logseq/pages/hls__TOSEM-2023-0377_Proof_hi_1700083339192_0.md

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• Configurable Graph Code Representation ls-type:: annotation hl-page:: 1 hl-color:: green id:: 6598171e-ee4c-4e6f-adb3-a95086f59c38
• Deep learning is widely used to uncover hidden patterns in large code corpora. ls-type:: annotation hl-page:: 2 hl-color:: green id:: 65981731-adca-4210-8ebb-fba055510702
• To achieve this, constructing a format that captures the relevant characteristics and features of source code is essential. ls-type:: annotation hl-page:: 2 hl-color:: green id:: 659817ec-e886-4536-887f-a3f878be06b6
• he output of these tools often lacks interoperability and results in excessively large graphs, making graph-based neural networks training slower and less scalable ls-type:: annotation hl-page:: 2 hl-color:: blue id:: 65981800-d43e-4a09-8e46-ab9bdd1b1585
• We introduce Concord, a domain-specific language to build customizable graph representations. ls-type:: annotation hl-page:: 2 hl-color:: purple id:: 65981816-5aa6-4360-85f4-46ce5a5a69cb
• It implements reduction heuristics to reduce graphs’ size complexity. ls-type:: annotation hl-page:: 2 hl-color:: purple id:: 65981824-e456-4b82-8841-233025c27972
• e demonstrate its effectiveness in code smell detection as an illustrative use case ls-type:: annotation hl-page:: 2 hl-color:: purple id:: 65981831-d2ba-45e9-829e-3aba0f53bbff
• address the issue of scalability in GNN models, ls-type:: annotation hl-page:: 2 hl-color:: yellow id:: 659819a2-98ea-4fc3-83a1-9d87caa5923e
• defect prediction ls-type:: annotation hl-page:: 2 hl-color:: green id:: 659904ce-dce1-4287-b2a4-2772cfd78263 hl-stamp:: 1704527067529
• code smell detection ls-type:: annotation hl-page:: 2 hl-color:: green id:: 659904d3-2bb3-48e2-b6b9-c514b3a09110 hl-stamp:: 1704527069827
• code summarization ls-type:: annotation hl-page:: 2 hl-color:: green id:: 659904d9-1500-4fea-b1c9-d6cf1570f0bf
• code is converted from its raw textual form to a numeric representation that can be processed and fed to dl-models ls-type:: annotation hl-page:: 2 hl-color:: blue id:: 659904f0-3a35-4dda-a849-2de831073313
• augmented ast with custom data flow edges and control flow edges to detect variable misuse in a code snippet ls-type:: annotation hl-page:: 3 hl-color:: green id:: 65990522-e532-4314-9141-56cd18c2d930
• The process of combining code representations, pre-processing them, and training a dl model is done manually and from scratch which wastes significant research time and resources ls-type:: annotation hl-page:: 3 hl-color:: green id:: 6599056a-6c94-48c2-82dd-c738d80f0557
• varied asts representation and hindering effective combinations. ls-type:: annotation hl-page:: 3 hl-color:: blue id:: 659bc9e3-73fd-487d-a881-61e248b79e7c
• Lack of interoperability ls-type:: annotation hl-page:: 3 hl-color:: purple id:: 659bc9e8-7e43-4eb5-af3e-c71c30877695
• Control Flow Graphs (cfgs) ls-type:: annotation hl-page:: 3 hl-color:: green id:: 659bca36-04a5-4c94-a707-d5584fe17dc7
• Graph Neural Networks (gnns) require large memory and computational resources during training, which renders them computationally expensive to train and difficult to scale. ls-type:: annotation hl-page:: 3 hl-color:: green id:: 659bca57-eadc-4294-ae70-27fab1ae6ee1
• gnn must store and manipulate large adjacency matrices representing the graph ls-type:: annotation hl-page:: 3 hl-color:: green id:: 659bca97-a175-42c2-8dc2-bd20e126b768
• computational requirements are directly proportional to the size of the graph and the number of layers in a gnn ls-type:: annotation hl-page:: 3 hl-color:: green id:: 659bcaad-1856-4cf4-8203-0ecc6af00f51
• One might conjecture that the removal of such statements can reduce source code and allow the model to learn better its implicit patterns and salient features to capture its intended behaviour. ls-type:: annotation hl-page:: 4 hl-color:: green id:: 659bcae2-6e4c-44e5-8e8d-ea5c4ee37051
• This paper introduces configurable code representation (Concord), a Domain Specific Language(dsl) and a unifying framework designed to automate the generation of custom code representations. ls-type:: annotation hl-page:: 4 hl-color:: purple id:: 659bcafc-05cf-4452-88d5-b8ca73ab1d6c
• Experimental results demonstrate that our proposed method achieves improved model performance at a lower computational cost. ls-type:: annotation hl-page:: 4 hl-color:: yellow id:: 659bcb29-167c-4364-af82-49714fa63d8a hl-stamp:: 1704708908005
