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

要旨

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.

著者
Raymond Fok
University of Washington, Seattle, Washington, United States
Mingyuan Zhong
University of Washington, Seattle, Washington, United States
Anne Spencer. Ross
Bucknell University, Lewisburg, Pennsylvania, United States
James Fogarty
University of Washington, Seattle, Washington, United States
Jacob O.. Wobbrock
University of Washington, Seattle, Washington, United States
論文URL

https://dl.acm.org/doi/abs/10.1145/3491102.3502143

動画

会議: CHI 2022

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2022.acm.org/)

セッション: Captioning Images, Videos and Applications

293
4 件の発表
2022-05-05 01:15:00
2022-05-05 02:30:00