ML Anthology

Deep Learning Meets Software Engineering: A Survey on Pre-Trained Models of Source Code

Changan Niu, Chuanyi Li, Bin Luo, Vincent Ng
IJCAI 2022 pp. 5546-5555
doi:10.24963/IJCAI.2022/775 /ijcai/2022/niu2022ijcai-deep/

Abstract

Recent years have seen the successful application of deep learning to software engineering (SE). In particular, the development and use of pre-trained models of source code has enabled state-of-the-art results to be achieved on a wide variety of SE tasks. This paper provides an overview of this rapidly advancing field of research and reflects on future research directions.

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Cite

Text

Niu et al. "Deep Learning Meets Software Engineering: A Survey on Pre-Trained Models of Source Code." International Joint Conference on Artificial Intelligence, 2022. doi:10.24963/IJCAI.2022/775

Markdown

[Niu et al. "Deep Learning Meets Software Engineering: A Survey on Pre-Trained Models of Source Code." International Joint Conference on Artificial Intelligence, 2022.](https://mlanthology.org/ijcai/2022/niu2022ijcai-deep/) doi:10.24963/IJCAI.2022/775

BibTeX

@inproceedings{niu2022ijcai-deep,
  title     = {{Deep Learning Meets Software Engineering: A Survey on Pre-Trained Models of Source Code}},
  author    = {Niu, Changan and Li, Chuanyi and Luo, Bin and Ng, Vincent},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2022},
  pages     = {5546-5555},
  doi       = {10.24963/IJCAI.2022/775},
  url       = {https://mlanthology.org/ijcai/2022/niu2022ijcai-deep/}
}