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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| Vydáno v: | Proceedings / International Conference on Software Engineering s. 835 - 846 |
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| Jazyk: | angličtina |
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IEEE
01.05.2021
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| ISBN: | 1665402962, 9781665402965 |
| ISSN: | 1558-1225 |
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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. |
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| 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 |
| Author_xml | – sequence: 1 givenname: Xinwei surname: Fang fullname: Fang, Xinwei email: xinwei.fang@york.ac.uk organization: University of York, UK – sequence: 2 givenname: Radu surname: Calinescu fullname: Calinescu, Radu email: radu.calinescu@york.ac.uk organization: University of York, UK – sequence: 3 givenname: Simos surname: Gerasimou fullname: Gerasimou, Simos email: simos.gerasimou@york.ac.uk organization: University of York, UK – sequence: 4 givenname: Faisal surname: Alhwikem fullname: Alhwikem, Faisal 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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| 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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