Generating customized low-code development platforms for digital twins

Dalibor, Manuela; Heithoff, Malte; Michael, Judith; Netz, Lukas; Pfeiffer, Jérôme; Rumpe, Bernhard; Varga, Simon; Wortmann, Andreas

doi:10.1016/j.cola.2022.101117

Cited by 26 publications

(7 citation statements)

References 28 publications

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“…Examples include, e.g., to use only classes, attributes, and relations in class diagrams for analysis and later on also methods and their signatures for generating code, or if we have human-centric information in requirements models that should be used in design modeling languages (Grundy et al 2021). Moreover, kind-typed symbol tables enable enhanced functionalities for reusable model libraries (Dalibor et al 2022), e.g., providing model sets of different languages which use symbols of each other and can be reused in combination.…”

Section: Discussionmentioning

confidence: 99%

Language Composition via Kind-Typed Symbol Tables.

Butting¹,

Michael²,

Rumpe³

2022

JOT

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The modularization of domain-specific modeling languages (DSMLs) fosters individual reuse of DSMLs in different contexts. Within this article, we discuss how it is possible to refer to model elements of other languages when composing different DSMLs. Related approaches usually rely on a DSML-agnostic language infrastructure that tests the compatibility of model elements via types encoded in Strings without any consistency checks. We propose the "strongly kind-typed" symbol table as an extension to the compiler approach to integrate the syntax of the languages using symbol tables that assign a symbol kind to each name definition. Our approach can be integrated into language workbenches that provide a symbol table infrastructure as part of a DSML implementation. The kind-typed symbol tables are integrated into the language workbench MontiCore. Strongly kind-typed symbol tables utilize the type system of the language workbench's host language to ensure type consistency between the language-specific symbol table infrastructures during DSML composition, which ultimately supports DSML engineering in the large.

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Section: Discussionmentioning

confidence: 99%

Language Composition via Kind-Typed Symbol Tables.

Butting¹,

Michael²,

Rumpe³

2022

JOT

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“…Several approaches utilize domain‐specific modeling languages, 27,29 metamodels, 30,31,60 and model‐driven techniques 32,61 for creating DTs in various domains such as DTs for managing graphical user interactions, 33–35 satellite systems, 36 and web applications 37 . Some approaches are dedicated to creating DTs for cyber‐physical systems (CPSs), such as References 28,38,39.…”

Section: Related Workmentioning

confidence: 99%

Model‐based digital twins of medicine dispensers for healthcare IoT applications

Sartaj,

Ali,

Yue

et al. 2024

Softw Pract Exp

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SummaryHealthcare applications with the Internet of Things (IoT) are often safety‐critical, thus, require extensive testing. Such applications are often connected to smart medical devices from various vendors. System‐level testing of such applications requires test infrastructures physically integrating medical devices, which is time and monetary‐wise expensive. Moreover, applications continuously evolve, for example, introducing new devices and users and updating software. Nevertheless, a test infrastructure enabling testing with a few devices is insufficient for testing healthcare IoT systems, hence compromising their dependability. In this paper, we propose a model‐based approach for the creation and operation of digital twins (DTs) of medicine dispensers as a replacement for physical devices to support the automated testing of IoT applications at scale. We evaluate our approach with an industrial IoT system with medicine dispensers in the context of Oslo City and its industrial partners, providing healthcare services to its residents. We study the fidelity of DTs in terms of their functional similarities with their physical counterparts: medicine dispensers. Results show that the DTs behave more than 92% similar to the physical medicine dispensers, providing a faithful replacement for the dispenser.

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“…The WebML approach (Moreno et al 2007) showed us how to use the UML for modeling and cutting out larger parts of a complete software system as done in MontiGem for generating financial management systems (Gerasimov, Heuser, et al 2020), digital twin cockpits (Dalibor et al 2020), low-code platforms (Dalibor et al 2022), or assistive systems (Michael et al 2020).…”

Section: Conclusion and Dedication To Antonio's Birthdaymentioning

confidence: 99%

A Catalog of Design Patterns for Compositional Language Engineering.

Drux¹,

Jansen²,

Rumpe³

2022

JOT

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When composing a domain-specific language from several language components, it is also necessary to compose analysis and synthesis techniques, which are individually defined on these components in an efficient, ideally black-box form.An effective way of allowing such compositions is to use specific design patterns, which are partly reflected in the tooling code, partly reflected in the language, but also partly reflected in the language workbench (one meta-level higher), and the generated/synthesized product code (one meta-level downward). Based on the experiences gained in compositional language development using the language workbench MontiCore, we in detail discuss several of those design patterns, namely the Mill, the RealThis object composition, the Template/Hook, and the TOP-Generator Patterns, and the hidden complexity of an extended visitor infrastructure coping with the above patterns. The patterns are recorded and described in a reusable way, as usual, allowing readers to participate from the gained insights and possible solutions.

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Generating customized low-code development platforms for digital twins

Cited by 26 publications

References 28 publications

Language Composition via Kind-Typed Symbol Tables.

Language Composition via Kind-Typed Symbol Tables.

Model‐based digital twins of medicine dispensers for healthcare IoT applications

A Catalog of Design Patterns for Compositional Language Engineering.

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