Computer Science ›› 2017, Vol. 44 ›› Issue (4): 229-233.doi: 10.11896/j.issn.1002-137X.2017.04.049

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Deep Belief Network Software Defect Prediction Model

GAN Lu, ZANG Lie and LI Hang   

  • Online:2018-11-13 Published:2018-11-13

Abstract: Software defect prediction technology plays an important role in detecting software defect and ensuring software quality.Using the neural network classification algorithm to build software defect prediction model has been used widely.But the neural network classification algorithm to train historical data is only shallow learning,this algorithm can’t deeply extract data features.To solve this problem,the deep belief network software defect prediction model (DBNSDPM) by using a deep belief nets which is composed of multilayer restricted Boltzmann machine was constructed.This model conducts feature integration and iteration firstly,then the characteristic data can be studied deeply.The simulation experiment proves that the prediction accuracy of DBNSDPM improves significantly than the traditional neural network prediction model.

Key words: Software defect prediction,Restricted Boltzmann machine,Deep learning,Deep belief network

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