Towards Generating Structurally Realistic Models by Generative Adversarial Networks

Abbas Rahimi, Massimo Tisi, Shekoufeh Kolahdouz Rahimi, Luca Berardinelli

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Abstract—Context. Several activities in model-driven engineering (MDE), like model transformation testing, would require
the availability of big sets of realistic models. However, the
community failed so far in producing large model repositories
and the lack of freely available industrial models has been raised
as one of the most important problems in MDE. Consequently,
MDE researchers have developed various tools and methods
to generate models using different approaches such as graph
grammar, partitioning, and random generation. However, these
tools rarely focus on producing new models considering their
Contribution. In this work, we utilize generative deep learning, in
particular, Generative Adversarial Networks (GANs), to present
an approach for generating new structurally realistic models.
Built atop the Eclipse Modeling Framework, the proposed tool
can produce new artificial models from a metamodel and one
big instance model as inputs. Graph-based metrics have been
used to evaluate the approach. The preliminary statistical results
illustrate that using GANs can be promising for creating new
realistic models.
Original languageEnglish
Title of host publicationMDE Intelligence
Publication statusE-pub ahead of print - 16 Aug 2023
EventMDE Intelligence - Sweden, Västerås, Sweden
Duration: 1 Oct 20233 Oct 2023


WorkshopMDE Intelligence
Internet address

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