Computer Science ›› 2021, Vol. 48 ›› Issue (5): 86-90.doi: 10.11896/jsjkx.210200055

• Computer Software • Previous Articles     Next Articles

Software Reliability Prediction Based on Continuous Deep Confidence Neural Network

QI Hui1, SHI Ying1,2, LI Deng-ao3, MU Xiao-fang1, HOU Ming-xing1   

  1. 1 Department of Computer,Taiyuan Normal University,Jinzhong,Shanxi 030619,China
    2 School of Computer and Information Technology,Shanxi University,Taiyuan 030006,China
    3 College of Data Science,Taiyuan University of Technology,Jinzhong,Shanxi 030600,China
  • Received:2021-02-05 Revised:2021-04-03 Online:2021-05-15 Published:2021-05-09
  • About author:QI Hui,born in 1981,master,associate professor,graduate supervisor.Her main research interests include machine learning and computational intelligence.
  • Supported by:
    National Major Scientific Research Instrument Development Project(6202780085),National Natural Science Foundation of China(62076177),Shanxi Province Key Core Technology and Common Technology R&D Project(2020xxx007) and Key Research and Development Project of Shanxi Province(201803D31055).

Abstract: In order to improve the accuracy of intelligent prediction of software reliability,continuous depth confidence neural network algorithm is used for software reliability prediction.Firstly,the core elements samples that affect software reliability are extracted,and the key features of the sample elements are obtained.Then,a software reliability prediction model based on conti-nuous deep belief neural network (DBN) is established.The samples to be predicted are input,and the parameters such as DBN weight are obtained through pre-processing training of multiple Restricted Boltzmann Machine (RBM) layers and multiple reverse fine-tuning iterations until the maximum number of RBM layers and the maximum number of reverse fine-tuning iterations are reached.Finally,a stable software reliability prediction model is obtained.Experiments show that good software reliability prediction accuracy and standard deviation can be obtained by reasonably setting the number of nodes in the hidden layer of DBN and the learning rate.Compared with commonly used software reliability prediction algorithms,this algorithm has high prediction accuracy,small standard deviation and high applicability in software reliability prediction.

Key words: Deep confidence neural network, Learning rate, Software failure, Software reliability

CLC Number: 

  • TP311.5
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