• We applied Concord to solve the code smell detection task, and managed to maintain up to 100% performance and reduce computations by 10.15%. ls-type:: annotation hl-page:: 4 hl-color:: yellow id:: 659bcb6b-d444-4a68-9eeb-934f7b00a3cd
• We show how the proposed pruning techniques can reduce size and computational time for gnn training while preserving performance ls-type:: annotation hl-page:: 4 hl-color:: yellow id:: 659bcba9-fb02-4cca-b635-7adb03216bdc
• Graph Neural Networks [54] operate on graph-structured data, using a message-passing mechanism to learn node and edge representations. ls-type:: annotation hl-page:: 4 hl-color:: green id:: 659bcc0a-d47c-4680-a611-e5a1ad4932c4
• The reason behind the prevalence of using such architecture is that source code is often modelled using graphs like in the case of syntax trees, control flow graphs and call graphs. ls-type:: annotation hl-page:: 4 hl-color:: blue id:: 659bcc40-1711-42c5-9a97-41ee9bc89003
• External dsls like css and sql, have their own syntax and parser that interprets the language or translates it into another language. ls-type:: annotation hl-page:: 4 hl-color:: green id:: 659bcc5d-32a5-46ca-b877-277ee93dea36
• data-centric cultural shift ls-type:: annotation hl-page:: 5 hl-color:: green id:: 659bcc8a-eed7-4ae3-a8e4-55bad8f27350
• Morales et al. [ 46] have proposed a dsl to model AI engineering processes, tailored for multidisciplinary teams developing AI-embedded software ls-type:: annotation hl-page:: 5 hl-color:: green id:: 659bccb1-a488-4830-a2f0-df30d314c0a3
• Sergio Morales, Robert Clarisó, and Jordi Cabot. 2022. Towards a DSL for AI Engineering Process Modeling. In Product-Focused Software Process Improvement, Davide Taibi, Marco Kuhrmann, Tommi Mikkonen, Jil Klünder, and Pekka Abrahamsson (Eds.). Springer International Publishing, Cham, 53–60. ls-type:: annotation hl-page:: 22 hl-color:: green id:: 659bccf0-1a77-4ea0-864c-8c75a095c58a
• Its goal is to facilitate the formalization of AI processes within organizations and seamlessly integrate with existing model-driven tools, advancing the adoption of AI engineering practices ls-type:: annotation hl-page:: 5 hl-color:: blue id:: 659bcdcd-0f74-41be-8b38-896468422a12
• Bieber et al. [ 9] provide an open-source Python library called python_graphs to construct graph representations of Python programs suitable for training machine learning models by employing static analysis. ls-type:: annotation hl-page:: 5 hl-color:: green id:: 659bd1aa-560a-4ac6-b3cd-f6b13b39d37c
• control-flow graphs, data-flow graphs, and composite “program graphs” that combine control-flow, data-flow, syntactic, and lexical information about a program. ls-type:: annotation hl-page:: 5 hl-color:: green id:: 659bd1b5-efcd-41b7-94a8-52d50132b076
• Limitations of existing work ls-type:: annotation hl-page:: 5 hl-color:: green id:: 659bd1f9-7c70-4639-ae9c-66f93c51d7ca
• each tool supports one programming language, either Python or Jav ls-type:: annotation hl-page:: 5 hl-color:: green id:: 659bd206-fa95-46e8-8de9-bd3f2fbefd24
• lack of composability and configurability. ls-type:: annotation hl-page:: 5 hl-color:: green id:: 659bd212-ed12-4e55-91fc-c105a56197ce
• the set of augmentations in these tools is disjoint, i.e., a list of operations is found in one tool but missing in another. ls-type:: annotation hl-page:: 6 hl-color:: green id:: 659bd231-75fd-4845-9e0a-f62457347012
• Zhang et al. [ 68] introduce an approach referred to as DietCode that leverages large pre-trained models such as CodeBERT [17] for source code. Through an empirical analysis of CodeBERT’s attention mechanism, they discovered that the model attends more to certain types of tokens and statements such as keywords and data-relevant statements ls-type:: annotation hl-page:: 6 hl-color:: green id:: 659bd3ea-511f-4a92-852b-3d2ef2022150
• selects statements and tokens with the highest attention weights ls-type:: annotation hl-page:: 6 hl-color:: green id:: 659bd49d-0157-44ff-b3d4-2f8bd8629f54
• 40% less computational cost during fine-tuning and testing ls-type:: annotation hl-page:: 6 hl-color:: green id:: 659bd4a9-9543-4e14-9f7f-498f7f143ae7
• Limitations of existing work: ls-type:: annotation hl-page:: 6 hl-color:: yellow id:: 659bd500-4f92-4ed3-83e9-b1471c4521ae
• The limitation of these works is that they require multiple rounds of expensive computation. ls-type:: annotation hl-page:: 6 hl-color:: yellow id:: 659bd64a-a3e1-471b-82a8-f9673e6a2cb1
