TY - GEN
T1 - The Metamorphosis
T2 - 37th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022
AU - Su, Yuhui
AU - Chen, Chunyang
AU - Wang, Junjie
AU - Liu, Zhe
AU - Wang, Dandan
AU - Li, Shoubin
AU - Wang, Qing
N1 - Publisher Copyright:
© 2022 ACM.
PY - 2022/9/19
Y1 - 2022/9/19
N2 - As the bridge between users and software, Graphical User Interface (GUI) is critical to the app accessibility. Scaling up the font or display size of GUI can help improve the visual impact, readability, and usability of an app, and is frequently used by the elderly and people with vision impairment. Yet this can easily lead to scaling issues such as text truncation, component overlap, which negatively influence the acquirement of the right information and the fluent usage of the app. Previous techniques for UI display issue detection and cross-platform inconsistency detection cannot work well for these scaling issues. In this paper, we propose an automated method, dVermin, for scaling issue detection, through detecting the inconsistency of a view under the default and a larger display scale. The evaluation result shows that dVermin achieves 97% precision and 97% recall in issue page detection, and 84% precision and 91% recall for issue view detection, outperforming two state-of-the-art baselines by a large margin. We also evaluate dVermin with popular Android apps on F-droid, and successfully uncover 21 previously-undetected scaling issues with 20 of them being confirmed/fixed.
AB - As the bridge between users and software, Graphical User Interface (GUI) is critical to the app accessibility. Scaling up the font or display size of GUI can help improve the visual impact, readability, and usability of an app, and is frequently used by the elderly and people with vision impairment. Yet this can easily lead to scaling issues such as text truncation, component overlap, which negatively influence the acquirement of the right information and the fluent usage of the app. Previous techniques for UI display issue detection and cross-platform inconsistency detection cannot work well for these scaling issues. In this paper, we propose an automated method, dVermin, for scaling issue detection, through detecting the inconsistency of a view under the default and a larger display scale. The evaluation result shows that dVermin achieves 97% precision and 97% recall in issue page detection, and 84% precision and 91% recall for issue view detection, outperforming two state-of-the-art baselines by a large margin. We also evaluate dVermin with popular Android apps on F-droid, and successfully uncover 21 previously-undetected scaling issues with 20 of them being confirmed/fixed.
KW - Accessibility Testing
KW - Android Testing
KW - Software Testing
KW - UI Inconsistency Issue
UR - http://www.scopus.com/inward/record.url?scp=85146933571&partnerID=8YFLogxK
U2 - 10.1145/3551349.3556935
DO - 10.1145/3551349.3556935
M3 - Conference contribution
AN - SCOPUS:85146933571
T3 - ACM International Conference Proceeding Series
BT - 37th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022
A2 - Aehnelt, Mario
A2 - Kirste, Thomas
PB - Association for Computing Machinery
Y2 - 10 October 2022 through 14 October 2022
ER -