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Drop-Down Menu Widget Identification Using HTML Structure Changes Classification

Drop-Down Menu Widget Identification Using HTML Structure Changes Classification Widgets have been deployed in rich internet applications for more than 10 years. However, many of the widgets currently available on the web do not implement current accessibility design solutions standardized in ARIA (Accessible Rich Internet Applications) specification, hence are not accessible to disabled users. This article sets out an approach for automatically identifying widgets on the basis of machine-learning algorithms and the classification of mutation records; it is an HTML5 technology that logs all changes that occur in the structure of a web application. Automatic widget identification is an essential component for the elaboration of automatic ARIA evaluation and adaptation strategies. Thus, the aim of this article is to take steps toward easing the software-engineering process of ARIA widgets. The proposed approach focuses on the identification of drop-down menu widgets. An experiment with real-world web applications was conducted and the results showed evidence that this approach is capable of identifying these widgets and can outperform previous state-of-the-art techniques based on an F-measure analysis conducted during the experiment. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Accessible Computing (TACCESS) Association for Computing Machinery

Drop-Down Menu Widget Identification Using HTML Structure Changes Classification

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2018 ACM
ISSN
1936-7228
eISSN
1936-7236
DOI
10.1145/3178854
Publisher site
See Article on Publisher Site

Abstract

Widgets have been deployed in rich internet applications for more than 10 years. However, many of the widgets currently available on the web do not implement current accessibility design solutions standardized in ARIA (Accessible Rich Internet Applications) specification, hence are not accessible to disabled users. This article sets out an approach for automatically identifying widgets on the basis of machine-learning algorithms and the classification of mutation records; it is an HTML5 technology that logs all changes that occur in the structure of a web application. Automatic widget identification is an essential component for the elaboration of automatic ARIA evaluation and adaptation strategies. Thus, the aim of this article is to take steps toward easing the software-engineering process of ARIA widgets. The proposed approach focuses on the identification of drop-down menu widgets. An experiment with real-world web applications was conducted and the results showed evidence that this approach is capable of identifying these widgets and can outperform previous state-of-the-art techniques based on an F-measure analysis conducted during the experiment.

Journal

ACM Transactions on Accessible Computing (TACCESS) – Association for Computing Machinery

Published: Jun 8, 2018

Keywords: ARIA

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