  For a given software bug report, identifying an appropriate developer who
could potentially fix the bug is the primary task of a bug triaging process. A
bug title (summary) and a detailed description is present in most of the bug
tracking systems. Automatic bug triaging algorithm can be formulated as a
classification problem, with the bug title and description as the input,
mapping it to one of the available developers (classes). The major challenge is
that the bug description usually contains a combination of free unstructured
text, code snippets, and stack trace making the input data noisy. The existing
bag-of-words (BOW) feature models do not consider the syntactical and
sequential word information available in the unstructured text. We propose a
novel bug report representation algorithm using an attention based deep
bidirectional recurrent neural network (DBRNN-A) model that learns a syntactic
and semantic feature from long word sequences in an unsupervised manner.
Instead of BOW features, the DBRNN-A based bug representation is then used for
training the classifier. Using an attention mechanism enables the model to
learn the context representation over a long word sequence, as in a bug report.
To provide a large amount of data to learn the feature learning model, the
unfixed bug reports (~70% bugs in an open source bug tracking system) are
leveraged, which were completely ignored in the previous studies. Another
contribution is to make this research reproducible by making the source code
available and creating a public benchmark dataset of bug reports from three
open source bug tracking system: Google Chromium (383,104 bug reports), Mozilla
Core (314,388 bug reports), and Mozilla Firefox (162,307 bug reports).
Experimentally we compare our approach with BOW model and machine learning
approaches and observe that DBRNN-A provides a higher rank-10 average accuracy.
