[Remote] Automated and Context-Aware Repair of Color-Related Accessibility Issues for Android Apps
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. It is unbelievable that one in seven people might not have the same level of access that other users have, which actually violates many legal and regulatory standards. On the contrary, if the apps are developed with accessibility in mind, it will drastically improve the user experience for all users as well as maximize revenue. Thus, 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. 8 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.
Wed 6 DecDisplayed time zone: Pacific Time (US & Canada) change
16:00 - 18:00 | Automated Repair IIJournal First / Research Papers at Golden Gate C3 Chair(s): Luciano Baresi Politecnico di Milano | ||
16:00 15mTalk | A Large-scale Empirical Review of Patch Correctness Checking Approaches Research Papers Jun Yang UIUC, Yuehan Wang University of Illinois at Urbana-Champaign, Yiling Lou Fudan University, Ming Wen Huazhong University of Science and Technology, Lingming Zhang University of Illinois at Urbana-Champaign Media Attached | ||
16:15 15mTalk | Program Repair Guided by Datalog-Defined Static Analysis Research Papers Yu Liu Beijing University of Technology, Sergey Mechtaev University College London, Pavle Subotic Microsoft, Abhik Roychoudhury National University of Singapore Media Attached | ||
16:30 15mTalk | SynShine: Improved Fixing of Syntax Errors Journal First Toufique Ahmed University of California at Davis, Noah Rose Ledesma UC Davis, Prem Devanbu University of California at Davis Media Attached | ||
16:45 15mTalk | Baldur: Whole-Proof Generation and Repair with Large Language Models Research Papers Emily First University of California, San Diego, Markus Rabe Google, Talia Ringer University of Illinois at Urbana-Champaign, Yuriy Brun University of Massachusetts Media Attached | ||
17:00 15mTalk | KG4CraSolver: Recommending Crash Solutions via Knowledge Graph Research Papers Xueying Du Fudan University, Yiling Lou Fudan University, Mingwei Liu Fudan University, Xin Peng Fudan University, Tianyong Yang Fudan University Pre-print Media Attached | ||
17:15 15mTalk | [Remote] Automated and Context-Aware Repair of Color-Related Accessibility Issues for Android Apps Research Papers Yuxin Zhang Tianjin University, Sen Chen College of Intelligence and Computing, Tianjin University, Lingling Fan College of Cyber Science, Nankai University, Chunyang Chen Monash University, Xiaohong Li Tianjin University Media Attached | ||
17:30 15mTalk | [Remote] Semantic Test Repair for Web applications Research Papers Xiaofang Qi School of Computer Science and Engineering, Southeast University, Xiang Qian School of Computer Science and Engineering, Southeast University, Yanhui Li Nanjing University Media Attached |