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Approximately 15% of the world's population is suffering from variousdisabilities or impairments. However, many mobile UX designers and developersdisregard the significance of accessibility for those with disabilities whendeveloping apps. A large number of studies and some effective tools fordetecting accessibility issues have been conducted and proposed to mitigatesuch a severe problem. However, compared with detection, the repair work isobviously falling behind. Especially for the color-related accessibilityissues, which is one of the top issues in apps with a greatly negative impacton vision and user experience. Apps with such issues are difficult to use forpeople with low vision and the elderly. Unfortunately, such an issue typecannot be directly fixed by existing repair techniques. To this end, we proposeIris, an automated and context-aware repair method to fix the color-relatedaccessibility issues (i.e., the text contrast issues and the image contrastissues) for apps. By leveraging a novel context-aware technique that resolvesthe optimal colors and a vital phase of attribute-to-repair localization, Irisnot only repairs the color contrast issues but also guarantees the consistencyof the design style between the original UI page and repaired UI page. Ourexperiments unveiled that Iris can achieve a 91.38% repair success rate withhigh effectiveness and efficiency. The usefulness of Iris has also beenevaluated by a user study with a high satisfaction rate as well as developers'positive feedback. 9 of 40 submitted pull requests on GitHub repositories havebeen accepted and merged into the projects by app developers, and another 4developers are actively discussing with us for further repair. Iris is publiclyavailable to facilitate this new research direction.

Automated and Context-Aware Repair of Color-Related Accessibility Issues for Android Apps