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

Manuel Andruccioli, Barry Bassi, Giovanni Delnevo () and Paola Salomoni
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Manuel Andruccioli: Department of Computer Science and Engineering, University of Bologna, 47522 Cesena, Italy
Barry Bassi: Department of Computer Science and Engineering, University of Bologna, 47522 Cesena, Italy
Giovanni Delnevo: Department of Computer Science and Engineering, University of Bologna, 47522 Cesena, Italy
Paola Salomoni: Department of Computer Science and Engineering, University of Bologna, 47522 Cesena, Italy

Data, 2025, vol. 10, issue 9, 1-13

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.

Keywords: web accessibility; large language models; open data; dynamic tables; vue components; human–AI interaction; digital sustainability (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2025
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