BAGEL: An Approach to Automatically Detect Navigation-Based Web Accessibility Barriers for Keyboard Users

Abstract

The Web has become an essential part of many people’s daily lives, enabling them to complete everyday and essential tasks online and access important information resources. The ability to navigate the Web via the keyboard interface is critical to people with various types of disabilities. However, modern websites often violate web accessibility guidelines for keyboard navigability. In this paper, we present a novel approach for automatically detecting web accessibility barriers that prevent or hinder keyboard users' ability to navigate web pages. An extensive evaluation of our technique on real-world subjects showed that our technique was able to detect navigation-based keyboard accessibility barriers in web applications with high precision and recall.

Award
Honorable Mention
Authors
Paul T.. Chiou
University of Southern California, Los Angeles, California, United States
Ali S.. Alotaibi
University of Southern California, Los Angeles, California, United States
William G.J.. Halfond
University of Southern California, Los Angeles, California, United States
Paper URL

https://doi.org/10.1145/3544548.3580749

Video

Conference: CHI 2023

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2023.acm.org/)

Session: Accessible Interaction Techniques A

Hall G2
6 items in this session
2023-04-25 14:30:00
2023-04-25 15:55:00