基于粗糙集-支持向量机的软件缺陷预测

软件缺陷预测已成为软件工程的重要研究课题,构造了一个基于粗糙集和支持向量机的软件缺陷预测模型。该模型通过粗糙集对原样本集进行属性约减,去掉冗余的和与缺陷预测无关的属性,利用粒子群对支持向量机的参数做选择。实验数据来源于NASA公共数据集,通过属性约减,特征属性由21个约减为5个。实验表明,属性约减后,Bayes分类器、CART树、神经网络和本文提出的粗糙集—支持向量机模型的预测性能均有所提高,本文提出的粗糙集支持向量机的预测性能好于其他三个模型。

Software defect prediction using rough sets and support vector machine

The prediction of software defects has been an important research topic in the field of software engineering. The paper focuses on the problem of defect prediction. A classification model for predicting software defects based on the integration of rough sets and support vector machine model (RS-SVM) is constructed. Rough sets work as a preprocessor in order to remove redundant information and reduce data dimensionality before the sample data are processed by support vector machine. As a solution to the difficulty of choosing parameters, the particle swarm optimization algorithm is used to choose the parameters of support vector machines. The experimental data are from the open source NASA datasets. The dimensions of the original data sets are reduced from 21 to 5 by rough sets. Experimental results indicate that the prediction performances of Bayes classifier, CART tree, RBF neural network and RS-SVM are all improved after the dimension of the original data sets are reduced from 21 to 5 by rough sets. Compared with the above three models, RS-SVM has a higher prediction performance.