Integrating the Support for Machine Learning of Inter-Model Relations in Model Views
Model-driven engineering (MDE) supports the engineering of complex systems via multiple models representing various aspects of the system. These interrelated models are usually heterogeneous and specified using complementary modeling languages. Thus, model-view solutions can be employed to federate these models more transparently. Inter-model links in model views can sometimes be automatically computed via explicitly written matching rules. However, in some cases, matching rules would be too complex (or even impossible) to write, but inter-model links may be inferred by analyzing previous examples instead. In this paper, we propose a Machine Learning (ML)-backed approach for expressing and computing such model views. Notably, we aim at making the use of ML in this context as simple as possible. To this end, we refined and extended the ViewPoint Definition Language (VPDL) from the EMF Views model-view solution to integrate the use of dedicated Heterogeneous Graph Neural Networks (HGNNs). These view-specific HGNNs are trained with appropriate sets of contributing models before being used for inferring links to be added to the views. We validated our approach by implementing a prototype combining EMF Views with PyEcore and PyTorch Geometric. Our experiments showed promising results regarding the ease-of-use of our approach and the relevance of the inferred inter-model links.
Tue 9 JulDisplayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change
11:00 - 12:30 | |||
11:00 30mResearch paper | Towards a Semantically Useful Definition of Conformance with a Reference Model ECMFA A: Marco Konersmann , A: Bernhard Rumpe RWTH Aachen University, A: Max Stachon RWTH Aachen University, A: Sebastian Stüber RWTH Aachen University, Chair of Software Engineering, A: Valdes Voufo RWTH Aachen University | ||
11:30 30mResearch paper | Integrating the Support for Machine Learning of Inter-Model Relations in Model Views ECMFA A: James Pontes Miranda IMT Atlantique, LS2N (UMR CNRS 6004), A: Hugo Bruneliere IMT Atlantique, LS2N (UMR CNRS 6004), A: Massimo Tisi IMT Atlantique, LS2N (UMR CNRS 6004), A: Gerson Sunyé IMT Atlantique; Nantes Université; École Centrale Nantes | ||
12:00 30mResearch paper | An Empirical Study on Leveraging LLMs for Metamodels and Code Co-evolution ECMFA A: Zohra Kaouter Kebaili Univ Rennes, CNRS, IRISA, A: Djamel Eddine Khelladi CNRS, IRISA, University of Rennes, A: Mathieu Acher University of Rennes, France / Inria, France / CNRS, France / IRISA, France, A: Olivier Barais University of Rennes, France / Inria, France / CNRS, France / IRISA, France |