No Arabic abstract
Stateflow models are complex software models, often used as part of safety-critical software solutions designed with Matlab Simulink. They incorporate design principles that are typically very hard to verify formally. In particular, the standard exhaustive formal verification techniques are unlikely to scale well for the complex designs that are developed in industry. Furthermore, the Stateflow language lacks a formal semantics, which additionally hinders the formal analysis. To address these challenges, we lay here the foundations of a scalable technique for provably correct formal analysis of Stateflow models, with respect to invariant properties, based on bounded model checking (BMC) over symbolic executions. The crux of our technique is: i) a representation of the state space of Stateflow models as a symbolic transition system (STS) over the symbolic configurations of the model, as the basis for BMC, and ii) application of incremental BMC, to generate verification results after each unrolling of the next-state relation of the transition system. To this end, we develop a symbolic structural operational semantics (SSOS) for Stateflow, starting from an existing structural operational semantics (SOS), and show the preservation of invariant properties between the two. Next, we define bounded invariant checking for STS over symbolic configurations as a satisfiability problem. We develop an automated procedure for generating the initial and next-state predicates of the STS, and propose an encoding scheme of the bounded invariant checking problem as a set of constraints, ready for automated analysis with standard, off-the-shelf satisfiability solvers. Finally, we present preliminary results from an experimental comparison of our technique against the Simulink Design Verifier, the proprietary built-in tool of the Simulink environment.
In this paper we investigate the applicability of standard model checking approaches to verifying properties in probabilistic programming. As the operational model for a standard probabilistic program is a potentially infinite parametric Markov decision process, no direct adaption of existing techniques is possible. Therefore, we propose an on-the-fly approach where the operational model is successively created and verified via a step-wise execution of the program. This approach enables to take key features of many probabilistic programs into account: nondeterminism and conditioning. We discuss the restrictions and demonstrate the scalability on several benchmarks.
Requirements engineering is a key phase in the development process. Ensuring that requirements are consistent is essential so that they do not conflict and admit implementations. We consider the formal verification of rt-consistency, which imposes that the inevitability of definitive errors of a requirement should be anticipated, and that of partial consistency, which was recently introduced as a more effective check. We generalize and formalize both notions for discrete-time timed automata, develop three incremental algorithms, and present experimental results.
Digital controllers have several advantages with respect to their flexibility and designs simplicity. However, they are subject to problems that are not faced by analog controllers. In particular, these problems are related to the finite word-length implementation that might lead to overflows, limit cycles, and time constraints in fixed-point processors. This paper proposes a new method to detect designs errors in digital controllers using a state-of-the art bounded model checker based on satisfiability modulo theories. The experiments with digital controllers for a ball and beam plant demonstrate that the proposed method can be very effective in finding errors in digital controllers than other existing approaches based on traditional simulations tools.
We present a survey of the saturation method for model-checking pushdown 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.