A Large-Scale Longitudinal Analysis of Missing Label Accessibility Failures in Android Apps


We present the first large-scale longitudinal analysis of missing label accessibility failures in Android apps. We developed a crawler and collected monthly snapshots of 312 apps over 16 months. We use this unique dataset in empirical examinations of accessibility not possible in prior datasets. Key large-scale findings include missing label failures in 55.6% of unique image-based elements, longitudinal improvement in ImageButton elements but not in more prevalent ImageView elements, that 8.8% of unique screens are unreachable without navigating at least one missing label failure, that app failure rate does not improve with number of downloads, and that effective labeling is neither limited to nor guaranteed by large software organizations. We then examine longitudinal data in individual apps, presenting illustrative examples of accessibility impacts of systematic improvements, incomplete improvements, interface redesigns, and accessibility regressions. We discuss these findings and potential opportunities for tools and practices to improve label-based accessibility.

In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Mingyuan Zhong
Mingyuan Zhong
PhD Student
Computer Science & Engineering

Currectly, I conduct research in accessibility, user interface, interaction techniques, and the intersection of these areas.