遗传优化支持向量机的软件可靠性预测模型

Software reliability prediction model based on support vector machine optimized by genetic algorithm

软件可靠性预测在软件开发的早期就能预测出哪些模块有出错倾向。提出一种改进的支持向量机来进行软件可靠性预测。针对支持向量机参数难选择的问题,将遗传算法引入到支持向量机的参数选择中,构造基于遗传算法优化支持向量机的软件可靠性预测模型,并用主成分分析的方法对软件度量数据进行降维,通过仿真实验,证明该模型比支持向量机、BP神经网络、分类回归树和聚类分析等预测模型具有更高的预测精度。

Software reliability prediction classifies software modules as fault-prone module and not fault-prone module at the early age of software development.This paper proposes an improved support vector machine to predict software reliability.As a solution to the difficulty of choosing parameters,it applies Genetic Algorithm（GA） to choose the parameters of Support Vector Machines （SVM）,and puts forward a model of predicting the software reliability based on GA-SVM.The method of principal component analysis is conducted to reduce the dimension of software metrics data.The simulation results show that this model can achieve the more precise prediction result than the prediction models of SVM,BP neural network,CART and clustering.