395,992 research outputs found
Statistical Model Checking : An Overview
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 model checking problem for stochastic systems
with respect to such logics is typically solved by a numerical approach that
iteratively computes (or approximates) the exact measure of paths satisfying
relevant subformulas; the algorithms themselves depend on the class of systems
being analyzed as well as the logic used for specifying the properties. Another
approach to solve the model checking problem is to \emph{simulate} the system
for finitely many runs, and use \emph{hypothesis testing} to infer whether the
samples provide a \emph{statistical} evidence for the satisfaction or violation
of the specification. In this short paper, we survey the statistical approach,
and outline its main advantages in terms of efficiency, uniformity, and
simplicity.Comment: non
Statistical Model Checking for Stochastic Hybrid Systems
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.Comment: In Proceedings HSB 2012, arXiv:1208.315
Efficient Parallel Statistical Model Checking of Biochemical Networks
We consider the problem of verifying stochastic models of biochemical
networks against behavioral properties expressed in temporal logic terms. Exact
probabilistic verification approaches such as, for example, CSL/PCTL model
checking, are undermined by a huge computational demand which rule them out for
most real case studies. Less demanding approaches, such as statistical model
checking, estimate the likelihood that a property is satisfied by sampling
executions out of the stochastic model. We propose a methodology for
efficiently estimating the likelihood that a LTL property P holds of a
stochastic model of a biochemical network. As with other statistical
verification techniques, the methodology we propose uses a stochastic
simulation algorithm for generating execution samples, however there are three
key aspects that improve the efficiency: first, the sample generation is driven
by on-the-fly verification of P which results in optimal overall simulation
time. Second, the confidence interval estimation for the probability of P to
hold is based on an efficient variant of the Wilson method which ensures a
faster convergence. Third, the whole methodology is designed according to a
parallel fashion and a prototype software tool has been implemented that
performs the sampling/verification process in parallel over an HPC
architecture
Verification of interlocking systems using statistical model checking
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.Comment: 12 pages, 3 figures, 2 table
MultiVeStA: Statistical Model Checking for Discrete Event Simulators
The modeling, analysis and performance evaluation of large-scale systems are difficult tasks. Due to the size and complexity of the considered systems, an approach typically followed by engineers consists in performing simulations of systems models to obtain statistical estimations of quantitative properties. Similarly, a technique used by computer scientists working on quantitative analysis is Statistical Model Checking (SMC), where rigorous mathematical languages (typically logics) are used to express systems properties of interest. Such properties can then be automatically estimated by tools performing simulations of the model at hand. These property specifications languages, often not popular among engineers, provide a formal, compact and elegant way to express systems properties without needing to hard-code them in the model definition. This paper presents MultiVeStA, a statistical analysis tool which can be easily integrated with existing discrete event simulators, enriching them with efficient distributed statistical analysis and SMC capabilities
Statistical Model Checking of e-Motions Domain-Specific Modeling Languages
Domain experts may use novel tools that allow them to de- sign and model their systems in a notation very close to the domain problem. However, the use of tools for the statistical analysis of stochas- tic systems requires software engineers to carefully specify such systems in low level and specific languages. In this work we line up both sce- narios, specific domain modeling and statistical analysis. Specifically, we have extended the e-Motions system, a framework to develop real-time domain-specific languages where the behavior is specified in a natural way by in-place transformation rules, to support the statistical analysis of systems defined using it. We discuss how restricted e-Motions sys- tems are used to produce Maude corresponding specifications, using a model transformation from e-Motions to Maude, which comply with the restrictions of the VeStA tool, and which can therefore be used to per- form statistical analysis on the stochastic systems thus generated. We illustrate our approach with a very simple messaging distributed system.Universidad de Málaga Campus de Excelencia Internacional AndalucĂa Tech. Research Project TIN2014-52034-R an
Parallel statistical model checking for safety verification in smart grids
By using small computing devices deployed at user premises, Autonomous Demand Response (ADR) adapts users electricity consumption to given time-dependent electricity tariffs. This allows end-users to save on their electricity bill and Distribution System Operators to optimise (through suitable time-dependent tariffs) management of the electric grid by avoiding demand peaks.
Unfortunately, even with ADR, users power consumption may deviate from the expected (minimum cost) one, e.g., because ADR devices fail to correctly forecast energy needs at user premises. As a result, the aggregated power demand may present undesirable peaks.
In this paper we address such a problem by presenting methods and a software tool (APD-Analyser) implementing them, enabling Distribution System Operators to effectively verify that a given time-dependent electricity tariff achieves the desired goals even when end-users deviate from their expected behaviour.
We show feasibility of the proposed approach through a realistic scenario from a medium voltage Danish distribution network
Formal Verification of Probabilistic SystemC Models with Statistical Model Checking
Transaction-level modeling with SystemC has been very successful in
describing the behavior of embedded systems by providing high-level executable
models, in which many of them have inherent probabilistic behaviors, e.g.,
random data and unreliable components. It thus is crucial to have both
quantitative and qualitative analysis of the probabilities of system
properties. Such analysis can be conducted by constructing a formal model of
the system under verification and using Probabilistic Model Checking (PMC).
However, this method is infeasible for large systems, due to the state space
explosion. In this article, we demonstrate the successful use of Statistical
Model Checking (SMC) to carry out such analysis directly from large SystemC
models and allow designers to express a wide range of useful properties. The
first contribution of this work is a framework to verify properties expressed
in Bounded Linear Temporal Logic (BLTL) for SystemC models with both timed and
probabilistic characteristics. Second, the framework allows users to expose a
rich set of user-code primitives as atomic propositions in BLTL. Moreover,
users can define their own fine-grained time resolution rather than the
boundary of clock cycles in the SystemC simulation. The third contribution is
an implementation of a statistical model checker. It contains an automatic
monitor generation for producing execution traces of the
model-under-verification (MUV), the mechanism for automatically instrumenting
the MUV, and the interaction with statistical model checking algorithms.Comment: Journal of Software: Evolution and Process. Wiley, 2017. arXiv admin
note: substantial text overlap with arXiv:1507.0818
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