logseq/pages/hls__MODELS23-DS_paper_11_1690904186006_0.md

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• software engineering involves implementing an executable program which principally includes writing code. ls-type:: annotation hl-page:: 1 hl-color:: green id:: 64c928f1-b5d1-4ef3-8a53-8479e4c55647
• managing data and training models ls-type:: annotation hl-page:: 1 hl-color:: green id:: 64c928fd-096c-4430-9d5c-769f186aac3a
• elatively less emphasis on the code writing ls-type:: annotation hl-page:: 1 hl-color:: purple id:: 64c92910-7bdb-44c2-8cdd-6bc7b977d842 hl-stamp:: 1690904850983
• how to improve the engineering of systems with AI components (SE4AI ls-type:: annotation hl-page:: 1 hl-color:: purple id:: 64c92922-edb7-45ed-b6f7-c001b8190a3a
• continuous learning and adaptation ls-type:: annotation hl-page:: 1 hl-color:: purple id:: 64c92933-20d1-4b68-a06e-89eb43f559b8
• ade-offs between software process modeling languages in AI/ML context must be re-examined ls-type:: annotation hl-page:: 1 hl-color:: yellow id:: 64c92944-7620-46e8-82a7-10994cdfe2b9
• In this paper, we propose a framework for modeling and executing software engineering process for AI-enabled system ls-type:: annotation hl-page:: 1 hl-color:: purple id:: 64c92956-2066-4199-a3d7-8e7e32b53c03
• Software process model ls-type:: annotation hl-page:: 1 hl-color:: green id:: 64c92b40-e90e-42ec-8a6d-b9f09927123d
• structured view of the processes, facilitating improvements, and permitting of the processes standardization and reu ls-type:: annotation hl-page:: 1 hl-color:: green id:: 64c92b4c-7ad7-45c0-bfc3-73852cd3d537
• activity-centric ls-type:: annotation hl-page:: 1 hl-color:: green id:: 64c92b5d-4413-4791-8250-c14b1c11189b
• artifact-centric ls-type:: annotation hl-page:: 1 hl-color:: green id:: 64c92b63-a12a-4b1d-8aae-a9461b6c2b29
• Artificial Intelligence-enabled Systems (AIS) are used increasingly in real-world applications. This is mainly due to the success of deep learning algorithms in the fields of image processing, speech recognition and machine translation ls-type:: annotation hl-page:: 1 hl-color:: purple id:: 64c92bc5-03bd-47a2-a429-fc5eee0ef973
• AIS is a software system including one or more ML components. ls-type:: annotation hl-page:: 1 hl-color:: purple id:: 64c92bd4-c5ff-4ef6-bf5b-d716404a359e
• his changes ls-type:: annotation hl-page:: 1 hl-color:: red id:: 64c92be9-44a2-479f-bb8b-78293d0d1f70
• new demands changes in the development process aros ls-type:: annotation hl-page:: 1 hl-color:: red id:: 64c92bf9-70db-441e-ae0c-7b69b70ec7f1
• Q1: What challenges emerge in the management of software engineering processes for AI-enabled systems ls-type:: annotation hl-page:: 1 hl-color:: purple id:: 64c92c23-013d-4d2a-928f-0cfec5691230
• An Artificial Intelligence-Enabled System (AIS) is a software system in which at least one of its components relies on one or more ML models ls-type:: annotation hl-page:: 2 hl-color:: purple id:: 64c92c7f-9de7-4bfc-88aa-d04a974419d5 hl-stamp:: 1692254584445
• ML model can be defined as ”a trained instance of a specific machine learning algorithm” [6]. ls-type:: annotation hl-page:: 2 hl-color:: purple id:: 64c92c8d-f876-4b69-b263-9dcff892b6f1 hl-stamp:: 1692254587588
• Managing Data and Training ML model [ ls-type:: annotation hl-page:: 2 hl-color:: green id:: 64c92f21-b214-4394-82a4-3009cdb64852
• In the development of AIS, it is essential not only to define the requirements for the final product but also to establish the requirements for the learning process ls-type:: annotation hl-page:: 2 hl-color:: blue id:: 64c9311c-47e0-46ab-b443-615081e46d2d hl-stamp:: 1692254744355
• ence, it can be stated that the effective satisfaction of NFRs becomes critic ls-type:: annotation hl-page:: 2 hl-color:: green id:: 64c932d2-6a74-4aaa-973f-6a0647afd612
• Software Testing (ST) is the process of verifying that a software behaves as specified, detecting errors, and validating that what has been specified is what the stakeholders actually needed [12] ls-type:: annotation hl-page:: 2 hl-color:: green id:: 64c932f6-9509-4030-8e23-1b658fbd1e6c
