The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Sorting and Transforming Program Repair Ingredients via Deep Learning Code Similarities
Open accessAbstract
In the field of automated program repair, the redundancy assumption claims large programs contain the seeds of their own repair. However, most redundancy-based program repair techniques do not reason about the repair ingredients- the code that is reused to craft a patch. We aim to reason about the repair ingredients by using code similarities to prioritize and transform statements in a codebase for patch generation. Our approach, DeepRepair, relies on deep learning to reason about code similarities. Code fragments at well-defined levels of granularity in a codebase can be sorted according to their similarity to suspicious elements (i.e., code elements that contain suspicious statements) and statements can be transformed by mapping out-of-scope identifiers to similar identifiers in scope. We examined these new search strategies for patch generation with respect to effectiveness from the viewpoint of a software maintainer. Our comparative experiments were executed on six open-source Java projects including 374 buggy program revisions and consisted of 19,949 trials spanning 2,616 days of computation time. Deep-Repair's search strategy using code similarities generally found compilable ingredients faster than the baseline, jGenProg, but this improvement neither yielded test-adequate patches in fewer attempts (on average) nor found significantly more patches (on average) than the baseline. Although the patch counts were not statistically different, there were notable differences between the nature of DeepRepair patches and jGenProg patches. The results show that our learning-based approach finds patches that cannot be found by existing redundancy-based repair techniques.
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Cites
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An analysis of patch plausibility and correctness for generate-and-validate patch generation systems
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Cited by
SEQUENCER: Sequence-to-Sequence Learning for End-to-End Program Repair
Chen, Z., Kommrusch, S. J., Tufano, M., Pouchet, L.-N., Poshyvanyk, D., & Monperrus, M. (2021) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2019.2940179 · Cited by 396
Self-citationAn 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
Self-citationShaping 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
A Systematic Literature Review on the Use of Deep Learning in Software Engineering Research
Watson, C., Cooper, N., Palacio, D. N., Moran, K., & Poshyvanyk, D. (2022) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3485275 · Cited by 137
Self-citationPerfection Not Required? Human-AI Partnerships in Code Translation
Weisz, J. D., Muller, M., Houde, S., Richards, J., Ross, S. I., Martinez, F., Agarwal, M., & Talamadupula, K. (2021) · 26th International Conference on Intelligent User Interfaces · doi:10.1145/3397481.3450656 · Cited by 129
Metadata
- Authors
- White, M., Tufano, M., Martinez, M., Monperrus, M., & Poshyvanyk, D.
- Venue
- 2019 IEEE 26th International Conference on Software Analysis, Evolution and Reengineering (SANER)
- Publisher
- IEEE
- Published
- 2019-02
- Pages
- 479-490
- OpenAlex
- W2736762043
- Cited by
- 163
- References
- 12
- FWCI
- 16.54
- Type
- conference paper
- Language
- en
- Open access
- green
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