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Automatic Bug Triage using Semi-Supervised Text Classification

被引：0
|
作者：
Xuan, Jifeng [1 ]
Jiang, He [2 ,3 ]
Ren, Zhilei [1 ]
Yan, Jun [4 ]
Luo, Zhongxuan [1 ,2 ]
机构：
[1] Dalian Univ Technol, Sch Math Sci, Dalian 116024, Peoples R China
[2] Dalian Univ Technol, Sch Software, Dalian 116621, Peoples R China
[3] Chinese Acad Sci, Inst Software, State Key Lab Comp Sci, Beijing 100190, Peoples R China
[4] Chinese Acad Sci, Inst Software, Technol Ctr Software Engn, Beijing 100190, Peoples R China
来源：
22ND INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING & KNOWLEDGE ENGINEERING (SEKE 2010) | 2010年
关键词：
automatic bug triage; expectation-maximization; semi-supervised text classification; weighted recommendation list;
D O I：
暂无
中图分类号：
TP31 [计算机软件];
学科分类号：
081202 ; 0835 ;
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 expectation-maximization 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.
引用
收藏
页码：209 / 214
页数：6
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