  As part of the process of resolving issues submitted by users via bug
reports, Android developers attempt to reproduce and observe the failures
described by the bug report. Due to the low-quality of bug reports and the
complexity of modern apps, the reproduction process is non-trivial and
time-consuming. Therefore, automatic approaches that can help reproduce Android
bug reports are in great need. However, current approaches to help developers
automatically reproduce bug reports are only able to handle limited forms of
natural language text and struggle to successfully reproduce failures for which
the initial bug report had missing or imprecise steps. In this paper, we
introduce a new fully automated Android bug report reproduction approach that
addresses these limitations. Our approach accomplishes this by leveraging
natural language process techniques to more holistically and accurately analyze
the natural language in Android bug reports and designing new techniques, based
on reinforcement learning, to guide the search for successful reproducing
steps. We conducted an empirical evaluation of our approach on 77 real world
bug reports. Our approach achieved 67% precision and 77% recall in accurately
extracting reproduction steps from bug reports, and reproduced 74% of the bug
reports, significantly outperforming state of the art techniques.

Software Engineering