Journal
JOURNAL OF SYSTEMS AND SOFTWARE
Volume 81, Issue 12, Pages 2146-2162Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.jss.2008.03.062
Keywords
Software quality; Software testing; Random testing; Adaptive random testing; Test case distribution
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Adaptive random testing (ART) has recently been proposed to enhance the failure-detection capability of random testing. In ART, test cases are not only randomly generated, but also evenly spread over the input domain. Various ART algorithms have been developed to evenly spread test cases in different ways. Previous studies have shown that some ART algorithms prefer to select test cases from the edge part of the input domain rather than from the centre part, that is, inputs do not have equal chance to be selected as test cases. Since we do not know where the failure-causing inputs are prior to testing, it is not desirable for inputs to have different chances of being selected as test cases. Therefore, in this paper, we investigate how to enhance some ART algorithms by offsetting the edge preference, and propose a new family of ART algorithms. A series of simulations have been conducted and it is shown that these new algorithms not only select test cases more evenly, but also have better failure detection capabilities. (C) 2008 Elsevier Inc. All rights reserved.
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