Electronic Science and Technology ›› 2020, Vol. 33 ›› Issue (1): 39-45.doi: 10.16180/j.cnki.issn1007-7820.2020.01.008

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Evaluation of Code Review Comments Based on Deep Learning

DUAN Yujia,JU Ting   

  1. School of Computer Science and Technology,Hangzhou Dianzi University,Hangzhou 310000,China
  • Received:2018-12-29 Online:2020-01-15 Published:2020-03-12
  • Supported by:
    Natural Science Foundation of Zhejiang(LQ17F020003)


Aiming at the code review comments during the code review process may be of no value to developers, a code review comments evaluation method based on deep learning Long Short-Term Memory networks was proposed, which can effectively extract the features related to validity of the code review comments, then a code review comments evaluation model based on these features was built to judge whether the review comments are useful to the developer. In order to verify the method, the extensive experiments were conducted based on the open source Eclipse project on GitHub as experimental data, and this paper compared the method with other machine learning methods. The experimental results demonstrated that the method effectively evaluate whether the review comment is meaningful and significantly better than the comparison methods.

Key words: software engineering, code review, deep learning, long short-term memory, word embedding, natural language processing

CLC Number: 

  • TP391.1