• [:span] ls-type:: annotation hl-page:: 5 hl-color:: green id:: 659bd67e-cf52-44c1-828c-4d7009f6e376 hl-type:: area hl-stamp:: 1704711804507
• To train a gnn model ls-type:: annotation hl-page:: 6 hl-color:: green id:: 659bd96b-a2e3-4eae-a82e-d7da151c4a80
• A dsl is typically characterized by a metamodel, which serves as a representation of its domain-specific entities and their interconnections. ls-type:: annotation hl-page:: 6 hl-color:: green id:: 659bd997-e61f-49f7-bd1b-c6d5e3bc297b
• arsing Expression Grammar (PEG) ls-type:: annotation hl-page:: 7 hl-color:: green id:: 659bd9a5-36d1-43e2-b4f8-46578d999b43
• node removal and edge addition ls-type:: annotation hl-page:: 7 hl-color:: yellow id:: 659bda02-94d9-48aa-9557-153458217366
• I. Dejanović, R. Vaderna, G. Milosavljević, and Ž. Vuković. 2017. TextX: A Python tool for Domain-Specific Languages implementation. Knowledge-Based Systems 115 (2017), 1–4. ls-type:: annotation hl-page:: 21 hl-color:: green id:: 659bdb96-81bf-444d-b020-6f2272216fa7
• 3.2 Graph generation process ls-type:: annotation hl-page:: 8 hl-color:: yellow id:: 659bdbfc-81cc-4f69-b85f-7d849579f6a3
• 3.3 Node remova ls-type:: annotation hl-page:: 8 hl-color:: yellow id:: 659bdc69-49f6-4e3e-a729-f78a729f9520
• simple assignment, print, and system exit statements ls-type:: annotation hl-page:: 8 hl-color:: green id:: 659bdd8b-9ee3-4545-99ae-0276314f7160
• Simple assignment statement to be removed ls-type:: annotation hl-page:: 10 hl-color:: yellow id:: 659bdf08-9037-43e1-a49d-c87be7c53814
• Edge addition ls-type:: annotation hl-page:: 10 hl-color:: yellow id:: 659bdf77-e9b2-43d5-a2be-0b3916021171
• 4 CASE STUDY: CODE SMELL DETECTION ls-type:: annotation hl-page:: 12 hl-color:: green id:: 659fe255-ed3d-43d6-967f-a06c667606ce
• we conducted a study to prepare code representations to classify code smells ls-type:: annotation hl-page:: 12 hl-color:: green id:: 659fe51d-5587-474d-933a-9452921a1ced
• poorly written code that requires refactoring ls-type:: annotation hl-page:: 12 hl-color:: green id:: 659fe528-62eb-4c39-abca-7880eee03031
• four types of code smells that were initially investigated by Sharma et al. [ 55]: Complex method, Complex conditional, Feature envy, and Multifaceted abstraction. ls-type:: annotation hl-page:: 12 hl-color:: green id:: 659fe567-2a99-4b9c-8149-b5a7a7ff3064
• eight repository characteristics ls-type:: annotation hl-page:: 12 hl-color:: yellow id:: 659fe5ba-f3af-42c0-ba05-7a3e3650faae
• we define three Concord configurations: R1, R2, and R ls-type:: annotation hl-page:: 12 hl-color:: green id:: 65a0196b-3a6e-4649-b072-3807554566d4
• training, validation, and test ls-type:: annotation hl-page:: 13 hl-color:: green id:: 65a019da-8373-4f35-9a2e-1e792f5f7884
• P ls-type:: annotation hl-page:: 14 hl-color:: yellow id:: 65a01a5a-a15f-42f6-a44e-ed8860605b0d
• solve the code smell classification task ls-type:: annotation hl-page:: 15 hl-color:: green id:: 65a01b04-66d5-4e3c-a372-9ca2c8159a9d
• we feed 𝑉 and the adjacency matrix 𝐸 to the ggnn layer. ls-type:: annotation hl-page:: 15 hl-color:: green id:: 65a01c0d-a32e-448c-b137-e421aebe7d03
• following equations ls-type:: annotation hl-page:: 15 hl-color:: yellow id:: 65a01c58-9ec9-4df7-8cd3-b3ee111ee962
• We train a classifier for each code smell-representation combination, giving a total of 12 trained models. ls-type:: annotation hl-page:: 16 hl-color:: yellow id:: 65a01c99-3061-4a1f-a818-6eaa31f62c18
• Matthews Correlation Coefficient (mcc) metric. ls-type:: annotation hl-page:: 16 hl-color:: green id:: 65a01cbd-aca4-401c-a5a9-d09f4379ce15
• flops (floating operation points) ls-type:: annotation hl-page:: 16 hl-color:: green id:: 65a01cdb-2a60-43a3-860f-ab44a6adedb6
• RQ. Does reducing the size complexity of code representation affect the performance and the needed computational operations? ls-type:: annotation hl-page:: 16 hl-color:: yellow id:: 65a01cfa-d6fa-4492-b41c-776dbf40fa58 hl-stamp:: 1704992204743
• The results indicate that our proposed approach generates code representation that can effectively capture the desired code features for code analysis tasks ls-type:: annotation hl-page:: 17 hl-color:: yellow id:: 65a01e93-c534-432f-97fb-075c24556185 hl-stamp:: 1704992405606
• It is a method to build representations in a configurable and customizable way. ls-type:: annotation hl-page:: 19 hl-color:: green id:: 65a01f88-6864-44cb-b7e5-eee10e22a809