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

• URL: http://arxiv.org/abs/2308.09029v1
• Date: Thu, 17 Aug 2023 15:03:11 GMT
• Title: Automated and Context-Aware Repair of Color-Related Accessibility Issues for Android Apps
• Authors: Yuxin Zhang, Sen Chen, Lingling Fan, Chunyang Chen, Xiaohong Li
• Abstract summary: We propose Iris, an automated and context-aware repair method to fix color-related accessibility issues for apps. By leveraging a novel context-aware technique, Iris resolves the optimal colors and a vital phase of attribute-to-repair localization. Our experiments unveiled that Iris can achieve a 91.38% repair success rate with high effectiveness and efficiency.
• Score: 28.880881834251227
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Does GenAI Make Usability Testing Obsolete? [7.863638253070439]
  This paper presents UX-LLM, a novel tool powered by a Large Vision-Language Model that predicts usability issues in iOS apps. To evaluate the performance of UX-LLM we predicted usability issues in two open-source apps of a medium complexity and asked usability experts to assess the predictions.
  arXiv  Detail & Related papers  (2024-11-01T14:45:34Z)
• Understanding Code Understandability Improvements in Code Reviews [79.16476505761582]
  We analyzed 2,401 code review comments from Java open-source projects on GitHub. 83.9% of suggestions for improvement were accepted and integrated, with fewer than 1% later reverted.
  arXiv  Detail & Related papers  (2024-10-29T12:21:23Z)
• Automated Code Fix Suggestions for Accessibility Issues in Mobile Apps [6.015259590468495]
  FixAlly is an automated tool designed to suggest source code fixes for accessibility issues detected by automated accessibility scanners. Our empirical study demonstrates FixAlly's capability in suggesting fixes that resolve issues found by accessibility scanners.
  arXiv  Detail & Related papers  (2024-08-07T15:06:07Z)
• Responsible Visual Editing [53.45295657891099]
  We formulate a new task, responsible visual editing, which entails modifying specific concepts within an image to render it more responsible while minimizing changes. To mitigate the negative implications of harmful images on research, we create a transparent and public dataset, AltBear, which expresses harmful information using teddy bears instead of humans. We find that the AltBear dataset corresponds well to the harmful content found in real images, offering a consistent experimental evaluation.
  arXiv  Detail & Related papers  (2024-04-08T14:56:26Z)
• Towards Automated Accessibility Report Generation for Mobile Apps [14.908672785900832]
  We propose a system to generate whole app accessibility reports. It combines varied data collection methods (e.g., app crawling, manual recording) with an existing accessibility scanner.
  arXiv  Detail & Related papers  (2023-09-29T19:05:11Z)
• Rethinking People Analytics With Inverse Transparency by Design [57.67333075002697]
  We propose a new design approach for workforce analytics we refer to as inverse transparency by design. We find that architectural changes are made without inhibiting core functionality. We conclude that inverse transparency by design is a promising approach to realize accepted and responsible people analytics.
  arXiv  Detail & Related papers  (2023-05-16T21:37:35Z)
• Giving Feedback on Interactive Student Programs with Meta-Exploration [74.5597783609281]
  Developing interactive software, such as websites or games, is a particularly engaging way to learn computer science. Standard approaches require instructors to manually grade student-implemented interactive programs. Online platforms that serve millions, like Code.org, are unable to provide any feedback on assignments for implementing interactive programs.
  arXiv  Detail & Related papers  (2022-11-16T10:00:23Z)
• Usability and Aesthetics: Better Together for Automated Repair of Web Pages [2.867517731896504]
  We propose an automated repair approach for web pages based on meta-heuristic algorithms. Our approach is able to successfully resolve mobile-friendly problems in 94% of the evaluation subjects.
  arXiv  Detail & Related papers  (2022-01-01T05:13:43Z)
• Emerging App Issue Identification via Online Joint Sentiment-Topic Tracing [66.57888248681303]
  We propose a novel emerging issue detection approach named MERIT. Based on the AOBST model, we infer the topics negatively reflected in user reviews for one app version. Experiments on popular apps from Google Play and Apple's App Store demonstrate the effectiveness of MERIT.
  arXiv  Detail & Related papers  (2020-08-23T06:34:05Z)
• Unblind Your Apps: Predicting Natural-Language Labels for Mobile GUI Components by Deep Learning [21.56849865328527]
  More than 77% apps have issues of missing labels, according to our analysis of 10,408 Android apps. We develop a deep-learning based model, called LabelDroid, to automatically predict the labels of image-based buttons. The experimental results show that our model can make accurate predictions and the generated labels are of higher quality than that from real Android developers.
  arXiv  Detail & Related papers  (2020-03-01T02:31:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.