  App stores allow users to give valuable feedback on apps, and developers to
find this feedback and use it for the software evolution. However, finding user
feedback that matches existing bug reports in issue trackers is challenging as
users and developers often use a different language. In this work, we introduce
DeepMatcher, an automatic approach using state-of-the-art deep learning methods
to match problem reports in app reviews to bug reports in issue trackers. We
evaluated DeepMatcher with four open-source apps quantitatively and
qualitatively. On average, DeepMatcher achieved a hit ratio of 0.71 and a Mean
Average Precision of 0.55. For 91 problem reports, DeepMatcher did not find any
matching bug report. When manually analyzing these 91 problem reports and the
issue trackers of the studied apps, we found that in 47 cases, users actually
described a problem before developers discovered and documented it in the issue
tracker. We discuss our findings and different use cases for DeepMatcher.
