SoK: Detection and Repair of Accessibility Issues

• URL: http://arxiv.org/abs/2411.19727v1
• Date: Fri, 29 Nov 2024 14:19:19 GMT
• Title: SoK: Detection and Repair of Accessibility Issues
• Authors: Liming Nie, Hao Liu, Jing Sun, Kabir Sulaiman Said, Shanshan Hong, Lei Xue, Zhiyuan Wei, Yangyang Zhao, Meng Li,
• Abstract summary: 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.
• Score: 10.134645262631983
• License:
• Abstract: There is an increasing global emphasis on information accessibility, with numerous researchers actively developing automated tools to detect and repair accessibility issues, thereby ensuring that individuals with diverse abilities can independently access software products and services. However, current research still encounters significant challenges in two key areas: the absence of a comprehensive taxonomy of accessibility issue types, and the lack of comprehensive analysis of the capabilities of detection and repair tools, as well as the status of corresponding datasets. To address these challenges, this paper introduces the Accessibility Issue Analysis (AIA) framework. Utilizing this framework, we develop a comprehensive taxonomy that categorizes 55 types of accessibility issues across four pivotal dimensions: Perceivability, Operability, Understandability, and Robustness. This taxonomy has been rigorously recognized through a questionnaire survey (n=130). Building on this taxonomy, we conduct an in-depth analysis of existing detection and repair tools, as well as the status of corresponding datasets. In terms of tools, our findings indicate that 14 detection tools can identify 31 issue types, achieving a 56.3% rate (31/55). Meanwhile, 9 repair tools address just 13 issue types, with a 23.6% rate. In terms of datasets, those for detection tools cover 21 issue types, at a 38.1% coverage rate, whereas those for repair tools cover only 7 types, at a 12.7% coverage rate.

Related papers

• SoK: On Closing the Applicability Gap in Automated Vulnerability Detection [0.18846515534317265]
  Automated Vulnerability Detection (AVD) aims to autonomously analyze source code to identify vulnerabilities. This paper addresses two primary research questions: How is current AVD research distributed across its core components, and what key areas should future research target to bridge the gap in the practical applicability of AVD throughout software development? We conduct a systematization over 79 AVD articles and 17 empirical studies, analyzing them across five core components: task formulation and granularity, input programming languages and representations, detection approaches and key solutions, evaluation metrics and datasets, and reported performance.
  arXiv  Detail & Related papers  (2024-12-15T14:01:41Z)
• SUPER: Evaluating Agents on Setting Up and Executing Tasks from Research Repositories [55.161075901665946]
  Super aims to capture the realistic challenges faced by researchers working with Machine Learning (ML) and Natural Language Processing (NLP) research repositories. Our benchmark comprises three distinct problem sets: 45 end-to-end problems with annotated expert solutions, 152 sub problems derived from the expert set that focus on specific challenges, and 602 automatically generated problems for larger-scale development. We show that state-of-the-art approaches struggle to solve these problems with the best model (GPT-4o) solving only 16.3% of the end-to-end set, and 46.1% of the scenarios.
  arXiv  Detail & Related papers  (2024-09-11T17:37:48Z)
• 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)
• Static Application Security Testing (SAST) Tools for Smart Contracts: How Far Are We? [14.974832502863526]
  In recent years, the importance of smart contract security has been heightened by the increasing number of attacks against them. To address this issue, a multitude of static application security testing (SAST) tools have been proposed for detecting vulnerabilities in smart contracts. In this paper, we propose an up-to-date and fine-grained taxonomy that includes 45 unique vulnerability types for smart contracts.
  arXiv  Detail & Related papers  (2024-04-28T13:40:18Z)
• Object Detectors in the Open Environment: Challenges, Solutions, and Outlook [95.3317059617271]
  The dynamic and intricate nature of the open environment poses novel and formidable challenges to object detectors. This paper aims to conduct a comprehensive review and analysis of object detectors in open environments. We propose a framework that includes four quadrants (i.e., out-of-domain, out-of-category, robust learning, and incremental learning) based on the dimensions of the data / target changes.
  arXiv  Detail & Related papers  (2024-03-24T19:32:39Z)
• A Comprehensive Study on Quality Assurance Tools for Java [15.255117038871337]
  Quality assurance (QA) tools are receiving more and more attention and are widely used by developers. Most existing research is limited in the following ways:. They compare tools without considering scanning rules analysis. They disagree on the effectiveness of tools due to the study methodology and benchmark dataset. There is no large-scale study on the analysis of time performance.
  arXiv  Detail & Related papers  (2023-05-26T10:48:02Z)
• How Robust is GPT-3.5 to Predecessors? A Comprehensive Study on Language Understanding Tasks [65.7949334650854]
  GPT-3.5 models have demonstrated impressive performance in various Natural Language Processing (NLP) tasks. However, their robustness and abilities to handle various complexities of the open world have yet to be explored. We show that GPT-3.5 faces some specific robustness challenges, including instability, prompt sensitivity, and number sensitivity.
  arXiv  Detail & Related papers  (2023-03-01T07:39:01Z)
• Weakly Supervised Anomaly Detection: A Survey [75.26180038443462]
  Anomaly detection (AD) is a crucial task in machine learning with various applications. We present the first comprehensive survey of weakly supervised anomaly detection (WSAD) methods. For each setting, we provide formal definitions, key algorithms, and potential future directions.
  arXiv  Detail & Related papers  (2023-02-09T10:27:21Z)
• Understanding metric-related pitfalls in image analysis validation [59.15220116166561]
  This work provides the first comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. Focusing on biomedical image analysis but with the potential of transfer to other fields, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy.
  arXiv  Detail & Related papers  (2023-02-03T14:57:40Z)
• Building Inspection Toolkit: Unified Evaluation and Strong Baselines for Damage Recognition [0.0]
  We introduce the building inspection toolkit -- bikit -- which acts as a simple to use data hub containing relevant open-source datasets in the field of damage recognition. The datasets are enriched with evaluation splits and predefined metrics, suiting the specific task and their data distribution. For the sake of compatibility and to motivate researchers in this domain, we also provide a leaderboard and the possibility to share model weights with the community.
  arXiv  Detail & Related papers  (2022-02-14T20:05:59Z)
• Unsupervised Domain Adaption of Object Detectors: A Survey [87.08473838767235]
  Recent advances in deep learning have led to the development of accurate and efficient models for various computer vision applications. Learning highly accurate models relies on the availability of datasets with a large number of annotated images. Due to this, model performance drops drastically when evaluated on label-scarce datasets having visually distinct images.
  arXiv  Detail & Related papers  (2021-05-27T23:34:06Z)

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.