Accessibility verification testing

A computer program product comprising:

• one or more computer readable storage media and program instructions collectively stored on the one or more computer readable storage media, the stored program instructions comprising program instructions configured to:
• identify a webpage comprising a set of user interface (UI) elements;
• analyze the set of UI elements to identify a set of interactable elements, including automatically and programmatically parse webpage source code corresponding to the one or more webpages;
• classify, for the set of interactable elements, each interactable element as focusable or as not focusable, thereby generating a subset of focusable interactable elements;
• generate a first training set comprising first feature vectors computed from first source code corresponding to focusable interactable elements of the subset of focusable interactable elements, each first feature vector including a plurality of fields encoding values parsed from the first source code corresponding to a respective focusable interactable element;
• and
• automatically train, using the first training set and labels indicating accessibility outcomes for corresponding interactable elements, a machine-learned classification model, thereby producing an accessibility issue detection model configured to predict whether a new source code instance corresponding to a UI element satisfies keyboard accessibility requirements.

A computer system comprising:

• one or more computer processors;
• one or more computer readable storage media;
• program instructions collectively stored on the one or more computer readable storage media for execution by at least one of the one or more computer processors, the stored program instructions comprising program instructions to:
• identify a webpage comprising a set of user interface (UI) elements;
• analyze the set of UI elements to identify a set of interactable elements, including automatically and programmatically parse webpage source code corresponding to the one or more webpages;
• classify, for the set of interactable elements, each interactable element as focusable or as not focusable, thereby generating a subset of focusable interactable elements;
• generate a first training set comprising first feature vectors computed from first source code corresponding to focusable interactable elements of the subset of focusable interactable elements, each first feature vector including a plurality of fields encoding values parsed from the first source code corresponding to a respective focusable interactable element;
• and
• automatically train, using the first training set and labels indicating accessibility outcomes for corresponding interactable elements, a machine-learned classification model, thereby producing an accessibility issue detection model configured to predict whether a new source code instance corresponding to a UI element satisfies keyboard accessibility requirements.