Integrating Simulink Models into the Model Checker Cosmos

Barbot, Benoît; Bérard, Béatrice; Duplouy, Yann; Haddad, Serge

doi:10.1007/978-3-319-91268-4_19

Cited by 5 publications

(3 citation statements)

References 16 publications

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“…Starting from the initial state of the automaton and the clock valuation equal to 0, the transition and the delay at step i are chosen using the inverse transform method for the distribution sampling with the reals (u 2i , u 2i+1 ) over the distribution indexed by (n − i). We also remark that in previous work [6] three tools were used to perform this sampling: Prism [34] for computing the zone graph, SageMath [42] for computing distributions and Cosmos [7] for the sampling. In the present work, the tool WordGen [8] combining the three steps has been developed which greatly increases the usability of the method.…”

Section: Transformation From the Unit Box To A Timed Polytopementioning

confidence: 99%

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Falsification of Cyber-Physical Systems with Constrained Signal Spaces

Barbot

Basset

Dang

et al. 2020

Lecture Notes in Computer Science

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Falsification has garnered much interest recently as a way to validate complex CPS designs with respect to a specification expressed via temporal logics. Using their quantitative semantics, the falsification problem can be formulated as robustness minimization problem. To make this infinite-dimensional problem tractable, a common approach is to restrict to classes of signals that can be defined using a finite number of parameters, such as piecewise-constant or piecewise-linear signals with fixed time intervals). A major drawback of this approach is that when the input signals must satisfy non-trivial temporal constraints, encoding these constraints into bounded domains for parameters can be difficult. In this work, to better capture temporal constraints on the input signal space, we use timed automata (TA) and make use of a transformation that allows sampling TA traces by sampling points in the unit box. We exploit this transformation to efficiently encode constrained CPS signals in the robustness minimization problem. This transformation also allows us to define an effective coverage measure of the constrained signal space so as to provide quantitative guarantees when no falsifying behaviour is found. In addition, this coverage is used to improve the black-box optimisation performance by detecting situations where the search is stuck near a local optimum. The approach is demonstrated on a ∆Σ modulator and a model of car automatic transmission subject to constraints describing usual driving patterns.

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Section: Transformation From the Unit Box To A Timed Polytopementioning

confidence: 99%

“…In this work, we assume that each π ai is an interval predicate 7 , and the set P v is thus a box, called a valued box. Hence, this coupling of time and value constraints leads to a polytope in R 2m : Π = {(τ, v) | τ ∈ P τ ∧ v ∈ P v }, called a timed-valued polytope.…”

Section: Encoding Constrained Signal Space In the Optimization Problemmentioning

confidence: 99%

Falsification of Cyber-Physical Systems with Constrained Signal Spaces

Barbot

Basset

Dang

et al. 2020

Lecture Notes in Computer Science

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“…Also, most optimisers do not take into account input constraints and may lead to trivial solutions that do not correspond to realistic scenarios. Statistical model checking based on Monte Carlo methods has also been applied to CPS [13,1,7]. The reader is referred to a survey on CPS validation approaches [8].…”

Section: Introductionmentioning

confidence: 99%

Generation of Signals Under Temporal Constraints for CPS Testing

Barbot

Basset

Dang

2019

Lecture Notes in Computer Science

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This work is concerned with validation of cyber-physical systems (CPS) via sampling of input signal spaces. Such a space is infinite and in general too difficult to treat symbolically, meaning that the only reasonable option is to sample a finite number of input signals and simulate the corresponding system behaviours. It is important to choose a sample so that it best "covers" the whole input signal space. We use timed automata to model temporal constraints, in order to avoid spurious bugs coming from unrealistic inputs and this can also reduce the input space to explore. We propose a method for low-discrepancy generation of signals under temporal constraints recognised by timed automata. The discrepancy notion reflects how uniform the input signal space is sampled and additionally allows deriving validation and performance guarantees. To evaluate testing quality, we also show a measure of uniformity of an arbitrary set of input signals. We describe a prototype tool chain and demonstrate the proposed methods on a Kinetic Battery Model (KiBaM) and a Σ∆ modulator.

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