Automatic detection finds navigation barriers for blind users on websites
A11yLTLNav: Automatic Detection of Accessibility Navigation Failures
Human-Computer InteractionSoftware Engineering
Summary
Websites that look accessible at first can become hard or impossible to use for blind or low-vision people when they start interacting with them. Current tools often miss these problems because they happen during interaction, like losing keyboard focus or missing feedback. The authors created A11yLTLNav, which watches how websites behave over time when users press keys randomly and checks for common accessibility navigation bugs. They tested it on websites inspired by real ones and found many real issues with high accuracy. This method turns knowledge about accessibility problems into repeatable automatic checks.
What this means in practice
- •For web developers: Automatically detect and fix navigation accessibility problems that appear only during user interaction on websites.
- •For accessibility testing teams: Enhance automated accessibility testing tools by integrating checks for dynamic navigation failures using temporal logic properties.
Authors
Chenming Ge, Kewen Peng, Chengyang Shi, Ben Greenman, Yue Jiang
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.