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Modified Adaptive Random Testing through Iterative Partitioning  

Lee, Kwang-Kyu (QA center, NHN Co.)
Shin, Seung-Hun (Graduate School of Information & Communication, Ajou University)
Park, Seung-Kyu (Graduate School of Information & Communication, Ajou University)
Publication Information
Journal of the Institute of Electronics Engineers of Korea CI / v.45, no.5, 2008 , pp. 180-191 More about this Journal
Abstract
An Adaptive Random Testing (ART) is one of test case generation algorithms that are designed to detect common failure patterns within input domain. The ART algorithm shows better performance than that of pure Random Testing (RT). Distance-bases ART (D-ART) and Restriction Random Testing (RRT) are well known examples of ART algorithms which are reported to have good performances. But significant drawbacks are observed as quadratic runtime and non-uniform distribution of test case. They are mainly caused by a huge amount of distance computations to generate test case which are distance based method. ART through Iterative Partitioning (IP-ART) significantly reduces the amount of computation of D-ART and RRT with iterative partitioning of input domain. However, non-uniform distribution of test case still exists, which play a role of obstacle to develop a scalable algerian. In this paper we propose a new ART method which mitigates the drawback of IP-ART while achieving improved fault-detection capability. Simulation results show that the proposed one has about 9 percent of improved F-measures with respect to other algorithms.
Keywords
Random Testing; Adaptive Random Testing; Test Case Generation;
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