关键词: 软件可靠性早期预测,特征选择,LASSO回归方法,LARS算法,LVQ神经网络

Abstract: To cope with the software complexity metric attributes dimension disaster which exists in the software reliability early prediction,this paper put forward a software complexity metric attribute feature selection method based on Least Absolute Shrinkage and Selection Operator(LASSO)method and the Least Angle Regression(LARS)algorithm．This method can filter out some software complexity metric attributes which have smaller influence on the early prediction results and can obtain the key attributes subsets associated most closely with the prediction result．This paper firstlyanalyzed the characteristics of LASSO regression method and its application in feature selection,secondly modified the LARS algorithm so that it can be used to solve the problems which LASSO method involves and get relevant complexity metric attribute subsets,lastly combined with the Learning Vector Quantization(LVQ)neural network to carry on the early software reliability prediction experiment．During the experiment,the authors used the 10-fold experiment methods．The experiment results indicate that the method can improve early prediction accuracy of software reliability.

Key words: Software reliability early prediction,Feature selection,LASSO regression method,LARS algorithm,LVQ neural network