A11yLTLNav：无障碍导航失败的自动检测

中文摘要 AI 辅助

对于盲人和低视力（BLV）屏幕阅读器用户而言，在静态快照中看似无障碍的网站，一旦开始交互就可能变得难以甚至无法导航。然而，大多数自动化无障碍检查器会遗漏涉及焦点、界面状态以及交互过程中无障碍反馈的失败。我们提出了A11yLTLNav，一种基于属性的方法，用于自动检测无障碍导航失败。通过对先前研究的结构化审查，我们将无障碍导航失败整理成一个失败分类法，并将浏览器可观察的子集形式化为可执行的线性时序逻辑（LTL）属性，作用于动作-状态轨迹。A11yLTLNav将随机键盘探索与运行时属性监控相结合，以在交互过程中检测这些失败。我们在基于真实世界网站和任务生成的31个网站上评估了A11yLTLNav。它报告了309个无障碍失败，其中274个被确认，实现了88.7%的精确率，并识别出比对比检查器更多的已确认失败。我们的结果表明，A11yLTLNav将无障碍知识转化为对界面行为随时间变化可复用的检查。

英文摘要

For blind and low-vision (BLV) screen-reader users, a website that appears accessible in a static snapshot can become difficult or impossible to navigate once interaction begins. Yet, most automated accessibility checkers miss failures involving focus, interface state, and accessible feedback across interactions. We present A11yLTLNav, a property-based approach for automatically detecting accessibility navigation failures. Through a structured review of prior research, we organize accessibility navigation failures into a failure taxonomy and formalize a browser-observable subset as executable Linear Temporal Logic properties over action-state traces. A11yLTLNav combines random keyboard exploration with runtime property monitoring to detect these failures during interactions. We evaluate A11yLTLNav on 31 generated websites based on real-world websites and tasks. It reported 309 accessibility failures, of which 274 were confirmed, achieving 88.7% precision and identifying more confirmed failures than the comparison checkers. Our results show that A11yLTLNav transforms accessibility knowledge into reusable checks of interface behavior over time.

发表机构

• University of Michigan（密歇根大学）
• University of Utah（犹他大学）

机构由 AI 辅助整理，请以论文原文为准。