Adaptive random testing (ART) has been proposed as an enhancement to random testing for situations where failure-causing inputs are clustered together. The basic idea of ART is to evenly spread test cases throughout the input domain. It has been shown by simulations and empirical analysis that ART frequently outperforms random testing. However, there are some outstanding issues on the cost-effectiveness and practicality of ART, which are the main foci of this thesis. Firstly, this thesis examines the basic factors that have an impact on the faultdetection effectiveness of adaptive random testing, and identifies favourable and unfavourable conditions for ART. Our study concludes that favourable conditions for ART occur more frequently than unfavourable conditions. Secondly, since all previous studies allow duplicate test cases, there has been a concern whether adaptive random testing performs better than random testing because ART uses fewer duplicate test cases. This thesis confirms that it is the even spread rather than less duplication of test cases which makes ART perform better than RT. Given that the even spread is the main pillar of the success of ART, an investigation has been conducted to study the relevance and appropriateness of several existing metrics of even spreading. Thirdly, the practicality of ART has been challenged for nonnumeric or high dimensional input domains. This thesis provides solutions that address these concerns. Finally, a new problem solving technique, namely, mirroring, has been developed. The integration of mirroring with adaptive random testing has been empirically shown to significantly increase the cost-effectiveness of ART. In summary, this thesis significantly contributes to both the foundation and the practical applications of adaptive random testing.

Software testing is essential for quality assurance, with automated techniques such as random testing and adaptive random testing being cost-effective solutions compared to others. Adaptive random testing seeks to enhance random testing, and there is a conception that adaptive random testing always should replace random testing. Our research question investigates this conception by addressing a gap in the literature, where a comparison between the two techniques in terms of certain key metrics is missing, namely defect detection efficiency and test case generation time. Defect detection efficiency is the amount of defects detected divided by the number defects in the system multiplied by one hundred. Test case generation time is the time it takes to generate all of the test case inputs. These metrics where chosen as they can be seen as a measurement of the techniques effectiveness and efficiency respectively. In order to address this research question we employ a quantitative experiment where we compare the performance of random testing and adaptive random testing with a sole focus on these two metrics. The comparison is performed by implementing and testing both algorithms on eight error-seeded numerical programs and measuring the results. The results displayed that adaptive random testing had a defect detection efficiency total average of 21.59% and a test case generation time total average of 35.37 (ms), while random testing had a defect detection efficiency total average of 22.28% and a test case generation time total average of 0.26 (ms). These results might contribute to disproving the conception that adaptive random testing always should replace random testing, as random testing evidently performed better on both the measured metrics.

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 countrys 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 modificationintentional or otherwiseof 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 testingfixed 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. / Software Engineering and Intelligent Systems

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