21,397 research outputs found
A First-Order Logic based Framework for Verifying Simulations
Modern science relies on simulation techniques for understanding phenomenon, exploring design options, or evaluating models. Assuring the correctness of simulators is a key problem where a multitude of solutions ranging from manual inspection to formal verification are applicable. Formal verification incorporates the rigor necessary but not all simulators are generated from formal specifications. Manual inspection is readily available but lacks the rigor and is prone to errors. In this paper, we describe an automated verification system (AVS) where the contraints that the system must adhere to are specified by the user in general purpose first-order logic. AVS translates these constraints into a verification program that scans the simulator trace and verifies that no constraints are violated. The advantage is the ability to verify any simulator trace using a formal specification of domain facts. Computer microarchitecture simulations were used to demonstrate the proposed approach. The system was implemented successfully to yield preliminary results
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
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
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Analog and Mixed Signal Verification
More and more electronic systems have components that are not purely digital. Verification of such systems is a much less developed discipline than the digital equivalents and the application of formal (mathematically complete) techniques is a nascent area. In this paper, we will discuss the nature of analog circuit design and describe the way verification is done in practice today. We will describe some “formal” approaches coming from the analog design community. We will describe some of the approaches to formal verification that have been presented in recent literature. Finally, we will mention some areas where there are opportunities for future work
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
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