Statistical Model Checking for Stochastic Hybrid Systems

This paper presents novel extensions and applications of the UPPAAL-SMC model checker. The extensions allow for statistical model checking of stochastic hybrid systems. We show how our race-based stochastic semantics extends to networks of hybrid systems, and indicate the integration technique applied for implementing this semantics in the UPPAAL-SMC simulation engine. We report on two applications of the resulting tool-set coming from systems biology and energy aware buildings.Comment: In Proceedings HSB 2012, arXiv:1208.315

Distributed Parametric and Statistical Model Checking

Statistical Model Checking (SMC) is a trade-off between testing and formal verification. The core idea of the approach is to conduct some simulations of the system and verify if they satisfy some given property. In this paper we show that SMC is easily parallelizable on a master/slaves architecture by introducing a series of algorithms that scale almost linearly with respect to the number of slave computers. Our approach has been implemented in the UPPAAL SMC toolset and applied on non-trivial case studies.Comment: In Proceedings PDMC 2011, arXiv:1111.006

Statistical Model Checking of LLVM Code

We present the new tool Lodin for statistical model checking of LLVM-bitcode. Lodin implements a simulation engine for LLVM-bitcode and implements classic statistical model checking algorithms on top of it. The simulation engine implements only the core of LLVM but supports extending this core through a plugin-architecture. Besides the statistical model checking algorithms Lodin also provides an interactive simulation front-end. The simulator front-end was integral for our second contribution - an integration of Lodin into Plasma-Lab. The integration with Plasma-Lab is integral to allow reasoning about rare properties of programs