A Hybrid Artificial Bee Colony and Harmony Search Algorithm to Generate Covering Arrays for Pair-wise Testing

Abstract

Abstract-Combinatorial Interaction Testing (CIT) is a cost effective testing technique that aims to detect interaction faults generated as a result of interaction between components or parameters in a software system. CIT requires the generation of effective test sets that cover all possible t-way (t denotes the strength of testing) interactions between parameters. Covering array (CA) and mixed covering array (MCA) are often used to represent test sets. This paper presents a hybrid algorithm that integrates artificial bee colony algorithm (ABC) and harmony search algorithm (HS) to construct CAs for testing all 2-way interactions (pair-wise testing) in software systems. The performance of the proposed hybrid algorithm ABCHS-CAG is compared and analyzed by performing experiments on a set of benchmark problems on pair-wise testing. The results show that ABCHS-CAG generates smaller CAs than its greedy counterparts whereas its performance is comparable to the existing state-of-the-art meta-heuristic algorithms.

A Simulated Annealing Algorithm to Construct Covering Perfect Hash Families

“…best global improved ← true (23) if ( ) = 0 then (24) return (25) end if (26) end if (27) end if (28) number of times for each temperature value. In this work, the number of perturbations of the current solution (the length of the Markov chain) done at each temperature value is not fixed.…”

“…The techniques used to construct covering arrays can be classified as exact, greedy, metaheuristic, algebraic, and 2 Mathematical Problems in Engineering recursive; see [11][12][13][14] for an overview of covering array construction methods. Metaheuristic algorithms to construct covering arrays include tabu search [15], genetic algorithms [16], simulated annealing [17], ant colony [18], particle swarm optimization [19], harmony search [20], hill climbing [21], bird swarm [22], a combination of bee colony and harmony search [23], Cuckoo search [24], differential evolution [25], tabu search as a hyperheuristic [26], and a combination of simulated annealing with a greedy algorithm [27].…”