Welcome to the website of the special event of AI Foundation Models and Software Engineering (FORGE 2024) in ICSE 2024!
The AI Foundation Models and Software Engineering event aims to bring researchers, practitioners, and educators from the AI and Software Engineering community to solve the new challenges that we meet in the era of foundation models.
Foundation models (e.g., ChatGPT and Llama) have attracted great attention from both academia and industry. In Software Engineering, several studies showed that Large-Language Models (LLMs) achieved remarkable performance in various tasks, including code generation, testing, code review, and program repair. Recently, many LLM-based development tools have been released to improve software development and show great potential, for example, GitHub Copilot and Amazon CodeWhisperer.
FORGE aims to bring together researchers and practitioners to explore new frontiers in using foundation models on SE tasks, boost software development productivity, and improve code quality.
Forge 2024 will be held on Sunday, April 14, 2024, in Lisbon, Portugal.
Sun 14 AprDisplayed time zone: Lisbon change
09:00 - 10:30 | FORGE2024 Opening / Keynote 1 / PanelKeynotes / Panel at Luis de Freitas Branco Chair(s): Xin Xia Huawei Technologies, Xing Hu Zhejiang University | ||
09:00 10mDay opening | Introduction from The Chairs Keynotes | ||
09:10 40mKeynote | Keynote 1: Large Language Models for Test Case Repair Keynotes Lionel Briand University of Ottawa, Canada; Lero centre, University of Limerick, Ireland | ||
09:50 40mPanel | Theme: Is There Space for Software Engineering Researchers to Contribute to AI4SE in The Era of Foundation Models? Panel Lionel Briand University of Ottawa, Canada; Lero centre, University of Limerick, Ireland, Denys Poshyvanyk William & Mary, Prem Devanbu University of California at Davis, Massimiliano Di Penta University of Sannio, Italy, David Lo Singapore Management University |
10:30 - 11:00 | |||
10:30 30mCoffee break | Break ICSE Catering |
11:00 - 12:30 | Foundation Models for Software Quality AssuranceResearch Track at Luis de Freitas Branco Chair(s): Matteo Ciniselli Università della Svizzera Italiana | ||
11:00 14mFull-paper | Deep Multiple Assertions GenerationFull Paper Research Track | ||
11:14 14mFull-paper | MeTMaP: Metamorphic Testing for Detecting False Vector Matching Problems in LLM Augmented GenerationFull Paper Research Track Guanyu Wang Beijing University of Posts and Telecommunications, Yuekang Li The University of New South Wales, Yi Liu Nanyang Technological University, Gelei Deng Nanyang Technological University, Li Tianlin Nanyang Technological University, Guosheng Xu Beijing University of Posts and Telecommunications, Yang Liu Nanyang Technological University, Haoyu Wang Huazhong University of Science and Technology, Kailong Wang Huazhong University of Science and Technology | ||
11:28 14mFull-paper | Planning to Guide LLM for Code Coverage PredictionFull Paper Research Track Hridya Dhulipala University of Texas at Dallas, Aashish Yadavally University of Texas at Dallas, Tien N. Nguyen University of Texas at Dallas | ||
11:42 7mShort-paper | The Emergence of Large Language Models in Static Analysis: A First Look through Micro-BenchmarksNew Idea Paper Research Track Ashwin Prasad Shivarpatna Venkatesh University of Paderborn, Samkutty Sabu University of Paderborn, Amir Mir Delft University of Technology, Sofia Reis Instituto Superior Técnico, U. Lisboa & INESC-ID, Eric Bodden | ||
11:49 14mFull-paper | Reality Bites: Assessing the Realism of Driving Scenarios with Large Language ModelsFull Paper Research Track Jiahui Wu Simula Research Laboratory and University of Oslo, Chengjie Lu Simula Research Laboratory and University of Oslo, Aitor Arrieta Mondragon University, Tao Yue Beihang University, Shaukat Ali Simula Research Laboratory and Oslo Metropolitan University | ||
12:03 7mShort-paper | Assessing the Impact of GPT-4 Turbo in Generating Defeaters for Assurance CasesNew Idea Paper Research Track Kimya Khakzad Shahandashti York University, Mithila Sivakumar York University, Mohammad Mahdi Mohajer York University, Alvine Boaye Belle York University, Song Wang York University, Timothy Lethbridge University of Ottawa | ||
