Abstract: The World Wide Web (Web) is in constant evolutionary change. This evolution occurs along many fronts and is led by infrastructure developers, Web designers, technologists, and users. These multiple stake holders ensure that the Web is a heterogeneous entity, not just in the nature of the content, but in the technology and agents used to deliver and render that content. It is precisely this heterogeneity which gives the Web its strength and its weakness. A weakness in technology adoption leading to an increasing disconnect between the actual user experience and the expected experience of the technology stakeholders. We are interested in the human factors surrounding the evolution of the Web interface; and believe that the wait is always too long for new accessibility recommendations, guidelines, and technology to be adopted. In this case, we describe a ten-year longitudinal study comprising approximately 6,000 home pages. From this study we conclude that as a rule-of-thumb mainstream technology is adopted at about 15% within the first three years, incremental version releases are adopted at about 10% within the first three years. However, sites which are most popular often exhibit enhanced adoption rates of between 10 and 15% over the same period. In addition, we see that accessibility guidelines are mostly ignored with only a 10% adoption rate after more than ten years. From this we infer that, for maximum accessibility adoption, guidelines might be supported and reflected in mainstream specifications instead of remaining only as a separate document.
Abstract: In previous work, we have shown that false positive detection of widgets can be reduced by searching for the Web page’s Document Object Model (DOM) elements that the widget monitors and updates. Using the profile of the JavaScript code’s structure from a Web page, the link between the widget’s set of JavaScript code and the DOM elements that interacts with the users can be established. The profiler can also be used as a platform to include tell-sign’s detection for widget identification research to be conducted. In this report, the architecture of the Widget Identification System (WIS), the process flow of the profiler, its limitations, the data structure that stores the profile of the analysed code and the evaluation results of the profiling approach are presented. Due to the size of the full JavaScript code from the top 10 Websites’s default pages, the techniques and assumptions made to overcome the scale of the evaluation are discussed. The profiling approach achieved a 100% detection accuracy from the evaluation, thus demonstrating the reliability of the platform.
