  Bug localization aims to reduce debugging time by recommending program
elements that are relevant for a specific bug report. To date, researchers have
primarily addressed this problem by applying different information retrieval
techniques that leverage similarities between a given bug report and source
code. However, with modern software development trending towards increased
speed of software change and continuous delivery to the user, the current
generation of bug localization techniques, which cannot quickly adapt to the
latest version of the software, is becoming inadequate. In this paper, we
propose a technique for online bug localization, which enables rapidly
updatable bug localization models. More specifically, we propose a streaming
bug localization technique, based on an ensemble of online topic models, that
is able to adapt to both specific (with explicit code mentions) and more
abstract bug reports. By using changesets (diffs) as the input instead of a
snapshot of the source code, the model naturally integrates defect prediction
and co-change information into its prediction. Initial results indicate that
the proposed approach improves bug localization performance for 42 out of 56
evaluation projects, with an average MAP improvement of 5.9%.
