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Deque Brings ML to Accessibility Testing

March 10, 2020

In an industry first, Deque has successfully integrated Machine Learning technology to perform powerful visual analyses within axe Pro’s automated and intelligent guided testing, which significantly reduces the amount of manual work required to identify and fix accessibility issues. Catching these issues quickly and easily is a crucial step to ensure that websites and apps are accessible to all people, including those with disabilities.

“Much of accessibility testing involves determining whether digital content is accurately conveyed to assistive technologies and the users who rely on them to access that content,” comments Preety Kumar, CEO, Deque Systems. “By leveraging Machine Learning technology, we’ve continued to automate many legacy manual testing efforts, drastically reducing testing costs and making better use of a developer’s time.”

Machine Learning - How It Works: axe Pro’s guided testing tool leverages Computer Vision, which pulls semantic data from digital images, to analyze the visual content of an HTML page’s interface elements and their relationships to one another. The visual text is then extracted from the page and fed into axe Pro’s guided testing tools, which then performs an accessibility analysis in a fully automated fashion. This greatly reduces the manual work typically required of such an analysis.

"Amazon, Google, Facebook and others have made huge advances in Machine Learning and Computer Vision technologies, which we are leveraging to apply to accessibility testing," says Dylan Barrell, CTO, Deque Systems. "When added to our proven technology, and our domain-specific data, we believe this first step constitutes a breakthrough that significantly reduces the amount of manual testing. We will continue to use these techniques to increasingly and steadily offer more automation and time savings far into the future.”

Deque continues to work on improvements and additions to the axe Pro beta. More exciting guided tests, new features and new Machine Learning-powered tools will be added in coming weeks.

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