Towards effective adaptive random testing for higher-dimensional input domains

Authors:
Johannes Mayer
Affiliations:
Ulm University, Ulm, Germany
Venue:
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Year:
2006

Citing 2
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Adaptive random testing

ASIAN'04 Proceedings of the 9th Asian Computing Science conference on Advances in Computer Science: dedicated to Jean-Louis Lassez on the Occasion of His 5th Cycle Birthday

Adaptive random testing with randomly translated failure region

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Abstract

Adaptive Random Testing subsumes a class of algorithms that detect the first failure with less test cases than Random Testing. The present paper shows that a "reference method" in the field of Adaptive Random Testing is not effective for higher dimensional input domains and clustered failure-causing inputs. The reason for this behavior is explained, and a modified method is proposed and analyzed.