Issue Tracking systems (ITS) such as Google Code Hosting and Bugzilla facilitate software maintenance activities through bug reporting, archiving and fixing. The large number of bug reports and their unstructured text makes it impractical for developers to manually extract actionable intelligence to expedite bug fixing. In this paper, we present an application of mining bug report description and threaded discussion comments using Latent Dirichlet Allocation (LDA) which is a topic modeling technique. We apply LDA on the Chromium Browser Project bug archives (open-source) to extract topics (discovery of semantically related terms) and the latent semantic relationship between documents (bug reports) and extracted topics for corpus exploration, trend analysis and understanding evolution in maintenance domain. We conduct a series of experiments to uncover latent topics potentially useful for developers and testers based on the bug meta-data such as time, priority, type, category and status. The analysis of reopened and duplicate bugs in particular, has important inferences for the developers and can help in applications such as expertise modeling, resource allocation and knowledge management.
['Ayushi Aggarwal', 'Gajendra Waghmare', 'Ashish Sureka']
Mining issue tracking systems using topic models for trend analysis, corpus exploration, and understanding evolution