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Software safety assessment based on a subordinated Markov chain

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Abstract

Software safety can be defined as the probability that any software hazard does not occur during a pre-specified time period. This paper proposes a new software safety assessment model based on a subordinated Markov chain, which consists of both non-homogeneous Poisson process (NHPP) and discrete time Markov chain (DTMC). In the model, NHPP and DTMC indicate the cumulative number of software errors experienced in testing phase and their impact, respectively. Using the data set of three types of software errors (critical, major and minor errors) collected in an actual software development project, we examine the effectiveness of the proposed model by compared with two existing models. Through the experiment, it is shown that the proposed model is superior to the existing ones in terms of fitting ability.

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Authors and Affiliations

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

    Hiroyuki Okamura & Tadashi Dohi

Authors
  1. Hiroyuki Okamura
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  2. Tadashi Dohi
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Correspondence to Hiroyuki Okamura.

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Okamura, H., Dohi, T. Software safety assessment based on a subordinated Markov chain. Int J Syst Assur Eng Manag 1, 307–315 (2010). https://doi.org/10.1007/s13198-011-0034-0

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  • DOI: https://doi.org/10.1007/s13198-011-0034-0

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