Accessibility verification testing

1 . A computer-implemented method comprising:

identifying one or more webpages comprising a set of user interface (UI) elements;

analyzing the set of UI elements to identify a set of interactable elements, including automatically and programmatically parsing webpage source code corresponding to the one or more webpages;

classifying, for the set of interactable elements, each interactable element as focusable or as not focusable, thereby generating a subset of focusable interactable elements;

generating 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 training, 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.

2 . The computer-implemented method of claim 1 , wherein analyzing the set of UI elements to identify the set of interactable elements further comprises: analyzing the webpage source code corresponding to the one or more webpages to identify one or more element types corresponding to the set of UI elements.

3 . The computer-implemented method of claim 2 , further comprising: determining which element types of the one or more identified element types correspond to the subset of focusable interactable elements.

4 . The computer-implemented method of claim 1 , further comprising: storing one or more passed results in a results database, wherein the one or more passed results correspond to one or more interactable elements of the set of interactable elements which are included in the subset of focusable interactable elements.

5 . The computer-implemented method of claim 1 , further comprising: extracting second features from second source code corresponding to a subset of not focusable interactable elements generated based on the classification.

6 . The computer-implemented method of claim 5 , further comprising: updating the accessibility issue detection model using the extracted second features from the second source code corresponding to the subset of not focusable interactable elements as second training data.

7 . The computer-implemented method of claim 1 , further comprising: storing one or more failed results in a results database, wherein the one or more failed results correspond to one or more interactable elements of the set of interactable elements which are included in the subset of not focusable interactable elements.

8 . The computer-implemented method of claim 1 , further comprising: receiving a webpage to be tested; and

using the accessibility issue detection model on one or more elements of the received webpage to determine whether the webpage meets a set of accessibility requirements.

9 . 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.

10 . The computer program product of claim 9 , wherein the program instructions to analyze the set of UI elements to identify the set of interactable elements further comprises: instructions to analyze the webpage source code corresponding to the one or more webpages to identify one or more element types corresponding to the set of UI elements.

11 . The computer program product of claim 9 , the program instructions further comprising: instructions to store one or more passed results in a results database, wherein the one or more passed results correspond to one or more interactable elements of the set of interactable elements which are included in the subset of focusable interactable elements.

12 . The computer program product of claim 9 , the program instructions further comprising: instructions to extract second features from second source code corresponding to a subset of not focusable interactable elements generated based on the classification.

13 . The computer program product of claim 12 , the program instructions further comprising: instructions to update the accessibility issue detection model using the extracted second features from the second source code corresponding to the subset of not focusable interactable elements as second training data.

14 . The computer program product of claim 9 , the program instructions further comprising: instructions to store one or more failed results in a results database, wherein the one or more failed results correspond to one or more interactable elements of the set of interactable elements which are included in the subset of not focusable interactable elements.

15 . 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.

16 . The computer system of claim 15 , wherein the program instructions to analyze the set of UI elements to identify the set of interactable elements further comprises: instructions to analyze the webpage source code corresponding to the one or more webpages to identify one or more element types corresponding to the set of UI elements.

17 . The computer system of claim 15 , the program instructions further comprising: instructions to store one or more passed results in a results database, wherein the one or more passed results correspond to one or more interactable elements of the set of interactable elements which are included in the subset of focusable interactable elements.

18 . The computer system of claim 15 , the program instructions further comprising: instructions to extract second features from second source code corresponding to a subset of not focusable interactable elements generated based on the classification.

19 . The computer system of claim 18 , the program instructions further comprising: instructions to update the accessibility issue detection model using the extracted second features from the second source code corresponding to the subset of not focusable interactable elements as second training data.

20 . The computer system of claim 15 , the program instructions further comprising: instructions to store one or more failed results in a results database, wherein the one or more failed results correspond to one or more interactable elements of the set of interactable elements which are included in the subset of not focusable interactable elements.