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Bayesian network based software reliability prediction with an operational profile

机译:具有操作配置文件的基于贝叶斯网络的软件可靠性预测

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This paper uses a Bayesian network to model software reliability prediction with an operational profile. Due to the complexity of software products and development processes, software reliability models need to possess the ability to deal with multiple parameters. A Bayesian network exhibits a strong ability to adapt to problems involving complex variant factors. A special kind of Bayesian network named a Markov Bayesian network has been applied successfully into modeling software reliability prediction. However, the existing research did not pay enough attention to the fact that the failure characteristics of many software systems often depend on the specific operation performed. In this paper, an extended Markov Bayesian network is developed to model software reliability prediction with an operational profile. The extended Markov Bayesian network proposed in the paper is focused on discrete-time failure data. Methods to solve the network are proposed, and an example is used to illustrate the utilization of the model.
机译:本文使用贝叶斯网络对具有运行状况的软件可靠性预测进行建模。由于软件产品和开发过程的复杂性,软件可靠性模型需要具有处理多个参数的能力。贝叶斯网络具有很强的适应复杂变数问题的能力。一种称为马尔可夫贝叶斯网络的特殊贝叶斯网络已成功地应用于建模软件可靠性预测中。但是,现有的研究并未充分注意许多软件系统的故障特性通常取决于所执行的特定操作这一事实。在本文中,开发了扩展的马尔可夫贝叶斯网络,以使用操作配置文件对软件可靠性预测进行建模。本文提出的扩展马尔可夫贝叶斯网络专注于离散时间故障数据。提出了求解网络的方法,并以一个实例说明了该模型的利用。

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