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Parametric model checking (PMC) computes algebraic formulae that express keynon-functional properties of a system (reliability, performance, etc.) asrational functions of the system and environment parameters. In softwareengineering, PMC formulae can be used during design, e.g., to analyse thesensitivity of different system architectures to parametric variability, or tofind optimal system configurations. They can also be used at runtime, e.g., tocheck if non-functional requirements are still satisfied after environmentalchanges, or to select new configurations after such changes. However, currentPMC techniques do not scale well to systems with complex behaviour and morethan a few parameters. Our paper introduces a fast PMC (fPMC) approach thatovercomes this limitation, extending the applicability of PMC to a broaderclass of systems than previously possible. To this end, fPMC partitions theMarkov models that PMC operates with into \emph{fragments} whose reachabilityproperties are analysed independently, and obtains PMC reachability formulae bycombining the results of these fragment analyses. To demonstrate theeffectiveness of fPMC, we show how our fPMC tool can analyse three systems(taken from the research literature, and belonging to different applicationdomains) with which current PMC techniques and tools struggle.

Fast Parametric Model Checking through Model Fragmentation