ISCAS OpenIR  > 2010软件所会议论文
automatic bug triage using semi-supervised text classification
Xuan Jifeng; Jiang He; Ren Zhilei; Yan Jun; Luo Zhongxuan
2010
会议名称22nd International Conference on Software Engineering and Knowledge Engineering, SEKE 2010
会议录名称SEKE 2010 - Proceedings of the 22nd International Conference on Software Engineering and Knowledge Engineering
页码209-214
会议日期44013
会议地点Redwood City, CA, United states
收录类别ei
出版地United States
ISBN1891706268
部门归属(1) School of Mathematical Sciences, Dalian University of Technology, Dalian, 116024, China; (2) School of Software, Dalian University of Technology, Dalian, 116621, China; (3) State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing, 100190, China; (4) Technology Center of Software Engineering, Institute of Software, Chinese Academy of Sciences, Beijing, 100190, China
摘要In this paper, we propose a semi-supervised text classification approach for bug triage to avoid the deficiency of labeled bug reports in existing supervised approaches. This new approach combines naive Bayes classifier and expectationmaximization to take advantage of both labeled and unlabeled bug reports. This approach trains a classifier with a fraction of labeled bug reports. Then the approach iteratively labels numerous unlabeled bug reports and trains a new classifier with labels of all the bug reports. We also employ a weighted recommendation list to boost the performance by imposing the weights of multiple developers in training the classifier. Experimental results on bug reports of Eclipse show that our new approach outperforms existing supervised approaches in terms of classification accuracy.
关键词Knowledge Engineering Locomotives Maximum Principle Optimization Railroad Cars Software Engineering
主办者Knowledge Systems Institute Graduate School
语种英语
内容类型会议论文
URI标识http://ir.iscas.ac.cn/handle/311060/8674
专题2010软件所会议论文
推荐引用方式
GB/T 7714
Xuan Jifeng,Jiang He,Ren Zhilei,et al. automatic bug triage using semi-supervised text classification[C]. United States,2010:209-214.
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