate = {2024-12-30}, keywords = {/unread,source\_mobile} } @online{UniversitaUsaueDove2025, title = {Università Usa-Ue: Dove i Soldi Fanno La Differenza | Milena Gabanelli}, shorttitle = {Università Usa-Ue}, date = {2025-03-20}, url = {https://www.corriere.it/dataroom-milena-gabanelli/universita-usa-ue-dove-i-soldi-fanno-la-differenza/c7855338-f66d-449a-8188-289175676xlk.shtml}, urldate = {2025-03-20}, abstract = {A confronto i due sistemi universitari: rette, borse di studio e prestiti d'onore. I Paesi Ue che ti pagano per studiare e i 43 milioni di americani indebitati}, langid = {english}, organization = {Corriere Della Sera}, keywords = {/unread} } @online{UniversitySouthAustralia, title = {University of {{South Australia}} {$>$} {{Course}}}, url = {http://programs.unisa.edu.au/public/pcms/course.aspx?pageid=101801&y=2016}, urldate = {2016-08-21} } @inproceedings{uppaal, title = {{{UPPAAL}} - a Tool Suite for Automatic Verification of Real-Time Systems}, booktitle = {Hybrid {{Syst}}. {{III Verification Control Proc}}. {{DIMACSSYCON Workshop Verification Control Hybrid Syst}}. {{Oct}}. 22-25 1995 {{Ruttgers Univ}}. {{N}}. {{B}}. {{NJ USA}}}, author = {Bengtsson, Johan and Larsen, Kim Guldstrand and Larsson, Fredrik and Pettersson, Paul and Yi, Wang}, editor = {Alur, Rajeev and Henzinger, Thomas A. and Sontag, Eduardo D.}, date = {1995}, series = {Lecture Notes in Computer Science}, volume = {1066}, pages = {232--243}, publisher = {Springer}, doi = {10.1007/BFB0020949}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Tue, 14 May 2019 10:00:42 +0200} } @online{UseMCPServers, title = {Use {{MCP}} Servers in {{VS}} Code (Preview)}, url = {https://code.visualstudio.com/docs/copilot/chat/mcp-servers}, urldate = {2025-04-11}, abstract = {Learn how to configure and use Model Context Protocol (MCP) servers with GitHub Copilot in Visual Studio Code.}, langid = {english} } @misc{UsingModelsRuntime, title = {Using {{Models}} at {{Runtime}} to {{Address Assurance}} for {{Self-Adaptive Systems}}} } @article{UsingRecommenderSystems, entrysubtype = {newspaper}, title = {Using {{Recommender Systems}} to {{Improve Proactive Modeling}}}, note = {TL;DR \par The results of the experiments show that extending proactive modeling with a recommender system results in an average reciprocal hit-rank of 0.871 and user feedback shows that integrating recommender systems into DSMLs increases usability and learnability.} } @online{UsingTorPrivoxy, title = {Using {{Tor}}, {{Privoxy}} and {{Polipo}} \textasciitilde{} {{A}} Little Bit of Everything}, url = {http://teebeenator.blogspot.it/2014/03/using-tor-privoxy-and-polipo.html}, urldate = {2015-03-30} } @inproceedings{Usman202125, title = {{{NEUROSPF}}: {{A}} Tool for the Symbolic Analysis of Neural Networks}, author = {Usman, M. and Noller, Y. and Pasareanu, C.S. and Sun, Y. and Gopinath, D.}, date = {2021}, series = {Proceedings - {{International Conference}} on {{Software Engineering}}}, pages = {25--28}, publisher = {IEEE Computer Society}, issn = {02705257}, doi = {10.1109/ICSE-Companion52605.2021.00027}, abstract = {This paper presents NEUROSPF, a tool for the symbolic analysis of neural networks. Given a trained neural network model, the tool extracts the architecture and model parameters and translates them into a Java representation that is amenable for analysis using the Symbolic PathFinder symbolic execution tool. Notably, NEUROSPF encodes specialized peer classes for parsing the model's parameters, thereby enabling efficient analysis. With NEUROSPF the user has the flexibility to specify either the inputs or the network internal parameters as symbolic, promoting the application of program analysis and testing approaches from software engineering to the field of machine learning. For instance, NEUROSPF can be used for coverage-based testing and test generation, finding adversarial examples and also constraint-based repair of neural networks, thus improving the reliability of neural networks and of the applications that use them. Video URL: https://youtu.be/seal8fG78L. © 2021 IEEE.}, author_keywords = {Neural Networks; Symbolic Execution; Symbolic PathFinder}, coden = {PCSED}, document_type = {Conference Paper}, isbn = {978-1-6654-1219-3}, source = {Scopus}, keywords = {Application programs,Architectures and models,Efficient analysis,Internal parameters,Model checking,Modeling parameters,Neural network model,Neural-networks,Software testing,Symbolic analysis,Symbolic execution,Symbolic pathfinder,Trained neural networks}, note = {cited By 2 \par TL;DR \par This paper presents NEUROSPF, a tool for the symbolic analysis of neural networks, which extracts the architecture and model parameters and translates them into a Java representation that is amenable for analysis using the Symbolic PathFinder symbolic execution tool.} } @article{Vakil-Baghmisheh2003, title = {A Fast Simplified Fuzzy {{ARTMAP}} Network}, author = {Vakil-Baghmisheh, Mohammad-Taghi and Pavešić, Nikola}, date = {2003-06-01}, journaltitle = {Neural Process. Lett.}, volume = {17}, number = {3}, pages = {273--316}, issn = {1573-773X}, doi = {10.1023/A:1026004816362}, abstract = {We present an algorithmic variant of the simplified fuzzy ARTMAP (SFAM) network, whose structure resembles those of feed-forward networks. Its difference with Kasuba's model is discussed, and their performances are compared on two benchmarks. We show that our algorithm is much faster than Kasuba's algorithm, and by increasing the number of training samples, the difference in speed grows enormously.} } @inproceedings{VallecilloGBWH12, title = {Formal Specification and Testing of Model Transformations}, booktitle = {Form. {{Methods Model-Driven Eng}}. - 12th {{Int}}. {{Sch}}. {{Form}}. {{Methods Des}}. {{Comput}}. {{Commun}}. {{Softw}}. {{Syst}}. {{SFM}} 2012 {{Bertinoro Italy June}} 18-23 2012 {{Adv}}. {{Lect}}.}, author = {Vallecillo, Antonio and Gogolla, Martin and Burgueño, Loli and Wimmer, Manuel and Hamann, Lars}, editor = {Bernardo, Marco and Cortellessa, Vittorio and Pierantonio, Alfonso}, date = {2012}, series = {Lecture Notes in Computer Science}, volume = {7320}, pages = {399--437}, publisher = {Springer}, doi = {10.1007/978-3-642-30982-3\_11}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Tue, 14 May 2019 10:00:44 +0200} } @inproceedings{vallecillosruizAgentdrivenAutomaticSoftware2024, title = {Agent-Driven Automatic Software Improvement}, booktitle = {Proc. 28th {{Int}}. {{Conf}}. {{Eval}}. {{Assess}}. {{Softw}}. {{Eng}}.}, author = {Vallecillos Ruiz, Fernando}, date = {2024-06-18}, pages = {470--475}, publisher = {ACM}, location = {Salerno Italy}, doi = {10.1145/3661167.3661171}, eventtitle = {{{EASE}} 2024: 28th {{International Conference}} on {{Evaluation}} and {{Assessment}} in {{Software Engineering}}}, isbn = {979-8-4007-1701-7}, langid = {english} } @article{vallecilloTypingModelTransformations2012, title = {Typing {{Model Transformations Using Tracts}}}, author = {Vallecillo, Antonio and Gogolla, Martin}, date = {2012}, journaltitle = {Theory Pract. Model Transform.}, volume = {7307}, pages = {56--71}, doi = {10.1007/978-3-642-30476-7_4}, note = {TL;DR \par A light-weight approach to type model transformations using tracts is presented, as well as the applicability of the proposal in several settings.} } @article{van2011numpy, title = {The {{NumPy}} Array: A Structure for Efficient Numerical Computation}, author = {Van Der Walt, Stefan and Colbert, S Chris and Varoquaux, Gael}, date = {2011}, journaltitle = {Comput. Sci. Eng.}, volume = {13}, number = {2}, pages = {22--30}, publisher = {IEEE} } @article{vanamstelUsingMetricsAssessing, title = {Using {{Metrics}} for {{Assessing}} the {{Quality}} of {{ATL Model Transformations}}}, author = {family=Amstel, prefix=van, useprefix=true and family=Brand, prefix=van den, useprefix=true}, note = {TL;DR \par This paper focuses on model transformations created using ATL, which ATL is currently one of the most widely used model transformation formalisms, and collects metrics data from a heterogeneous collection of seven model transformations to assess various quality attributes.} } @inproceedings{vanderdoncktApplyingDeepLearning2020, title = {Applying Deep Learning to Reduce Large Adaptation Spaces of Self-Adaptive Systems with Multiple Types of Goals}, booktitle = {Proc. {{IEEEACM}} 15th {{Int}}. {{Symp}}. {{Softw}}. {{Eng}}. {{Adapt}}. {{Self-Manag}}. {{Syst}}.}, author = {Van Der Donckt, Jeroen and Weyns, Danny and Quin, Federico and Van Der Donckt, Jonas and Michiels, Sam}, date = {2020-06-29}, pages = {20--30}, publisher = {ACM}, location = {Seoul Republic of Korea}, doi = {10.1145/3387939.3391605}, abstract = {When a self-adaptive system needs to adapt, it has to analyze the possible options for adaptation, i.e., the adaptation space. For systems with large adaptation spaces, this analysis process can be resource- and time-consuming. One approach to tackle this problem is using machine learning techniques to reduce the adaptation space to only the relevant adaptation options. However, existing approaches only handle threshold goals, while practical systems often need to address also optimization goals. To tackle this limitation, we propose a two-stage learning approach called Deep Learning for Adaptation Space Reduction (DLASeR). DLASeR applies a deep learner first to reduce the adaptation space for the threshold goals and then ranks these options for the optimization goal. A benefit of deep learning is that it does not require feature engineering. Results on two instances of the DeltaIoT artifact (with different sizes of adaptation space) show that DLASeR outperforms a state-of-the-art approach for settings with only threshold goals. The results for settings with both threshold goals and an optimization goal show that DLASeR is effective with a negligible effect on the realization of the adaptation goals. Finally, we observe no noteworthy effect on the effectiveness of DLASeR for larger sizes of adaptation spaces.}, eventtitle = {{{SEAMS}} '20: {{IEEE}}/{{ACM}} 15th {{International Symposium}} on {{Software Engineering}} for {{Adaptive}} and {{Self-Managing Systems}}}, isbn = {978-1-4503-7962-5}, langid = {english}, note = {TL;DR \par This work proposes a two-stage learning approach called Deep Learning for Adaptation Space Reduction (DLASeR), which applies a deep learner first to reduce the adaptation space for the threshold goals and then ranks these options for the optimization goal.} } @inproceedings{VanDerWaa2018, title = {The Design and Validation of an Intuitive Confidence Measure}, author = {Van Der Waa, J. and Van DIggelen, J. and Neerincx, M.}, editor = {Said A., Komatsu T.}, date = {2018}, series = {{{CEUR Workshop Proceedings}}}, volume = {2068}, publisher = {CEUR-WS}, issn = {16130073}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85044532830&partnerID=40&md5=2ed349cd86e9fe55b202cfbb8c2a5f7d}, abstract = {Explainable AI becomes increasingly important as the use of intelligent systems becomes more widespread in high-risk domains. In these domains it is important that the user knows to which degree the system's decisions can be trusted. To facilitate this, we present the Intuitive Confidence Measure (ICM): A lazy learning meta-model that can predict how likely a given decision is correct. ICM is intended to be easy to understand which we validated in an experiment. We compared ICM with two different methods of computing confidence measures: The numerical output of the model and an actively learned metamodel. The validation was performed using a smart assistant for maritime professionals. Results show that ICM is easier to understand but that each user is unique in its desires for explanations. This user studies with domain experts shows what users need in their explanations and that personalization is crucial. © 2018 Copyright for the individual papers remains with the authors.}, document_type = {Conference Paper}, source = {Scopus}, note = {cited By 0 \par TL;DR \par A lazy learning meta-model that can predict how likely a given decision is correct and is intended to be easy to understand, which is validated in an experiment and shows that each user is unique in its desires for explanations.} } @article{vandeursen2000domain, title = {Domain-Specific Languages: {{An}} Annotated Bibliography}, author = {family=Deursen, given=Arie, prefix=van, useprefix=true and Klint, Paul and Visser, Joost}, date = {2000}, journaltitle = {ACM SIGPLAN Not.}, volume = {35}, number = {6}, pages = {26--36}, publisher = {ACM} } @article{VanGelder2014245, title = {Comparative Study of Metamodelling Techniques in Building Energy Simulation: {{Guidelines}} for Practitioners}, author = {Van Gelder, L. and Das, P. and Janssen, H. and Roels, S.}, date = {2014}, journaltitle = {Simul. Model. Pract. Theory}, volume = {49}, pages = {245--257}, publisher = {Elsevier}, issn = {1569190X}, doi = {10.1016/j.simpat.2014.10.004}, abbrev_source_title = {Simul. Model. Pract. Theory}, affiliation = {Building Physics Section, Department of Civil Engineering, KU Leuven, Kasteelpark Arenberg 40 Bus 2447, Heverlee, 3001, Belgium; Bartlett School of Graduate Studies, University College London, Central House, 14 Upper Woburn Place, London, WC1H 0NN, United Kingdom}, correspondence_address1 = {Van Gelder, L.; Building Physics Section, Department of Civil Engineering, KU Leuven, Kasteelpark Arenberg 40 Bus 2447, Belgium}, document_type = {Article}, langid = {english}, source = {Scopus}, note = {cited By 67} } @article{vanhooffFrameworkTransformationChain2006, title = {A {{Framework}} for {{Transformation Chain Development Processes}}}, author = {Vanhooff, Bert and Ayed, Dhouha and Berbers, Yolande}, date = {2006}, pages = {3--8} } @article{vanhooffTransformationChainModeling2006, title = {Towards a {{Transformation Chain Modeling Language}}}, author = {Vanhooff, Bert and Baelen, Stefan and Hovsepyan, Aram and Joosen, Wouter and Berbers, Yolande}, date = {2006}, journaltitle = {Embed. Comput. Syst. Archit. Model. Simul.}, volume = {4017}, pages = {39--48}, doi = {10.1007/11796435_6}, note = {TL;DR \par This paper proposes a metamodel for a transformation chain modeling language that enables implementation independent composition of transformations and proposes a concrete syntax for this language that is based on UML activity diagrams.} } @inproceedings{varaminybahnemiry2021automated, title = {Automated Patch Generation for Fixing Semantic Errors in {{ATL}} Transformation Rules}, booktitle = {2021 {{ACMIEEE}} 24th {{Int}}. {{Conf}}. {{Model Driven Eng}}. {{Lang}}. {{Syst}}. {{MODELS}}}, author = {VaraminyBahnemiry, Zahra and Galasso, Jessie and Belharbi, Khalid and Sahraoui, Houari}, date = {2021}, pages = {13--23}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {TL;DR \par This paper proposes an approach for fixing semantic errors in ATL transformation rules without predefined patch templates for specific error types, and shows that it can fix most of the errors for transformations with one or two errors.} } @article{Varaminybahnemiry2021fixing, title = {Fixing Multiple Type Errors in Model Transformations with Alternative Oracles to Test Cases}, author = {family=Varaminybahnemiry¡/a¿, given=¡a, prefix=href="/contents.php?query=Varaminybahnemiry"¿Zahra, useprefix=false and family=Galasso¡/a¿, given=¡a, prefix=href="/contents.php?query=Galasso"¿Jessie, useprefix=false and family=Sahraoui¡/a¿, given=¡a, prefix=href="/contents.php?query=Sahraoui"¿Houari, useprefix=false}, date = {2021}, journaltitle = {J. Object Technol.}, volume = {20}, number = {3}, pages = {9:1-14}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found} } @inproceedings{Vargas_sales_diversity_14, title = {Improving Sales Diversity by Recommending Users to Items}, booktitle = {Eighth {{ACM Conf}}. {{Recomm}}. {{Syst}}. {{RecSys}} 14 {{Foster City Silicon Val}}. {{CA USA}} - {{Oct}}. 06 - 10 2014}, author = {Vargas, Saúl and Castells, Pablo}, date = {2014}, pages = {145--152}, doi = {10.1145/2645710.2645744}, bibsource = {dblp computer science bibliography, http://dblp.org}, biburl = {http://dblp.uni-trier.de/rec/bib/conf/recsys/VargasC14}, note = {TL;DR \par This work explores the inversion of the recommendation task as a means to enhance sales diversity - and indirectly novelty - by selecting which users an item should be recommended to instead of the other way around, and addresses the inverted task by inverting the rating matrix.}, timestamp = {Thu, 02 Oct 2014 08:41:01 +0200} } @inproceedings{vargasRankRelevanceNovelty2011, ids = {10.1145/2043932.2043955}, title = {Rank and Relevance in Novelty and Diversity Metrics for Recommender Systems}, booktitle = {Proc. {{Fifth ACM Conf}}. {{Recomm}}. {{Syst}}.}, author = {Vargas, Saúl and Castells, Pablo}, date = {2011}, series = {{{RecSys}} '11}, pages = {109--116}, publisher = {ACM}, location = {New York, NY, USA}, url = {http://doi.acm.org/10.1145/2043932.2043955}, acmid = {2043955}, isbn = {978-1-4503-0683-6}, nodoi = {10.1145/2043932.2043955}, numpages = {8}, pagetotal = {8}, keywords = {diversity,evaluation,metrics,novelty,recommender systems}, note = {TL;DR \par A formal framework for the definition of novelty and diversity metrics is presented that unifies and generalizes several state of the art metrics and identifies three essential ground concepts at the roots of noveltyand diversity: choice, discovery and relevance, upon which the framework is built.} } @article{vargasRealisticPublicDataset2019, title = {A Realistic and Public Dataset with Rare Undesirable Real Events in Oil Wells}, author = {Vargas, Ricardo Emanuel Vaz and Munaro, Celso José and Ciarelli, Patrick Marques and Medeiros, André Gonçalves and family=Amaral, given=Bruno Guberfain, prefix=do, useprefix=false and Barrionuevo, Daniel Centurion and family=Araújo, given=Jean Carlos Dias, prefix=de, useprefix=false and Ribeiro, Jorge Lins and Magalhães, Lucas Pierezan}, date = {2019-10}, journaltitle = {Journal of Petroleum Science and Engineering}, volume = {181}, pages = {106223}, issn = {09204105}, doi = {10.1016/j.petrol.2019.106223}, abstract = {Detection of undesirable events in oil and gas wells can help prevent production losses, environmental accidents, and human casualties and reduce maintenance costs. The scarcity of measurements in such processes is a drawback due to the low reliability of instrumentation in such hostile environments. Another issue is the absence of adequately structured data related to events that should be detected. To contribute to providing a priori knowledge about undesirable events for diagnostic algorithms in offshore naturally flowing wells, this work presents an original and valuable dataset with instances of eight types of undesirable events characterized by eight process variables. Many hours of expert work were required to validate historical instances and to produce simulated and hand-drawn instances that can be useful to distinguish normal and abnormal actual events under different operating conditions. The choices made during this dataset's preparation are described and justified, and specific benchmarks that practitioners and researchers can use together with the published dataset are defined. This work has resulted in two relevant contributions. A challenging public dataset that can be used as a benchmark for the development of (i) machine learning techniques related to inherent difficulties of actual data, and (ii) methods for specific tasks associated with detecting and diagnosing undesirable events in offshore naturally flowing oil and gas wells. The other contribution is the proposal of the defined benchmarks.}, langid = {english} } @inproceedings{varro2007automating, title = {Automating Model Transformation by Example Using Inductive Logic Programming}, booktitle = {Proc. 2007 {{ACM Symp}}. {{Appl}}. {{Comput}}.}, author = {Varró, Dániel and Balogh, Zoltán}, date = {2007}, pages = {978--984}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {TL;DR \par This paper automates the approach to model transformation by example using inductive logic programming which aims at the inductive construction of first-order clausal theories from examples and background knowledge.} } @inproceedings{vasilescu_stackoverflow_2013, title = {{{StackOverflow}} and {{GitHub}}: {{Associations}} between {{Software Development}} and {{Crowdsourced Knowledge}}}, shorttitle = {{{StackOverflow}} and {{GitHub}}}, booktitle = {2013 {{Int}}. {{Conf}}. {{Soc}}. {{Comput}}.}, author = {Vasilescu, Bogdan and Filkov, Vladimir and Serebrenik, Alexander}, date = {2013-09}, pages = {188--195}, doi = {10.1109/SocialCom.2013.35}, abstract = {Stack Overflow is a popular on-line programming question and answer community providing its participants with rapid access to knowledge and expertise of their peers, especially benefitting coders. Despite the popularity of Stack Overflow, its role in the work cycle of open-source developers is yet to be understood: on the one hand, participation in it has the potential to increase the knowledge of individual developers thus improving and speeding up the development process. On the other hand, participation in Stack Overflow may interrupt the regular working rhythm of the developer, hence also possibly slow down the development process. In this paper we investigate the interplay between Stack Overflow activities and the development process, reflected by code changes committed to the largest social coding repository, GitHub. Our study shows that active GitHub committers ask fewer questions and provide more answers than others. Moreover, we observe that active Stack Overflow askers distribute their work in a less uniform way than developers that do not ask questions. Finally, we show that despite the interruptions incurred, the Stack Overflow activity rate correlates with the code changing activity in GitHub.}, keywords = {Communities,crowdsourced knowledge,Electronic mail,GitHub,Indexes,knowledge based systems,Merging,online programming question-and-answer community,Productivity,Rhythm,social coding repository,social media,Software,software development,software maintenance,StackOverflow}, note = {TL;DR \par This paper investigates the interplay between Stack Overflow activities and the development process, reflected by code changes committed to the largest social coding repository, GitHub, and shows that active GitHub committers ask fewer questions and provide more answers than others.} } @article{vasilescuHowHealthyAre2014, title = {How Healthy Are Software Engineering Conferences?}, author = {Vasilescu, Bogdan and Serebrenik, Alexander and Mens, Tom and family=Brand, given=Mark G.J., prefix=van den, useprefix=true and Pek, Ekaterina}, date = {2014-09}, journaltitle = {Sci. Comput. Program.}, volume = {89}, pages = {251--272}, issn = {01676423}, doi = {10.1016/j.scico.2014.01.016}, langid = {english} } @inproceedings{vassevAutonomyRequirementsEngineering2013, title = {Autonomy Requirements Engineering: A Case Study on the {{BepiColombo}} Mission}, shorttitle = {Autonomy Requirements Engineering}, booktitle = {Proc. {{Int}}. {{C Conf}}. {{Comput}}. {{Sci}}. {{Softw}}. {{Eng}}.}, author = {Vassev, Emil and Hinchey, Mike}, date = {2013}, pages = {31--41}, publisher = {ACM}, url = {http://dl.acm.org/citation.cfm?id=2494472}, urldate = {2016-08-21} } @article{Vaswani:2017lxt, title = {Attention {{Is All You Need}}}, author = {Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N. and Kaiser, Lukasz and Polosukhin, Illia}, date = {2017-12-05}, journaltitle = {arXiv:1706.03762 [cs.CL]}, eprint = {1706.03762}, eprinttype = {arXiv}, eprintclass = {cs.CL}, url = {http://arxiv.org/abs/1706.03762}, urldate = {2022-08-26}, abstract = {The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.}, keywords = {Computer Science - Computation and Language,Computer Science - Machine Learning,LOGSEQ}, note = {Comment: 15 pages, 5 figures} } @online{vaswaniAttentionAllYou2023, title = {Attention {{Is All You Need}}}, author = {Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N. and Kaiser, Lukasz and Polosukhin, Illia}, date = {2023-08-01}, eprint = {1706.03762}, eprinttype = {arXiv}, eprintclass = {cs}, doi = {10.48550/arXiv.1706.03762}, abstract = {The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.}, pubstate = {prepublished}, keywords = {Computer Science - Computation and Language,Computer Science - Machine Learning}, note = {Comment: 15 pages, 5 figures} } @article{vathy-fogarassyUniformDataAccess2017, title = {Uniform Data Access Platform for {{SQL}} and {{NoSQL}} Database Systems}, author = {Vathy-Fogarassy, Ágnes and Hugyák, Tamás}, date = {2017-09}, journaltitle = {Inf. Syst.}, volume = {69}, pages = {93--105}, issn = {03064379}, doi = {10.1016/j.is.2017.04.002}, abstract = {Integration of data stored in heterogeneous database systems is a very challenging task and it may hide several difficulties. As NoSQL databases are growing in popularity, integration of different NoSQL systems and interoperability of NoSQL systems with SQL databases become an increasingly important issue. In this paper, we propose a novel data integration methodology to query data individually from different relational and NoSQL database systems. The suggested solution does not support joins and aggregates across data sources; it only collects data from different separated database management systems according to the filtering options and migrates them. The proposed method is based on a metamodel approach and it covers the structural, semantic and syntactic heterogeneities of source systems. To introduce the applicability of the proposed methodology, we developed a web-based application, which convincingly confirms the usefulness of the novel method.}, langid = {english} } @inproceedings{vazEmpiricalStudyTask2019, title = {An {{Empirical Study}} on {{Task Documentation}} in {{Software Crowdsourcing}} on {{TopCoder}}}, booktitle = {2019 {{ACMIEEE}} 14th {{Int}}. {{Conf}}. {{Glob}}. {{Softw}}. {{Eng}}. {{ICGSE}}}, author = {Vaz, Luis and Steinmacher, Igor and Marczak, Sabrina}, date = {2019-05}, pages = {48--57}, publisher = {IEEE}, location = {Montreal, QC, Canada}, doi = {10.1109/ICGSE.2019.00041}, eventtitle = {2019 {{ACM}}/{{IEEE}} 14th {{International Conference}} on {{Global Software Engineering}} ({{ICGSE}})}, isbn = {978-1-5386-9196-0} } @thesis{velazquez-rodriguezUncoveringLibraryFeatures, title = {Uncovering {{Library Features}} from {{Incomplete Information}} on {{Stack Overflow}}}, author = {Velázquez-Rodríguez, Camilo}, langid = {english}, keywords = {LOGSEQ}, note = {\section{Annotazioni\\ (4/3/2024, 15:16:19)} \par - \par “Camilo Velázquez-Rodríguez” (Velázquez-Rodríguez, p. 1) \#66ff66 \par \mkbibemph{ } - \par “lack of automated tool support.” (Velázquez-Rodríguez, p. i) \#ff00ff \par \mkbibemph{ } - \par “Overview of the Approach” (Velázquez-Rodríguez, p. 4) \#ffd400 \par \mkbibemph{By looking at the structure, it seems the thesis is a collection of papers; we are missing a chapter playing the role of glue among all of them... it improves later! } - \par “This dissertation presents contributions in two main research areas: i) automated library feature uncovering and ii) API type resolution for incomplete code snippets” (Velázquez-Rodríguez, p. 5) \#ffd400 \par \mkbibemph{See my previous comment, it is not clear the role of the second part, and how it is linked to the first one. } - \par “recommendation of multiple tags” (Velázquez-Rodríguez, p. 5) \#ffd400 \par \mkbibemph{Existing baselines have not been considered why? See for instance: Juri Di Rocco,~Davide Di Ruscio,~Claudio Di Sipio,~Phuong T. Nguyen,~Riccardo Rubei:\\ HybridRec: A recommender system for tagging GitHub repositories.~Appl. Intell.~53(8):~9708-9730~(2023) } - \par “In summary, we stress the need for an approach that can extract information from syntactically incorrect code snippets. Such an approach should also resolve missing external API references in incomplete code snippets. The extracted information and the API reference resolution may improve the current SO code processing. Discussed approaches have not addressed these problems commonly found on SO code snippets” (Velázquez-Rodríguez, p. 39) \#ffd400 \par \mkbibemph{The goal has been clearly written only at page 39. } - \par “3.6. Conclusion” (Velázquez-Rodríguez, p. 40) \#ffd400 \par \mkbibemph{At the end of chapter 3 the question is: what's the granularity of "features"? \#question } - \par “simple names of API type” (Velázquez-Rodríguez, p. 43) \#ffd400 \par \mkbibemph{What does it mean simple names? \#question } - \par “alance Datase” (Velázquez-Rodríguez, p. 52) \#ffd400 \par \mkbibemph{How have you balanced your dataset? \#question } - \par “ML Classifiers” (Velázquez-Rodríguez, p. 52) \#ffd400 \par \mkbibemph{What has been used here? \#question } - \par “Select Best Model” (Velázquez-Rodríguez, p. 52) \#ffd400 \par \mkbibemph{Is this done once? \#question } - \par “100 most frequent libraries” (Velázquez-Rodríguez, p. 53) \#ffd400 \par \mkbibemph{Have you considered the removal of the most frequent libraries, e.g., log4j \#question \#popularitybias } - \par “The FQNs with fewer occurrences than the defined threshold are not considered for the training phase and are therefore excluded.” (Velázquez-Rodríguez, p. 53) \#ffd400 \par \mkbibemph{This is a \#popularitybias It is a common issue, how to deal with it? } - \par “External Datasets” (Velázquez-Rodríguez, p. 54) \#ffd400 \par \mkbibemph{Are they balanced? } - \par “The machine learning algorithms employed within RESICO” (Velázquez-Rodríguez, p. 59) \#ffd400 \par \mkbibemph{What are the criteria you used to select ML algorithms? \#question } - \par “each of the RESICO classifiers” (Velázquez-Rodríguez, p. 59) \#ffd400 \par \mkbibemph{How to decide, which one to be used for the task at hand? } - \par “We conclude that the best classifier of our approach (i.e., KNN) is more effective than COSTER on this dataset” (Velázquez-Rodríguez, p. 62) \#ffd400 \par \mkbibemph{Any qualitative motivation / discussion supporting such conclusions? } - \par “In the three external datasets considered to evaluate the generalisability of the performance, RESICO-trained models outperform COSTER with a notable difference in some cases.” (Velázquez-Rodríguez, p. 64) \#ffd400 \par \mkbibemph{In the case of unbalanced datasets, how do the two approach compare? } - \par “Our approach is more complex than COSTER since it involves training several machine learning models; hence, it consumes more computational resources during training.” (Velázquez-Rodríguez, p. 73) \#ffd400 \par \mkbibemph{From the user perspectives, how is the model selection done / supported? } - \par “features offered by each library.” (Velázquez-Rodríguez, p. 75) \#f0ff00 \par \mkbibemph{it's interesting to see the given definition of features. } - \par “We adopt their definition in this dissertation so that features comprise the API elements that realise them, as well as their textual description.” (Velázquez-Rodríguez, p. 76) \#00b036 \par \mkbibemph{ } - \par “AutoCat and MUTAMA are not focused on fine-grained features, but target more coarse forms of features such as categories and tags.” (Velázquez-Rodríguez, p. 76) \#00b036 \par \mkbibemph{ } - \par “users could inspect a list of features that are commonly or rarely implemented by the libraries in a selected category.” (Velázquez-Rodríguez, p. 77) \#00b036 \par \mkbibemph{ } - \par “Current tool support does not allow developers to efficiently evaluate and compare candidate libraries with respect to all the desired features.” (Velázquez-Rodríguez, p. 77) \#00b036 \par \mkbibemph{ } - \par “The features, however, can be described at several levels of granularity, from the artifact level to the code level.” (Velázquez-Rodríguez, p. 78) \#00b036 \par \mkbibemph{\#IMPORTANT Features are given at different level of granularity. } - \par “AutoCat, automatically categorises a library into one of the top-level library categories used by ecosystem indices.” (Velázquez-Rodríguez, p. 78) \#00b036 \par \mkbibemph{autocat } - \par “LiFUSO, describes a library in terms of features consisting of the API elements that implement them and a natural language description.” (Velázquez-Rodríguez, p. 78) \#00b036 \par \mkbibemph{lofuso } - \par “The proposed approach is based on text classification machine learning algorithms trained and evaluated on a corpus of text extracted from the libraries. We obtain this corpus of text by extracting the identifiers of public classes and methods from a library’s JAR file using the Apache BCEL8 library. For those identifiers following the CamelCase naming convention (e.g., getAccountNumber), we separate the identifier into distinct words (e.g., get, Account, Number)” (Velázquez-Rodríguez, p. 79) \#f0ff00 \par \mkbibemph{thus, readme files or alike are not considered right? This could have been useful for the task of categorization..... } - \par “The generated vectors capture the context around a word (e.g., a window size of five tokens); hence it is possible to relate different words by the surrounding context. Default parameters for the Word2Vec process are selected for its training.” (Velázquez-Rodríguez, p. 79) \#f0ff00 \par \mkbibemph{an illustrative example would have been useful here. } - \par “The task of the machine learning algorithm is to learn and predict a discrete label for each vector that corresponds to the MVNRepository category to which the library belongs. We consider five machine learning algorithms to instantiate our approach: Gaussian Naive Bayes (GNB) as well as Bernoulli Naive Bayes (BNB) [JL95], Support Vector Machines (SVC) [HDO+98], K -Nearest Neighbors (KNN) [Das91] and Random Forest (RF) [Ho95, Bre01] (cf. Section 2.4.2).” (Velázquez-Rodríguez, p. 79) \#f0ff00 \par \mkbibemph{I suspect the dataset is not balanced, isn't it? } - \par “five MVNRepository categories: Collections, Dependency Injection, Http Clients, Compression and JSON libraries.” (Velázquez-Rodríguez, p. 79) \#f0ff00 \par \mkbibemph{is there any reason around the selection of such five categories? } - \par “This small experiment (i.e., only 15 libraries) returns promising results regarding the automatic classification of libraries based on their implementation.” (Velázquez-Rodríguez, p. 79) \#f0ff00 \par \mkbibemph{it's a bit obscure without an example } - \par “Limitations of Category-based Approaches to Feature Uncovering” (Velázquez-Rodríguez, p. 80) \#f0ff00 \par \mkbibemph{Why no comparisons have been done with related baseline? } - \par “An automated approach to suggesting feature tags for a software library could overcome this problem and thereby facilitate ecosystem search.” (Velázquez-Rodríguez, p. 81) \#f0ff00 \par \mkbibemph{check if it has been compared with our approach. } - \par “Table 5.3.: Multi-tag predictions made by the best trained multi-label model.” (Velázquez-Rodríguez, p. 87) \#f0ff00 \par \mkbibemph{Also in this case, the dataset can be unbalanced. How have you addressed the problem? } - \par “Limitations of Tag-based Approaches for Features Discovery” (Velázquez-Rodríguez, p. 88) \#f0ff00 \par \mkbibemph{in terms of conveied knowledge, what can we say, given a same project, about the recommended tags and category? Do you think such a cross-cuttings analysis can help or give some more insights? } - \par “Figure 5.5.:” (Velázquez-Rodríguez, p. 89) \#f0ff00 \par \mkbibemph{does the collected features make sense without any initial human intervention? \#question } - \par “example usages of this API from SO snippets (step 3 in Figure 5.5). To collect” (Velázquez-Rodríguez, p. 89) \#f0ff00 \par \mkbibemph{this is a bit obscure at this point... } - \par “we collect all class names that were once considered part of it.” (Velázquez-Rodríguez, p. 89) \#f0ff00 \par \mkbibemph{not clear.... } - \par “has been tagged with the name of the library (e.g., guava, pdfbox).” (Velázquez-Rodríguez, p. 89) \#f0ff00 \par \mkbibemph{this limit the applicability of the approach, isn't it? \#question } - \par “its strictness minimises false positives.” (Velázquez-Rodríguez, p. 90) \#f0ff00 \par \mkbibemph{but still, in my opinion it limits the applicability of the approach in practice, \\ I don't think the are so many tagged answers } - \par “Parsers generated by an island grammar [Moo01] focus on some constructs of interest (i.e., islands) and consider the remainder of the text to parse as irrelevant (i.e., water). They have been shown well-suited to parsing and lightweight analysis of code that is grammatically incomplete (e.g., a statement without a surrounding method) or that contains syntax errors (e.g., three dots instead of an expression) such as the snippets on SO.” (Velázquez-Rodríguez, p. 90) \#00b036 \par \mkbibemph{ } - \par “clusters with the most frequent name pairs and API references are outputted.” (Velázquez-Rodríguez, p. 94) \#f0ff00 \par \mkbibemph{why pairs? } - \par “Figure 5.11.: Front page of the LiFUSO tool with the Search feature tab activated.” (Velázquez-Rodríguez, p. 97) \#f0ff00 \par \mkbibemph{I'm not sure on how significant are the shown features for users in practice. I think a user study would have been needed here. } - \par “cohesiveness” (Velázquez-Rodríguez, p. 97) \#f0ff00 \par \mkbibemph{What does it mean? } - \par “The evaluation of our approach focuses on the API calls of the generated features.” (Velázquez-Rodríguez, p. 97) \#f0ff00 \par \mkbibemph{what does that mean ? } - \par “research questions:” (Velázquez-Rodríguez, p. 97) \#f0ff00 \par \mkbibemph{Without a user study the significance and usefulness of the given features cannot be evaluated properly. A qualitative evaluation is needed. } - \par “documented tutorial features?” (Velázquez-Rodríguez, p. 98) \#f0ff00 \par \mkbibemph{how and when are these uncovered? } - \par “Tutorial features are compared one by one with all uncovered clusters.” (Velázquez-Rodríguez, p. 101) \#f0ff00 \par \mkbibemph{it means that there is a kind of shared / common vocabulary that is manually curated. isn't it? \#question } - \par “When matches occur, we store the uncovered feature identifier (i.e., a number) and the tutorial feature that was matched.” (Velázquez-Rodríguez, p. 101) \#f0ff00 \par \mkbibemph{is there a kind of hasmap defined somewhere? \#question } - \par “High relevance scores indicate that uncovered features are highly similar to tutorial features.” (Velázquez-Rodríguez, p. 104) \#f0ff00 \par \mkbibemph{there is a bias here, which is related to the process that has been followed to identify tutorial features. What can you say about this? \#question } - \par “Libraries.io to” (Velázquez-Rodríguez, p. 105) \#00b036 \par \mkbibemph{this website seems to be interesting for our experiments \#ideas } - \par “Table 5.8.: Newly matched features from GitHub client projects.” (Velázquez-Rodríguez, p. 106) \#f0ff00 \par \mkbibemph{there is the usual comment about the not clear definition of what is a feature. Explanatory examples would help here. } - \par “Another limitation of our approach is that it relies on SO posts being tagged with the name of the library for which features need to be uncovered.” (Velázquez-Rodríguez, p. 109) \#f0ff00 \par \mkbibemph{I agree...this has been a limiting decision. } - \par “Figure 5.12.: Shared features for the studied libraries.” (Velázquez-Rodríguez, p. 111) \#f0ff00 \par \mkbibemph{some features are missing context. Create pdf is indeed clear, add page is not. } - \par “for less popular libraries for which there is little usage in SO answers” (Velázquez-Rodríguez, p. 113) \#f0ff00 \par \mkbibemph{this is related to the popularity bias problem mentioned also for the other works. Evaluating the work by filtering out popular libraries would have helped to gain some more insights about the overall accuracy of the work. } - \par “new GitHub corpus and its application to the whole SO dataset. Additionally, we present the results of our strategies through various evaluation” (Velázquez-Rodríguez, p. 116) \#f0ff00 \par \mkbibemph{what are the characteristics of such a new Github datast? \#question } - \par “some manual input is required for parts of the proposed pipeline such as the meta-data of a library (e.g., groupId and artifactId) as well as its corresponding tag name. Additionally, the GitHub repository hosting the library is required as input to the ghtopdep tool, which retrieves the dependent repositories.” (Velázquez-Rodríguez, p. 140) \#f0ff00 \par \mkbibemph{This is related to what I was mentioning about the need of having humans in the loop while curatig the creation of the feature taxonomy. } - \par “LiFUSO-supported libraries” (Velázquez-Rodríguez, p. 140) \#f0ff00 \par \mkbibemph{this raises questions about the applicability of the approach in practice \#question } - \par “source of information are unit test cases and their text descriptions, which may scale to many libraries since most of them include a test suite.” (Velázquez-Rodríguez, p. 141) \#00b036 \par \mkbibemph{that is very interesting \#IMPORTANT } - \par “Features are defined as API usage patterns with a corresponding description in natural language.” (Velázquez-Rodríguez, p. 143) \#f0ff00 \par \mkbibemph{I don't see in the Web based tool such descriptions in natural language. } - \par “RESICO leverages a dataset of library API usage within complete and correct code to learn word embeddings and the most likely fully qualified name for a simple name in a specific context.” (Velázquez-Rodríguez, p. 144) \#f0ff00 \par \mkbibemph{the support for continuous learning is needed in this domain and it is also necessary to address the coldstart problem. What do you think? \#question } - \par “API usage from Stack Overflow” (Velázquez-Rodríguez, p. 146) \#f0ff00 \par \mkbibemph{how to deal with ambiguous cases. Add page can be add a pdf page or a page in a Web base system. It is necessary to include the application domain. \#question } - \par “An unsupervised machine learning algorithm is used to form clusters of API usage.” (Velázquez-Rodríguez, p. 149) \#f0ff00 \par \mkbibemph{I think this should include supervision. } - \par “tutorials and cookbooks of the libraries under analysis.” (Velázquez-Rodríguez, p. 149) \#f0ff00 \par \mkbibemph{to what extent this is manual / semi-automated? } - \par “RDSN+20] Riccardo” (Velázquez-Rodríguez, p. 166) \#f0ff00 \par \mkbibemph{This is the only one cited? }} } @article{venChallengesStrategiesUse2008, title = {Challenges and Strategies in the Use of {{Open Source Software}} by {{Independent Software Vendors}}}, author = {Ven, Kris and Mannaert, Herwig}, date = {2008-08}, journaltitle = {Inf. Softw. Technol.