Metaheuristic algorithms for building Covering Arrays: A review

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.

• Fecha de publicación: 2016
• Institución: Universidad Pedagógica y Tecnológica de Colombia
• Repositorio: RiUPTC: Repositorio Institucional UPTC
• Idioma: eng
• OAI Identifier: oai:repositorio.uptc.edu.co:001/14153
• Acceso en línea: https://revistas.uptc.edu.co/index.php/ingenieria/article/view/5295; https://repositorio.uptc.edu.co/handle/001/14153
• Palabra clave: ant colony optimization; Covering Array; genetic algorithms; harmony search algorithm; metaheuristics; particle swarm optimization; simulated annealing; tabu search; algoritmo de búsqueda armónica; algoritmos genéticos; búsqueda tabú; Covering Array; metaheurística; optimización por colonia de hormigas; optimización por enjambre de partículas; recocido simulado
• License: http://purl.org/coar/access_right/c_abf47