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基于深度学习的代码生成方法研究进展

Summary

This survey, written in Chinese, reviews deep learning methods that turn natural-language requirements into source code in general-purpose languages such as Python, Java and C++. It treats this text-to-code task as separate from code completion. The authors queried six literature databases, including ACM DL, IEEE Xplore, Google Scholar and CNKI. They then filtered the results by hand and used forward and backward snowballing to arrive at a curated set of studies. The survey sorts these approaches into three families. The first, feature-based generation, splits into supervised encoder–decoder models and pre-trained models. The other two families build on it, either by adding retrieval of external code or knowledge, or by adding post-processing steps such as reranking, test-based filtering or compiler feedback. The paper also catalogues the benchmark corpora and evaluation metrics used in this area, noting a shift from translation-style scores toward test-based ones. Finally, it discusses open problems, including ambiguous user intent, very large output search spaces and limited human evaluation. For the library, it serves as a structured map of code-generation research up to the arrival of ChatGPT and GPT-4.

Key points

  • The corpus covers 54 directly relevant papers collected up to November 2022. Output rose sharply after CodeBERT appeared in 2020, with 22 papers in 2022 alone. Industry reports such as PanGu-Coder and AlphaCode are included even though they were not peer-reviewed.
  • The supervised line of work traced in the survey begins with Ling et al. (2016) on card-game code for HearthStone and Magic the Gathering. It continues through syntax-aware decoders: Yin et al.'s AST-action model, abstract syntax networks (ASDL), TranX with reranking, a grammar-based CNN and TreeGen. Iyer et al. contributed context-aware generation along with the CONCODE dataset, which is now part of CodeXGLUE.
  • The post-processing and retrieval families include CodeT (generated tests), CodeRL (reinforcement learning), Jigsaw, and compiler-feedback approaches.
  • The benchmarks discussed include CONCODE, APPS, MBPP, MultiPL-E, DS-1000, AixBench, Lyra, XLCoST and MCoNaLa. The metrics covered are BLEU, CodeBLEU and test-based measures such as SPoC-style pass rates.
  • On bibliographic details: the paper appeared online in October 2023, and the issue is Journal of Software 2024, 35(2): 604–628, doi 10.13328/j.cnki.jos.006981.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Linguistics & languages · Natural language processing

Cited by

1 paper of our library cites it (of the 140673 whose bibliographies we read); each has a page here.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.13328/j.cnki.jos.006981

@article{2023,
  title = {{基于深度学习的代码生成方法研究进展}},
  author = {杨泽洲 and 陈思榕 and 高翠芸 and 李振昊 and 李戈 and 吕荣聪},
  journal = {软件学报 (Journal of Software)},
  year = {2023},
  doi = {10.13328/j.cnki.jos.006981},
  url = {https://doi.org/10.13328/j.cnki.jos.006981}
}

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