A11yLTLNav: Automatic Detection of Accessibility Navigation Failures

2026-09-15

AI Summary

研究针对盲人和视障用户在网站交互中遇到的导航问题，提出了A11yLTLNav方法，通过随机键盘探索与运行时属性监控自动检测这些故障。

Abstract

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.

Problem

• accessibility
• navigation failures
• screen-reader users
• interaction
• focus

Innovation

• property-based approach
• Linear Temporal Logic
• random keyboard exploration
• runtime property monitoring