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Automated Program Repair (APR) has evolved significantly with the advent of Large Language Models (LLMs).
R. Just, D. Jalali, and M. D. Ernst, “Defects4j: A database of existing faults to enable controlled testing studies for java programs,” in Proceedings of the 2014 International Symposium on Software Testing and Analysis . ACM, 2014, pp. 437–440
2014
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J.-R. Falleri, F. Morandat, X. Blanc, M. Martinez, and M. Monperrus, “Fine-grained and accurate source code differencing,” in Proceedings of the 29th ACM/IEEE international conference on Automated software engineering , 2014, pp. 313–324
2014
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R. Pawlak, M. Monperrus, N. Petitprez, C. Noguera, and L. Seinturier, “Spoon: A library for implementing analyses and transformations of java source code,” Softw. Pract. Exper. , vol. 46, no. 9, p. 1155–1179, Sep. 2016. [Online]. Available: https://doi.org/10.1002/spe.2346
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R. Gupta, S. Pal, A. Kanade, and S. Shevade, “Deepfix: Fixing common c language errors by deep learning,” in Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence , ser. AAAI’17. AAAI Press, 2017, p. 1345–1351
2017
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M. Monperrus, “Automatic software repair: A bibliography,” ACM Computing Surveys (CSUR) , vol. 51, no. 1, pp. 1–24, 2018
2018
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Z. Chen, S. 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 , vol. 47, no. 9, pp. 1943–1959, 2019
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 , ser. ICSE ’19, 2019, p. 13–24
2019
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Z. Chen, S. J. Kommrusch, M. Tufano, L. 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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M. Yasunaga and P. Liang, “Graph-based, self-supervised program repair from diagnostic feedback,” in International Conference on Machine Learning . PMLR, 2020, pp. 10 799–10 808
2020
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C. Le Goues, M. Pradel, A. Roychoudhury, and S. Chandra, “Automatic program repair,” IEEE Software , vol. 38, no. 4, pp. 22–27, 2021
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 , ser. ESEC/FSE 2021. ACM, 2021, p. 341–353
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 ACM/IEEE 43rd International Conference on Software Engineering , 2021
2021
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Z. Chen, V. J. Hellendoorn, P. Lamblin, P. Maniatis, P.-A. Manzagol, D. Tarlow, and S. Moitra, “Plur: A unifying, graph-based view of program learning, understanding, and repair,” Advances in Neural Information Processing Systems , vol. 34, pp. 23 089–23 101, 2021
2021
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E. J. Hu, P. Wallis, Z. Allen-Zhu, Y. Li, S. Wang, L. Wang, W. Chen et al. , “Lora: Low-rank adaptation of large language models,” in International Conference on Learning Representations , 2021
2021
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M. Monperrus, M. Martinez, H. Ye, F. Madeiral, T. Durieux, and Z. Yu, “Megadiff: A dataset of 600k java source code changes categorized by diff size,” 2021
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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2021
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K. Abhinav, V. Sharvani, A. Dubey, M. D’Souza, N. Bhardwaj, S. Jain, and V. Arora, “Repairnet: contextual sequence-to-sequence network for automated program repair,” in International Conference on Artificial Intelligence in Education . Springer, 2021, pp. 3–15
2021
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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 Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering , 2022, pp. 1–13
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
Earlier work this paper cites.
W. Yuan, Q. Zhang, T. He, C. Fang, N. Q. V. Hung, X. Hao, and H. Yin, “Circle: Continual repair across programming languages,” in Proceedings of the 31st ACM SIGSOFT International Symposium on Software Testing and Analysis , 2022, pp. 678–690
2022
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2022
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M. Namavar, N. Nashid, and A. Mesbah, “A controlled experiment of different code representations for learning-based program repair,” Empirical Software Engineering , vol. 27, no. 7, p. 190, 2022
2022
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2022
Cited alongside, same era.
J. Zhang, S. Panthaplackel, P. Nie, J. J. Li, and M. Gligoric, “Coditt5: Pretraining for source code and natural language editing,” in Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering , 2022, pp. 1–12
2022
Cited alongside, same era.
Z. Chen, S. Kommrusch, and M. Monperrus, “Neural transfer learning for repairing security vulnerabilities in c code,” IEEE Transactions on Software Engineering , vol. 49, no. 1, pp. 147–165, 2022
2022
Cited alongside, same era.
C. Wang, Y. Yang, C. Gao, Y. Peng, H. Zhang, and M. R. Lyu, “No more fine-tuning? an experimental evaluation of prompt tuning in code intelligence,” in Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering , 2022, pp. 382–394
2023
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2022
Cited alongside, same era.
N. Jiang, K. Liu, T. Lutellier, and L. Tan, “Impact of code language models on automated program repair,” in 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) , 2023, pp. 1430–1442
2023
Cited alongside, same era.
C. S. Xia, Y. Ding, and L. Zhang, “The plastic surgery hypothesis in the era of large language models,” in 2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE) . IEEE, 2023, pp. 522–534
2023
Cited alongside, same era.
W. Wang, Y. Wang, S. Joty, and S. C. Hoi, “Rap-gen: Retrieval-augmented patch generation with codet5 for automatic program repair,” in Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering , 2023, pp. 146–158
2023
Cited alongside, same era.
H. Joshi, J. C. Sanchez, S. Gulwani, V. Le, G. Verbruggen, and I. Radiček, “Repair is nearly generation: Multilingual program repair with llms,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 37, no. 4, 2023, pp. 5131–5140
2023
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Z. Fu, H. Yang, A. M.-C. So, W. Lam, L. Bing, and N. Collier, “On the effectiveness of parameter-efficient fine-tuning,” in Proceedings of the AAAI conference on artificial intelligence , vol. 37, no. 11, 2023, pp. 12 799–12 807
2023
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Y. Choi and J.-H. Lee, “Codeprompt: Task-agnostic prefix tuning for program and language generation,” in Findings of the Association for Computational Linguistics: ACL 2023 , 2023, pp. 5282–5297
2023
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2024
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A. Silva, N. Saavedra, and M. Monperrus, “Gitbug-java: A reproducible benchmark of recent java bugs,” in 2024 IEEE/ACM 21st International Conference on Mining Software Repositories (MSR) . IEEE, 2024, pp. 118–122
2024
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2024
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“Repairllama - statistical test,” https://github.com/ASSERT-KTH/repairllama/blob/main/src/patch_analysis/statistical_testing.md , [Accessed 05-11-2024]
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M. AI, “Codestral: Hello, world!” https://mistral.ai/news/codestral/ , 2024, [Accessed 04-12-2024]
2024
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A. Zirak and H. Hemmati, “Improving automated program repair with domain adaptation,” ACM Transactions on Software Engineering and Methodology , vol. 33, no. 3, pp. 1–43, 2024
2024
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S. B. Hossain, N. Jiang, Q. Zhou, X. Li, W.-H. Chiang, Y. Lyu, H. Nguyen, and O. Tripp, “A deep dive into large language models for automated bug localization and repair,” Proceedings of the ACM on Software Engineering , vol. 1, no. FSE, pp. 1471–1493, 2024
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