}, volume = {50}, number = {9--10}, pages = {991--1002}, issn = {09505849}, doi = {10.1016/j.infsof.2007.09.001}, langid = {english} } @article{venkateshScalableApplicationDesignIoT2017, entrysubtype = {magazine}, title = {Scalable-{{Application Design}} for the {{IoT}}}, author = {Venkatesh, Jagannathan and Aksanli, Baris and Chan, Christine S. and Akyurek, Alper S. and Rosing, Tajana S. and {undefined} and {undefined} and {undefined} and {undefined}}, date = {2017}, journaltitle = {IEEE Software}, volume = {34}, number = {1}, pages = {62--70}, issn = {0740-7459}, abstract = {The Internet of Things envisions a web-connected infrastructure of sensing and actuation devices. However, the current state of the art presents another reality: monolithic end-to-end applications tightly coupled to a limited set of sensors and actuators. Growing such applications with new devices or behaviors, or extending the existing infrastructure with new applications, involves redesign and deployment. A proposed approach breaks these applications up into an equivalent set of functional units called context engines, whose I/O transformations are driven by general-purpose machine learning. This approach decreases computational redundancy and complexity with a minimal impact on accuracy. Researchers evaluated this approach's scalability--how the context engines' overhead grows as the input data and number of computational nodes increase. In a large-scale case study of residential smart-grid control, this approach provided better accuracy and scaling than the state-of-the-art single-stage approach.}, keywords = {internet of things,software engineering} } @inproceedings{verma_fairness_2018, title = {Fairness {{Definitions Explained}}}, booktitle = {2018 {{IEEEACM Int}}. {{Workshop Softw}}. {{Fairness FairWare}}}, author = {Verma, Sahil and Rubin, Julia}, date = {2018-05}, pages = {1--7}, doi = {10.1145/3194770.3194776}, abstract = {Algorithm fairness has started to attract the attention of researchers in AI, Software Engineering and Law communities, with more than twenty different notions of fairness proposed in the last few years. Yet, there is no clear agreement on which definition to apply in each situation. Moreover, the detailed differences between multiple definitions are difficult to grasp. To address this issue, this paper collects the most prominent definitions of fairness for the algorithmic classification problem, explains the rationale behind these definitions, and demonstrates each of them on a single unifying case-study. Our analysis intuitively explains why the same case can be considered fair according to some definitions and unfair according to others.}, keywords = {Artificial intelligence,Conferences,definitions of fairness,Employment,History,machine learning,Software,Software algorithms,Software engineering,survey}, note = {TL;DR \par This paper collects the most prominent definitions of fairness for the algorithmic classification problem, explains the rationale behind these definitions, and demonstrates each of them on a single unifying case-study.} } @book{vermesanInternetThingsApplications2014, title = {Internet of Things Applications - from Research and Innovation to Market Deployment.}, author = {Vermesan, Ovidiu}, date = {2014}, publisher = {River Publishers}, location = {Place of publication not identified}, isbn = {978-87-93102-94-1}, langid = {english}, note = {TL;DR \par The chapter describes the Calipso communication architecture for IP connectivity in wireless sensor networks and the Smart Parking application scenario developed within the Project, and shows how the Smart parking application takes advantage of different modules within the architecture.} } @book{vermesanInternetThingsConverging2013, title = {Internet of {{Things}}: {{Converging Technologies}} for {{Smart Environments}}}, shorttitle = {Internet of {{Things}}}, author = {Vermesan, Ovidiu}, date = {2013}, publisher = {River Publishers}, langid = {english} } @article{vermesanInternetThingsStrategic2011, title = {Internet of Things Strategic Research Roadmap}, author = {Vermesan, Ovidiu and Friess, Peter and Guillemin, Patrick and Gusmeroli, Sergio and Sundmaeker, Harald and Bassi, Alessandro and Jubert, Ignacio Soler and Mazura, Margaretha and Harrison, Mark and Eisenhauer, M. and others}, date = {2011}, journaltitle = {Internet Things-Glob. Technol. Soc. Trends}, pages = {9--52}, url = {http://books.google.com/books?hl=en&lr=&id=Eug-RvslW30C&oi=fnd&pg=PA9&dq=%22by+individuals+and+organisations+around+the%22+%22Services+(IoS),+into+a+common+global+IT+platform+of+seamless+networks%22+%22networks+and+Internet.+Research+on+SOA,+Web/enterprise%22+%22interconnects+growing+population+of+users+while+promoting+their%22+&ots=3Tx7vGjxCw&sig=jz8DKE3sstPdA9juWvxatLigzxs}, urldate = {2016-06-03} } @incollection{vermolenReconstructingComplexMetamodel2012, title = {Reconstructing {{Complex Metamodel Evolution}}}, booktitle = {Software {{Language Engineering}}}, author = {Vermolen, Sander D. and Wachsmuth, Guido and Visser, Eelco}, date = {2012}, series = {Lecture {{Notes}} in {{Computer Science}}}, volume = {6940}, pages = {201--221} } @incollection{vieiraMetricsMeasureChange2014, title = {Metrics to {{Measure}} the {{Change Impact}} in {{ATL Model Transformations}}}, booktitle = {Product-{{Focused Software Process Improvement}}}, author = {Vieira, Andreza and Ramalho, Franklin}, date = {2014}, series = {Lecture {{Notes}} in {{Computer Science}}}, volume = {8892}, pages = {254--268} } @article{vignagaTypingArtifactsMegamodeling2011, title = {Typing Artifacts in Megamodeling}, author = {Vignaga, Andrés and Jouault, Frédéric and Bastarrica, María Cecilia and Brunelière, Hugo}, date = {2011}, journaltitle = {Softw. Syst. Model.}, volume = {12}, number = {1}, pages = {105--119}, doi = {10.1007/s10270-011-0191-2}, note = {TL;DR \par This paper presents the core elements of a type system for GMM that improves its original typing approach and enables both typechecking and type inference on artifacts within a megamodel, and presents a prototypical implementation of this type system.} } @incollection{vignagaTypingModelManagement2009, title = {Typing in {{Model Management}}}, booktitle = {Theory and {{Practice}} of {{Model Transformations}}}, author = {Vignaga, Andrés and Jouault, Frédéric and Bastarrica, María Cecilia and Brunelière, Hugo}, editor = {Paige, Richard F.}, date = {2009}, series = {Lecture {{Notes}} in {{Computer Science}}}, number = {5563}, pages = {197--212}, publisher = {Springer Berlin Heidelberg}, url = {http://link.springer.com/chapter/10.1007/978-3-642-02408-5_14}, urldate = {2015-04-01}, abstract = {Model management is essential for coping with the complexity introduced by the increasing number and varied nature of artifacts involved in MDE-based projects. Global Model Management (GMM) addresses this issue enabling the representation of artifacts, particularly transformation composition and execution, by a model called a megamodel. Typing information about artifacts can be used for preventing type errors during execution. In this work, we present a type system for GMM that improves its current typing approach and enables formal reasoning about the type of artifacts within a megamodel. This type system is able to capture non-trivial situations such as the use of higher order transformations.}, isbn = {978-3-642-02407-8 978-3-642-02408-5}, langid = {english}, keywords = {software engineering}, note = {TL;DR \par This work presents a type system for GMM that improves its current typing approach and enables formal reasoning about the type of artifacts within a megamodel, able to capture non-trivial situations such as the use of higher order transformations.} } @article{viroliSASO2014Selected2016, title = {{{SASO}} 2014: {{Selected}}, {{Revised}}, and {{Extended Best Papers}}}, shorttitle = {{{SASO}} 2014}, author = {Viroli, Mirko and Diaconescu, Ada and Kandasamy, Nagarajan}, date = {2016-07-25}, journaltitle = {ACM Trans. Auton. Adapt. Syst.}, volume = {11}, number = {2}, pages = {1--2}, issn = {15564665}, doi = {10.1145/2939206}, langid = {english}, note = {TL;DR \par This special issue of ACM TAAS champions some of the most solid research results of SASO 2016, presenting selected, revised, and extended best articles.} } @book{virvouHandbookArtificialIntelligenceEmpowered2022, title = {Handbook on {{Artificial Intelligence-Empowered Applied Software Engineering}}: {{VOL}}.1: {{Novel Methodologies}} to {{Engineering Smart Software Systems}}}, shorttitle = {Handbook on {{Artificial Intelligence-Empowered Applied Software Engineering}}}, editor = {Virvou, Maria and Tsihrintzis, George A. and Bourbakis, Nikolaos G. and Jain, Lakhmi C.}, date = {2022}, series = {Artificial {{Intelligence-Enhanced Software}} and {{Systems Engineering}}}, volume = {2}, publisher = {Springer International Publishing}, location = {Cham}, doi = {10.1007/978-3-031-08202-3}, isbn = {978-3-031-08201-6 978-3-031-08202-3}, langid = {english} } @article{vladHypersonicModelAnalysis, title = {Hypersonic: {{Model Analysis}} and {{Checking}} in the {{Cloud}}}, author = {Vlad, Acretoaie and Harald, Storrle}, note = {TL;DR \par This paper investigates the conceptual and technical feasibility of a new software architecture for modeling tools, where certain advanced features are factored out of the client and moved towards the Cloud, by applying standards such as REST and JSON in combination with Prolog as an implementation language.} } @inproceedings{vogelsangRequirementsEngineeringMachine2017, title = {Requirements {{Engineering}} for {{Machine Learning}}: {{Perspectives}} from {{Data Scientists}}}, shorttitle = {Requirements {{Engineering}} for {{Machine Learning}}}, booktitle = {2019 {{IEEE}} 27th {{Int}}. {{Requir}}. {{Eng}}. {{Conf}}. {{Workshop REW}}}, author = {Vogelsang, Andreas and Borg, Markus}, date = {2017-01}, eprint = {1908.04674}, eprinttype = {arXiv}, pages = {245--251}, publisher = {IEEE}, location = {Jeju Island, Korea (South)}, doi = {10.1109/REW.2019.00050}, abstract = {Machine learning (ML) is used increasingly in real-world applications. In this paper, we describe our ongoing endeavor to define characteristics and challenges unique to Requirements Engineering (RE) for ML-based systems. As a first step, we interviewed four data scientists to understand how ML experts approach elicitation, specification, and assurance of requirements and expectations. The results show that changes in the development paradigm, i.e., from coding to training, also demands changes in RE. We conclude that development of ML systems demands requirements engineers to: (1) understand ML performance measures to state good functional requirements, (2) be aware of new quality requirements such as explainability, freedom from discrimination, or specific legal requirements, and (3) integrate ML specifics in the RE process. Our study provides a first contribution towards an RE methodology for ML systems.}, eventtitle = {2019 {{IEEE}} 27th {{International Requirements Engineering Conference Workshops}} ({{REW}})}, isbn = {978-1-7281-5165-6}, langid = {english} } @book{volter2013model, title = {Model-Driven Software Development: Technology, Engineering, Management}, author = {Völter, Markus and Stahl, Thomas and Bettin, Jorn and Haase, Arno and Helsen, Simon}, date = {2013}, publisher = {John Wiley \& Sons}, note = {TL;DR \par MDSD Tools: Roles, Architecture, SelectionCriteria, Selection Criteria, and Pointers, and MDSD Process Building Blocks and Best Practices.} } @online{VWMonDataLogger, title = {{{VWMon}}: {{Data}} Logger and Remote Control for the {{Vaillant}} Heat Pump | Construction Blog by {{Katja}} \& {{Alexey}}}, shorttitle = {{{VWMon}}}, url = {http://baublog.ozerov.de/waermepumpe/vwmon-datenlogger-fuer-die-vaillant-waermepumpe/}, urldate = {2015-03-27} } @inproceedings{walensteinSimilarityPrograms2006, title = {Similarity in Programs}, booktitle = {Duplic. {{Redundancy Similarity Softw}}. 2307 - 26072006}, author = {Walenstein, Andrew and El-Ramly, Mohammad and Cordy, James R. and Evans, William S. and Mahdavi, Kiarash and Pizka, Markus and Ramalingam, Ganesan and family=Gudenberg, given=Jürgen Wolff, prefix=von, useprefix=true}, date = {2006}, url = {http://drops.dagstuhl.de/opus/volltexte/2007/968}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/bib/conf/dagstuhl/WalensteinECEMPRG06}, note = {TL;DR \par The paper is intended to be a starting point for a more comprehensive analysis of the subject of similarity in programs, which is critical to understand if progress is to be made in fields such as clone detection.}, timestamp = {Thu, 23 Aug 2018 15:56:31 +0200} } @article{walter2014ontology, title = {An Ontology-Based Framework for Domain-Specific Modeling}, author = {Walter, Tobias and Parreiras, Fernando Silva and Staab, Steffen}, date = {2014}, journaltitle = {Software Syst. Model.}, volume = {13}, number = {1}, pages = {83--108}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {TL;DR \par A framework that allows the use of ontology technologies to describe and reason on DSLs and the formal semantics of OWL together with reasoning services allows for addressing constraint definition, progressive evaluation, suggestions, and debugging.} } @article{Wan2022423, title = {A Variational Bayesian Inference-Inspired Unrolled Deep Network for {{MIMO}} Detection}, author = {Wan, Q. and Fang, J. and Huang, Y. and Duan, H. and Li, H.}, date = {2022}, journaltitle = {IEEE Trans. Signal Process.}, volume = {70}, pages = {423--437}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {1053587X}, doi = {10.1109/TSP.2022.3140926}, abstract = {The great success of deep learning (DL) has inspired researchers to develop more accurate and efficient symbol detectors for multi-input multi-output (MIMO) systems. Existing DL-based MIMO detectors, however, suffer several drawbacks. To address these issues, in this paper, we develop a model-driven DL detector based on variational Bayesian inference. Specifically, the proposed unrolled DL architecture is inspired by an inverse-free variational Bayesian learning framework which circumvents matrix inversion via maximizing a relaxed evidence lower bound. Two networks are respectively developed for independent and identically distributed (i.i.d.) Gaussian channels and arbitrarily correlated channels. The proposed networks, referred to as VBINet, have only a few learnable parameters and thus can be efficiently trained with a moderate amount of training samples. The proposed VBINet-based detectors can work in both offline and online training modes. An important advantage of our proposed networks over state-of-the-art MIMO detection networks such as OAMPNet and MMNet is that the VBINet can automatically learn the noise variance from data, thus yielding a significant performance improvement over the OAMPNet and MMNet in the presence of noise variance uncertainty. Simulation results show that the proposed VBINet-based detectors achieve competitive performance for both i.i.d. Gaussian and realistic 3GPP MIMO channels. © 1991-2012 IEEE.}, coden = {ITPRE}, document_type = {Article}, source = {Scopus}, note = {cited By 0 \par TL;DR \par The proposed unrolled DL architecture is inspired by an inverse-free variational Bayesian learning framework which circumvents matrix inversion via maximizing a relaxed evidence lower bound and can automatically learn the noise variance from data, thus yielding a significant performance improvement over the OAMPNet and MMNet in the presence of noise variance uncertainty.} } @article{wang_adversarial_2020, title = {Adversarial {{Training-Based Mean Bayesian Personalized Ranking}} for {{Recommender System}}}, author = {Wang, Jianfang and Han, Pengfei}, date = {2020}, journaltitle = {IEEE Access}, volume = {8}, pages = {7958--7968}, issn = {2169-3536}, doi = {10.1109/ACCESS.2019.2963316}, abstract = {Users' feedback information as the ground-truth has attracted a lot of attention in recommender systems. However, the feedback that could be contaminated by users' misoperations or malicious operations is probably not true in real scenarios. This work aims to develop a technique based on an improved Bayesian personalized ranking (BPR), called adversarial training-based mean Bayesian personalized ranking (AT-MBPR). In this method, we divide the feedback information into three categories based on the mean Bayesian personalized ranking (MBPR), then gain the implicit feedback from the mean and non-observed items of each user, following which, adversarial perturbations are added on the embedding vectors of the users and items by playing a minimax game to reduce the noise. The experiments demonstrate in five datasets that our approach outperforms the traditional BPR methods and state-of-the-art methods used for the recommendation. Our implementation is available at: https://github.com/HanXia001/Adversarial-Training-based-Mean-BPR-for-Recommender.}, keywords = {adversarial perturbations,Adversarial training,adversarial training-based mean Bayesian personalized ranking,Bayes methods,BPR,BPR methods,Business process re-engineering,Collaboration,collaborative filtering,embedding vectors,feedback information,game theory,implicit feedback,malicious operations,minimax game,minimax techniques,Negative feedback,recommender system,recommender systems,Recommender systems,Robustness,Training}, note = {Conference Name: IEEE Access \par TL;DR \par This work aims to develop a technique based on an improved Bayesian personalized ranking (BPR), called adversarial training-based mean Bayesian generalized ranking (AT-MBPR), which outperforms the traditional BPR methods and state-of-the-art methods used for the recommendation.} } @inproceedings{wang_bridging_2022, title = {Bridging Pre-Trained Models and Downstream Tasks for Source Code Understanding}, booktitle = {Proc. 44th {{Int}}. {{Conf}}. {{Softw}}. {{Eng}}.}, author = {Wang, Deze and Jia, Zhouyang and Li, Shanshan and Yu, Yue and Xiong, Yun and Dong, Wei and Liao, Xiangke}, date = {2022-07}, series = {{{ICSE}} '22}, pages = {287--298}, publisher = {Association for Computing Machinery}, location = {New York, NY, USA}, doi = {10.1145/3510003.3510062}, abstract = {With the great success of pre-trained models, the pretrain-then-finetune paradigm has been widely adopted on downstream tasks for source code understanding. However, compared to costly training a large-scale model from scratch, how to effectively adapt pre-trained models to a new task has not been fully explored. In this paper, we propose an approach to bridge pre-trained models and code-related tasks. We exploit semantic-preserving transformation to enrich downstream data diversity, and help pre-trained models learn semantic features invariant to these semantically equivalent transformations. Further, we introduce curriculum learning to organize the transformed data in an easy-to-hard manner to fine-tune existing pre-trained models. We apply our approach to a range of pre-trained models, and they significantly outperform the state-of-the-art models on tasks for source code understanding, such as algorithm classification, code clone detection, and code search. Our experiments even show that without heavy pre-training on code data, natural language pre-trained model RoBERTa fine-tuned with our lightweight approach could outperform or rival existing code pre-trained models fine-tuned on the above tasks, such as CodeBERT and GraphCodeBERT. This finding suggests that there is still much room for improvement in code pre-trained models.}, isbn = {978-1-4503-9221-1}, keywords = {curriculum learning,data augmentation,fine-tuning,test-time augmentation}, note = {TL;DR \par This paper exploits semantic-preserving transformation to enrich downstream data diversity, and helps pre-trained models learn semantic features invariant to these semantically equivalent transformations, and introduces curriculum learning to or-ganize the transformed data in an easy-to-hard manner to fine-tune existing pre- trained models.} } @article{Wang10, title = {An Automatic Documentation Generator Based on Model-Driven Techniques}, author = {Wang, Chao and Li, Hong and Gao, Zhigang and Yao, Min and Yang, Yuhao}, date = {2010-01}, journaltitle = {Int. Conf. Comput. Eng. Technol. Proc. (ICCET)}, volume = {4}, doi = {10.1109/ICCET.2010.5485654}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {TL;DR \par This paper proposes a new methodology for automatic documentation generation, which is capable of maintaining the consistency between software documentation and the corresponding software system in model-driven development.} } @inproceedings{Wang2015689, title = {An Accurate {{ACOSSO}} Metamodeling Technique for Processor Architecture Design Space Exploration}, author = {Wang, H. and Zhu, Z. and Shi, J. and Su, Y.}, date = {2015}, series = {20th {{Asia}} and {{South Pacific Design Automation Conference}}, {{ASP-DAC}} 2015}, pages = {689--694}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/ASPDAC.2015.7059090}, abbrev_source_title = {Asia South Pac. Des. Autom. Conf., ASP-DAC}, affiliation = {Beijing Key Laboratory of Mobile Computing and Pervasive Device, Beijing, 100190, China; Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing, 100190, China}, art_number = {7059090}, document_type = {Conference Paper}, isbn = {978-1-4799-7792-5}, langid = {english}, source = {Scopus}, note = {cited By 3 \par TL;DR \par An accurate and efficient adaptive component selection and smoothing operator (ACOSSO) metamodel assisted NSGA-II (MA-NSGA- II) multi-objective optimization (MOO) technique for processor DSE that achieves higher prediction accuracy and better architecture optimization results.} } @inproceedings{Wang2019, title = {Fast Robustness Prediction for Deep Neural Network}, author = {Wang, Y. and Li, Z. and Xu, J. and Yu, P. and Ma, X.}, date = {2019}, series = {{{ACM International Conference Proceeding Series}}}, publisher = {Association for Computing Machinery}, doi = {10.1145/3361242.3361243}, abstract = {Deep neural networks (DNNs) have achieved impressive performance in many difficult tasks. However, DNN models are essentially uninterpretable to humans, and unfortunately prone to adversarial attacks, which hinders their adoption in security and safety-critical scenarios. The robustness of a DNN model, which measures its stableness against adversarial attacks, becomes an important topic in both the machine learning and the software engineering communities. Analytical evaluation of DNN robustness is difficult due to the high-dimensionality of inputs, the huge amount of parameters, and the nonlinear network structure. In practice, the degree of robustness of DNNs is empirically approximated with adversarial searching, which is computationally expensive and cannot be applied in resource constrained settings such as embedded computing. In this paper, we propose to predict the robustness of a DNN model for each input with another DNN model, which takes the output of neurons of the former model as input. We train a regression model to encode the connections between output of the penultimate layer of a DNN model and its robustness. With this trained model, the robustness for an input can be predicted instantaneously. Experiments with MNIST and CIFAR10 datasets and LeNet, VGG and ResNet DNN models were conducted to evaluate the efficacy of the proposed approach. The results indicated that our approach achieved 0.05-0.21 mean absolute errors and significantly outperformed confidence and surprise adequacy-based approaches. © 2019 Association for Computing Machinery.}, art_number = {3361243}, author_keywords = {Deep Neural Networks; Prediction; Robustness}, document_type = {Conference Paper}, isbn = {978-1-4503-7701-0}, source = {Scopus}, keywords = {Analytical evaluation,Deep neural networks,Degree of robustness,Embedded computing,Engineering community,Forecasting,High dimensionality,Mean absolute error,Neural networks,Regression analysis,Regression model,Robustness (control systems),Safety engineering,Software engineering,Stableness}, note = {cited By 0 \par TL;DR \par A regression model is trained to encode the connections between output of the penultimate layer of a DNN model and its robustness and, with this trained model, the robustness for an input can be predicted instantaneously.} } @article{Wang2021107, title = {{{InDuDoNet}}: {{An}} Interpretable Dual Domain Network for {{CT}} Metal Artifact Reduction}, author = {Wang, H. and Li, Y. and Zhang, H. and Chen, J. and Ma, K. and Meng, D. and Zheng, Y.}, editor = {family=Bruijne M., de Bruijne M., given=Cattin P.C., suffix=Cotin S., Padoy N., Speidel S., Zheng Y., Essert C., prefix=de, useprefix=true}, date = {2021}, journaltitle = {Lect. Notes Comput. Sci. Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinforma.}, volume = {12906 LNCS}, pages = {107--118}, publisher = {{Springer Science and Business Media Deutschland GmbH}}, issn = {03029743}, doi = {10.1007/978-3-030-87231-1_11}, abstract = {For the task of metal artifact reduction (MAR), although deep learning (DL)-based methods have achieved promising performances, most of them suffer from two problems: 1) the CT imaging geometry constraint is not fully embedded into the network during training, leaving room for further performance improvement; 2) the model interpretability is lack of sufficient consideration. Against these issues, we propose a novel interpretable dual domain network, termed as InDuDoNet, which combines the advantages of model-driven and data-driven methodologies. Specifically, we build a joint spatial and Radon domain reconstruction model and utilize the proximal gradient technique to design an iterative algorithm for solving it. The optimization algorithm only consists of simple computational operators, which facilitate us to correspondingly unfold iterative steps into network modules and thus improve the interpretablility of the framework. Extensive experiments on synthesized and clinical data show the superiority of our InDuDoNet. Code is available in https://github.com/hongwang01/InDuDoNet. © 2021, Springer Nature Switzerland AG.}, document_type = {Conference Paper}, isbn = {9783030872304}, source = {Scopus}, note = {cited By 1 \par TL;DR \par This work builds a joint spatial and Radon domain reconstruction model and utilizes the proximal gradient technique to design an iterative algorithm for solving it, which combines the advantages of model-driven and data-driven methodologies and improves the interpretablility of the framework.} } @article{Wang20212270, title = {A Model-Driven {{DL}} Algorithm for {{PAPR}} Reduction in {{OFDM}} System}, author = {Wang, X. and Jin, N. and Wei, J.}, date = {2021}, journaltitle = {IEEE Commun. Lett.}, volume = {25}, number = {7}, pages = {2270--2274}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {10897798}, doi = {10.1109/LCOMM.2021.3076605}, abstract = {Deep learning (DL) has dramatically improved the peak-to-average power ratio (PAPR) performance. However, the high computational complexity and excessive training data constitute a significant hurdle. In this letter, a model-driven deep learning algorithm is proposed for PAPR reduction in orthogonal frequency division multiplexing (OFDM) system. Precisely, an iterative peak-canceling signal generation scheme is unfolded as a layer structure of the DL network. The scheme falls into the category of tone reservation technique. A set of trainable parameters, which optimizes the clipping threshold and weights time-domain kernel function, has been designed and introduced into the iterative scheme. Compared with the existing approaches, the simulation results demonstrate that the proposed algorithm achieves comparable PAPR performance with low complexity and training costs. © 2021 IEEE.}, art_number = {9419069}, coden = {ICLEF}, document_type = {Article}, source = {Scopus}, note = {cited By 2 \par TL;DR \par A model-driven deep learning algorithm is proposed for PAPR reduction in orthogonal frequency division multiplexing (OFDM) system and simulation results demonstrate that the proposed algorithm achieves comparable P APR performance with low complexity and training costs.} } @article{Wang20212385, title = {Pilot-Assisted {{SIMO-NOMA}} Signal Detection with Learnable Successive Interference Cancellation}, author = {Wang, X. and Zhu, P. and Li, D. and Xu, Y. and You, X.}, date = {2021}, journaltitle = {IEEE Commun. Lett.}, volume = {25}, number = {7}, pages = {2385--2389}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {10897798}, doi = {10.1109/LCOMM.2021.3070705}, abstract = {In this letter, we propose a pilot-assisted receiver scheme based on learnable successive interference cancellation (PA-LSIC) for uplink single-input multiple-output (SIMO) non-orthogonal multiple access (NOMA) systems. The PA-LSIC combines the successive interference cancellation (SIC) structure with the model-driven deep learning network. Considering the noise impact of channel estimation and the incomplete detection and cancellation in SIC process, we introduce some new parameters, such as noise cancellation factor and interference cancellation factor, which are optimized by using the back-propagation algorithm and random gradient descent algorithm. Numerical results show that the PA-LSIC has superior bit error rate (BER) performance and lower complexity during training and implementation. © 2021 IEEE.}, art_number = {9393981}, coden = {ICLEF}, document_type = {Article}, source = {Scopus}, note = {cited By 4 \par TL;DR \par Numerical results show that the PA-LSIC has superior bit error rate (BER) performance and lower complexity during training and implementation, and introduces some new parameters, such as noise cancellation factor and interference cancellation factor, which are optimized by using the back-propagation algorithm and random gradient descent algorithm.} } @inproceedings{Wang2021639, title = {Morphling: {{Fast}}, near-Optimal Auto-Configuration for Cloud-Native Model Serving}, author = {Wang, L. and Yang, L. and Yu, Y. and Wang, W. and Li, B. and Sun, X. and He, J. and Zhang, L.}, date = {2021}, series = {{{SoCC}} 2021 - {{Proceedings}} of the 2021 {{ACM Symposium}} on {{Cloud Computing}}}, pages = {639--653}, publisher = {Association for Computing Machinery, Inc}, doi = {10.1145/3472883.3486987}, abbrev_source_title = {SoCC - Proc. ACM Symp. Cloud Comput.}, affiliation = {HKUST, Alibaba Group, Hong Kong}, document_type = {Conference Paper}, isbn = {978-1-4503-8638-8}, langid = {english}, source = {Scopus}, keywords = {notion}, note = {cited By 1} } @article{Wang20223440, title = {Better Constraints of Imperceptibility, Better Adversarial Examples in the Text}, author = {Wang, W. and Wang, L. and Wang, R. and Ye, A. and Ke, J.}, date = {2022}, journaltitle = {Int. J. Intell. Syst.}, volume = {37}, number = {6}, pages = {3440--3459}, publisher = {{John Wiley and Sons Ltd}}, issn = {08848173}, doi = {10.1002/int.22696}, abstract = {State-of-the-art adversarial attacks in the text domain have shown their power to induce machine learning models to produce abnormal outputs. The samples generated in these attacks have three important attributes: attack ability, transferability, and imperceptibility. However, compared with the other two attributes, the imperceptibility of adversarial examples has not been well investigated. Unlike the pixel-level perturbations in images, adversarial perturbations in the text are usually traceable, reflecting changes in characters, words, or sentences. The generation of imperceptible samples in texts is more difficult than in images. Therefore, how to constrain adversarial perturbations added in the text is a crucial step to construct more natural adversarial texts. Unfortunately, recent studies merely select measurements to constrain the added adversarial perturbations, but none of them explain where these measurements are suitable, which one is better, and how they perform in different kinds of adversarial attacks. In this paper, we fill this gap by comparing the performance of these metrics in various attacks. Furthermore, we propose a stricter constraint for word-level attacks to obtain more imperceptible samples. It is also helpful to enhance existing word-level attacks for adversarial training. © 2021 Wiley Periodicals LLC.}, author_keywords = {adversarial texts; imperceptibility; measurements; semantic and visual similarity; visual optimization}, coden = {IJISE}, document_type = {Article}, source = {Scopus}, keywords = {Adversarial text,Imperceptibility,Machine learning models,Pixel level,Power,Semantic similarity,Semantics,Software engineering,State of the art,Visual optimization,Visual similarity,Word level}, note = {cited By 2} } @article{wangCoCoSumContextualCode2021, title = {{{CoCoSum}}: {{Contextual Code Summarization}} with {{Multi-Relational Graph Neural Network}}}, shorttitle = {{{CoCoSum}}}, author = {Wang, Yanlin and Shi, Ensheng and Du, Lun and Yang, Xiaodi and Hu, Yuxuan and Han, Shi and Zhang, Hongyu and Zhang, Dongmei}, date = {2021-07-05}, journaltitle = {ArXiv210701933 Cs}, eprint = {2107.01933}, eprinttype = {arXiv}, eprintclass = {cs}, url = {http://arxiv.org/abs/2107.01933}, urldate = {2022-01-28}, abstract = {Source code summaries are short natural language descriptions of code snippets that help developers better understand and maintain source code. There has been a surge of work on automatic code summarization to reduce the burden of writing summaries manually. However, most contemporary approaches mainly leverage the information within the boundary of the method being summarized (i.e., local context), and ignore the broader context that could assist with code summarization. This paper explores two global contexts, namely intra-class and inter-class contexts, and proposes the model CoCoSUM: Contextual Code Summarization with Multi-Relational Graph Neural Networks. CoCoSUM first incorporates class names as the intra-class context to generate the class semantic embeddings. Then, relevant Unified Modeling Language (UML) class diagrams are extracted as inter-class context and are encoded into the class relational embeddings using a novel Multi-Relational Graph Neural Network (MRGNN). Class semantic embeddings and class relational embeddings, together with the outputs from code token encoder and AST encoder, are passed to a decoder armed with a two-level attention mechanism to generate high-quality, context-aware code summaries. We conduct extensive experiments to evaluate our approach and compare it with other automatic code summarization models. The experimental results show that CoCoSUM is effective and outperforms state-of-the-art methods. Our source code and experimental data are available in the supplementary materials and will be made publicly available.}, langid = {english}, keywords = {Computer Science - Software Engineering} } @software{wangGaryYufeiAlignLLMHumanSurvey2024, title = {{{GaryYufei}}/{{AlignLLMHumanSurvey}}}, author = {Wang, Yufei}, date = {2024-09-24T08:58:21Z}, origdate = {2023-07-23T06:41:56Z}, url = {https://github.com/GaryYufei/AlignLLMHumanSurvey}, urldate = {2024-09-24}, abstract = {Aligning Large Language Models with Human: A Survey}, keywords = {awesome,chatgpt,chinese-llama,gpt-4,large-language-models,llama,llama2,LLMs,rlhf,supervised-finetuning,survey} } @online{wangHowLLMsAid2024, title = {How {{LLMs Aid}} in {{UML Modeling}}: {{An Exploratory Study}} with {{Novice Analysts}}}, shorttitle = {How {{LLMs Aid}} in {{UML Modeling}}}, author = {Wang, Beian and Wang, Chong and Liang, Peng and Li, Bing and Zeng, Cheng}, date = {2024-06-10}, eprint = {2404.17739}, eprinttype = {arXiv}, eprintclass = {cs}, doi = {10.48550/arXiv.2404.17739}, abstract = {Since the emergence of GPT-3, Large Language Models (LLMs) have caught the eyes of researchers, practitioners, and educators in the field of software engineering. However, there has been relatively little investigation regarding the performance of LLMs in assisting with requirements analysis and UML modeling. This paper explores how LLMs can assist novice analysts in creating three types of typical UML models: use case models, class diagrams, and sequence diagrams. For this purpose, we designed the modeling tasks of these three UML models for 45 undergraduate students who participated in a requirements modeling course, with the help of LLMs. By analyzing their project reports, we found that LLMs can assist undergraduate students as novice analysts in UML modeling tasks, but LLMs also have shortcomings and limitations that should be considered when using them.}, pubstate = {prepublished}, keywords = {Computer Science - Software Engineering}, note = {Comment: The 21st IEEE International Conference on Software Services Engineering (SSE) \par Comment: The 21st IEEE International Conference on Software Services Engineering (SSE)} } @inproceedings{wangMiningSuccinctHighcoverage2013, ids = {Wang2013Mining}, title = {Mining Succinct and High-Coverage {{API}} Usage Patterns from Source Code}, booktitle = {10th {{Work}}. {{Conf}}. {{Min}}. {{Softw}}. {{Repos}}.