  Automatic crash reporting systems have become a de-facto standard in software
development. These systems monitor target software, and if a crash occurs they
send details to a backend application. Later on, these reports are aggregated
and used in the development process to 1) understand whether it is a new or an
existing issue, 2) assign these bugs to appropriate developers, and 3) gain a
general overview of the application's bug landscape. The efficiency of report
aggregation and subsequent operations heavily depends on the quality of the
report similarity metric. However, a distinctive feature of this kind of report
is that no textual input from the user (i.e., bug description) is available: it
contains only stack trace information.
  In this paper, we present S3M ("extreme") -- the first approach to computing
stack trace similarity based on deep learning. It is based on a siamese
architecture that uses a biLSTM encoder and a fully-connected classifier to
compute similarity. Our experiments demonstrate the superiority of our approach
over the state-of-the-art on both open-sourced data and a private JetBrains
dataset. Additionally, we review the impact of stack trace trimming on the
quality of the results.
