Dandelion: A scalable, cloud-based graphical language workbench for industrial low-code development

Abstract

There is an increasing demand nowadays for low-code development platforms (LCDPs). As they rely heavily on graphical languages rather than writing code, these platforms enable citizen developers to participate in software development. However, creating new LCDPs is very costly, since it requires building support for graphical modelling and its integration with services like model validation, recommendation systems, or code generation. While Model-driven Engineering (MDE) has developed technologies to create these components, most of them are not cloud-based, as required by LCDPs. In particular, a cloud-based graphical workbench capable of providing the scalability required by industrial applications and adequately supporting technological heterogeneity is currently missing. To fill this gap we introduce Dandelion, a cloud-based graphical language workbench for LCDPs built following an MDE approach. The tool handles model heterogeneity by using a harmonising meta-model to uniformly represent models from diverse technologies, and supports a customisable level of conformance between models and meta-models. Scalability is addressed by persisting models in a distributed, highly flexible database whose infrastructure is designed to conform to the harmonising meta-model, thus favouring model retrieval. Additionally, a customisable scalability component is introduced for lazy model loading. This paper describes the concepts and principles behind the tool design and reports on an evaluation on large synthetic process mining models, and on domain-specific languages and large industrial models used within the UGROUND company, showing promising results.