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To support software developers in finding and fixing software bugs, several automated program repair techniques have been introduced.
C. Le Goues, T. Nguyen, S. Forrest, and W. Weimer, “GenProg: A generic method for automatic software repair,” Ieee transactions on software engineering , vol. 38, no. 1, pp. 54–72, 2011
2011
Earlier work this paper cites.
2015
Earlier work this paper cites.
T. Durieux and M. Monperrus, “Dynamoth: dynamic code synthesis for automatic program repair,” in Proceedings of the 11th International Workshop on Automation of Software Test , 2016, pp. 85–91
2016
Earlier work this paper cites.
J. Xuan, M. Martinez, F. Demarco, M. Clement, S. L. Marcote, T. Durieux, D. Le Berre, and M. Monperrus, “Nopol: Automatic repair of conditional statement bugs in Java programs,” IEEE Transactions on Software Engineering , vol. 43, no. 1, pp. 34–55, 2016
2016
Earlier work this paper cites.
W. E. Wong, X. Li, and P. A. Laplante, “Be more familiar with our enemies and pave the way forward: A review of the roles bugs played in software failures,” Journal of Systems and Software , vol. 133, pp. 68–94, 2017
2017
Earlier work this paper cites.
S. O. Haraldsson, J. R. Woodward, A. E. Brownlee, and K. Siggeirsdottir, “Fixing bugs in your sleep: How genetic improvement became an overnight success,” in Proceedings of the Genetic and Evolutionary Computation Conference Companion , 2017, pp. 1513–1520
2017
Earlier work this paper cites.
J. Petke, S. O. Haraldsson, M. Harman, W. B. Langdon, D. R. White, and J. R. Woodward, “Genetic improvement of software: a comprehensive survey,” IEEE Transactions on Evolutionary Computation , vol. 22, no. 3, pp. 415–432, 2017
2017
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in neural information processing systems , 2017, pp. 5998–6008
2017
Cited alongside, same era.
D. Lin, J. Koppel, A. Chen, and A. Solar-Lezama, “QuixBugs: A multi-lingual program repair benchmark set based on the Quixey Challenge,” in Proceedings Companion of the 2017 ACM SIGPLAN international conference on systems, programming, languages, and applications: software for humanity , 2017, pp. 55–56
2017
Cited alongside, same era.
L. Gazzola, D. Micucci, and L. Mariani, “Automatic software repair: a survey,” in Proceedings of the 40th International Conference on Software Engineering, ICSE 2018, Gothenburg, Sweden, May 27 - June 03, 2018 , M. Chaudron, I. Crnkovic, M. Chechik, and M. Harman, Eds. ACM, 2018, p. 1219. [Online]. Available: https://doi.org/10.1145/3180155.3182526
2018
Cited alongside, same era.
C. Clement, D. Drain, J. Timcheck, A. Svyatkovskiy, and N. Sundaresan, “PyMT5: Multi-mode translation of natural language and Python code with transformers,” in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) , 2020, pp. 9052–9065
2020
Later among the works it cites.
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 ISSTA ’20: 29th ACM SIGSOFT International Symposium on Software Testing and Analysis, Virtual Event, USA, July 18-22, 2020 , S. Khurshid and C. S. Pasareanu, Eds. ACM, 2020, pp. 101–114. [Online]. Available: https://doi.org/10.1145/3395363.3397369
2020
Later among the works it cites.
2021
Later among the works it cites.
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2018
Cited alongside, same era.
M. Martinez and M. Monperrus, “Ultra-large repair search space with automatically mined templates: The cardumen mode of astor,” in International Symposium on Search Based Software Engineering . Springer, 2018, pp. 65–86
2018
Cited alongside, same era.
M. Martinez and M. Monperrus, “Astor: Exploring the design space of generate-and-validate program repair beyond GenProg,” Journal of Systems and Software , vol. 151, pp. 65–80, 2019
2019
Cited alongside, same era.
Z. Feng, D. Guo, D. Tang, N. Duan, X. Feng, M. Gong, L. Shou, B. Qin, T. Liu, D. Jiang et al. , “CodeBERT: A pre-trained model for programming and natural languages,” in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings , 2020, pp. 1536–1547
2020
Cited alongside, same era.
H. Ye, M. Martinez, T. Durieux, and M. Monperrus, “A comprehensive study of automatic program repair on the QuixBugs benchmark,” Journal of Systems and Software , vol. 171, p. 110825, 2021
2021
Later among the works it cites.
D. Sobania, M. Briesch, and F. Rothlauf, “Choose your programming copilot: a comparison of the program synthesis performance of GitHub Copilot and genetic programming,” in Proceedings of the Genetic and Evolutionary Computation Conference , 2022, pp. 1019–1027
2022
Later among the works it cites.
J. A. Prenner, H. Babii, and R. Robbes, “Can OpenAI’s codex fix bugs? an evaluation on QuixBugs,” in Proceedings of the Third International Workshop on Automated Program Repair , 2022, pp. 69–75
2022
Later among the works it cites.