Automated LLM-Based Accessibility Remediation: From Conventional Websites to Angular Single-Page Applications

TL;DR

This paper introduces an automated system leveraging Large Language Models to efficiently fix accessibility issues in both static websites and Angular SPAs, significantly reducing manual effort and improving web accessibility.

Contribution

It presents a modular LLM-based framework capable of automatically correcting accessibility issues in static and dynamic web applications, including Angular projects.

Findings

Fixes 80% of issues on public static websites

Fixes 86% of issues on Angular applications

Generates meaningful image descriptions without disrupting design

Abstract

Web accessibility remains an unresolved issue for a large part of the web content. There are many tools to detect errors automatically, but fixing those issues is still mostly a manual, slow, and costly process in which it is easy for developers to overlook specific details. The situation becomes even more complex with modern Single-Page Applications (SPAs), whose dynamic nature makes traditional static analysis approaches inadequate. This work proposes a system that aims to address this challenge by using Large Language Models (LLMs) to automate accessibility fixes. The proposal presents a modular workflow applicable to both static websites and complex Angular projects. The framework actively implements corrections within the DOM of static web pages or the source code of SPAs. The system was tested on 12 static websites and 6 open-source Angular projects, fixing 80% of the accessibility issues on public websites and 86% of the issues on Angular applications. Our proposal also generates meaningful visual descriptions for images while preserving the application's design and stability. This work contributes to ensuring that accessibility stops being a technical debt deferred to the future and becomes a natural part of everyday development workflows.