A three-parameter fault-detection software reliability model with the uncertainty of operating environments

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• 作者：Kwang Yoon Song ; In Hong Chang ; Hoang Pham
• 关键词：Nonhomogeneous Poisson process ; software reliability ; mean squared error ; predictive ratio risk ; predictive power ; fault detection
• 刊名：Journal of Systems Science and Systems Engineering
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：26
• 期：1
• 页码：121-132
• 全文大小：
• 刊物类别：Engineering
• 刊物主题：Complexity; Economic Theory/Quantitative Economics/Mathematical Methods; Operation Research/Decision Theory;
• 出版者：Springer Berlin Heidelberg
• ISSN：1861-9576
• 卷排序：26

文摘

As requirements for system quality have increased, the need for high system reliability is also increasing. Software systems are extremely important, in terms of enhanced reliability and stability, for providing high quality services to customers. However, because of the complexity of software systems, software development can be time-consuming and expensive. Many statistical models have been developed in the past years to estimate software reliability. In this paper, we propose a new three-parameter fault-detection software reliability model with the uncertainty of operating environments. The explicit mean value function solution for the proposed model is presented. Examples are presented to illustrate the goodness-of-fit of the proposed model and several existing non-homogeneous Poisson process (NHPP) models based on three sets of failure data collected from software applications. The results show that the proposed model fits significantly better than other existing NHPP models based on three criteria such as mean squared error (MSE), predictive ratio risk (PRR), and predictive power (PP).