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Large language models of code have shown remarkable effectiveness across various software engineering tasks.
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DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
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Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf. 2019b · 2019
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Distilling task-specific knowledge from bert into simple neural networks
Raphael Tang, Yao Lu, Linqing Liu, Lili Mou, Olga Vechtomova, and Jimmy Lin. 2019 · 2019
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Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks. In Advances in Neural Information Processing Systems , H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, and R. Garnett (Eds.), Vol. 32. Curran Associates, Inc
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Gareth Ari Aye and Gail E Kaiser. 2020 · 2020
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Angela Fan, Edouard Grave, and Armand Joulin. 2020 · 2020
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CodeBERT: A Pre-Trained Model for Programming and Natural Languages. In Findings of the Association for Computational Linguistics: EMNLP 2020 . Association for Computational Linguistics, Online, 1536–1547
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Deep Learning Meets Software Engineering: A Survey on Pre-Trained Models of Source Code. In Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI 2022, Vienna, Austria, 23-29 July 2022 , Luc De Raedt (Ed.). ijcai.org, 5546–5555
Changan Niu, Chuanyi Li, Bin Luo, and Vincent Ng. 2022 · 2022
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Can identifier splitting improve open-vocabulary language model of code?. In 2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) . IEEE, 1134–1138
Jieke Shi, Zhou Yang, Junda He, Bowen Xu, and David Lo. 2022a · 2022
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Natural Attack for Pre-Trained Models of Code. In Proceedings of the 44th International Conference on Software Engineering (Pittsburgh, Pennsylvania) (ICSE ’22) . Association for Computing Machinery, New York, NY, USA, 1482–1493
Zhou Yang, Jieke Shi, Junda He, and David Lo. 2022 · 2022
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An Extensive Study on Pre-Trained Models for Program Understanding and Generation. In Proceedings of the 31st ACM SIGSOFT International Symposium on Software Testing and Analysis (Virtual, South Korea) (ISSTA 2022) . Association for Computing Machinery, New York, NY, USA, 39–51
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Diet code is healthy: Simplifying programs for pre-trained models of code. In Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering . 1073–1084
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Large Language Models for Software Engineering: A Systematic Literature Review
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An Empirical Comparison of Pre-Trained Models of Source Code. In 45th IEEE/ACM International Conference on Software Engineering, ICSE 2023, Melbourne, Australia, May 14-20, 2023 . IEEE, 2136–2148
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