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A Survey of Automatic Generation of Source Code Comments: Algorithms and Techniques

  • Published:2019 Issue: Volume:7 Page:111411-111428
  • ISSN:2169-3536
  • Container-title:IEEE Access
  • language:
  • Short-container-title:IEEE Access

Author:

Song Xiaotao,Sun HailongORCID,Wang Xu,Yan Jiafei

Funder

National Basic Research Program of China

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science

Cited by 31 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Semantic similarity loss for neural source code summarization;Journal of Software: Evolution and Process;2024-07-07

2. Context-Focused Prompt Tuning Pre-Trained Code Models to Improve Code Summarization;2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC);2024-07-02

3. Do Code Summarization Models Process Too Much Information? Function Signature May Be All That Is Needed;ACM Transactions on Software Engineering and Methodology;2024-06-27

4. MESIA: Understanding and Leveraging Supplementary Nature of Method-level Comments for Automatic Comment Generation;Proceedings of the 32nd IEEE/ACM International Conference on Program Comprehension;2024-04-15

5. Beyond Syntax: Unleashing the Power of Computational Notebooks Code Metrics in Documentation Generation;Proceedings of the IEEE/ACM 3rd International Conference on AI Engineering - Software Engineering for AI;2024-04-14
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