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Automated program repair is a crucial task for improving the efficiency of software developers.
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2017
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M. Monperrus, “Automatic software repair: A bibliography,” ACM Computing Surveys , vol. 51, no. 1, pp. 17:1–17:24, 2018
2018
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2018
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M. Monperrus, “The living review on automated program repair,” HAL Archives Ouvertes, [Technical Report] hal-01956501, 2018
2018
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J. Hua, M. Zhang, K. Wang, and S. Khurshid, “Towards practical program repair with on-demand candidate generation,” in Proceedings of the 40th International Conference on Software Engineering , 2018, pp. 12–23
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Y. Wang, W. Wang, S. R. Joty, and S. C. H. 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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A. Afzal, M. Motwani, K. T. Stolee, Y. Brun, and C. L. Goues, “Sosrepair: Expressive semantic search for real-world program repair,” IEEE Transactions on Software Engineering , vol. 47, no. 10, pp. 2162–2181, 2021
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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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L. Gazzola, D. Micucci, and L. Mariani, “Automatic software repair: A survey,” IEEE Transactions on Software Engineering , vol. 45, no. 1, pp. 34–67, 2019
2019
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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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M. Tufano, C. Watson, G. Bavota, M. D. Penta, M. White, and D. Poshyvanyk, “An empirical study on learning bug-fixing patches in the wild via neural machine translation,” ACM Transactions on Software Engineering and Methodology , vol. 28, no. 4, pp. 19:1–19:29, 2019
2019
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A. Ghanbari, S. Benton, and L. Zhang, “Practical program repair via bytecode mutation,” in Proceedings of the 28th ACM SIGSOFT International Symposium on Software Testing and Analysis , 2019, pp. 19–30
2019
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S. Saha, R. K. Saha, and M. R. Prasad, “Harnessing evolution for multi-hunk program repair,” in Proceedings of the 41st International Conference on Software Engineering , 2019, pp. 13–24
2019
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F. Madeiral, S. Urli, M. de Almeida Maia, and M. Monperrus, “BEARS: an extensible java bug benchmark for automatic program repair studies,” in Proceedings of the 26th IEEE International Conference on Software Analysis, Evolution and Reengineering , 2019, pp. 468–478
2019
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2019
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2021
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N. Jiang, T. Lutellier, and L. Tan, “CURE: code-aware neural machine translation for automatic program repair,” in Proceedings of the 43rd IEEE/ACM International Conference on Software Engineering , 2021, pp. 1161–1173
2021
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E. Mashhadi and H. Hemmati, “Applying codebert for automated program repair of java simple bugs,” in Proceedings of the 18th IEEE/ACM International Conference on Mining Software Repositories , 2021, pp. 505–509
2021
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S. Chakraborty and B. Ray, “On multi-modal learning of editing source code,” in Proceedings of the 36th IEEE/ACM International Conference on Automated Software Engineering , 2021, pp. 443–455
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2021
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2021
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H. Ye, M. Martinez, and M. Monperrus, “Automated patch assessment for program repair at scale,” Empirical Software Engineering , vol. 26, no. 2, p. 20, 2021
2021
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X. Gao, Y. Noller, and A. Roychoudhury, “Program repair,” CoRR , vol. abs/2211.12787, 2022
2022
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C. S. Xia and L. Zhang, “Less training, more repairing please: Revisiting automated program repair via zero-shot learning,” in Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering , 2022, pp. 959–971
2022
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2022
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Y. Li, S. Wang, and T. N. Nguyen, “DEAR: A novel deep learning-based approach for automated program repair,” in Proceedings of the 44th IEEE/ACM 44th International Conference on Software Engineering , 2022, pp. 511–523
2022
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W. Zhong, H. Ge, H. Ai, C. Li, K. Liu, J. Ge, and B. Luo, “Standup4npr: Standardizing setup for empirically comparing neural program repair systems,” in Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering , 2022, pp. 97:1–97:13
2022
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2022
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H. Ye, M. Martinez, and M. Monperrus, “Neural program repair with execution-based backpropagation,” in Proceedings of the 44th IEEE/ACM 44th International Conference on Software Engineering , 2022, pp. 1506–1518
2022
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Y. Noller, R. Shariffdeen, X. Gao, and A. Roychoudhury, “Trust enhancement issues in program repair,” in Proceedings of the 44th IEEE/ACM International Conference on Software Engineering , 2022, pp. 2228–2240
2022
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