Yu, L., Wang, H., Luo, X., Zhang, T., Liu, K., Chen, J., Zhou, H., Tang, Y. and Xiao, X. (2023) Towards automatically localizing function errors in mobile apps with user reviews. IEEE Transactions on Software Engineering, 49(4), pp. 1464-1486. (doi: 10.1109/TSE.2022.3178096)
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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 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 tools (i.e., ChangeAdvisor and Where2Change ).
| Item Type: | Articles |
|---|---|
| Additional Information: | This work was supported in part by the Hong Kong RGC Projects under Grants PolyU15223918 and PolyU15224121, in part by HKPolyU Start-up Fund (BD7H), by the National Natural Science Foundation of China under Grants 61972359 and 61831022, in part by the Zhejiang Provincial Natural Science Foundation of China under Grant LY19F020052, and in part by National Science Foundation https://doi.org/10.13039/100000001 under the Grant CCF-2046953. |
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Tang, Dr Yutian |
| Authors: | Yu, L., Wang, H., Luo, X., Zhang, T., Liu, K., Chen, J., Zhou, H., Tang, Y., and Xiao, X. |
| College/School: | College of Science and Engineering > School of Computing Science |
| Journal Name: | IEEE Transactions on Software Engineering |
| Publisher: | IEEE |
| ISSN: | 0098-5589 |
| ISSN (Online): | 1939-3520 |
| Published Online: | 26 May 2022 |
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