  The task of finding the best developer to fix a bug is called bug triage.
Most of the existing approaches consider the bug triage task as a
classification problem, however, classification is not appropriate when the
sets of classes change over time (as developers often do in a project).
Furthermore, to the best of our knowledge, all the existing models use textual
sources of information, i.e., bug descriptions, which are not always available.
  In this work, we explore the applicability of existing solutions for the bug
triage problem when stack traces are used as the main data source of bug
reports. Additionally, we reformulate this task as a ranking problem and
propose new deep learning models to solve it. The models are based on a
bidirectional recurrent neural network with attention and on a convolutional
neural network, with the weights of the models optimized using a ranking loss
function. To improve the quality of ranking, we propose using additional
information from version control system annotations. Two approaches are
proposed for extracting features from annotations: manual and using an
additional neural network. To evaluate our models, we collected two datasets of
real-world stack traces. Our experiments show that the proposed models
outperform existing models adapted to handle stack traces. To facilitate
further research in this area, we publish the source code of our models and one
of the collected datasets.
