Towards Semi-automatic Bug Triage and Severity Prediction Based on Topic Model and Multi-feature of Bug Reports

Abstract

Bug fixing is an essential activity in the software maintenance, because most of the software systems have unavoidable defects. When new bugs are submitted, triagers have to find and assign appropriate developers to fix the bugs. However, if the bugs are at first assigned to inappropriate developers, they may later have to be reassigned to other developers. That increases the time and cost for fixing bugs. Therefore, finding appropriate developers becomes a key to bug resolution. When triagers assign a new bug report, it is necessary to decide how quickly the bug report should be addressed. Thus, the bug severity is an important factor in bug fixing. In this paper, we propose a novel method for the bug triage and bug severity prediction. First, we extract topic(s) from historical bug reports in the bug repository and find bug reports related to each topic. When a new bug report arrives, we decide the topic(s) to which the report belongs. Then we utilize multi-feature to identify corresponding reports that have the same multi-feature (e.g., Component, product, priority and severity) with the new bug report. Thus, given a new bug report, we are able to recommend the most appropriate developer to fix each bug and predict its severity. To evaluate our approach, we not only measured the effectiveness of our study by using about 30,000 golden bug reports extracted from three open source projects (Eclipse, Mozilla, and Net beans), but also compared some related studies. The results show that our approach is likely to effectively recommend the appropriate developer to fix the given bug and predict its severity.