基于RBF神经网络的多标签蜕变关系预测方法

摘要

蜕变测试技术被广泛应用于不同软件测试领域，但在实际应用中也遇到一些问题———缺少实用的蜕变关系构造方法，本文提出一种新的方法，使用RBF神经网络自动的预测函数可能满足的多个蜕变关系。首先根据函数的源码使用Soot分析工具生成控制流图和相应带标签的控制流图；然后从函数的控制流图中提取节点和路径特性构成多标签的数据集；最后通过对样本数据集的学习建立多标签RBF神经网络模型来预测被测函数可能满足的多个蜕变关系。实验结果表明，该方法是有效的。%A multi-label metamorphic relations prediction approach based on Radial Basis Function( RBF) neural network is pro-posed, which uses Soot analysis tool to generate the control flow graph and the corresponding control flow graph with labels. And then, nodes and path properties extracted from functional control flow graph constitute multi-label data sets. Finally, a multi-label RBF neural network prediction model established by learning the sample data sets is used to predict the function that can satisfy multiple metamorphic relations. Experiment results prove that the method is effective.