From control law diagrams to Ada via
            <i>Circus</i>

Cavalcanti, Ana; n, Phil Clayton_aff; O'Halloran, Colin

doi:10.1007/s00165-010-0170-3

Cited by 26 publications

(22 citation statements)

References 44 publications

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“…That proof, however, uses existing Circus laws rather than the UTP-based semantics of Circus Time we recaptured in Section 7.2. We note that standard Circus laws like those in [CSW03,CCO11] remain valid in Circus Time.…”

Section: Resultsmentioning

confidence: 76%

Laws of mission-based programming

Zeyda

Cavalcanti

2015

Form. Asp. Comput.

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Safety-Critical Java (SCJ) is a recent technology that changes the execution and memory model of Java in such a way that applications can be statically analysed and certified for their real-time properties and safe use of memory. Our interest is in the development of comprehensive and sound techniques for the formal specification, refinement, design, and implementation of SCJ programs, using a correct-by-construction approach. As part of this work, we present here an account of laws and patterns that are of general use for the refinement of SCJ mission specifications into designs of parallel handlers, as they are used in the SCJ programming paradigm. Our refinement notation is a combination of languages from the Circus family, supporting state-rich reactive models with the addition of class objects and real-time properties. Starting from a sequential and centralised Circus specification, our laws permit refinement into Circus models of SCJ program designs. Automation and proof of the refinement laws is examined here, too. Our work is an important step towards eliciting laws of programming for SCJ and fits into a refinement strategy that we have developed previously to derive SCJ programs from specifications in a rigorous manner.

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Section: Resultsmentioning

confidence: 76%

Laws of mission-based programming

Zeyda

Cavalcanti

2015

Form. Asp. Comput.

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“…It can be automatically generated if there is a more complete model of the FMU. For example, [7] shows the case if a discrete-time Simulink model is available.…”

Section: Specific Fmu Modelsmentioning

confidence: 99%

“…To establish determinism in that sense, we need to consider a highly parallel model with all valid execution orders respecting the port dependency graph. This is the approach in [7], where verification uses theorem proving. The approach taken here is more amenable to model checking and sufficient to verify sequential implementations of simulations.…”

Section: Master Algorithmsmentioning

confidence: 99%

Behavioural Models for FMI Co-simulations

Cavalcanti

Woodcock

Amálio

2016

Lecture Notes in Computer Science

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Abstract. Simulation is a favoured technique for analysis of cyberphysical systems. With their increase in complexity, co-simulation, which involves the coordinated use of heterogeneous models and tools, has become widespread. An industry standard, FMI, has been developed to support orchestration; we provide the first behavioural semantics of FMI. We use the state-rich process algebra, Circus, to present our modelling approach, and indicate how models can be automatically generated from a description of the individual simulations and their dependencies. We illustrate the work using three algorithms for orchestration. A stateless version of the models can be verified using model checking via translation to CSP. With that, we can prove important properties of these algorithms, like termination and determinism, for example. We also show that the example provided in the FMI standard is not a valid algorithm.

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“…A strategy to translate Simulink diagrams into Circus and to prove that a parallel ADA implementation refines this Circus specification is presented in (CAVALCANTI et al, 2011). Simulink is a block-based language for control applications, similar to FBD (Function Block Diagram); although it is not used in PLCs their principals are much alike.…”

Section: (Cavalcanti Et Al 2011)mentioning

confidence: 99%