Learning in the presence of concept drift and hidden contexts
Machine Learning
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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 available.