Automated Code Fix Suggestions for Accessibility Issues in Mobile Apps
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913460610138112 |
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| author | Mehralian, Forough Barik, Titus Nichols, Jeff Swearngin, Amanda |
| author_facet | Mehralian, Forough Barik, Titus Nichols, Jeff Swearngin, Amanda |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_03827 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Automated Code Fix Suggestions for Accessibility Issues in Mobile Apps Mehralian, Forough Barik, Titus Nichols, Jeff Swearngin, Amanda Software Engineering Artificial Intelligence Human-Computer Interaction D.2.5; I.2 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. |
| title | Automated Code Fix Suggestions for Accessibility Issues in Mobile Apps |
| topic | Software Engineering Artificial Intelligence Human-Computer Interaction D.2.5; I.2 |
| url | https://arxiv.org/abs/2408.03827 |