Towards Automatically Localizing Function Errors in Mobile Apps with User Reviews

Le Yu, Haoyu Wang, Xiapu Luo, Tao Zhang, Kang Liu, Jiachi Chen, Hao Zhou, Yutian Tang, Xusheng Xiao

Research output: Journal article publicationJournal articleAcademic researchpeer-review

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 focus on review classification, requirements engineering, sentiment analysis, and summarization [1]. They do not localize the function errors described in user reviews in apps' code. Moreover, recent studies on mapping reviews to problematic source files look for the matching between the words in reviews and that in source code, bug reports, commit messages, and stack traces, thus may result in false positives and false negatives since they do not consider the semantic meaning and part of speech tag of each word. 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 Review Solver, and carefully evaluate it with reviews of real apps. The experimental result shows that Review Solverhas much better performance than the state-of-the-art tools (i.e.,Change Advisor and Where2Change).
Original languageEnglish
Pages (from-to)1-21
JournalIEEE Transactions on Software Engineering
Volume14
Issue number8
DOIs
Publication statusPublished - 26 May 2022

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