Metaheuristic algorithms for building Covering Arrays: A review


  • Jimena Adriana Timaná-Peña Universidad del Cauca (Popayán-Cauca, Colombia).
  • Carlos Alberto Cobos-Lozada Universidad del Cauca (Popayán-Cauca, Colombia).
  • Jose Torres-Jimenez Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional (Ciudad Victoria-Tamaulipas, México).



ant colony optimization, Covering Array, genetic algorithms, harmony search algorithm, metaheuristics, particle swarm optimization, simulated annealing, tabu search


Covering Arrays (CA) are mathematical objects used in the functional testing of software components. They enable the testing of all interactions of a given size of input parameters in a procedure, function, or logical unit in general, using the minimum number of test cases. Building CA is a complex task (NP-complete problem) that involves lengthy execution times and high computational loads. The most effective methods for building CAs are algebraic, Greedy, and metaheuristic-based. The latter have reported the best results to date. This paper presents a description of the major contributions made by a selection of different metaheuristics, including simulated annealing, tabu search, genetic algorithms, ant colony algorithms, particle swarm algorithms, and harmony search algorithms. It is worth noting that simulated annealing-based algorithms have evolved as the most competitive, and currently form the state of the art.


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How to Cite

Timaná-Peña, J. A., Cobos-Lozada, C. A., & Torres-Jimenez, J. (2016). Metaheuristic algorithms for building Covering Arrays: A review. Revista Facultad De Ingeniería, 25(43), 31–45.




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