Fast Parametric Model Checking through Model Fragmentation

Parametric model checking (PMC) computes algebraic formulae that express key non-functional properties of a system (reliability, performance, etc.) as rational functions of the system and environment parameters. In software engineering, PMC formulae can be used during design, e.g., to analyse the se...

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Published in:Proceedings / International Conference on Software Engineering pp. 835 - 846
Main Authors: Fang, Xinwei, Calinescu, Radu, Gerasimou, Simos, Alhwikem, Faisal
Format: Conference Proceeding
Language:English
Published: IEEE 01.05.2021
Subjects:
Analytical models
discrete time Markov chains
Markov processes
non-functional properties
Parametric model checking
Parametric statistics
Runtime
Sensitivity
Systems architecture
ISBN:1665402962, 9781665402965
ISSN:1558-1225
Online Access:Get full text
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Abstract Parametric model checking (PMC) computes algebraic formulae that express key non-functional properties of a system (reliability, performance, etc.) as rational functions of the system and environment parameters. In software engineering, PMC formulae can be used during design, e.g., to analyse the sensitivity of different system architectures to parametric variability, or to find optimal system configurations. They can also be used at runtime, e.g., to check if non-functional requirements are still satisfied after environmental changes, or to select new configurations after such changes. However, current PMC techniques do not scale well to systems with complex behaviour and more than a few parameters. Our paper introduces a fast PMC (fPMC) approach that overcomes this limitation, extending the applicability of PMC to a broader class of systems than previously possible. To this end, fPMC partitions the Markov models that PMC operates with into fragments whose reachability properties are analysed independently, and obtains PMC reachability formulae by combining the results of these fragment analyses. To demonstrate the effectiveness of fPMC, we show how our fPMC tool can analyse three systems (taken from the research literature, and belonging to different application domains) with which current PMC techniques and tools struggle.
AbstractList Parametric model checking (PMC) computes algebraic formulae that express key non-functional properties of a system (reliability, performance, etc.) as rational functions of the system and environment parameters. In software engineering, PMC formulae can be used during design, e.g., to analyse the sensitivity of different system architectures to parametric variability, or to find optimal system configurations. They can also be used at runtime, e.g., to check if non-functional requirements are still satisfied after environmental changes, or to select new configurations after such changes. However, current PMC techniques do not scale well to systems with complex behaviour and more than a few parameters. Our paper introduces a fast PMC (fPMC) approach that overcomes this limitation, extending the applicability of PMC to a broader class of systems than previously possible. To this end, fPMC partitions the Markov models that PMC operates with into fragments whose reachability properties are analysed independently, and obtains PMC reachability formulae by combining the results of these fragment analyses. To demonstrate the effectiveness of fPMC, we show how our fPMC tool can analyse three systems (taken from the research literature, and belonging to different application domains) with which current PMC techniques and tools struggle.
Author Gerasimou, Simos
Alhwikem, Faisal
Fang, Xinwei
Calinescu, Radu
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  surname: Alhwikem
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  email: faisal.alhwikem@york.ac.uk
  organization: University of York, UK
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Snippet Parametric model checking (PMC) computes algebraic formulae that express key non-functional properties of a system (reliability, performance, etc.) as rational...
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StartPage 835
SubjectTerms Analytical models
discrete time Markov chains
Markov processes
non-functional properties
Parametric model checking
Parametric statistics
Runtime
Sensitivity
Systems architecture
Title Fast Parametric Model Checking through Model Fragmentation
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