Towards a Formal Specification of Multi-paradigm Modelling

Amrani, Moussa; Blouin, Dominique; Heinrich, Robert; Rensink, Arend; Vangheluwe, Hans; Wortmann, Andreas

doi:10.1109/models-c.2019.00067

Cited by 10 publications

(14 citation statements)

References 13 publications

(12 reference statements)

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“…This paper is a continuation of an effort started during the COST Action IC1404 1 "Multi-Paradigm Modelling for Cyber-Physical Systems" (MPM4CPS), which surveyed languages and tools used for engineering CPSs [12] and captured the relationships between them in an ontology. Moreover, it significantly extends, and complements, a preliminary version of our Descriptive Framework [3] by (i) capturing the various components of a paradigm explicitly and (ii) demonstrating a simple paradigm combination resulting in a valid paradigm, which could suggest that our framework is closed under the usual combination operators required for the multiparadigms necessary for modelling CPSs.…”

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confidence: 74%

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Multi-paradigm modelling for cyber–physical systems: a descriptive framework

et al. 2021

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The complexity of cyber–physical systems (CPSs) is commonly addressed through complex workflows, involving models in a plethora of different formalisms, each with their own methods, techniques, and tools. Some workflow patterns, combined with particular types of formalisms and operations on models in these formalisms, are used successfully in engineering practice. To identify and reuse them, we refer to these combinations of workflow and formalism patterns as modelling paradigms. This paper proposes a unifying (Descriptive) Framework to describe these paradigms, as well as their combinations. This work is set in the context of Multi-Paradigm Modelling (MPM), which is based on the principle to model every part and aspect of a system explicitly, at the most appropriate level(s) of abstraction, using the most appropriate modelling formalism(s) and workflows. The purpose of the Descriptive Framework presented in this paper is to serve as a basis to reason about these formalisms, workflows, and their combinations. One crucial part of the framework is the ability to capture the structural essence of a paradigm through the concept of a paradigmatic structure. This is illustrated informally by means of two example paradigms commonly used in CPS: Discrete Event Dynamic Systems and Synchronous Data Flow. The presented framework also identifies the need to establish whether a paradigm candidate follows, or qualifies as, a (given) paradigm. To illustrate the ability of the framework to support combining paradigms, the paper shows examples of both workflow and formalism combinations. The presented framework is intended as a basis for characterisation and classification of paradigms, as a starting point for a rigorous formalisation of the framework (allowing formal analyses), and as a foundation for MPM tool development.

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confidence: 74%

“…Section 2 presents an informal notion of paradigm to serve as a tutorial introduction to our Descriptive Framework, which itself is described in Sect. 3. Section 4 exemplifies the framework with two well-known paradigms used for CPS development.…”

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confidence: 99%

Multi-paradigm modelling for cyber–physical systems: a descriptive framework

et al. 2021

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“…In the perspective of low-code platforms, a multi-strategy engine should be fully automated, from the automatic strategy selection to the automatic con guration and deployment on distributed infrastructures. Our approach is di erent from the multi-strategy approach proposed in [3] which is focused on languages and their salient features. e conception of a multi-strategy engine leads to many scienti c challenges that we divide in two parts in the rest of this section: the challenges related to the code and the challenges related to DevOps.…”

Section: Challenges In Multi-strategy Model-managementmentioning

confidence: 99%

Towards transparent combination of model management execution strategies for low-code development platforms

Philippe

Coullon

Tisi

et al. 2020

Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedi

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Low-code development platforms are taking an important place in the model-driven engineering ecosystem, raising new challenges, among which transparent e ciency or scalability. Indeed, the increasing size of models leads to di culties in interacting with them e ciently. To tackle this scalability issue, some tools are built upon speci c computational strategies exploiting reactivity, or parallelism. However, their performances may vary depending on the speci c nature of their usage. Choosing the most suitable computational strategy for a given usage is a di cult task which should be automated. Besides, the most e cient solutions may be obtained by the use of several strategies at the same time. is paper motivates the need for a transparent multi-strategy execution mode for model-management operations. We present an overview of the di erent computational strategies used in the model-driven engineering ecosystem, and use a running example to introduce the bene ts of mixing strategies for performing a single computation. is example helps us present our design ideas for a multi-strategy model-management system. e code-related and DevOps challenges that emerged from this analysis are also presented. CCS CONCEPTS •So ware and its engineering →So ware creation and management; •Computing methodologies →Parallel algorithms;

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“…There can be different models with specific paradigms and formalisms for a complex system such as a sCPSoS in which each of the models represents one aspect of the system. This approach is called Multi-Paradigm Modeling (MPM) [2]. The modeling approach can be used for different purposes, such as model based engineering [34] which is a software and systems development paradigm that emphasizes the application of modeling principles and best practices throughout the System Development Life Cycle; and M&S in which the models are basis for simulation of the system to develop data utilized for analysis and decision making for the system.…”

Section: Introductionmentioning

confidence: 99%

“…The modeling approach can be used for different purposes, such as model based engineering [34] which is a software and systems development paradigm that emphasizes the application of modeling principles and best practices throughout the System Development Life Cycle; and M&S in which the models are basis for simulation of the system to develop data utilized for analysis and decision making for the system. This vision paper aims to elaborate the lifecycle of sCPSoS using MPM [2] (both for M&S and development), to use proper paradigms/formalisms with the philosophy of modeling different aspects/phases of sCPSoS lifecycle explicitly in an appropriate level of abstraction (in design and/or run time). The idea of using different paradigms and formalisms in the lifecycle of sCPSoS can be challenging, as the integration of different paradigms may be a hard problem, if it is not possible at all in some cases.…”

Section: Introductionmentioning

confidence: 99%

Towards employing ABM and MAS integrated with MBSE for the lifecycle of sCPSoS

Challenger

Vangheluwe

2020

Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedi

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Cyber-Physical Systems (CPSs) are natural evolutions of embedded systems consisting of embedded computing devices and networks interacting with physical processes and possibly with a human. By introduction of Internet of Things (IoT), and Industry 4.0, CPSs are used in an interconnected way each of which may belong to a different stakeholder, building a complex System of Systems (SoS) called CPSoS. These systems need to employ some techniques to transform the collected data to knowledge with which the system can make better decisions. These capabilities can create smart CPSoS or sCPSoS. However, these systems are highly complex from both structural and behavioural point of views. Naturally, there is a need for multiple abstraction levels using different paradigms to model these system, called Multi-Paradigm modeling (MPM). In this paper, the challenges and opportunities of using agent technologies, including intelligent agents, Agent-based modeling and Simulation (ABM), and Multi-Agent Systems (MAS) (or Agentoriented Software Engineering (AOSE)) in an integrated way with Model-based System Engineering (MBSE) techniques are discussed to cover the whole lifecycle of sCPSoS, from simulation and analysis, to development, operation and monitoring. CCS CONCEPTS • Software and its engineering → Model-driven software engineering; • Computing methodologies → Multi-agent systems; • Computer systems organization → Embedded and cyber-physical systems.

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Towards a Formal Specification of Multi-paradigm Modelling

Cited by 10 publications

References 13 publications

Multi-paradigm modelling for cyber–physical systems: a descriptive framework

Multi-paradigm modelling for cyber–physical systems: a descriptive framework

Towards transparent combination of model management execution strategies for low-code development platforms

Towards employing ABM and MAS integrated with MBSE for the lifecycle of sCPSoS

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