• Input-level Testing ls-type:: annotation hl-page:: 2 hl-color:: green id:: 64c93311-5d7a-483a-8896-708aec8a40fe
• Model-level Testin ls-type:: annotation hl-page:: 2 hl-color:: green id:: 64c93316-7cd6-49e2-853e-072a855914bc
• ntegration Testing ls-type:: annotation hl-page:: 2 hl-color:: green id:: 64c9331e-2631-4ed8-9b44-d4ff2e32515c
• System Testing ls-type:: annotation hl-page:: 2 hl-color:: green id:: 64c93322-7155-44ce-a5ce-f540288cc1c7
• heir primary purpose distinguish them from each other. ls-type:: annotation hl-page:: 2 hl-color:: green id:: 64c93450-86d4-4840-b1a8-9490ccd8e3c4
• maintaining of the Traceability. Traceability imposes the conditions that the interdependencies among the artifacts be made explicit and that each artifact be trackable longitudinally through the entire development process. ls-type:: annotation hl-page:: 2 hl-color:: green id:: 64c93460-8516-4f71-8aba-427db12bc628
• We provide partial model of ML workflow practiced by Microsoft team. Microsoft’ ML workflow is nine stages with some data-oriented stages (e.g., data cleaning, and data labeling) and others model-oriented(e.g., feature engineering, and model training) [2] ls-type:: annotation hl-page:: 2 hl-color:: purple id:: 64c93490-b68c-49a0-b42e-82e1ec896e60
• (2) Artifacts of novel types are emerged ls-type:: annotation hl-page:: 3 hl-color:: green id:: 64c93526-d05f-42e1-8c1b-b9f6ddedeb7a
• (1) Joint effort of roles from different backgrounds involved in the development process ls-type:: annotation hl-page:: 3 hl-color:: green id:: 64c9352e-2c1f-4d82-9f66-1ed59a6754a4
• Compatibility ensures that the ML components necessary for achieving reproducibility work together without conflicts ls-type:: annotation hl-page:: 3 hl-color:: green id:: 64c93574-9723-481c-b6b0-d71d1d042589
• complex ls-type:: annotation hl-page:: 3 hl-color:: yellow id:: 64c93642-2f81-4249-a1b4-3ddd616cf7d9
• effectively model SE processes for AIS ls-type:: annotation hl-page:: 2 hl-color:: green id:: 64caccc4-bd9d-44a6-9dca-e7cec9e57a39
• the same set of dependencies and versions are used consistently across different platforms and collaborators) ls-type:: annotation hl-page:: 3 hl-color:: purple id:: 64cacd41-b191-49d1-89ad-fabee626a9a0
• refinement of the ML model to achieve the desired performanc ls-type:: annotation hl-page:: 3 hl-color:: purple id:: 64cacdb0-8843-4df6-9efb-90b89656d54b
• the need to revisit the data management, feature engineering, and model training steps multiple times to experiment with different approaches and refine the ML solution. ls-type:: annotation hl-page:: 3 hl-color:: green id:: 64cacdc1-5647-42b8-92fc-9bf0fd453298
• r, ls-type:: annotation hl-page:: 1 hl-color:: red id:: 64ddc115-d113-4060-9906-c2e9808d39e6
• Q2: What are the essential characteristics that process modeling should encompass to address the novel challenges introduced by AI-based systems development processes? ls-type:: annotation hl-page:: 1 hl-color:: purple id:: 64ddc160-cb14-4de4-a7c7-884bfef31415
• demands change ls-type:: annotation hl-page:: 2 hl-color:: red id:: 64ddc1ea-a881-471e-ba0c-7004ee7bdbc8
• (1) Define Domain-specific modeling (DSL) for creating models that are specific to SE for AIS (including the specific related AI tasks, roles, and artifacts). Defining these concepts proves beneficial for enhancing communication among different participants, automating trace link generation, enabling more flexible execution, and facilitating partial process modeling. ls-type:: annotation hl-page:: 4 hl-color:: yellow id:: 64ddc444-165f-485d-bd43-f80a5a692e46 hl-stamp:: 1692255302067
• We address this lack by proposing a goal-oriented ACPM methodology for systematically modeling the processes [15]. ls-type:: annotation hl-page:: 4 hl-color:: yellow id:: 64ddc4ef-3de7-4836-8994-3d0066c252f3
• (3) Develop a data-driven process engine capable of executing fragmented artifact process models, and generating a macro model derived from the partially modeled process. ls-type:: annotation hl-page:: 4 hl-color:: yellow id:: 64ddc53c-0a3a-4590-987d-5b594e458d7d