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The World Wide Web (Web) has evolved from a collection of static pages that need reloading every time the content changes, into dynamic pages where parts of the page updates independently, without reloading it. As such, users are required to work with dynamic pages with components that react to events either from human interaction or machine automation. Often elderly and visually impaired users are the most disadvantaged when dealing with this form of interaction. Operating widgets require the user to have the conceptual design knowledge of the widget to complete the task. Users must have prior experience with the widget or have to learn to operate it independently, because often no user documentation is available. An automated Widget Prediction Framework (WPF) is proposed to address the issues discussed. It is a pre-emptive approach that predicts different types of widget and their locations in the page. Widgets with similar characteristics and functionalities are categorised based on a definition provided by widget design pattern libraries. Some design patterns are more loosely defined than others, and this causes confusion and ambiguity when identifying them. A formal method to model widgets based on a Widget Ontology was developed. The paradigm of the ontology provides a framework for developers to communicate their ideas, while reducing ambiguity between different types of widget. A Widget Prediction System (WPS) was developed using the concepts of the WPF. To select the types of widget for WPS evaluation, a widget popularity investigation was conducted. Seven of the most popular widgets from the investigation, done across fifty Websites, were selected. To demonstrate how WPF can be applied to predict widgets, fifty websites were used to evaluate the feasibility of the approach using WPS. On average, WPS achieved 61.98% prediction accuracy with two of the widgets > 84% accuracy. These results demonstrated the feasibility of the framework as the backend for tools that support elderly or visually impaired users.

Abstract: This thesis presents an extensive survey of 100,000 websites as the basis for understanding the deployment of cookies across the Internet. The survey indicates cookie deployment on the Internet is approaching universal levels. The survey identifies the presence of P3P policies and dynamic web technologies as major predictors of cookie usage, and a number of significant relationships are established between the origin of the web application and cookie deployment. Large associations are identified between third-party persistent cookie usage and a country’s e-business environment. Cookie collection testing (CCT), a strategy for testing web applications, is presented. Cookies maintained in a browser are explored in light of anti random testing techniques, culminating in the definition of seeding vectors as the basis for a scalable test suite. Essentially CCT seeks to verify web application robustness against the modification—intentional or otherwise—of an application's internal state variables. Automation of CCT is outlined through the definition of test oracles and evaluation criterion. Evolutionary adaptive random (eAR) testing is proposed for application to the cookie collection testing strategy. A simulation study is undertaken to evaluate eAR against the current state-of-the-art in adaptive random testing—fixed size candidate set, restricted random testing, quasi-random testing, and random testing. eAR is demonstrated to be superior to the other techniques for block pattern simulations. For fault patterns of increased complexity, eAR is shown to be comparable to the other methods. An empirical investigation of CCT is undertaken. CCT is demonstrated to reveal defects within web applications, and is found to have a substantial fault-triggering rate. Furthermore, CCT is demonstrated to interact with the underlying application, not just the technological platform upon which an application is implemented. Both seeding and generated vectors are found to be useful in triggering defects. A synergetic relationship is found to exist between the seeding and generated vectors with respect to distinct fault detection. Finally, a large significant relationship is established between structural and content similarity measures of web application responses, with a composite of the two similarity measures observed to be superior in the detection of faults.