PSCV: A Runtime Verification Tool for Probabilistic SystemC Models

Ngo, Van Chan; Legay, Axel; Joloboff, Vania

doi:10.1007/978-3-319-41528-4_5

Cited by 7 publications

(2 citation statements)

References 14 publications

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“…We have implemented an SMC‐based verification tool,() PSCV, that contains 2 main original components: a Monitor and Aspect‐advice Generator (MAG) and a Statistical Model Checker (SystemC Plugin). The tool whose architecture is depicted in Figure is considered as a runtime verification tool for probabilistic temporal properties.…”

Section: Methodsmentioning

confidence: 99%

Formal verification of probabilistic SystemC models with statistical model checking

Ngo

Legay

2017

J Software Evolu Process

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SUMMARYTransaction-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, e.g., random data and unreliable components. It thus is 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 (PMC). 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 (SMC) to carry out 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 in Bounded Linear Temporal Logic (BLTL) 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 BLTL. 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 (MUV), the mechanism for automatically instrumenting the MUV, and the interaction with statistical model checking algorithms.

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Section: Methodsmentioning

confidence: 99%

Formal verification of probabilistic SystemC models with statistical model checking

Ngo

Legay

2017

J Software Evolu Process

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“…Plasma Lab and the SystemC plugin is embedded in the toolchain of the Probabilistic SystemC Verifier (PSCV) tool [20]. The tool chain is presented in Fig.…”

Section: Systemcmentioning

confidence: 99%

Plasma Lab: A Modular Statistical Model Checking Platform

Legay

Sedwards

Traonouez

2016

Leveraging Applications of Formal Methods, Verification and Validation: Foundational Techniques

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Abstract. We present an overview of Plasma Lab, a modular statistical model checking (SMC) platform that facilitates multiple SMC algorithms, multiple modelling and query languages and has multiple modes of use. Plasma Lab may be used as a stand-alone tool with a graphical development environment or invoked from the command line for high performance scripting applications. Plasma Lab is written in Java for maximum cross-platform compatibility, but it may interface with tools and libraries written in arbitrary programming languages. Plasma Lab's API also allows it to be incorporated as a library within other tools. We first describe the motivation and architecture of Plasma Lab, then proceed to describe some of its important algorithms, including those for rare events and nondeterminism. We conclude with a number of industrially-relevant case studies and applications.

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