12:10 20mOther | Discussion Research Track |
12:30 - 14:00 | |||
12:30 90mLunch | Lunch ICSE Catering |
15:30 - 16:00 | |||
15:30 30mCoffee break | Break ICSE Catering |
16:00 - 17:30 | FORGE2024 Awards & Foundation Models for Code and Documentation GenerationResearch Track at Luis de Freitas Branco Chair(s): Antonio Mastropaolo Università della Svizzera italiana | ||
16:00 10mAwards | Award Ceremony Research Track | ||
16:10 7mShort-paper | Fine Tuning Large Language Model for Secure Code GenerationNew Idea Paper Research Track Junjie Li Concordia University, Aseem Sangalay Delhi Technological University, Cheng Cheng Concordia University, Yuan Tian Queen's University, Kingston, Ontario, Jinqiu Yang Concordia University | ||
16:17 14mFull-paper | Investigating the Performance of Language Models for Completing Code in Functional Programming Languages: a Haskell Case StudyFull Paper Research Track Tim van Dam Delft University of Technology, Frank van der Heijden Delft University of Technology, Philippe de Bekker Delft University of Technology, Berend Nieuwschepen Delft University of Technology, Marc Otten Delft University of Technology, Maliheh Izadi Delft University of Technology | ||
16:31 7mShort-paper | On Evaluating the Efficiency of Source Code Generated by LLMsNew Idea Paper Research Track Changan Niu Software Institute, Nanjing University, Ting Zhang Singapore Management University, Chuanyi Li Nanjing University, Bin Luo Nanjing University, Vincent Ng Human Language Technology Research Institute, University of Texas at Dallas, Richardson, TX 75083-0688 | ||
16:38 14mFull-paper | PathOCL: Path-Based Prompt Augmentation for OCL Generation with GPT-4Full Paper Research Track Seif Abukhalaf Polytechnique Montreal, Mohammad Hamdaqa Polytechnique Montréal, Foutse Khomh École Polytechnique de Montréal | ||
16:52 7mShort-paper | Creative and Correct: Requesting Diverse Code Solutions from AI Foundation ModelsNew Idea Paper Research Track Scott Blyth Monash University, Christoph Treude Singapore Management University, Markus Wagner Monash University, Australia | ||
16:59 7mShort-paper | Commit Message Generation via ChatGPT: How Far Are We?New Idea Paper Research Track | ||
17:06 24mOther | Discussion Research Track |
Accepted Papers
Call for Papers
The special event of AI Foundation Models and Software Engineering (FORGE 2024) in ICSE 2024 aims to bring researchers, practitioners, and educators from the AI and Software Engineering community to solve the new challenges that we meet in the era of foundation models.
Foundation models (e.g., ChatGPT and Llama) have attracted great attention from both academia and industry. In Software Engineering, several studies showed that Large-Language Models (LLMs) achieved remarkable performance in various tasks, including code generation, testing, code review, and program repair. Recently, many LLM-based development tools have been released to improve software development and show great potential, for example, GitHub Copilot and Amazon CodeWhisperer.
FORGE aims to bring together researchers and practitioners to explore new frontiers in using foundation models on SE tasks, boost software development productivity, and improve code quality.
FORGE 2024 will be held on Sunday, April 14, 2024, in Lisbon, Portugal.
Topic of Interests
We solicit submissions describing original and unpublished results of theoretical, empirical, conceptual, and experimental software engineering research related to Software Engineering with Foundation Models. Topics of interest include but are not limited to:
- FM for Requirement Engineering and Software Design
- FM for Code Generation/Reuse
- FM for Software Quality Assurance (e.g., including code review, analysis, testing, and debugging)
- FM to support software evolution (e.g., refactoring, technical debt management)
- FM for Software Security and Privacy
- FM for AIOps
- FM for software supply chain management, e.g., FM-based vulnerability identification, software composition analysis
- LLM Agents for SE tasks, e.g., how to use various FMs (e.g., LangChain) to complete a SE task.
