This work is partially supported by the National Natural Science Foundation of China (62162046), the Major Project of Inner Mongolia Natural Science Foundation (2019ZD15), Research and Application of Key Technology of Big Data for Discipline Inspection and Supervision (No.2019GG372).
Quantum Algorithms --Testing
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 contraction-expansion 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.
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Guo, X., Song, X., & Zhou, J. T. (2022). A synergic quantum particle swarm optimisation for constrained combinatorial test generation. IET Software.