A Mixed Poisson process and Empirical Bayes estimation based Software Reliability Growth Model and Simulation

被引：0

作者：

Barraza, Nestor Ruben ^{[1
]}

机构：

[1] UNTREF, Dept Engn, Caseros, Argentina

来源：

2017 IEEE INTERNATIONAL CONFERENCE ON SOFTWARE QUALITY, RELIABILITY AND SECURITY COMPANION (QRS-C) | 2017年

关键词：

Pure Birth process; Simulation of Software Failures; Software Reliability; Empirical Bayes; Mixed Poisson; Inverse Gaussian;

D O I：

10.1109/QRS-C.2017.114

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

A new Software Reliability Model based on a Mixed Poisson process where the failure rate follows an Inverse Gaussian distribution is proposed. By using the Empirical Bayes estimate of the failure rate, our estimate depends just on the number of failures and the total past time, it is not necessary to know the exact instants of time were the failures have occurred. We simulate the failure detection process with exponential waiting times randomly generated where the failure rate is constantly updated with the Empirical Bayes estimate. Results of simulations are compared with two real projects.

引用

页码：612 / 613

页数：2

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