A Generalized Bivariate Modeling Framework of Fault Detection and Correction Processes
A Generalized Bivariate Modeling Framework of Fault Detection and Correction Processes
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DOI：
10.1109/issre.2017.22
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发表时间：
2017-10
期刊：
影响因子：
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通讯作者：
H. Okamura;T. Dohi
中科院分区：
文献类型：
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作者：
H. Okamura;T. Dohi
本文提出了具有二元分布的故障检测和纠正过程的通用建模框架。所提出的框架包括几乎所有现有的软件可靠性增长模型，即故障检测和纠正过程均由非齐次泊松过程描述的模型。在我们的框架中，故障纠正时间的时间依赖性对应于故障检测和纠正时间之间的相关性。此外，我们提出了一种新的具有超Erlang分布的故障检测和纠正过程模型，并通过EM（期望最大化）算法开发了模型参数估计算法。通过数值例子，我们展示了hyper-Erlang模型与开源项目的实际故障检测和纠正数据的数据拟合能力。
This paper presents a generalized modeling framework of fault detection and correction processes with bivariate distributions. The presented framework includes almost all existing software reliability growth models, namely the models in which both fault detection and correction processes are described by non-homogeneous Poisson processes. In our framework, the time dependency of fault correction time corresponds to the correlation between fault detection and correction times. Moreover, we propose a new fault detection and correction process model with hyper-Erlang distributions, and develop the model parameter estimation algorithm via EM (expectation-maximization) algorithm. In numerical examples, we demonstrate the data fitting ability of hyper-Erlang model with actual fault detection and correction data of open source projects.