linguistic analysis of bug report titles with respect to the dimension of bug importance
We perform linguistic analysis of bug-report titles obtained from the publicly available Bugzilla defect tracking tool for the open-source Firefox browser (Mozilla project) and present the results of our analysis. Our motivation is to gain insights on how people describe software defects and do a feasibility study on the possibility of building a predictive model (a classifier) for categorizing bug report based only on the titles to one of the predefined severity levels (bug importance). We observed that in general bug titles do not contain enough information for automatically predicting its importance with high accuracy. However, we noticed that two of the bug importance categories such as critical and enhancement have characteristics or features in the title that can be exploited to assign the correct severity level. We perform statistical analysis on part-of-speech, word frequency and distribution across various severity levels.
bug report mining text analytics data mining