Open Software

• RL4MT

  This project demonstrates the use of Reinforcement Learning (RL) methods for rule-orchestrated model transformations. In that matter, the MOMoT Framework is employed which represents problem domains by means of Ecore meta-models and problem instances as models to be optimized through executing graph transformation rules. The framework is extended by two different RL approaches, named value-based and policy-based, which portray alternative optimization methods to so far implemented metaheuristics for finding valuable rule compositions. The project includes case studies that primarily evaluate the RL agents against the Non-Dominated Sorting Algorithm (NSGA-II) and demonstrate the feasibility of RL methods for search-based model optimization. [RL4MT - Github Repository]

• Coverage for DSLs

  we propose a generic coverage computation and fault localization framework for DSLs. It is implemented as part of the GEMOC Studio. [Coverage for DSLs - Github Repository]

• CoQuaDe

  is a modeling language and design framework for quantum circuits. This language allow the definition of composite operators advocating a higher-level quantum algorithm design, together with automated code generation for the circuit execution. The proposed approach comes with a separation of the quantum operation definitions from the quantum circuit syntax, which allows for an independent design and the use of customized libraries. [CoQuaDe - Github Repository]

• ModulER

  is a tool that supports the automatic modularization of ER models. ModulER uses the Eclipse Modeling Framework and the Sirius framework. This tool provides a graphical editor for the creation and editing of modularized ER models and also provides the interface to execute the genetic algorithm. The algorithm was implemented with Jenetics. [ModulEER - Github Repository]

• VERSO (Variability injEctoR for Sirius editOrs)

  is a tool support to automatically adapt existing graphical Sirius editors to allow the definition of models with variability. [VERSO - Github Repository]

• Modelling Production System Families with AutomationML

  We added the variability functionality to the AutomationML environment using the VERSO tool. We demostrate how it works in the project example: caex.caex30.feature.variability.ppu. This project is based on the Pick and Place Unit (PPU) and generate the variants of six documented scenarios (Sc0, Sc1, Sc2, Sc3, Sc4a and Sc4b). [Variability Functionality for AutomationML - Github Repository]