A Dataset of High Impact Bugs: Manually-Classified Issue Reports

The importance of supporting test and maintenance
activities in software development has been increasing, since
recent software systems have become large and complex. Although
in the field of Mining Software Repositories (MSR) there
are many promising approaches to predicting, localizing, and
triaging bugs, most of them do not consider impacts of each
bug on users and developers but rather treat all bugs with equal
weighting, excepting a few studies on high impact bugs including
security, performance, blocking, and so forth. To make MSR
techniques more actionable and effective in practice, we need
deeper understandings of high impact bugs. In this paper we
introduced our dataset of high impact bugs which was created
by manually reviewing four thousand issue reports in four open
source projects (Ambari, Camel, Derby and Wicket).