Yuan Huang
Yingfei Xiong
ABSTRACT
Code commenting is a common programming practice of practical importance to help developers review and comprehend source code. However, there is a lack of thorough specifications to help developers make their commenting decisions in current practice. To reduce the effort of making commenting decisions, we propose a novel method, CommentSuggester, to guide developers regarding appropriate commenting locations in the source code. We extract context information of source code and employ machine learning techniques to identify possible commenting locations in the source code. The encouraging experimental results demonstrated the feasibility and effectiveness of our commenting suggestion method.
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Cross Ref
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- Lili Mou, Ge Li, Lu Zhang, Tao Wang, and Zhi Jin. Convolutional neural networks over tree structures for programming language processing. In Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, AAAI' 16, pages 1287--1293, 2016. Google ScholarDigital Library
- Y. Huang, Q. Zheng, X. Chen, Y. Xiong, Z. Liu, and X. Luo. Mining version control system for automatically generating commit comment. In 2017 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM), pages 414--423, Nov 2017. Google ScholarDigital Library
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