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.

翻译：网络无障碍问题仍是大部分网络内容尚未解决的难题。目前已有众多自动化检测工具，但修复这些问题仍主要依赖人工操作，过程缓慢且成本高昂，开发人员极易忽略特定细节。对于现代单页应用而言，情况更为复杂——其动态特性使得传统静态分析方法难以适用。本研究提出一种利用大型语言模型实现无障碍问题自动化修复的系统。该方案设计了适用于静态网站和复杂Angular项目的模块化工作流程，能够直接在静态网页的DOM或单页应用的源代码中实施修正。系统在12个静态网站和6个开源Angular项目上进行了测试，成功修复了公共网站中80%的无障碍问题以及Angular应用中86%的问题。该方案还能在保持应用设计与稳定性的前提下，为图像生成具有实际意义的视觉描述。本研究成果有助于推动无障碍建设从积压的技术债务转变为日常开发流程的自然组成部分。