A Declarative Approach for Transforming SysML Models to Executable Simulation Models

Kapos, George-Dimitrios; Tsadimas, Anargyros; Kotronis, Christos; Dalakas, Vassilis; Νικολαϊδου, Μάρα; Anagnostopoulos, Dimosthenis

doi:10.1109/tsmc.2019.2922153

Cited by 14 publications

(6 citation statements)

References 37 publications

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“…To enable a consistent transformation of a system model into a domain-specific model, an automated approach (Figure 4h) reduces the risk of biased human interpretation. (Kapos et al 2021). It does not involve the return of information into the initial system model.…”

Section: Model Transformationmentioning

confidence: 99%

Utilization of system models in model-based systems engineering: definition, classes and research directions based on a systematic literature review

Wilking,

Horber,

Goetz

et al. 2024

Des. Sci.

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The use of system models within model-based systems engineering (MBSE) is essential for improved communication or system documentation. Previous publications have investigated further reuse of these system models, for example, transforming them directly into discipline-specific models for reuse. The authors refer to this as the term “Utilization” of system models. It aims the compensation of modelling efforts and a further integration of linked models within MBSE. Motivated by a lack of common understanding of this term, a systematic literature review of the state of the art is presented. With this systematic overview, a definition and classification system for different use cases and system life cycle stages are created. These are key results to support engineers and researchers in adopting existing or discovering new utilization approaches. This supports the mission of advanced systems engineering and aims the identification of new research directions coming along with SysML v2 and the advanced systems engineering methods.

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Section: Model Transformationmentioning

confidence: 99%

Utilization of system models in model-based systems engineering: definition, classes and research directions based on a systematic literature review

Wilking,

Horber,

Goetz

et al. 2024

Des. Sci.

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“…These simulations are all iterative and difficult to manage, which leads to the low efficiency of the whole verification processes for the AI-enabled CPS [29]. Researchers provide solutions for improving the consistency and traceability management for automated verifications, such as Huang et al [30] and Kapos et al [31]. Bagnato et al [32] proposed a tool-chain to support CPS design and co-simulation using a developed model-transformer.…”

Section: A Literature Reviewmentioning

confidence: 99%

Model-Based Systems Engineering Tool-Chain for Automated Parameter Value Selection

Chen

Wang

et al. 2022

IEEE Trans. Syst. Man Cybern, Syst.

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Cyber-physical systems (CPSs) integrate heterogeneous systems and process sensor data using digital services. As the complexity of CPS increases, it becomes more challenging to efficiently formalize the integrated multidomain views with flexible automated verification across the entire lifecycle. This article illustrates a model-based systems engineering tool-chain to support CPS development with an emphasis on automated parameter value selection for co-simulation. First, a domain-specific modeling approach is introduced to support the formalizations of CPS artifacts, development processes, and simulation configurations. The domain-specific models are used as the basis to generate a Web-based process management system for automated parameter value selections, which coordinates Open Services for Lifecycle Collaboration services of development information and technical resources (models, data, and tools) in order to support automated co-simulation. The services are deployed by a service orchestrator based on a decision-making algorithm for parameter value selection. Finally, developers make use of the WPMS to implement simulations and to select system parameter values for co-simulation automatically. The approach is illustrated by a case study on auto-braking system development and we evaluate the efficiency of this tool-chain by both qualitative and quantitative methods. The results show that parameter values are selected more efficiently and effectively when implementing co-simulations using our tool-chain. Index Terms-Automated parameter value selection, cyberphysical systems (CPSs), model-based systems engineering (MBSE).

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“…Model-driven approaches for modeling and simulation so far have mostly focused on the generation of (executable) simulation models [24]- [26], models in distributed settings [27], or multi-formalism modeling [28].…”

Section: Related Workmentioning

confidence: 99%

A Model-Driven Approach for Conducting Simulation Experiments

Wilsdorf¹,

Heller²,

Budde³

et al. 2021

Preprint

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With the increasing complexity of simulation studies, and thus increasing complexity of simulation experiments, there is a high demand for better support for their conduction. Recently, model-driven approaches have been explored for facilitating the specification, execution, and reproducibility of simulation experiments. However, a more general approach that is suited for a variety of modeling and simulation areas, experiment types, and tools, which also allows for further automation, is still missing. Therefore, we present a novel model-driven engineering (MDE) framework for simulation studies that extends the state-of-the-art by means for knowledge sharing across domains, increased productivity and quality of complex simulation experiments, as well as reusability and automation. We demonstrate the practicality of our approach using case studies from three different fields of simulation (stochastic discrete-event simulation of a cell signaling pathway, virtual prototyping of a neurostimulator, and finite element analysis of electric fields), and various experiment types (global sensitivity analysis, time course analysis, and convergence testing). The proposed framework can be the starting point for further automation of simulation experiments, and therefore can assist in conducting simulation studies in a more systematic and effective manner. For example, based on this MDE framework, approaches for automatically selecting and parametrizing experimentation methods, or for planning following activities depending on the context of the simulation study, could be developed.

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A Declarative Approach for Transforming SysML Models to Executable Simulation Models

Cited by 14 publications

References 37 publications

Utilization of system models in model-based systems engineering: definition, classes and research directions based on a systematic literature review

Utilization of system models in model-based systems engineering: definition, classes and research directions based on a systematic literature review

Model-Based Systems Engineering Tool-Chain for Automated Parameter Value Selection

A Model-Driven Approach for Conducting Simulation Experiments

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