Scenario-Driven and Context-Aware Automated Accessibility Testing for Android Apps

Mobile accessibility is increasingly important nowadays as it enables people with disabilities to use mobile applications to perform daily tasks. Ensuring mobile accessibility not only benefits those with disabilities but also enhances the user experience for all users, making applications more intu...

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Bibliographic Details
Published in:Proceedings / International Conference on Software Engineering pp. 2777 - 2789
Main Authors: Zhang, Yuxin, Chen, Sen, Xie, Xiaofei, Liu, Zibo, Fan, Lingling
Format: Conference Proceeding
Language:English
Published: IEEE 26.04.2025
Subjects:
Accessibility testing
Accuracy
Android app
Context-aware analysis
Graphical user interfaces
Mobile accessibility
Mobile applications
People with disabilities
Runtime
Testing
UI exploration
User experience
ISSN:1558-1225
Online Access:Get full text
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Summary:Mobile accessibility is increasingly important nowadays as it enables people with disabilities to use mobile applications to perform daily tasks. Ensuring mobile accessibility not only benefits those with disabilities but also enhances the user experience for all users, making applications more intuitive and user-friendly. Although numerous tools are available for testing and detecting accessibility issues in Android applications, a large number of false negatives and false positives persist due to limitations in the existing approaches, i.e., low coverage of UI scenarios and lack of consideration of runtime context. To address these problems, in this paper, we propose a scenario-driven exploration method for improving the coverage of UI scenarios, thereby detecting accessibility issues within the application, and ultimately reducing false negatives. Furthermore, to reduce false positives caused by not considering the runtime context, we propose a context-aware detection method that provides a more fine-grained detection capability. Our experimental results reveal that A11yScan can detect 1.7X more issues surpassing current state-of-the-art approaches like Xbot ( \mathbf{3, 9 9 1} vs. \mathbf{2, 3 2 1} ), thereby reducing the false negative rate by \mathbf{4 1. 8 4 \%} . Additionally, it outperforms established UI exploration techniques such as SceneDroid (952 vs. 661 UI scenarios), while achieving comparable activity coverage to recent leading GUI testing tools like GPTDroid on the available dataset (73 % vs. 71%). Meanwhile, with the context-aware detection method, A11yScan effectively reduces the false positive rate by 21 %, validated with a 90.56 % accuracy rate through a user study.
ISSN:1558-1225
DOI:10.1109/ICSE55347.2025.00093