DeepReview:Automatic Code Review Using Deep Multi-instance Learning

摘要：

　　Code review,an inspection of code changes in order to identify and fix defects before integration,is essential in Software Quality Assurance(SQA).Code review is a time-consuming task since the reviewers need to understand,analysis and provide comments manually.To alleviate the burden of reviewers,automatic code review is needed.However,this task has not been well studied before.To bridge this research gap,in this paper,we formalize automatic code review as a multi-instance learning task that each change consisting of multiple hunks is regarded as a bag,and each hunk is described as an instance.We propose a novel deep learning model named DeepReview based on Convolutional Neural Network(CNN),which is an end-to-end model that learns feature representation to predict whether one change is approved or rejected.Experimental results on open source projects show that DeepReview is effective in automatic code review tasks.In terms of F1 score and AUC,DeepReview outperforms the performance of traditional single-instance based model TFIDF-SVM and the state-of-the-art deep feature based model Deeper.

关键词： Software mining Machine learning Multi-instance learning Automatic code review

作者: Heng-Yi Li Shu-Ting Shi Ferdian Thung Xuan Huo Bowen Xu Ming Li David Lo

作者单位: National Key Laboratory for Novel Software Technology,Nanjing University,Nanjing 210023,China School of Information Systems,Singapore Management University,Singapore,Singapore

会议类型: 国际会议

会议名称: The 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining (第23届亚太知识发现和数据挖掘国际会议(PAKDD2019)

会议地点: 澳门

会议语种:英文

页码: 318-330

在线出版日期: 2019-04-14（万方平台首次上网日期，不代表论文的发表时间）