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基于贝叶斯估计的软件可靠性综合评估模型 被引量:7

Software Reliability Integrated Evaluation Model Based on Bayesian Estimation
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摘要 软件可靠性定量评估与预测是软件可靠性工程的重要组成部分。提出利用支持向量机回归分析方法建立基于软件质量度量的软件可靠性预测模型,并将基于贝叶斯估计的现代可信性理论引入该领域,利用可信性因子合理组合基于软件质量度量的软件可靠性预测模型和基于失效数据的软件可靠性增长模型的评估结果,从而全面利用与软件可靠性相关的多方面信息,得到更合理的软件可靠性定量评估结果。根据方差分解和最优线性非齐次估计给出基于贝叶斯估计的软件可靠性综合评估模型的数学描述公式,并举例说明可信性因子的求取方法。数据分析表明该模型具有合理性和可行性。 Quantitative evaluation and prediction of software reliability are the important parts of software reliability engineering. Support vector machine regression was applied to build up a software reliability prediction model based on the metrics of software quality, and the creditability theory based on Bayesian estimation was introduced into the field. By this means, the prediction result of software reliability model based on the metrics of software quality and the evaluation result of software reliability growth model based on failure data were combined rationally via the credibility factor so that the extensive information about software reliability was utilized to obtain a more reasonable evaluation result of software reliability. According to variance disassemble and optimal linear non-homogeneous credibility estimator, the mathematic expression of this software reliability integrated evaluation model based on Bayesian estimation was described in detail, and the obtaining method of credibility factor was illustrated by the practical example. The simulation experiment indicates that the model possesses reasonableness and feasibility.
出处 《兵工学报》 EI CAS CSCD 北大核心 2008年第4期440-445,共6页 Acta Armamentarii
基金 总装备部通用装备保障科研项目(2005装司字第580号)
关键词 系统评估与可行性分析 软件可靠性 支持向量机回归分析 贝叶斯估计 可信性因子 systematic evaluation and feasibility analysis software reliability support vector machine regression Bayesian estimate credibility factor
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参考文献6

  • 1Musa J D.软件可靠性工程[M].韩柯,译.北京:机械工业出版社,2003.
  • 2ZHANG Xue-mei, Hoang Pham. An analysis of factors affecting software reliability [J]. Journal of System and Software, 2000,50 : 43 - 56.
  • 3杜树新,吴铁军.用于回归估计的支持向量机方法[J].系统仿真学报,2003,15(11):1580-1585. 被引量:141
  • 4Chang Chih-Chung, Lin Chin-Jen. LIBSVM2.81-A library for support vector machines [CP/OL]. Taipei: National Taiwan University, 2005[2005-10-21]. http: //www. csie. ntu. edu. tw/-cjlin/libsvm/.
  • 5成世学.关于可信性模型的若干评注[J].应用概率统计,2002,18(4):438-448. 被引量:10
  • 6Gerber.H.U.数学风险论导引[M].成世学,严顿译.北京:世界图书出版公司,1997.

二级参考文献26

  • 1严颖,成世学,程侃.保险精算方法(三)信度理论[J].数理统计与管理,1996,15(6):59-64. 被引量:5
  • 2Vapnik V N. Statistical learning theory[M]. New York, 1998.
  • 3Scholkoph B, Smola A J, Bartlett P L. New support vectoral gorithms[J]. Neural Computation, 2000, 12:1207-1245.
  • 4Suykens J A K, Branbanter J K, Lukas L, et al. Weighted least squares support vector machines: robustness and spare approximation [J]. Neurocomputing, 2002, 48(1): 85-105.
  • 5Lin C-F, Wang S-D. Fuzzy support vector machines[J]. IEEE Trans on Neural Networks, 2002, 13(2): 464-471.
  • 6Tay F E H, Cao L J. Modified support vector machines in financial time series forecasting[J]. Neurocomputing, 2002, 48: 847-861.
  • 7Tay F E H, Cao L J. ε-Descending support vector machines for financial time series forecasting[J]. Neural Processing Letters, 2002, 15(2): 179-195.
  • 8Keoman V, Hadzic I. Support vectors selection by linear programming[A]. Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks[J. Como, Italy, 2000, 5: 193-198.
  • 9Osuna E, Freund R, Girosi F. An improved training algorithm for support vector machine[A]. Proc the 1997 IEEE workshop on neural networks for signal processing[C]. Amelea Island, FL, 1997, 276-285.
  • 10Laskov P. Feasible direction decomposition algorithms for training support vector machines[J]. Machine Learning, 2002, 46(1): 315-349.

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