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The Tabular Accessibility Dataset: A Benchmark for LLM-Based Web Accessibility Auditing

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Pubblicato in:Data vol. 10, no. 9 (2025), p. 149-162
Autore principale: Andruccioli Manuel
Altri autori: Bassi, Barry, Delnevo Giovanni, Salomoni Paola
Pubblicazione:
MDPI AG
Soggetti:
Datasets
Accessibility
Violations
Source code
Static code analysis
Large language models
Artificial intelligence
JavaScript
Keyboards
Chatbots
Semantics
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Abstract:This dataset was developed to support research at the intersection of web accessibility and Artificial Intelligence, with a focus on evaluating how Large Language Models (LLMs) can detect and remediate accessibility issues in source code. It consists of code examples written in PHP, Angular, React, and Vue.js, organized into accessible and non-accessible versions of tabular components. A substantial portion of the dataset was collected from student-developed Vue components, implemented using both the Options and Composition APIs. The dataset is structured to enable both a static analysis of source code and a dynamic analysis of rendered outputs, supporting a range of accessibility research tasks. All files are in plain text and adhere to the FAIR principles, with open licensing (CC BY 4.0) and long-term hosting via Zenodo. This resource is intended for researchers and practitioners working on LLM-based accessibility validation, inclusive software engineering, and AI-assisted frontend development. Dataset: https://www.doi.org/10.5281/zenodo.17062188. Dataset License: Creative Commons Attribution 4.0 International
ISSN:2306-5729
DOI:10.3390/data10090149
Fonte:Advanced Technologies & Aerospace Database