Fallzahl Standort Koblenz: 2 (Warnstufe Gelb bis 30.04.2021) Maßnahmenkonzept

Forschungspraktikum / Research Practical


  • Information event: Wednesday, February 03 2021, 13:00, online (Slides)
    • The link to the virtual room has been published via e-mail lists. If you did not receive the link, please ask your fellow students or, if that is not possible, send a mail to konersmann@uni-koblenz.de with the subject "Request for research practical kickoff room".



Topic:  Automated Detection of Model Transformations Intents 

Your supervisor: Dr. Qusai Ramadan.

Model transformation is a promising direction in software development and it represents the cornerstone of Model-Driven Engineering (MDE) [1][2].  Model transformation can be used for a variety of intents, such as the models editing and refinement, model composition, analysis, and model visualization [3]. Model transformations that share the same goal belong to the same intent group. Each intent group is associated with properties that can be concretized into transformation properties. Examples of such properties include termination, traceability, and preservation of semantic aspects. These properties help in validating the model transformations.

The identification of a model transformation intent will enable mapping the appropriate validation methods to the model transformation in question. Generally, identifying the intent of a model transformation facilitate the documentation, maintenance, validation, or reuse of the transformation [3]. Currently, experts are responsible for the identification of a model transformation intent and its properties. In the absence of experts, software engineers can identify transformation intents manually by following guidelines such as those presented in [3].  But manually classifying the model transformations based on their intents is an error-prone process. The literature shows several automated approaches that can support the software artifacts classification process. Examples of such approaches include automated classification [4] and clustering [5] for software models. However, so far, no approach helps in identifying the transformation intents automatically.

Objectives: In this research practical, we aim at designing a machine learning pipeline that enables classifying model transformations based on their intents.

Outcomes: An automatic tool that enables classifying model transformations based on their intents. We also aim to write a tool-paper that describes our contribution.

Background: Participants have to be active, and self-motivated, willing to contribute during the practical research. We expect that participants should have some background in machine learning, programming skills preferably in Python, and good expertise in latex.



[1] Shane Sendall and Wojtek Kozaczynski. Model transformation: The heart and soul of model-driven software development. IEEE software, 20(5):42–45, 2003.

[2] Sendall, S.,Kozaczynski,W.: Model transformation: the heart and soul of model-driven software development. IEEE Softw. 20(5), 42–45 (2003)

[3] Lúcio, Levi, et al. "Model transformation intents and their properties." Software & systems modeling 15.3 (2016): 647-684.

[4] Nguyen, Phuong T., et al. "Automated Classification of Metamodel Repositories: A Machine Learning Approach." 2019 ACM/IEEE 22nd International Conference on Model Driven Engineering Languages and Systems (MODELS). IEEE, 2019.

[5] Basciani, Francesco, et al. "Automated clustering of metamodel repositories." International Conference on Advanced Information Systems Engineering. Springer, Cham, 2016.   

Proof of performance / certificate

The grade will be put together from the following parts:

  • a written tool paper,
  • a tool implementation,
  • a presentation.

You will obtain further information during the information event or personally.


We are really interested in accompanying feedback to directly respond to change requests. Please express your comments subsequent to a lecture via e-mail or the anonymous contact form of our research group (in the latter case please mention the lecure the comment refers to). Many thanks!