Browsing by Subject "Markov chain usage models"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    On A Simpler and Faster Derivation of Single Use Reliability Mean and Variance for Model-Based Statistical Testing
    (KSI Research, 2018-07) Xue, Yufeng; Lin, Lan; Sun, Xin; Song, Fengguang; Computer and Information Science, School of Science
    Markov chain usage-based statistical testing has proved sound and effective in providing audit trails of evidence in certifying software-intensive systems. The system end-toend reliability is derived analytically in closed form, following an arc-based Bayesian model. System reliability is represented by an important statistic called single use reliability, and defined as the probability of a randomly selected use being successful. This paper continues our earlier work on a simpler and faster derivation of the single use reliability mean, and proposes a new derivation of the single use reliability variance by applying a well-known theorem and eliminating the need to compute the second moments of arc failure probabilities. Our new results complete a new analysis that could be shown to be simpler, faster, and more direct while also rendering a more intuitive explanation. Our new theory is illustrated with three simple Markov chain usage models with manual derivations and experimental results.
  • Thumbnail Image
    Item
    A Simpler and More Direct Derivation of System Reliability Using Markov Chain Usage Models
    (KSI, 2017) Lin, Lan; Xue, Yufeng; Song, Fengguang; Computer and Information Science, School of Science
    Markov chain usage-based statistical testing has been around for more than two decades, and proved sound and effective in providing audit trails of evidence in certifying software-intensive systems. The system end-to-end reliability is derived analytically in closed form, following an arc-based Bayesian model. System reliability is represented by an important statistic called single use reliability, and defined as the probability of a randomly selected use being successful. This paper reviews the analytical derivation of the single use reliability mean, and proposes a simpler, faster, and more direct way to compute the expected value that renders an intuitive explanation. The new derivation is illustrated with two examples.