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Accessibility landmarks identification in web applications based on DOM elements classification

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Abstract

Landmarks are defined in the ARIA—Accessible Rich Internet Applications specification, and are important regions of web applications that blind users access through keyboard shortcuts. However, ARIA landmarks are not always included in web applications, given developer’s lack of knowledge of accessibility guidelines and specifications. The primary goal of this study was to investigate the efficacy in using supervised machine learning for identifying ARIA landmarks in web applications. In this paper, we present an approach for identifying landmarks based on classification of elements in web applications. Our approach uses a classification model with features extracted from the structure of web applications. Then, we cluster elements of the web application based on their landmarks class probability, position and size in the web application, reporting only the element with the highest probability as a landmark for each cluster. Our goal was to use landmarks correctly implemented in web applications to fit a classifier and then use this classifier to identify landmarks in web applications which did not implement them. Our evaluation reported different accuracy results for each landmark, identifying multiple landmarks in web applications with precision values higher than 0.75 to the landmarks: banner, contentinfo, navigation, region and search. The conducted evaluation showed the potential of the proposed approach in the identification of specific types of landmarks and quantifies the amount of elements that could be automatically identified and adapted, enhancing the accessibility of web applications. Furthermore, the results set a baseline accuracy for future studies in the area.

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Data availability

To favor the replicability of this methodology, the data of the web applications and all the steps necessary for generating the LEARNING and TEST DATASET, model evaluation, landmarks prediction, clustering and report generation were made available in an open-source project https://github.com/watinha/aria-landmarks-identification [38].

Notes

  1. https://www.section508.gov/.

  2. https://emag.governoeletronico.gov.br/.

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  6. https://docs.aws.amazon.com/alexa-top-sites/index.html.

  7. https://github.com/watinha/website-downloader.

  8. https://github.com/watinha/aria-landmarks-identification.

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Acknowledgements

We thank UTFPR, DACOM-CP and PPGI supporting this research.

Funding

The authors did not receive support from any organization for the submitted work.

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Authors and Affiliations

  1. DACOM-CP, Federal University of Technology - Paraná, Av. Aberto Carazzai, Cornélio Procópio, Paraná, 86300-000, Brazil

    Willian M. Watanabe, Guilherme de Lemos & Rene Willian Nascimento

  2. Trimble Brasil, Av. Santos Dumont, 271, Londrina, Paraná, 86039-090, Brazil

    Guilherme de Lemos

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  1. Willian M. Watanabe
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  3. Rene Willian Nascimento
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Contributions

WMW performed the methodology, software and writing—original draft. RWN performed the conceptualization and validation. GdL performed the conceptualization.

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Correspondence to Willian M. Watanabe.

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Watanabe, W.M., de Lemos, G. & Nascimento, R.W. Accessibility landmarks identification in web applications based on DOM elements classification. Univ Access Inf Soc 23, 765–777 (2024). https://doi.org/10.1007/s10209-022-00959-8

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