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

Abstract

Combinatorial interaction testing has been addressed as an effective software testing technique recently. It shows its ability to reduce the number of test cases that have to be considered for softwareunder-test by taking the combinations of parameters as an interaction of input. 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. Evidence showed that this test suite could detect most of the faults in the software-under-test as compared to higher interactions. This paper presents a new technique to generate pairwise combinatorial test suites. Also, Dragon Fly (DF), a new swarm intelligent optimization algorithm, is assessed. The design and adaptation of the algorithm are addresses in the paper in detail. 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 optimization algorithm for future investigations.

Construction of Prioritized T-Way Test Suite Using Bi-Objective Dragonfly Algorithm

“…In combinatorial testing, there has been adaptation and implementation of DA. Implementations of DA in the combinatorial testing has shown proofs for the efficiency of DA in test suite generation mainly in terms of test suite size [10].…”

“…The proposed strategy can function with high interaction strength, which is up to 6. Table 2 shows the performances of BDA and other strategies in terms of final test suite size (weight based tests) and also the performance of BDA in weight and priority based tests for the configuration CA(N; t, 2 10 ), where 2 ≤ t ≤ 6.…”

“…In weight and priority based tests, BDA generates test suites with sizes not much bigger than the sizes of test suites generated by BDA in weight based tests. As an example, for CA(N; t, 2 10 ) with t = 5, the test suite generated by BDA in the weight and priority based test has a size of 83 (best value), and the test suite generated by BDA in the weight based test has a size of 80 (best value). Here, the difference between these two test suite sizes is only 3.…”

“…BDA has been very effective in the prioritized test suite generation as well as maintaining smaller test suite sizes. Table 5 shows the performance of BDA against SCA and TLBO in terms of test case weight and priority for the configuration CA(N; t, 2 10 ), 2 ≤ t ≤ 6. We have applied our developed strategy on SCA and TLBO also so that there can be an evaluation on these algorithms against BDA in terms of prioritized test suite generation.…”