International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Evaluating Large Language Models for Accessible Web Code Generation: A Comparative Study Using the FeedA11y Framework

Author(s) Ms. DIYA SINGH, Prof. Sanjay Kumar Sharma
Country India
Abstract The role of Large Language Models (LLMs) in software development has become quite common, integrating them more deeply into the daily routines of developers. This makes it increasingly important for LLMs to generate code that is not only coherent but also easy for humans to read, especially for creating different web applications. While previous studies have focused on individual LLMs regarding their functionality or security, there’s a noticeable lack of research comparing multiple LLM platforms in terms of accessibility, correctness, and usability. This gap is significant and serves as the driving force behind our work. We conducted a comprehensive empirical study to evaluate the coding capabilities of eight leading LLMs: Chat GPT, Claude Ai, Gemini, Mistral, Qwen Ai, Phind, Grok and Blackbox Ai. Our assessment not only looked at how well these models generate forms, user interfaces, and semantic ARIA roles for webpages but also measured five key criteria: compliance with WCAG 2.0, code accuracy, responsiveness to prompts, cost-effectiveness, and user-friendliness. To enhance efficiency, we also modified the FeedA11y framework to automate evaluations using tools like AChecker for automated web accessibility assessments. Our findings highlight significant differences among the LLMs. Lastly, we outline the proposed solutions and frameworks and discuss the implications of LLMs on inclusive development processes.
Keywords LLM, FeedAlly.
Field Computer Applications
Published In Volume 7, Issue 3, May-June 2025
Published On 2025-06-15
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E-ISSN 2582-2160
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