AI-enhanced testing

AI-enhanced accessibility testing improves traditional rule-based checks by evaluating the meaning and context of UI elements, images, and app features. AI can identify missing or unclear labels, suggest more accurate alternatives, and classify issues based on severity. This helps teams catch complex issues earlier and improve app usability efficiently for all users.

Use cases

AI-enhanced accessibility testing can be used to:

• Detect accessibility issues
  Identify accessibility issues that traditional rule-based checks may not detect. For example, it can analyze an image’s visual content and determine whether the associated accessibility label accurately describes the image content.

• Classify issues
  Classify detected issues based on severity, helping teams prioritize fixes and improve the overall app accessibility.

• Assess the accuracy of accessibility solutions
  Evaluate whether implemented accessibility solutions are accurate. For example, it can analyze image elements and their associated labels to determine the label’s accuracy.

• Suggest solutions
  Analyze your app’s features, UI elements, and content to suggest accessibility solutions. For example, it can generate meaningful labels for images based on their visual content.

• Provide localization support
  Generate content-oriented solutions in the same language as the screen content to ensure a consistent user experience.

AI-enhanced rules

Currently, the following rule is enhanced with AI capabilities:

• ImageView element accessibility label