On the Challenges of Model Decorations for Capturing Complex Metadata

Model decorations have proven useful as an extension mechanism to provide bespoke language extensions for particular scenarios in the language's domain. However, the current state of the art has only explored extension mechanisms that allow capturing basic metadata, e.g. additional attributes. In this paper we explore the challenges encountered when decorations must capture more complex metadata, in particular metadata that targets model management operations. Additionally, we provide an initial take on these challenges through automated language extension generation. The generated extensions provide enhanced model decoration capabilities that can support metadata of higher complexity.