A THREE-PARAMETER FAULT-DETECTION SOFTWARE RELIABILITY MODEL WITH THE UNCERTAINTY OF OPERATING ENVIRONMENTS

作     者：Kwang Yoon Song In Hong Chang Hoang Pham 

作者机构：Department of Computer Science and Statistics Chosun University Gwangju 62022 Korea Department of Industrial and Systems Engineering Rutgers University NJ 08855-8018 USA 

出 版 物：《Journal of Systems Science and Systems Engineering》 (系统科学与系统工程学报（英文版）)

年 卷 期：2017年第26卷第1期

页      面：121-132页

核心收录：

学科分类：07[理学] 08[工学] 

基　　金：supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education 

主　　题：Nonhomogeneous Poisson process ratio risk predictive power fault detection software reliability mean squared error predictive 

摘      要：As requirements for system quality have increased, the need for high system reliability is also increasing. Soflnvare 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, soft-ware development can be time-consuming and expensive. Many statistical models have been developed in the past years to estimate soflnvare 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）.