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Websites are integral to people’s daily lives, with billions in use today. However, due to limited awareness of accessibility and its guidelines, developers often release web apps that are inaccessible to people with disabilities, who make up around 16% of the global population. To ensure a baseline of accessibility, software engineers rely on automated checkers that assess a webpage’s compliance based on predefined rules. Unfortunately, these tools typically cover only a small subset of accessibility guidelines and often overlook violations that require a semantic understanding of the webpage. The advent of generative AI, known for its ability to comprehend textual and visual content, has created new possibilities for detecting accessibility violations. We began by studying the most widely used guideline, WCAG, to determine the testable success criteria that generative AI could address. This led to the development of an automated tool called GenA11y, which extracts elements from a page related to each success criterion and inputs them into an LLM prompted to detect accessibility issues on the web. Evaluations of GenA11y showed its effectiveness, with a precision of 94.5% and a recall of 87.61%. Additionally, when tested on real websites, GenA11y identified an average of eight more types of accessibility violations than the combination of existing tools.
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Ma11y: A Mutation Framework for Web Accessibility Testing
Despite the availability of numerous automatic accessibility testing solutions, web accessibility issues persist on many websites. Moreover, there is a lack of systematic evaluations of the efficacy of current accessibility testing tools. To address this gap, we present the first mutation analysis framework, called Ma11y, designed to assess web accessibility testing tools. Ma11y includes 25 mutation operators that intentionally violate various accessibility principles and an automated oracle to determine whether a mutant is detected by a testing tool. Evaluation on real-world websites demonstrates the practical applicability of the mutation operators and the framework’s capacity to assess tool performance. Our results demonstrate that the current tools cannot identify nearly 50% of the accessibility bugs injected by our framework, thus underscoring the need for the development of more effective accessibility testing tools. Finally, the framework’s accuracy and performance attest to its potential for seamless and automated application in practical settings
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- Award ID(s):
- 2211790
- PAR ID:
- 10544080
- Publisher / Repository:
- ACM
- Date Published:
- ISBN:
- 9798400706127
- Page Range / eLocation ID:
- 100 to 111
- Subject(s) / Keyword(s):
- Software Accessibility, Mutation Testing, Web
- Format(s):
- Medium: X
- Location:
- Vienna Austria
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3650212.3652113
