Automated software accessibility testing

Not everyone experiences vision the same way. Many software users are blind or have low vision, yet accessibility testing tools often miss critical issues. Traditionally, these tools rely on manual inspection or source code analysis, limiting their effectiveness. In this PhD, you will explore how we can overcome these limitations and push the boundaries of visual accessibility testing using state-of-the-art machine learning and software testing techniques. Your work has the potential for significant societal impact: 1.4 million people in Canada alone are blind or have low vision.

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Motivation Statement

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