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Based on the intuition that widely spread test cases should have greater chance of hitting the nonpoint failure-causing regions, several adaptive random testing (ART) methods have recently been proposed to improve traditional random testing (RT). However, most of the ART methods require additional distance computations to ensure an even spread of test(More)
Adaptive Random Testing (ART) is an effective improvement of Random Testing (RT) in the sense that fewer test cases are needed to detect the first failure. It is based on the observation that failure-causing inputs are normally clustered in one or more contiguous regions in the input domain. Hence, it has been proposed that test case generation should refer(More)
Adaptive Random Testing (ART) has been proposed to improve the fault-detection capability of Random Testing (RT). Lattice-based ART (L-ART) is a distinctive ART method which generates test cases by systematically placing and then randomly shifting lattice nodes in the input domain. Previous studies showed that L-ART has a better fault-detection capability(More)
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(More)
Recently, Adaptive Random Testing through Iterative Partitioning (IP-ART) has been proposed as a random testing method that is more effective than pure Random Testing. Besides this, it is supposed to be equally effective as very good random testing techniques, namely Distance-Based Adaptive Random Testing and Restricted Random Testing, while only having(More)
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