Paper: Learning from evolving data streams: online triage of bug reports

ACL ID E12-1063
Title Learning from evolving data streams: online triage of bug reports
Venue Annual Meeting of The European Chapter of The Association of Computational Linguistics
Session Main Conference
Year 2012
Authors

Open issue trackers are a type of social me- dia that has received relatively little atten- tion from the text-mining community. We investigate the problems inherent in learn- ing 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 available.