摘要
基于核函数的软件可靠性模型一般对软件失效时间数据与发生在其之前的m次失效时间数据的关系进行建模,着重研究了m取值不同时,其对核函数可靠性模型预测能力的影响。在5个不同类型失效数据集上,采用Mann-Kendall检验观测到m值增大时模型预测能力逐渐下降,说明现时失效时间数据能比较久之前观测的失效时间数据更好地用于预测未来,通过把m的取值划分成几个区间,运用配对T检验进行实验研究,结果表明当m∈{6,7,8,9,10}时,模型能够得到最好的预测性能。
The high complexity of software is the major contributing factor of software reliabihty problems, and traditional parametric models may exhibit different predictive capabilities among different software projects, it is hard to select a suitable model for every software projects. Compared to traditional models, kernel based models could achieve better prediction accuracy, and had arouse the interesting of many researchers. The RVM learning scheme was applied to model the failure time data so as to capture the inner correlation between software failure time data and the m nearest failure time data. In addition, the trend of predictive accuracy with the varying of m was detected by way of Mann-KendaU test method. Thereupon, the reasonable value range of m was achieved,thus m∈ {6,7,8,9,10} through paired T-test in 5 common used software failure data.
出处
《电信科学》
北大核心
2015年第9期90-96,共7页
Telecommunications Science
基金
国家自然科学基金资助项目(No.61103051)
浙江省自然科学基金资助项目(No.LY15F020018)
浙江省科技计划公益项目(No.2015C33247)~~
