Computer Science ›› 2016, Vol. 43 ›› Issue (7): 186-190.doi: 10.11896/j.issn.1002-137X.2016.07.034

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Software Defect Prediction Model Based on Adaboost Algorithm

XIONG Jing, GAO Yan and WANG Ya-yu   

  • Online:2018-12-01 Published:2018-12-01

Abstract: 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.

Key words: Software defect,Software defect prediction,BP neural network,Adaboost,Cascade classifier

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