7,339 research outputs found
A model checker for performance and dependability properties
Markov chains are widely used in the context of
performance and reliability evaluation of systems of various
nature. Model checking of such chains with respect to
a given (branching) temporal logic formula has been proposed
for both the discrete [8] and the continuous time setting
[1], [3]. In this short paper, we describe the prototype
model checker for discrete and continuous-time
Markov chains, where properties are expressed in appropriate
extensions of CTL.We illustrate the general benefits
of this approach and discuss the structure of the tool
Model checking learning agent systems using Promela with embedded C code and abstraction
As autonomous systems become more prevalent, methods for their verification will become more
widely used. Model checking is a formal verification technique that can help ensure the safety of autonomous
systems, but in most cases it cannot be applied by novices, or in its straight \off-the-shelf" form. In order
to be more widely applicable it is crucial that more sophisticated techniques are used, and are presented
in a way that is reproducible by engineers and verifiers alike. In this paper we demonstrate in detail two
techniques that are used to increase the power of model checking using the model checker SPIN. The first
of these is the use of embedded C code within Promela specifications, in order to accurately re
ect robot
movement. The second is to use abstraction together with a simulation relation to allow us to verify multiple
environments simultaneously. We apply these techniques to a fairly simple system in which a robot moves
about a fixed circular environment and learns to avoid obstacles. The learning algorithm is inspired by the
way that insects learn to avoid obstacles in response to pain signals received from their antennae. Crucially,
we prove that our abstraction is sound for our example system { a step that is often omitted but is vital if
formal verification is to be widely accepted as a useful and meaningful approach
Verifiably-safe software-defined networks for CPS
Next generation cyber-physical systems (CPS) are expected to be deployed in domains which require scalability as well as performance under dynamic conditions. This scale and dynamicity will require that CPS communication networks be programmatic (i.e., not requiring manual intervention at any stage), but still maintain iron-clad safety guarantees. Software-defined networking standards like OpenFlow provide a means for scalably building tailor-made network architectures, but there is no guarantee that these systems are safe, correct, or secure. In this work we propose a methodology and accompanying tools for specifying and modeling distributed systems such that existing formal verification techniques can be transparently used to analyze critical requirements and properties prior to system implementation. We demonstrate this methodology by iteratively modeling and verifying an OpenFlow learning switch network with respect to network correctness, network convergence, and mobility-related properties. We posit that a design strategy based on the complementary pairing of software-defined networking and formal verification would enable the CPS community to build next-generation systems without sacrificing the safety and reliability that these systems must deliver
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