Statistical Model Checking of Black-Box Probabilistic Systems

@inproceedings{Sen2004StatisticalMC,
  title={Statistical Model Checking of Black-Box Probabilistic Systems},
  author={Koushik Sen and Mahesh Viswanathan and Gul A. Agha},
  booktitle={CAV},
  year={2004}
}
We propose a new statistical approach to analyzing stochastic systems against specifications given in a sublogic of continuous stochastic logic (CSL). Unlike past numerical and statistical analysis methods, we assume that the system under investigation is an unknown, deployed black-box that can be passively observed to obtain sample traces, but cannot be controlled. Given a set of executions (obtained by Monte Carlo simulation) and a property, our algorithm checks, based on statistical… Expand
A Survey of Statistical Model Checking
TLDR
SMC provides a more widely applicable and scalable alternative to analysis of properties of stochastic systems using numerical and symbolic methods, while emphasizing current limitations and tradeoffs between precision and scalability. Expand
Statistical Model Checking: An Overview
TLDR
This tutorial surveys the statistical approach to model checking, and outlines its main advantages in terms of efficiency, uniformity, and simplicity. Expand
Bayesian statistical model checking with application to Stateflow/Simulink verification
TLDR
It is proved that Bayesian SMC can make the probability of giving a wrong answer arbitrarily small, which is essential for scaling up to large Stateflow/Simulink models. Expand
Bayesian statistical model checking with application to Simulink/Stateflow verification
TLDR
It is proved that Bayesian SMC can make the probability of giving a wrong answer arbitrarily small, which enables faster verification than state-of-the-art statistical techniques, while retaining the same error bounds. Expand
Statistical Model Checking with Change Detection
TLDR
An algorithm that can be used to monitor changes in the probability distribution to satisfy a bounded-time property at runtime and is illustrated by using Plasma Lab to verify a Simulink case study modelling a pig shed temperature controller. Expand
Model-Based Testing of Probabilistic Systems
TLDR
This paper provides algorithms to generate, execute and evaluate test cases from a probabilistic requirements model, and connects ioco-theory for model-based testing and statistical hypothesis testing: these algorithms handle the functional aspects, while statistical methods assess if the frequencies observed during test execution correspond to the probabilities specified in the requirements. Expand
Contributions to Statistical Model Checking
TLDR
This thesis proposes several contributions to increase the efficiency of SMC and to wider its applicability to a larger class of systems and shows how to extend the applicability ofSMC to estimate the probability of rare-events and considers the problem of detecting probability changes at runtime. Expand
Simulation + Hypothesis Testing for Model Checking Probabilistic Systems A Tutorial
Quantitative properties of stochastic systems are usually specified in logics that allow one to compare the measure of executions satisfying certain temporal properties with thresholds. The modelExpand
PVeStA: A Parallel Statistical Model Checking and Quantitative Analysis Tool
TLDR
PVESTA is presented, an extension and parallelization of the VESTA statistical model checking tool, which supports statistical model Checking of probabilistic real-time systems specified as either discrete or continuous Markov Chains; or Probabilistic rewrite theories in Maude. Expand
On Statistical Model Checking of Stochastic Systems
TLDR
A statistical model checking algorithm that also verifies CSL formulas with unbounded untils, based on Monte Carlo simulation of the model and hypothesis testing of the samples, as opposed to sequential hypothesis testing is presented. Expand
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 23 REFERENCES
Numerical vs. Statistical Probabilistic Model Checking: An Empirical Study
TLDR
This study relies on highly efficient sequential acceptance sampling tests, which enables statistical solution techniques to quickly return a result with some uncertainty in CSL model checking, and proposes a novel combination of the two solution techniques for verifying CSL queries with nested probabilistic operators. Expand
Approximate Probabilistic Model Checking
TLDR
An approximation method to verify quantitative properties on discrete Markov chains using a randomized algorithm to approximate the probability that a property expressed by some positive LTL formula is satisfied with high confidence by a probabilistic system. Expand
Probabilistic Verification of Discrete Event Systems Using Acceptance Sampling
TLDR
A model independent procedure for verifying properties of discrete event systems based on Monte Carlo simulation and statistical hypothesis testing that is probabilistic in two senses and carried out in an anytime manner. Expand
Symbolic Model Checking for Probabilistic Processes
TLDR
A symbolic model checking procedure for Probabilistic Computation Tree Logic PCTL over labelled Markov chains as models is introduced, based on the algorithm used by Hansson and Jonsson [24], and is efficient because it avoids explicit state space construction. Expand
A Markov Chain Model Checker
TLDR
A prototype model checker for discrete and continuous-time Markov chains, the Erlangen-Twente Markov Chain Checker (E ⊢ MC2), where properties are expressed in appropriate extensions of CTL, is described. Expand
PRISM: Probabilistic Symbolic Model Checker
TLDR
PRISM has been successfully used to analyse probabilistic termination, performance, and quality of service properties for a range of systems, including randomized distributed algorithms, manufacturing systems and workstation clusters. Expand
Verifying Quantitative Properties of Continuous Probabilistic Timed Automata
TLDR
This work develops a model checking method for continuous probabilistic timed automata, which improves on the previously known techniques in that it allows the verification of quantitative probability bounds, as opposed to qualitative properties which can only refer to bounds of probability 0 or 1. Expand
Model Checking of Probabalistic and Nondeterministic Systems
TLDR
Model-checking algorithms for extensions of pCTL and p CTL* to systems in which the probabilistic behavior coexists with nondeterminism are presented, and it is shown that these algorithms have polynomial-time complexity in the size of the system. Expand
Model-Checking for Probabilistic Real-Time Systems (Extended Abstract)
TLDR
This paper extends model-checking to stochastic real-time systems, whose behavior depends on probabilistic choice and quantitative time, with a model that can express constraints like “the delay between the request and the response is distributed uniformly between 2 to 4 seconds”. Expand
Approximate Symbolic Model Checking of Continuous-Time Markov Chains
TLDR
A symbolic approximate method for solving the integrals using MTDDs (multi-terminal decision diagrams), a generalisation of MTBDDs, suitable for numerical integration using quadrature formulas based on equally-spaced abscissas, like trapezoidal, Simpson and Romberg integration schemes. Expand
...
1
2
3
...