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Formal verification of probabilistic SystemC models with statistical model checking : Formal verification of probabilistic SystemC models with statistical model checking

Authors: Ngo, Van Chan; Legay, Axel;

Formal verification of probabilistic SystemC models with statistical model checking : Formal verification of probabilistic SystemC models with statistical model checking

Abstract

Transaction-level modeling with SystemC has been very successful in describing the behavior of embedded systems by providing high-level executable models, in which many of them have inherent probabilistic behaviors, eg, random data and unreliable components. It is thus crucial to have both quantitative and qualitative analysis of the probabilities of system properties. Such analysis can be conducted by constructing a formal model of the system under verification and using Probabilistic Model Checking. However, this method is infeasible for large systems, due to the state space explosion. In this article, we demonstrate the successful use of statistical model checking to conduct such analysis directly from large SystemC models and allow designers to express a wide range of useful properties. The first contribution of this work is a framework to verify properties expressed inBounded Linear Temporal Logic for SystemC models with both timed and probabilistic characteristics. Second, the framework allows users to expose a rich set of user code primitives as atomic propositions in Bounded Linear Temporal Logic. Moreover, users can define their own fine-grained time resolution rather than the boundary of clock cycles in the SystemC simulation. The third contribution is an implementation of a statistical model checker. It contains an automatic monitor generation for producing execution traces of the model-under-verification, the mechanism for automatically instrumenting the model-under-verification, and the interaction with statistical model checking algorithms.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green