}, author = {Wang, J. and Dang, Y. and Zhang, H. and Chen, K. and Xie, T. and Zhang, D.}, date = {2013}, pages = {319--328}, publisher = {IEEE}, location = {Piscataway}, issn = {2160-1852}, keywords = {API usage,application program interfaces,application programming interface,client code mining,Clustering algorithms,Context,data mining,Data mining,high-coverage API usage pattern mining,Indexes,large-scale Microsoft codebase,MAPO,Measurement,mining software repositories,Probabilistic logic,Redundancy,sequence mining,software development,software reusability,software reuse,source code,succinct API usage pattern mining,UP-miner,usage pattern,usage pattern discovery,usage pattern miner}, note = {TL;DR \par This paper proposes two quality metrics (succinctness and coverage) for mined usage patterns, and proposes a novel approach called Usage Pattern Miner (UP-Miner) that mines succinct and high-coverage usage patterns of API methods from source code.} } @article{wangPersonalizingLabelPrediction2022, title = {Personalizing Label Prediction for {{GitHub}} Issues}, author = {Wang, Jun and Zhang, Xiaofang and Chen, Lin and Xie, Xiaoyuan}, date = {2022-01}, journaltitle = {Information and Software Technology}, pages = {106845}, issn = {09505849}, doi = {10.1016/j.infsof.2022.106845}, abstract = {Objective: These factors inspire us to propose a method to identify these synonymous labels automatically and recommend personalized labels for different open-source projects. Method: In this paper, we propose a Personalizing Label Prediction framework for Issues named PLPI. PLPI identifies labels with similar meanings by representing labels as semantic vectors and applying clustering methods. PLPI can predict personalized labels from the existing labels in the open-source project. Result: We conduct a comprehensive study to compare seven commonly adopted labeling models with our approach. The experimental results demonstrate the advantages of our approach. Finally, we show some representative examples and discuss the visualization results of synonyms clustering by dimension reduction. Conclusion: The experimental results show that our method PLPI can improve label prediction performance and provide personalized label recommendation results for different open-source projects.}, langid = {english} } @article{wangSurveyLargeLanguage2024, title = {A Survey on Large Language Model Based Autonomous Agents}, author = {Wang, Lei and Ma, Chen and Feng, Xueyang and Zhang, Zeyu and Yang, Hao and Zhang, Jingsen and Chen, Zhiyuan and Tang, Jiakai and Chen, Xu and Lin, Yankai and Zhao, Wayne Xin and Wei, Zhewei and Wen, Ji-Rong}, date = {2024-12}, journaltitle = {Front. Comput. Sci.}, volume = {18}, number = {6}, eprint = {2308.11432}, eprinttype = {arXiv}, eprintclass = {cs}, pages = {186345}, issn = {2095-2228, 2095-2236}, doi = {10.1007/s11704-024-40231-1}, abstract = {Autonomous agents have long been a prominent research focus in both academic and industry communities. Previous research in this field often focuses on training agents with limited knowledge within isolated environments, which diverges significantly from human learning processes, and thus makes the agents hard to achieve human-like decisions. Recently, through the acquisition of vast amounts of web knowledge, large language models (LLMs) have demonstrated remarkable potential in achieving human-level intelligence. This has sparked an upsurge in studies investigating LLM-based autonomous agents. In this paper, we present a comprehensive survey of these studies, delivering a systematic review of the field of LLM-based autonomous agents from a holistic perspective. More specifically, we first discuss the construction of LLM-based autonomous agents, for which we propose a unified framework that encompasses a majority of the previous work. Then, we present a comprehensive overview of the diverse applications of LLM-based autonomous agents in the fields of social science, natural science, and engineering. Finally, we delve into the evaluation strategies commonly used for LLM-based autonomous agents. Based on the previous studies, we also present several challenges and future directions in this field. To keep track of this field and continuously update our survey, we maintain a repository of relevant references at https://github.com/Paitesanshi/LLM-Agent-Survey.}, langid = {english}, keywords = {[[LLM]],Computer Science - Artificial Intelligence,Computer Science - Computation and Language,MOSAICO}, note = {Comment: Correcting several typos, 35 pages, 5 figures, 3 tables} } @inproceedings{wangUnityStrengthCollaborative2024, title = {Unity Is Strength: Collaborative {{LLM-based}} Agents for Code Reviewer Recommendation}, shorttitle = {Unity Is Strength}, booktitle = {Proc. 39th {{IEEEACM Int}}. {{Conf}}. {{Autom}}. {{Softw}}. {{Eng}}.}, author = {Wang, Luqiao and Zhou, Yangtao and Zhuang, Huiying and Li, Qingshan and Cui, Di and Zhao, Yutong and Wang, Lu}, date = {2024-10-27}, pages = {2235--2239}, publisher = {ACM}, location = {Sacramento CA USA}, doi = {10.1145/3691620.3695291}, eventtitle = {{{ASE}} '24: 39th {{IEEE}}/{{ACM International Conference}} on {{Automated Software Engineering}}}, isbn = {979-8-4007-1248-7}, langid = {english}, keywords = {/unread} } @inproceedings{wangVerifyingMetamodelCoverage2006, title = {Verifying Metamodel Coverage of Model Transformations}, author = {Wang, J. and Kim, S.-K. and Carrington, D.}, date = {2006}, pages = {10 pp.-282}, publisher = {IEEE}, doi = {10.1109/ASWEC.2006.55}, isbn = {978-0-7695-2551-8} } @inproceedings{wangWuKongScalableAccurate2015, title = {{{WuKong}}: {{A}} Scalable and Accurate Two-Phase Approach to Android App Clone Detection}, booktitle = {Proc. 2015 {{Int}}. {{Symp}}. {{Softw}}. {{Test}}. {{Anal}}.}, author = {Wang, Haoyu and Guo, Yao and Ma, Ziang and Chen, Xiangqun}, date = {2015}, series = {{{ISSTA}} 2015}, pages = {71--82}, publisher = {ACM}, location = {New York, NY, USA}, url = {http://doi.acm.org/10.1145/2771783.2771795}, acmid = {2771795}, isbn = {978-1-4503-3620-8}, nodoi = {10.1145/2771783.2771795}, numpages = {12}, keywords = {Android,Clone detection,mobile applications,repackaging,third-party library}, note = {TL;DR \par WuKong is proposed, a two-phase detection approach that includes a coarse-grained detection phase to identify suspicious apps by comparing light-weight static semantic features, and a fine- grained phase to compare more detailed features for only those apps found in the first phase.} } @article{waszkowskiLowcodePlatformAutomating2019, ids = {WASZKOWSKI2019376}, title = {Low-Code Platform for Automating Business Processes in Manufacturing}, author = {Waszkowski, Robert}, date = {2019}, journaltitle = {IFAC-PapersOnLine}, volume = {52}, number = {10}, pages = {376--381}, issn = {24058963}, doi = {10.1016/j.ifacol.2019.10.060}, langid = {english}, keywords = {Aurea BPM,business processes,Low-code platform,manufacturing,process automation}, note = {13th IFAC Workshop on Intelligent Manufacturing Systems IMS 2019 \par Referred in \href{zotero://note/u/WN7FRTZI/?ignore=1&line=-1}{PLACEMENT}} } @book{watzoldtModelingCollaborationsSelfadaptive2015, title = {Modeling Collaborations in Self-Adaptive Systems of Systems: Terms, Characteristics, Requirements, and Scenarios}, shorttitle = {Modeling Collaborations in Self-Adaptive Systems of Systems}, author = {Wätzoldt, Sebastian and Giese, Holger}, namea = {{Hasso-Plattner-Institut für Softwaresystemtechnik}}, nameatype = {collaborator}, date = {2015}, series = {Technische {{Berichte}} Des {{Hasso-Plattner-Instituts}} Für {{Softwaresystemtechnik}} an Der {{Universität Potsdam}}}, number = {96}, publisher = {Univ.-Verl}, location = {Potsdam}, isbn = {978-3-86956-324-4}, langid = {english}, pagetotal = {72}, note = {Literaturverzeichnis S. 62 - 72} } @article{Weber2020403, title = {A Model Management Platform for Industry 4.0 – Enabling Management of Machine Learning Models in Manufacturing Environments}, author = {Weber, C. and Hirmer, P. and Reimann, P.}, editor = {Abramowicz W., Klein G.}, date = {2020}, journaltitle = {Lect. Notes Bus. Inf. Process.}, volume = {389 LNBIP}, pages = {403--417}, publisher = {Springer}, issn = {18651348}, doi = {10.1007/978-3-030-53337-3_30}, abstract = {Industry 4.0 use cases such as predictive maintenance and product quality control make it necessary to create, use and maintain a multitude of different machine learning models. In this setting, model management systems help to organize models. However, concepts for model management systems currently focus on data scientists, but do not support non-expert users such as domain experts and business analysts. Thus, it is difficult for them to reuse existing models for their use cases. In this paper, we address these challenges and present an architecture, a metadata schema and a corresponding model management platform. © Springer Nature Switzerland AG 2020.}, document_type = {Conference Paper}, isbn = {9783030533366}, source = {Scopus}, note = {cited By 4 \par TL;DR \par This paper presents an architecture, a metadata schema and a corresponding model management platform for non-expert users such as domain experts and business analysts to reuse existing models for their use cases in industry 4.0.} } @inproceedings{Weber202091, title = {{{MMP}} - {{A}} Platform to Manage Machine Learning Models in Industry 4.0 Environments}, author = {Weber, C. and Reimann, P.}, date = {2020}, series = {Proceedings - {{IEEE International Enterprise Distributed Object Computing Workshop}}, {{EDOCW}}}, volume = {2020-October}, pages = {91--94}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {15417719}, doi = {10.1109/EDOCW49879.2020.00025}, abstract = {In manufacturing environments, machine learning models are being built for several use cases, such as predictive maintenance and product quality control. In this context, the various manufacturing processes, machines, and product variants make it necessary to create and use lots of different machine learning models. This calls for a software system that is able to manage all these diverse machine learning models and associated metadata. However, current model management systems do not associate models with business and domain context to provide non-expert users with tailored functions for model search and discovery. Moreover, none of the existing systems provides a comprehensive overview of all models within an organization. In our demonstration, we present the MMP, our model management platform that addresses these issues. The MMP provides a model metadata extractor, a model registry, and a context manager to store model metadata in a central metadata store. On top of this, the MMP provides frontend components that offer the above-mentioned functionalities. In our demonstration, we show two scenarios for model management in Industry 4.0 environments that illustrate the novel functionalities of the MMP. We demonstrate to the audience how the platform and its metadata, linking models to their business and domain context, help non-expert users to search and discover models. Furthermore, we show how to use MMP's powerful visualizations for model reporting, such as a dashboard and a model landscape view. © 2020 IEEE.}, art_number = {9233284}, document_type = {Conference Paper}, isbn = {978-1-7281-6471-7}, source = {Scopus}, note = {cited By 1 \par TL;DR \par The MMP is presented, the authors' model management platform that addresses issues of model management, and it is demonstrated to the audience how the platform and its metadata, linking models to their business and domain context, help non-expert users to search and discover models.} } @inproceedings{Wei2020, title = {Learned Conjugate Gradient Descent Network for Massive {{MIMO}} Detection}, author = {Wei, Y. and Zhao, M.-M. and Hong, M. and Zhao, M.-J. and Lei, M.}, date = {2020}, series = {{{IEEE International Conference}} on {{Communications}}}, volume = {2020-June}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {15503607}, doi = {10.1109/ICC40277.2020.9149227}, abstract = {In this work, we consider the use of model-driven deep learning (DL) techniques for signal detection in massive multiple-input multiple-output (MIMO) system. Massive MIMO promises improved spectral efficiency, coverage and reliability, compared to conventional MIMO systems. Unfortunately, these benefits usually come at the cost of significantly increased computational complexity. To address this difficulty, a learned conjugate gradient descent network, referred to as LcgNet, is presented by unfolding the iterative conjugate gradient descent (CG) detector. In the proposed network, instead of calculating the exact values of the scalar step-sizes for every problem instance, we explicitly learn their universal values. We show that the performance of the proposed network can be greatly improved by augmenting the dimensions of these step-sizes. Furthermore, due to the limited learnable parameters to be optimized, the proposed networks are easy and fast to train. Numerical results demonstrate that this approach can achieve superior performance over some state-of-the-art MIMO detectors such as the CG detector, the linear minimum mean squared error (LMMSE) detector etc., with much lower computational complexity. © 2020 IEEE.}, art_number = {9149227}, document_type = {Conference Paper}, isbn = {978-1-7281-5089-5}, source = {Scopus}, note = {cited By 2 \par TL;DR \par This work considers the use of model-driven deep learning techniques for signal detection in massive multiple-input multiple-output (MIMO) system and proposes a learned conjugate gradient descent network, referred to as LcgNet, which can achieve superior performance over some state-of-the-art MIMO detectors with much lower computational complexity.} } @article{Wei20206336, title = {Learned Conjugate Gradient Descent Network for Massive {{MIMO}} Detection}, author = {Wei, Y. and Zhao, M.-M. and Hong, M. and Zhao, M.-J. and Lei, M.}, date = {2020}, journaltitle = {IEEE Trans. Signal Process.}, volume = {68}, pages = {6336--6349}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {1053587X}, doi = {10.1109/TSP.2020.3035832}, abstract = {In this work, we consider the use of model-driven deep learning techniques for massive multiple-input multiple-output (MIMO) detection. Compared with conventional MIMO systems, massive MIMO promises improved spectral efficiency, coverage and range. Unfortunately, these benefits are at the expense of significantly increased computational complexity. To reduce the complexity of signal detection and guarantee the performance, we present a learned conjugate gradient descent network (LcgNet), which is constructed by unfolding the iterative conjugate gradient descent (CG) detector. In the proposed network, instead of calculating the exact values of the scalar step-sizes, we explicitly learn their universal values. Also, we can enhance the proposed network by augmenting the dimensions of these step-sizes. Furthermore, in order to reduce the memory costs, a novel quantized LcgNet is proposed, where a low-resolution nonuniform quantizer is used to quantize the learned parameters. The quantizer is based on a specially designed soft staircase function with learnable parameters to adjust its shape. Meanwhile, due to fact that the number of learnable parameters is limited, the proposed networks are relatively easy to train. Numerical results demonstrate that the proposed network can achieve promising performance with much lower complexity. © 2020 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.}, art_number = {3035832}, coden = {ITPRE}, document_type = {Article}, source = {Scopus}, note = {cited By 16} } @article{weilEnsuringConsistencyInformation2017, title = {Ensuring the Consistency of Information between Two Versions of a Mechanical Drawing}, author = {Weil, Alexandre Monnier}, date = {2017}, abstract = {Mechanical drawings are crucial in industry for manufacturing and information retrieval. Companies face a recurring issue with managing their hand-drawn drawings produced in the mid-20th century. To address this, the engineers convert hand-drawn drawings into CAD drawings. This transformation process must be error-free to avoid material waste and significant costs. To ensure this, quality assurance engineers carefully verify the final CAD drawings to ensure all information is preserved. This verification process is time-consuming and laborious, often requiring the dedicated effort of two engineers for an entire week per drawing.}, langid = {english}, keywords = {LOGSEQ}, note = {\section{Annotazioni\\ (23/7/2024, 17:32:02)} \par - “Companies face a recurring issue with managing their hand-drawn drawings produced in the mid-20th century” (Weil, 2017, p. 1) \#5fb236 \par - “This transformation process must be error-free to avoid material waste and significant costs.” (Weil, 2017, p. 1) \#5fb236 \par - “This verification process is time-consuming and laborious, often requiring the dedicated effort of two engineers for an entire week per drawing” (Weil, 2017, p. 1) \#5fb236 \par - “Identifying these dissimilarities will reduce the effort of the engineers to perform the necessary corrections” (Weil, 2017, p. 1) \#a28ae5 \par - “Our approach is divided into two aspects:” (Weil, 2017, p. 1) \#5fb236 \par - “interpreting the drawings to extract information” (Weil, 2017, p. 1) \#a28ae5 \par - “comparing the drawings based on this information” (Weil, 2017, p. 1) \#a28ae5 \par - “formalism to describe the information contained in mechanical drawings” (Weil, 2017, p. 1) \#5fb236 \par - “graph-based structure” (Weil, 2017, p. 1) \#5fb236 \par - “Figure 2.” (Weil, 2017, p. 1) \#ffd400\\ \mkbibemph{This should come before Fig. 1, isn't it?} \par - “Any mistake can propagate through the production process, resulting in material waste and significant costs” (Weil, 2017, p. 1) \#a28ae5 \par - “This verification process is time-consuming and laborious, often requiring the dedicated effort of two engineers for an entire week per drawing” (Weil, 2017, p. 1) \#a28ae5 \par - “automate this verification step;” (Weil, 2017, p. 1) \#5fb236 \par - “dissimilarity as a divergence of information that leads to inconsistencies in the CAD drawing with respect to the original hand-drawn version.” (Weil, 2017, p. 1) \#a28ae5 \par - “Identifying these dissimilarities will reduce the effort of the engineers to perform the necessary corrections.” (Weil, 2017, p. 1) \#a28ae5 \par - “the placement of annotations is different between the versions” (Weil, 2017, p. 2) \#ffd400\\ \mkbibemph{Does this represent a problem?} \par - “For example, in Figure 2, the dimension values are not in the same location, and the arrowheads in the hand-drawn drawing point outward, while in the CAD drawing, they point inward.” (Weil, 2017, p. 2) \#ffd400\\ \mkbibemph{There are also some values that are different.} \par - “This complexity shows that an interpretation step of the drawings is necessary before considering comparison.” (Weil, 2017, p. 2) \#a28ae5 \par - “Graph construction” (Weil, 2017, p. 2) \#2ea8e5 \par - “Drawing interpretation” (Weil, 2017, p. 2) \#2ea8e5 \par - “developing a methodology for segmenting annotations and pure geometry” (Weil, 2017, p. 2) \#5fb236 \par - “incrementally adding new examples to enhance annotation detection.” (Weil, 2017, p. 2) \#a28ae5 \par - “Interpretation-Based comparison” (Weil, 2017, p. 2) \#2ea8e5 \par - “Integrated methodology validation” (Weil, 2017, p. 2) \#2ea8e5 \par - “scanned hand-drawn drawings are algorithmically cleaned of all unwanted information, such as smudges, creases, and the noise often present in scans of hand-drawn mechanical drawings” (Weil, 2017, p. 2) \#a28ae5 \par - “our first three contributions” (Weil, 2017, p. 2) \#a28ae5 \par - “rule-based and heuristic systems” (Weil, 2017, p. 2) \#a28ae5 \par - “advancements in deep learning have shown promise in this field” (Weil, 2017, p. 2) \#ffd400\\ \mkbibemph{Are this referred to "modern techniques" previously mentioned?} \par - “The focus of our research is to digitize mechanical drawings with the goal of developing a modular interpretation system that can easily incorporate new annotation standards.” (Weil, 2017, p. 2) \#e56eee \par - “This refinement reduces the number of shapes to describe the drawing.” (Weil, 2017, p. 2) \#5fb236 \par - “Graph matching involves associating one graph with another” (Weil, 2017, p. 3) \#a28ae5 \par - “exact methods” (Weil, 2017, p. 3) \#2ea8e5 \par - “approximate methods, that allow for a certain degree of tolerance [17]” (Weil, 2017, p. 3) \#2ea8e5 \par - “We are particularly interested in the approximate methods as they are easily adaptable and do not require training.” (Weil, 2017, p. 3) \#a28ae5 \par - “This approach involves defining a set of editing rules to transform one graph into another.” (Weil, 2017, p. 3) \#5fb236 \par - “this method calculates a distance between two graphs based on the total cost of actions required for the transformation” (Weil, 2017, p. 3) \#5fb236 \par - “This tracking provides a more detailed understanding of how one graph is transformed into another, which can be particularly useful for explainability.” (Weil, 2017, p. 3) \#ffd400\\ \mkbibemph{What is the complexity of this operation?} \par - “querying of large databases of such drawings.” (Weil, 2017, p. 3) \#a28ae5 \par - “This method does not enable precise comparison of information nor effectively highlights the differences between the drawings.” (Weil, 2017, p. 3) \#ffd400 \par - “Piping and Instrumentation Diagrams (P\&IDs).” (Weil, 2017, p. 3) \#5fb236 \par - “Binarized hand-drawn drawing Image” (Weil, 2017, p. 3) \#ffd400\\ \mkbibemph{This is the main issue that I see. How can we be sure that hand-drawn images are correctly vectorized? This is the main shortcomings.} \par - “Metamodel” (Weil, 2017, p. 3) \#ffd400\\ \mkbibemph{What's the use of this?} \par - “annotations are represented geometrically, they should be regarded as metadata.” (Weil, 2017, p. 3) \#ffd400 \par - “The vectorisation algorithm is considered a parameter of our approach, meaning the detected shapes depend on the specific algorithm used.” (Weil, 2017, p. 4) \#a28ae5 \par - “we apply a vectorisation algorithm to raster images, resulting in a list of detected shapes.” (Weil, 2017, p. 4) \#5fb236 \par - “treating each intersection point as vertices in the graph” (Weil, 2017, p. 4) \#5fb236 \par - “segments s1 and s2 and the intersection i1 are grouped into a hyperedge a1.” (Weil, 2017, p. 4) \#5fb236 \par - “4.3 Comparison” (Weil, 2017, p. 5) \#ffd400\\ \mkbibemph{All the model comparison work done in the modeling community has been neglected. In the end, once CAD artifacts are represented as graphs, they are models that can be compared by using existing technologhies, that should be considere din the work.} \par - “function f that maps a node in the initial graph to a corresponding node in the target graph.” (Weil, 2017, p. 5) \#5fb236 \par - “We will assess whether our comparison method can detect all the dissimilarities that engineers can identify in order to evaluate the comparison approach. We will compare the efficiency of our method with the engineers’ performance to evaluate if our method enhances the overall detection process.” (Weil, 2017, p. 5) \#5fb236} } @inproceedings{weissModelDrivenDevelopmentSelfDescribing2011, title = {Model-{{Driven Development}} of {{Self-Describing Components}} for {{Self-Adaptive Distributed Embedded Systems}}}, author = {Weiss, Gereon and Becker, Klaus and Kamphausen, Benjamin and Radermacher, Ansgar and Gerard, Sebastien}, date = {2011-08}, pages = {477--484}, publisher = {IEEE}, doi = {10.1109/SEAA.2011.78}, isbn = {978-1-4577-1027-8}, note = {TL;DR \par This work introduces a novel concept for the model-driven development of self-adaptive embedded systems, and presents a self-x profile, a modeling extension for describing the adaptation, and the respective design flow with built-in transformations.} } @online{wengLLMPoweredAutonomous2023, title = {{{LLM Powered Autonomous Agents}}}, author = {Weng, Lilian}, date = {2023-06-23T00:00:00+00:00}, url = {https://lilianweng.github.io/posts/2023-06-23-agent/}, urldate = {2024-02-07}, abstract = {Building agents with LLM (large language model) as its core controller is a cool concept. Several proof-of-concepts demos, such as AutoGPT, GPT-Engineer and BabyAGI, serve as inspiring examples. The potentiality of LLM extends beyond generating well-written copies, stories, essays and programs; it can be framed as a powerful general problem solver. Agent System Overview In a LLM-powered autonomous agent system, LLM functions as the agent’s brain, complemented by several key components:}, langid = {english} } @inproceedings{WenzKT21, title = {Detecting Quality Problems in Data Models by Clustering Heterogeneous Data Values}, booktitle = {{{ACMIEEE Int}}. {{Conf}}. {{Model Driven Eng}}. {{Lang}}. {{Syst}}. {{Companion MODELS}} 2021 {{Companion Fukuoka Jpn}}. {{Oct}}. 10-15 2021}, author = {Wenz, Viola and Kesper, Arno and Taentzer, Gabriele}, date = {2021}, pages = {150--159}, publisher = {IEEE}, doi = {10.1109/MODELS-C53483.2021.00027}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {TL;DR \par This work proposes a bottom-up approach to detecting quality problems in data models that manifest in heterogeneous data values that supports an explorative analysis of the existing data and can be configured by domain experts according to their domain knowledge.}, timestamp = {Wed, 23 Feb 2022 12:16:57 +0100} } @inproceedings{wermelingerDesignPrinciplesArchitectural2008, title = {Design Principles in Architectural Evolution: {{A}} Case Study}, shorttitle = {Design Principles in Architectural Evolution}, booktitle = {2008 {{IEEE Int}}. {{Conf}}. {{Softw}}. {{Maint}}.}, author = {Wermelinger, Michel and Yu, Yijun and Lozano, Angela}, date = {2008-09}, pages = {396--405}, publisher = {IEEE}, location = {Beijing, China}, doi = {10.1109/ICSM.2008.4658088}, abstract = {We wish to investigate how structural design principles are used in practice, in order to assess the utility and relevance of such principles to the maintenance of large, complex, long-lived, successful systems. In this paper we take Eclipse as the case study and check whether its architecture follows, throughout multiple releases, some principles proposed in the literature.}, eventtitle = {2008 {{IEEE International Conference}} on {{Software Maintenance}} ({{ICSM}})}, isbn = {978-1-4244-2613-3}, langid = {english} } @article{Westfechtel14, title = {Merging of {{EMF}} Models - Formal Foundations}, author = {Westfechtel, Bernhard}, date = {2014}, journaltitle = {Software Syst. Model.}, volume = {13}, number = {2}, pages = {757--788}, doi = {10.1007/S10270-012-0279-3}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, timestamp = {Fri, 18 Sep 2020 11:19:20 +0200} } @inproceedings{weynsApplyingArchitectureBasedAdaptation2018, title = {Applying {{Architecture-Based Adaptation}} to {{Automate}} the {{Management}} of {{Internet-of-Things}}}, booktitle = {Softw. {{Archit}}.}, author = {Weyns, Danny and Iftikhar, M. Usman and Hughes, Danny and Matthys, Nelson}, editor = {Cuesta, Carlos E. and Garlan, David and Pérez, Jennifer}, date = {2018}, series = {Lecture {{Notes}} in {{Computer Science}}}, pages = {49--67}, publisher = {Springer International Publishing}, location = {Cham}, doi = {10.1007/978-3-030-00761-4_4}, abstract = {Architecture-based adaptation equips a software-intensive system with a feedback loop that enables the system to adapt itself at runtime to changes to maintain its required quality goals. To guarantee the required goals, existing adaptation approaches apply exhaustive verification techniques at runtime. However these approaches are restricted to small-scale settings, which often limits their applicability in practice. To tackle this problem, we introduce an innovative architecture-based adaptation approach to solve a concrete practical problem of VersaSense: automating the management of Internet-of-Things (IoT). The approach, called MARTAS, equips a software system with a feedback loop that employs Models At Run Time and Statistical techniques to reason about the system and adapt it to ensure the required goals. We apply MARTAS to a building security case system, which is a representative IoT system deployed by VersaSense. The application comprises a set of IoT devices that communicate sensor data over a time synchronized smart mess network to a central monitoring facility. We demonstrate how MARTAS outperforms a conservative approach that is typically applied in practice and a state-of-the-art adaptation approach for different quality goals, and we report lessons learned from this industrial case.}, isbn = {978-3-030-00761-4}, langid = {english}, keywords = {DONE}, note = {TL;DR \par An innovative architecture-based adaptation approach to solve a concrete practical problem of VersaSense: automating the management of Internet-of-Things (IoT) and demonstrates how MARTAS outperforms a conservative approach that is typically applied in practice and a state- of-the-art adaptation approach for different quality goals.} } @article{Weyssow20221071, ids = {weyssow2022recommending}, title = {Recommending Metamodel Concepts during Modeling Activities with Pre-Trained Language Models}, author = {Weyssow, M. and Sahraoui, H. and Syriani, E.}, date = {2022}, journaltitle = {Software Syst. Model.}, volume = {21}, number = {3}, pages = {1071--1089}, publisher = {{Springer Science and Business Media Deutschland GmbH}}, issn = {16191366}, doi = {10.1007/s10270-022-00975-5}, abbrev_source_title = {Softw. Syst. Model.}, affiliation = {DIRO, Université de Montréal, Montreal, Canada}, correspondence_address1 = {Weyssow, M.; DIRO, Canada; email: martin.weyssow@umontreal.ca}, document_type = {Article}, langid = {english}, source = {Scopus}, keywords = {/unread,⛔ No INSPIRE recid found,GOAL\_Model-Assistance,notion,TECHNIQUE\_LSTM}, note = {cited By 0 \par TL;DR \par The preliminary results show that the trained model is able to provide accurate top 5 lists of relevant recommendations for concept renaming scenarios, and the results are less compelling for the scenario of the iterative construction of the metamodel, in part because of the conservative strategy used to evaluate the recommendations.} } @article{weyssow2024codeultrafeedback, title = {{{CodeUltraFeedback}}: An {{LLM-as-a-judge}} Dataset for Aligning Large Language Models to Coding Preferences}, author = {Weyssow, Martin and Kamanda, Aton and Sahraoui, Houari}, date = {2024}, journaltitle = {arXiv prepr. arXiv:2403,09032}, eprint = {2403.09032}, eprinttype = {arXiv}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {TL;DR \par This paper introduces CodeUltraFeedback, a preference dataset of 10,000 complex instructions to tune and align LLMs to coding preferences through AI feedback, and shows the utility of CodeUltraFeedback for preference tuning.} } @article{whalenRequirementsArchitecturesSecure2016, title = {Requirements and {{Architectures}} for {{Secure Vehicles}}}, author = {Whalen, Michael W. and Cofer, Darren and Gacek, Andrew}, date = {2016}, journaltitle = {IEEE Softw.}, volume = {33}, number = {4}, pages = {22--25}, url = {http://ieeexplore.ieee.org/abstract/document/7498541/}, urldate = {2016-09-28} } @online{WhatDifferenceEvolution, title = {What Is the Difference between Evolution and Change? | {{WikiDiff}}}, url = {https://wikidiff.com/evolution/change}, urldate = {2020-02-10} } @online{WhatLowCode2020, title = {What {{Is Low-Code}}? [2020 {{Update}}]}, url = {https://www.outsystems.com/blog/what-is-low-code.html}, urldate = {2020-04-08}, keywords = {lowcode} } @online{WhatsDifferenceAutonomous, title = {What's the Difference between Autonomous Systems, {{ISPs}} and {{RIRs}}? - {{Network Engineering Stack Exchange}}}, url = {http://networkengineering.stackexchange.com/questions/25951/whats-the-difference-between-autonomous-systems-isps-and-rirs}, urldate = {2016-08-26} } @online{WhatSpacedRepetition, title = {What Is Spaced Repetition Study Method: 101 Guide}, shorttitle = {What Is Spaced Repetition Study Method}, url = {https://ankipro.net/blog/spaced-repetition}, urldate = {2025-02-19}, abstract = {Let's be honest, those late-night dates with textbooks aren't just not romantic; they're also terribly ineffective. Read to understand the spaced repetition system and apply it to your study routine!}, langid = {english}, organization = {Spaced Repetition: Learn Anything and Make it Stick - Anki Pro}, keywords = {/unread} } @misc{WhenHowUse, title = {When and {{How}} to {{Use Multi-Level Modelling}}.Pdf} } @online{whitePromptPatternCatalog2023, title = {A {{Prompt Pattern Catalog}} to {{Enhance Prompt Engineering}} with {{ChatGPT}}}, author = {White, Jules and Fu, Quchen and Hays, Sam and Sandborn, Michael and Olea, Carlos and Gilbert, Henry and Elnashar, Ashraf and Spencer-Smith, Jesse and Schmidt, Douglas C.}, date = {2023-02-21}, eprint = {2302.11382}, eprinttype = {arXiv}, eprintclass = {cs}, url = {http://arxiv.org/abs/2302.11382}, urldate = {2024-09-17}, abstract = {Prompt engineering is an increasingly important skill set needed to converse effectively with large language models (LLMs), such as ChatGPT. Prompts are instructions given to an LLM to enforce rules, automate processes, and ensure specific qualities (and quantities) of generated output. Prompts are also a form of programming that can customize the outputs and interactions with an LLM.}, langid = {english}, pubstate = {prepublished}, keywords = {Computer Science - Artificial Intelligence,Computer Science - Software Engineering} } @article{whitmoreInternetThingsSurvey2015, title = {The {{Internet}} of {{Things}}—{{A}} Survey of Topics and Trends}, author = {Whitmore, Andrew and Agarwal, Anurag and Da Xu, Li}, date = {2015-04}, journaltitle = {Inf. Syst. Front.}, volume = {17}, number = {2}, pages = {261--274}, issn = {1387-3326, 1572-9419}, doi = {10.1007/s10796-014-9489-2}, langid = {english} } @inproceedings{whittleIndustrialAdoptionModeldriven2013, title = {Industrial Adoption of Model-Driven Engineering: {{Are}} the Tools Really the Problem?}, shorttitle = {Industrial Adoption of Model-Driven Engineering}, booktitle = {Int. {{Conf}}. {{Model Driven Eng}}. {{Lang}}. {{Syst}}.}, author = {Whittle, Jon and Hutchinson, John and Rouncefield, Mark and family=Burden, given=H\textbackslash a, prefix=akan, useprefix=false and Heldal, Rogardt}, date = {2013}, pages = {1--17}, publisher = {Springer}, url = {http://link.springer.com/chapter/10.1007/978-3-642-41533-3_1}, urldate = {2017-02-22}, note = {TL;DR \par A taxonomy of tool-related considerations is presented, based on industry data, which can be used to reflect on the tooling landscape as well as inform future research on MDE tools.} } @online{WhyHuggingFaceKaggle, title = {Why {{HuggingFace}} and {{Kaggle Win}} with {{AI}} — {{And What You}}’re {{Missing}} | by {{Mahmudur R Manna}} | {{Dec}}, 2024 | {{AI Advances}}}, url = {https://ai.gopubby.com/millions-invested-yet-no-ai-results-uncovering-the-missing-link-between-data-readiness-and-real-135b857ec8b2}, keywords = {source\_mobile} } @incollection{Wilcoxon1992, title = {Individual Comparisons by Ranking Methods}, booktitle = {Breakthroughs in Statistics: {{Methodology}} and Distribution}, author = {Wilcoxon, Frank}, editor = {Kotz, Samuel and Johnson, Norman L.}, date = {1992}, pages = {196--202}, publisher = {Springer New York}, location = {New York, NY}, doi = {10.1007/978-1-4612-4380-9₁6}, abstract = {The comparison of two treatments generally falls into one of the following two categories: (a) we may have a number of replications for each of the two treatments, which are unpaired, or (b) we may have a number of paired comparisons leading to a series of differences, some of which may be positive and some negative. The appropriate methods for testing the significance of the differences of the means in these two cases are described in most of the textbooks on statistical methods.}, isbn = {978-1-4612-4380-9} } @online{WileyAutonomousSystem, title = {Wiley: {{The Autonomous System}}: {{A Foundational Synthesis}} of the {{Sciences}} of the {{Mind}} - {{Szabolcs Michael}} de {{Gyurky}}, {{Mark A}}. {{Tarbell}}}, url = {http://eu.wiley.com/WileyCDA/WileyTitle/productCd-1118294246,subjectCd-EE79.html}, urldate = {2016-08-22} } @inproceedings{Williams2020, title = {Deriving Metamodels to Relate Machine Learning Quality to Design Repository Characteristics in the Context of Additive Manufacturing}, author = {Williams, G. and Meisel, N.A. and Simpson, T.W. and McComb, C.}, date = {2020}, series = {Proceedings of the {{ASME Design Engineering Technical Conference}}}, volume = {11A-2020}, publisher = {American Society of Mechanical Engineers (ASME)}, doi = {10.1115/DETC2020-22518}, abbrev_source_title = {Proc. ASME Des. Eng. Tech. Conf.}, affiliation = {Pennsylvania State University, University Park, PA, United States}, art_number = {V11AT11A006}, document_type = {Conference Paper}, isbn = {978-0-7918-8400-3}, langid = {english}, source = {Scopus}, note = {cited By 0 \par TL;DR \par The results suggest that metamodels predicting the convolutional neural network coefficient of determination, as opposed to computational effort, were most accurate and the size of a design repository, the average complexity of its constituent designs, and the average and spread of design spatial diversity were the best predictors of convolutionic neural network accuracy.} } @article{williamsEngineeringSecurityVulnerability2018, title = {Engineering {{Security Vulnerability Prevention}}, {{Detection}}, and {{Response}}}, author = {Williams, L. and McGraw, G. and Migues, S.}, date = {2018-09}, journaltitle = {IEEE Softw.}, volume = {35}, number = {5}, pages = {76--80}, issn = {0740-7459}, doi = {10.1109/MS.2018.290110854}, abstract = {Around the turn of the 21st century, practices began to emerge to guide teams toward engineering software to stop attackers and users from utilizing unintended functionality by violating the system designer’s assumptions to cause a security breach. Yet, breaches are reported daily in the news in all domains—from the casual to the critical. The goal of this article is to help software engineers, software engineering educators, and security researchers understand opportunities for education and research through an analysis of current software security practices. The analysis is conducted on data on the use of a subset of 113 software security practices by 109 firms over 42 months, as reported in the Building Security In Maturity Model (BSIMM) Version 8 report. This article is part of a theme issue on software engineering’s 50th anniversary.}, keywords = {software engineering}, note = {TL;DR \par The goal of this article is to help software engineers, software engineering educators, and security researchers understand opportunities for education and research through an analysis of current software security practices.} } @inproceedings{williamsModelBasedAutonomousSystems1996, title = {Model-{{Based Autonomous Systems}} in the {{New Millenium}}.}, booktitle = {{{AIPS}}}, author = {Williams, Brian C.}, date = {1996}, pages = {275--282}, url = {http://www.aaai.org/Papers/AIPS/1996/AIPS96-035.pdf}, urldate = {2016-08-21}, note = {TL;DR \par Self modeling, self configuration, deliberated reactions and compositional, model-based programming are the four key elements of a model- based autonomous systems architecture that is taking us into the New Millenium.