The AETG System: An Approach to Testing Based on Combinatorial Design
IEEE Transactions on Software Engineering
A threshold of ln n for approximating set cover
Journal of the ACM (JACM)
In-Parameter-Order: A Test Generation Strategy for Pairwise Testing
HASE '98 The 3rd IEEE International Symposium on High-Assurance Systems Engineering
Designs, Codes and Cryptography
Software Fault Interactions and Implications for Software Testing
IEEE Transactions on Software Engineering
Using Artificial Life Techniques to Generate Test Cases for Combinatorial Testing
COMPSAC '04 Proceedings of the 28th Annual International Computer Software and Applications Conference - Volume 01
A framework of greedy methods for constructing interaction test suites
Proceedings of the 27th international conference on Software engineering
Upper bounds for covering arrays by tabu search
Discrete Applied Mathematics
Memetic algorithms for constructing binary covering arrays of strength three
EA'09 Proceedings of the 9th international conference on Artificial evolution
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A Covering Array (CA), denoted by CA(N ; t, k, v), is a matrix of size N ×k with entries from the set {0,1,2,...,v−1}, where in each submatrix of size N ×t appears each combination of symbols derived from vt, at least once. The Covering Arrays (CAs) are combinatorial structures that have applications in software testing. This paper defines the Problem of Optimal Shortening of Covering ARrays (OSCAR), gives its NP-Completeness proof and presents an exact and a greedy algorithms to solve it. The OSCAR problem is an optimization problem that for a given matrix M consists in finding a submatrix M′ that is close to be a CA. An algorithm that solves the OSCAR problem has application as an initialization function of a metaheuristic algorithm that constructs CAs. Our algorithms were tested on a benchmark formed by 20 instances of the OSCAR problem, derived from 12 CAs taken from the scientific literature. From the solutions of the 20 instances of the OSCAR problem, 12 were transformed into CAs through a previously reported metaheuristic algorithm for the construction of CAs.