Improving automated bug triaging with specialized topic model

• Main Authors: XIA, Xin, LO, David, DING, Ying, AL-KOFAHI, Jafar M., NGUYEN, Tien N., WANG, Xinyu
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2017
• Subjects: bug triaging; Developer; feature information; topic model; Databases and Information Systems; Information Security
• Online Access: https://ink.library.smu.edu.sg/sis_research/3691; https://ink.library.smu.edu.sg/context/sis_research/article/4693/viewcontent/ImprovingAutomatedBugTriaging_2017.pdf
• Institution: Singapore Management University

Bug triaging refers to the process of assigning a bug to the most appropriate developer to fix. It becomes more and more difficult and complicated as the size of software and the number of developers increase. In this paper, we propose a new framework for bug triaging, which maps the words in the bug reports (i.e., the term space) to their corresponding topics (i.e., the topic space). We propose a specialized topic modeling algorithm named multi-feature topic model (MTM) which extends Latent Dirichlet Allocation (LDA) for bug triaging. MTM considers product and component information of bug reports to map the term space to the topic space. Finally, we propose an incremental learning method named TopicMiner which considers the topic distribution of a new bug report to assign an appropriate fixer based on the affinity of the fixer to the topics. We pair TopicMiner with MTM (TopicMinerMTM ). We have evaluated our solution on 5 large bug report datasets including GCC, OpenOffice, Mozilla, Netbeans, and Eclipse containing a total of 227,278 bug reports. We show that TopicMiner MTM can achieve top-1 and top-5 prediction accuracies of 0.4831-0.6868, and 0.7686-0.9084, respectively. We also compare TopicMinerMTM with Bugzie, LDA-KL, SVM-LDA, LDA-Activity, and Yang et al.'s approach. The results show that TopicMiner MTM on average improves top-1 and top-5 prediction accuracies of Bugzie by 128.48 and 53.22 percent, LDA-KL by 262.91 and 105.97 percent, SVM-LDA by 205.89 and 110.48 percent, LDA-Activity by 377.60 and 176.32 percent, and Yang et al.'s approach by 59.88 and 13.70 percent, respectively.