A generic data-driven software reliability model with model mining technique

A generic data-driven software reliability model with model mining technique
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采用模型挖掘技术的通用数据驱动软件可靠性模型

DOI:
10.1016/j.ress.2010.02.006
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发表时间:
2010-06
影响因子:
8.1
通讯作者:
Xie, Min
Xie, Min
中科院分区:
工程技术1区
文献类型:
--
作者:
Tan, Feng;Li, Xiang;Yang, Bo;Xie, Min

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复杂的系统包含硬件和软件,软件可靠性在系统可靠性方面变得越来越重要。近年来,人们提出并研究了具有多延迟输入单输出(MDISO)架构的数据驱动软件可靠性模型(DDSRM)。对于这些模型,软件故障过程被视为时间序列,并且假设软件故障与最近的故障密切相关。实际上,这个假设可能不成立,因此模型的性能会受到影响。在本文中,我们通过放宽这种不切实际的假设,提出了一种具有 MDISO 架构的通用 DDSRM。所提出的模型可以满足各种故障相关性,现有的 DDSRM 是所提出模型的特例。开发了一种基于混合遗传算法(GA)的算法,该算法采用模型挖掘技术来发现故障的相关性并获得最优模型参数。给出了数值示例,结果表明所提出的模型优于现有的 DDSRM。
Complex systems contain both hardware and software, and software reliability becomes more and more essential in system reliability context. In recent years, data-driven software reliability models (DDSRMs) with multiple-delayed-input single-output (MDISO) architecture have been proposed and studied. For these models, the software failure process is viewed as a time series and it is assumed that a software failure is strongly correlated with the most recent failures. In reality, this assumption may not be valid and hence the model performance would be affected. In this paper, we propose a generic DDSRM with MDISO architecture by relaxing this unrealistic assumption. The proposed model can cater for various failure correlations and existing DDSRMs are special cases of the proposed model. A hybrid genetic algorithm (GA)-based algorithm is developed which adopts the model mining technique to discover the correlation of failures and to obtain optimal model parameters. Numerical examples are presented and results reveal that the proposed model outperforms existing DDSRMs.
DOI: 10.1016/s0951-8320(02)00160-6
发表时间: 2003-03
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影响因子: --
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