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Sequence-to-sequence models have been used to transform erroneous programs into correct ones when trained with a large enough dataset.
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2019
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T. Lutellier, V. H. Pham, L. Pang, Y. Li, M. Wei, and L. Tan, “Coconut: Combining context-aware neural translation models using ensemble for program repair,” 2020
2020
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2020
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2020
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2022
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M. L. Siddiq, S. H. Majumder, M. R. Mim, S. Jajodia, and J. C. S. Santos, “An empirical study of code smells in transformer-based code generation techniques,” in 2022 IEEE 22nd Int’l Working Conf. on Source Code Analysis and Manipulation (SCAM) , 2022, pp. 71–82
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M. A. Hadi, I. N. B. Yusuf, F. Thung, K. G. Luong, J. Lingxiao, F. H. Fard, and D. Lo, “On the effectiveness of pretrained models for api learning,” in Proc. of the 30th IEEE/ACM Int’l Conf. on Program Comprehension , ser. ICPC ’22. New York, NY, USA: ACM, 2022, p. 309–320. [Online]. Available: https://doi.org/10.1145/3524610.3527886
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R. Logan IV, I. Balazevic, E. Wallace, F. Petroni, S. Singh, and S. Riedel, “Cutting down on prompts and parameters: Simple few-shot learning with language models,” in Findings of the Association for Computational Linguistics: ACL 2022 . Dublin, Ireland: Association for Computational Linguistics, May 2022, pp. 2824–2835. [Online]. Available: https://aclanthology.org/2022.findings-acl.222
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J. A. Prenner, H. Babii, and R. Robbes, “Can openai’s codex fix bugs?: An evaluation on quixbugs,” in 2022 IEEE/ACM Intl. Workshop on Automated Program Repair (APR) , 2022, pp. 69–75
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2023
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M. L. Siddiq, A. Samee, S. R. Azgor, M. A. Haider, S. I. Sawraz, and J. C. Santos, “Zero-shot prompting for code complexity prediction using github copilot,” in 2023 The 2nd Intl. Workshop on NL-based Software Engineering , 2023
2023
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M. L. Siddiq, J. C. S. Santos, R. H. Tanvir, N. Ulfat, F. A. Rifat, and V. C. Lopes, “Exploring the effectiveness of large language models in generating unit tests,” 2023
2023
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2023
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2023
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P. Liu, W. Yuan, J. Fu, Z. Jiang, H. Hayashi, and G. Neubig, “Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing,” ACM Comput. Surv. , vol. 55, no. 9, jan 2023. [Online]. Available: https://doi.org/10.1145/3560815
2023
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