Automated Code Fix Suggestions for Accessibility Issues in Mobile Apps
This addresses the challenge of improving app accessibility for developers, particularly in mobile contexts, by providing actionable fix suggestions, though it is incremental as it builds on existing accessibility scanners.
The paper tackles the problem of developers struggling to fix accessibility issues in mobile apps by introducing FixAlly, an automated tool that suggests source code fixes, achieving 77% effectiveness in generating plausible suggestions and 69.4% acceptance from developers.
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.