The DOI matches its Crossref record; 51 of the 51 references it cites were found in a registry or an index.
SEQUENCER: Sequence-to-Sequence Learning for End-to-End Program Repair
Open accessAbstract
This paper presents a novel end-to-end approach to program repair based on sequence-to-sequence learning. We devise, implement, and evaluate a technique, called SequenceR, for fixing bugs based on sequence-to-sequence learning on source code. This approach uses the copy mechanism to overcome the unlimited vocabulary problem that occurs with big code. Our system is data-driven; we train it on 35,578 samples, carefully curated from commits to open-source repositories. We evaluate SequenceR on 4,711 independent real bug fixes, as well on the Defects4J benchmark used in program repair research. SequenceR is able to perfectly predict the fixed line for 950/4,711 testing samples, and find correct patches for 14 bugs in Defects4J benchmark. SequenceR captures a wide range of repair operators without any domain-specific top-down design.
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Cites
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Sutskever, I., Vinyals, O., & Le, Q. V. (2014) · arXiv · doi:10.48550/arxiv.1409.3215 · Cited by 17,766
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Bahdanau, D., Cho, K., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1409.0473 · Cited by 16,213
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Get To The Point: Summarization with Pointer-Generator Networks
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Cited by
Unified Pre-training for Program Understanding and Generation
Ahmad, W., Chakraborty, S., Ray, B., & Chang, K.-W. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2021.naacl-main.211 · Cited by 642
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
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
“Many learning methods have been proposed for program repair [Allamanis et al., 2018b, Tarlow et al., 2019, Hellendoorn et al., 2019, Dinella et al., 2019, Yasunaga and Liang, 2020, Chen et al., 2019b, Pradel and Sen, 2018].”
MethodAutomated Repair of Programs from Large Language Models
Fan, Z., Gao, X., Mirchev, M., Roychoudhury, A., & Tan, S. H. (2023) · 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) · doi:10.1109/icse48619.2023.00128 · Cited by 256
Less training, more repairing please: revisiting automated program repair via zero-shot learning
Xia, C. S., & Zhang, L. (2022) · Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering · doi:10.1145/3540250.3549101 · Cited by 231
Metadata
- Authors
- Chen, Z., Kommrusch, S. J., Tufano, M., Pouchet, L.-N., Poshyvanyk, D., & Monperrus, M.
- Venue
- IEEE Transactions on Software Engineering
- Published
- 2021
- Pages
- 1-1
- ISSN
- 0098-5589 (ISSN-L)
- 1939-3520
- 2326-3881
- OpenAlex
- W2907705732
- Cited by
- 396
- References
- 51
- FWCI
- 45.92
- Type
- journal article
- Language
- en
- Open access
- green
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