基于关联维计算的软件失效混沌识别研究
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摘要
针对大数据环境下软件失效行为的复杂性问题,提出了一种基于关联维数的软件失效混沌识别算法.通过计算软件失效数据是否具有关联维特征来验证失效的混沌性,利用相空间方法重构软件失效系统,验证了软件失效行为的混沌性.实验结果表明,混沌关联维算法能够有效识别软件失效的混沌性,精确地描述软件失效行为的无序性和无规则性,同时也能解决复杂软件系统失效、大数据混沌性这类复杂问题.
In view of the disorder of software failure behavior in large data environment,a software failure identification method based on chaotic correlation dimension is proposed.With the help of the G-P algorithm,the characteristics of the correlation dimension and the embedding dimension of the failure data are calculated,and the uncertainty of the software failure data is determined by the chaotic characteristic quantity.The experimental results show that the chaos correlation dimension algorithm not only can effectively identify software failure,and accurately describe the software failure behavior disorder and irregular,but also can solve the complex software system failure,and big data chaos of this kind of complex problem.
In view of the disorder of software failure behavior in large data environment,a software failure identification method based on chaotic correlation dimension is proposed.With the help of the G-P algorithm,the characteristics of the correlation dimension and the embedding dimension of the failure data are calculated,and the uncertainty of the software failure data is determined by the chaotic characteristic quantity.The experimental results show that the chaos correlation dimension algorithm not only can effectively identify software failure,and accurately describe the software failure behavior disorder and irregular,but also can solve the complex software system failure,and big data chaos of this kind of complex problem.
引文
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[3]汪北阳,吕金虎.复杂软件系统的软件网络结点影响分析[J].软件学报,2013,24(12):2814-2829.
[4]YANG J F,ZHAO M.Maximum likelihood estimation for software reliability with masked failure data[J].Journal of Systems Engineering&Electronics,2013,35(12):2665-2669.
[5]程跃华,崔艳.组合模型在软件可靠性预测中的建模与仿真[J].计算机仿真,2011,28(6):371-374.
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[7]LIN C T,HUANG C Y.Enhancing and measuring the predictive capabilities of testing-effort dependent software reliability models[J].Journal of Systems&Software,2008,81(6):1025-1038.
[8]楼俊钢,蒋云良,申情,等.软件可靠性预测中不同核函数的预测能力评估[J].计算机学报,2013,36(6):1303-1311.
[9]INOUE S,HAYASHIDA S,YAMADA S.Toward Practical Software Reliability Assessment with Change-Point Based on Hazard Rate Models[C]//Computer Software and Applications Conference.IEEE Computer Society,2013:268-273.
[10]LANDON J,OZEKICI S,SOYER R.A Markov modulated Poisson model for software reliability[J].European Journal of Operational Research,2013,229(2):404-410.
[11]ZHENG J.Predicting software reliability with neural network ensembles[J].Expert Systems with Applications An International Journal,2009,36(2):2116-2122.
[12]BARU C,BHANDARKAR M,CURINO C,et al.Discussion of BigBench:A Proposed Industry Standard Performance Benchmark for Big Data[M].[s.l.]:Springer International Publishing,2014:44-63.
[13]LYU M R.Handbook of Software Reliability Engineering[M/OL].Hong Kong:Department of computer science and engineering,Chinese University Hong Kong,2005[2016-11-23].http://www.cse.cuhk.edu.hk/lyu/book/reliability/.