Automatic Web Accessibility Metrics

Several works of literature contributed to the web evaluation process in recent years to promote digital inclusion by addressing several accessibility guidelines, methods, processes, and techniques. Researchers have investigated how the web evaluation process could be facilitated by including accessibility issues to obtain an inclusive and accessible solution to improve the user experience and increase user satisfaction. Three systematic literature reviews (SLRs) have been conducted in the context of past research, considering such research focuses. This paper presents a new SLR approach concerning accessibility in the web evaluation process, considering the period from 2010 to 2021. The review of 92 primary studies showed the contribution of publications on different phases of the web evaluation process mainly by highlighting the significant studies in the framework design and testing process. To the best of our knowledge, this is the first study focused on the web accessibility literature reporting the engineering assets for evaluation of new accessible and inclusive web-based solutions (e.g., websites). Besides, in this study, we aim to provide a new direction to the web designers and developers with an updated view of process, methods, techniques, tools, and other crucial aspects to contribute to the accessible process enrichment, as well as depict the gaps and challenges that may be worthy to be investigated in the future. The findings of this SLR introduce a new dimension in web accessibility research on determining and mitigating the research gap of web accessibility issues for web designers, developers, and other practitioners.

Section: Challenges (C)mentioning

confidence: 99%

Section: Improvement Directions (Id)mentioning

confidence: 99%

“…(1) metrics for accessibility evaluation concerning validity, reliability, sensitivity, and adequacy are challenging to ensure [42].…”

Section: (C 2 ) Assets Of Success Criteria Validationmentioning

confidence: 99%

Section: (C 2 ) Assets Of Success Criteria Validationmentioning

confidence: 99%

See 2 more Smart Citations

Accessibility engineering in web evaluation process: a systematic literature review

Ara

Sik-Lányi

Kelemen

2023

“…To compute the accessibility level of a web page we used an accessibility metric, more specifically the A3 aggregation function [8]. This metric is an extension of the UWEM 0.5 metric [8,35] but improves upon its predecessor by considering the complexity of the resources and the needs of different ability groups. A3 is characterized by a limited range of scores (from 0 to 1).…”

Section: Measurementmentioning

confidence: 99%

A large-scale web accessibility analysis considering technology adoption

Martins

Duarte

2023

This paper reports the results of the automated accessibility evaluation of nearly three million web pages. The analysis of the evaluations allowed us to characterize the status of web accessibility. On average, we identified 30 errors per web page, and only a very small number of pages had no accessibility barriers identified. The more frequent problems found were inadequate text contrast and lack of accessible names. Additionally, we identified the technologies present in the websites evaluated, which allowed us to relate web technologies with the accessibility level, as measured by A3, an accessibility metric. Our findings show that most categories of web technologies impact the accessibility of web pages, but that even for those categories that show a negative impact, it is possible to select technologies that improve or do not impair the accessibility of the web content.

Large-scale study of web accessibility metrics

Martins

Duarte

2022

Evaluating the accessibility of web resources is usually done by checking the conformance of the resource against a standard or set of guidelines (e.g., the WCAG 2.1). The result of the evaluation will indicate what guidelines are respected (or not) by the resource. While it might hint at the accessibility level of web resources, often it will be complicated to compare the level of accessibility of different resources or of different versions of the same resource from evaluation reports. Web accessibility metrics synthesize the accessibility level of a web resource into a quantifiable value. The fact that there is a wide number of accessibility metrics, makes it challenging to choose which ones to use. In this paper, we explore the relationship between web accessibility metrics. For that purpose, we investigated eleven web accessibility metrics. The metrics were computed from automated accessibility evaluations obtained using QualWeb. A set of around three million web pages were evaluated. By computing the metrics over this sample of nearly three million web pages, it was possible to identify groups of metrics that offer similar results. Our analysis shows that there are metrics that behave similarly, which, when deciding what metrics to use, assists in picking the metric that is less resource intensive or for which it might be easier to collect the inputs.