The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Repairing Programs with Semantic Code Search (T)
Abstract
Automated program repair can potentially reduce debugging costs and improve software quality but recent studies have drawn attention to shortcomings in the quality of automatically generated repairs. We propose a new kind of repair that uses the large body of existing open-source code to find potential fixes. The key challenges lie in efficiently finding code semantically similar (but not identical) to defective code and then appropriately integrating that code into a buggy program. We present SearchRepair, a repair technique that addresses these challenges by(1) encoding a large database of human-written code fragments as SMT constraints on input-output behavior, (2) localizing a given defect to likely buggy program fragments and deriving the desired input-output behavior for code to replace those fragments, (3) using state-of-the-art constraint solvers to search the database for fragments that satisfy that desired behavior and replacing the likely buggy code with these potential patches, and (4) validating that the patches repair the bug against program testsuites. We find that SearchRepair repairs 150 (19%) of 778 benchmark C defects written by novice students, 20 of which are not repaired by GenProg, TrpAutoRepair, and AE. We compare the quality of the patches generated by the four techniques by measuring how many independent, not-used-during-repairtests they pass, and find that SearchRepair-repaired programs pass 97.3% ofthe tests, on average, whereas GenProg-, TrpAutoRepair-, and AE-repaired programs pass 68.7%, 72.1%, and 64.2% of the tests, respectively. We concludethat SearchRepair produces higher-quality repairs than GenProg, TrpAutoRepair, and AE, and repairs some defects those tools cannot.
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Citations
Cites
de Moura, L., & Bjørner, N. (2008) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-78800-3_24 · Cited by 6,595
An analysis of patch plausibility and correctness for generate-and-validate patch generation systems
Qi, Z., Long, F., Achour, S., & Rinard, M. (2015) · Proceedings of the 2015 International Symposium on Software Testing and Analysis · doi:10.1145/2771783.2771791 · Cited by 420
The ManyBugs and IntroClass benchmarks for automated repair of C programs
Le Goues, C., Holtschulte, N., Smith, E. K., Brun, Y., Devanbu, P., Forrest, S., & Weimer, W. (2015) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2015.2454513 · Cited by 326 · cited 4×
“IntroClass is intended specifically for evaluating automatic program repair research [43].”
BackgroundMethodInfluentialSelf-citationIs the cure worse than the disease? overfitting in automated program repair
Smith, E. K., Barr, E. T., Le Goues, C., & Brun, Y. (2015) · Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering · doi:10.1145/2786805.2786825 · Cited by 323
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Cited by
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
DeepFL: integrating multiple fault diagnosis dimensions for deep fault localization
Li, X., Li, W., Zhang, Y., & Zhang, L. (2019) · Proceedings of the 28th ACM SIGSOFT International Symposium on Software Testing and Analysis · doi:10.1145/3293882.3330574 · Cited by 336
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
Is the cure worse than the disease? overfitting in automated program repair
Smith, E. K., Barr, E. T., Le Goues, C., & Brun, Y. (2015) · Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering · doi:10.1145/2786805.2786825 · Cited by 323
Self-citationAn Analysis of the Search Spaces for Generate and Validate Patch Generation Systems
Long, F., & Rinard, M. (2016) · Proceedings of the 38th International Conference on Software Engineering · doi:10.1145/2884781.2884872 · Cited by 167
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
(2025) · doi:10.1109/icse55347.2025.00157 · Cited by 157
A comparison of code similarity analysers
Ragkhitwetsagul, C., Krinke, J., & Clark, D. (2018) · Empirical Software Engineering · doi:10.1007/s10664-017-9564-7 · Cited by 122
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
Self-citationAutomated 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
Metadata
- Authors
- Ke, Y., Stolee, K. T., Goues, C. L., & Brun, Y.
- Venue
- 2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE)
- Publisher
- IEEE
- Published
- 2015-11
- Pages
- 295-306
- OpenAlex
- W1475493299
- Cited by
- 192
- References
- 4
- FWCI
- 21.21
- Type
- conference paper
- Language
- en
- Open access
- closed
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