Abstract
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
realism.
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.
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
realism.
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 language | English |
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Title of host publication | MDE Intelligence |
Publication status | E-pub ahead of print - 16 Aug 2023 |
Event | MDE Intelligence - Sweden, Västerås, Sweden Duration: 1 Oct 2023 → 3 Oct 2023 https://mde-intelligence.github.io/ |
Workshop
Workshop | MDE Intelligence |
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Country/Territory | Sweden |
City | Västerås |
Period | 1/10/23 → 3/10/23 |
Internet address |