Recently, adaptive random testing (ART) has been developed to enhance the fault- detection effectiveness of random testing (RT). It has been known in general that the fault-detection effectiveness of ART depends on the distribution of failure-causing inputs,
yet this understanding is in coarse terms without precise details. In this paper, we conduct an in-depth investigation into the factors related to the distribution of failure- causing inputs that have an impact on the fault-detection effectiveness of ART. This paper gives a comprehensive analysis of the favourable conditions for ART. Our study contributes to the knowledge of ART and provides useful information for testers to decide when it is more cost-effective to use ART.