AccessGuru: leveraging LLMs to detect and correct web accessibility violations in HTML code

Abstract

The vast majority of Web pages fail to comply with established Web accessibility guidelines, excluding a range of users with diverse abilities from interacting with their content. Making Web pages accessible to all users requires dedicated expertise and additional manual efforts from Web page providers. To lower their efforts and, thus, promote inclusiveness, we aim to automatically detect and correct Web accessibility violations in HTML code. While previous work has made progress in detecting certain types of accessibility violations, the problem of automatically detecting and correcting accessibility violations remains an open challenge that we address. We introduce a novel taxonomy classifying Web accessibility violations into three key categories— Syntactic, Semantic, and Layout. This taxonomy provides a structured foundation for developing our detection and correction method and selecting and redefining evaluation metrics. We propose our novel method, AccessGuru, which combines existing accessibility testing tools and Large Language Models (LLMs) to detect accessibility violations of Web accessibility guidelines and taxonomy-driven prompting strategies of LLMs to correct all three accessibility violation categories. To evaluate these capabilities, we have developed a novel benchmark encompassing Web accessibility violations from real-world Web pages. Our benchmark quantifies syntactic and layout compliance and judges semantic accuracy through a comparative analysis against human expert corrections. Evaluation against our benchmark demonstrates that our method achieves up to 84% average violation score decrease on our benchmark dataset, significantly outperforming existing methods, which achieve at most 50% average violation score decrease.

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ORCID for Steffen Staab: orcid.org/0000-0002-0780-4154

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