Statistical Model

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Axel Legay - One of the best experts on this subject based on the ideXlab platform.

  • FM - Statistical Model Checking of LLVM Code
    Formal Methods, 2018
    Co-Authors: Axel Legay, Dirk Nowotka, Danny Bøgsted Poulsen, Louis-marie Tranouez
    Abstract:

    We present the new tool Lodin for Statistical Model checking of LLVM-bitcode. Lodin implements a simulation engine for LLVM-bitcode and implements classic Statistical Model checking algorithms on top of it. The simulation engine implements only the core of LLVM but supports extending this core through a plugin-architecture. Besides the Statistical Model checking algorithms Lodin also provides an interactive simulation front-end. The simulator front-end was integral for our second contribution - an integration of Lodin into Plasma-Lab. The integration with Plasma-Lab is integral to allow reasoning about rare properties of programs.

  • Statistical Model Checking of LLVM Code
    2017
    Co-Authors: Louis-marie Traonouez, Axel Legay, Dirk Nowotka, Danny Poulsen
    Abstract:

    We present our work in providing Statistical Model Checking for programs in LLVM bitcode. As part of this work we develop a semantics for programs that separates the program itself from its environment. The program interact with the environment through function calls. The environment is furthermore allowed to perform actions that alter the state of the C-program-useful for mimicking an interrupt system. On top of this semantics we build a probabilistic semantics and present an algorithm for simulating traces under that semantics.. This paper also includes the development of the new tool component Lodin that provides a Statistical Model checking infrastructure for LLVM programs. The tool currently implement standard Monte Carlo algorithms and a simulator component to manually inspect the behaviour of programs. The simulator also proves useful in one of our other main contributions; namely producing the first tool capable of doing importance splitting on LLVM code. Importance splitting is implemented by integrating Lodin with the existing Statistical Model checking tool Plasma-Lab.

  • Verification of Interlocking Systems Using Statistical Model Checking
    2017
    Co-Authors: Quentin Cappart, Louis-marie Traonouez, Jean Quilbeuf, Christophe Limbrée, Pierre Schaus, Axel Legay
    Abstract:

    In the railway domain, an interlocking is the system ensuring safe train traffic inside a station by controlling its active elements such as the signals or points. Modern interlockings are configured using particular data, called application data, reflecting the track layout and defining the actions that the interlocking can take. The safety of the train traffic relies thereby on application data correctness, errors inside them can cause safety issues such as derailments or collisions. Given the high level of safety required by such a system, its verification is a critical concern. In addition to the safety, an interlocking must also ensure that availability properties, stating that no train would be stopped forever in a station, are satisfied. Most of the research dealing with this verification relies on Model checking. However, due to the state space explosion problem, this approach does not scale for large stations. More recently, a discrete event simulation approach limiting the verification to a set of likely scenarios, was proposed. The simulation enables the verification of larger stations, but with no proof that all the interesting scenarios are covered by the simulation. In this paper, we apply an intermediate Statistical Model checking approach, offering both the advantages of Model checking and simulation. Even if exhaustiveness is not obtained, Statistical Model checking evaluates with a parametrizable confidence the reliability and the availability of the entire system.

  • On the Power of Statistical Model Checking
    2016
    Co-Authors: Kim Guldstrand Larsen, Axel Legay
    Abstract:

    This paper contains material for our tutorial presented at STRESS 2016. This includes an introduction to Statistical Model Checking algorithms and their rare event extensions, as well as an introduction to two well-known SMC tools: Plasma and Uppaal.

  • Statistical Model Checking for Product Lines
    2016
    Co-Authors: Maurice H Ter Beek, Axel Legay, Alberto Lluch Lafuente, Andrea Vandin
    Abstract:

    We report on the suitability of Statistical Model checking for the analysis of quantitative properties of product line Models by an extended treatment of earlier work by the authors. The type of analysis that can be performed includes the likelihood of specific product behaviour, the expected average cost of products (in terms of the attributes of the products’ features) and the probability of features to be (un)installed at runtime. The product lines must be Modelled in QFLan, which extends the probabilistic feature-oriented language PFLan with novel quantitative constraints among features and on behaviour and with advanced feature installation options. QFLan is a rich process-algebraic specifi- cation language whose operational behaviour interacts with a store of constraints, neatly separating product configuration from product behaviour. The resulting probabilistic configurations and probabilistic behaviour converge in a discrete-time Markov chain semantics, enabling the analysis of quantitative properties. Technically, a Maude implementation of QFLan, integrated with Microsoft’s SMT constraint solver Z3, is combined with the distributed Statistical Model checker MultiVeStA, developed by one of the authors. We illustrate the feasibility of our framework by applying it to a case study of a product line of bikes.

Sean Sedwards - One of the best experts on this subject based on the ideXlab platform.

  • An efficient Statistical Model checker for nondeterminism and rare events
    International Journal on Software Tools for Technology Transfer, 2020
    Co-Authors: Carlos E. Budde, Arnd Hartmanns, Pedro R. D’argenio, Sean Sedwards
    Abstract:

    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 article, we present modes : a Statistical Model checker that combines fully automated importance splitting to 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 using experiments across multi-core and distributed setups on three case studies and report on an extensive performance comparison with three current Statistical Model checkers.

