A synergic quantum particle swarm optimisation for constrained combinatorial test generation

Abstract

Combinatorial testing (CT) can efficiently detect failures caused by interactions of parameters of software under test. The CT study has undergone a transition from traditional CT to constrained CT, which is crucial for real-world systems testing. Under this scenario, constrained covering array generation (CCAG), a vital combinatorial optimisation issue targeted with constructing a test suite of minimal size while properly addressing constraints, remains challenging in CT. To the authors' best knowledge, this paper presents a synergic method first based on quantum particle swarm optimisation (QPSO) for the CCAG problems. Three auxiliary strategies, including contractionexpansion coefficient adaptive change strategy, differential evolution strategy, and discretisation strategy, are proposed to improve the performance of QPSO. Meanwhile, the improved QPSO method combines with the three different constraint handling strategies and an enhanced one-test-at-a-time strategy as a synergic QPSO method named QPIO to solve the CCAG problem. In the experiment, we investigate the impacts of parameter settings on the performance of the QPIO. Extensive experimental results show that the QPIO algorithm is a competitive method compared to the representative methods for CCAG. Besides, the QPIO method enriches the application of the QPSO algorithm in the context of CT.

Innovations in t-way test creation based on a hybrid hill climbing-greedy algorithm

“…A combination of greedy and metaheuristic algorithms, as well as the partition of the process into three stages, has resulted in a significant number of improvements for our technique. It is necessary to compare HGHC's effectiveness in decreasing the size of the test suite with that of other existing approaches as deliberated in [34]- [36]. A total of five sets of comparisons are made in the experiment: − HGHC is compared to the results of techniques for various setups involving t=2.…”

“…The most significant contribution of this research is the adaptation of GSA to the production of t-way test data for the first time. Recently, Guo et al [36] provides a synergistic solution for the constrained covering array generation (CCAG) problems that is initially based on quantum particle swarm optimisation (QPSO). Three auxiliary procedures are presented to increase QPSO's performance: contraction-expansion coefficient adaptive modification, differential evolution, and discretization.…”

Test Case Prioritization, Selection, and Reduction Using Improved Quantum-Behaved Particle Swarm Optimization

“…The bigger the value, the wider the particle search range; on the other hand, the smaller value, the more narrow the search range. The evolution velocity coefficient α is introduced to adaptively alter θ [34]:…”

“…Therefore, Equation ( 8) is replaced with: θ = θ max − αθ min (10) here θ min and θ max are the minimum and maximum values, and α is the evolution velocity coefficient's weight. Further, the difference between P(t) − G(t) approaches zero as the iterations proceed, so this value is replaced with the mutation operator x rj (t) − x sj (t), where r and s are the particles selected randomly from the population [34]. It is mathematically formulated as:…”

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

“…PSO has also been proved to be effective in combinatorial testing. Quantum Particle Swarm Optimization (QPSO) strategy has successfully generated constrained combinatorial test suites [30]. A synergic QPSO technique called QPIO enriches QPSO's application in the context of combinatorial testing.…”