Robert G. Merkel

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Random testing is not only a useful testing technique in itself, but also plays a core role in many other testing methods. Hence, any significant improvement to random testing has an impact throughout the software testing community. Recently, Adaptive Random Testing (ART) was proposed as an effective alternative to random testing. This paper presents a(More)
Adaptive random testing (ART) has recently been introduced to improve the fault-detection effectiveness of random testing (RT) for certain types of failurecausing patterns. However, ART requires extra computations to ensure an even spread of test cases, which may render ART to be less cost-effective than RT. In this paper, we introduce an innovative(More)
Quasi-random sequences, also known as low-discrepancy or low-dispersion sequences, are sequences of points in an n-dimensional unit hypercube. These sequences have the property that points are spread more evenly throughout the cube than random point sequences, which result in regions where there are clusters of points and others that are sparsely populated.(More)
A web service may evolve autonomously, making peer web services in the same service composition uncertain as to whether the evolved behaviors may still be compatible to its originally collaborative agreement. Although peer services may wish to conduct regression testing to verify the original collaboration, the source code of the former service can be(More)
We examine the statistical variability of three commonly used software testing effectiveness measures—the E-measure (expected number of failures detected), P-measure (probability of detecting at least one failure), and F-measure (number of tests required to detect the first failure). We show that for random testing with replacement, the F-measure will be(More)
Adaptive random testing (ART) is a method for improving the fault-finding effectiveness of random testing. Fixed-size candidate set ART is the most studied variant of this approach. However, existing implementations of FSCS-ART have had substantial selection overhead, with n test cases requiring O(n/sup 2/) time to generate. We describe the use of a(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)
Failure patterns describe typical ways in which inputs revealing program failure are distributed across the input domain—in many cases, clustered together in contiguous regions. Based on these observations several debug testing methods have been developed. We examine the upper bound of debug testing effectiveness improvements possible through making(More)