Fast Parametric Model Checking through Model Fragmentation
This work provides a method for software engineers to analyze the sensitivity of system architectures and find optimal configurations, as well as to check and select new configurations at runtime, for a broader class of complex systems than previously possible.
This paper introduces Fast Parametric Model Checking (fPMC) to address the scalability limitations of existing Parametric Model Checking (PMC) techniques for complex systems with multiple parameters. fPMC partitions Markov models into fragments, analyzes their reachability properties independently, and combines the results to obtain PMC reachability formulae, enabling analysis of systems previously intractable for PMC.
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 \emph{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.