Automated Code Fix Suggestions for Accessibility Issues in Mobile Apps

• URL: http://arxiv.org/abs/2408.03827v1
• Date: Wed, 7 Aug 2024 15:06:07 GMT
• Title: Automated Code Fix Suggestions for Accessibility Issues in Mobile Apps
• Authors: Forough Mehralian, Titus Barik, Jeff Nichols, Amanda Swearngin,
• Abstract summary: 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.
• Score: 6.015259590468495
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• 2FA: Navigating the Challenges and Solutions for Inclusive Access [55.2480439325792]
  Two-Factor Authentication (2FA) has emerged as a critical solution to protect online activities. This paper examines the intricacies of deploying 2FA in a way that is secure and accessible to all users. An analysis was conducted to examine the implementation and availability of various 2FA methods across popular online platforms.
  arXiv  Detail & Related papers  (2025-02-17T12:23:53Z)
• Closing the Gap: A User Study on the Real-world Usefulness of AI-powered Vulnerability Detection & Repair in the IDE [5.824774194964031]
  We implement a vulnerability detection and fix tool with professional software developers on real projects that they own. DeepVulGuard scans code for vulnerabilities, suggests fixes, provides natural-language explanations for alerts and fixes, leveraging chat interfaces. We find that although state-of-the-art AI-powered detection and fix tools show promise, they are not yet practical for real-world use due to a high rate of false positives and non-applicable fixes.
  arXiv  Detail & Related papers  (2024-12-18T20:19:56Z)
• SoK: Detection and Repair of Accessibility Issues [10.134645262631983]
  We develop a comprehensive taxonomy that categorizes 55 types of accessibility issues across four pivotal dimensions: Perceivability, Operability, Understandability, and Robustness. We conduct an in-depth analysis of existing detection and repair tools, as well as the status of corresponding datasets.
  arXiv  Detail & Related papers  (2024-11-29T14:19:19Z)
• 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)
• Agent-Driven Automatic Software Improvement [55.2480439325792]
  This research proposal aims to explore innovative solutions by focusing on the deployment of agents powered by Large Language Models (LLMs) The iterative nature of agents, which allows for continuous learning and adaptation, can help surpass common challenges in code generation. We aim to use the iterative feedback in these systems to further fine-tune the LLMs underlying the agents, becoming better aligned to the task of automated software improvement.
  arXiv  Detail & Related papers  (2024-06-24T15:45:22Z)
• Learning Task Decomposition to Assist Humans in Competitive Programming [90.4846613669734]
  We introduce a novel objective for learning task decomposition, termed value (AssistV) We collect a dataset of human repair experiences on different decomposed solutions. Under 177 hours of human study, our method enables non-experts to solve 33.3% more problems, speeds them up by 3.3x, and empowers them to match unassisted experts.
  arXiv  Detail & Related papers  (2024-06-07T03:27:51Z)
• 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)
• Automated and Context-Aware Repair of Color-Related Accessibility Issues for Android Apps [28.880881834251227]
  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.
  arXiv  Detail & Related papers  (2023-08-17T15:03:11Z)
• SUPERNOVA: Automating Test Selection and Defect Prevention in AAA Video Games Using Risk Based Testing and Machine Learning [62.997667081978825]
  Testing video games is an increasingly difficult task as traditional methods fail to scale with growing software systems. We present SUPERNOVA, a system responsible for test selection and defect prevention while also functioning as an automation hub. The direct impact of this has been observed to be a reduction in 55% or more testing hours for an undisclosed sports game title.
  arXiv  Detail & Related papers  (2022-03-10T00:47:46Z)
• Adversarial Patch Generation for Automated Program Repair [0.0]
  NEVERMORE is a novel learning-based mechanism inspired by the adversarial nature of bugs and fixes. NEVERMORE is built upon the Generative Adrial Networks architecture and trained on historical bug fixes to generate repairs that closely mimic human-produced fixes. Our empirical evaluation on 500 real-world bugs demonstrates the effectiveness of NEVERMORE in bug-fixing, generating repairs that match human fixes for 21.2% of the examined bugs.
  arXiv  Detail & Related papers  (2020-12-21T00:34:29Z)
• On the Social and Technical Challenges of Web Search Autosuggestion Moderation [118.47867428272878]
  Autosuggestions are typically generated by machine learning (ML) systems trained on a corpus of search logs and document representations. While current search engines have become increasingly proficient at suppressing such problematic suggestions, there are still persistent issues that remain. We discuss several dimensions of problematic suggestions, difficult issues along the pipeline, and why our discussion applies to the increasing number of applications beyond web search.
  arXiv  Detail & Related papers  (2020-07-09T19:22:00Z)

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.