Automated Detection of Web Application Navigation Barriers for Screen Reader Users

An estimated 43.3 million people worldwide live with blindness and rely on screen readers (SRs) to access the web. To support accessible development, software teams often rely on automated tools like WAVE and Lighthouse to detect accessibility issues. However, these tools primarily rely on static rule-based analysis and are largely limited to detecting labeling errors relevant to screen reader users. They fail to capture dynamic accessibility issues—specifically, whether user interface (UI) elements can be located and activated using a screen reader, which is essential for accessing core webpage functionality. To address this gap, we present A1 1yNavigator, an automated accessibility testing tool that simulates screen reader navigation to detect UI elements that cannot be either (1) located or (2) activated via the screen reader. A11yNavigator leverages NVDA, one of the most widely used screen readers, and supports three common navigation strategies: Tab, Arrow, and quick Navigation keys. We evaluate A1 1yNavigator across 26 real-world websites and demonstrate its effectiveness in uncovering issues missed by existing tools. Our results highlight its high precision and recall in detecting barriers that go beyond static analysis.