Hybrid Particle Swarm and Ranked Firefly Metaheuristic Optimization-Based Software Test Case Minimization
نویسندگان
چکیده
Software testing is a valuable and time-consuming activity that aims to improve the software quality. Due its significance, combinatorial focuses on fault identification by interaction of small amount input factors. But, deep not sufficient due time or resources availability. To select optimal test cases with least computation time, Hybrid Multi Criteria Particle Swarm Ranked Firefly Metaheuristic Optimization(HMCPW-RFMO) technique are introduced. Initially, population randomly initialized. Then fitness calculated pairwise coverage, execution cost, detection capability average frequency. RFM approach starts ‘n’ fireflies. The light intensity each firefly gets initialized.If one minor than other one, it moves near brighter one. Next, rank given based their intensity. Lastly, high ranked chosen as global best solution.The result reveals HMCPW-RFMO improves
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ژورنال
عنوان ژورنال: International Journal of Applied Metaheuristic Computing
سال: 2021
ISSN: ['1947-8291', '1947-8283']
DOI: https://doi.org/10.4018/ijamc.290534