基于深度学习的代码审查意见有效性评估

doi:10.16180/j.cnki.issn1007-7820.2020.01.008

Abstract

Abstract:

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

DUAN Yujia,JU Ting. Evaluation of Code Review Comments Based on Deep Learning[J].Electronic Science and Technology, 2020, 33(1): 39-45.

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References 20

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	Bayes	SVM	RNN	LSTM
准确率	0.597	0.715	0.802	0.884
召回率	0.625	0.613	0.633	0.627
F₁ score	0.611	0.661	0.708	0.734

Evaluation of Code Review Comments Based on Deep Learning

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