RELATIONSHIP BETWEEN CBS QUALITY PARAMETERS FOR ASSESSMENT OF COMPUTATIONAL INTELLIGENCE TECHNIQUES

Shivani Yadav, B. Kishan
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Abstract

Software reliability plays a vital role in the emerging field of digitalization. Everyone wants cost and time-efficient software along with reliability which is achieved using CBS. In CBS, if the individual components are computed for a large or complicated system, then integration becomes complex which results in difficulty in predicting CBSR. To solve this problem several computational intelligence techniques such as SVM, ACO, PSO, ABC, GA, Neural network, are used but in our paper, we have focused on optimization techniques Fuzzy, ACO, ABC, PSO. These techniques help in estimating and predicting reliability models for CBS. Also, we have done, an assessment and comparative analysis based on a literature review of ABC, ACO, and PSO that have also been presented, for choosing suitable parameters for software reliability modeling.
用于评估计算智能技术的CBS质量参数之间的关系
软件可靠性在数字化这一新兴领域中起着至关重要的作用。每个人都想要成本和时间效率高的软件以及使用CBS实现的可靠性。在CBS中,如果为大型或复杂的系统计算单个组件,则集成变得复杂,从而导致难以预测CBSR。为了解决这一问题,使用了几种计算智能技术,如支持向量机,蚁群算法,粒子群算法,ABC算法,遗传算法,神经网络,但在本文中,我们重点研究了模糊优化技术,蚁群算法,ABC算法,粒子群算法。这些技术有助于估计和预测CBS的可靠性模型。此外,我们还根据ABC、ACO和PSO的文献综述进行了评估和比较分析,以便为软件可靠性建模选择合适的参数。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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