Verification of general and cyclic covering arrays using grid computing
Globe'10 Proceedings of the Third international conference on Data management in grid and peer-to-peer systems
Advances in Software Engineering - Special issue on software test automation
A virtualized environment and orthogonal array as a method for software testing
MMACTEE'09 Proceedings of the 11th WSEAS international conference on Mathematical methods and computational techniques in electrical engineering
Information Sciences: an International Journal
Tuple density: a new metric for combinatorial test suites (NIER track)
Proceedings of the 33rd International Conference on Software Engineering
Using binary decision diagrams for combinatorial test design
Proceedings of the 2011 International Symposium on Software Testing and Analysis
Evolutionary algorithm for prioritized pairwise test data generation
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Pairwise testing for software product lines: comparison of two approaches
Software Quality Control
Supercomputing and grid computing on the verification of covering arrays
The Journal of Supercomputing
CarFast: achieving higher statement coverage faster
Proceedings of the ACM SIGSOFT 20th International Symposium on the Foundations of Software Engineering
Minimizing test suites in software product lines using weight-based genetic algorithms
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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Many software developers have encountered failures that occur only as the result of an interaction between two components. For example, a Web application might work correctly on Linux/Apache or Windows/IIS platforms but fail when run on a Windows XP box running an Apache server. The application error is triggered by an interaction of two components: the operating system and the Web server. Testers often use pairwise testing—all pairs of parameter values—to detect such interactions. Combinatorial testing beyond pairwise is rarely used because good algorithms for higher strength combinations, such as four-way or more, have not been available. Nevertheless, empirical evidence shows that some errors are triggered only by the interaction of three, four, or more parameters. With new algorithms and tools, developers can apply high-strength combinatorial testing capable of detecting these elusive errors.