} } @article{willinkWorkshopOCLTextual, title = {Workshop on {{OCL}} and Textual Modelling: {{Re-engineering}} Eclipse {{MDT}}/{{OCL}} for Xtext}, author = {Willink, Edward}, abstract = {The current tooling used for the Eclipse OCL project uses an LALR parser generator. Enhancing the tooling to support editing motivated a migration to exploit the inherently model-driven characteristics of Xtext. This paper summarizes the experiences of that migration, identifies the many benefits and discusses a few changes in implementation approach that were required. Objective performance and size comparisons between the old LALR and new Xtext approach are provided.}, langid = {english} } @article{Wills06googlespagerank, title = {Google's {{PageRank}}: {{The}} Math behind the Search Engine}, author = {Wills, Rebecca S.}, date = {2006}, journaltitle = {Math. Intelligencer}, pages = {6--10} } @inproceedings{WimmerB13, title = {Testing {{M2T}}/{{T2M}} Transformations}, booktitle = {Model-{{Driven Eng}}. {{Lang}}. {{Syst}}. - 16th {{Int}}. {{Conf}}. {{MODELS}} 2013 {{Miami FL USA Sept}}. 29 - {{Oct}}. 4 2013 {{Proc}}.}, author = {Wimmer, Manuel and Burgueño, Loli}, editor = {Moreira, Ana and Schätz, Bernhard and Gray, Jeff and Vallecillo, Antonio and Clarke, Peter J.}, date = {2013}, series = {Lecture Notes in Computer Science}, volume = {8107}, pages = {203--219}, publisher = {Springer}, doi = {10.1007/978-3-642-41533-3\_13}, bibsource = {dblp computer science bibliography, https://dblp.org}, langid = {english}, keywords = {/unread,⛔ No INSPIRE recid found}, note = {TL;DR \par Tracts, a M2M transformation testing approach, is extended, to represent text within a generic metamodel, and applied to evaluate code generation capabilities of several existing UML tools.}, timestamp = {Mon, 21 Jun 2021 12:26:18 +0200} } @article{wimmerCatalogueRefactoringsModeltoModel2012, title = {A {{Catalogue}} of {{Refactorings}} for {{Model-to-Model Transformations}}.}, author = {Wimmer, Manuel and Martínez, Salvador and Jouault, Frédéric and Cabot, Jordi}, date = {2012}, journaltitle = {J. Object Technol.}, volume = {11}, number = {2}, pages = {2:1}, doi = {10.5381/jot.2012.11.2.a2}, note = {TL;DR \par A dedicated catalogue of refactorings for improving the quality of model transformations in model-to-model (M2M) transfor- mations and applicable also to other M2M transformation languages.} } @article{wimmerHowWeb202008, title = {How {{Web}} 2.0 Can Leverage {{Model Engineering}} in {{Practice}}}, author = {Wimmer, Manuel and Schauerhuber, Andrea and Michael, Strommer and Jürgen, Flandorfer and Gerti, Kappel}, date = {2008}, journaltitle = {Workshop Domänspezifische Modellier.} } @inproceedings{wimmerPlugPlayModel2010, title = {Plug \& Play Model Transformations: A {{DSL}} for Resolving Structural Metamodel Heterogeneities}, shorttitle = {Plug \& Play Model Transformations}, booktitle = {Proc. 10th {{Workshop Domain-Specif}}. {{Model}}.}, author = {Wimmer, Manuel and Retschitzegger, W. and Kappel, G. and Schoenboeck, J. and Kusel, A. and Schwinger, Wieland}, date = {2010}, pages = {7}, publisher = {ACM}, url = {http://dl.acm.org/citation.cfm?id=2060348}, urldate = {2015-06-24} } @article{wimmerReusingModelTransformations2011, title = {Reusing {{Model Transformations}} across {{Heterogeneous Metamodels}}}, author = {Wimmer, Manuel}, date = {2011} } @inproceedings{winterMonitoringawareIDEs2019, ids = {winterMonitoringawareIDEs2019a}, title = {Monitoring-Aware {{IDEs}}}, booktitle = {Proc. 2019 27th {{ACM Jt}}. {{Meet}}. {{Eur}}. {{Softw}}. {{Eng}}. {{Conf}}. {{Symp}}. {{Found}}. {{Softw}}. {{Eng}}.}, author = {Winter, Jos and Aniche, Maurício and Cito, Jürgen and family=Deursen, given=Arie, prefix=van, useprefix=false}, date = {2019-08-12}, pages = {420--431}, publisher = {ACM}, location = {Tallinn Estonia}, doi = {10.1145/3338906.3338926}, eventtitle = {{{ESEC}}/{{FSE}} '19: 27th {{ACM Joint European Software Engineering Conference}} and {{Symposium}} on the {{Foundations}} of {{Software Engineering}}}, isbn = {978-1-4503-5572-8}, langid = {english}, keywords = {devops,IDE,Integrated Development Environment,runtime monitoring,software engineering,systems monitoring}, note = {\textbf{Contents} \par \begin{itemize} \item \href{zotero://open-pdf/0_IL3NR2SD/2}{Abstract} \item \href{zotero://open-pdf/0_IL3NR2SD/2}{1 Introduction} \item \href{zotero://open-pdf/0_IL3NR2SD/3}{2 Background} \begin{itemize} \item \href{zotero://open-pdf/0_IL3NR2SD/3}{2.1 Related Work} \item \href{zotero://open-pdf/0_IL3NR2SD/4}{2.2 Monitoring and DevOps} \end{itemize} \item \href{zotero://open-pdf/0_IL3NR2SD/5}{3 Monitoring-Aware IDEs} \item \href{zotero://open-pdf/0_IL3NR2SD/5}{4 Monitoring-aware IDE prototype} \item \href{zotero://open-pdf/0_IL3NR2SD/6}{5 Field Experiment} \begin{itemize} \item \href{zotero://open-pdf/0_IL3NR2SD/6}{5.1 Methodology} \item \href{zotero://open-pdf/0_IL3NR2SD/7}{5.2 Participants} \end{itemize} \item \href{zotero://open-pdf/0_IL3NR2SD/7}{6 Results} \begin{itemize} \item \href{zotero://open-pdf/0_IL3NR2SD/7}{6.1 RQ1: How do developers interact with a Monitoring-Aware IDE?} \item \href{zotero://open-pdf/0_IL3NR2SD/8}{6.2 RQ2: What impact does a Monitoring-Aware IDE bring to software development teams?} \item \href{zotero://open-pdf/0_IL3NR2SD/9}{6.3 RQ3: What are the developers' perceptions about the usefulness of a Monitoring-Aware IDE to support their monitoring practices?} \end{itemize} \item \href{zotero://open-pdf/0_IL3NR2SD/10}{7 Discussion} \begin{itemize} \item \href{zotero://open-pdf/0_IL3NR2SD/10}{7.1 Building Monitoring-Aware IDEs} \item \href{zotero://open-pdf/0_IL3NR2SD/11}{7.2 Threats to Validity} \end{itemize} \item \href{zotero://open-pdf/0_IL3NR2SD/11}{8 Conclusions} \item \href{zotero://open-pdf/0_IL3NR2SD/12}{References} \end{itemize} \par TL;DR \par This paper implemented a prototype of a Monitoring-Aware IDE, connected to the monitoring systems of Adyen, a large-scale payment company that performs intense monitoring in their software systems, and concluded that monitoring-aware IDEs can play an essential role in improving how developers perform monitoring.} } @article{wintersSoftwareEngineeringGoogle, title = {Software {{Engineering}} at {{Google}}}, author = {Winters, Titus and Manschreck, Tom and Wright, Hyrum}, pages = {602}, langid = {english}, note = {TL;DR \par The authors catalog and describe Google’s key software engineering practices, which include the server-side software behind speech recognition and voice actions, and speech synthesis.} } @article{Wollstadt2022, title = {{{CarHoods10k}}: {{An}} Industry-Grade Data Set for Representation Learning and Design Optimization in Engineering Applications}, author = {Wollstadt, P. and Bujny, M. and Ramnath, S. and Shah, J.J. and Detwiler, D. and Menzel, S.}, date = {2022}, journaltitle = {IEEE Trans. Evol. Comput.}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {1089778X}, doi = {10.1109/TEVC.2022.3147013}, abbrev_source_title = {IEEE Trans Evol Comput}, affiliation = {Honda Research Institute Europe, Offenbach/Main, Germany. (e-mail: patricia.wollstadt@honda-ri.de); Honda Research Institute Europe, Offenbach/Main, Germany.; Simulation Innovation \&\#x0026; Modeling Center, The Ohio State University Columbus, Ohio, USA.; Digital Design \&\#x0026; Manufacturing Lab, The Ohio State University Columbus, Ohio, USA.; Honda Development \&\#x0026; Manufacturing of America, Raymond, Ohio, USA.}, coden = {ITEVF}, document_type = {Article}, langid = {english}, source = {Scopus}, note = {cited By 0 \par TL;DR \par The OSU-Honda Automobile Hood Dataset (CarHoods10k), an industry-grade 3-D vehicle hood data set of over 10 000 shapes along with mechanical performance data that were validated against real-world hood designs by industry experts, is introduced.} } @article{wongPerformanceEvaluationClassification2015, title = {Performance Evaluation of Classification Algorithms by K-Fold and Leave-One-out Cross Validation}, author = {Wong, Tzu-Tsung}, date = {2015}, journaltitle = {Pattern Recognit.}, volume = {48}, number = {9}, pages = {2839--2846}, publisher = {Elsevier}, address = {New York}, issn = {0031-3203}, nodoi = {10.1016/j.patcog.2015.03.009}, numpages = {8} } @inproceedings{Worsey2021265, title = {Observations from Using a Portable {{LIDAR}} Scanner to Capture {{RF}} Propagation Modelling Environments}, author = {Worsey, J. and Hindmarch, I. and Armour, S. and Bull, D.}, date = {2021}, series = {Proceedings - 2021 {{IEEE}} 4th {{5G World Forum}}, {{5GWF}} 2021}, pages = {265--268}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/5GWF52925.2021.00053}, abbrev_source_title = {Proc. - IEEE 5G World Forum, 5GWF}, affiliation = {University of Bristol, Department of Electrical And Electronic Engineering, Bristol, United Kingdom}, document_type = {Conference Paper}, isbn = {978-1-6654-4308-1}, langid = {english}, source = {Scopus}, note = {cited By 0} } @article{wortmannModelingLanguagesIndustry2019, title = {Modeling Languages in {{Industry}} 4.0: An Extended Systematic Mapping Study}, shorttitle = {Modeling Languages in {{Industry}} 4.0}, author = {Wortmann, Andreas and Barais, Olivier and Combemale, Benoit and Wimmer, Manuel}, date = {2019-09-20}, journaltitle = {Softw Syst Model}, issn = {1619-1366, 1619-1374}, doi = {10.1007/s10270-019-00757-6}, abstract = {Industry 4.0 integrates cyber-physical systems with the Internet of Things to optimize the complete value-added chain. Successfully applying Industry 4.0 requires the cooperation of various stakeholders from different domains. Domain-specific modeling languages promise to facilitate their involvement through leveraging (domain-specific) models to primary development artifacts. We aim to assess the use of modeling in Industry 4.0 through the lens of modeling languages in a broad sense. Based on an extensive literature review, we updated our systematic mapping study on modeling languages and modeling techniques used in Industry 4.0 (Wortmann et al., Conference on model-driven engineering languages and systems (MODELS’17), IEEE, pp 281–291, 2017) to include publications until February 2018. Overall, the updated study considers 3344 candidate publications that were systematically investigated until 408 relevant publications were identified. Based on these, we developed an updated map of the research landscape on modeling languages and techniques for Industry 4.0. Research on modeling languages in Industry 4.0 focuses on contributing methods to solve the challenges of digital representation and integration. To this end, languages from systems engineering and knowledge representation are applied most often but rarely combined. There also is a gap between the communities researching and applying modeling languages for Industry 4.0 that originates from different perspectives on modeling and related standards. From the vantage point of modeling, Industry 4.0 is the combination of systems engineering, with cyber-physical systems, and knowledge engineering. Research currently is splintered along topics and communities and accelerating progress demands for multi-disciplinary, integrated research efforts.}, langid = {english} } @software{wsteven24Wsteven24NodeRedWorkflowBPMN2022, title = {Wsteven24/{{NodeRed-Workflow-BPMN}}}, author = {{wsteven24}}, date = {2022-08-08T07:57:23Z}, origdate = {2021-09-13T07:41:05Z}, url = {https://github.com/wsteven24/NodeRed-Workflow-BPMN}, urldate = {2024-05-02}, abstract = {An approach to workflow management system for embedded devices using Node-RED for implementation, selection, and execution of BPMN files}, keywords = {bpmn,node-red,workflow-management} } @article{wu_effective_2016, title = {Effective Detection of Android Malware Based on the Usage of Data Flow {{APIs}} and Machine Learning}, author = {Wu, Songyang and Wang, Pan and Li, Xun and Zhang, Yong}, date = {2016-07}, journaltitle = {Inf. Softw. Technol.}, volume = {75}, pages = {17--25}, issn = {09505849}, doi = {10.1016/j.infsof.2016.03.004}, abstract = {Objective. This study proposes an Android malware detecting system that provides highly accurate classification and efficient sensitive data transmission analysis. Method. The study adopts a machine learning approach that leverages the use of dataflow application program interfaces (APIs) as classification features to detect Android malware. We conduct a thorough analysis to extract dataflow-related API-level features and improve the k-nearest neighbour classification model. The dataflow-related API list is further optimized through machine learning, which enables us to improve considerably the efficiency of sensitive data transmission analysis, whereas analytical accuracy is approximated to that of the experiment using a full dataflow-related API list. Results. The proposed scheme is evaluated using 1160 benign and 1050 malicious samples. Results show that the system can achieve an accuracy rate of as high as 97.66\% in detecting unknown Android malware. Our experiment of static dataflow analysis shows that more than 85\% of sensitive data transmission paths can be determined using the refined API subset, whereas time of analysis decreases by nearly 40\%. Conclusion. The usage of dataflow-related APIs is a valid feature for identifying Android malware. The proposed scheme provides an efficient approach to detecting Android malware and investigating privacy violations in malicious apps.}, langid = {english} } @inproceedings{Wu201911899, title = {Domain Adaptation with Asymmetrically-Relaxed Distribution Alignment}, author = {Wu, Y. and Winston, E. and Kaushik, D. and Lipton, Z.C.}, date = {2019}, series = {36th {{International Conference}} on {{Machine Learning}}, {{ICML}} 2019}, volume = {2019-June}, pages = {11899--11914}, publisher = {International Machine Learning Society (IMLS)}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078282922&partnerID=40&md5=f9cb636540ab2ed0339cb1978717dd35}, abstract = {Domain adaptation addresses the common situation in which the target distribution generating our test data differs from the source distribution generating our training data. While absent assumptions, domain adaptation is impossible, strict conditions, e.g. covariate or label shift, enable principled algorithms. Recently-proposed domain-adversarial approaches consist of aligning source and target encodings, an approach often motivated as minimizing two (of three) terms in a theoretical bound on target error. Unfortunately, this minimization can cause arbitrary increases in the third term, a problem guaranteed to arise under shifting label distributions. We propose asymmetrically-relaxed distribution alignment, a new approach that overcomes some limitations of standard domain-adversarial algorithms. Moreover, we characterize precise assumptions under which our algorithm is theoretically principled and demonstrate empirical benefits on both synthetic and real datasets. Copyright © 2019 ASME}, document_type = {Conference Paper}, isbn = {978-1-5108-8698-8}, source = {Scopus}, keywords = {Artificial intelligence,Domain adaptation,Label distribution,Machine learning,New approaches,Real data sets,Software engineering,Source distribution,Standard domains,Theoretical bounds,Training data}, note = {cited By 24 \par TL;DR \par Asymmetrically-relaxed distribution alignment is proposed, a new approach that overcomes some limitations of standard domain-adversarial algorithms and characterize precise assumptions under which the algorithm is theoretically principled and demonstrate empirical benefits on both synthetic and real datasets.} } @article{Wu20202230, title = {Ensemble Generalized Multiclass Support-Vector-Machine-Based Health Evaluation of Complex Degradation Systems}, author = {Wu, J. and Guo, P. and Cheng, Y. and Zhu, H. and Wang, X.-B. and Shao, X.}, date = {2020}, journaltitle = {IEEEASME Trans. Mechatron.}, volume = {25}, number = {5}, pages = {2230--2240}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {10834435}, doi = {10.1109/TMECH.2020.3009449}, abbrev_source_title = {IEEE ASME Trans Mechatron}, affiliation = {School of Naval Architecture and Ocean Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China; School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China; College of Electrical Engineering, Henan University of Technology, Zhengzhou, 450001, China}, art_number = {9141518}, coden = {IATEF}, correspondence_address1 = {Wang, X.-B.; College of Electrical Engineering, China; email: xb\textsubscript{w}ang@live.com}, document_type = {Article}, langid = {english}, source = {Scopus}, note = {cited By 22} } @article{Wu2021, title = {Causal Artificial Neural Network and Its Applications in Engineering Design}, author = {Wu, D. and Wang, G.G.}, date = {2021}, journaltitle = {Eng. Appl. Artif. Intell.}, volume = {97}, publisher = {Elsevier Ltd}, issn = {09521976}, doi = {10.1016/j.engappai.2020.104089}, abbrev_source_title = {Eng Appl Artif Intell}, affiliation = {Product Design and Optimization Laboratory, Mechatronic System Engineering Department, Simon Fraser University, Surrey, BCV3T 0A3, Canada}, art_number = {104089}, coden = {EAAIE}, correspondence_address1 = {Wu, D.; Product Design and Optimization Laboratory, Surrey, BC, Canada; email: dwa88@sfu.ca}, document_type = {Article}, langid = {english}, source = {Scopus}, note = {cited By 4} } @article{Wu20211915, title = {Channel Prediction in High-Mobility Massive {{MIMO}}: {{From}} Spatio-Temporal Autoregression to Deep Learning}, author = {Wu, C. and Yi, X. and Zhu, Y. and Wang, W. and You, L. and Gao, X.}, date = {2021}, journaltitle = {IEEE J. Sel. Areas Commun.}, volume = {39}, number = {7}, pages = {1915--1930}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {07338716}, doi = {10.1109/JSAC.2021.3078503}, abstract = {While massive multiple-input multiple-output (MIMO) has achieved tremendous success in both theory and practice, it faces a crisis of sharp performance degradation in moderate or high-mobility scenarios (e.g., 30 km/h), due to the breach of uplink-downlink channel duality. Such a 'curse of mobility' has spurred the research on channel prediction in high-mobility scenarios. Instead of predicting channel response matrix in the space-frequency domain, we investigate it in the angle-delay domain by utilizing the high angle-delay resolution of wideband massive MIMO systems. Specifically, we study the general angle-delay domain channel characterization and obtain that: 1) the correlations between the angle-delay domain channel response matrix (ADCRM) elements are decoupled significantly; 2) when the number of antennas and bandwidth are limited, the decoupling is insufficient and residual correlations between the neighboring ADCRM elements exist. Then focusing on the ADCRM, we propose two channel prediction methods: a spatio-temporal autoregressive (ST-AR) model-driven unsupervised-learning method and a deep learning (DL) based data-driven supervised-learning method. While the model-driven method provides a principled way for channel prediction, the data-driven method is generalizable to various channel scenarios. In particular, ST-AR exploits the residual spatio-temporal correlations of the channel element with its most neighboring elements, and DL realizes element-wise angle-delay domain channel prediction utilizing a complex-valued neural network (CVNN). Simulation results under the 3GPP non-line-of-sight (NLOS) scenarios indicate that, compared to the state-of-the-art Prony-based angular-delay domain (PAD) prediction method, both the proposed ST-AR and the CVNN-based channel prediction methods can enhance the channel prediction accuracy. © 1983-2012 IEEE.}, art_number = {9427230}, coden = {ISACE}, document_type = {Article}, source = {Scopus}, note = {cited By 2 \par TL;DR \par Simulation results under the 3GPP non-line- of-sight (NLOS) scenarios indicate that, compared to the state-of-the-art Prony-based angular-delay domain (PAD) prediction method, both the proposed ST-AR and the CVNN-based channel prediction methods can enhance the channel prediction accuracy.} } @online{wuAutoGenEnablingNextGen2023, title = {{{AutoGen}}: {{Enabling Next-Gen LLM Applications}} via {{Multi-Agent Conversation}}}, shorttitle = {{{AutoGen}}}, author = {Wu, Qingyun and Bansal, Gagan and Zhang, Jieyu and Wu, Yiran and Li, Beibin and Zhu, Erkang and Jiang, Li and Zhang, Xiaoyun and Zhang, Shaokun and Liu, Jiale and Awadallah, Ahmed Hassan and White, Ryen W. and Burger, Doug and Wang, Chi}, date = {2023-10-03}, eprint = {2308.08155}, eprinttype = {arXiv}, eprintclass = {cs}, url = {http://arxiv.org/abs/2308.08155}, urldate = {2024-01-26}, abstract = {AutoGen is an open-source framework that allows developers to build LLM applications via multiple agents that can converse with each other to accomplish tasks. AutoGen agents are customizable, conversable, and can operate in various modes that employ combinations of LLMs, human inputs, and tools. Using AutoGen, developers can also flexibly define agent interaction behaviors. Both natural language and computer code can be used to program flexible conversation patterns for different applications. AutoGen serves as a generic infrastructure to build diverse applications of various complexities and LLM capacities. Empirical studies demonstrate the effectiveness of the framework in many example applications, with domains ranging from mathematics, coding, question answering, operations research, online decision-making, entertainment, etc.}, pubstate = {prepublished}, keywords = {Computer Science - Artificial Intelligence,Computer Science - Computation and Language}, note = {Comment: 43 pages (10 pages for the main text, 3 pages for references, and 30 pages for appendices)} } @article{wuGraphNeuralNetworks2021, title = {Graph {{Neural Networks}} in {{Recommender Systems}}: {{A Survey}}}, shorttitle = {Graph {{Neural Networks}} in {{Recommender Systems}}}, author = {Wu, Shiwen and Sun, Fei and Zhang, Wentao and Cui, Bin}, date = {2021-04-19}, journaltitle = {ArXiv201102260 Cs}, eprint = {2011.02260}, eprinttype = {arXiv}, eprintclass = {cs}, url = {http://arxiv.org/abs/2011.02260}, urldate = {2021-10-19}, abstract = {Owing to the superiority of GNN in learning on graph data and its efficacy in capturing collaborative signals and sequential patterns, utilizing GNN techniques in recommender systems has gain increasing interests in academia and industry. In this survey, we provide a comprehensive review of the most recent works on GNN-based recommender systems. We proposed a classification scheme for organizing existing works. For each category, we briefly clarify the main issues, and detail the corresponding strategies adopted by the representative models. We also discuss the advantages and limitations of the existing strategies. Furthermore, we suggest several promising directions for future researches. We hope this survey can provide readers with a general understanding of the recent progress in this field, and shed some light on future developments.}, keywords = {Computer Science - Information Retrieval,Computer Science - Machine Learning}, note = {TL;DR \par This article provides a taxonomy of GNN-based recommendation models according to the types of information used and recommendation tasks and systematically analyze the challenges of applying GNN on different types of data.} } @inproceedings{wuLowComplexityModelDriven2021, title = {A {{Low Complexity Model-Driven Deep Learning LDPC Decoding Algorithm}}}, booktitle = {2021 {{IEEE}} 6th {{Int}}. {{Conf}}. {{Comput}}. {{Commun}}. {{Syst}}. {{ICCCS}} 2021}, author = {Wu, Q. and Tang, S.-K. and Liang, Y. and Lam, C.T. and Ma, Y.}, date = {2021}, pages = {558--563}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/ICCCS52626.2021.9449266}, abstract = {A novel Neural Offset Min-Sum(NOMS) Belief Propagation(BP) decoding algorithm based on model-driven is proposed which applied to LDPC decoding. NOMS is improved multiplication in Neural Normalized Min-Sum(NNMS) into addition operation to reduce the complexity of calculation., a better Bit Error Rate (BER) performance is simultaneously achieved in the same condition. Secondly, considering that there are still many multiplication operations in NOMS, we propose a novel Shared Offset Min-Sum(SNOMS) to reduce the number of weights in the network by sharing parameters. Finally, codebook-based quantization is used to further reduce the memory consumption. Simulation experimental results show that the proposed method has a better BER performance, and the decoding accuracy of the decoder is 0.65dB higher than that of the NNMS after 5 iterations. In addition, SNOMS decoding method achieves almost the same decoding performance comparable to that of NOMS, but requires less complex calculation. Proposed quantization of code-book method reduces memory requirement significantly with slight performance loss. © 2021 IEEE.}, isbn = {978-0-7381-2604-3}, keywords = {Belief propagation decoding algorithms,Bit error rate,Bit error rate (BER) performance,Complex networks,Complexity modeling,Decoding performance,Deep learning,Iterative decoding,Memory consumption,Memory requirements,Multiplication operations,Performance loss}, note = {cited By 0 \par TL;DR \par A novel Neural Offset Min-Sum (NOMS) Belief Propagation (BP) decoding algorithm based on model-driven is proposed which applied to LDPC decoding, and proposed quantization of codebook-based quantization is used to further reduce the memory consumption.} } @article{Xanthopoulos201869, title = {Cluster Analysis and Neural Network-Based Metamodeling of Priority Rules for Dynamic Sequencing}, author = {Xanthopoulos, A.S. and Koulouriotis, D.E.}, date = {2018}, journaltitle = {J. Intell. Manuf.}, volume = {29}, number = {1}, pages = {69--91}, publisher = {Springer New York LLC}, issn = {09565515}, doi = {10.1007/s10845-015-1090-0}, abbrev_source_title = {J Intell Manuf}, affiliation = {Department of Production and Management Engineering, Democritus University of Thrace, V. Sofias 12, Xanthi, 67100, Greece}, coden = {JIMNE}, correspondence_address1 = {Xanthopoulos, A.S.; Department of Production and Management Engineering, V. Sofias 12, Greece; email: axanthop@pme.duth.gr}, document_type = {Article}, langid = {english}, source = {Scopus}, note = {cited By 4 \par TL;DR \par The results of this article can be used as a component for prediction systems of dispatching rule output, as a guideline for building new dispatching heuristic with entirely different characteristics than existing ones, and to significantly decrease the length of what-if simulation studies.} } @article{xhafaInternetThingsEngineering2018, title = {Internet of {{Things}}: {{Engineering Cyber Physical Human Systems}}}, shorttitle = {Internet of {{Things}}}, author = {Xhafa, Fatos}, date = {2018-09}, journaltitle = {Internet Things}, volume = {1--2}, pages = {iii}, issn = {25426605}, doi = {10.1016/S2542-6605(18)30099-4}, langid = {english} } @inproceedings{xia:tag:2013, title = {Tag Recommendation in Software Information Sites}, booktitle = {Proc. 10th {{Work}}. {{Conf}}. {{Min}}. {{Softw}}. {{Repos}}.}, author = {Xia, Xin and Lo, David and Wang, Xinyu and Zhou, Bo}, date = {2013}, series = {{{MSR}} '13}, pages = {287--296}, publisher = {IEEE Press}, location = {Piscataway, NJ, USA}, url = {http://dl.acm.org/citation.cfm?id=2487085.2487140}, isbn = {978-1-4673-2936-1}, note = {TL;DR \par This paper proposes TagCombine, an automatic tag recommendation method which analyzes objects in software information sites and recommends tags after analyzing the terms in the objects.} } @online{xiaoFoundationsLargeLanguage2025, title = {Foundations of {{Large Language Models}}}, author = {Xiao, Tong and Zhu, Jingbo}, date = {2025-01-16}, eprint = {2501.09223}, eprinttype = {arXiv}, eprintclass = {cs}, doi = {10.48550/arXiv.2501.09223}, abstract = {This is a book about large language models. As indicated by the title, it primarily focuses on foundational concepts rather than comprehensive coverage of all cutting-edge technologies. The book is structured into four main chapters, each exploring a key area: pre-training, generative models, prompting techniques, and alignment methods. It is intended for college students, professionals, and practitioners in natural language processing and related fields, and can serve as a reference for anyone interested in large language models.}, langid = {english}, pubstate = {prepublished}, keywords = {/unread,Computer Science - Artificial Intelligence,Computer Science - Computation and Language,Computer Science - Machine Learning} } @online{xieOSWorldBenchmarkingMultimodal2024, title = {{{OSWorld}}: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments}, shorttitle = {{{OSWorld}}}, author = {Xie, Tianbao and Zhang, Danyang and Chen, Jixuan and Li, Xiaochuan and Zhao, Siheng and Cao, Ruisheng and Hua, Toh Jing and Cheng, Zhoujun and Shin, Dongchan and Lei, Fangyu and Liu, Yitao and Xu, Yiheng and Zhou, Shuyan and Savarese, Silvio and Xiong, Caiming and Zhong, Victor and Yu, Tao}, date = {2024-05-30}, eprint = {2404.07972}, eprinttype = {arXiv}, eprintclass = {cs}, doi = {10.48550/arXiv.2404.07972}, abstract = {Autonomous agents that accomplish complex computer tasks with minimal human interventions have the potential to transform human-computer interaction, significantly enhancing accessibility and productivity. However, existing benchmarks either lack an interactive environment or are limited to environments specific to certain applications or domains, failing to reflect the diverse and complex nature of real-world computer use, thereby limiting the scope of tasks and agent scalability. To address this issue, we introduce OSWorld, the first-of-its-kind scalable, real computer environment for multimodal agents, supporting task setup, execution-based evaluation, and interactive learning across various operating systems such as Ubuntu, Windows, and macOS. OSWorld can serve as a unified, integrated computer environment for assessing open-ended computer tasks that involve arbitrary applications. Building upon OSWorld, we create a benchmark of 369 computer tasks involving real web and desktop apps in open domains, OS file I/O, and workflows spanning multiple applications. Each task example is derived from real-world computer use cases and includes a detailed initial state setup configuration and a custom execution-based evaluation script for reliable, reproducible evaluation. Extensive evaluation of state-of-the-art LLM/VLM-based agents on OSWorld reveals significant deficiencies in their ability to serve as computer assistants. While humans can accomplish over 72.36\% of the tasks, the best model achieves only 12.24\% success, primarily struggling with GUI grounding and operational knowledge. Comprehensive analysis using OSWorld provides valuable insights for developing multimodal generalist agents that were not possible with previous benchmarks. Our code, environment, baseline models, and data are publicly available at https://os-world.github.io.}, langid = {english}, pubstate = {prepublished}, keywords = {/unread,Computer Science - Artificial Intelligence,Computer Science - Computation and Language}, note = {Comment: 51 pages, 21 figures} } @article{xieSystematicMappingStudy2021, title = {Systematic {{Mapping Study}} on the {{Machine Learning Lifecycle}}}, author = {Xie, Yuanhao and Cruz, Luís and Heck, Petra and Rellermeyer, Jan S.}, date = {2021-03-11}, journaltitle = {ArXiv210310248 Cs}, eprint = {2103.10248}, eprinttype = {arXiv}, eprintclass = {cs}, url = {http://arxiv.org/abs/2103.10248}, urldate = {2021-03-23}, abstract = {The development of artificial intelligence (AI) has made various industries eager to explore the benefits of AI. There is an increasing amount of research surrounding AI, most of which is centred on the development of new AI algorithms and techniques. However, the advent of AI is bringing an increasing set of practical problems related to AI model lifecycle management that need to be investigated. We address this gap by conducting a systematic mapping study on the lifecycle of AI model. Through quantitative research, we provide an overview of the field, identify research opportunities, and provide suggestions for future research. Our study yields 405 publications published from 2005 to 2020, mapped in 5 different main research topics, and 31 sub-topics. We observe that only a minority of publications focus on data management and model production problems, and that more studies should address the AI lifecycle from a holistic perspective.}, keywords = {68T01 (Primary),Computer Science - Artificial Intelligence,Computer Science - Machine Learning,Computer Science - Software Engineering,D.2.9,I.2.5}, note = {Comment: Accepted at WAIN21: 1st Workshop on AI Engineering - Software Engineering for AI \par TL;DR \par A systematic mapping study on the lifecycle of AI model management, observing that only a minority of publications focus on data management and model production problems, and that more studies should address the AI lifecycle from a holistic perspective.} } @article{xing_api-evolution_2007, title = {{{API-Evolution Support}} with {{Diff-CatchUp}}}, author = {Xing, Zhenchang and Stroulia, Eleni}, date = {2007-12}, journaltitle = {IEEE Trans. Softw. Eng.}, volume = {33}, number = {12}, pages = {818--836}, doi = {10.1109/TSE.2007.70747}, abstract = {Applications built on reusable component frameworks are subject to two independent, and potentially conflicting, evolution processes. The application evolves in response to the specific requirements and desired qualities of the application's stakeholders. On the other hand, the evolution of the component framework is driven by the need to improve the framework functionality and quality while maintaining its generality. Thus, changes to the component framework frequently change its API on which its client applications rely and, as a result, these applications break. To date, there has been some work aimed at supporting the migration of client applications to newer versions of their underlying frameworks, but it usually requires that the framework developers do additional work for that purpose or that the application developers use the same tools as the framework developers. In this paper, we discuss our approach to tackle the API-evolution problem in the context of reuse-based software development, which automatically recognizes the API changes of the reused framework and proposes plausible replacements to the "obsolete" API based on working examples of the framework code base. This approach has been implemented in the Diff-CatchUp tool. We report on two case studies that we have conducted to evaluate the effectiveness of our approach with its Diff-CatchUp prototype.}, keywords = {API-evolution support,application program interfaces,Application software,client application migration,component framework functionality,component framework maintenance,component framework quality,Costs,D.2.10.g Object-oriented design methods,D.2.2.eProgrammer workbench,D.2.3.aObject-oriented programming,D.2.3Coding Tools and Techniques,Diff-CatchUp tool,Documentation,object-oriented programming,Programming,Prototypes,reusable component framework evolution,reuse-based software development,Software engineering,software maintenance,software prototyping,Software prototyping,software quality,software reusability,software tools,Software tools} } @article{Xiong2022, title = {Towards a Robust and Trustworthy Machine Learning System Development: {{An}} Engineering Perspective}, author = {Xiong, P. and Buffett, S. and Iqbal, S. and Lamontagne, P. and Mamun, M. and Molyneaux, H.}, date = {2022}, journaltitle = {J. Inf. Secur. Appl.}, volume = {65}, publisher = {Elsevier Ltd}, issn = {22142134}, doi = {10.1016/j.jisa.2022.103121}, abstract = {While Machine Learning (ML) technologies are widely adopted in many mission critical fields to support intelligent decision-making, concerns remain about system resilience against ML-specific security attacks and privacy breaches as well as the trust that users have in these systems. In this article, we present our recent systematic and comprehensive survey on the state-of-the-art ML robustness and trustworthiness from a security engineering perspective, focusing on the problems in system threat analysis, design and evaluation faced in developing practical machine learning applications, in terms of robustness and user trust. Accordingly, we organize the presentation of this survey intended to facilitate the convey of the body of knowledge from this angle. We then describe a metamodel we created that represents the body of knowledge in a standard and visualized way. We further illustrate how to leverage the metamodel to guide a systematic threat analysis and security design process which extends and scales up the classic process. Finally, we propose the future research directions motivated by our findings. Our work differs itself from the existing surveys by (i) exploring the fundamental principles and best practices to support robust and trustworthy ML system development, and (ii) studying the interplay of robustness and user trust in the context of ML systems. We expect this survey provides a big picture for machine learning security practitioners. © 2022}, art_number = {103121}, document_type = {Article}, source = {Scopus}, keywords = {GOAL\_ML-System-Development}, note = {cited By 0} } @article{xiuExploratoryStudyMachine2021, title = {An {{Exploratory Study}} of {{Machine Learning Model Stores}}}, author = {Xiu, Minke and Jiang, Zhen Ming Jack and Adams, Bram}, date = {2021-01}, journaltitle = {IEEE Softw.}, volume = {38}, number = {1}, pages = {114--122}, issn = {0740-7459, 1937-4194}, doi = {10.1109/MS.2020.2975159}, note = {TL;DR \par Three stores that provide public access to pretrained machine learning models and infrastructure are examined and the information they provide is compared against two mobileapp stores and among themselves.} } @inproceedings{xu_meditor_2019, title = {Meditor: {{Inference}} and {{Application}} of {{API Migration Edits}}}, shorttitle = {Meditor}, booktitle = {2019 {{IEEEACM}} 27th {{Int Conf Program Comprehension ICPC}}}, author = {Xu, Shengzhe and Dong, Ziqi and Meng, Na}, date = {2019-05}, pages = {335--346}, doi = {10.1109/ICPC.2019.00052}, abstract = {Developers build programs based on software libraries. When a library evolves, programmers need to migrate their client code from the library's old release(s) to new release(s). Due to the API backwards incompatibility issues, such code migration may require developers to replace API usage and apply extra edits (e.g., statement insertions or deletions) to ensure the syntactic or semantic correctness of migrated code. Existing tools extract API replacement rules without handling the additional edits necessary to fulfill a migration task. This paper presents our novel approach, Meditor, which extracts and applies the necessary edits together with API replacement changes. Meditor has two phases: inference and application of migration edits. For edit inference, Meditor mines open source repositories for migration-related (MR) commits, and conducts program dependency analysis on changed Java files to locate and cluster MR code changes. From these changes, Meditor further generalizes API migration edits by abstracting away unimportant details (e.g., concrete variable identifiers). For edit application, Meditor matches a given program with inferred edits to decide which edit is applicable, customizes each applicable edit, and produces a migrated version for developers to review. We applied Meditor to four popular libraries: Lucene, CraftBukkit, Android SDK, and Commons IO. By searching among 602,249 open source projects on GitHub, Meditor identified 1,368 unique migration edits. Among these edits, 885 edits were extracted from single updated statements, while the other 483 more complex edits were from multiple co-changed statements. We sampled 937 inferred edits for manual inspection and found all of them to be correct. Our evaluation shows that Meditor correctly applied code migrations in 218 out of 225 cases. This research will help developers automatically adapt client code to different library versions.