Incremental Causal Connection for Self-Adaptive Systems Based on Relational Reference Attribute Grammars

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.