Open Access
Issue
Wuhan Univ. J. Nat. Sci.
Volume 29, Number 4, August 2024
Page(s) 349 - 356
DOI https://doi.org/10.1051/wujns/2024294349
Published online 04 September 2024
  1. Gupta P, Mehrotra N, Purandare R. JCoffee: Using compiler feedback to make partial code snippets compilable[C]//2020 IEEE International Conference on Software Maintenance and Evolution (ICSME). New York: IEEE, 2020: 810-813. [CrossRef] [Google Scholar]
  2. Thummalapenta S, Xie T. Parseweb: A programmer assistant for reusing open source code on the web[C]//Proceedings of the 22nd IEEE/ACM International Conference on Automated Software Engineering. New York: ACM, 2007: 204-213 . [CrossRef] [Google Scholar]
  3. Zhou Y Q, Liu S Q, Siow J K, et al. Devign: Effective vulnerability identification by learning comprehensive program semantics via graph neural networks[C]//Proceedings of the 33rd International Conference on Neural Information Processing Systems. New York: Curran Associates Inc, 2019:10197-10207. [Google Scholar]
  4. Phan H, Nguyen H A, Tran N M, et al. Statistical learning of API fully qualified names in code snippets of online forums[C]//Proceedings of the 40th International Conference on Software Engineering. New York: ACM, 2018: 632-642 . [CrossRef] [Google Scholar]
  5. Khaled Saifullah C M, Asaduzzaman M, Roy C K. Learning from examples to find fully qualified names of API elements in code snippets[C]//2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE). New York: IEEE, 2019: 243-254. [Google Scholar]
  6. Dong Y W, Gu T X, Tian Y Q, et al. SNR: Constraint-based type inference for incomplete Java code snippets[C]//Proceedings of the 44th International Conference on Software Engineering. New York: ACM, 2022: 1982-1993. [CrossRef] [Google Scholar]
  7. Huang Q, Yuan Z Q, Xing Z C, et al. Prompt-tuned code language model as a neural knowledge base for type inference in statically-typed partial code[C]//Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering. New York: ACM, 2022: 1-13 . [Google Scholar]
  8. Feng Z Y, Guo D Y, Tang D Y, et al. Codebert: A pre-trained model for programming and natural languages. [2020-11-03]. https://arXivpreprintarXiv:2002.08155. [Google Scholar]
  9. Allamanis M, Barr E T, Devanbu P, et al. A survey of machine learning for big code and naturalness[J]. ACM Computing Surveys, 2019, 51(4): 1-37. [CrossRef] [Google Scholar]
  10. Guo D, Ren S, Lu S, et al. Analyzing CodeBERT's performance on natural language code search[EB/OL]. [2022-11-03]. https://api.semanticscholar.org/CorpusID:252587541. [Google Scholar]
  11. Wu Y H, Schuster M, Chen Z F, et al. Google's neural machine translation system: Bridging the gap between human and machine translation[EB/OL]. [2016-12-26]. http://arxiv.org/abs/1609.08144. [Google Scholar]
  12. Papineni K, Roukos S, Ward T, et al. BLEU: A method for automatic evaluation of machine translation[C]//Proceedings of the 40th Annual Meeting on Association for Computational Linguistics — ACL '02. Morristown: Association for Computational Linguistics, 2001: 311-318. [CrossRef] [Google Scholar]

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