On-the-fly confluence detection for statistical model checking (extended version)

Arnd Hartmanns, Mark Timmer

Research output: Book/ReportReportAcademic

12 Downloads (Pure)

Abstract

Statistical model checking is an analysis method that circumvents the state space explosion problem in model-based verification by combining probabilistic simulation with statistical methods that provide clear error bounds. As a simulation-based technique, it can only provide sound results if the underlying model is a stochastic process. In verification, however, models are usually variations of nondeterministic transition systems. The notion of confluence allows the reduction of such transition systems in classical model checking by removing spurious nondeterministic choices. In this paper, we show that confluence can be adapted to detect and discard such choices on-the-fly during simulation, thus extending the applicability of statistical model checking to a subclass of Markov decision processes. In contrast to previous approaches that use partial order reduction, the confluence-based technique can handle additional kinds of nondeterminism. In particular, it is not restricted to interleavings. We evaluate our approach, which is implemented as part of the modes simulator for the Modest modelling language, on a set of examples that highlight its strengths and limitations and show the improvements compared to the partial order-based method.
Original languageEnglish
Place of PublicationEnschede
PublisherCentre for Telematics and Information Technology (CTIT)
Number of pages18
Publication statusPublished - Mar 2013

Publication series

NameCTIT Technical Report Series
PublisherUniversity of Twente, Centre for Telematica and Information Technology (CTIT)
No.TR-CTIT-13-04
ISSN (Print)1381-3625

Fingerprint

Model checking
Random processes
Explosions
Statistical methods
Simulators
Acoustic waves
Statistical Models

Keywords

  • Confluence
  • EWI-23163
  • On-the-fly algorithm
  • Statistical Model Checking
  • Markov Decision Processes
  • IR-85175
  • METIS-296354

Cite this

Hartmanns, A., & Timmer, M. (2013). On-the-fly confluence detection for statistical model checking (extended version). (CTIT Technical Report Series; No. TR-CTIT-13-04). Enschede: Centre for Telematics and Information Technology (CTIT).
Hartmanns, Arnd ; Timmer, Mark. / On-the-fly confluence detection for statistical model checking (extended version). Enschede : Centre for Telematics and Information Technology (CTIT), 2013. 18 p. (CTIT Technical Report Series; TR-CTIT-13-04).
@book{802b0abb5f024e51ba067af665b04f64,
title = "On-the-fly confluence detection for statistical model checking (extended version)",
abstract = "Statistical model checking is an analysis method that circumvents the state space explosion problem in model-based verification by combining probabilistic simulation with statistical methods that provide clear error bounds. As a simulation-based technique, it can only provide sound results if the underlying model is a stochastic process. In verification, however, models are usually variations of nondeterministic transition systems. The notion of confluence allows the reduction of such transition systems in classical model checking by removing spurious nondeterministic choices. In this paper, we show that confluence can be adapted to detect and discard such choices on-the-fly during simulation, thus extending the applicability of statistical model checking to a subclass of Markov decision processes. In contrast to previous approaches that use partial order reduction, the confluence-based technique can handle additional kinds of nondeterminism. In particular, it is not restricted to interleavings. We evaluate our approach, which is implemented as part of the modes simulator for the Modest modelling language, on a set of examples that highlight its strengths and limitations and show the improvements compared to the partial order-based method.",
keywords = "Confluence, EWI-23163, On-the-fly algorithm, Statistical Model Checking, Markov Decision Processes, IR-85175, METIS-296354",
author = "Arnd Hartmanns and Mark Timmer",
year = "2013",
month = "3",
language = "English",
series = "CTIT Technical Report Series",
publisher = "Centre for Telematics and Information Technology (CTIT)",
number = "TR-CTIT-13-04",
address = "Netherlands",

}

Hartmanns, A & Timmer, M 2013, On-the-fly confluence detection for statistical model checking (extended version). CTIT Technical Report Series, no. TR-CTIT-13-04, Centre for Telematics and Information Technology (CTIT), Enschede.

On-the-fly confluence detection for statistical model checking (extended version). / Hartmanns, Arnd; Timmer, Mark.

Enschede : Centre for Telematics and Information Technology (CTIT), 2013. 18 p. (CTIT Technical Report Series; No. TR-CTIT-13-04).

Research output: Book/ReportReportAcademic

TY - BOOK

T1 - On-the-fly confluence detection for statistical model checking (extended version)

AU - Hartmanns, Arnd

AU - Timmer, Mark

PY - 2013/3

Y1 - 2013/3

N2 - Statistical model checking is an analysis method that circumvents the state space explosion problem in model-based verification by combining probabilistic simulation with statistical methods that provide clear error bounds. As a simulation-based technique, it can only provide sound results if the underlying model is a stochastic process. In verification, however, models are usually variations of nondeterministic transition systems. The notion of confluence allows the reduction of such transition systems in classical model checking by removing spurious nondeterministic choices. In this paper, we show that confluence can be adapted to detect and discard such choices on-the-fly during simulation, thus extending the applicability of statistical model checking to a subclass of Markov decision processes. In contrast to previous approaches that use partial order reduction, the confluence-based technique can handle additional kinds of nondeterminism. In particular, it is not restricted to interleavings. We evaluate our approach, which is implemented as part of the modes simulator for the Modest modelling language, on a set of examples that highlight its strengths and limitations and show the improvements compared to the partial order-based method.

AB - Statistical model checking is an analysis method that circumvents the state space explosion problem in model-based verification by combining probabilistic simulation with statistical methods that provide clear error bounds. As a simulation-based technique, it can only provide sound results if the underlying model is a stochastic process. In verification, however, models are usually variations of nondeterministic transition systems. The notion of confluence allows the reduction of such transition systems in classical model checking by removing spurious nondeterministic choices. In this paper, we show that confluence can be adapted to detect and discard such choices on-the-fly during simulation, thus extending the applicability of statistical model checking to a subclass of Markov decision processes. In contrast to previous approaches that use partial order reduction, the confluence-based technique can handle additional kinds of nondeterminism. In particular, it is not restricted to interleavings. We evaluate our approach, which is implemented as part of the modes simulator for the Modest modelling language, on a set of examples that highlight its strengths and limitations and show the improvements compared to the partial order-based method.

KW - Confluence

KW - EWI-23163

KW - On-the-fly algorithm

KW - Statistical Model Checking

KW - Markov Decision Processes

KW - IR-85175

KW - METIS-296354

M3 - Report

T3 - CTIT Technical Report Series

BT - On-the-fly confluence detection for statistical model checking (extended version)

PB - Centre for Telematics and Information Technology (CTIT)

CY - Enschede

ER -

Hartmanns A, Timmer M. On-the-fly confluence detection for statistical model checking (extended version). Enschede: Centre for Telematics and Information Technology (CTIT), 2013. 18 p. (CTIT Technical Report Series; TR-CTIT-13-04).