The DOI matches its Crossref record; 47 of the 47 references it cites were found in a registry or an index.
History Driven Program Repair
Abstract
Effective automated program repair techniques have great potential to reduce the costs of debugging and maintenance. Previously proposed automated program repair (APR) techniques often follow a generate-and-validate and test-case-driven procedure: They first randomly generate a large pool of fix candidates and then exhaustively validate the quality of the candidates by testing them against existing or provided test suites. Unfortunately, many real-world bugs cannot be repaired by existing techniques even after more than 12 hours of computation in a multi-core cloud environment. More work is needed to advance the capabilities of modern APR techniques. We propose a new technique that utilizes the wealth of bug fixesacross projects in their development history to effectively guide and drive a programrepair process. Our main insight is that recurring bug fixes are common inreal-world applications, and that previously-appearing fix patterns canprovide useful guidance to an automated repair technique. Based on this insight, our technique first automaticallymines bug fix patterns from the history of many projects. We then employ existingmutation operators to generate fix candidates for a given buggy program. Candidates that match frequently occurring historical bug fixes are consideredmore likely to be relevant, and we thus give them priority inthe random search process. Finally, candidates thatpass all the previously failed test cases are recommended as likely fixes. We compare our technique against existinggenerate-and-validate and test-driven APR approaches using 90 bugs from 5 Javaprograms. The experiment results show that our technique can producegood-quality fixes for many more bugs as compared to the baselines, while beingreasonably computationally efficient: it takes less than 20minutes, on average, to correctly fix a bug.
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Cited by
Automated Program Repair in the Era of Large Pre-trained Language Models
Xia, C. S., Wei, Y., & Zhang, L. (2023) · 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) · doi:10.1109/icse48619.2023.00129 · Cited by 433
An Empirical Study on Learning Bug-Fixing Patches in the Wild via Neural Machine Translation
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Shaping program repair space with existing patches and similar code
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Context-aware patch generation for better automated program repair
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Metadata
- Authors
- Le, X. B. D., Lo, D., & Le Goues, C.
- Venue
- 2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering (SANER)
- Publisher
- IEEE
- Published
- 2016-03
- Pages
- 213-224
- OpenAlex
- W2400994325
- Cited by
- 332
- References
- 47
- FWCI
- 28.72
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Le, X. B. D., Lo, D., & Le Goues, C. (2016). History Driven Program Repair. 2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering (SANER). https://doi.org/10.1109/saner.2016.76
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