On Even Spread of Test Cases in Adaptive Random Testing

for Baohua. Random testing (RT) is a basic and simple software testing technique. However, it is controversial whether RT is an effective method to detect software failures. Based on the common observation that inputs revealing software failures tend to cluster into contiguous regions, adaptive random testing (ART) has been proposed to enhance the failure-detection capability of RT. The basic intuition of ART is to evenly spread random test cases over the whole input domain. This thesis investigates a number of topics about such an “even spread ” intuition. It has been generally believed that how evenly an ART algorithm spreads test cases has an impact on how effectively it detects software failures, and an even distribution of test cases brings a good failure-detection capability. This thesis investigates some existing ART algorithms, and proposes several families of new ART algorithms. Some simulations are conducted and the experimental results show that these new algorithms significantly enhance the failure-detection capability of ART. Test case distributions o