}, keywords = {Android SDK,API backwards incompatibility issues,API migration edits,API replacement rules,API usage,application program interfaces,automatic program transformation,client code,cluster MR code changes,code migration,Commons IO,CraftBukkit,data mining,edit inference,Java,library,Lucene,Meditor,migrated code,migrated version,migration task,migration-related commits,open source repository mining,program dependency analysis,public domain software,software libraries} } @article{Xu2013653, title = {Crashworthiness Design of Multi-Component Tailor-Welded Blank ({{TWB}}) Structures}, author = {Xu, F. and Sun, G. and Li, G. and Li, Q.}, date = {2013}, journaltitle = {Struct. Multidiscip. Optim.}, volume = {48}, number = {3}, pages = {653--667}, publisher = {Springer Verlag}, issn = {1615147X}, doi = {10.1007/s00158-013-0916-7}, abbrev_source_title = {Struct. Mutltidiscip. Opt.}, affiliation = {State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha 410082, China; Key Laboratory of Manufacture and Test Techniques for Automobile Parts, Ministry of Education, Chongqing 400054, China; School of Aerospace, Mechanical and Mechatronic Engineering, University of Sydney, Sydney NSW 2006, Australia}, coden = {SMOTB}, correspondence_address1 = {Li, G.; State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, , Changsha 410082, China; email: gyli@hnu.edu.cn}, document_type = {Article}, langid = {english}, source = {Scopus}, note = {cited By 59} } @article{Xu2015, title = {A Comprehensive Survey of Clustering Algorithms}, author = {Xu, Dongkuan and Tian, Yingjie}, date = {2015-06-01}, journaltitle = {Ann. Data Sci.}, volume = {2}, number = {2}, pages = {165--193}, issn = {2198-5812}, doi = {10.1007/s40745-015-0040-1}, abstract = {Data analysis is used as a common method in modern science research, which is across communication science, computer science and biology science. Clustering, as the basic composition of data analysis, plays a significant role. On one hand, many tools for cluster analysis have been created, along with the information increase and subject intersection. On the other hand, each clustering algorithm has its own strengths and weaknesses, due to the complexity of information. In this review paper, we begin at the definition of clustering, take the basic elements involved in the clustering process, such as the distance or similarity measurement and evaluation indicators, into consideration, and analyze the clustering algorithms from two perspectives, the traditional ones and the modern ones. All the discussed clustering algorithms will be compared in detail and comprehensively shown in Appendix Table 22.}, note = {TL;DR \par This review paper begins at the definition of clustering, takes the basic elements involved in the clustering process, such as the distance or similarity measurement and evaluation indicators, into consideration, and analyzes the clustered algorithms from two perspectives, the traditional ones and the modern ones.} } @inproceedings{Xu2016, title = {Mixed-Variable Metamodeling Methods for Designing Multimaterial Structures}, author = {Xu, H. and Chuang, C.-H. and Yang, R.-J.}, date = {2016}, series = {Proceedings of the {{ASME Design Engineering Technical Conference}}}, volume = {2B-2016}, publisher = {American Society of Mechanical Engineers (ASME)}, doi = {10.1115/DETC2016-59176}, abbrev_source_title = {Proc. ASME Des. Eng. Tech. Conf.}, affiliation = {Research and Advanced Engineering, Ford Motor Company, Dearborn, MI, United States}, correspondence_address1 = {Xu, H.; Research and Advanced Engineering, United States; email: hxu41@ford.com}, document_type = {Conference Paper}, isbn = {978-0-7918-5011-4}, langid = {english}, source = {Scopus}, note = {cited By 7} } @inproceedings{Xu2021160, title = {Conv-{{Reluplex}}: {{A}} Verification Framework for Convolution Neural Networks}, author = {Xu, J. and Li, Z. and Zhang, M. and Du, B.}, date = {2021}, series = {Proceedings of the {{International Conference}} on {{Software Engineering}} and {{Knowledge Engineering}}, {{SEKE}}}, volume = {2021-July}, pages = {160--165}, publisher = {Knowledge Systems Institute Graduate School}, issn = {23259000}, doi = {10.18293/SEKE2021-085}, abstract = {In recent years, machine learning has demonstrated impressive performance in many real-world tasks, especially in computer vision and natural language processing. However, to apply them in safety-critical systems one needs formal guarantees on the neural network outputs. The Reluplex tool is proposed to verify the safety of deep neural networks (DNNs), and in case the DNN fails to give a correct output, can generate adversarial examples. Since the tool can only handle DNNs, it is necessary to extend the tool to process image data. Therefore, in this paper, we propose the Conv-Reluplex framework, which is designed to verify the convolutional layer and pooling layer in convolutional neural networks(CNNs), and generate adversarial examples when classification is misguided. We conduct several experiments on MNIST to evaluate our approaches. The results show that the original CNN is improved using the adversarial examples generated by our tool, and the precision of classification can be increased significantly. © 2021 Knowledge Systems Institute Graduate School. All rights reserved.}, author_keywords = {Adversarial robustness; Reluplex algorithm; Verification framework}, document_type = {Conference Paper}, isbn = {1-891706-52-7}, source = {Scopus}, keywords = {Convolution,Convolution neural network,Convolutional neural networks,Deep neural networks,Image data,Multilayer neural networks,NAtural language processing,Natural language processing systems,Real-world task,Safety critical systems,Safety engineering,Software engineering,Verification framework}, note = {cited By 2} } @inproceedings{xuREPERSPRecommendingPersonalized2017, title = {{{REPERSP}}: {{Recommending Personalized Software Projects}} on {{GitHub}}}, shorttitle = {{{REPERSP}}}, author = {Xu, Wenyuan and Sun, Xiaobing and Hu, Jiajun and Li, Bin}, date = {2017-09}, pages = {648--652}, publisher = {IEEE}, doi = {10.1109/ICSME.2017.20}, isbn = {978-1-5386-0992-7} } @article{Yan2018343, title = {Data-Driven Prediction of Mechanical Properties in Support of Rapid Certification of Additively Manufactured Alloys}, author = {Yan, F. and Chan, Y.-C. and Saboo, A. and Shah, J. and Olson, G.B. and Chen, W.}, date = {2018}, journaltitle = {CMES - Comput. Model. Eng. Sci.}, volume = {117}, number = {3}, pages = {343--366}, publisher = {Tech Science Press}, issn = {15261492}, doi = {10.31614/cmes.2018.04452}, abbrev_source_title = {CMES Comput. Model. Eng. Sci.}, affiliation = {Department of Materials Science and Engineering, Northwestern University, 2220 Campus Dr, Evanston, IL 60208, United States; Department of Mechanical Engineering, Northwestern University, 2145 Sheridan Rd, Evanston, IL 60208, United States; QuesTek Innovations LLC, 1820 Ridge Ave, Evanston, IL 60201, United States; Product Development and Analysis LLC, 1776 Legacy Circle, Suite \#115, Naperville, IL 60563, United States}, correspondence_address1 = {Chen, W.; Department of Mechanical Engineering, 2145 Sheridan Rd, United States; email: weichen@northwestern.edu}, document_type = {Article}, langid = {english}, source = {Scopus}, note = {cited By 10 \par TL;DR \par A rapid prediction framework that replaces the physics-based mechanistic models with Gaussian process (GP) metamodels, a type of machine learning model for limited data and statistical inference, that can predict the varying properties in an entire part in a fraction of the time is proposed.} } @inproceedings{Yang2017, title = {A Domain-Driven Approach to Metamodeling in Additive Manufacturing}, author = {Yang, Z. and Hagedorn, T. and Eddy, D. and Krishnamurty, S. and Grosse, I. and Denno, P. and Lu, Y. and Witherell, P.}, date = {2017}, series = {Proceedings of the {{ASME Design Engineering Technical Conference}}}, volume = {1}, publisher = {American Society of Mechanical Engineers (ASME)}, doi = {10.1115/DETC2017-67807}, abbrev_source_title = {Proc. ASME Des. Eng. Tech. Conf.}, affiliation = {Department of Mechanical and Industrial Engineering, University of Massachusetts Amherst, Amherst, MA 01003, United States; Engineering Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899, United States}, art_number = {67807}, document_type = {Conference Paper}, isbn = {978-0-7918-5811-0}, langid = {english}, source = {Scopus}, note = {cited By 6} } @inproceedings{Yang2018, title = {A Super-Metamodeling Framework to Optimize System Predictability}, author = {Yang, Z. and Eddy, D. and Krishnamurty, S. and Grosse, I. and Lu, Y.}, date = {2018}, series = {Proceedings of the {{ASME Design Engineering Technical Conference}}}, volume = {1A-2018}, publisher = {American Society of Mechanical Engineers (ASME)}, doi = {10.1115/DETC201886055}, abbrev_source_title = {Proc. ASME Des. Eng. Tech. Conf.}, affiliation = {University of Massachusetts Amherst, Department of Mechanical and Industrial Engineering, Amherst, MA 01003, United States; National Institute of Standards and Technology Engineering Laboratory, Gaithersburg, MD 20899, United States}, correspondence_address1 = {Eddy, D.; University of Massachusetts Amherst, United States; email: dceddy@engin.umass.edu}, document_type = {Conference Paper}, isbn = {978-0-7918-5172-2}, langid = {english}, source = {Scopus}, note = {cited By 7 \par TL;DR \par This paper introduces a super-metamodel optimization framework (SMOF) to improve overall prediction accuracy by integrating different metamodeling techniques without a need for additional data.} } @inproceedings{Yang2021, title = {Deep Joint Source Channel Coding for Wireless Image Transmission with {{OFDM}}}, author = {Yang, M. and Bian, C. and Kim, H.-S.}, date = {2021}, series = {{{IEEE International Conference}} on {{Communications}}}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {15503607}, doi = {10.1109/ICC42927.2021.9500996}, abstract = {We present a deep learning based joint source channel coding (JSCC) scheme for wireless image transmission over multipath fading channels with non-linear signal clipping. The proposed encoder and decoder use convolutional neural networks (CNN) and directly map the source images to complex-valued baseband samples for orthogonal frequency division multiplexing (OFDM) transmission. The proposed model-driven machine learning approach eliminates the need for separate source and channel coding while integrating an OFDM datapath to cope with multipath fading channels. The end-to-end JSCC communication system combines trainable CNN layers with non-trainable but differentiable layers representing the multipath channel model and OFDM signal processing blocks. Our results show that injecting domain expert knowledge by incorporating OFDM baseband processing blocks into the machine learning framework significantly enhances the overall performance compared to an unstructured CNN. Our method outperforms conventional schemes that employ state-of-the-art but separate source and channel coding such as BPG and LDPC with OFDM. Moreover, our method is shown to be robust against non-linear signal clipping in OFDM for various channel conditions that do not match the model parameter used during the training. © 2021 IEEE.}, document_type = {Conference Paper}, isbn = {978-1-7281-7122-7}, source = {Scopus}, note = {cited By 2 \par TL;DR \par The results show that injecting domain expert knowledge by incorporating OFDM baseband processing blocks into the machine learning framework significantly enhances the overall performance compared to an unstructured CNN.} } @article{yangActionableAnalyticsSoftware2018, title = {Actionable {{Analytics}} for {{Software Engineering}}}, author = {Yang, Ye and Falessi, Davide and Menzies, Tim and Hihn, Jairus}, date = {2018}, journaltitle = {IEEE Softw.}, volume = {35}, number = {1}, pages = {51--53}, note = {TL;DR \par This theme issue aims to reflect on actionable analytics for software engineering and to document a catalog of success stories in which analytics has been proven actionable and useful, in some significant way, in an organization.} } @article{yangIoTStreamProcessing2017, title = {{{IoT Stream Processing}} and {{Analytics}} in the {{Fog}}}, author = {Yang, Shusen}, date = {2017-08}, journaltitle = {IEEE Commun. Mag.}, volume = {55}, number = {8}, pages = {21--27}, issn = {0163-6804, 1558-1896}, doi = {10.1109/MCOM.2017.1600840}, keywords = {Data analysis,DONE,internet of things}, note = {TL;DR \par The general models and architecture of fog data streaming are presented, by analyzing the common properties of several typical applications, and the design space of fog streaming is analyzed with the consideration of four essential dimensions (system, data, human, and optimization).} } @article{yangMultistepIterativeAutomated2018, title = {Multi-Step {{Iterative Automated Domain Modeling}} with {{Large Language Models}}}, author = {Yang, Yujing and Chen, Boqi and Chen, Kua and Mussbacher, Gunter and Varró, Dániel}, date = {2018}, abstract = {Domain modeling, which represents the concepts and relationships in a problem domain, is an essential part of software engineering. As large language models (LLMs) have recently exhibited remarkable ability in language understanding and generation, many approaches are designed to automate domain modeling with LLMs. However, these approaches usually formulate all input information to the LLM in a single step, resulting in many missing modeling elements and advanced patterns. This paper presents a novel framework designed to improve the performance of fully automated domain model generation leveraging LLMs. The multi-step automated domain modeling approach extracts model elements (e.g., classes, attributes, and relationships) from problem descriptions. Improving from earlier work, the proposed approach includes instructions and human knowledge in each step and uses an iterative process to identify complex patterns, repeatedly extracting the pattern from various instances and then synthesizing these extractions into a summarized overview. Furthermore, the framework incorporates a self-reflection mechanism. This mechanism assesses each generated model element, offering feedback for necessary modifications or removals, and integrates the domain model with the generated feedback. The proposed approach is assessed in experiments, comparing it with a baseline single-step approach. It improves the 𝐹1-score of identifying classes by 22.71\% and relationships by 75.18\%, while the 𝐹1-score is similar for attributes. Furthermore, it improves the 𝐹1-score by 10.39\% for identifying complex design patterns. Our approach, dataset, and evaluation provide valuable insight for future research in automated LLM-based domain modeling.}, langid = {english}, keywords = {LOGSEQ}, note = {\section{Annotazioni\\ (30/7/2024, 11:52:09)} \par - “Multi-step Iterative Automated Domain Modeling with Large Language Models” (Yang et al., 2018, p. 1) \#5fb236 \par - “language understanding and generation, many approaches are designed to automate domain modeling with LLMs” (Yang et al., 2018, p. 1) \#5fb236 \par - “This paper presents a novel framework designed to improve the performance of fully automated domain model generation leveraging LLMs” (Yang et al., 2018, p. 1) \#5fb236 \par - “proposed approach includes instructions and human knowledge in each step and uses an iterative process to identify complex patterns” (Yang et al., 2018, p. 1) \#5fb236 \par - “self-reflection mechanism” (Yang et al., 2018, p. 1) \#a28ae5 \par - “integrates the domain model with the generated feedback.” (Yang et al., 2018, p. 1) \#5fb236 \par - “baseline single-step approach” (Yang et al., 2018, p. 1) \#a28ae5 \par - “Recent advances in large language models (LLMs) have shown remarkable generalizability to tasks beyond natural language processing” (Yang et al., 2018, p. 1) \#5fb236 \par - “With different prompt designs, LLMs can achieve impressive performance on various tasks by only using a few labeled examples in the prompt.” (Yang et al., 2018, p. 1) \#a28ae5 \par - “Previous work [7] f” (Yang et al., 2018, p. 1) \#5fb236 \par - “the recall of identifying classes, attributes, and relationships is, in general, much lower than the precision” (Yang et al., 2018, p. 1) \#a28ae5 \par - “o modeling patterns (that capture modeling best practices) have been identified i” (Yang et al., 2018, p. 1) \#a28ae5 \par - “task into more simple sub-tasks and including human knowledge to solve these sub-tasks” (Yang et al., 2018, p. 2) \#a28ae5 \par - “a feedback mechanism can be included in automated domain modeling to further enhance the quality of output models” (Yang et al., 2018, p. 2) \#ffd400\\ \mkbibemph{Kind or reinforcement learning?} \par - “novel approach for fully automated domain modeling using LLMs” (Yang et al., 2018, p. 2) \#ffd400\\ \mkbibemph{Fully automated, thus no human intervention is required???} \par - “automated domain modeling approach” (Yang et al., 2018, p. 2) \#5fb236 \par - “iterative process to identify best-practice patterns” (Yang et al., 2018, p. 2) \#5fb236 \par - “self-reflection mechanism to generate internal feedback according to human knowledge and then integrate the generated feedback into the domain model for improvement” (Yang et al., 2018, p. 2) \#a28ae5 \par - “text generation problem, where a generative function f directly converts a domain specification d into a domain model M” (Yang et al., 2018, p. 2) \#5fb236 \par - “player-role pattern [11] and abstraction-occurrence pattern” (Yang et al., 2018, p. 2) \#2ea8e5 \par - “player-role pattern because it is one of the most used patterns while LLMs perform poorly in identifying it.” (Yang et al., 2018, p. 2) \#ffd400\\ \mkbibemph{Can you give a supporting reference for this statement?} \par - “This method involves a single prompt, including a problem description, task description, and output format description, to generate the domain model with an LLM.” (Yang et al., 2018, p. 2) \#5fb236 \par - “this approach often misses elements in the domain model (low recall)” (Yang et al., 2018, p. 2) \#5fb236 \par - “achieves the functionality” (Yang et al., 2018, p. 2) \#ffd400\\ \mkbibemph{What does it mean in general? Human in the loop is required, right?} \par - “if x is in exact match or semantically equivalent” (Yang et al., 2018, p. 2) \#ffd400\\ \mkbibemph{I would have distinguished these. Indeed exact match is also semantically equivalent, but not the other way around.} \par - “standard statistical metrics of precision, recall, and F1-score over the classes, relationships, and attributes” (Yang et al., 2018, p. 2) \#5fb236 \par - “multi-step automated domain modeling” (Yang et al., 2018, p. 3) \#5fb236 \par - “task description to describe the overall domain model generation task” (Yang et al., 2018, p. 3) \#a28ae5 \par - “modeling problem description written in natural language to describe a problem domain” (Yang et al., 2018, p. 3) \#a28ae5 \par - “example store that contains few-shot examples explaining the definition of model elements and patterns” (Yang et al., 2018, p. 3) \#a28ae5 \par - “partial model” (Yang et al., 2018, p. 3) \#ffd400\\ \mkbibemph{How are the different partial models linked together? Or is it a monothonic approach where models can be only incrementally increased? (from each iteration, elements can be only added to the domain model being created),} \par - “sequence of these outputs can be combined” (Yang et al., 2018, p. 3) \#ffd400\\ \mkbibemph{Such a combination operation requires details.} \par - “iterative” (Yang et al., 2018, p. 3) \#ffd400\\ \mkbibemph{See my previous comments. It's not clear if domain models can be only enlarged?} \par - “The post-processor uses rule-based methods to parse and extract relevant results from the LLM’s response to create the final domain model.” (Yang et al., 2018, p. 3) \#ffd400\\ \mkbibemph{Does the LLM's response need to adhere some syntax to facilitate post-processing / parsing?} \par - “evaluation” (Yang et al., 2018, p. 3) \#ffd400\\ \mkbibemph{So models are not compared directly with the ground truth, but instead the are indirectly compared by considering their precision, recall, and F1-score.} \par - “single-step process into smaller, pre-defined subprocesses.” (Yang et al., 2018, p. 3) \#5fb236 \par - “human instructions and examples can be included in the individual process to improve the performance of identifying modeling elements” (Yang et al., 2018, p. 3) \#ffd400\\ \mkbibemph{When such instructions are given?} \par - “k times” (Yang et al., 2018, p. 4) \#ffd400\\ \mkbibemph{How is k identified?} \par - “non-zero temperature” (Yang et al., 2018, p. 4) \#ffd400\\ \mkbibemph{Same here, how is temperature value determined?} \par - “integrates the player-role pattern into the previous partial model from Step 1 with the problem description and the extracted patterns” (Yang et al., 2018, p. 4) \#ffd400\\ \mkbibemph{How is this integration done?} \par - “Self-reflection” (Yang et al., 2018, p. 5) \#5fb236 \par - “generate feedback for the partial domain model” (Yang et al., 2018, p. 5) \#ffd400 \par - “improve the partial domain model according to the feedback” (Yang et al., 2018, p. 5) \#ffd400\\ \mkbibemph{How is this feedback given and automatically processed?} \par - “self-refinement [13] and reflexion [18]” (Yang et al., 2018, p. 5) \#a28ae5 \par - “good understanding of where the model made mistakes as well as the ability to generate a summary containing actionable insights for improvement” (Yang et al., 2018, p. 5) \#ffd400\\ \mkbibemph{So, human intervention seems to be required here. If this is the case, it is need to smooth the "automated" aspect of the approach (even in the title)} \par - “problem description” (Yang et al., 2018, p. 5) \#5fb236 \par - “partial model revised from previous step” (Yang et al., 2018, p. 5) \#5fb236 \par - “One example of the generated feedback for the class Dimension (String length) is shown below: It is unnecessary as the dimension can be an attribute of the Item class.” (Yang et al., 2018, p. 5) \#ffd400\\ \mkbibemph{It is not clear how feedback is generated automatically!} \par - “Integrate feedback into the partial model.” (Yang et al., 2018, p. 5) \#5fb236 \par - “feedback for each class as input.” (Yang et al., 2018, p. 5) \#ffd400\\ \mkbibemph{It is not clear how this gets generated for each class. It should be also for structural features (individually and not induced by class feedback), isn't it?} \par - “Table 1: Comparison of precision, recall, and F1-score for single-step and MIG approaches for each model element type” (Yang et al., 2018, p. 6) \#ffd400\\ \mkbibemph{What are the values that are used for k and temperature?} \par - “we carry out a more fine-grained analysis by highlighting which modeling aspects the LLMs struggle with.” (Yang et al., 2018, p. 6) \#5fb236 \par - “For the single-step approach, the precision is higher than the recall for almost all model elements, indicating that the identified elements are accurate, but there are many missing elements.” (Yang et al., 2018, p. 6) \#ffd400\\ \mkbibemph{SO this means that it is not a clear win, isn't it?} \par - “This indicates that our generated model covers many more elements in the solution model.” (Yang et al., 2018, p. 6) \#ffd400\\ \mkbibemph{yes, but with reduced precision...} \par - “Table 2: Precision, recall, and F1-score of three approaches of identifying player-role patterns: zero-shot single-step, twoshot single-step, and multi-step” (Yang et al., 2018, p. 6) \#ffd400\\ \mkbibemph{Same comment for the previous Table.} \par - “This RQ shows that our proposed approach overall improves the performance of automating domain modeling compared to the single-step approach” (Yang et al., 2018, p. 6) \#ffd400\\ \mkbibemph{It's not clear like this.} \par - “In the research question, we further investigate the performance of identifying the playerrole pattern and compare it with the single-step approach.” (Yang et al., 2018, p. 6) \#5fb236 \par - “One of the potential future directions is that we can design components to identify other advanced patterns, for example, the abstraction-occurrence pattern.” (Yang et al., 2018, p. 7) \#ffd400\\ \mkbibemph{It's not clear how the identified patterns are checked if they are correct or not. I think humans should be involved here this it not mentioned in the paper.} \par - “Furthermore, we address this variation by experimenting with a set of eight diverse domain examples.” (Yang et al., 2018, p. 7) \#5fb236 \par - “without any human interaction or supervised training” (Yang et al., 2018, p. 8) \#ffd400\\ \mkbibemph{This is not clear in the paper.} \par - “generate and integrate feedback without any external interaction.” (Yang et al., 2018, p. 8) \#ffd400\\ \mkbibemph{Can be the case that feedback given in not correct or accurate?}} } @article{yangNaturalAttackPretrained2022, title = {Natural {{Attack}} for {{Pre-trained Models}} of {{Code}}}, author = {Yang, Zhou and Shi, Jieke and He, Junda and Lo, David}, date = {2022-01-21}, journaltitle = {ArXiv220108698 Cs}, eprint = {2201.08698}, eprinttype = {arXiv}, eprintclass = {cs}, doi = {10.1145/3510003.3510146}, abstract = {Pre-trained models of code have achieved success in many important software engineering tasks. However, these powerful models are vulnerable to adversarial attacks that slightly perturb model inputs to make a victim model produce wrong outputs. Current works mainly attack models of code with examples that preserve operational program semantics but ignore a fundamental requirement for adversarial example generation: perturbations should be natural to human judges, which we refer to as naturalness requirement.}, langid = {english}, keywords = {Computer Science - Software Engineering}, note = {Comment: Accepted to the Technical Track of ICSE 2022 \par TL;DR \par This paper proposes ALERT (Naturalness Aware Attack), a black-box attack that adversarially transforms inputs to make victim models produce wrong outputs and investigates the value of the generated adversarial examples to harden victim models through an adversarial fine-tuning procedure.} } @inproceedings{yangStackOverflowGithub2017, title = {Stack Overflow in Github: Any Snippets There?}, shorttitle = {Stack Overflow in Github}, booktitle = {Min. {{Softw}}. {{Repos}}. {{MSR}} 2017 {{IEEEACM}} 14th {{Int}}. {{Conf}}. {{On}}}, author = {Yang, Di and Martins, Pedro and Saini, Vaibhav and Lopes, Cristina}, date = {2017}, pages = {280--290}, publisher = {IEEE} } @article{yanPopularityBiasCorrelation2024, title = {Popularity {{Bias}} in {{Correlation Graph}} Based {{API Recommendation}} for {{Mashup Creation}}}, author = {Yan, Chao and Zhong, Weiyi and Zhai, Dengshuai and Khan, Arif Ali and Gong, Wenwen and Xu, Yanwei and Xin, Baogui}, date = {2024-04-02}, journaltitle = {ACM Trans. Intell. Syst. Technol.}, issn = {2157-6904}, doi = {10.1145/3654445}, abstract = {The explosive growth of the API economy in recent years has led to a dramatic increase in available APIs. Mashup development, a dominant approach for creating data-centric applications based on APIs, has experienced a surge in popularity. However, the vast array of choices poses a challenge for mashup developers when selecting appropriate API compositions to meet specific business requirements. Correlation graph-based recommendation approaches have been designed to assist developers in discovering related and compatible API compositions for mashup creation. Unfortunately, these approaches often suffer from popularity bias issues, leading to an inequality in API usage and potential disruptions to the entire API ecosystem. To address these challenges, our research begins with a theoretical analysis of the popularity bias introduced by correlation graph-based API recommendation approaches. Subsequently, we empirically validate the presence of popularity bias in API recommendations through a data-driven study. Finally, we introduce the popularity bias aware web API recommendation (PB-WAR) approach to mitigate popularity bias in correlation graph-based API recommendations. Experimental results over a real world dataset demonstrate that PB-WAR offers the optimal trade-off between accuracy and debiasing performance compared to other competitive methods.}, keywords = {⛔ No INSPIRE recid found,API recommendation,correlation graph,mashup,popularity bias} } @online{yanPromisesChallengesGenerative2024, title = {Promises and Challenges of Generative Artificial Intelligence for Human Learning}, author = {Yan, Lixiang and Greiff, Samuel and Teuber, Ziwen and Gašević, Dragan}, date = {2024-09-05}, eprint = {2408.12143}, eprinttype = {arXiv}, doi = {10.48550/arXiv.2408.12143}, abstract = {Generative artificial intelligence (GenAI) holds the potential to transform the delivery, cultivation, and evaluation of human learning. This Perspective examines the integration of GenAI as a tool for human learning, addressing its promises and challenges from a holistic viewpoint that integrates insights from learning sciences, educational technology, and human-computer interaction. GenAI promises to enhance learning experiences by scaling personalised support, diversifying learning materials, enabling timely feedback, and innovating assessment methods. However, it also presents critical issues such as model imperfections, ethical dilemmas, and the disruption of traditional assessments. Cultivating AI literacy and adaptive skills is imperative for facilitating informed engagement with GenAI technologies. Rigorous research across learning contexts is essential to evaluate GenAI's impact on human cognition, metacognition, and creativity. Humanity must learn with and about GenAI, ensuring it becomes a powerful ally in the pursuit of knowledge and innovation, rather than a crutch that undermines our intellectual abilities.}, langid = {english}, pubstate = {prepublished}, keywords = {Computer Science - Human-Computer Interaction} } @inproceedings{yaoIntelligentManufacturingSmart2017, title = {From {{Intelligent Manufacturing}} to {{Smart Manufacturing}} for {{Industry}} 4.0 {{Driven}} by {{Next Generation Artificial Intelligence}} and {{Further On}}}, booktitle = {2017 5th {{Int}}. {{Conf}}. {{Enterp}}. {{Syst}}. {{ES}}}, author = {Yao, Xifan and Zhou, Jiajun and Zhang, Jiangming and Boer, Claudio R.}, date = {2017-09}, pages = {311--318}, publisher = {IEEE}, location = {Beijing}, doi = {10.1109/ES.2017.58}, abstract = {Smart manufacturing (SM) is emerging as a new version of intelligent manufacturing (IM), reflecting the magnitude and impact of smart technologies such the Internet of Things, Cloud Computing, Cyber-Physical Systems and Big Data on Industry 4.0. This paper addresses how IM evolves to SM along with artificial intelligence (AI) evolution. To this end, this study first summarizes how the symbolic AI (called AI 1.0) characterized by structured contents and centralized control structures evolves into the next-generation AI (called AI 2.0) characterized by unstructured contents, decentralized control structures and machine learning (especially deep learning), and explain show IM enabled by AI 1.0 evolves into SM by AI 2.0 accordingly. Then, the comparison of IM and SM is discussed in detail. Finally, the further development of smart manufacturing for Industry 4.0 is given.}, eventtitle = {2017 5th {{International Conference}} on {{Enterprise Systems}} ({{ES}})}, isbn = {978-1-5386-0936-1}, langid = {english}, note = {TL;DR \par This study summarizes how the symbolic AI characterized by structured contents and centralized control structures evolves into the next-generation AI (called AI 2.0) characterized by unstructured contents, decentralized control structures and machine learning (especially deep learning).} } @inproceedings{Yarom2020725, title = {Artificial Neural Networks and Reinforcement Learning for Model-Based Design of an Automated Vehicle Guidance System}, author = {Yarom, O.A. and Scherler, S. and Goellner, M. and Liu-Henke, X.}, editor = {Rocha A., Steels L., van den Herik J.}, date = {2020}, series = {{{ICAART}} 2020 - {{Proceedings}} of the 12th {{International Conference}} on {{Agents}} and {{Artificial Intelligence}}}, volume = {2}, pages = {725--733}, publisher = {SciTePress}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083099118&partnerID=40&md5=7022f6c914439fde6f66fb48b414ff4f}, abbrev_source_title = {ICAART - Proc. Int. Conf. Agents Artif. Intell.}, affiliation = {Ostfalia University of Applied Sciences, Salzdahulmer Str. 46/48, Wolfenbuettel, 38302, Germany}, document_type = {Conference Paper}, isbn = {978-989-758-395-7}, langid = {english}, source = {Scopus}, keywords = {GOAL\_Model-Assistance,notion}, note = {cited By 4 \par TL;DR \par The model-based development of a function for lateral control of an automated vehicle using Artificial Neural Networks (ANN) and Genetic Algorithms (GA) is presented and the driving function is designed in the form of a functional structure.} } @inproceedings{Yavanoglu20172186, title = {A Review on Cyber Security Datasets for Machine Learning Algorithms}, author = {Yavanoglu, O. and Aydos, M.}, editor = {Nie J.-Y., Obradovic Z., Ghosh R., Nambiar R., Wang C., Zang H., Baeza-Yates R., Baeza-Yates R., Hu X., Kepner J., Cuzzocrea A., Tang J., Toyoda M., Suzumura T.}, date = {2017}, series = {Proceedings - 2017 {{IEEE International Conference}} on {{Big Data}}, {{Big Data}} 2017}, volume = {2018-January}, pages = {2186--2193}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/BigData.2017.8258167}, abstract = {It is an undeniable fact that currently information is a pretty significant presence for all companies or organizations. Therefore protecting its security is crucial and the security models driven by real datasets has become quite important. The operations based on military, government, commercial and civilians are linked to the security and availability of computer systems and network. From this point of security, the network security is a significant issue because the capacity of attacks is unceasingly rising over the years and they turn into be more sophisticated and distributed. The objective of this review is to explain and compare the most commonly used datasets. This paper focuses on the datasets used in artificial intelligent and machine learning techniques, which are the primary tools for analyzing network traffic and detecting abnormalities. © 2017 IEEE.}, document_type = {Conference Paper}, isbn = {978-1-5386-2714-3}, source = {Scopus}, note = {cited By 51 \par TL;DR \par The objective of this review is to explain and compare the most commonly used datasets used in artificial intelligent and machine learning techniques, which are the primary tools for analyzing network traffic and detecting abnormalities.} } @article{ye_misim_nodate, title = {{{MISIM}}: {{A Novel Code Similarity System}}}, author = {Ye, Fangke and Zhou, Shengtian and Venkat, Anand and Marcus, Ryan and Tatbul, Nesime and Tithi, Jesmin J and Hasabnis, Niranjan and Petersen, Paul and Mattson, Timothy and Kraska, Tim and Dubey, Pradeep and Sarkar, Vivek and Gottschlich, Justin E}, pages = {22}, abstract = {Code similarity systems are integral to a range of applications from code recommendation to automated software defect correction. We argue that code similarity is now a first-order problem that must be solved. To begin to address this, we present machine Inferred Code Similarity (MISIM), a novel end-to-end code similarity system that consists of two core components. First, MISIM uses a novel context-aware semantic structure, which is designed to aid in lifting semantic meaning from code syntax. Second, MISIM provides a neural-based code similarity scoring algorithm, which can be implemented with various neural network architectures with learned parameters. We compare MISIM to three state-of-the-art code similarity systems: (i) code2vec, (ii) Neural Code Comprehension, and (iii) Aroma. In our experimental evaluation across 328,155 programs (over 18 million lines of code), MISIM has 1.5x to 43.4x better accuracy than all three systems.}, langid = {english}, note = {TL;DR \par This work presents machine Inferred Code Similarity (MISIM), a novel end-to-end code similarity system that consists of two core components: a novel context-aware semantic structure and a neural-based code similarity scoring algorithm that can be implemented with various neural network architectures with learned parameters.} } @article{Ye202166, title = {Deep-Learning-Enhanced {{NOMA}} Transceiver Design for Massive {{MTC}}: {{Challenges}}, State of the Art, and Future Directions}, author = {Ye, N. and An, J. and Yu, J.}, date = {2021}, journaltitle = {IEEE Wirel. Commun.}, volume = {28}, number = {4}, pages = {66--73}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {15361284}, doi = {10.1109/MWC.001.2000472}, abstract = {Non-orthogonal multiple access (NOMA) is a promising evolution path to meet the requirements of massive machine type communications (mMTC) in 5G and beyond. However, the deployment of NOMA is hindered by the non-unified signal processing architectures of various NOMA schemes and the inflexibility resulting from the offline design paradigm. The block-wise optimized transceivers make its performance far from the limit. The recent breakthrough of deep learning and its positive applications to wireless communications have paved the way to tackle these challenges. This article studies the effectiveness and efficiency of deep learning in enhancing NOMA performance. Specifically, we first present the deep neural network (DNN), which is constructed via a uniform signal processing architecture, and use it as the unified multiuser receiver in both data and model-driven approaches. This enables the end-to-end optimization of NOMA transceivers due to the universal function approximation property of DNN. On the other hand, with DNN we can automatically extract the user access behaviors out of the time-series signals and optimize the transceivers to match these cross-lay-er behaviors. We further analyze the integration of non-orthogonal communication and neural computation to accomplish high-efficiency data transmission at low cost. Finally, we identify some essential future directions of deep-learning-en-hanced NOMA from the perspectives of online reconfigurability and adaptability toward the ever changing environment in future mMTC. © 2002-2012 IEEE.}, art_number = {9535455}, coden = {IWCEA}, document_type = {Article}, source = {Scopus}, note = {cited By 1} } @inproceedings{yeSupportingReuseDelivering2002, title = {Supporting Reuse by Delivering Task-Relevant and Personalized Information}, booktitle = {Proc. 24th {{Int}}. {{Conf}}. {{Softw}}. {{Eng}}.}, author = {Ye, Yunwen and Fischer, Gerhard}, date = {2002}, series = {{{ICSE}} '02}, pages = {513--523}, publisher = {ACM}, location = {New York, NY, USA}, url = {http://doi.acm.org/10.1145/581339.581402}, acmid = {581402}, isbn = {1-58113-472-X}, nodoi = {10.1145/581339.581402}, numpages = {11}, note = {TL;DR \par This research has explored a new interaction style between software developers and reuse repository systems enabled by information delivery mechanisms, where information delivery autonomously locates and presents components by using the developers' partially written programs as implicit queries.