- Prompt Engineering for Software Development
- Legal Aspects of using FM
Awards
The best papers will be awarded with an ACM SIGSOFT Distinguished Paper Award at FORGE. A selection of the best papers will be invited to a Special Issue of Empirical Software Engineering (EMSE).
How to Submit
We accept both full and new idea papers:
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Full Papers are expected to present new techniques, and/or provide research results, and/or report industry/open-source practice when applying foundation models for SE, and should be evaluated in a scientific way. Full Paper must not exceed 10 pages for the main text, inclusive of all figures, tables, appendices, etc. Two more pages containing only references are permitted.
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New Idea Papers should present new ideas in the field, e.g., new directions or techniques that are not yet fully developed and/or evaluated, or visions that show the future of AI foundation models and SE. Accepted new idea papers will present their ideas in a short lightning talk. New Idea Paper must not exceed 4 pages for the main text, inclusive of all figures, tables, appendices, etc. Two more pages containing only references are permitted.
All submissions must be in PDF. The page limit is strict, and it will not be possible to purchase additional pages at any point in the process (including after acceptance).
Formatting instructions are available at https://www.acm.org/publications/proceedings-template for both LaTeX and Word users. LaTeX users must use the provided acmart.cls
and ACM-Reference-Format.bst
without modification, enable the conference format in the preamble of the document (i.e., \documentclass[sigconf,review]{acmart}
), and use the ACM reference format for the bibliography (i.e., \bibliographystyle{ACM-Reference-Format}
). The review option adds line numbers, thereby allowing referees to refer to specific lines in their comments.
Note, we use double-anonymous reviewing. Be sure to remove the list of authors from the submitted paper. If citing your own prior work, please do so in the third person to obscure the relationship you have with it. For advice, guidance, and explanation about the double-anonymous review process, see ICSE Research Track’s Q&A page.
By submitting your article to an ACM Publication, you are hereby acknowledging that you and your co-authors are subject to all ACM Publications Policies, including ACM’s new Publications Policy on Research Involving Human Participants and Subjects. Alleged violations of this policy or any ACM Publications Policy will be investigated by ACM and may result in a full retraction of your paper, in addition to other potential penalties, as per ACM Publications Policy.
Please ensure that you and your co-authors obtain an ORCID ID, so you can complete the publishing process for your accepted paper. ACM has been involved in ORCID from the start and we have recently made a commitment to collect ORCID IDs from all of our published authors. The collection process has started and will roll out as a requirement throughout 2022. We are committed to improving author discoverability, ensuring proper attribution, and contributing to ongoing community efforts around name normalization; your ORCID ID will help in these efforts.
All papers must be written in English.
All papers should be made accessible to people with disabilities. Some guidelines from the SIGACCESS community are available here: https://assets21.sigaccess.org/creating_accessible_pdfs.html.
Please submit your paper on HotCRP: https://forge-2024.hotcrp.com/
Review Criteria
Following the review criteria of ICSE 2024, each paper submitted to the FORGE 2024 will be evaluated based on the following criteria:
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Novelty: The novelty and innovativeness of contributed solutions, problem formulations, methodologies, theories, and/or evaluations, i.e., the extent to which the paper is sufficiently original with respect to the state-of-the-art.
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Rigor: The soundness, clarity, and depth of a technical or theoretical contribution, and the level of thoroughness and completeness of an evaluation.
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Relevance: The significance and/or potential impact of the research to the field of software engineering.
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Verifiability and Transparency: The extent to which the paper includes sufficient information to understand how an innovation works; to understand how data was obtained, analyzed, and interpreted; and how the paper supports independent verification or replication of the paper’s claimed contributions. Any artifacts attached to or linked from the paper may be checked by one reviewer.
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Presentation: The clarity of the exposition in the paper.
Reviewers will carefully consider all of the above criteria during the review process, and authors should take great care in clearly addressing them all. The paper should clearly explain and justify the claimed contributions.