基于泛函网络的软件可靠性多模型综合预测方法  

Multi-model Synthesis Prediction of Software Reliability Based on Functional Networks

作　　者：王二威 吴祈宗 

机构地区：[1]理工大学珠海学院,珠海519088 [2]理工大学管理与经济学院,北京100081

出　　处：《计算机科学》2015年第10期175-179,共5页Computer Science

基　　金：广东省软科学基金项目(2013B070206004)资助

摘　　要：将泛函网络引入软件可靠性预测,利用其比神经网络更好的解释性及其他性能,提出了基于泛函网络的软件可靠性多模型综合预测方法。首先阐述了泛函网络的结构和学习过程,然后将多个单一模型的预测值作为泛函网络的输入,将实际值作为输出,建立泛函网络结构,给出了泛函网络的学习算法,制定了3种训练策略,并进行了实验分析。实验结果表明:在第三种训练策略下,基于泛函网络的软件可靠性多模型综合预测方法有较高的预测精度,其预测效果比单个模型和Lyu提出的线性综合模型都好。The functional networks were introduced into the prediction of software reliability,and based on its better ex- planatory and other attributes than neural network, a multi-model synthesis prediction method of software reliability based on functional networks was proposed. The estimated values of many single models were taken as the input, and the actual value was taken as the output, thus the structure of functional networks was established. The learning algo- rithm of functional networks was proposed and three training strategies were designed, all of which were conducted in tests accordingly. The test results show that in the third training strategy, the multi-model prediction method based on functional networks has better predictive accuracy, and is more effective than single-model and linear integrated models proposed by Lyu.

关 键 词：软件可靠性 泛函网络 多模型综合 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]