Abstract
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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.