Learning from evolving data streams: online triage of bug reports
In: EACL 2012, Avignon, France(2012).
Open issue trackers are a type of social media that has received relatively little attention from the text-mining community. We
investigate the problems inherent in learning to triage bug reports from time-varying
data. We demonstrate that concept drift is
an important consideration. We show the
effectiveness of online learning algorithms
by evaluating them on several bug report
datasets collected from open issue trackers
associated with large open-source projects.
We make this collection of data publicly