DeepTriage: Exploring the Effectiveness of Deep Learning for Bug Triaging

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, which takes the bug title and description as the input, mapping it to one of the available developers (class labels). 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 highly noisy. The existing bag-of-words (BOW) feature models do not consider the syntactical and sequential word information available in the unstructured text. In this research, 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 (constitute about 70% bugs in an open source bug tracking system) are leveraged upon as an important contribution of this research, which were completely ignored in the previous studies. Another major 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, Mozilla Core, and Mozilla Firefox. For our experiments, we use 383,104 bug reports from Google Chromium, 314,388 bug reports from Mozilla Core, and 162,307 bug reports from Mozilla Firefox. Experimentally we compare our approachwith BOWmodel and softmax classifier, support vector machine, naive Bayes, and cosine distance and observe that DBRNN-A provides a higher rank-10 average accuracy.