Intelligent Run-Time Partitioning of Low-Code System Models

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Over the last 2 decades, several dedicated languages have been proposed to support model management activities such as model validation, transformation, and code generation. As software systems become more complex, underlying system models grow proportionally in both size and complexity. To keep up, model management languages and their execution engines need to provide increasingly more sophisticated mechanisms for making the most efficient use of the available system resources. Efficiency is particularly important when model-driven technologies are used in the context of low-code platforms where all model processing happens in pay-per-use cloud resources. In this paper, we present our vision for an approach that leverages sophisticated static program analysis of model management programs to identify, load, process and transparently discard relevant model partitions - instead of naively loading the entire models into memory and keeping them loaded for the duration of the execution of the program. In this way, model management programs will be able to process system models faster with a reduced memory footprint, and resources will be freed that will allow them to accommodate even larger models.
Original languageEnglish
Title of host publicationProceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings
Place of PublicationNew York, NY, USA
ISBN (Print)9781450381352
Publication statusPublished - 2020

Publication series

NameMODELS '20
PublisherAssociation for Computing Machinery


  • partial loading
  • memory management
  • model partitioning
  • model-driven software engineering

Cite this