基于Adaboost算法的软件缺陷预测模型

Software Defect Prediction Model Based on Adaboost Algorithm

将Adaboost算法应用到软件缺陷预测模型中是软件缺陷预测的一种新思路,Adaboost算法原理通过训练多个弱分类器构成一个更强的级联分类器,有效地避免了过拟合问题。通过采用美国国家航空航天局(NASA)的软件缺陷数据库的仿真实验,分别对原始BP神经网络算法和Adaboost算法进行分析对比,其中Adaboost的弱分类器采用神经网络。实验结果表明,Adaboost级联分类器有效地提高了软件缺陷预测模型的预测性能。

A new software defect prediction method was proposed in this paper, which used Adaboost cascade classifier as its prediction model. The principle of Adaboost algorithm is to train multiple weak classifiers and combine them into another stronger cascade classifier, which can avoid over-fitting problem effectively. In this paper, comparative experiments based on NSNA software defect data sets are carried out between the original BP network and Adaboost with the weak classifier of BP network. The experimental results show that, the software defect prediction model based on Adaboost cascade classifier can improve the prediction performance significantly.