Statistical Model Checking of Simulink Models with Plasma Lab

Legay, Axel; Traonouez, Louis-Marie

doi:10.1007/978-3-319-29510-7_15

Cited by 8 publications

(3 citation statements)

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“…Plasma Lab includes a simulator for the Reactive Module Language (RML) of the probabilistic modelchecker Prism [17] that allows to specify discrete and continuous time Markov chains, as well as Markov decision processes. It also includes interfaces to external simulators such as SystemC [22], LLVM bytecode, or MATLAB/Simulink models [21].…”

Section: Implementation Using Plasma Labmentioning

confidence: 99%

Statistical Model Checking of Incomplete Stochastic Systems

Arora

Legay

Richmond

et al. 2018

Leveraging Applications of Formal Methods, Verification and Validation. Verification

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We study incomplete stochastic systems that are missing some parts of their design, or are lacking information about some components. It is interesting to get early analysis results of the requirements of these systems, in order to adequately refine their design. In previous works, models for incomplete systems are analysed using model checking techniques for three-valued temporal logics. In this paper, we propose statistical model checking algorithms for these logics. We illustrate our approach on a case-study of a network system that is refined after the analysis of early designs.

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Section: Implementation Using Plasma Labmentioning

confidence: 99%

Statistical Model Checking of Incomplete Stochastic Systems

Arora

Legay

Richmond

et al. 2018

Leveraging Applications of Formal Methods, Verification and Validation. Verification

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“…Rather than translating Simulink models to a specific formal language, we have been able to directly interface Plasma Lab and Simulink [18]. We thus apply SMC algorithms by using the simulation engine provided by Simulink.…”

Section: Matlab/simulinkmentioning

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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“…Several toolboxes are available for SMC application. Some of them can be directly applied on Matlab/Simulink models, using plugins of the existing software (e.g., Breach [13], S-TaLiRo [14] or Plasma Lab [15]) or by analysing execution traces collected from the simulations (e.g., with ThEodorE [16]). It is also possible to model the system using timed automaton (TA) (i.e., finite state machine) structures, which are applied to define a timed behaviour and analyse realtime systems [17], e.g., using the UPPAAL SMC toolbox [18].…”

Section: Introductionmentioning

confidence: 99%