One-test-at-a-time heuristic search for interaction test suites
Proceedings of the 9th annual conference on Genetic and evolutionary computation
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Workshop on Domain specific approaches to software test automation: in conjunction with the 6th ESEC/FSE joint meeting
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Discrete Applied Mathematics
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LATIN'08 Proceedings of the 8th Latin American conference on Theoretical informatics
A survey of combinatorial testing
ACM Computing Surveys (CSUR)
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MICAI'10 Proceedings of the 9th Mexican international conference on Artificial intelligence conference on Advances in soft computing: Part II
Construction of mixed covering arrays of variable strength using a tabu search approach
COCOA'10 Proceedings of the 4th international conference on Combinatorial optimization and applications - Volume Part I
Locating Errors Using ELAs, Covering Arrays, and Adaptive Testing Algorithms
SIAM Journal on Discrete Mathematics
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New bounds for binary covering arrays using simulated annealing
Information Sciences: an International Journal
Efficient conditional expectation algorithms for constructing hash families
IWOCA'11 Proceedings of the 22nd international conference on Combinatorial Algorithms
SP 800-142. Practical Combinatorial Testing
SP 800-142. Practical Combinatorial Testing
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Journal of Discrete Algorithms
Randomized post-optimization of covering arrays
European Journal of Combinatorics
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The Journal of Supercomputing
Constraints dependent t-way test suite generation using harmony search strategy
PKAW'12 Proceedings of the 12th Pacific Rim conference on Knowledge Management and Acquisition for Intelligent Systems
MICAI'12 Proceedings of the 11th Mexican international conference on Advances in Computational Intelligence - Volume Part II
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There are many published algorithms for generating interaction test suites for software testing, exemplified by AETG, IPO, TCG, TConfig, simulated annealing and other heuristic search, and combinatorial design techniques. Among these, greedy one-test-at-a-time methods (such as AETG and TCG) have proven to be a reasonable compromise between the needs for small test suites, fast test-suite generation, and flexibility to accommodate a variety of testing scenarios. However, such methods suffer from the lack of a worst-case logarithmic guarantee on test suite size, while methods that provide such a guarantee at present are less efficient or flexible, or do not produce test suites that are competitive in size for practical testing scenarios. In this paper, a new algorithm establishes that efficient, greedy, one-test-at-a-time methods can indeed produce a logarithmic worst-case guarantee on the test suite size. In addition, this can be done while still producing test suites that are of competitive size, and in a time that is comparable to the published methods. It is deterministic, guaranteeing reproducibility. It generates only one candidate test at a time, permits users to ‘seed’ the test suite with specified tests, and allows users to specify constraints of combinations that should be avoided. Further, statistical analysis examines the impact of five variables used to tune this density algorithm for execution time and test suite size: weighting of density for factors, scaling of density, tie-breaking, use of multiple candidates, and multiple repetitions using randomization. Copyright © 2007 John Wiley & Sons, Ltd.