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Large language models such as Codex, have shown the capability to produce code for many programming tasks.
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S. H. Tan, H. Yoshida, M. R. Prasad, and A. Roychoudhury, “Anti-patterns in search-based program repair,” in Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering , 2016, pp. 727–738
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S. H. Tan, H. Yoshida, M. R. Prasad, and A. Roychoudhury, “Anti-patterns in search-based program repair,” in Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering . ACM, 2016, pp. 727–738
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J. Xuan, M. Martinez, F. DeMarco, M. Clement, S. L. Marcote, T. Durieux, D. L. Berre, and M. Monperrus, “Nopol: Automatic repair of conditional statement bugs in java programs,” IEEE Transactions on Software Engineering , vol. PP, no. 99, pp. 1–1, 2016
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S. H. Tan, J. Yi, S. Mechtaev, A. Roychoudhury et al. , “Codeflaws: a programming competition benchmark for evaluating automated program repair tools,” in 2017 IEEE/ACM 39th International Conference on Software Engineering Companion (ICSE-C) . IEEE, 2017, pp. 180–182
2017
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J. Yi, U. Z. Ahmed, A. Karkare, S. H. Tan, and A. Roychoudhury, “A feasibility study of using automated program repair for introductory programming assignments,” in Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering , 2017, pp. 740–751
2017
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R. Gupta, S. Pal, A. Kanade, and S. Shevade, “Deepfix: Fixing common c language errors by deep learning,” in Thirty-First AAAI Conference on Artificial Intelligence , 2017
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2018
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M. Wen, J. Chen, R. Wu, D. Hao, and S.-C. Cheung, “Context-aware patch generation for better automated program repair,” in 2018 IEEE/ACM 40th International Conference on Software Engineering (ICSE) . IEEE, 2018, pp. 1–11
2018
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J. Jiang, Y. Xiong, H. Zhang, Q. Gao, and X. Chen, “Shaping program repair space with existing patches and similar code,” ser. ISSTA, 2018
2018
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S. Mechtaev, X. Gao, S. H. Tan, and A. Roychoudhury, “Test-equivalence analysis for automatic patch generation,” ACM Transactions on Software Engineering and Methodology (TOSEM) , vol. 27, 2018
2018
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Y. Yuan and W. Banzhaf, “Arja: Automated repair of java programs via multi-objective genetic programming,” IEEE Transactions on software engineering , vol. 46, no. 10, pp. 1040–1067, 2018
2018
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R. Puri, D. S. Kung, G. Janssen, W. Zhang, G. Domeniconi, V. Zolotov, J. Dolby, J. Chen, M. Choudhury, L. Decker et al. , “Project codenet: a large-scale ai for code dataset for learning a diversity of coding tasks,” ArXiv. Available at https://arxiv. org/abs , vol. 2105, 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
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H. Ye, J. Gu, M. Martinez, T. Durieux, and M. Monperrus, “Automated classification of overfitting patches with statically extracted code features,” IEEE Transactions on Software Engineering , 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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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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C. L. Goues, M. Pradel, and A. Roychoudhury, “Automated program repair,” Communications of the ACM , vol. 62, pp. 56–65, 2019
2019
Cited alongside, same era.
K. Liu, A. Koyuncu, D. Kim, and T. F. Bissyandé, “Avatar: Fixing semantic bugs with fix patterns of static analysis violations,” in 2019 IEEE 26th International Conference on Software Analysis, Evolution and Reengineering (SANER) . IEEE, 2019, pp. 1–12
2019
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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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T. Lutellier, H. V. Pham, L. Pang, Y. Li, M. Wei, and L. Tan, “Coconut: combining context-aware neural translation models using ensemble for program repair,” in Proceedings of the 29th ACM SIGSOFT international symposium on software testing and analysis , 2020, pp. 101–114
2020
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S. Wang, M. Wen, B. Lin, H. Wu, Y. Qin, D. Zou, X. Mao, and H. Jin, “Automated patch correctness assessment: How far are we?” in Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering , 2020, pp. 968–980
2020
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A. Ghanbari, “Objsim: lightweight automatic patch prioritization via object similarity,” in Proceedings of the 29th ACM SIGSOFT International Symposium on Software Testing and Analysis , 2020, pp. 541–544
2020
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2021
Later among the works it cites.
2021
Later among the works it cites.
K. Rahmani, M. Raza, S. Gulwani, V. Le, D. Morris, A. Radhakrishna, G. Soares, and A. Tiwari, “Multi-modal program inference: A marriage of pre-trained language models and component-based synthesis,” Proc. ACM Program. Lang. , vol. 5, no. OOPSLA, oct 2021. [Online]. Available: https://doi.org/10.1145/3485535
2021
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2021
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2022
Closest in time.
“Codex edit mode,” 2022. [Online]. Available: https://openai.com/blog/gpt-3-edit-insert
2022
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“Codex model,” 2022. [Online]. Available: https://https://beta.openai.com/playground
2022
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“Leetcode contest,” 2022. [Online]. Available: https://leetcode.com/contest
2022
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2022
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“Amazon codewhisperer,” 2022. [Online]. Available: https://aws.amazon.com/codewhisperer/
2022
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N. Jain, S. Vaidyanath, A. Iyer, N. Natarajan, S. Parthasarathy, S. Rajamani, and R. Sharma, “Jigsaw: Large language models meet program synthesis,” in Proceedings of the 44th International Conference on Software Engineering , 2022, pp. 1219–1231
2022
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N. Nguyen and S. Nadi, “An empirical evaluation of github copilot’s code suggestions,” in 2022 IEEE/ACM 19th International Conference on Mining Software Repositories (MSR) . IEEE, 2022, pp. 1–5
2022
Closest in time.
J. A. Prenner, H. Babii, and R. Robbes, “Can openai’s codex fix bugs?: An evaluation on quixbugs,” in 2022 IEEE/ACM International Workshop on Automated Program Repair (APR) . IEEE, 2022, pp. 69–75
2022
Closest in time.