Plasma Lab: A Modular Statistical Model Checking Platform

Legay, Axel; Sedwards, Sean; Traonouez, Louis-Marie

doi:10.1007/978-3-319-47166-2_6

Cited by 27 publications

(20 citation statements)

References 22 publications

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“…The method works as follows [4]: The method takes into account both the structure of the goal set formula and the structure of the state space. This is in contrast to the approach of Jégourel et al [32], implemented in a semi-automated fashion in Plasma lab [34,40], that only considers the structure of the (more complex linear-time) logical property. The memory usage of the compositional method is determined by the number of discrete local states (required to be finite) over all components.…”

Section: Automated Rare Event Simulationmentioning

confidence: 95%

“…3. Alongside Plasma lab [40], which implements automatic importance sampling [33] and semi-automatic importance splitting [32,34] for Markov chains (with APIs allowing for extensions to other models), modes is one of the most automated tools for RES on formal models today. In particular, we are not aware of any other tool that provides fully automated RES on general stochastic models.…”

Section: Introductionmentioning

confidence: 99%

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A Statistical Model Checker for Nondeterminism and Rare Events

Budde

D’Argenio

Hartmanns

et al. 2018

Tools and Algorithms for the Construction and Analysis of Systems

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Statistical model checking avoids the state space explosion problem in verification and naturally supports complex non-Markovian formalisms. Yet as a simulation-based approach, its runtime becomes excessive in the presence of rare events, and it cannot soundly analyse nondeterministic models. In this tool paper, we present modes: a statistical model checker that combines fully automated importance splitting to efficiently estimate the probabilities of rare events with smart lightweight scheduler sampling to approximate optimal schedulers in nondeterministic models. As part of the Modest Toolset, it supports a variety of input formalisms natively and via the Jani exchange format. A modular software architecture allows its various features to be flexibly combined. We highlight its capabilities with an experimental evaluation across multi-core and distributed setups on three exemplary case studies.

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Section: Automated Rare Event Simulationmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

A Statistical Model Checker for Nondeterminism and Rare Events

Budde

D’Argenio

Hartmanns

et al. 2018

Tools and Algorithms for the Construction and Analysis of Systems

Self Cite

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“…We have implemented our three-valued estimation algorithm and the qSMC hypothesis testing algorithm in the tool Plasma Lab [4,19]. Plasma Lab is a generic platform for performing statistical model checking of stochastic systems.…”

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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“…Other statistical model checkers offer res methods to some degree of automation. Plasma Lab implements automatic is [18] and semiautomatic isplit [21] for Markov chains. Its isplit engine offers a wizard that guides the user to choose an importance function.…”

Section: Introductionmentioning

confidence: 99%

FIG: The Finite Improbability Generator

Budde

2020

Tools and Algorithms for the Construction and Analysis of Systems

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This paper introduces the statistical model checker FIG 1.2, that estimates transient and steady-state reachability properties in stochastic automata. This software tool specialises in Rare Event Simulation via importance splitting, and implements the algorithms restart and Fixed Effort. FIG is pushbutton automatic since the user need not define an importance function: this function is derived from the model specification plus the property query. The tool operates with Input/Output Stochastic Automata with Urgency, aka iosa models, described either in the native syntax or in the jani exchange format. The theory backing FIG has demonstrated good efficiency, comparable to optimal importance splitting implemented ad hoc for specific models. Written in C++, FIG can outperform other state-of-the-art tools for Rare Event Simulation.

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Plasma Lab: A Modular Statistical Model Checking Platform

Cited by 27 publications

References 22 publications

A Statistical Model Checker for Nondeterminism and Rare Events

A Statistical Model Checker for Nondeterminism and Rare Events

Statistical Model Checking of Incomplete Stochastic Systems

FIG: The Finite Improbability Generator

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