On adaptive random testing through iterative partitioning

Random Testing (RT) is an important and fundamental approach to testing computer

software. Adaptive Random Testing (ART) has been proposed to improve the faultdetection

capability of RT. ART employs the location information of successful test

cases (those that have been executed but not revealed a failure) to enforce an even spread

of random test cases across the input domain. Distance-based ART (D-ART) and Restriction-

based ART (R-ART) are the first two ART methods, which have considerably

improved the fault-detection capability of RT. Both these methods, however, require additional

computation to ensure the generation of evenly spread test cases. To reduce the

overhead in test case generation, we present in this paper a new ART method using the

notion of iterative partitioning. The input domain is divided into equally sized cells by a

grid. The grid cells are categorized into three different groups according to their relative

locations to successful test cases. In this way, our method can easily identify those grid

cells that are far apart from all successful test cases for test case generation. Our method

significantly reduces the time complexity, while keeping the high fault-detection capability.