ICSE 2024
Fri 12 - Sun 21 April 2024 Lisbon, Portugal
Wed 17 Apr 2024 15:00 - 15:15 at Pequeno Auditório - Program Repair 2 Chair(s): Xiang Gao

Automated program repair (APR) tools have unlocked the potential for the rapid rectification of codebase issues. However, to encourage wider adoption of program repair in practice, it is necessary to address the usability concerns related to the generation of irrelevant or out-of-context patches: when software engineers are presented with patches they deem uninteresting or unhelpful, they are burdened with having to deal with more “noise” in their workflows, and become less likely to engage with APR tools in future. This paper presents a novel approach to optimally time, target, and present auto-patches to software engineers. To achieve this, we designed, developed and deployed a new tool, dubbed B-Assist, which leverages GitHub’s Suggested Changes interface to seamlessly integrate automated suggestions into active pull requests (PRs), as opposed to initiating new, potentially distracting PRs. This strategy ensures that suggestions are not only timely but also contextually relevant and delivered to the most suitable software engineers. Evaluation among Bloomberg software engineers demonstrated their preference for this approach. From our user study, B-Assist’s efficacy is evident, with the acceptance rate of patch suggestions being as high as 74.56%; the suggestions were also found useful with ratings of at least 4 out of 5 in 78.2% of cases. Further, this paper sheds light on persisting usability challenges in APR and lays the groundwork for enhancing the user experience in future APR tools.

Wed 17 Apr

Displayed time zone: Lisbon change

14:00 - 15:30
14:00
15m
Talk
Practical Program Repair via Preference-based Ensemble Strategy
Research Track
Wenkang Zhong State Key Laboratory for Novel Software and Technology, Nanjing University, 22 Hankou Road, Nanjing, China, Chuanyi Li Nanjing University, Kui Liu Huawei, Tongtong Xu Huawei, Jidong Ge Nanjing University, Tegawendé F. Bissyandé University of Luxembourg, Bin Luo Nanjing University, Vincent Ng Human Language Technology Research Institute, University of Texas at Dallas, Richardson, TX 75083-0688
14:15
15m
Talk
Learning and Repair of Deep Reinforcement Learning Policies from Fuzz-Testing Data
Research Track
Martin Tappler TU Graz; Silicon Austria Labs, Andrea Pferscher Institute of Software Technology, Graz University of Technology , Bernhard Aichernig Graz University of Technology, Bettina Könighofer Graz University of Technology
14:30
15m
Talk
BinAug: Enhancing Binary Similarity Analysis with Low-Cost Input Repairing
Research Track
WONG Wai Kin Hong Kong University of Science and Technology, Huaijin Wang Hong Kong University of Science and Technology, Li Zongjie Hong Kong University of Science and Technology, Shuai Wang Hong Kong University of Science and Technology
14:45
15m
Talk
Constraint Based Program Repair for Persistent Memory Bugs
Research Track
Zunchen Huang University of Southern California, Chao Wang University of Southern California
15:00
15m
Talk
User-Centric Deployment of Automated Program Repair at Bloomberg
Software Engineering in Practice
David Williams University College London, James Callan UCL, Serkan Kirbas Bloomberg LP, Sergey Mechtaev University College London, Justyna Petke University College London, Thomas Prideaux-Ghee Bloomberg LP, Federica Sarro University College London
15:15
7m
Talk
AIBugHunter: A Practical Tool for Predicting, Classifying and Repairing Software Vulnerabilities
Journal-first Papers
Michael Fu Monash University, Kla Tantithamthavorn Monash University, Trung Le Monash University, Australia, Yuki Kume Monash University, Van Nguyen Monash University, Dinh Phung Monash University, Australia, John Grundy Monash University
Link to publication DOI Pre-print
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