Abstract
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.
Highlights
Over the past few decades, software testing has been the most widely used method for ensuring software quality [1]
These are methods that enable the building of optimal Covering Arrays (CA), but they are only practical for building small covering arrays [6]
In 2012, Ahmed et al [59] demonstrated the efficiency of Particle Swarm-based t-way Test Generator (PSTG), a strategy for generating uniform CAs of variable strength, which copes with high interaction strengths of up to t = 6
Summary
Over the past few decades, software testing has been the most widely used method for ensuring software quality [1]. Combinatorial tests provide an alternative to exhaustive testing, which select test cases by a sampling mechanism that systematically covers the combinations ofinput parameter values, defining a smaller set of tests that is relatively easy to manage and run [3]. This type of testing reduces costs, and significantly increases the effectiveness of software testing. One of the main applications of these objects is generating the smallest number of sets or software test cases to cover all interactions sets of input parameters of a procedure, function or software logical unit [5].
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