베이지안 소프트웨어 신뢰성 성장 모형에 관한 연구

Abstract

This paper presents a stochastic model for the software failure phenomenon based on a Nonhomogeneous Poisson Process(NHPP) and performs Bayesian inference using Conjugate prior and noninformative prior by Laplace. The failure process is analyzed to develop a suitable mean value function for the NHPP; expressions are given for several performance measure. The parametric inference of the model using Goel-Okumoto model, and Rayleigh model is discussed. Sum of relative error was used for efficient bayesian selection. The numerical results of this models are applied to real software failure data. We could avoid multiple integration using Gibbs sampling, which is a kind of Markov Chain Monte Carlo method to compute the posterior distribution, and numerical estimates were used for the NTDS data.