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WIREs Comp Stat

Software reliability

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In this article, we present an overview of the state of the art in software reliability. We present some of the traditional software reliability models as well as recent advances in modeling. In so doing, we discuss use of hidden Markov models, as well as nonparametric models including mixtures of Dirichlet processes. Furthermore, we review decision problems in software reliability such as testing strategies and optimal stopping rules. We discuss computational issues associated with use of the models, their statistical analyses and development of optimal strategies. WIREs Comp Stat 2011 3 269–281 DOI: 10.1002/wics.159

This article is categorized under:

  • Statistical and Graphical Methods of Data Analysis > Bayesian Methods and Theory
  • Statistical and Graphical Methods of Data Analysis > Reliability, Survivability, and Quality Control
  • Software for Computational Statistics > Software/Statistical Software
  • Data: Types and Structure > Time Series, Stochastic Processes, and Functional Data
Figure 1.

Musa's system 40 data.

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Figure 2.

Posterior distributions of λ15, λ45, λ85, and λ101.

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Figure 3.

Posterior distributions of λi's over different stages testing.

[ Normal View | Magnified View ]
Figure 4.

Posterior distribution of K.

[ Normal View | Magnified View ]
Figure 5.

The m‐stage decision tree for the optimal release problem.

[ Normal View | Magnified View ]

Browse by Topic

Software for Computational Statistics > Software/Statistical Software
Statistical and Graphical Methods of Data Analysis > Bayesian Methods and Theory
Statistical and Graphical Methods of Data Analysis > Reliability, Survivability, and Quality Control
Data: Types and Structure > Time Series, Stochastic Processes, and Functional Data

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