Effort-Aware Tri-Training for Semi-supervised Just-in-Time Defect Prediction

摘要：

　　In recent years,just-in-time(JIT)defect prediction has gained considerable interest as it enables developers to identify risky changes at check-in time.Previous studies tried to conduct research from both supervised and unsupervised perspectives.Since the label of change is hard to acquire,it would be more desirable for applications if a prediction model doesnt highly rely on the label information.However,the performance of the unsupervised models proposed by previous work isnt good in terms of precision and F1 due to the lack of supervised information.To overcome this weakness,we try to study the JIT defect prediction from the semi-supervised perspective,which only requires a few labeled data for training.In this paper,we propose an Effort-Aware Tri-Training(EATT)semi-supervised model for JIT defect prediction based on sample selection.We compare EATT with the state-of-the-art supervised and unsupervised models with respect to different labeled rates.The experimental results on six open-source projects demonstrate that EATT performs better than existing supervised and unsupervised models for effort-aware JIT defect prediction.

关键词： Defect prediction Just-in-time Tri-training Effort-aware

作者: Wenzhou Zhang Weiwei Li Xiuyi Jia

作者单位: School of Computer Science and Engineering,Nanjing University of Science and Technology,Nanjing 2100 College of Astronautics,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China School of Computer Science and Engineering,Nanjing University of Science and Technology,Nanjing 2100

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: 293-304

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）