Automated Accessibility Failure Detection in Software Interfaces
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current digital accessibility testing in software development is time-consuming and inefficient, often requiring human intervention and separate accessibility teams, leading to delays in identifying and addressing accessibility issues in user interfaces for visually impaired users.
Innovation Solution
A tool that automatically detects accessibility failures in user interfaces using a screen reader and machine learning models to analyze audio output, providing real-time feedback and remediation recommendations to developers, allowing for proactive identification and resolution of issues during the coding process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual accessibility testing is performed by separate accessibility teams, then accessibility issues can be identified through human interpretation, but the testing process takes weeks or longer and significantly delays software release
Solution Approach 1:
The patent replaces the mechanical human testing process with an automated system that uses screen readers to generate audio output, speech recognition to convert audio to text, and machine learning models to analyze accessibility issues. This substitution eliminates the need for manual human interpretation while maintaining detection capability, thereby resolving the contradiction between accurate accessibility issue identification and excessive testing time.
2Reliability
If accessibility testing is performed separately from coding, then dedicated accessibility evaluation can be conducted, but the iterative development process is significantly lengthened by waiting weeks for results
Solution Approach 1:
The patent implements preliminary accessibility testing by integrating the automated accessibility evaluation tool directly into the development environment, allowing accessibility checks to be performed during the coding process itself rather than as a separate subsequent step. This enables developers to receive immediate feedback and correct issues before formal testing, maintaining reliability while dramatically improving development speed.
Solution Approach 2:
The patent merges accessibility testing with the coding process by integrating the automated evaluation tool into the development environment. This combination allows accessibility evaluation to occur concurrently with development activities rather than as a separate sequential step, thereby maintaining evaluation reliability while eliminating the time loss associated with separate testing phases.
3Measurement precision
If developers wait for formal accessibility testing results before making changes, then comprehensive accessibility issues can be identified, but the time required to make new software features available is significantly extended
Solution Approach 1:
The patent implements continuous feedback by providing real-time accessibility evaluation results directly to developers during the coding process. The automated tool analyzes the user interface as it is being developed and immediately reports accessibility issues, allowing developers to make corrections iteratively throughout development rather than waiting for comprehensive post-development testing. This maintains issue identification completeness while dramatically reducing time to market.
Data Source
AI summary
Systems and methods for detecting accessibility failures. In some aspects, the system receives a request for evaluating accessibility for a user interface of an updated version of a software application, wherein the request includes the updated and previous versions of the software application, and previous textual data corresponding to the previous version. The system generates audio output by using a screen reader to process the user interface. Textual data is generated by processing the audio output using speech recognition. The system compares the textual data to previous textual data to determine whether feature differences are present between the updated and previous versions. The one or more feature differences are analyzed to identify a feature difference corresponding to an accessibility failure point. In response to identifying the accessibility failure point, the system determines actions for removing the accessibility failure point.