Accessibility is a critical software quality affecting more than 15% of the world’s population with some form of disabilities. Modern mobile platforms, i.e., iOS and Android, provide guidelines and testing tools for developers to assess the accessibility of their apps. The main focus of the testing tools is on examining a particular screen’s compliance with some predefined rules derived from accessibility guidelines. Unfortunately, these tools cannot detect accessibility issues that manifest themselves in interactions with apps using assistive services, e.g., screen readers. A few recent studies have proposed assistive-service driven testing; however, they require manually constructed inputs from developers to evaluate a specific screen or presume availability of UI test cases. In this work, we propose an automated accessibility crawler for mobile apps, Groundhog, that explores an app with the purpose of finding accessibility issues without any manual effort from developers. Groundhog assesses the functionality of UI elements in an app with and without assistive services and pinpoints accessibility issues with an intuitive video of how to replicate them. Our experiments show Groundhog is highly effective in detecting accessibility barriers that existing techniques cannot discover. Powered by Groundhog, we conducted an empirical study on a large set of real-world apps and found new classes of critical accessibility issues that should be the focus of future work in this area.

@inproceedings{paperbot7239,
  title = {Groundhog: An Automated Accessibility Crawler for Mobile Apps},
  author = {Navid Salehnamadi and Forough Mehralian and Sam Malek},
  booktitle = {ASE},
  year = {2022},
  doi = {10.1145/3551349.3556905}
}