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Automatic patch generation by learning correct code
Open accessAbstract
We present Prophet, a novel patch generation system that works with a set of successful human patches obtained from open- source software repositories to learn a probabilistic, application-independent model of correct code. It generates a space of candidate patches, uses the model to rank the candidate patches in order of likely correctness, and validates the ranked patches against a suite of test cases to find correct patches. Experimental results show that, on a benchmark set of 69 real-world defects drawn from eight open-source projects, Prophet significantly outperforms the previous state-of-the-art patch generation system.
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Citations
Cited by
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Gupta, R., Pal, S., Kanade, A., & Shevade, S. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.10742 · Cited by 426
Le Goues, C., Pradel, M., & Roychoudhury, A. (2019) · Communications of the ACM · doi:10.1145/3318162 · Cited by 410
Program Synthesis with Large Language Models
Austin, J., Odena, A., Nye, M., Bosma, M., Michalewski, H., Dohan, D., Jiang, E., Cai, C., Terry, M., Le, Q., et al. (2021) · arXiv · doi:10.48550/arxiv.2108.07732 · Cited by 388
“Program repair is the problem of automatically fixing bugs in programs, often based on a test suite [Le Goues et al., 2012, Long and Rinard, 2016].”
An Empirical Study on Learning Bug-Fixing Patches in the Wild via Neural Machine Translation
Tufano, M., Watson, C., Bavota, G., Penta, M. D., White, M., & Poshyvanyk, D. (2019) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3340544 · Cited by 370
Shaping program repair space with existing patches and similar code
Jiang, J., Xiong, Y., Zhang, H., Gao, Q., & Chen, X. (2018) · Proceedings of the 27th ACM SIGSOFT International Symposium on Software Testing and Analysis · doi:10.1145/3213846.3213871 · Cited by 329
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Lin, D., Koppel, J., Chen, A., & Solar-Lezama, A. (2017) · Proceedings Companion of the 2017 ACM SIGPLAN International Conference on Systems, Programming, Languages, and Applications: Software for Humanity · doi:10.1145/3135932.3135941 · Cited by 242
Identifying patch correctness in test-based program repair
Xiong, Y., Liu, X., Zeng, M., Zhang, L., & Huang, G. (2018) · Proceedings of the 40th International Conference on Software Engineering · doi:10.1145/3180155.3180182 · Cited by 162
Baldur: Whole-Proof Generation and Repair with Large Language Models
First, E., Rabe, M. N., Ringer, T., & Brun, Y. (2023) · Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering · doi:10.1145/3611643.3616243 · Cited by 94
Automated clustering and program repair for introductory programming assignments
Gulwani, S., Radiček, I., & Zuleger, F. (2018) · ACM SIGPLAN Notices · doi:10.1145/3296979.3192387 · Cited by 82
SynShine: Improved Fixing of Syntax Errors
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SpecRover: Code Intent Extraction via LLMs
Ruan, H., Zhang, Y., & Roychoudhury, A. (2024) · arXiv · doi:10.48550/arxiv.2408.02232 · Cited by 28
DeepDev-PERF: a deep learning-based approach for improving software performance
Garg, S., Moghaddam, R. Z., Clement, C. B., Sundaresan, N., & Wu, C. (2022) · Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering · doi:10.1145/3540250.3549096 · Cited by 24
Search, align, and repair: data-driven feedback generation for introductory programming exercises
Wang, K., Singh, R., & Su, Z. (2018) · ACM SIGPLAN Notices · doi:10.1145/3296979.3192384 · Cited by 17
(2020) · doi:10.1145/3382494.3422164 · Cited by 13
Metadata
- Authors
- Long, F., & Rinard, M.
- Venue
- Proceedings of the 43rd Annual ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages
- Publisher
- ACM
- Published
- 2016-01-11
- Pages
- 298-312
- OpenAlex
- W2274071363
- Cited by
- 481
- References
- 0
- FWCI
- 65.53
- Type
- conference paper
- Language
- en
- Open access
- bronze
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