关于Gibbs算法的NHPP软件可信模型的Bayesian接近方法

The Bayesian approach for NHPP software reliability model using Gibbs algorithm

研究了软件可信成长模型的bayesian推论和模型选择方法。如果用内在错误和错误之间的时间间隔来模化成长模型,在软件开发阶段,可以非常有效的利用其成长模型。本文在解决多重积分问题时利用Gibbs抽样方法来计算出算后分布。一般顺序统计量模型依赖具有扩散算前分布特性的软件。对一般顺序统计量模型依实行了bayesian总体参数的推断,还实行了有效的模型选择方法。模型的设定和判断标准可用于利用方差相乘和的适合度鉴定和趋势鉴定。为了取得相应的值例,本文在分析错误资料时利用了AllenP.NikoraandMichaelR.Lyu[13]提出的SYS2软件错误资料。

Bayesian inference and model selection method for software reliability growth models are studied.Software reliability growth models are used in testing stages of software development to model the error content and time intervals between software failures. In this paper, could avoid multiple integration using of Gibbs sampling, which is a kind of Markov Chain Monte Carlo method to compute the posterior distribution. Bayesian inference for general order statistics models in software reliability with diffuse information and model selection method are studied.For model de- determination and selection, explored goodness of fit （the error sum of squares）, trend tests.The methodology develoed in this paper is exemplified with a software reliability data set introduced by SYS 2（Allen P.Nikora and Michael R, Lyu 13）.