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A Bayesian Inference Tool for NHPP-Based Software Reliability Assessment

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Future Generation Information Technology (FGIT 2009)

Part of the book series: Lecture Notes in Computer Science ((LNCCN,volume 5899))

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Abstract

In this paper, we concern a sampling method for Markov chain Monte Carlo (MCMC) in estimating software reliability, and propose a unified MCMC algorithm based on the Metropolis-Hasting method regardless of model on data structures. The resulting MCMC algorithm is implemented as a Java-based tool. Using the Java-based Bayesian inference tool, we illustrate how to assess the software reliability in actual software development processes.

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Author information

Authors and Affiliations

  1. Department of Information Engineering, Graduate School of Engineering, Hiroshima University, 1–4–1 Kagamiyama, Higashi-Hiroshima, 739-8527, Japan

    Takumi Hirata, Hiroyuki Okamura & Tadashi Dohi

Authors
  1. Takumi Hirata
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  2. Hiroyuki Okamura
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  3. Tadashi Dohi
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Editor information

Editors and Affiliations

  1. Sogang University, South Korea

    Young-hoon Lee

  2. Hannam University, Daejeon, South Korea

    Tai-hoon Kim

  3. National Chiao Tung University, Hsinchu, Taiwan

    Wai-chi Fang

  4. University of Warsaw & Infobright Inc., Poland

    Dominik Ślęzak

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© 2009 Springer-Verlag Berlin Heidelberg

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Hirata, T., Okamura, H., Dohi, T. (2009). A Bayesian Inference Tool for NHPP-Based Software Reliability Assessment. In: Lee, Yh., Kim, Th., Fang, Wc., Ślęzak, D. (eds) Future Generation Information Technology. FGIT 2009. Lecture Notes in Computer Science, vol 5899. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10509-8_26

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  • DOI: https://doi.org/10.1007/978-3-642-10509-8_26

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-10508-1

  • Online ISBN: 978-3-642-10509-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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