Assisting bug Triage in Large Open Source Projects Using Approximate String Matching

In this paper, we propose a novel approach for assisting human bug triagers in large open source software projects by semi-automating the bug assignment process. Our approach employs a simple and efficient n-gram-based algorithm for approximate string matching on the character level. We propose and implement a recommender prototype which collects the natural language textual information available in the summary and description fields of the previously resolved bug reports and classifies that information in a number of separate inverted lists with respect to the resolver of each issue. These inverted lists are considered as vocabulary-based expertise and interest models of the developers. Given a new bug report, the recommender creates all possible n-grams of the strings, evaluates their similarities to the available expertise models concerning a number of well-known string similarity measures, namely Cosine, Dice, Jaccard and Overlap coefficients. Finally, the top three developers are recommended as proper candidates for resolving this new issue. Experimental results on 5200 bug reports of the Eclipse JDT project show weighted average precision value of 90.1% and weighted average recall value of 45.5%. Keywords-software deployment and maintenance; semiautomated bug triage; approximate string retrieval; open source software.