Massive Stochastic Testing of SQL
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Random Program Generator for Java JIT Compiler Test System
QSIC '03 Proceedings of the Third International Conference on Quality Software
Adaptive Random Testing Through Dynamic Partitioning
QSIC '04 Proceedings of the Quality Software, Fourth International Conference
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 by bisection and localization
FATES'05 Proceedings of the 5th international conference on Formal Approaches to Software Testing
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
Experimental assessment of random testing for object-oriented software
Proceedings of the 2007 international symposium on Software testing and analysis
Automatic test data generation using particle systems
Proceedings of the 2008 ACM symposium on Applied computing
ARTOO: adaptive random testing for object-oriented software
Proceedings of the 30th international conference on Software engineering
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Quasi-random sequences, also known as low-discrepancy or low-dispersion sequences, are sequences of points in an n-dimensional unit hypercube. These sequences have the property that points are spread more evenly throughout the cube than random point sequences, which result in regions where there are clusters of points and others that are sparsely populated. Based on the observation that program faults tend to lead to contiguous failure regions within a program's input domain, and that an even spread of random tests enhances the failure detection effectiveness for certain failure patterns, we examine the use of these sequences as a replacement for random sequences in automated testing.The limited number of quasi-random sequences available from the standard algorithms poses significant practical problems for use when testing real programs, and especially for evaluating its effectiveness. We examine the use of randomised quasi-random sequences, which are permuted in a nondeterministic fashion but still retain their low discrepancy properties, to overcome this problem, and show that testing using randomised quasi-random sequences is often significantly more effective than random testing.