Towards effective adaptive random testing for higher-dimensional input domains

摘要

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.