  • On Statistical Model Checking with PLASMA
    2014
    Co-Authors: Axel Legay, Sean Sedwards
    Abstract:

    This paper surveys the main functionalities of the PLASMA Statistical Model checking platform developed at Inria.

  • PLASMA-lab: A Flexible, Distributable Statistical Model Checking Library
    2013
    Co-Authors: Benot Boyer, Axel Legay, Kevin Corre, Sean Sedwards
    Abstract:

    We present PLASMA-lab, a Statistical Model checking (SMC) library that provides the functionality to create custom Statistical Model checkers based on arbitrary discrete event Modelling languages. PLASMA-lab is written in Java for maximum cross-platform compatibility and has already been incorporated in various performance-critical software and embedded hardware platforms. Users need only implement a few simple methods in a simulator class to take advantage of our efficient SMC algorithms. PLASMA-lab may be instantiated from the command line or from within other software. We have constructed a graphical user interface (GUI) that exposes the functionality of PLASMA-lab and facilitates its use as a standalone application with multiple 'drop-in' Modelling languages. The GUI adds the notion of projects and experiments, and implements a simple, practical means of distributing simulations using remote clients.

  • Importance Splitting for Statistical Model Checking Rare Properties
    2013
    Co-Authors: Cyrille Jegourel, Axel Legay, Sean Sedwards
    Abstract:

    Statistical Model checking avoids the intractable growth of states associated with probabilistic Model checking by estimating the probability of a property from simulations. Rare properties are often important, but pose a challenge for simulation-based approaches: the relative error of the estimate is unbounded. A key objective for Statistical Model checking rare events is thus to reduce the variance of the estimator. Importance splitting achieves this by estimating a sequence of conditional probabilities, whose product is the required result. To apply this idea to Model checking it is necessary to define a score function based on logical properties, and a set of levels that delimit the conditional probabilities. In this paper we motivate the use of importance splitting for Statistical Model checking and describe the necessary and desirable properties of score functions and levels. We illustrate how a score function may be derived from a property and give two importance splitting algorithms: one that uses fixed levels and one that discovers optimal levels adaptively.

  • Statistical Model Checking for Stochastic Hybrid Systems
    2012
    Co-Authors: Alexandre David, Axel Legay, Kim Guldstrand Larsen, Marius Mikučionis, Danny Bogsten Poulsen, Sean Sedwards
    Abstract:

    This paper presents novel extensions and applications of the UPPAAL-SMC Model checker. The extensions allow for Statistical Model checking of stochastic hybrid systems. We show how our race-based stochastic semantics extends to networks of hybrid systems, and indicate the integration technique applied for implementing this semantics in the UPPAAL-SMC simulation engine. We report on two applications of the resulting tool-set coming from systems biology and energy aware buildings.

Nam H. Kim - One of the best experts on this subject based on the ideXlab platform.

  • Review of Statistical Model calibration and validation—from the perspective of uncertainty structures
    Structural and Multidisciplinary Optimization, 2019
    Co-Authors: Guesuk Lee, Hyunseok Oh, Byeng D. Youn, Wongon Kim, Nam H. Kim
    Abstract:

    Computer-aided engineering (CAE) is now an essential instrument that aids in engineering decision-making. Statistical Model calibration and validation has recently drawn great attention in the engineering community for its applications in practical CAE Models. The objective of this paper is to review the state-of-the-art and trends in Statistical Model calibration and validation, based on the available extensive literature, from the perspective of uncertainty structures. After a brief discussion about uncertainties, this paper examines three problem categories—the forward problem, the inverse problem, and the validation problem—in the context of techniques and applications for Statistical Model calibration and validation.

Liming Yang - One of the best experts on this subject based on the ideXlab platform.

  • parton distribution of proton in a simple Statistical Model
    Physics Letters B, 2002
    Co-Authors: Yongjun Zhang, Liming Yang
    Abstract:

    Taking proton as an ensemble of quark-gluon Fock states and using the principle of detailed balance, we construct a simple Statistical Model for parton distribution of proton. The recent observed Bjorken-x dependent light flavor sea quark asymmetry (d) over bar (x) - (u) over bar (x) can be well reproduced by Monte Carlo simulation as a pure Statistical effect. (C) 2002 Elsevier Science B.V. All rights reserved.

Guesuk Lee - One of the best experts on this subject based on the ideXlab platform.

  • Review of Statistical Model calibration and validation—from the perspective of uncertainty structures
    Structural and Multidisciplinary Optimization, 2019
    Co-Authors: Guesuk Lee, Hyunseok Oh, Byeng D. Youn, Wongon Kim, Nam H. Kim
    Abstract:

    Computer-aided engineering (CAE) is now an essential instrument that aids in engineering decision-making. Statistical Model calibration and validation has recently drawn great attention in the engineering community for its applications in practical CAE Models. The objective of this paper is to review the state-of-the-art and trends in Statistical Model calibration and validation, based on the available extensive literature, from the perspective of uncertainty structures. After a brief discussion about uncertainties, this paper examines three problem categories—the forward problem, the inverse problem, and the validation problem—in the context of techniques and applications for Statistical Model calibration and validation.