Orthogonal Latin squares: an application of experiment design to compiler testing
Communications of the ACM
The AETG System: An Approach to Testing Based on Combinatorial Design
IEEE Transactions on Software Engineering
In-Parameter-Order: A Test Generation Strategy for Pairwise Testing
HASE '98 The 3rd IEEE International Symposium on High-Assurance Systems Engineering
Transportation Modeling: An Artificial Life Approach
ICTAI '02 Proceedings of the 14th IEEE International Conference on Tools with Artificial Intelligence
On Honey Bees and Dynamic Server Allocation in Internet Hosting Centers
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Software Testing: Fundamental Principles and Essential Knowledge
Software Testing: Fundamental Principles and Essential Knowledge
Engineering optimizations via nature-inspired virtual bee algorithms
IWINAC'05 Proceedings of the First international work-conference on the Interplay Between Natural and Artificial Computation conference on Artificial Intelligence and Knowledge Engineering Applications: a bioinspired approach - Volume Part II
Cooperative bees swarm for solving the maximum weighted satisfiability problem
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
RuleML '09 Proceedings of the 2009 International Symposium on Rule Interchange and Applications
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
An auto-adapted method to generate pairwise test data set
AICI'12 Proceedings of the 4th international conference on Artificial Intelligence and Computational Intelligence
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Pairwise testing is a combinatorial technique used to reduce the number of test case inputs to a system in situations where exhaustive testing with all possible inputs is not feasible. The generation of pairwise test sets with a minimal size is an NP-complete problem and several deterministic algorithms have been published. This paper presents the results of generating pairwise test sets using a simulated bee colony algorithm. Compared to published results for seven benchmark problems, the simulated bee colony approach produced test sets which were comparable or better in terms of size for all seven problems. However, the simulated bee colony approach required significantly longer generation time than deterministic approaches in all cases. The results demonstrate that the generation of pairwise test sets using a simulated bee colony algorithm is possible, and suggest that the approach may be useful in testing scenarios where pairwise test set data will be reused.