Computer Science ›› 2016, Vol. 43 ›› Issue (8): 159-164.doi: 10.11896/j.issn.1002-137X.2016.08.033

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Hidden Markov Software Reliability Model with EM Method

ZHANG Ting-ting, ZHANG De-ping and LIU Guo-qiang   

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

Abstract: In view of the problem that single software reliability model doesn’t precisely describe the failure behavior of the software,and doesn’t accurately predict the software reliability,this paper studied a hidden Markov chain software reliability model incorporating the change point analysis.The formulation of the hidden Markov chain software reliability prediction approach involves a hidden state variable that indicates the regime change.This variable is specified to be detected by software failure data in each regime.The model parameters are estimated using expectation/maximization (EM) algorithm.Some numerical examples were performed based on some real software failure data sets.Experimental results show that the proposed framework to incorporate multiple change points for software reliability model has fairly accurate and efficient change-point detection capability,and can significantly improve software reliability fitting accuracy.

Key words: Software reliability,Hidden Markov chain model,Expectation/Maximization algorithm,Change point analysis

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