Automated Code Fix Suggestions for Accessibility Issues in Mobile Apps

TL;DR

This paper presents FixAlly, an automated tool leveraging multi-agent LLMs to generate source code fixes for accessibility issues in mobile apps, improving developer support and accessibility compliance.

Contribution

Introduction of FixAlly, a novel multi-agent LLM-based system that suggests source code fixes for accessibility issues detected in mobile apps.

Findings

77% effectiveness in generating plausible fixes

Developers willing to accept 69.4% of suggestions

FixAlly successfully localizes and proposes code modifications

Abstract

Accessibility is crucial for inclusive app usability, yet developers often struggle to identify and fix app accessibility issues due to a lack of awareness, expertise, and inadequate tools. Current accessibility testing tools can identify accessibility issues but may not always provide guidance on how to address them. We introduce FixAlly, an automated tool designed to suggest source code fixes for accessibility issues detected by automated accessibility scanners. FixAlly employs a multi-agent LLM architecture to generate fix strategies, localize issues within the source code, and propose code modification suggestions to fix the accessibility issue. Our empirical study demonstrates FixAlly's capability in suggesting fixes that resolve issues found by accessibility scanners -- with an effectiveness of 77% in generating plausible fix suggestions -- and our survey of 12 iOS developers finds they would be willing to accept 69.4% of evaluated fix suggestions.