} } @article{yildizMicroArcEventDriven, title = {{{MicroArc}}: {{Event Driven Analysis}} and {{Design Method}} for {{Microservice Based Systems}}}, author = {Yildiz, Ali and Demirors, Onur}, abstract = {The rapid development of the Internet infrastructure has enabled software applications to leverage almost unlimited and scalable resources. Microservice-based architecture has emerged as a solution to harness the benefits of a distributed cloud-based infrastructure. Event-driven architecture is a powerful approach for addressing challenges in distributed systems, such as scalability, distributed data, and sharing of data at scale. In an event-driven microservice architecture, decoupled services interact by responding to events and event streams facilitate data sharing between them. Despite these advantages, there is no de facto method for the analysis and design of systems within microservice architecture. Organizations often face difficulties in developing microservice-based systems, owing to the lack of well-defined methodologies for analysis and design. In this study, we present an analysis and design method for microservicebased systems. MicroArc is a method for analyzing and designing microservice-based systems, and comprises modeling notations and guiding processes to articulate how the method is applied. The MicroArc approach enables the identification of events and microservice candidates by modeling the flow of processes in the early phase of development.}, langid = {english}, keywords = {LOGSEQ}, note = {\section{Annotazioni\\ (21/6/2024, 17:39:41)} \par - “no de facto method for the analysis and design of systems within microservice architecture” (Yildiz e Demirors, p. 1) \#5fb236\\ \mkbibemph{ } \par - “difficulties in developing microservice-based systems, owing to the lack of well-defined methodologies for analysis and design” (Yildiz e Demirors, p. 1) \#a28ae5\\ \mkbibemph{ } \par - “analysis and design method for microservicebased systems.” (Yildiz e Demirors, p. 1) \#a28ae5\\ \mkbibemph{ } \par - “new” (Yildiz e Demirors, p. 1) \#ffd400\\ \mkbibemph{?? not so new, isn't it? } \par - “updated or changed, and the failure of the microservice does not affect the entire system.” (Yildiz e Demirors, p. 1) \#5fb236\\ \mkbibemph{ } \par - “The most obvious difference is to eliminate the coupling of objects, stop using the database for coordination, and enable asynchronous communication between services” (Yildiz e Demirors, p. 1) \#5fb236\\ \mkbibemph{ } \par - “Organizations transitioning to software development using microservice architectures encounter challenges in distributing responsibilities and eliminating dependencies among developer teams.” (Yildiz e Demirors, p. 2) \#5fb236\\ \mkbibemph{ } \par - “Additionally, traditional analysis and design techniques have been found to be ineffective for microservice architectures” (Yildiz e Demirors, p. 2) \#5fb236\\ \mkbibemph{ } \par - “MicroArc” (Yildiz e Demirors, p. 3) \#5fb236\\ \mkbibemph{ } \par - “BMC (Business Model Canvas), EPC (Event-driven Process Chain), and MCD (Microservices Communication Diagram).” (Yildiz e Demirors, p. 3) \#5fb236\\ \mkbibemph{ } \par - “MicroArc is a method for analyzing and designing microservice-based systems that employs three modeling notations: the Business Model Canvas (BMC), Event-driven Process Chain (EPC), and Microservice Communication Diagram (MCD).” (Yildiz e Demirors, p. 3) \#ffd400\\ \mkbibemph{This is repeated. } \par - “Business Model Canvas (BMC)” (Yildiz e Demirors, p. 3) \#ffd400\\ \mkbibemph{This is from 2004 } \par - “Key Activitie” (Yildiz e Demirors, p. 3) \#5fb236\\ \mkbibemph{ } \par - “Key Resources” (Yildiz e Demirors, p. 3) \#5fb236\\ \mkbibemph{ } \par - “Key Partners:” (Yildiz e Demirors, p. 3) \#5fb236\\ \mkbibemph{ } \par - “Key activities in BMC enable the elicitation of high-level requirements for the software that will be developed. Thus, rough boundaries about domains can be learned from key activities.” (Yildiz e Demirors, p. 3) \#5fb236\\ \mkbibemph{ } \par - “Event-Driven Process Chain (EPC)” (Yildiz e Demirors, p. 3) \#ffd400\\ \mkbibemph{This from 2000 } \par - “It provides a high-level overview of how various components communicate and collaborate in a micro-service-based system.” (Yildiz e Demirors, p. 4) \#ffd400\\ \mkbibemph{ } \par - “In this study, an event-driven analysis and design method for microservice-based systems called MicroArc is presented. The goal is to analyze and design microservice-based systems in an event-driven manner. It comprises modeling notations and a guiding process to articulate how the method is applied. MicroArc allows transitions between models; therefore, integration between models can be established, and changes can be reflected in connected models. Using this approach, developers can identify events and microservice candidates by modeling the flow of processes in the early phase of development” (Yildiz e Demirors, p. 6) \#ffd400\\ \mkbibemph{The paper does not highlight the novelty of the proposed approach compared with the methodologies presented in the related work or with an existing baseline. The MicroArc methodologies rely on three different notations, BMC and EPC are existing languages developed a twe decades ago, and MCD, which seems to be conceived by the authors. None of the these languages have been discussed with existing languages, link BPMN. The evaluation of the methodology is not convincing as the authors discussed at a high level of abstraction the application of the approach on a case study. Said that, the paper cannot be accepted as it is, as it lacks details and discussions on the strenghts and limitations of the proposed languages with respect to existing notations and tools. }} } @article{Yin2022, title = {Deep {{CSI}} Compression for Massive {{MIMO}}: {{A}} Self-Information Model-Driven Neural Network}, author = {Yin, Z. and Xu, W. and Xie, R. and Zhang, S. and Ng, D.W.K. and You, X.}, date = {2022}, journaltitle = {IEEE Trans. Wirel. Commun.}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {15361276}, doi = {10.1109/TWC.2022.3170576}, abstract = {In order to fully exploit the advantages of massive multiple-input multiple-output (mMIMO), it is critical for the transmitter to accurately acquire the channel state information (CSI). Deep learning (DL)-based methods have been proposed for CSI compression and feedback to the transmitter. Although most existing DL-based methods consider the CSI matrix as an image, structural features of the CSI image are rarely exploited in neural network design. As such, we propose a model of self-information that dynamically measures the amount of information contained in each patch of a CSI image from the perspective of structural features. Then, by applying the self-information model, we propose a model-and-data-driven network for CSI compression and feedback, namely IdasNet. The IdasNet includes the design of a module of self-information deletion and selection (IDAS), an encoder of informative feature compression (IFC), and a decoder of informative feature recovery (IFR). In particular, the model-driven module of IDAS pre-compresses the CSI image by removing informative redundancy in terms of the self-information. The encoder of IFC then conducts feature compression to the pre-compressed CSI image and generates a feature codeword which contains two components, i.e., codeword values and position indices of the codeword values. Subsequently, the IFR decoder decouples the codeword values as well as position indices to recover the CSI image. Experimental results verify that the proposed IdasNet noticeably outperforms existing DL-based networks under various compression ratios while it has the number of network parameters reduced by orders-of-magnitude compared with various existing methods. IEEE}, document_type = {Article}, source = {Scopus}, note = {cited By 0 \par TL;DR \par Experimental results verify that the proposed IdasNet noticeably outperforms existing DL-based networks under various compression ratios while it has the number of network parameters reduced by orders-of-magnitude compared with various existing methods.} } @article{yinDynamicDataMining2020, title = {Dynamic {{Data Mining}} of {{Sensor Data}}}, author = {Yin, Yunfei and Long, Lianjie and Deng, Xiyu}, date = {2020}, journaltitle = {IEEE Access}, volume = {8}, pages = {41637--41648}, issn = {2169-3536}, doi = {10.1109/ACCESS.2020.2976699}, abstract = {The research of data mining has aroused widespread concern in academia and industry. However, an important mark of the Internet of Things era is that sensor data replaces artificially compiled data. How to extract valuable knowledge and patterns from a large amount of data generated by sensors is a meaningful research topic. This paper proposes a dynamic data mining framework for processing sensor data. A sensor data mining model which can be used in the process of dynamic change is constructed. In this model, different sensor network environments are considered as different physical systems. The physical system and its parameters are trained by collecting and mining historical changes in sensor data; the associations between different sensor network environments are discovered by mining the associations between the parameters of different physical systems. In our limited experimental environment, the physical quantities considered included transmission distance, transmission delay, sensor data, data changes, and so on. Experiments were carried out on the designated experimental platform, and the results showed that the model could mine the dynamic data and find stable patterns. Through the analysis of the experimental results, it was found that the model had reference value for the dynamic mining of sensor data, and was expected to construct new methods for industrial big data analysis.}, langid = {english}, note = {TL;DR \par A dynamic data mining framework for processing sensor data is proposed and a model which can be used in the process of dynamic change is constructed, finding that the model had reference value for the dynamic mining of sensor data, and was expected to construct new methods for industrial big data analysis.} } @article{yingSelectionPresentationPractices, title = {Selection and {{Presentation Practices}} for {{Code Example Summarization}}}, author = {Ying, Annie T T and Robillard, Martin P}, pages = {12}, abstract = {Code examples are an important source for answering questions about software libraries and applications. Many usage contexts for code examples require them to be distilled to their essence: e.g., when serving as cues to longer documents, or for reminding developers of a previously known idiom. We conducted a study to discover how code can be summarized and why. As part of the study, we collected 156 pairs of code examples and their summaries from 16 participants, along with over 26 hours of think-aloud verbalizations detailing the decisions of the participants during their summarization activities. Based on a qualitative analysis of this data we elicited a list of practices followed by the participants to summarize code examples and propose empirically-supported hypotheses justifying the use of specific practices. One main finding was that none of the participants exclusively extracted code verbatim for the summaries, motivating abstractive summarization. The results provide a grounded basis for the development of code example summarization and presentation technology.}, langid = {english} } @article{Yılmaz20214305, title = {Artificial Neural Network Metamodeling-Based Design Optimization of a Continuous Motorcyclists Protection Barrier System}, author = {Yılmaz, İ. and Yelek, İ. and Özcanan, S. and Atahan, A.O. and Hiekmann, J.M.}, date = {2021}, journaltitle = {Struct. Multidiscip. Optim.}, volume = {64}, number = {6}, pages = {4305--4323}, publisher = {{Springer Science and Business Media Deutschland GmbH}}, issn = {1615147X}, doi = {10.1007/s00158-021-03080-1}, abbrev_source_title = {Struct. Mutltidiscip. Opt.}, affiliation = {Borçelik Çelik San. ve Tic. A.Ş., Ata Mh. 125. Sk. No:1, Gemlik, Bursa, 16601, Turkey; Department of Civil Engineering, Sirnak University, M. Emin Acar Campus, Sirnak, 73000, Turkey; Department of Civil Engineering, İstanbul Technical University, Ayazağa Campus, İstanbul, 34469, Turkey; Pass+Co Road Restraint Systems, Seigen, Germany}, coden = {SMOTB}, correspondence_address1 = {Yılmaz, İ.; Borçelik Çelik San. ve Tic. A.Ş., Ata Mh. 125. Sk. No:1, Gemlik, Turkey; email: ilyilmaz@borcelik.com}, document_type = {Article}, langid = {english}, source = {Scopus}, note = {cited By 1} } @inproceedings{yohannisModelbasedBiasMitigation2022, title = {Towards Model-Based Bias Mitigation in Machine Learning}, booktitle = {Proc. 25th {{Int}}. {{Conf}}. {{Model Driven Eng}}. {{Lang}}. {{Syst}}.}, author = {Yohannis, Alfa and Kolovos, Dimitris}, date = {2022-10-23}, pages = {143--153}, publisher = {ACM}, location = {Montreal Quebec Canada}, doi = {10.1145/3550355.3552401}, abstract = {Models produced by machine learning are not guaranteed to be free from bias, particularly when trained and tested with data produced in discriminatory environments. The bias can be unethical, mainly when the data contains sensitive attributes, such as sex, race, age, etc. Some approaches have contributed to mitigating such biases by providing bias metrics and mitigation algorithms. The challenge is users have to implement their code in general/statistical programming languages, which can be demanding for users with little programming and fairness in machine learning experience. We present FairML, a model-based approach to facilitate bias measurement and mitigation with reduced software development effort. Our evaluation shows that FairML requires fewer lines of code to produce comparable measurement values to the ones produced by the baseline code.}, eventtitle = {{{MODELS}} '22: {{ACM}}/{{IEEE}} 25th {{International Conference}} on {{Model Driven Engineering Languages}} and {{Systems}}}, isbn = {978-1-4503-9466-6}, langid = {english}, keywords = {LOGSEQ}, note = {TL;DR \par This work presents FairML, a model-based approach to facilitate bias measurement and mitigation with reduced software development effort, and shows that FairML requires fewer lines of code to produce comparable measurement values to the ones produced by the baseline code.} } @article{Yoo2022, title = {Deep Learning-Based Efficient Metamodeling via Domain Knowledge-Integrated Designable Data Augmentation with Transfer Learning: Application to Vehicle Crash Safety}, author = {Yoo, Y. and Park, C.-K. and Lee, J.}, date = {2022}, journaltitle = {Struct. Multidiscip. Optim.}, volume = {65}, number = {7}, publisher = {{Springer Science and Business Media Deutschland GmbH}}, issn = {1615147X}, doi = {10.1007/s00158-022-03290-1}, abbrev_source_title = {Struct. Mutltidiscip. Opt.}, affiliation = {School of Mechanical Engineering, Yonsei University, Seoul, 03722, South Korea; Department of Automotive Engineering, Gwangju University, Gwangju, 61743, South Korea}, art_number = {189}, coden = {SMOTB}, correspondence_address1 = {Lee, J.; School of Mechanical Engineering, South Korea; email: jleej@yonsei.ac.kr}, document_type = {Article}, langid = {english}, source = {Scopus}, note = {cited By 0 \par TL;DR \par This study proposes a deep learning-based efficient metamodeling method called domain knowledge-integrated designable data augmentation (DDA) with transfer learning for engineering design that was applied to the design of a bumper considering vehicle crash safety.} } @inproceedings{Yoon202010468, title = {Balanced Depth Completion between Dense Depth Inference and Sparse Range Measurements via {{KISS-GP}}}, author = {Yoon, S. and Kim, A.}, date = {2020}, series = {{{IEEE International Conference}} on {{Intelligent Robots}} and {{Systems}}}, pages = {10468--10475}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {21530858}, doi = {10.1109/IROS45743.2020.9341769}, abstract = {Estimating a dense and accurate depth map is the key requirement for autonomous driving and robotics. Recent advances in deep learning have allowed depth estimation in full resolution from a single image. Despite this impressive result, many deep-learning-based monocular depth estimation (MDE) algorithms have failed to keep their accuracy yielding a meter-level estimation error. In many robotics applications, accurate but sparse measurements are readily available from Light Detection and Ranging (LiDAR). Although they are highly accurate, the sparsity limits full resolution depth map reconstruction. Targeting the problem of dense and accurate depth map recovery, this paper introduces the fusion of these two modalities as a depth completion (DC) problem by dividing the role of depth inference and depth regression. Utilizing the state-of-the-art MDE and our Gaussian process (GP) based depth-regression method, we propose a general solution that can flexibly work with various MDE modules by enhancing its depth with sparse range measurements. To overcome the major limitation of GP, we adopt Kernel Interpolation for Scalable Structured (KISS)-GP and mitigate the computational complexity from O(N3) to O(N). Our experiments demonstrate that the accuracy and robustness of our method outperform state-of-the-art unsupervised methods for sparse and biased measurements. © 2020 IEEE.}, art_number = {9341769}, coden = {85RBA}, document_type = {Conference Paper}, isbn = {978-1-7281-6212-6}, source = {Scopus}, note = {cited By 2} } @online{yousefizadehUsingCooperativeCoevolutionary2025, title = {Using {{Cooperative Co-evolutionary Search}} to {{Generate Metamorphic Test Cases}} for {{Autonomous Driving Systems}}}, author = {Yousefizadeh, Hossein and Gu, Shenghui and Briand, Lionel C. and Nasr, Ali}, date = {2025-04-15}, eprint = {2412.03843}, eprinttype = {arXiv}, eprintclass = {cs}, doi = {10.48550/arXiv.2412.03843}, abstract = {Autonomous Driving Systems (ADSs) rely on Deep Neural Networks, allowing vehicles to navigate complex, open environments. However, the unpredictability of these scenarios highlights the need for rigorous system-level testing to ensure safety, a task usually performed with a simulator in the loop. Though one important goal of such testing is to detect safety violations, there are many undesirable system behaviors, that may not immediately lead to violations, that testing should also be focusing on, thus detecting more subtle problems and enabling a finer-grained analysis. This paper introduces Cooperative Co-evolutionary MEtamorphic test Generator for Autonomous systems (CoCoMEGA), a novel automated testing framework aimed at advancing system-level safety assessments of ADSs. CoCoMEGA combines Metamorphic Testing (MT) with a search-based approach utilizing Cooperative Co-Evolutionary Algorithms (CCEA) to efficiently generate a diverse set of test cases. CoCoMEGA emphasizes the identification of test scenarios that present undesirable system behavior, that may eventually lead to safety violations, captured by Metamorphic Relations (MRs). When evaluated within the CARLA simulation environment on the Interfuser ADS, CoCoMEGA consistently outperforms baseline methods, demonstrating enhanced effectiveness and efficiency in generating severe, diverse MR violations and achieving broader exploration of the test space. These results underscore CoCoMEGA as a promising, more scalable solution to the inherent challenges in ADS testing with a simulator in the loop. Future research directions may include extending the approach to additional simulation platforms, applying it to other complex systems, and exploring methods for further improving testing efficiency such as surrogate modeling.}, pubstate = {prepublished}, keywords = {Computer Science - Neural and Evolutionary Computing,Computer Science - Software Engineering} } @inproceedings{Yu2020310, title = {Architectural Facade Recognition and Generation through Generative Adversarial Networks}, author = {Yu, Q. and Malaeb, J. and Ma, W.}, date = {2020}, series = {Proceedings - 2020 {{International Conference}} on {{Big Data}} and {{Artificial Intelligence}} and {{Software Engineering}}, {{ICBASE}} 2020}, pages = {310--316}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/ICBASE51474.2020.00072}, abstract = {With the development of artificial intelligence technology, the ideas of machine learning have been introduced into the field of design in recent years. The research methods of 'AI + Architecture' have brought new ideas for solving traditional problems. Generative Adversarial Network (GAN) is a machine learning model for image generation. Pix2pix is an improved version of GAN, which is specially designed to learn and generate pairs of image data with similar characteristics. In this study, Pix2pix is applied to the recognition and generation of building facade. The purpose is to explore the feasibility of using image generation technology to achieve rapid recognition and generation of building facade based on pix2pix. This paper also discusses the application scenarios of this technology. The existing building façade datasets and the self-made Chinese traditional building datasets are used to test and verify that pix2pix under different types of datasets can nicely identify and generate facade images. Then we summarize a set of working methods based on GAN to realize the overall or local reconstruction design of the facade, so as to provide new ideas for the improvement of the efficiency of related industries and the expansion of teaching tools. © 2020 IEEE.}, art_number = {9403820}, author_keywords = {facade; GAN; image generation; image recognition}, document_type = {Conference Paper}, isbn = {978-1-7281-9619-0}, source = {Scopus}, keywords = {Adversarial networks,Application scenario,Architectural facades,Artificial intelligence technologies,Big data,Building facades,Chinese traditional buildings,Facades,Image enhancement,Image generations,Machine learning,Machine learning models,Software engineering,Turing machines}, note = {cited By 2} } @inproceedings{yuAPIBookEffectiveApproach2016, title = {{{APIBook}}: An Effective Approach for Finding {{APIs}}}, shorttitle = {{{APIBook}}}, author = {Yu, Haibo and Song, Wenhao and Mine, Tsunenori}, date = {2016}, pages = {45--53}, publisher = {ACM Press}, doi = {10.1145/2993717.2993727}, isbn = {978-1-4503-4829-4}, langid = {english}, note = {TL;DR \par A search-based recommendation algorithm on API methods is proposed that combines semantic relevance, type relevance and the extent of degree that API method is used to sort these API methods and rank those that are highly relevant and widely used in the top positions.} } @inproceedings{yuEfficientSimRankComputation2013, title = {Towards Efficient {{SimRank}} Computation on Large Networks.}, booktitle = {{{ICDE}}}, author = {Yu, Weiren and Lin, Xuemin and Zhang, Wenjie}, editor = {Jensen, Christian S. and Jermaine, Christopher M. and Zhou, Xiaofang}, date = {2013}, pages = {601--612}, publisher = {IEEE Computer Society}, url = {http://dblp.uni-trier.de/db/conf/icde/icde2013.html#YuLZ13}, added-at = {2013-06-27T00:00:00.000+0200}, biburl = {http://www.bibsonomy.org/bibtex/2026a998396ad1c9c6555439949e04747/dblp}, ee = {http://doi.ieeecomputersociety.org/10.1109/ICDE.2013.6544859}, interhash = {22d1f058c9d3b87edb546d64328413e8}, intrahash = {026a998396ad1c9c6555439949e04747}, isbn = {978-1-4673-4909-3}, keywords = {dblp}, timestamp = {2013-06-27T00:00:00.000+0200} } @article{yuRapidApplicationDevelopment2011, title = {Towards the {{Rapid Application Development Based}} on {{Predefined Frameworks}}}, author = {Yu, Dongjin}, date = {2011-08-01}, journaltitle = {JSW}, volume = {6}, number = {9}, pages = {1795--1804}, issn = {1796-217X}, doi = {10.4304/jsw.6.9.1795-1804}, abstract = {The development of large or medium-sized domain application systems usually involves intensive workforce due to its complexity. Reuse of existing components, especially those architectural ones, could dramatically reduce the production cost and improve the quality. However, the problems related with making and adapting reusable components among different systems often inhibit the introduction of reuse. Fortunately, domainoriented application systems, especially those data-centric ones, usually share similar behaviors no matter what they server for. This paper extracts the common behaviors existing in different domains and introduces the templates based application framework, called RADF. RADF provides application skeletons and confines domain specific coding in predefined templates of classes and configuration files. The proprietary behavior of domain specific applications could be realized via simply filling codes in these templates. RADF not only consolidates the programming paradigm and provides the supporting classes for default behaviors expected in different domains, but also allows manually extending and reassembling these supporting classes. Four cases of RADF-based development have proved that RADF helps rapid application development with significantly reduced number of manually-coded source lines.}, langid = {english} } @inproceedings{zaheriConsistencyManagementLowcode2022, title = {Towards Consistency Management in Low-Code Platforms}, booktitle = {Proc. 25th {{Int}}. {{Conf}}. {{Model Driven Eng}}. {{Lang}}. {{Syst}}. {{Companion Proc}}.}, author = {Zaheri, MohammadAmin}, date = {2022-10-23}, pages = {176--181}, publisher = {ACM}, location = {Montreal Quebec Canada}, doi = {10.1145/3550356.3558510}, eventtitle = {{{MODELS}} '22: {{ACM}}/{{IEEE}} 25th {{International Conference}} on {{Model Driven Engineering Languages}} and {{Systems}}}, isbn = {978-1-4503-9467-3}, langid = {english} } @inproceedings{Zahoor2020198, title = {An Investigation of Smart Parking Tools, Technologies, \& Challenges}, author = {Zahoor, T. and Azam, F. and Anwar, M.W. and Tariq, A. and Javaid, H.A.}, date = {2020}, series = {{{ACM International Conference Proceeding Series}}}, pages = {198--203}, publisher = {Association for Computing Machinery}, doi = {10.1145/3436829.3436851}, abbrev_source_title = {ACM Int. Conf. Proc. Ser.}, affiliation = {Department of Computer and Software Engineering, College of EandME, National University of Sciences and Technology (NUST), H-12 Islamabad, Pakistan}, document_type = {Conference Paper}, isbn = {978-1-4503-7721-8}, langid = {english}, source = {Scopus}, note = {cited By 0 \par TL;DR \par The key tools, techniques \& challenges proposed in the recent research studies in smart parking systems are investigated and will definitely assist the practitioners while deciding the appropriate selections.} } @article{zambonelliKeyAbstractionsIoTOriented2017, title = {Key {{Abstractions}} for {{IoT-Oriented Software Engineering}}}, author = {Zambonelli, Franco}, date = {2017-01}, journaltitle = {IEEE Softw.}, volume = {34}, number = {1}, pages = {38--45}, issn = {0740-7459}, doi = {10.1109/MS.2017.3}, langid = {english} } @inproceedings{zambonelliSelfAdaptationSelfExpressionSelfAwareness2011, title = {On {{Self-Adaptation}}, {{Self-Expression}}, and {{Self-Awareness}} in {{Autonomic Service Component Ensembles}}}, author = {Zambonelli, Franco and Bicocchi, Nicola and Cabri, Giacomo and Leonardi, Letizia and Puviani, Mariachiara}, date = {2011-10}, pages = {108--113}, publisher = {IEEE}, doi = {10.1109/SASOW.2011.24}, isbn = {978-1-4577-2029-1 978-0-7695-4545-5}, note = {TL;DR \par This position paper frame and discuss the above issues, survey the state of the art in the area, and sketch the main research challenges that will be faced in the ASCENS project towards the definition of a fully-fledged framework for autonomic services.} } @inproceedings{zampettiHowOpenSource2017, title = {How {{Open Source Projects Use Static Code Analysis Tools}} in {{Continuous Integration Pipelines}}}, author = {Zampetti, Fiorella and Scalabrino, Simone and Oliveto, Rocco and Canfora, Gerardo and Di Penta, Massimiliano}, date = {2017-05}, pages = {334--344}, publisher = {IEEE}, doi = {10.1109/MSR.2017.2}, isbn = {978-1-5386-1544-7}, note = {TL;DR \par Study of the usage of static analysis tools in 20 Java open source projects hosted on GitHub and using Travis CI as continuous integration infrastructure reveals that build breakages are quickly fixed by actually solving the problem, rather than by disabling the warning, and are often properly documented.} } @inproceedings{zayanEffectsUsingExamples2014, title = {Effects of Using Examples on Structural Model Comprehension: A Controlled Experiment}, shorttitle = {Effects of Using Examples on Structural Model Comprehension}, booktitle = {Proc. 36th {{Int}}. {{Conf}}. {{Softw}}. {{Eng}}.}, author = {Zayan, Dina and Antkiewicz, Micha\textbackslash l and Czarnecki, Krzysztof}, date = {2014}, pages = {955--966}, publisher = {ACM}, url = {http://dl.acm.org/citation.cfm?id=2568270}, urldate = {2015-05-17} } @article{zelkowitzExperimentalModelsValidating1998, title = {Experimental Models for Validating Technology}, author = {Zelkowitz, M.V. and Wallace, D.R.}, date = {1998-05}, journaltitle = {Computer}, volume = {31}, number = {5}, pages = {23--31}, issn = {00189162}, doi = {10.1109/2.675630}, langid = {english} } @inproceedings{Zeng2017982, title = {Metamodel of the Two-Dimensional Magnet-Rail Relationship Based on a {{BP}} Neural Network}, author = {Zeng, S. and Liu, Y. and Li, J. and Zhou, S.}, date = {2017}, series = {Proceedings - 2017 {{Chinese Automation Congress}}, {{CAC}} 2017}, volume = {2017-January}, pages = {982--987}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/CAC.2017.8242909}, abbrev_source_title = {Proc. - Chin. Autom. Congr., CAC}, affiliation = {College of Mechatronics Engineering and Automation, National University of Defense Technology, Changsha, China}, document_type = {Conference Paper}, isbn = {978-1-5386-3524-7}, langid = {english}, source = {Scopus}, keywords = {notion}, note = {cited By 1} } @inproceedings{zennaroMachineLearningApproach2018, title = {A Machine Learning Approach for Area Prediction of Hardware Designs from Abstract Specifications}, booktitle = {Proc. - 21st {{Euromicro Conf}}. {{Digit}}. {{Syst}}. {{Des}}. {{DSD}} 2018}, author = {Zennaro, E. and Servadei, L. and Devarajegowda, K. and Ecker, W.}, editor = {Konofaos N., Novotny M., Skavhaug A.}, date = {2018}, pages = {413--420}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/DSD.2018.00076}, abstract = {Advancements of Machine Learning (ML) in the field of computer vision have paved the way for its potential application in many other fields. Researchers and hardware domain experts are exploring possible applications of Machine Learning in optimizing many aspects of hardware development process. In this paper, we propose a novel approach for predicting the area of hardware components from specifications. The flow uses an existing RTL generation framework, for generating valid data samples that enable ML algorithms to train the learning models. The approach has been successfully employed to predict the area of real-life hardware components such as Control and Status Register (CSR) interfaces that are ubiquitous in embedded systems. With this approach we are able to predict the area with more than 98\% accuracy and 600x faster than the existing methods. In addition, we are able to rank the features according to their importance in final area estimations. © 2018 IEEE.}, isbn = {978-1-5386-7376-8}, keywords = {Area estimation,Artificial intelligence,Code Generation,Computer hardware,Design productivity,Embedded systems,Forecasting,Hardware,Learning systems,Meta model,Model driven architectures,Software architecture,Software design,Specifications,Systems analysis}, note = {cited By 11} } @misc{zeshanPromptInjectionBasedAdversarial, title = {Prompt-{{Injection}} Based {{Adversarial Attacks}} in {{Large Language Models}}}, author = {Zeshan, Muhammad Umar}, langid = {english}, keywords = {LOGSEQ,Mentoring,people/Umar}, note = {\section{Annotazioni\\ (19/1/2024, 16:08:19)} \par “trendy” (Zeshan, p. 1) \#ff6666 \par “prompt attacks in Large Language Models” (Zeshan, p. 1) \#a28ae5 \par “LLMs” (Zeshan, p. 1) \#ff6666 \par “better” (Zeshan, p. 1) \#ffd400 \\ \mkbibemph{Nothing has been said so far. Better than that?} \par “ethical issues, bias, and responsible use become crucial factors to take into account” (Zeshan, p. 1) \#ffd400 \\ \mkbibemph{These are critical points. Are you mentioning them because you want to plan to deal with them? If not, why are you mentioning them?} \par “insufficient safety assessments and guardrails are accompanying this drive toward AI integration” (Zeshan, p. 1) \#5fb236 \par “A prompt should ideally generate an answer that is accurate, sufficient in both form and content, and of the appropriate length” (Zeshan, p. 2) \#5fb236 \par “several human feedback-incorporating fine-tuning procedures have been developed to guarantee that LLM outputs are both safe and consistent with human values.” (Zeshan, p. 2) \#5fb236 \par “around the security.” (Zeshan, p. 2) \#ffd400 \\ \mkbibemph{Some more details about security issues one can have with LLMs are needed.} \par “According to Zou et al.’s research from 2023, adversarial sequence creation can be automated, producing an infinite number of these attacks. Furthermore, they demonstrate how safety precautions can be circumvented by appending a single adversarial sequence to several damaging prompts” (Zeshan, p. 2) \#ffd400 \\ \mkbibemph{Some illustrative examples are needed here!} \par “Adversarial attacks detection in RSSE” (Zeshan, p. 2) \#ffd400 \\ \mkbibemph{This section should be on presenting Rec Systems in Software Engineering. Thus the title should be changed and the content should be expanded with the aim of presenting a quick overview on RSSE.} \par “Adversarial attempts produce perturbations to trick and confuse systems by breaking them down, impairing their ability to provide recommendations. For instance, an adversarial attack on recommender systems may support or disparage a product, depending on the intent, which would have a detrimental effect on the final recommendations. Similarly, malicious users may expose recommender systems to hazardous artifacts by altering training data that is accessible through OSS platforms. Software system disruptions may arise if a recommender is trained to deliver harmful outcomes based on Adversaries. For instance, a recent study reveals that there have been attempts to force Android apps to open ports covertly, enabling unwanted access. Security concerns in machine learning systems and all-purpose recommender systems are investigated via research on adversarial machine learning (AML)” (Zeshan, p. 3) \#ffd400 \\ \mkbibemph{I would show some technical details that will be used later in the text.} \par “Adversarial Attacks in Promptbased Learning” (Zeshan, p. 4) \#ffd400 \\ \mkbibemph{All the different approaches that are overviewed in this section need to be expanded with concrete examples. It is essential to expand because, as far as I understood, this will underpin and motivate the planned work.} \par “trendy” (Zeshan, p. 4) \#ff6666 \\ \mkbibemph{I don't like it; it is not for scientific documents.} \par “backdoor attacks” (Zeshan, p. 4) \#5fb236 \par “Our Methodology” (Zeshan, p. 5) \#ffd400 \\ \mkbibemph{For what? It is necessary to conclude the previous section by listing the challenges you plan to address! By clearly listing the challenges at the end of the previous section, in this section you can discuss how you plant to deal with them.} \par “Limitations in previous methods” (Zeshan, p. 6) \#ffd400 \\ \mkbibemph{This must be moved to the previous section before listing the challenges that you plan to address.} \par “The main drawback of previous research is that it is case study oriented; for example, it did not define the attacker’s objective in rapid injection assaults in terms of the type of the attack, attacker wants to carry.” (Zeshan, p. 6) \#ffd400 \\ \mkbibemph{The document is plenty of sentences like this one, we are missing concreteness, you have to provide explanatory examples to help reader understand what's the problem by referring to real cases.} \par “Rather than simply translating a text into English, they demonstrated how an assailant may lead an LLM astray and have them compose a sonnet about pandas. The main drawback of this kind of caseby-case research is how difficult it is to come up with novel prompt injection attacks or carry out a thorough analysis and comparison of other prompt injection assaults. Even the latest studies are focusing on some particular type of prompt injections, which also allow for enhancing the attack by combining all the possible attack scenarios.” (Zeshan, p. 6) \#ffd400 \\ \mkbibemph{Suddenly, we lost the reference to RSSE!} \par “Research Questions” (Zeshan, p. 6) \#ffd400 \\ \mkbibemph{Are you still interested in RSSE? The research questions you defined are not related to RSSE.} \par “ll kinds of adversarial attacks defined in literature are discussed which are relevant in this new trendy topic of prompt learning attacks.” (Zeshan, p. 6) \#ffd400 \\ \mkbibemph{Thus, what's the plan? Are you planning to work on a survey work? What do you expect to do further than expanding what you have listed in 3.2?} \par “threat model is proposed, in which a combined attack model is applied which concatenates all the possible types of attacks in prompts by the attacker.” (Zeshan, p. 6) \#ffd400 \\ \mkbibemph{I don't see it in Section 3.2} \par “Prompt Injection Attacks” (Zeshan, p. 8) \#ffd400 \\ \mkbibemph{Is this supposed to be a new type of attack? But it is not new, isn't it?}} } @inproceedings{zhang_automatic_2012, title = {Automatic Parameter Recommendation for Practical {{API}} Usage}, booktitle = {2012 34th {{Int}}. {{Conf}}. {{Softw}}. {{Eng}}. {{ICSE}}}, author = {Zhang, Cheng and Yang, Juyuan and Zhang, Yi and Fan, Jing and Zhang, Xin and Zhao, Jianjun and Ou, Peizhao}, date = {2012-06}, pages = {826--836}, doi = {10.1109/ICSE.2012.6227136}, abstract = {Programmers extensively use application programming interfaces (APIs) to leverage existing libraries and frameworks. However, correctly and efficiently choosing and using APIs from unfamiliar libraries and frameworks is still a non-trivial task. Programmers often need to ruminate on API documentations (that are often incomplete) or inspect code examples (that are often absent) to learn API usage patterns. Recently, various techniques have been proposed to alleviate this problem by creating API summarizations, mining code examples, or showing common API call sequences. However, few techniques focus on recommending API parameters. In this paper, we propose an automated technique, called Precise, to address this problem. Differing from common code completion systems, Precise mines existing code bases, uses an abstract usage instance representation for each API usage example, and then builds a parameter usage database. Upon a request, Precise queries the database for abstract usage instances in similar contexts and generates parameter candidates by concretizing the instances adaptively. The experimental results show that our technique is more general and applicable than existing code completion systems, specially, 64\% of the parameter recommendations are useful and 53\% of the recommendations are exactly the same as the actual parameters needed. We have also performed a user study to show our technique is useful in practice.}, keywords = {abstract usage instance representation,Abstracts,API,API call sequences,API documentations,API summarizations,API usage pattern learning,application program interfaces,application programming interfaces,argument,Arrays,automatic parameter recommendation,code base mining,code completion,code completion systems,code example mining,Complexity theory,Context,data mining,database querying,Documentation,Indexes,inspect code examples,learning (artificial intelligence),parameter,parameter usage database,practical API usage,Precise,query processing,recommendation,recommender systems}, note = {ISSN: 1558-1225 \par TL;DR \par An automated technique, called Precise, is proposed, which mines existing code bases, uses an abstract usage instance representation for each API usage example, and then builds a parameter usage database and generates parameter candidates by concretizing the instances adaptively.