Improving Automated Bug Triaging with Specialized Topic Model
Xin Xia, David Lo, Ying Ding, Jafar M. Al-Kofahi, Tien N. Nguyen, Xinyu Wang · IEEE Transactions on Software Engineering · 2016
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 namedmulti-feature topic model (MTM)which extends Latent Dirichlet Allocation (LDA) for bug triaging.MTMconsiders product and component information of bug reports to map the term space to the topic space. Finally, we propose an incremental learning method namedTopicMinerwhich 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 pairTopicMinerwith MTM (TopicMiner$^{MTM}$). 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 thatTopicMiner$^{MTM}$can achieve top-1 and top-5 prediction accuracies of 0.4831-0.6868, and 0.7686-0.9084, respectively. We also compareTopicMiner$^{MTM}$with Bugzie, LDA-KL, SVM-LDA, LDA-Activity, and Yang et al.'s approach. The results show thatTopicMiner$^{MTM}$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.