Abstract
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).
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Acknowledgments
The authors would like to thank the reviewers for their comments and suggestions. This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2013R1A1A2009277).
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Kwang Yoon Song received the PhD degree in the Department of Computer Science and Statistics at Chosun University, Korea in 2016. He received the BS degrees from Chosun University, Korea in 2009. His research interests include reliability, cost model and Bayesian statistics.
In Hong Chang is a professor in the Department of Computer Science and Statistics at Chosun University, Korea. He received his MS and PhD degrees from Hanyang University, Korea. He has served as an editor and a board member of many journals relating to statistics and reliability. He is a member of the Korean Statistical Society and the Korean Reliability Society. His research interests include reliability, statistical inference and Bayesian statistics.
Hoang Pham is a Distinguished Professor and former Chairman (2007-2013) of the Department of Industrial and Systems Engineering at Rutgers University. He received his MS in statistics from the University of Illinois at Urbana-Champaign, and both MS and PhD in industrial engineering from State University of New York at Buffalo. His research interests include software reliability and system reliability modelling. He has been served as editor-in-chief and editor of several journals. He is the editor of Springer Book Series in Reliability Engineering and has served as Conference Chair and Program Chair of over 30 international conferences. He is the author or coauthor of 6 books and has published over 140 journal articles, and edited 10 books. His numerous awards include 2009 IEEE Reliability Society Engineer of the Year Award. He is a Fellow of the IEEE and IIE.
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Song, K.Y., Chang, I.H. & Pham, H. A three-parameter fault-detection software reliability model with the uncertainty of operating environments. J. Syst. Sci. Syst. Eng. 26, 121–132 (2017). https://doi.org/10.1007/s11518-016-5322-4
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DOI: https://doi.org/10.1007/s11518-016-5322-4