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Automated program repair is the task of automatically repairing software bugs.
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C. L. Goues, M. Pradel, and A. Roychoudhury, “Automated program repair,” Communications of the ACM , vol. 62, no. 12, pp. 56–65, 2019
2019
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M. Yasunaga and P. Liang, “Break-it-fix-it: Unsupervised learning for program repair,” in International Conference on Machine Learning (ICML) , 2021
2021
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K. Liu, A. Koyuncu, D. Kim, and T. F. Bissyandé, “Tbar: revisiting template-based automated program repair,” in Proceedings of the 28th ACM SIGSOFT International Symposium on Software Testing and Analysis , 2019, pp. 31–42
2019
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Z. Chen, S. J. Kommrusch, M. Tufano, L.-N. Pouchet, D. Poshyvanyk, and M. Monperrus, “Sequencer: Sequence-to-sequence learning for end-to-end program repair,” IEEE Transactions on Software Engineering , 2019
2019
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F. Madeiral, S. Urli, M. Maia, and M. Monperrus, “Bears: An extensible java bug benchmark for automatic program repair studies,” in 2019 IEEE 26th International Conference on Software Analysis, Evolution and Reengineering (SANER) . IEEE, 2019, pp. 468–478
2019
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M. Tufano, C. Watson, G. Bavota, M. Di Penta, M. White, and D. Poshyvanyk, “Learning how to mutate source code from bug-fixes,” in 2019 IEEE International Conference on Software Maintenance and Evolution (ICSME) . IEEE, 2019, pp. 301–312
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S. Saha et al. , “Harnessing evolution for multi-hunk program repair,” in 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE) . IEEE, 2019, pp. 13–24
2019
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K. Liu, A. Koyuncu, T. F. Bissyandé, D. Kim, J. Klein, and Y. Le Traon, “You cannot fix what you cannot find! an investigation of fault localization bias in benchmarking automated program repair systems,” in 2019 12th IEEE conference on software testing, validation and verification (ICST) . IEEE, 2019, pp. 102–113
2019
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J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,” in Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) , 2019, pp. 4171–4186
2019
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W. Ahmad, S. Chakraborty, B. Ray, and K.-W. Chang, “Unified pre-training for program understanding and generation,” in Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies . Association for Computational Linguistics, Jun. 2021, pp. 2655–2668
2021
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Q. Zhu, Z. Sun, Y.-a. Xiao, W. Zhang, K. Yuan, Y. Xiong, and L. Zhang, “A syntax-guided edit decoder for neural program repair,” in Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering , 2021, pp. 341–353
2021
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Y. Li, S. Wang, and T. Nguyen, “Fault localization with code coverage representation learning,” in 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE) . IEEE, 2021, pp. 661–673
2021
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N. Jiang, T. Lutellier, and L. Tan, “Cure: Code-aware neural machine translation for automatic program repair,” in 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE) . IEEE, 2021, pp. 1161–1173
2021
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Y. Wang, W. Wang, S. Joty, and S. C. Hoi, “Codet5: Identifier-aware unified pre-trained encoder-decoder models for code understanding and generation,” in Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing , 2021, pp. 8696–8708
2021
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2021
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2021
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H. Ye, M. Martinez, and M. Monperrus, “Neural program repair with execution-based backpropagation,” in Proceedings of the 44th International Conference on Software Engineering , 2022, pp. 1506–1518
2022
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Z. Chen, S. J. Kommrusch, and M. Monperrus, “Neural transfer learning for repairing security vulnerabilities in c code,” IEEE Transactions on Software Engineering , 2022
2022
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H. Ye, M. Martinez, X. Luo, T. Zhang, and M. Monperrus, “Selfapr: Self-supervised program repair with test execution diagnostics,” in 37th IEEE/ACM International Conference on Automated Software Engineering , 2022, pp. 1–13
2022
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B. Loriot, F. Madeiral, and M. Monperrus, “Styler: learning formatting conventions to repair checkstyle violations,” Empirical Software Engineering , vol. 27, no. 6, pp. 1–36, 2022
2022
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J. Wang, S. Si, Z. Zhu, X. Qu, Z. Hong, and J. Xiao, “Leveraging causal inference for explainable automatic program repair,” in 2022 International Joint Conference on Neural Networks (IJCNN) . IEEE, 2022, pp. 1–6
2022
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2022
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