Auto-generating models from their semantics and constraints
Domain-specific models powered using domain-specific modeling languages are traditionally created manually by modelers. There exist model intelligence techniques, such as constraint solvers and model guidance, which alleviate challenges associated with manually creating models, however parts of the modeling process are still manual. Moreover, state-of-the-art model intelligence techniques are—in essence—reactive (i.e., invoked by the modeler). This thesis therefore provides two contributions to model-driven engineering research using domain-specific modeling language (DSML). First, it discusses how DSML semantic and constraint can enable proactive modeling, which is a form of model intelligence that foresees model transformations, automatically executes these model transformations, and prompts the modeler for assistance when necessary. Secondly, this thesis shows how we integrated proactive modeling into the Generic Modeling Environment (GME). Our experience using proactive modeling shows that it can reduce modeling effort by both automatically generating required model elements, and by guiding modelers to select what actions should be executed on the model.
Hill, Purdue University.
Information Technology|Computer science
Off-Campus Purdue Users:
To access this dissertation, please log in to our