An Investigation of the Applicability of Design of Experiments to Software Testing
SEW '02 Proceedings of the 27th Annual NASA Goddard Software Engineering Workshop (SEW-27'02)
Software Fault Interactions and Implications for Software Testing
IEEE Transactions on Software Engineering
Roux-type constructions for covering arrays of strengths three and four
Designs, Codes and Cryptography
The density algorithm for pairwise interaction testing: Research Articles
Software Testing, Verification & Reliability
A New Backtracking Algorithm for Constructing Binary Covering Arrays of Variable Strength
MICAI '09 Proceedings of the 8th Mexican International Conference on Artificial Intelligence
Randomized Postoptimization of Covering Arrays
Combinatorial Algorithms
Constructions of new orthogonal arrays and covering arrays of strength three
Journal of Combinatorial Theory Series A
Covering arrays from cyclotomy
Designs, Codes and Cryptography
A survey of combinatorial testing
ACM Computing Surveys (CSUR)
A survey of methods for constructing covering arrays
Programming and Computing Software
An exact approach to maximize the number of wild cards in a covering array
MICAI'11 Proceedings of the 10th Mexican international conference on Advances in Artificial Intelligence - Volume Part I
Information and Software Technology
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The development of a new software system involves extensive tests on the software functionality in order to identify possible failures. It will be ideal to test all possible input cases (configurations), but the exhaustive approach usually demands too large cost and time. The test suite reduction problem can be defined as the task of generating small set of test cases under certain requirements. A way to design test suites is through interaction testing using a matrix called Covering Array, CA(N;t,k,v), which guarantees that all configurations among every t parameters are covered. This paper presents a simple strategy that reduces the number of rows of a CA. The algorithms represent a post-optimization process which detects wild cards (values that can be changed arbitrarily without the CA losses its degree of coverage) and uses them to merge rows. In the experiment, 667 CAs, created by a state-of-the-art algorithm, were subject to the reduction process. The results report a reduction in the size of 347 CAs (52% of the cases). As part of these results, we report the matrix for CA(42;2,8,6) constructed from CA(57;2,8,6) with an impressive reduction of 15 rows, which is the best upper bound so far.