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.