Nonstationary software testing models with cox distribution for fault resolution duration

Хомоненко, А. Д.; Danilov, A. I.; Gerasimenko, P. V.; Данилов, А. А.

doi:10.1109/scm.2016.7519730

Cited by 1 publication

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“…The applications of the Erlang distributions and their derivations in science and engineering is quite vast. To illustrate this fact, we list in the following some results, obviously, without intention to be exaustive: In computer-based medical systems, for modeling deseases [23], in realibility engineering, for modeling failure rate [13]; In electrical systems, for modeling the delay behavior of electromagnetic pulses [5] [27] in performance analysis of antennas; In communication systems to model personal communication netwoks [14] and to analyse network security [2]; In Smart Grid Sytems to construct models for power allocation in Home Area Networks [20]; In Computer systems, for analysis of computer networks [9] and software testing [16].…”

Section: Introductionmentioning

confidence: 99%

Stochastic Event Generation Through Markovian Jumps: Generalized Distribution and Minimal Representations

Maia

2019

Preprint

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Stochastic Event Timing is a fundamental issue in developing both analytic and simulation models for stochastic systems. Generalized Erlang distributions are quite useful for generating those random events in a quite general way by inserting intermediary states with markovian jumps. One very important and celebrated generalization of the Erlang distribution was made by D. R. Cox in the middle 50's. This paper discuss further the Cox generalization and presents an even more general topology, capable of representing any practical distribution. As an application, we revisit the classical problem of the first two moments matching, and derive minimal topologies in terms of number of states, then the results are compared with those found in literature. At the end of the paper, we show how the generalized structure can be use for timing general stochastic discrete-event models for analytic and simulation purposes.

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Section: Introductionmentioning

confidence: 99%

Stochastic Event Generation Through Markovian Jumps: Generalized Distribution and Minimal Representations

Maia

2019

Preprint

View full text Add to dashboard Cite

show abstract

Nonstationary software testing models with cox distribution for fault resolution duration

Cited by 1 publication

References 4 publications

Stochastic Event Generation Through Markovian Jumps: Generalized Distribution and Minimal Representations

Stochastic Event Generation Through Markovian Jumps: Generalized Distribution and Minimal Representations

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