scholarly journals A Markov Reward Model for Software Reliability

2007 ◽  
Author(s):  
YoungMin Kwon ◽  
Gul Agha
Keyword(s):  
Software Reliability ◽  
Markov Reward ◽  
Markov Reward Model

A Symbolic Approach to the Analysis of Multi-Formalism Markov Reward Models

2013 ◽  
pp. 170-195
Author(s):  
Kai Lampka ◽  
Markus Siegle
Keyword(s):  
Functional System ◽  
State Variables ◽  
State Charts ◽  
Level Model ◽  
System Properties ◽  
Markov Reward ◽  
High Level ◽  
Symbolic Approach ◽  
State Space Generation ◽  
Markov Reward Model

When modelling large systems, modularity is an important concept, as it aids modellers to master the complexity of their model. Moreover, employing different modelling formalisms within the same modelling project has the potential to ease the description of various parts or aspects of the overall system. In the area of performability modelling, formalisms such as stochastic reward nets, stochastic process algebras, stochastic automata, or stochastic UML state charts are often used, and several of these may be employed within one modelling project. This chapter presents an approach for efficiently constructing a symbolic representation in the form of a zero-suppressed Binary Decision Diagram (BDD), which represents the Markov Reward Model underlying a multi-formalism high-level model. In this approach, the interaction between the submodels may be established either by the sharing of state variables or by the synchronisation of common activities. It is shown that the Decision Diagram data structure and the associated algorithms enable highly efficient state space generation and different forms of analysis of the underlying Markov Reward Model (e.g. calculation of reward measures or asserting non-functional system properties by means of model checking techniques).

Reliability measures for a new general system using fuzzy non-homogenous Markov reward model under d-degradation levels

2020 ◽  
Vol 37 (9/10) ◽  
pp. 1301-1323
Author(s):  
Neama Temraz
Keyword(s):  
Differential Equations ◽  
General System ◽  
System Of Differential Equations ◽  
Reliability Measures ◽  
Content Type ◽  
Different Types ◽  
Complete Failure ◽  
Markov Reward ◽  
New Procedures ◽  
Markov Reward Model

PurposeIn this paper, new procedures for a fuzzy Markov reward model are introduced to find the reliability measures.Design/methodology/approachIt is supposed that the introduced system consisted of n identical units connected in parallel and each unit has m different types of failures. Also, each unit of the system is allowed to have d levels of degradation from a working state to complete failure. Non-homogeneous Markov reward model is used to construct the system of differential equations of the model. Procedures are proposed to obtain reliability measures of the model under considering that the failure and repair rates of the systems unit are fuzzy. An application is constructed to analyze a system of 2-unit, and results are shown graphically.FindingsNon-homogeneous Markov reward model is used to construct the system of differential equations of the model.Originality/valueAll papers in literature assumed Markov reward model with deterministic parameters. In this paper, a generalization of classical Markov reward model is introduced.

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