  Many contemporary software products have subsystems for automatic crash
reporting. However, it is well-known that the same bug can produce slightly
different reports. To manage this problem, reports are usually grouped, often
manually by developers. Manual triaging, however, becomes infeasible for
products that have large userbases, which is the reason for many different
approaches to automating this task. Moreover, it is important to improve
quality of triaging due to the big volume of reports that needs to be processed
properly. Therefore, even a relatively small improvement could play a
significant role in overall accuracy of report bucketing. The majority of
existing studies use some kind of a stack trace similarity metric, either based
on information retrieval techniques or string matching methods. However, it
should be stressed that the quality of triaging is still insufficient. In this
paper, we describe TraceSim -- a novel approach to address this problem which
combines TF-IDF, Levenshtein distance, and machine learning to construct a
similarity metric. Our metric has been implemented inside an industrial-grade
report triaging system. The evaluation on a manually labeled dataset shows
significantly better results compared to baseline approaches.
