On test case distributions of adaptive random testing
Tsong Yueh Chen, Fei‐Ching Kuo, Huai Liu · Swinburne figshare (Swinburne University of Technology) · 2007
Adaptive Random Testing (ART) has recently been proposed as an approach to enhancing the fault-detection effectiveness of Random Testing (RT). The basic principle of ART is to enforce randomly selected test cases as evenly spread over the input domain as possible. Many ART methods have been proposed to evenly spread test cases in different ways, but no comparison has been made among these methods in terms of their test case distributions. In this paper, we conduct a comprehensive investigation on test case distributions of various ART methods. Our work shows many interesting aspects related to ART’s performance and its test case distribution. Furthermore, it points out a new research direction on enhancing ART.