ICSE 2024
Fri 12 - Sun 21 April 2024 Lisbon, Portugal
Wed 17 Apr 2024 11:30 - 11:45 at Pequeno Auditório - Program Repair 1 Chair(s): Sergey Mechtaev

Industrial deployments of automated program repair (APR), e.g., at Facebook and Bloomberg, signal a new milestone for this exciting and potentially impactful technology. In these deployments, developers use APR-generated patch suggestions as part of a human-driven debugging process. Unfortunately, little is known about how using patch suggestions affects developers during debugging. This paper conducts a controlled user study with 40 developers with a median of 6 years of experience. The developers engage in debugging tasks on nine naturally-occurring defects in real-world, open-source, Java projects, using Recoder, SimFix, and TBar, three state-of-the-art APR tools. For each debugging task, the developers either have access to the project’s tests, or, also, to code suggestions that make all the tests pass. These suggestions are either developer-written or APR-generated, which can be correct or deceptive. Deceptive suggestions, which are a common APR occurrence, make all the available tests pass but fail to generalize to the intended specification. Through a total of 160 debugging sessions, we find that access to a code suggestion significantly increases the odds of submitting a patch. Access to correct APR suggestions increase the odds of debugging success by 14,000% as compared to having access only to tests, but access to deceptive suggestions decrease the odds of success by 65%. Correct suggestions also speed up debugging. Surprisingly, we observe no significant difference in how novice and experienced developers are affected by APR, suggesting that APR may find uses across the experience spectrum. Overall, developers come away with a strong positive impression of APR, suggesting promise for APR-mediated, human-driven debugging, despite existing challenges in APR-generated repair quality.

Wed 17 Apr

Displayed time zone: Lisbon change

11:00 - 12:30
Program Repair 1Research Track / Journal-first Papers / Industry Challenge Track at Pequeno Auditório
Chair(s): Sergey Mechtaev University College London
11:00
15m
Talk
Domain Knowledge Matters: Improving Prompts with Fix Templates for Repairing Python Type Errors
Research Track
Yun Peng The Chinese University of Hong Kong, Shuzheng Gao The Chinese University of Hong Kong, Cuiyun Gao Harbin Institute of Technology, Yintong Huo The Chinese University of Hong Kong, Michael Lyu The Chinese University of Hong Kong
11:15
15m
Talk
VeRe: Verification Guided Synthesis for Repairing Deep Neural Networks
Research Track
Jianan Ma Hangzhou Dianzi University, China; Zhejiang University, Hangzhou, China, Pengfei Yang Institute of Software at Chinese Academy of Sciences, China, Jingyi Wang Zhejiang University, Youcheng Sun The University of Manchester, Cheng-Chao Huang Nanjing Institute of Software Technology, ISCAS, Zhen Wang Hangzhou Dianzi University, China
11:30
15m
Talk
Automated Program Repair, What Is It Good For? Not Absolutely Nothing!
Research Track
Hadeel Eladawy University of Massachusetts, Claire Le Goues Carnegie Mellon University, Yuriy Brun University of Massachusetts
DOI Pre-print Media Attached
11:45
15m
Talk
When Large Language Models Confront Repository-Level Automatic Program Repair: How Well They Done?
Industry Challenge Track
YuXiao Chen Institute of Software, Chinese Academy of Sciences, Jingzheng Wu Institute of Software, The Chinese Academy of Sciences, Xiang Ling Institute of Software, Chinese Academy of Sciences, Changjiang Li Penn State, ZHIQING RUI Institute of Software, Chinese Academy of Sciences; University of Chinese Academy of Sciences, Tianyue Luo Institute of Software, Chinese Academy of Sciences, Yanjun Wu Institute of Software, Chinese Academy of Sciences
12:00
7m
Talk
Katana: Dual Slicing Based Context for Learning Bug Fixes
Journal-first Papers
Mifta Sintaha University of British Columbia, Noor Nashid University of British Columbia, Ali Mesbah University of British Columbia (UBC)
Link to publication Pre-print
12:07
7m
Talk
Poracle: Testing Patches Under Preservation Conditions to Combat the Overfitting Problem of Program Repair
Journal-first Papers
Elkhan Ismayilzada UNIST, Md Mazba Ur Rahman UNIST, Dongsun Kim Kyungpook National University, Jooyong Yi UNIST
12:14
7m
Talk
APR4Vul: An empirical study of automatic program repair techniques on real-world Java vulnerabilities
Journal-first Papers
Quang-Cuong Bui Hamburg University of Technology, Ranindya Paramitha University of Trento, Duc-Ly Vu University of Information Technology, Ho Chi Minh City, Fabio Massacci University of Trento; Vrije Universiteit Amsterdam, Riccardo Scandariato Hamburg University of Technology
DOI Pre-print