首页|Performance Evaluation of Software in Large Data Environments Utilizing Time-Managed Computation Tree Logic
Performance Evaluation of Software in Large Data Environments Utilizing Time-Managed Computation Tree Logic
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The research aims to solve the problems of unstable performance parameters and insufficient coverage in software testing and proposes a big data platform software performance testing system based on the clock-controlled computation tree logic method. The particle swarm algorithm finds the optimal solution through the movement and cooperation of particles in the search space. The genetic algorithm evolves the population through selection, crossover, and mutation operations, ultimately finding the optimal solution. Secondly, long short-term memory networks and linear autoregressive models also have advantages in software testing, which can improve the effectiveness and efficiency of software testing through reasonable selection and combined use. The research uses the algorithmic logic of the particle swarm and genetic algorithms to confirm the software testing system's moment parameters and other information. At the same time, an algorithmic model researches the joint coverage and the use of the system's value, and finally, the big data platform is used to analyze the research system. The innovative combination of the CCTL method and optimization algorithm in the research has improved the accuracy and stability of software testing. The research results show that using the system to test software can achieve a coverage rate of 100% for its component use cases, while the functional coverage rates of the genetic algorithm and particle swarm algorithm reach 90.36% and 91.32%, respectively. The accuracy of software testing for researching usage methods is 5% and 6% higher than testing methods. When the moment range of the particle parameter position information of the model is [150 ms, 250 ms], the maximum value of the target parameter velocity is 80 m/s, and the minimum value is o m/s. The maximum value of the target azimuth velocity is 20 rad/s, and the minimum is o rad/s. The system can determine the various parameters of the software, and at the same time, if the software test results on the test results are typical, fault analysis can be completed typically; the performance of the use of algorithms is also better than other algorithms, and the study of the use of algorithms with a higher degree of stability. It can be seen that the system and methods used in this research are better than traditional methods, and the test results in software testing have improved, providing a research direction for software testing after the research.
Software TestingParticle Swarm AlgorithmBell-controlled Computational Tree Logic MethodGenetic AlgorithmTesting Effectiveness
Yuan Sun、Md Gapar Md Johar、Jacquline Tham
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Postgrduate Center Management and Science University Shah Alam 40100, Malaysia||School of Information Engineering GongQing Institute of Science and Technology Jiujiang 332020, China
Postgrduate Center Management and Science University Shah Alam 40100, Malaysia
NETL
NSTL
