Approximately 15% of the world's population is suffering from various disabilities or impairments. However, many mobile UX designers and developers disregard the significance of accessibility for those with disabilities when developing apps. A large number of studies and some effective tools for detecting accessibility issues have been conducted and proposed to mitigate such a severe problem. However, compared with detection, the repair work is obviously falling behind. Especially for the color-related accessibility issues, which is one of the top issues in apps with a greatly negative impact on vision and user experience. Apps with such issues are difficult to use for people with low vision and the elderly. Unfortunately, such an issue type cannot be directly fixed by existing repair techniques. To this end, we propose Iris, an automated and context-aware repair method to fix the color-related accessibility issues (i.e., the text contrast issues and the image contrast issues) for apps. By leveraging a novel context-aware technique that resolves the optimal colors and a vital phase of attribute-to-repair localization, Iris not only repairs the color contrast issues but also guarantees the consistency of the design style between the original UI page and repaired UI page. Our experiments unveiled that Iris can achieve a 91.38% repair success rate with high effectiveness and efficiency. The usefulness of Iris has also been evaluated by a user study with a high satisfaction rate as well as developers' positive feedback. 9 of 40 submitted pull requests on GitHub repositories have been accepted and merged into the projects by app developers, and another 4 developers are actively discussing with us for further repair. Iris is publicly available to facilitate this new research direction.

翻译：全球约15%的人口患有各类残疾或障碍。然而，许多移动端用户体验设计师和开发者在开发应用时忽视了可访问性对残障人群的重要性。为缓解这一严峻问题，已有大量研究及有效的可访问性问题检测工具被提出和开发。然而，相较于检测环节，修复工作明显滞后，尤其是颜色相关的可访问性问题——这是应用中最为突出的问题之一，对视力和用户体验有极大的负面影响。存在此类问题的应用对视力低下者和老年人而言难以使用。遗憾的是，现有修复技术无法直接解决这类问题。为此，我们提出Iris——一种自动且上下文感知的修复方法，用于修复应用中的颜色相关可访问性问题（即文本对比度问题和图像对比度问题）。通过利用一种能够确定最优颜色的新型上下文感知技术，以及属性与修复定位的关键阶段，Iris不仅能修复颜色对比度问题，还能保证原始UI页面与修复后UI页面之间设计风格的一致性。实验表明，Iris可实现91.38%的高效修复成功率。用户研究也验证了Iris的有用性，用户满意度高且开发者反馈积极。在提交至GitHub代码仓库的40个拉取请求中，有9个已被应用开发者接受并合并到项目中，另有4位开发者正积极与我们讨论进一步修复方案。Iris现已公开可用，以推动这一新研究方向的发展。