A synergic quantum particle swarm optimisation for constrained combinatorial test generation

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