SoK: Detection and Repair of Accessibility Issues
Liming Nie, Hao Liu, Jing Sun, Kabir Sulaiman Said, Shanshan Hong, Lei, Xue, Zhiyuan Wei, Yangyang Zhao, and Meng Li

TL;DR
This paper introduces the AIA framework, creating a comprehensive accessibility issue taxonomy and analyzing detection and repair tools and datasets, revealing significant gaps in coverage and capabilities.
Contribution
It develops a detailed taxonomy of 55 accessibility issues and provides an in-depth analysis of existing tools and datasets, highlighting current limitations.
Findings
14 detection tools cover 31 issue types (56.3%)
9 repair tools address 13 issue types (23.6%)
Datasets for detection cover 21 issue types (38.1%)
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…
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Taxonomy
TopicsDigital Accessibility for Disabilities
