Incremental causal connection for self-adaptive systems based on relational reference attribute grammars.

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Abstract

Even though model-driven engineering reduces complexity during the development of self-adaptive systems and models@run.time enables using them during runtime, connecting models to different external systems still involves manual work. Those connections are essential to the complete system, as they enable external systems to react to changes in the internal model and vice versa. In our case, the model is based on Relational Reference Attribute Grammars, an extension of Attribute Grammars to enable conceptual models at runtime while retaining their benefits of modular specification and an incremental evaluation scheme. We present an approach to enable concise specification of the causal connection and needed transformations to match required formats or semantics. To show its applicability, a case study showing the coordination of multiple industrial robot arms using models is presented. We show that using our approach, connections can be specified more concisely while maintaining the same efficiency as hand-written code. The artefact comprising all source code and an executable version of the case studies is available at https://doi.org/10.5281/zenodo.7009758.

Details

Original languageEnglish
Title of host publicationMoDELS
Pages1-12
Number of pages12
ISBN (electronic)9781450394666
Publication statusPublished - 26 Oct 2022
Peer-reviewedYes

Conference

Title25th ACM/IEEE International Conference on Model Driven Engineering Languages and Systems
Abbreviated titleMODELS' 2022
Conference number25
Duration23 - 28 October 2022
Website
Degree of recognitionInternational event
LocationUniversity of Montreal & online
CityMontreal
CountryCanada

External IDs

ORCID /0000-0002-3247-0264/work/142248602
Scopus 85141832184
ORCID /0000-0003-1537-7815/work/168720061
ORCID /0000-0002-3513-6448/work/168720190

Keywords

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