SoK: Detection and Repair of Accessibility Issues
Liming Nie, Hao Liu, Jing Sun, Kabir Sulaiman Said, Shanshan Hong, Lei Xue, Zhiyuan Wei, Yangyang Zhao, Meng Li
TL;DR
The paper tackles the fragmentation in accessibility research by introducing the Accessibility Issue Analysis (AIA) framework, which builds a comprehensive taxonomy of 55 accessibility issue types aligned with WCAG 2.1 and evaluates current detection and repair tools, along with the status of related datasets. Through a systematic literature review (n=42), taxonomy construction (from 124 initial types to 55), and a questionnaire survey (n=$130$), the study reveals that 14 detection tools cover $31$ types ($56.3 ext{%}$) while 9 repair tools cover only $13$ types ($23.6 ext{%}$); detection datasets cover $21$ of $55$ types ($38.1 ext{%}$) and repair datasets cover $7$ types ($12.7 ext{%}$). The results highlight substantial gaps in cross-platform coverage, tooling capabilities, and dataset breadth, underscoring the need for broader datasets, more capable repair tools, and potential WCAG guideline extensions. The framework and findings provide a standardized reference for researchers and practitioners to improve accessibility tooling, inform guideline evolution, and guide future dataset development to support inclusive software and web experiences.
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.