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  • Open Access


    Implementation of Hybrid Particle Swarm Optimization for Optimized Regression Testing

    V. Prakash*, S. Gopalakrishnan

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2575-2590, 2023, DOI:10.32604/iasc.2023.032122

    Abstract Software test case optimization improves the efficiency of the software by proper structure and reduces the fault in the software. The existing research applies various optimization methods such as Genetic Algorithm, Crow Search Algorithm, Ant Colony Optimization, etc., for test case optimization. The existing methods have limitations of lower efficiency in fault diagnosis, higher computational time, and high memory requirement. The existing methods have lower efficiency in software test case optimization when the number of test cases is high. This research proposes the Tournament Winner Genetic Algorithm (TW-GA) method to improve the efficiency of software test case optimization. Hospital Information… More >

  • Open Access


    Case Optimization Using Improved Genetic Algorithm for Industrial Fuzzing Test

    Ming Wan1, Shiyan Zhang1, Yan Song2, Jiangyuan Yao3,*, Hao Luo1, Xingcan Cao4

    Intelligent Automation & Soft Computing, Vol.28, No.3, pp. 857-871, 2021, DOI:10.32604/iasc.2021.017214

    Abstract Due to the lack of security consideration in the original design of industrial communication protocols, industrial fuzzing test which can successfully exploit various potential security vulnerabilities has become one new research hotspot. However, one critical issue is how to improve its testing efficiency. From this point of view, this paper proposes a novel fuzzing test case optimization approach based on improved genetic algorithm for industrial communication protocols. Moreover, a new individual selection strategy is designed as the selection operator in this genetic algorithm, which can be actively engaged in the fuzzing test case optimization process. In this individual selection strategy,… More >

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