A component-based back-propagation reliability model with low complexity for complex software systems  

作　　者：聂鹏 Geng Ji Qin Zhiguang 

机构地区：[1]School of Computer Science & Engineering,University of Electronic Science and Technology of China [2]Jiangxi University of Finance and Economics

出　　处：《High Technology Letters》2013年第3期273-282,共10页高技术通讯（英文版）

基　　金：Supported by the National Natural Science Foundation of China(No.60973118,60873075)

摘　　要：Since most of the available component-based software reliability models consume high computational cost and suffer from the evaluating complexity for the software system with complex structures,a component-based back-propagation reliability model(CBPRM)with low complexity for the complex software system reliability evaluation is presented in this paper.The proposed model is based on the artificial neural networks and the component reliability sensitivity analyses.These analyses are performed dynamically and assigned to the neurons to optimize the reliability evaluation.CBPRM has a linear increasing complexity and outperforms the state-based and the path-based reliability models.Another advantage of CBPRM over others is its robustness.CBPRM depends on the component reliabilities and the correlative sensitivities,which are independent from the software system structure.Based on the theory analysis and experiment results,it shows that the complexity of CBPRM is evidently lower than the contrast models and the reliability evaluating accuracy is acceptable when the software system structure is complex.Since most of the available component-based software reliability models consume high computa- tional cost and suffer from the evaluating complexity for the software system with complex structures, a component-based back-propagation reliability model （CBPRM） with low complexity for the com- plex software system reliability evaluation is presented in this paper. The proposed model is based on the artificial neural networks and the component reliability sensitivity analyses. These analyses are performed dynamically and assigned to the neurons to optimize the reliability evaluation. CBPRM has a linear increasing complexity and outperforms the state-based and the path-based reliability models. Another advantage of CBPRM over others is its robustness. CBPRM depends on the component relia- bilities and the correlative sensitivities, which are independent from the software system structure. Based on the theory analysis and experiment results, it shows that the complexity of CBPRM is evi- dently lower than the contrast models and the reliability evaluating accuracy is acceptable when the software system structure is complex.

关 键 词：software reliability evaluation component-based software system component reli-ability sensitivity artificial neural networks 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TP311.52[自动化与计算机技术—控制科学与工程]