Automated Bug Reproduction from User Reviews for Android Applications

Bug-related user reviews of mobile applications have negative influence on their reputation and competence, and thus these reviews are highly regarded by developers. Before bug fixing, developers need to manually reproduce the bugs reported in user reviews, which is an extremely time-consuming and tedious task. Hence, it is highly expected to automate this process. However, it is challenging to do so since user reviews are hard to understand and poorly informative for bug reproduction (especially lack of reproduction steps). In this paper, we propose RepRev to automatically Reproduce Android application bugs from user Reviews. Specifically, RepRev leverages natural language processing techniques to extract valuable information for bug reproduction. Then, it ranks GUI components by semantic similarity with the user review and dynamically searches on apps with a novel one-step exploration technique. To evaluate RepRev, we construct a benchmark including 63 crash-related user reviews from Google Play, which have been reproduced successfully by three graduate students. On this benchmark, RepRev presents comparable performance with humans, which successfully reproduces 44 user reviews in our benchmark (about 70%) with 432.2 seconds average time. We make the implementation of our approach publicly available, along with the artifacts and experimental data we used [4].