On adaptive random testing through iterative partitioning
RIS ID
38382
Abstract
Random Testing (RT) is an important and fundamental approach to testing computersoftware. Adaptive Random Testing (ART) has been proposed to improve the faultdetectioncapability of RT. ART employs the location information of successful testcases (those that have been executed but not revealed a failure) to enforce an even spreadof 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 considerablyimproved the fault-detection capability of RT. Both these methods, however, require additionalcomputation to ensure the generation of evenly spread test cases. To reduce theoverhead in test case generation, we present in this paper a new ART method using thenotion of iterative partitioning. The input domain is divided into equally sized cells by agrid. The grid cells are categorized into three different groups according to their relativelocations to successful test cases. In this way, our method can easily identify those gridcells that are far apart from all successful test cases for test case generation. Our methodsignificantly reduces the time complexity, while keeping the high fault-detection capability.
Publication Details
Chen, T. Yueh., Huang, D. Hao. & Zhou, Z. Quan. (2011). On adaptive random testing through iterative partitioning. Journal of Information Science and Engineering, 27 (4), 1449-1472.