Generating Pairwise Combinatorial Interaction Test Suites Using Single Objective Dragonfly Optimisation Algorithm

Abstract

Combinatorial interaction testing has been addressed as an important and effective software testing technique recently. The technique can significantly reduce the number of test cases. It provides an alternative method to exhaustive testing and design of experiments (DOE) by allowing a minimised set of tests to represent the actual set of test cases. The reduction of the test cases is based on the combination of input parameters for the system-under-test. This combination could be considered as input-configuration of different software families. Pairwise combinatorial test suite takes the interaction of two input parameters into consideration instead of many parameter interactions. Evidences showed that this test suite can detect most of the faults in the software-under-test as compared to higher interactions. This paper shows the adaptation and assessment of Dragon Fly (DF), a novel swarm intelligent optimisation algorithm, for pairwise combinatorial test generation. The design of the algorithm is addressed in the paper. The algorithm is evaluated extensively through different experiments and benchmarks. The evaluation shows the efficiency of the proposed technique for test suite generation and the usefulness of DF optimisation algorithm for future investigations.