Detecting keyboard accessibility issues in web applications
Inventors
Chiou, Paul • Alotaibi, Ali • Halfond, William
Assignees
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Publication Number
US-12229390-B2
Publication Date
2025-02-18
Expiration Date
Abstract
A method for detecting and/or localizing keyboard accessibility failures (KAFs) in a web page is disclosed. A document object model of a web page is read. A keyboard navigation flow model is generated from the document object model of the web page based on interactions of a user with the web page. The model includes states representing user interfaces displayed by the web page, nodes representing keyboard inputs in the states, and edges representing transitions that occur in the web page between the nodes. KAFs are detected based on an analysis of the keyboard navigation flow model. A report of the detected one or more KAFs on the web page is produced. The KAFs include unintuitive navigation failures, reflow related failures, and dialog related failures.
Core Innovation
The invention provides a system and method for detecting keyboard accessibility failures (KAFs) on a web page. The method reads a document object model of a web page and generates, via a processor, a keyboard navigation flow model from interactions of a user with the web page. The keyboard navigation flow model includes states representing user interfaces displayed by the web page, nodes representing keyboard inputs in the states, and edges representing transitions that occur in the web page between the nodes.
The method detects one or more KAFs based on an analysis of the keyboard navigation flow model and produces a report of the detected one or more KAFs on the web page. The disclosure also describes responsive accessibility failure detection by comparing keyboard functionalities between a full-sized UI and a reflow UI, and keyboard dialog failure detection using a Keyboard Dialog Flow Graph (KDFG) and dialog-related heuristics.
Claims Coverage
The provided set includes three independent claim families. Across these independents, the claims share an architecture centered on reading a DOM, generating a keyboard navigation flow model with states, nodes, and edges, analyzing that model to detect KAFs, and producing a report.
Generate a keyboard navigation flow model from the document object model
A keyboard navigation flow model is generated from the document object model of a web page based on interactions of a user with the web page, where the model includes states representing user interfaces displayed by the web page, nodes representing keyboard inputs in the states, and edges representing transitions that occur in the web page between the nodes.
Detect keyboard accessibility failures based on an analysis of the keyboard navigation flow model
One or more keyboard accessibility failures (KAFs) are detected based on an analysis of the keyboard navigation flow model.
Produce a report of detected keyboard accessibility failures on the web page
A report is produced of the detected one or more KAFs on the web page.
Each independent claim covers the same overall detection workflow: read the document object model, generate a keyboard navigation flow model defined by states, nodes, and edges from user interactions, detect one or more KAFs by analyzing the model, and produce a report on the web page.
Stated Advantages
Detecting and localizing keyboard accessibility failures (KAFs) on web pages.
Producing a report of the detected one or more KAFs on the web page.
Extending detection to responsive accessibility failure (RAF) by comparing keyboard-accessible functionalities between full-size and reflow user interfaces.
Extending detection to dialog accessibility failures using a keyboard dialog flow graph (KDFG).
Documented Applications
Detecting and localizing keyboard accessibility failures (KAFs) in web pages by modeling runtime keyboard navigation from the page’s document object model (DOM).
Detecting inaccessible functionalities (IAFs) and keyboard traps (KTFs) using graph-based web application modeling (KNFG and PCNFG).
Detecting unintuitive navigation failures including navigation order, change of context, and unapparent keyboard focus using a keyboard focus flow/graph model.
Detecting responsive accessibility failure (RAF) by comparing keyboard-accessible functionalities between a full-size web page and a reflow user interface.
Detecting dialog accessibility failures using a keyboard dialog flow graph (KDFG) for non-initialization-in/out, non-containment, and non-dismissible dialogs.
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