Localizing Function Errors in Mobile Apps with User Reviews

Abstract

Removing all function errors is critical for making successful mobile apps. Since app testing may miss some function errors given limited time and resource, the user reviews of mobile apps are very important to developers for learning the uncaught errors. Unfortunately, manually handling each review is time-consuming and even error-prone. Existing studies on mobile apps' reviews could not help developers effectively locate the problematic code according to the reviews, because the majority of such research does not take into account apps' code. Moreover, recent studies on mapping reviews to problematic source files just look for the matching between the words in reviews and that in source code, and thus result in many false positives and false negatives. In this paper, we propose a novel approach to localize function errors in mobile apps by exploiting the context information in user reviews and correlating the reviews and bytecode through their semantic meanings. We realize our new approach as a tool named ReviewSolver, and carefully evaluate it with reviews of real apps. The experimental result shows that ReviewSolver has much better performance than the state-of-the-art tool.