TY - GEN
T1 - PAID
T2 - 26th IEEE International Symposium on Software Reliability Engineering, ISSRE 2015
AU - Gao, Cuiyun
AU - Wang, Baoxiang
AU - He, Pinjia
AU - Zhu, Jieming
AU - Zhou, Yangfan
AU - Lyu, Michael R.
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2016/1/13
Y1 - 2016/1/13
N2 - User review analysis is critical to the bug-fixing and version-modification process for app developers. Many research efforts have been put to user review mining in discovering app issues, including laggy user interface, high memory overhead, privacy leakage, etc. Existing exploration of app reviews generally depends on static collections. As a result, they largely ignore the fact that user reviews are tightly related to app versions. Furthermore, the previous approaches require a developer to spend much time on filtering out trivial comments and digesting the informative textual data. This would be labor-intensive especially to popular apps with tremendous reviews. In the paper, we target at designing a framework in Prioritizing App Issues for Developers (PAID) with minimal manual power and good accuracy. The PAID design is based on the fact that the issues presented in the level of phrase, i.e., a couple of consecutive words, can be more easily understood by developers than in long sentences. Hence, we aim at recommending phrase-level issues of an app to its developers by tracking reviews over the release versions of the app. To assist developers in better comprehending the app issues, PAID employs ThemeRiver to visualize the analytical results to developers. Finally, PAID also allows the developers to check the most related reviews, when they want to obtain a deep insight of a certain issue. In contrast to the traditional evaluation methods such as manual labeling or examining the discussion forum, our experimental study exploits the first-hand information from developers, i.e., app changelogs, to measure the performance of PAID. We analyze millions of user reviews from 18 apps with 117 app versions and the results show that the prioritized issues generated by PAID match the official changelogs with high precision.
AB - User review analysis is critical to the bug-fixing and version-modification process for app developers. Many research efforts have been put to user review mining in discovering app issues, including laggy user interface, high memory overhead, privacy leakage, etc. Existing exploration of app reviews generally depends on static collections. As a result, they largely ignore the fact that user reviews are tightly related to app versions. Furthermore, the previous approaches require a developer to spend much time on filtering out trivial comments and digesting the informative textual data. This would be labor-intensive especially to popular apps with tremendous reviews. In the paper, we target at designing a framework in Prioritizing App Issues for Developers (PAID) with minimal manual power and good accuracy. The PAID design is based on the fact that the issues presented in the level of phrase, i.e., a couple of consecutive words, can be more easily understood by developers than in long sentences. Hence, we aim at recommending phrase-level issues of an app to its developers by tracking reviews over the release versions of the app. To assist developers in better comprehending the app issues, PAID employs ThemeRiver to visualize the analytical results to developers. Finally, PAID also allows the developers to check the most related reviews, when they want to obtain a deep insight of a certain issue. In contrast to the traditional evaluation methods such as manual labeling or examining the discussion forum, our experimental study exploits the first-hand information from developers, i.e., app changelogs, to measure the performance of PAID. We analyze millions of user reviews from 18 apps with 117 app versions and the results show that the prioritized issues generated by PAID match the official changelogs with high precision.
UR - https://www.scopus.com/pages/publications/84964819177
U2 - 10.1109/ISSRE.2015.7381797
DO - 10.1109/ISSRE.2015.7381797
M3 - 会议稿件
AN - SCOPUS:84964819177
T3 - 2015 IEEE 26th International Symposium on Software Reliability Engineering, ISSRE 2015
SP - 35
EP - 45
BT - 2015 IEEE 26th International Symposium on Software Reliability Engineering, ISSRE 2015
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 2 November 2015 through 5 November 2015
ER -