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