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Efficient bug triaging procedures are an important precondition forsuccessful collaborative software engineering projects. Triaging bugs canbecome a laborious task particularly in open source software (OSS) projectswith a large base of comparably inexperienced part-time contributors. In thispaper, we propose an efficient and practical method to identify valid bugreports which a) refer to an actual software bug, b) are not duplicates and c)contain enough information to be processed right away. Our classification isbased on nine measures to quantify the social embeddedness of bug reporters inthe collaboration network. We demonstrate its applicability in a case study,using a comprehensive data set of more than 700,000 bug reports obtained fromthe Bugzilla installation of four major OSS communities, for a period of morethan ten years. For those projects that exhibit the lowest fraction of validbug reports, we find that the bug reporters' position in the collaborationnetwork is a strong indicator for the quality of bug reports. Based on thisfinding, we develop an automated classification scheme that can easily beintegrated into bug tracking platforms and analyze its performance in theconsidered OSS communities. A support vector machine (SVM) to identify validbug reports based on the nine measures yields a precision of up to 90.3% withan associated recall of 38.9%. With this, we significantly improve the resultsobtained in previous case studies for an automated early identification of bugsthat are eventually fixed. Furthermore, our study highlights the potential ofusing quantitative measures of social organization in collaborative softwareengineering. It also opens a broad perspective for the integration of socialawareness in the design of support infrastructures.

Categorizing Bugs with Social Networks: A Case Study on Four Open Source Software Communities