} } @inproceedings{zhang_cyber-guided_2020, title = {Cyber-Guided {{Deep Neural Network}} for {{Malicious Repository Detection}} in {{GitHub}}}, booktitle = {2020 {{IEEE Int}}. {{Conf}}. {{Knowl}}. {{Graph ICKG}}}, author = {Zhang, Yiming and Fan, Yujie and Hou, Shifu and Ye, Yanfang and Xiao, Xusheng and Li, Pan and Shi, Chuan and Zhao, Liang and Xu, Shouhuai}, date = {2020-08}, pages = {458--465}, doi = {10.1109/ICBK50248.2020.00071}, abstract = {As the largest source code repository, GitHub has played a vital role in modern social coding ecosystem to generate production software. Despite the apparent benefits of such social coding paradigm, its potential security risks have been largely overlooked (e.g., malicious codes or repositories could be easily embedded and distributed). To address this imminent issue, in this paper, we propose a novel framework (named GitCyber) to automate malicious repository detection in GitHub at the first attempt. In GitCyber, we first extract code contents from the repositories hosted in GitHub as the inputs for deep neural network (DNN), and then we incorporate cybersecurity domain knowledge modeled by heterogeneous information network (HIN) to design cyber-guided loss function in the learning objective of the DNN to assure the classification performance while preserving consistency with the observational domain knowledge. Comprehensive experiments based on the large-scale data collected from GitHub demonstrate that our proposed GitCyber outperforms the state-of-the-arts in malicious repository detection.}, keywords = {Cyber-guided DNN,Data mining,Encoding,Feature extraction,heterogeneous information network,Knowledge engineering,malicious repository detection,Neural networks,Security,Software}, note = {TL;DR \par Comprehensive experiments based on the large-scale data collected from GitHub demonstrate that the proposed GitCyber outperforms the state-of-the-arts in malicious repository detection.} } @article{Zhang:1997:BND:593415.593443, title = {{{BIRCH}}: {{A}} New Data Clustering Algorithm and Its Applications}, author = {Zhang, Tian and Ramakrishnan, Raghu and Livny, Miron}, date = {1997-01}, journaltitle = {Data Min. Knowl. Discov.}, volume = {1}, number = {2}, pages = {141--182}, publisher = {Kluwer Academic Publishers}, address = {Hingham, MA, USA}, issn = {1384-5810}, url = {https://doi.org/10.1023/A:1009783824328}, acmid = {593443}, issue_date = {1997}, nodoi = {10.1023/A:1009783824328}, numpages = {42}, keywords = {Data Classification and Compression,Data Clustering,Incremental Algorithm,Very Large Databases} } @article{ZHANG2011625, ids = {zhang2011identifying}, title = {Identifying Relevant Studies in Software Engineering}, author = {Zhang, He and Babar, Muhammad Ali and Tell, Paolo}, date = {2011}, journaltitle = {Inf. Softw. Technol.}, volume = {53}, number = {6}, pages = {625--637}, publisher = {Elsevier}, issn = {0950-5849}, doi = {10.1016/j.infsof.2010.12.010}, abstract = {Context Systematic literature review (SLR) has become an important research methodology in software engineering since the introduction of evidence-based software engineering (EBSE) in 2004. One critical step in applying this methodology is to design and execute appropriate and effective search strategy. This is a time-consuming and error-prone step, which needs to be carefully planned and implemented. There is an apparent need for a systematic approach to designing, executing, and evaluating a suitable search strategy for optimally retrieving the target literature from digital libraries. Objective The main objective of the research reported in this paper is to improve the search step of undertaking SLRs in software engineering (SE) by devising and evaluating systematic and practical approaches to identifying relevant studies in SE. Method We have systematically selected and analytically studied a large number of papers (SLRs) to understand the state-of-the-practice of search strategies in EBSE. Having identified the limitations of the current ad-hoc nature of search strategies used by SE researchers for SLRs, we have devised a systematic and evidence-based approach to developing and executing optimal search strategies in SLRs. The proposed approach incorporates the concept of ‘quasi-gold standard’ (QGS), which consists of collection of known studies, and corresponding ‘quasi-sensitivity’ into the search process for evaluating search performance. Results We conducted two participant–observer case studies to demonstrate and evaluate the adoption of the proposed QGS-based systematic search approach in support of SLRs in SE research. Conclusion We report their findings based on the case studies that the approach is able to improve the rigor of search process in an SLR, as well as it can serve as a supplement to the guidelines for SLRs in EBSE. We plan to further evaluate the proposed approach using a series of case studies on varying research topics in SE.}, keywords = {Evidence-based software engineering,Quasi-gold standard,Search strategy,Systematic literature review}, note = {Special Section: Best papers from the APSEC} } @article{Zhang2017, title = {Performing Global Uncertainty and Sensitivity Analysis from given Data in Tunnel Construction}, author = {Zhang, L. and Wu, X. and Zhu, H. and Abourizk, S.M.}, date = {2017}, journaltitle = {J. Comput. Civ. Eng.}, volume = {31}, number = {6}, publisher = {American Society of Civil Engineers (ASCE)}, issn = {08873801}, doi = {10.1061/(ASCE)CP.1943-5487.0000714}, abbrev_source_title = {J. Comput. Civ. Eng.}, affiliation = {College of Design, School of Building Construction, Georgia Institute of Technology, 280 Ferst Dr., Atlanta, GA 30332-0680, United States; School of Civil Engineering and Mechanics, Huazhong Univ. of Science and Technology, Wuhan, Hubei, 430074, China; Dept. of Civil and Environmental Engineering, Hole School of Construction Engineering, Univ. of Alberta, 5-047 Markin/Canadian Natural Resources Limited, Natural Resources Engineering Facility, Edmonton, AB T6G 2W2, Canada}, art_number = {04017065}, coden = {JCCEE}, correspondence_address1 = {Zhu, H.; School of Civil Engineering and Mechanics, China; email: hpzhu@mail.hust.edu.cn}, document_type = {Article}, langid = {english}, source = {Scopus}, note = {cited By 31} } @inproceedings{Zhang2019, title = {Fingerprint-Based Localization Using Commercial {{LTE}} Signals: {{A}} Field-Trial Study}, author = {Zhang, H. and Zhang, Z. and Zhang, S. and Xu, S. and Cao, S.}, date = {2019}, series = {{{IEEE Vehicular Technology Conference}}}, volume = {2019-September}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {15502252}, doi = {10.1109/VTCFall.2019.8891257}, abstract = {Wireless localization for mobile device has attracted more and more interests by increasing the demand for location based services. Fingerprint-based localization is promising, especially in non-Line-of-Sight (NLoS) or rich scattering environments, such as urban areas and indoor scenarios. In this paper, we propose a novel fingerprint-based localization technique based on deep learning framework under commercial long term evolution (LTE) systems. Specifically, we develop a software defined user equipment to collect the real time channel state information (CSI) knowledge from LTE base stations and extract the intrinsic features among CSI observations. On top of that, we propose a time domain fusion approach to assemble multiple positioning estimations. Experimental results demonstrated that the proposed localization technique can significantly improve the localization accuracy and robustness, e.g. achieves Mean Distance Error (MDE) of 0.47 meters for indoor and of 19.9 meters for outdoor scenarios, respectively. © 2019 IEEE.}, art_number = {8891257}, coden = {IVTCD}, document_type = {Conference Paper}, isbn = {978-1-7281-1220-6}, source = {Scopus}, note = {cited By 14 \par TL;DR \par A novel fingerprint-based localization technique based on deep learning framework under commercial long term evolution (LTE) systems is proposed, which develops a software defined user equipment to collect the real time channel state information (CSI) knowledge from LTE base stations and extract the intrinsic features among CSI observations.} } @inproceedings{Zhang201912744, title = {Self-Attention Generative Adversarial Networks}, author = {Zhang, H. and Goodfellow, I. and Metaxas, D. and Odena, A.}, date = {2019}, series = {36th {{International Conference}} on {{Machine Learning}}, {{ICML}} 2019}, volume = {2019-June}, pages = {12744--12753}, publisher = {International Machine Learning Society (IMLS)}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078069179&partnerID=40&md5=5849359dc99edfeab0013f81d7c62855}, abstract = {In this paper, we propose the Self-Attention Generative Adversarial Network (SAGAN) which allows attention-driven, long-range dependency modeling for image generation tasks. Traditional convolutional GAXs generate high-resolution details as a function of only spatially local points in lower-resolution feature maps. In SAGAN, details can be generated using cues from all feature locations. Moreover, the discriminator can check that highly detailed features in distant portions of the image are consistent with each other. Furthermore, recent work has shown that generator conditioning affects GAN performance. Leveraging this insight, we apply spectral normalization to the GAN generator and find that this improves training dynamics. The proposed SAGAN performs better than prior work1, boosting the best published Inception score from 36.8 to 52.52 and reducing Fréhet Inception distance from 27.62 to 18.65 on the challenging ImageNet dataset. Visualization of the attention layers shows that the generator leverages neighborhoods that correspond to object shapes rather than local regions of fixed shape. © 36th International Conference on Machine Learning, ICML 2019. All rights reserved.}, document_type = {Conference Paper}, isbn = {978-1-5108-8698-8}, source = {Scopus}, keywords = {Adversarial networks,Artificial intelligence,Feature location,High-Resolution Details,Image generations,Local region,Long-range dependencies,Lower resolution,Machine learning,Software engineering,Spectral normalization}, note = {cited By 1392 \par TL;DR \par The proposed SAGAN achieves the state-of-the-art results, boosting the best published Inception score from 36.8 to 52.52 and reducing Frechet Inception distance from 27.62 to 18.65 on the challenging ImageNet dataset.} } @inproceedings{Zhang2019376, title = {{{PS2}}: {{Parameter}} Server on Spark}, author = {Zhang, Z. and Cui, B. and Shao, Y. and Yu, L. and Jiang, J. and Miao, X.}, date = {2019}, series = {Proceedings of the {{ACM SIGMOD International Conference}} on {{Management}} of {{Data}}}, pages = {376--388}, publisher = {Association for Computing Machinery}, issn = {07308078}, doi = {10.1145/3299869.3314038}, abstract = {Most of the data is extracted and processed by Spark in Tencent Machine Learning Platform. However, seldom of them use Spark MLlib, an official machine learning (ML) library on top of Spark due to its inefficiency. In contrast, systems like parameter servers, XGBoost and TensorFlow are more used, which incur expensive cost of transferring data in and out of Spark ecosystem. In this paper, we identify the causes of inefficiency in Spark MLlib and solve the problem by building parameter servers on top of Spark. We propose PS2, a parameter server architecture that integrates Spark without hacking the core of Spark. With PS2, we leverage the power of Spark for data processing and ML training, and parameter servers for maintaining ML models. By carefully analyzing Tencent ML workloads, we figure out a widely existing computation pattern for ML models-element-wise operations among multiple high dimensional vectors. Based on this observation, we propose a new data abstraction, called Dimension Co-located Vector (DCV) for efficient model management in PS2. A DCV is a distributed vector that considers locality in parameter servers and enables efficient computation with multiple co-located distributed vectors. For ease-of-use, we also design a wide variety of advanced operators for operating DCVs. Finally, we carefully implement the PS2 system and evaluate it against existing systems on both public and Tencent workloads. Empirical results demonstrate that PS2 can outperform Spark MLlib by up to 55.6× and specialized ML systems like Petuum by up to 3.7×. © 2019 Association for Computing Machinery.}, document_type = {Conference Paper}, isbn = {978-1-4503-5643-5}, source = {Scopus}, note = {cited By 12} } @inproceedings{Zhang20201513, title = {{{ColumnSGD}}: {{A}} Column-Oriented Framework for Distributed Stochastic Gradient Descent}, author = {Zhang, Z. and Wu, W. and Jiang, J. and Yu, L. and Cui, B. and Zhang, C.}, date = {2020}, series = {Proceedings - {{International Conference}} on {{Data Engineering}}}, volume = {2020-April}, pages = {1513--1524}, publisher = {IEEE Computer Society}, issn = {10844627}, doi = {10.1109/ICDE48307.2020.00134}, abstract = {Distributed machine learning (ML) has triggered tremendous research interest in recent years. Stochastic gradient descent (SGD) is one of the most popular algorithms for training ML models, and has been implemented in almost all distributed ML systems, such as Spark MLlib, Petuum, MXNet, and TensorFlow. However, current implementations often incur huge communication and memory overheads when it comes to large models. One important reason for this inefficiency is the row-oriented scheme (RowSGD) that existing systems use to partition the training data, which forces them to adopt a centralized model management strategy that leads to vast amount of data exchange over the network.We propose a novel, column-oriented scheme (ColumnSGD) that partitions training data by columns rather than by rows. As a result, ML model can be partitioned by columns as well, leading to a distributed configuration where individual data and model partitions can be collocated on the same machine. Following this locality property, we develop a simple yet powerful computation framework that significantly reduces communication overheads and memory footprints compared to RowSGD, for large-scale ML models such as generalized linear models (GLMs) and factorization machines (FMs). We implement ColumnSGD on top of Apache Spark, and study its performance both analytically and experimentally. Experimental results on both public and real-world datasets show that ColumnSGD is up to 930 x faster than MLlib, 63 x faster than Petuum, and 14 x faster than MXNet. © 2020 IEEE.}, art_number = {9101731}, document_type = {Conference Paper}, isbn = {978-1-7281-2903-7}, source = {Scopus}, note = {cited By 1 \par TL;DR \par A novel, column-oriented scheme (ColumnSGD) that partitions training data by columns rather than by rows, leading to a distributed configuration where individual data and model partitions can be collocated on the same machine.} } @article{Zhang2020903, title = {An Efficient Data-Model Dual-Drive Algorithm for Compressed Sensing {{MRI}} [数据与模型双驱动的高效压缩感知磁共振成像重构算法]}, author = {Zhang, Y. and Ma, L. and Liu, R. and Cheng, S. and Fan, X. and Luo, Z.}, date = {2020}, journaltitle = {Jisuanji Fuzhu Sheji Yu Tuxingxue XuebaoJournal Comput.-Aided Des. Comput. Graph.}, volume = {32}, number = {6}, pages = {903--910}, publisher = {Institute of Computing Technology}, issn = {10039775}, doi = {10.3724/SP.J.1089.2020.17999}, abstract = {Traditional compressed sensing MRI methods that focus on constructing better prior regularizations or numerical iterative optimizations usually suffer from heavy computational burden. Recently developed deep learning based approaches rely too much on the selection of training data and deep architecture, thus have poor abilities of generalization. To address these issues, we propose an efficient and robust algorithm to achieve the balance between reconstruction accuracy and efficiency. We construct a model-driven priori expression process and a data-driven prediction process for details restoration and artifacts correction, in a complementary perspective, realizing an integration of domain knowledge and deep representation. Further, the iteratively alternating mechanism ensures that the output propagation can be corrected in time and guided towards the desired solution in expected direction. Detailed experiments on T1 and T2 weighted data demonstrate that compared with the state-of-the-art, our method achieves higher reconstruction accuracy for all three kinds of sampling patterns and five sampling ratios, as well as higher computation efficiency on both GPU and CPU. Further experiments show that our method provides stronger robustness to data variations and noise pollution. © 2020, Beijing China Science Journal Publishing Co. Ltd. All right reserved.}, coden = {JFTXF}, document_type = {Article}, source = {Scopus}, note = {cited By 1} } @article{Zhang2021141, title = {Deep Learning Techniques for Advancing {{6G}} Communications in the Physical Layer}, author = {Zhang, S. and Liu, J. and Rodrigues, T.K. and Kato, N.}, date = {2021}, journaltitle = {IEEE Wirel. Commun.}, volume = {28}, number = {5}, pages = {141--147}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {15361284}, doi = {10.1109/MWC.001.2000516}, abstract = {As current 5G communication systems cannot fulfill the stringent requirements brought by emerging applications, 6G will innovatively employ deep learning (DL) techniques to fundamentally rethink the communication systems design problem from the bottom to top layers. Although recent evidence has shown the power of DL techniques in the communication domain, the exploration and utilization of DL techniques in communication systems is still in its infancy and should come in a progressive manner. To effectively and efficiently implement DL techniques in future 6G communications in the physical layer, we give some potential deployment strategies and key enabling technologies that relate to 6G in terms of joint design of block-structured and end-to-end DL, integration of model-driven and data-driven DL, combination of online and offline training, ubiquitous learning and explainable DL techniques. © 2021 IEEE.}, coden = {IWCEA}, document_type = {Article}, source = {Scopus}, note = {cited By 0 \par TL;DR \par Some potential deployment strategies and key enabling technologies that relate to 6G in terms of joint design of block-structured and end-to-end DL, integration of model-driven and data-driven DL, combination of online and offline training, ubiquitous learning and explainable DL techniques are given.} } @article{Zhang2022, title = {An Adaptive Dendrite-{{HDMR}} Metamodeling Technique for High-Dimensional Problems}, author = {Zhang, Q. and Wu, Y. and Lu, L. and Qiao, P.}, date = {2022}, journaltitle = {J. Mech. Des. Trans. ASME}, volume = {144}, number = {8}, publisher = {American Society of Mechanical Engineers (ASME)}, issn = {10500472}, doi = {10.1115/1.4053526}, abbrev_source_title = {J Mech Des, Trans ASME}, affiliation = {National CAD Supported Software Engineering Centre, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China}, art_number = {081701}, coden = {JMDEE}, correspondence_address1 = {Wu, Y.; National CAD Supported Software Engineering Centre, China; email: cad.wyz@hust.edu.cn}, document_type = {Article}, langid = {english}, source = {Scopus}, note = {cited By 0} } @article{Zhang20221037, title = {Data Augmentation Empowered Neural Precoding for Multiuser {{MIMO}} with {{MMSE}} Model}, author = {Zhang, S. and Xu, J. and Xu, W. and Wang, N. and Ng, D.W.K. and You, X.}, date = {2022}, journaltitle = {IEEE Commun. Lett.}, volume = {26}, number = {5}, pages = {1037--1041}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {10897798}, doi = {10.1109/LCOMM.2022.3156946}, abstract = {Precoding design exploiting deep learning methods has been widely studied for multiuser multiple-input multiple-output (MU-MIMO) systems. However, conventional neural precoding design applies black-box-based neural networks which are less interpretable. In this letter, we propose a deep learning-based precoding method based on an interpretable design of a neural precoding network, namely iPNet. In particular, the iPNet mimics the classic minimum mean-squared error (MMSE) precoding and approximates the matrix inversion in the design of the neural network architecture. Specifically, the proposed iPNet consists of a model-driven component network, responsible for augmenting the input channel state information (CSI), and a data-driven sub-network, responsible for precoding calculation from this augmented CSI. The latter data-driven module is explicitly interpreted as an unsupervised learner of the MMSE precoder. Simulation results show that by exploiting the augmented CSI, the proposed iPNet achieves noticeable performance gain over existing black-box designs and also exhibits enhanced generalizability against CSI mismatches. © 1997-2012 IEEE.}, coden = {ICLEF}, document_type = {Article}, source = {Scopus}, note = {cited By 0 \par TL;DR \par A deep learning-based precoding method based on an interpretable design of a neural precoding network, namely iPNet, which mimics the classic minimum mean-squared error (MMSE) precoding and approximates the matrix inversion in the design of the neural network architecture.} } @article{ZHANG2022106922, title = {Improving {{Stack Overflow}} Question Title Generation with Copying Enhanced {{CodeBERT}} Model and Bi-Modal Information}, author = {Zhang, Fengji and Yu, Xiao and Keung, Jacky and Li, Fuyang and Xie, Zhiwen and Yang, Zhen and Ma, Caoyuan and Zhang, Zhimin}, date = {2022}, journaltitle = {Inf. Softw. Technol.}, volume = {148}, pages = {106922}, issn = {0950-5849}, doi = {10.1016/j.infsof.2022.106922}, abstract = {Context: Stack Overflow is very helpful for software developers who are seeking answers to programming problems. Previous studies have shown that a growing number of questions are of low quality and thus obtain less attention from potential answerers. Gao et al. proposed an LSTM-based model (i.e., BiLSTM-CC) to automatically generate question titles from the code snippets to improve the question quality. However, only using the code snippets in the question body cannot provide sufficient information for title generation, and LSTMs cannot capture the long-range dependencies between tokens. Objective: This paper proposes CCBERT, a deep learning based novel model to enhance the performance of question title generation by making full use of the bi-modal information of the entire question body. Method: CCBERT follows the encoder–decoder paradigm and uses CodeBERT to encode the question body into hidden representations, a stacked Transformer decoder to generate predicted tokens, and an additional copy attention layer to refine the output distribution. Both the encoder and decoder perform the multi-head self-attention operation to better capture the long-range dependencies. This paper builds a dataset containing around 200,000 high-quality questions filtered from the data officially published by Stack Overflow to verify the effectiveness of the CCBERT model. Results: CCBERT outperforms all the baseline models on the dataset. Experiments on both code-only and low-resource datasets show the superiority of CCBERT with less performance degradation. The human evaluation also shows the excellent performance of CCBERT concerning both readability and correlation criteria. Conclusion: CCBERT is capable of automatically capturing the bi-modal semantic information from the entire question body and parsing the long-range dependencies to achieve better performance. Therefore, CCBERT is an effective approach for generating Stack Overflow question titles.}, keywords = {CodeBERT,Copy mechanism,Stack Overflow,Title generation} } @article{Zhang2022190, title = {Meta-Learning Based Multi-Fidelity Deep Neural Networks Metamodel Method [基于元学习的多可信度深度神经网络代理模型]}, author = {Zhang, L. and Chen, J. and Xiong, F. and Ren, C. and Li, C.}, date = {2022}, journaltitle = {Jixie Gongcheng XuebaoJournal Mech. Eng.}, volume = {58}, number = {1}, pages = {190--200}, publisher = {Chinese Mechanical Engineering Society}, issn = {05776686}, doi = {10.3901/JME.2022.01.190}, abbrev_source_title = {Jixie Gongcheng Xuebao}, affiliation = {School of Astronautics, Beijing Institute of Technology, Beijing, 100081, China; China Aerodynamic Research and Development Center, Mianyang, 621000, China}, coden = {CHHKA}, correspondence_address1 = {Xiong, F.; School of Astronautics, China; email: fenfenx@bit.edu.cn}, document_type = {Article}, langid = {chinese}, source = {Scopus}, note = {cited By 0} } @article{Zhang20222368, title = {Model-Driven Learning for Generic {{MIMO}} Downlink Beamforming with Uplink Channel Information}, author = {Zhang, J. and You, M. and Zheng, G. and Krikidis, I. and Zhao, L.}, date = {2022}, journaltitle = {IEEE Trans. Wirel. Commun.}, volume = {21}, number = {4}, pages = {2368--2382}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {15361276}, doi = {10.1109/TWC.2021.3111843}, abstract = {Accurate downlink channel information is crucial to the beamforming design, but it is difficult to obtain in practice. This paper investigates a deep learning-based optimization approach of the downlink beamforming to maximize the system sum rate, when only the uplink channel information is available. Our main contribution is to propose a model-driven learning technique that exploits the structure of the optimal downlink beamforming to design an effective hybrid learning strategy with the aim to maximize the sum rate performance. This is achieved by jointly considering the learning performance of the downlink channel, the power and the sum rate in the training stage. The proposed approach applies to generic cases in which the uplink channel information is available, but its relation to the downlink channel is unknown and does not require an explicit downlink channel estimation. We further extend the developed technique to massive multiple-input multiple-output scenarios and achieve a distributed learning strategy for multicell systems without an inter-cell signalling overhead. Simulation results verify that our proposed method provides the performance close to the state of the art numerical algorithms with perfect downlink channel information and significantly outperforms existing data-driven methods in terms of the sum rate. © 2002-2012 IEEE.}, document_type = {Article}, source = {Scopus}, note = {cited By 0 \par TL;DR \par This paper investigates a deep learning-based optimization approach of the downlink beamforming to maximize the system sum rate, when only the uplink channel information is available and significantly outperforms existing data-driven methods in terms of the sum rate.} } @article{zhangCodeBERTAttackAdversarialAttack2023, title = {{{CodeBERT}}‐{{Attack}}: {{Adversarial}} Attack against Source Code Deep Learning Models via Pre‐trained Model}, shorttitle = {{{CodeBERT}}‐{{Attack}}}, author = {Zhang, Huangzhao and Lu, Shuai and Li, Zhuo and Jin, Zhi and Ma, Lei and Liu, Yang and Li, Ge}, date = {2023-05-15}, journaltitle = {J Software Evolu Process}, pages = {e2571}, issn = {2047-7473, 2047-7481}, doi = {10.1002/smr.2571}, langid = {english} } @inproceedings{zhangOptimalityNaiveBayes2004, title = {The Optimality of Naive Bayes}, booktitle = {Proc. {{Seventeenth Int}}. {{Fla}}. {{Artif}}. {{Intell}}. {{Res}}. {{Soc}}. {{Conf}}. {{FLAIRS}} 2004}, author = {Zhang, Harry}, editor = {Barr, Valerie and Markov, Zdravko}, date = {2004}, publisher = {AAAI Press}, abstract = {Naive Bayes is one of the most efficient and effective inductive learning algorithms for machine learning and data mining. Its competitive performance in classification is surprising, because the conditional independence assumption on which it is based, is rarely true in realworld applications. An open question is: what is the true reason for the surprisingly good performance of naive Bayes in classification? In this paper, we propose a novel explanation on the superb classification performance of naive Bayes. We show that, essentially, the dependence distribution; i.e., how the local dependence of a node distributes in each class, evenly or unevenly, and how the local dependencies of all nodes work together, consistently (supporting a certain classification) or inconsistently (canceling each other out), plays a crucial role. Therefore, no matter how strong the dependences among attributes are, naive Bayes can still be optimal if the dependences distribute evenly in classes, or if the dependences cancel each other out. We propose and prove a sufficient and necessary conditions for the optimality of naive Bayes. Further, we investigate the optimality of naive Bayes under the Gaussian distribution. We present and prove a sufficient condition for the optimality of naive Bayes, in which the dependence between attributes do exist. This provides evidence that dependence among attributes may cancel out each other. In addition, we explore when naive Bayes works well.}, added-at = {2011-03-27T19:35:34.000+0200}, biburl = {https://www.bibsonomy.org/bibtex/29288cd3adf6e5273ce7f8b74beb4c6e2/cocus}, booktitleaddon = {May 17-19, 2004}, interhash = {a8e31b4197a90abcb0bdb2b93504acda}, intrahash = {9288cd3adf6e5273ce7f8b74beb4c6e2}, owner = {CK}, venue = {Miami Beach, Florida, USA}, keywords = {bayesian,naive-bayes}, timestamp = {2011-03-27T19:35:45.000+0200} } @article{zhangSTAF_2025_paper_39, title = {{{STAF}}\_2025\_paper\_39}, author = {Zhang, Weixing and Hebig, Regina and Strüber, Daniel}, abstract = {Software languages evolve over time for various reasons, such as the addition of new features. When the language’s grammar definition evolves, textual instances that originally conformed to the grammar become outdated. For DSLs in a model-driven engineering context, there exists a plethora of techniques to co-evolve models with the evolving metamodel. However, these techniques are not geared to support DSLs with a textual syntax — applying them to textual language definitions and instances may lead to the loss of information from the original instances, such as comments and layout information, which are valuable for software comprehension and maintenance. This study explores the potential of Large Language Model (LLM)-based solutions in achieving grammar and instance co-evolution, with attention to their ability to preserve auxiliary information when directly processing textual instances. By applying two advanced language models, Claude-3.5 and GPT-4o, and conducting experiments across seven case languages, we evaluated the feasibility and limitations of this approach. Our results indicate a good ability of the considered LLMs for migrating textual instances in small-scale cases, which are representative of a subset of cases encountered in practice, where DSLs are often conceived as “small languages“ for specialized problems. In addition, we observe significant challenges with the scalability of LLM-based solutions to larger languages and instances, leading to insights that are useful for informing future research.}, langid = {english}, note = {\section{()Annotazioni\\ (25/2/2025, 16:59:44)} \par - “However, these techniques are not geared to support DSLs with a textual syntax — applying them to textual language definitions and instances may lead to the loss of information from the original instances, such as comments and layout information, which are valuable for software comprehension and maintenance.” (Zhang et al., p. 1) \#5fb236 \par - “(LLM)-based solutions in achieving grammar and instance co-evolution” (Zhang et al., p. 1) \#2ea8e5 \par - “ability to preserve auxiliary information when directly processing textual instances” (Zhang et al., p. 1) \#5fb236 \par - “Claude-3.5 and GPT-4o” (Zhang et al., p. 1) \#a28ae5 \par - “migrating textual instances in small-scale cases” (Zhang et al., p. 1) \#5fb236 \par - “significant challenges with the scalability of LLM-based solutions to larger languages and instances, leading to insights that are useful for informing future research.” (Zhang et al., p. 1) \#a28ae5 \par - “To that end, the original instance needs to be parsed into the form of a model and transformed back into textual form after the model is co-evolved.” (Zhang et al., p. 2) \#5fb236 \par - “it serves a critical purpose during tasks such as code maintenance, debugging, and understanding design intent” (Zhang et al., p. 2) \#5fb236 \par - “We harness our recently published dataset on Xtext-based language evolution cases retrieved from GitHub (Zhang \& Strüber 2024),” (Zhang et al., p. 2) \#a28ae5 \par - “the following” (Zhang et al., p. 2) \#ff6666 \par - “RQ1: How can we use LLMs to automate the co-evolution of instances with evolving Xtext grammars?” (Zhang et al., p. 2) \#e56eee \par - “build two LLM-based co-evolution solutions” (Zhang et al., p. 2) \#2ea8e5 \par - “We designed a dedicated prompt to guide the LLMs to analyze the two grammars and perform evolution operations on the instance that conforms to the grammar before the evolution.” (Zhang et al., p. 2) \#2ea8e5 \par - “analyze whether the new instance follows the evolved grammar and whether it retains the auxiliary information (such as comments) from the original instance.” (Zhang et al., p. 2) \#2ea8e5 \par - “RQ2: How do grammar size, instance size, and grammar change extent affect the capability of LLMs to produce correct solutions when co-evolving textual instances?” (Zhang et al., p. 2) \#e56eee \par - “RQ3: How do these factors affect LLMs’ capability i” (Zhang et al., p. 2) \#e56eee \par - “ity in preserving auxiliary information during DSL co-evolution?” (Zhang et al., p. 2) \#e56eee \par - “exploration of the potential of LLM for the co-evolution of grammar and instances of DSLs” (Zhang et al., p. 2) \#5fb236 \par - “In Xtext language projects, there is a close relationship between grammar and metamodel” (Zhang et al., p. 2) \#5fb236 \par - “ring .” (Zhang et al., p. 3) \#ff6666 \par - “This is illustrated by Listing 2 showing the outcome of applying such an approach to the exam instance, which leads to the loss of all comments and formatting information.” (Zhang et al., p. 3) \#5fb236 \par - “The evolution of programming languages is primarily driven by the need to improve readability, maintainability, and execution efficiency while supporting increasingly complex software systems.” (Zhang et al., p. 3) \#5fb236 \par - “DSL evolution is similar to general-purpose programming languages, but due to its domain-specific nature, evolution is typically constrained by domain requirements, necessitating a balance between extensibility and stability.” (Zhang et al., p. 3) \#5fb236 \par - “Recent models like GPT-4o and Claude have been widely explored in code-related applications, with several studies reporting promising results in code completion, generation, and transformation tasks” (Zhang et al., p. 4) \#5fb236 \par - “framework for evaluating tool support for DSL co-evolution” (Zhang et al., p. 4) \#5fb236 \par - “In contrast, our work addresses the challenges of textual DSL evolution, particularly the preservation of auxiliary information in source code (such as comments and layout).” (Zhang et al., p. 4) \#a28ae5 \par - “proposed a technical framework to guide LLM adoption in MDE” (Zhang et al., p. 4) \#5fb236 \par - “provides a valuable reference for our handling of textual instance evolution.” (Zhang et al., p. 4) \#a28ae5 \par - “They proposed a prompt engineeringbased approach that guides LLMs through structured prompts containing metamodel abstraction gaps, change information, and erroneous code” (Zhang et al., p. 4) \#a28ae5 \par - “Although the research objectives differ, their work provides valuable insights for our research, and their results confirm the potential of LLMs in handling software artifact evolution problems, which aligns with our findings” (Zhang et al., p. 4) \#5fb236 \par - “we selected a diverse set of seven DSLs as evaluation objects” (Zhang et al., p. 4) \#2ea8e5 \par - “We then designed an LLM-based method to co-evolve grammar and instances and implemented it as two solutions based on two LLMs (i.e., Claude-3.5 and GPT-4o)” (Zhang et al., p. 4) \#2ea8e5 \par - “we applied these two solutions to the selected case languages and analyze the results to evaluate the potential of LLM to co-evolve grammar and instances” (Zhang et al., p. 4) \#2ea8e5 \par - “from in” (Zhang et al., p. 4) \#ff6666 \par - “Once the grammar versions before and after the evolution step are determined, for each of them, we look for an instance that complies with them.” (Zhang et al., p. 5) \#5fb236 \par - “the original grammar” (Zhang et al., p. 5) \#5fb236 \par - “instance conforming to it” (Zhang et al., p. 5) \#5fb236 \par - “the evolved grammar,” (Zhang et al., p. 5) \#5fb236 \par - “LLMs are expected to analyze the grammar differences and perform instance evolution accordingly” (Zhang et al., p. 5) \#5fb236 \par - “Claude-3.5 requires Anthropic’s API key,” (Zhang et al., p. 5) \#5fb236 \par - “GPT4o requires OpenAI’s API key” (Zhang et al., p. 5) \#5fb236 \par - “Although the two scripts call different APIs, they follow the same workflow” (Zhang et al., p. 5) \#ffd400\\ \mkbibemph{But the prompts might be different, isn't it?} \par - “the prompting technique we adopt is Zero-shot Chain-of-thoughts prompting” (Zhang et al., p. 5) \#ffd400\\ \mkbibemph{Double check this.} \par - “2015-07-18 2546850 15 2015-09-05 f0380e8” (Zhang et al., p. 6) \#ffd400\\ \mkbibemph{This time from the first to the second date is quite long.} \par - “2015-05-03 55911bf 42 2015-07-27 3fa6e6d” (Zhang et al., p. 6) \#ffd400\\ \mkbibemph{See my previous comment} \par - “do” (Zhang et al., p. 6) \#ff6666 \par - “initial prompt that we iteratively refined” (Zhang et al., p. 6) \#ffd400\\ \mkbibemph{Interesting to discuss on the external validity of the obtained prompts, supposed to be the best ones at the end of the iterative process.} \par - “we adjusted and optimized the prompt according to the problem, and entered the next iteration” (Zhang et al., p. 6) \#ffd400\\ \mkbibemph{See my previous comment.} \par - “To this end, we repeated the co-evolution When the instance was correctly co-evolved, we performed nine more co-evolution runs with the same prompt.” (Zhang et al., p. 6) \#ff6666 \par - “The grammar before evolution is shown in Table 3, which contains five grammar rules.” (Zhang et al., p. 6) \#ff6666 \par - “default in the grammar rule Feature which means that LLMs need to identify more changes in the grammar.” (Zhang et al., p. 7) \#5fb236 \par - “we applied two Python scripts containing the final version of prompting text” (Zhang et al., p. 8) \#ffd400\\ \mkbibemph{How does the prompt look like? It is interesting to see how different it is with respect the initial prompt previously shown.} \par - “i.e., the instances evolved by the LLMs according to the evolution of the grammar” (Zhang et al., p. 8) \#ffd400\\ \mkbibemph{It is necessary to give some more details about the used dataset especially concerning the kinds of language changes that are involved and why not reusing the braking resolvable/unresolvable and unbraking changes classification?