Adaptive random testing with randomly translated failure region
Proceedings of the 1st international workshop on Random testing
An empirical analysis and comparison of random testing techniques
Proceedings of the 2006 ACM/IEEE international symposium on Empirical software engineering
Adaptive random testing through iterative partitioning revisited
Proceedings of the 3rd international workshop on Software quality assurance
Automated cookie collection testing
ACM Transactions on Software Engineering and Methodology (TOSEM)
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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(^2) time to generate. We describe the use of a geometric data structure known as the Voronoi Diagram to reduce this overhead to no worse than O(nvn) and, with further optimization, O(n log n). We demonstrate experimentally that practical improvements in selection overhead can be gained using this improved implementation.