TY - JOUR
T1 - Towards Automatically Localizing Function Errors in Mobile Apps With User Reviews
AU - Yu, Le
AU - Wang, Haoyu
AU - Luo, Xiapu
AU - Zhang, Tao
AU - Liu, Kang
AU - Chen, Jiachi
AU - Zhou, Hao
AU - Tang, Yutian
AU - Xiao, Xusheng
N1 - Publisher Copyright:
© 1976-2012 IEEE.
PY - 2023/4/1
Y1 - 2023/4/1
N2 - 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).
AB - 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).
KW - Function error localization
KW - mobile apps
KW - user reviews
UR - http://www.scopus.com/inward/record.url?scp=85142446731&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85142446731&partnerID=8YFLogxK
U2 - 10.1109/TSE.2022.3178096
DO - 10.1109/TSE.2022.3178096
M3 - Article
AN - SCOPUS:85142446731
SN - 0098-5589
VL - 49
SP - 1464
EP - 1486
JO - IEEE Transactions on Software Engineering
JF - IEEE Transactions on Software Engineering
IS - 4
ER -