软件可靠性预测的相关向量机模型

摘要

The relevance vector machines (RVM) is very efficient in solving regression problems.A RVM-based generic model adaptive to the characteristic of the given data set is used for software failure time prediction.The RVM learning scheme is applied to the failure time data,forcing the network to learn and recognize the inherent internal temporal property of software failure sequence in order to capture the most current feature hidden inside the software failure behavior.We also compare the prediction accuracy of software reliability prediction models based on RVM,support vector machine (SVM) and artificial neural network (ANN).Experimental results by four data sets show that our RVM-based software reliability prediction model could achieve higher prediction accuracy than that of the ANN-based or SVM-based models.It is also shown that recent failure data could give more accurate prediction of near future failure event.The reasonable values of m and r can be achieved by experiments.%相关向量机是一种解决回归问题非常有效的方法,针对软件失效时间及其之前的m个失效时间数据使用相关向量机进行学习,以建立失效时间之间内在的依赖关系,由此构建新的基于相关向量机的软件可靠性预测模型.在4个数据集上的实验结果表明,新模型在预测能力上较之广泛使用的基于支持向量机或人工神经网络的软件可靠性预测模型有明显的提高,同时也表明现时失效数据的预测能力比很久之前观测的失效数据更强,最后通过实验对合理的m值及不同数据集上核函数参数取值进行研究.