} \par - “We verified this version of the prompt text 10 times for both LLM solutions, and in most of these ten times, the instances obtained complied with the evolved grammar.” (Zhang et al., p. 8) \#5fb236 \par - “an” (Zhang et al., p. 8) \#ff6666 \par - “compare” (Zhang et al., p. 8) \#ff6666\\ \mkbibemph{Decide the present or the past form and keep it consistent throughout the text} \par - “Answer to RQ1: We developed two automated solutions based on Claude-3.5 and GPT-4o, each consisting of a dedicated Python script and an optimized prompting text. These solutions take the grammar before evolution, the evolved grammar, and the instance that conforms to the grammar before the evolution, then use LLMs to analyze grammar differences and generate an evolved instance that conforms to the evolved grammar.” (Zhang et al., p. 9) \#ffd400\\ \mkbibemph{The answer should reflect the results that you obtained. Essentially the results shown in Tab 4 and Tab 5.\\ However, I suggest to present the results in terms of typical metrics like precision, recall, F-measure. Such metrics permit to grasp the quality of the approach more easily.} \par - “However, applying an LLM-based solution to large practical cases is currently not advisable.” (Zhang et al., p. 11) \#5fb236 \par - “Thus, while we can judge the correctness of the evolution done by the LLMs as well as the loss of auxiliary information, we currently cannot use the real updated instances to compare whether a pure co-evolution would be performed similarly by human developers. Future work is required to understand better whether LLMs and human developers choose the same solutions during co-evolution, given multiple technically valid ways of evolving an instance.” (Zhang et al., p. 11) \#5fb236\\ \mkbibemph{So essentially, you claiming the importance of good quality data for supporting the evaluation.} \par - “It is also important to study larger changes, therefore, in future work we plan to study two versions of the language with larger changes, for example, to cover the changes between different UML versions.” (Zhang et al., p. 11) \#5fb236 \par - “future work should investigate improved techniques for handling large-scale LLM-based DSL evolution” (Zhang et al., p. 12) \#2ea8e5 \par - “ways to leverage multiple prompts” (Zhang et al., p. 12) \#2ea8e5 \par - “retrieval-based augmentation” (Zhang et al., p. 12) \#2ea8e5 \par - “having the LLM generate migration code.” (Zhang et al., p. 12) \#2ea8e5} } @article{zhangTPPFAMUseThreshold2014, title = {{{TPPFAM}}: {{Use}} of Threshold and Posterior Probability for Category Reduction in Fuzzy {{ARTMAP}}}, author = {Zhang, Yongquan and Ji, Hongbing and Zhang, Wenbo}, date = {2014}, journaltitle = {Neurocomputing}, volume = {124}, pages = {63--71}, issn = {0925-2312}, url = {http://www.sciencedirect.com/science/article/pii/S0925231213008151}, abstract = {The issue of category proliferation caused by the overlapping classes in fuzzy ARTMAP (FAM) is addressed in this paper. A new FAM-based neural architecture called TTPFAM is proposed, which can reduce category proliferation by performing a threshold filtering mechanism before a new category created during training, and improve the classification accuracy by combining prediction distributed by the dynamic Q-max rule and posterior probability estimated during testing. The TPPFAM can produce a small size of neural network architecture without degradation of the classification accuracy. The algorithm is evaluated in terms of the classification accuracy and the number of categories by experiments on both artificial and real data, and the results show that the performance of TPPFAM is better than that of the other models.}, nodoi = {https://doi.org/10.1016/j.neucom.2013.07.042}, keywords = {Category proliferation,Fuzzy ARTMAP (FAM),Overlapping classes,Posterior probability,Threshold adjustment parameter} } @article{zhao2005, title = {Hierarchical Clustering Algorithms for Document Datasets}, author = {Zhao, Ying and Karypis, George and Fayyad, Usama}, date = {2005-03}, journaltitle = {Data Min. Knowl. Discov.}, volume = {10}, number = {2}, pages = {141--168}, publisher = {Kluwer Academic Publishers}, address = {Hingham, MA, USA}, issn = {1384-5810}, url = {http://dl.acm.org/citation.cfm?id=1061897.1061908}, abstract = {Fast and high-quality document clustering algorithms play an important role in providing intuitive navigation and browsing mechanisms by organizing large amounts of information into a small number of meaningful clusters. In particular, clustering algorithms that build meaningful hierarchies out of large document collections are ideal tools for their interactive visualization and exploration as they provide data-views that are consistent, predictable, and at different levels of granularity. This paper focuses on document clustering algorithms that build such hierarchical solutions and (i) presents a comprehensive study of partitional and agglomerative algorithms that use different criterion functions and merging schemes, and (ii) presents a new class of clustering algorithms called constrained agglomerative algorithms, which combine features from both partitional and agglomerative approaches that allows them to reduce the early-stage errors made by agglomerative methods and hence improve the quality of clustering solutions. The experimental evaluation shows that, contrary to the common belief, partitional algorithms always lead to better solutions than agglomerative algorithms; making them ideal for clustering large document collections due to not only their relatively low computational requirements, but also higher clustering quality. Furthermore, the constrained agglomerative methods consistently lead to better solutions than agglomerative methods alone and for many cases they outperform partitional methods, as well.}, acmid = {1061908}, added-at = {2011-09-18T22:25:48.000+0200}, biburl = {http://www.bibsonomy.org/bibtex/21afe6065cc536f52534a7c15eed599c3/jil}, interhash = {fdb19684fd849b0c44ac0fb996b7888e}, intrahash = {1afe6065cc536f52534a7c15eed599c3}, nodoi = {10.1007/s10618-005-0361-3}, numpages = {28}, keywords = {cluster clustering entropy evaluation f-score fscore measure measures purity}, note = {TL;DR \par The experimental evaluation shows that, contrary to the common belief, partitional algorithms always lead to better solutions than agglomerative algorithms; making them ideal for clustering large document collections due to not only their relatively low computational requirements, but also higher clustering quality.}, timestamp = {2013-11-23T20:11:51.000+0100} } @article{ZHAO20192018EDP7227, title = {Accurate Library Recommendation Using Combining Collaborative Filtering and Topic Model for Mobile Development}, author = {Zhao, Xiaoqiong and Li, Shanping and Yu, Huan and Wang, Ye and Qiu, Weiwei}, date = {2019}, journaltitle = {IEICE Trans. Inf. Syst.}, volume = {E102.D}, number = {3}, pages = {522--536}, doi = {10.1587/transinf.2018EDP7227} } @article{Zhao2022, title = {Interaction Design System for Artificial Intelligence User Interfaces Based on {{UML}} Extension Mechanisms}, author = {Zhao, Y.}, date = {2022}, journaltitle = {Mob. Inf. Syst.}, volume = {2022}, publisher = {Hindawi Limited}, issn = {1574017X}, doi = {10.1155/2022/3534167}, abbrev_source_title = {Mob. Inf. Sys.}, affiliation = {Department of Art and Design, Shijiazhuang University of Applied Technology, Shijiazhuang, 050081, China}, art_number = {3534167}, correspondence_address1 = {Zhao, Y.; Department of Art and Design, China; email: 2013010678@sjzpt.edu.cn}, document_type = {Article}, langid = {english}, source = {Scopus}, note = {cited By 0 \par TL;DR \par This research creates a user interface framework based on interaction behavior from the user’s perspective and incorporates user requirements and provides a scientific reference for the development and design of user interfaces.} } @online{zhaoAutomaticModelSelection2023, title = {Automatic {{Model Selection}} with {{Large Language Models}} for {{Reasoning}}}, author = {Zhao, James Xu and Xie, Yuxi and Kawaguchi, Kenji and He, Junxian and Xie, Michael Qizhe}, date = {2023-10-23}, eprint = {2305.14333}, eprinttype = {arXiv}, eprintclass = {cs}, doi = {10.48550/arXiv.2305.14333}, abstract = {Chain-of-Thought (CoT) and Program-Aided Language Models (PAL) represent two distinct reasoning methods, each with its own strengths. CoT employs natural language, offering flexibility and interpretability, while PAL utilizes programming language, yielding more structured and rigorous logic. We introduce a model selection method to combine the best of both worlds by employing a large language model (LLM) to dynamically select between them. Our theoretical analysis underscores the feasibility of this method, which is further corroborated by empirical results. Our proposed method demonstrates significant performance improvements across eight reasoning datasets with Codex, ChatGPT, and GPT-4. Additionally, our method is complementary to self-consistency; when integrated, it can further enhance performance while significantly reducing computation costs. Moreover, we achieve new state-of-the-art results on GSM8K and SVAMP, with respective accuracies of 96.8\% and 93.7\%. Our code, data and prompts are available at https://github.com/XuZhao0/Model-Selection-Reasoning}, pubstate = {prepublished}, keywords = {Computer Science - Artificial Intelligence,Computer Science - Computation and Language}, note = {Comment: EMNLP 2023 Findings \par TL;DR \par This work introduces a model selection method to combine the best of both worlds by employing a large language model (LLM) to dynamically select between them, and demonstrates significant performance improvements across eight reasoning datasets with Codex, ChatGPT, and GPT-4.} } @article{zhaoOndemandServiceAggregation2019, ids = {Zhao2019S3}, title = {An On-Demand Service Aggregation and Service Recommendation Method Based on {{RGPS}}}, author = {Zhao, Y. and Guo, J. and He, K.}, date = {2019}, journaltitle = {Intell. Data Anal.}, volume = {23}, number = {S1}, pages = {S3-S23}, publisher = {IOS Press}, issn = {1088467X}, doi = {10.3233/IDA-192628}, abbrev_source_title = {Intell. Data Anal.}, abstract = {'Internet plus' application service recommendation is challenged by two issues: One is the increase in service volume and the disorderliness of the service organizations. A second is the diversification of user requirements. The research focus of this study was to investigate how to achieve more ordered aggregation and recommend services that meet the individualized requirements of users. This paper addresses the disorderliness of conventional service aggregation and considers the aggregation requirements of QoS weights with non-functional targets. Based on semantic relevance using the role (R), goal (G), process (P), service (S) demand metamodel, an RGPS association is proposed that is a weighted network for ordered QoS service aggregation. An individualized service recommendation method then is provided, based on an LSTM neural network with role and target backstepping using RGPS association network, that can achieve a high-quality precision service. Finally, a simulation experiment was carried out on service recommendations in the tourism domain, which verified the precision, effectiveness and application value of the service recommendation method. © 2019 - IOS Press and the authors. All rights reserved.}, affiliation = {Department School of Computer Science, Wuhan University, Wuhan, China; Department School of Data Science and Software Engineering, Qingdao University, Qingdao, China}, correspondence_address1 = {Zhao, Y.; Department School of Computer Science, China; email: ivwepriu@sina.com}, document_type = {Conference Paper}, source = {Scopus}, keywords = {Application services,Conventional services,Long short-term memory,Meta model,nonfunctional target requirement,On-demand services,Semantic relevance,Semantics,Service organizations,Service recommendations}, note = {cited By 0 \par cited By 0} } @online{zhaoSurveyLargeLanguage2024, title = {A {{Survey}} of {{Large Language Models}}}, author = {Zhao, Wayne Xin and Zhou, Kun and Li, Junyi and Tang, Tianyi and Wang, Xiaolei and Hou, Yupeng and Min, Yingqian and Zhang, Beichen and Zhang, Junjie and Dong, Zican and Du, Yifan and Yang, Chen and Chen, Yushuo and Chen, Zhipeng and Jiang, Jinhao and Ren, Ruiyang and Li, Yifan and Tang, Xinyu and Liu, Zikang and Liu, Peiyu and Nie, Jian-Yun and Wen, Ji-Rong}, date = {2024-09-24}, eprint = {2303.18223}, eprinttype = {arXiv}, doi = {10.48550/arXiv.2303.18223}, abstract = {Language is essentially a complex, intricate system of human expressions governed by grammatical rules. It poses a significant challenge to develop capable AI algorithms for comprehending and grasping a language. As a major approach, language modeling has been widely studied for language understanding and generation in the past two decades, evolving from statistical language models to neural language models. Recently, pre-trained language models (PLMs) have been proposed by pre-training Transformer models over large-scale corpora, showing strong capabilities in solving various NLP tasks. Since researchers have found that model scaling can lead to performance improvement, they further study the scaling effect by increasing the model size to an even larger size. Interestingly, when the parameter scale exceeds a certain level, these enlarged language models not only achieve a significant performance improvement but also show some special abilities that are not present in small-scale language models. To discriminate the difference in parameter scale, the research community has coined the term large language models (LLM) for the PLMs of significant size. Recently, the research on LLMs has been largely advanced by both academia and industry, and a remarkable progress is the launch of ChatGPT, which has attracted widespread attention from society. The technical evolution of LLMs has been making an important impact on the entire AI community, which would revolutionize the way how we develop and use AI algorithms. In this survey, we review the recent advances of LLMs by introducing the background, key findings, and mainstream techniques. In particular, we focus on four major aspects of LLMs, namely pre-training, adaptation tuning, utilization, and capacity evaluation. Besides, we also summarize the available resources for developing LLMs and discuss the remaining issues for future directions.}, pubstate = {prepublished}, keywords = {Computer Science - Artificial Intelligence,Computer Science - Computation and Language,LLMs,LOGSEQ,MOSAICO} } @inproceedings{zhaoUserbasedCollaborativefilteringRecommendation2010, title = {User-Based Collaborative-Filtering Recommendation Algorithms on Hadoop}, booktitle = {Proc. 2010 {{Third Int}}. {{Conf}}. {{Knowl}}. {{Discov}}. {{Data Min}}.}, author = {Zhao, Zhi-Dan and Shang, Ming-sheng}, date = {2010}, series = {{{WKDD}} '10}, pages = {478--481}, publisher = {IEEE Computer Society}, location = {Washington, DC, USA}, url = {https://doi.org/10.1109/WKDD.2010.54}, acmid = {1749278}, isbn = {978-0-7695-3923-2}, nodoi = {10.1109/WKDD.2010.54}, numpages = {4}, keywords = {cloud computing,collaborative filtering,hadoop,Map-Reduce,recommender systems}, note = {TL;DR \par This paper implements user-based CF algorithm on a cloud computing platform, namely Hadoop, to solve the scalability problem of CF.} } @article{Zheng201210, title = {Model-Driven Centerline Extraction for Severely Occluded Major Coronary Arteries}, author = {Zheng, Y. and Shen, J. and Tek, H. and Funka-Lea, G.}, date = {2012}, journaltitle = {Lect. Notes Comput. Sci. Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinforma.}, volume = {7588 LNCS}, pages = {10--18}, issn = {03029743}, doi = {10.1007/978-3-642-35428-1_2}, abstract = {Almost all previous approaches on coronary artery centerline extraction are data-driven, which try to trace a centerline from an automatically detected or manually specified coronary ostium. No or little high level prior information is used; therefore, the centerline tracing procedure may terminate early at a severe occlusion or an anatomically inconsistent centerline course may be generated. In this work, we propose a model-driven approach to extracting the three major coronary arteries. The relative position of the major coronary arteries with respect to the heart chambers is stable, therefore the automatically segmented chambers can be used to predict the initial position of these coronary centerlines. The initial centerline is further refined using a machine learning based vesselness measurement. The proposed approach can handle variations in the length and topology of an artery, and it is more robust under severe occlusions than a data-driven approach. The extracted centerlines are already labeled, therefore no additional vessel labeling procedure is needed. Quantitative comparison on 54 cardiac CT datasets demonstrates the robustness of the proposed method over a state-of-the-art data-driven approach. © 2012 Springer-Verlag.}, document_type = {Conference Paper}, isbn = {9783642354274}, source = {Scopus}, note = {cited By 6} } @inproceedings{Zheng201375, title = {{{IVAMS}}: {{Intelligent}} Metamodel-Integrated {{Verilog-AMS}} for Circuit-Accurate System-Level Mixed-Signal Design Exploration}, author = {Zheng, G. and Mohanty, S.P. and Kougianos, E. and Okobiah, O.}, date = {2013}, series = {Proceedings of the {{International Conference}} on {{Application-Specific Systems}}, {{Architectures}} and {{Processors}}}, pages = {75--78}, issn = {10636862}, doi = {10.1109/ASAP.2013.6567553}, abbrev_source_title = {Proc Int Conf Appl Spec Syst Arcitec Process Proc}, affiliation = {NanoSystem Design Laboratory, University of North Texas, Denton, TX 76203, United States}, art_number = {6567553}, coden = {PIAAF}, correspondence_address1 = {NanoSystem Design Laboratory, , Denton, TX 76203, United States}, document_type = {Conference Paper}, isbn = {978-1-4799-0492-1}, langid = {english}, source = {Scopus}, note = {cited By 0} } @inproceedings{zhengCodEXSourceCode2018, title = {{{CodEX}}: {{Source}} Code Plagiarism Detection Based on Abstract Syntax Tree}, booktitle = {Proc. 26th {{AIAI Ir}}. {{Conf}}. {{Artif}}. {{Intell}}. {{Cogn}}. {{Sci}}. {{Trinity Coll}}. {{Dublin Dublin Irel}}. {{Dec}}. 6-7th 2018}, author = {Zheng, Mengya and Pan, Xingyu and Lillis, David}, date = {2018}, pages = {362--373}, url = {http://ceur-ws.org/Vol-2259/aics_33.pdf}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/bib/conf/aics/ZhengPL18}, timestamp = {Tue, 08 Jan 2019 17:34:19 +0100} } @inproceedings{zhou_assessing_2021, title = {Assessing {{Generalizability}} of {{CodeBERT}}}, booktitle = {2021 {{IEEE Int}}. {{Conf}}. {{Softw}}. {{Maint}}. {{Evol}}. {{ICSME}}}, author = {Zhou, Xin and Han, DongGyun and Lo, David}, date = {2021-09}, pages = {425--436}, doi = {10.1109/ICSME52107.2021.00044}, abstract = {Pre-trained models like BERT have achieved strong improvements on many natural language processing (NLP) tasks, showing their great generalizability. The success of pre-trained models in NLP inspires pre-trained models for programming language. Recently, CodeBERT, a model for both natural language (NL) and programming language (PL), pre-trained on code search dataset, is proposed. Although promising, CodeBERT has not been evaluated beyond its pre-trained dataset for NL-PL tasks. Also, it has only been shown effective on two tasks that are close in nature to its pre-trained data. This raises two questions: Can CodeBERT generalize beyond its pre-trained data? Can it generalize to various software engineering tasks involving NL and PL? Our work answers these questions by performing an empirical investigation into the generalizability of CodeBERT. First, we assess the generalizability of CodeBERT to datasets other than its pre-training data. Specifically, considering the code search task, we conduct experiments on another dataset containing Python code snippets and their corresponding documentation. We also consider yet another dataset of questions and answers collected from Stack Overflow about Python programming. Second, to assess the generalizability of CodeBERT to various software engineering tasks, we apply CodeBERT to the just-in-time defect prediction task. Our empirical results support the generalizability of CodeBERT on the additional data and task. CodeBERT-based solutions can achieve higher or comparable performance than specialized solutions designed for the code search and just-in-time defect prediction tasks. However, the superior performance of the CodeBERT requires a tradeoff; for example, it requires much more computation resources as compared to specialized code search approaches.}, keywords = {CodeBERT,Codes,Computational modeling,Conferences,Documentation,generalizability,Natural language processing,pre-trained model,Programming,Software maintenance}, note = {ISSN: 2576-3148} } @article{zhou_boosting_2020, title = {Boosting {{API Recommendation}} with {{Implicit Feedback}}}, author = {Zhou, Yu and Yang, Xinying and Chen, Taolue and Huang, Zhiqiu and Ma, Xiaoxing and Gall, Harald}, date = {2020-02}, journaltitle = {ArXiv200201264 Cs}, eprint = {2002.01264}, eprinttype = {arXiv}, eprintclass = {cs}, abstract = {Developers often need to use appropriate APIs to program efficiently, but it is usually a difficult task to identify the exact one they need from a vast of candidates. To ease the burden, a multitude of API recommendation approaches have been proposed. However, most of the currently available API recommenders do not support the effective integration of users’ feedback into the recommendation loop. In this paper, we propose a framework, BRAID (Boosting RecommendAtion with Implicit FeeDback), which leverages learningto-rank and active learning techniques to boost recommendation performance. By exploiting users’ feedback information, we train a learning-to-rank model to re-rank the recommendation results. In addition, we speed up the feedback learning process with active learning. Existing query-based API recommendation approaches can be plugged into BRAID. We select three state-of-the-art API recommendation approaches as baselines to demonstrate the performance enhancement of BRAID measured by Hit@k (Top-k), MAP, and MRR. Empirical experiments show that, with acceptable overheads, the recommendation performance improves steadily and substantially with the increasing percentage of feedback data, comparing with the baselines.}, langid = {english}, nourl = {http://arxiv.org/abs/2002.01264}, keywords = {Computer Science - Information Retrieval,Computer Science - Software Engineering}, note = {arXiv: 2002.01264 \par Comment: 13 pages, 4 figures \par TL;DR \par A framework, BRAID (Boosting RecommendAtion with Implicit FeeDback), which leverages learning-to-rank and active learning techniques to boost recommendation performance and speed up the feedback learning process with active learning is proposed.} } @inproceedings{Zhou201913073, title = {Lipschitz Generative Adversarial Nets}, author = {Zhou, Z. and Liang, J. and Song, Y. and Yu, L. and Wang, H. and Zhang, W. and Yu, Y. and Zhang, Z.}, date = {2019}, series = {36th {{International Conference}} on {{Machine Learning}}, {{ICML}} 2019}, volume = {2019-June}, pages = {13073--13082}, publisher = {International Machine Learning Society (IMLS)}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078069337&partnerID=40&md5=57519b6411b2d299d1e26de2d419dcef}, abstract = {In this paper we show that generative adversarial networks (GANs) without restriction on the discriminative function space commonly suffer from the problem that the gradient produced by the discriminator is uninformative to guide the generator. By contrast, Wasserstein GAN (WGAN), where the discriminative function is restricted to 1-Lipschitz, does not suffer from such a gradient uninformativeness problem. We further show in the paper that the model with a compact dual form of Wasserstein distance, where the Lipschitz condition is relaxed, may also theoretically suffer from this issue. This implies the importance of Lipschitz condition and motivates us to study the general formulation of GANs with Lipschitz constraint, which leads to a new family of GANs that we call Lipschitz GANs (LGANs). We show that LGANs guarantee the existence and uniqueness of the optimal discriminative function as well as the existence of a unique Nash equilibrium. We prove that LGANs are generally capable of eliminating the gradient uninformativeness problem. According to our empirical analysis, LGANs arc more stable and generate consistently higher quality samples compared with WGAN. © 36th International Conference on Machine Learning, ICML 2019. All rights reserved.}, document_type = {Conference Paper}, isbn = {978-1-5108-8698-8}, source = {Scopus}, keywords = {TECHNIQUE\_CNN}, note = {cited By 8 \par TL;DR \par LGANs guarantee the existence and uniqueness of the optimal discriminative function as well as the existence of a unique Nash equilibrium and it is proved that LGANs are generally capable of eliminating the gradient uninformativeness problem.} } @article{zhouLargeLanguageModel2024, title = {Large Language Model for Vulnerability Detection and Repair: Literature Review and the Road Ahead}, shorttitle = {Large Language Model for Vulnerability Detection and Repair}, author = {Zhou, Xin and Cao, Sicong and Sun, Xiaobing and Lo, David}, date = {2024-12-18}, journaltitle = {ACM Trans. Software Eng. Methodol.}, pages = {3708522}, issn = {1049-331X, 1557-7392}, doi = {10.1145/3708522}, abstract = {The significant advancements in Large Language Models (LLMs) have resulted in their widespread adoption across various tasks within Software Engineering (SE), including vulnerability detection and repair. Numerous studies have investigated the application of LLMs to enhance vulnerability detection and repair tasks. Despite the increasing research interest, there is currently no existing survey that focuses on the utilization of LLMs for vulnerability detection and repair. In this paper, we aim to bridge this gap by offering a systematic literature review of approaches aimed at improving vulnerability detection and repair through the utilization of LLMs. The review encompasses research work from leading SE, AI, and Security conferences and journals, encompassing 43 papers published across 25 distinct venues, along with 15 high-quality preprint papers, bringing the total to 58 papers. By answering three key research questions, we aim to (1) summarize the LLMs employed in the relevant literature, (2) categorize various LLM adaptation techniques in vulnerability detection, and (3) classify various LLM adaptation techniques in vulnerability repair. Based on our findings, we have identified a series of limitations of existing studies. Additionally, we have outlined a roadmap highlighting potential opportunities that we believe are pertinent and crucial for future research endeavors. CCS Concepts: • Security and privacy → Software and application security.}, langid = {english} } @inproceedings{zhouMoreAccurateContent2014, title = {Towards More Accurate Content Categorization of {{API}} Discussions}, booktitle = {Proc. {{22Nd Int}}. {{Conf}}. {{Program Comprehension}}}, author = {Zhou, Bo and Xia, Xin and Lo, David and Tian, Cong and Wang, Xinyu}, date = {2014}, series = {{{ICPC}} 2014}, pages = {95--105}, publisher = {ACM}, location = {New York, NY, USA}, url = {http://doi.acm.org/10.1145/2597008.2597142}, acmid = {2597142}, isbn = {978-1-4503-2879-1}, nodoi = {10.1145/2597008.2597142}, numpages = {11}, keywords = {API Discussion,Cache-Based Method,Composite Method,Text Categorization}, note = {TL;DR \par A Cache-bAsed compoSitE algorithm, short formed as CASE, to automatically categorize API discussions, which outperforms the state-of-the-art method proposed by Hou and Mo by 11\%, 10\%, and 2\%, respectively.} } @article{Zhu20215434, title = {Deep-Learned Approximate Message Passing for Asynchronous Massive Connectivity}, author = {Zhu, W. and Tao, M. and Yuan, X. and Guan, Y.}, date = {2021}, journaltitle = {IEEE Trans. Wirel. Commun.}, volume = {20}, number = {8}, pages = {5434--5448}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, issn = {15361276}, doi = {10.1109/TWC.2021.3067903}, abstract = {This paper considers the massive connectivity problem in an asynchronous grant-free random access system, where a huge number of devices sporadically transmit data to a base station (BS) with imperfect synchronization. The goal is to design algorithms for joint user activity detection, delay detection, and channel estimation. By exploiting the sparsity on both user activity and delays, we formulate a hierarchical sparse signal recovery problem in both the single-antenna and the multiple-antenna scenarios. While traditional compressed sensing algorithms can be applied to these problems, they suffer high computational complexity and often require the perfect statistical information of channel and devices. This paper solves these problems by designing the Learned Approximate Message Passing (LAMP) network, which belongs to model-driven deep learning approaches and ensures efficient performance without tremendous training data. Particularly, in the multiple-antenna scenario, we design three different LAMP structures, namely, distributed, centralized and hybrid ones, to balance the performance and complexity. Simulation results demonstrate that the proposed LAMP networks can significantly outperform the conventional AMP method thanks to their ability of parameter learning. It is also shown that LAMP has robust performance to the maximal delay spread of the asynchronous users. © 2002-2012 IEEE.}, art_number = {9390399}, document_type = {Article}, source = {Scopus}, note = {cited By 4} } @online{zhuCanChatGPTReproduce2023, title = {Can {{ChatGPT Reproduce Human-Generated Labels}}? {{A Study}} of {{Social Computing Tasks}}}, shorttitle = {Can {{ChatGPT Reproduce Human-Generated Labels}}?}, author = {Zhu, Yiming and Zhang, Peixian and Haq, Ehsan-Ul and Hui, Pan and Tyson, Gareth}, date = {2023-04-20}, eprint = {2304.10145}, eprinttype = {arXiv}, eprintclass = {cs}, url = {http://arxiv.org/abs/2304.10145}, urldate = {2023-04-21}, abstract = {The release of ChatGPT has uncovered a range of possibilities whereby large language models (LLMs) can substitute human intelligence. In this paper, we seek to understand whether ChatGPT has the potential to reproduce human-generated label annotations in social computing tasks. Such an achievement could significantly reduce the cost and complexity of social computing research. As such, we use ChatGPT to re-label five seminal datasets covering stance detection (2x), sentiment analysis, hate speech, and bot detection. Our results highlight that ChatGPT does have the potential to handle these data annotation tasks, although a number of challenges remain. ChatGPT obtains an average precision 0.609. Performance is highest for the sentiment analysis dataset, with ChatGPT correctly annotating 64.9\% of tweets. Yet, we show that performance varies substantially across individual labels. We believe this work can open up new lines of analysis and act as a basis for future research into the exploitation of ChatGPT for human annotation tasks.}, pubstate = {prepublished}, keywords = {Computer Science - Artificial Intelligence,Computer Science - Computation and Language,LOGSEQ} } @inproceedings{zhuMiningAPIUsage2014, title = {Mining {{API Usage Examples}} from {{Test Code}}}, author = {Zhu, Zixiao and Zou, Yanzhen and Xie, Bing and Jin, Yong and Lin, Zeqi and Zhang, Lu}, date = {2014-09}, pages = {301--310}, publisher = {IEEE}, doi = {10.1109/ICSME.2014.52}, abstract = {Lack of effective usage examples in API documents has been proven to be a great obstacle to API learning. To deal with this issue, several approaches have been proposed to automatically extract usage examples from client code or related web pages, which are unfortunately not available for newly released API libraries. In this paper, we propose a novel approach to mining API usage examples from test code. Although test code can be a good source of usage examples, the issue of multiple test scenarios might lead to repetitive and interdependent API usages in a test method, which make it complicated and difficult to extract API usage examples. To address this issue, we study the JUnit test code and summarize a set of test code patterns. We employ a code pattern based heuristic slicing approach to separate test scenarios into code examples. Then we cluster the similar usage examples for recommendation. An evaluation on four open source software libraries demonstrates that the accuracy of our approach is much higher than the state-of-art approach eXoaDoc on test code. Furthermore, we have developed an Eclipse plugin tool UsETeC.}, isbn = {978-1-4799-6146-7}, langid = {english} } @article{zolotasRESTsecLowcodePlatform2018, title = {{{RESTsec}}: A Low-Code Platform for Generating Secure by Design Enterprise Services}, shorttitle = {{{RESTsec}}}, author = {Zolotas, Christoforos and Chatzidimitriou, Kyriakos C. and Symeonidis, Andreas L.}, date = {2018-10-21}, journaltitle = {Enterprise Information Systems}, volume = {12}, number = {8--9}, pages = {1007--1033}, issn = {1751-7575, 1751-7583}, doi = {10.1080/17517575.2018.1462403}, abstract = {In the modern business world it is increasingly often that Enterprises opt to bring their business model online, in their effort to reach out to more end users and increase their customer base. While transitioning to the new model, enterprises consider securing their data of pivotal importance. In fact, many efforts have been introduced to automate this ‘webification’ process; however, they all fall short in some aspect: a) they either generate only the security infrastructure, assigning implementation to the developers, b) they embed mainstream, less powerful authorisation schemes, or c) they disregard the merits of the dominating REST architecture and adopt less suitable approaches. In this paper we present RESTsec, a Low-Code platform that supports rapid security requirements modelling for Enterprise Services, abiding by the state of the art ABAC authorisation scheme. RESTsec enables the developer to seamlessly embed the desired access control policy and generate the service, the security infrastructure and the code. Evaluation shows that our approach is valid and can help developers deliver secure by design enterprise services in a rapid and automated manner.}, langid = {english}, keywords = {lowcode} } @article{zolotasTypeInferenceFlexible2018, title = {Type Inference in Flexible Model-Driven Engineering Using Classification Algorithms}, author = {Zolotas, Athanasios and Matragkas, Nicholas and Devlin, Sam and Kolovos, Dimitrios S. and Paige, Richard F.}, date = {2018-01-23}, journaltitle = {Softw. Syst. Model.}, issn = {1619-1366, 1619-1374}, doi = {10.1007/s10270-018-0658-5}, langid = {english}, note = {TL;DR \par The use of classification algorithms are proposed to help with the inference of models with elements that are unintentionally left untyped and the correct type prediction varies from 23 to 100\% depending on the domain, the proportion of elements that were left untyped and the prediction algorithm used.} } @online{ZoomingPanningHTML5, title = {Zooming \& {{Panning}} in {{HTML5}} \& {{JavaScript Chart}}}, url = {http://canvasjs.com/docs/charts/basics-of-creating-html5-chart/zooming-panning/}, urldate = {2015-04-02}, abstract = {Often Zooming and Panning are required Chart Interactions when plotting a chart with large data. By making zoomEnabled to true, you can zoom into area of interest.}, organization = {CanvasJS} } @article{zotero-26975, entrysubtype = {newspaper} } @online{zotero-27977, url = {https://www.computer.org/csdl/journal/ts/2024/11/10752650/21QnqRYUu4M} } @online{zotero-28204, url = {https://www.linkedin.com/posts/dr-faheem-ullah_phd-research-supervision-activity-7279088776570654720-nW7a?utm_source=share&utm_medium=member_android}, keywords = {source\_mobile} } @online{zotero-item-28080, url = {https://www.linkedin.com/posts/jcabot_huggingface-empiricalstudies-hf-activity-7274761232258105344-Q6To?utm_source=share&utm_medium=member_android}, keywords = {MOSAICO} } @online{zotero-item-28120, url = {https://www.linkedin.com/posts/jason-thatcher-0329764_academicresearch-activity-7279662548377907200-E4Bh?utm_source=share&utm_medium=member_android}, keywords = {source\_mobile} } @online{zotero-item-28564, url = {https://www.linkedin.com/posts/avi-chawla_5-%F0%9D%97%BA%F0%9D%97%BC%F0%9D%98%80%F0%9D%98%81-%F0%9D%97%BD%F0%9D%97%BC%F0%9D%97%BD%F0%9D%98%82%F0%9D%97%B9%F0%9D%97%AE%F0%9D%97%BF-%F0%9D%97%94%F0%9D%97%B4%F0%9D%97%B2%F0%9D%97%BB%F0%9D%98%81%F0%9D%97%B6%F0%9D%97%B0-activity-7288083231944388608-V-iW?utm_source=share&utm_medium=member_android}, keywords = {source\_mobile} } @online{zotero-item-28574, url = {https://www.linkedin.com/posts/manthanpatell_how-do-ai-agents-work-the-diagram-below-activity-7286619276130611201-qjTh?utm_source=share&utm_medium=member_android}, keywords = {source\_mobile} } @online{zotero-item-28579, url = {https://www.linkedin.com/posts/greg-coquillo_foundation-of-large-language-models-ugcPost-7289698398499221505-PtVy?utm_source=share&utm_medium=member_android}, keywords = {source\_mobile} } @online{zotero-item-28783, url = {https://links.newsletter.rcsmediagroup.it/email_browser_view?uid=75f119ca-55cd-4a60-9aa0-5d53f831d1ea&mid=b159de12-830b-4a72-9105-5e29e7e9dbdc&txnid=6713ab35-701a-4f49-83bd-6a7299bb975f&eid=73b8402d-804d-4905-a293-db11768f62e2&utid=75f119ca-55cd-4a60-9aa0-5d53f831d1ea-Newsletter_COR_ONE_MORE_THING&bsft_ek=2025-02-12T05%3A00%3A00Z&bsft_aaid=72bb9dec-3452-4075-a63c-0f8d60246a1e&bsft_mime_type=text&bsft_tv=373&bsft_lx=1}, urldate = {2025-02-17}, keywords = {/unread} } @online{zotero-item-28884, url = {https://www.linkedin.com/posts/andreashorn1_%F0%9D%97%9C%F0%9D%97%BB-%F0%9D%97%B0%F0%9D%97%AE%F0%9D%98%80%F0%9D%97%B2-%F0%9D%98%86%F0%9D%97%BC%F0%9D%98%82-%F0%9D%98%84%F0%9D%97%B2%F0%9D%97%BF%F0%9D%97%B2-%F0%9D%98%84%F0%9D%97%BC%F0%9D%97%BB%F0%9D%97%B1%F0%9D%97%B2%F0%9D%97%BF%F0%9D%97%B6%F0%9D%97%BB%F0%9D%97%B4-activity-7298224223477481472-YodZ?utm_source=share&utm_medium=member_android&rcm=ACoAAACaoXoBMvdj060syLix4vC3rwOgjAE9MpU}, keywords = {source\_mobile} } @online{zotero-item-28885, url = {https://www.linkedin.com/posts/genai-works_artificialintelligence-llm-openai-activity-7298687915109359616-x7-5?utm_source=share&utm_medium=member_android&rcm=ACoAAACaoXoBMvdj060syLix4vC3rwOgjAE9MpU}, keywords = {source\_mobile} } @online{zotero-item-28886, url = {https://www.linkedin.com/feed/update/urn:li:activity:7297563101116223489?utm_source=share&utm_medium=member_android&rcm=ACoAAACaoXoBMvdj060syLix4vC3rwOgjAE9MpU}, keywords = {source\_mobile} } @online{zotero-item-30533, url = {https://dl.acm.org/doi/10.1145/3731559}, urldate = {2025-05-16} } @inproceedings{Zou2018174, title = {Improved Sliding-Mode on-Line Adaptive Position Control for {{AMT}} Clutch Systems Based on Neural Networks}, author = {Zou, J. and Huang, H. and Gühmann, C.}, date = {2018}, series = {{{IEEE Intelligent Vehicles Symposium}}, {{Proceedings}}}, volume = {2018-June}, pages = {174--178}, publisher = {{Institute of Electrical and Electronics Engineers Inc.}}, doi = {10.1109/IVS.2018.8500691}, abbrev_source_title = {IEEE Intell Veh Symp Proc}, affiliation = {Center for Automotive Electronics, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China; Schaeffler Technologies AG and Co. KG, Germany; Electronic Measurement and Diagnostic Technology, Technische Universität, Berlin, Germany}, art_number = {8500691}, document_type = {Conference Paper}, isbn = {978-1-5386-4452-2}, langid = {english}, source = {Scopus}, note = {cited By 0} }