Abstract
© 2024, [Elsevier]. This is an author produced version of a paper published in SoftwareX uploaded in accordance with the publisher’s self- archiving policy. The final published version (version of record) is available online at the link. Some minor differences between this version and the final published version may remain. We suggest you refer to the final published version should you wish to cite from it.
The methodology under the term model-based software engineering (MBSE)
gained importance already around 20 years ago, after the publication of the
MDA initiative by the OMG. This development methodology continues to
evolve, giving rise to recent proposals such as low-code or no-code. Something
that has not changed, as recent surveys point out, is the need for powerful
testing approaches and tools for these new methodologies. In MBSE, test
inputs are models, so it is key to have frameworks for model generation.
However, the main shortcomings of existing model-generation frameworks
are their performance limitations and the need for domain-specific knowledge,
which seriously hampers their industrial adoption. In this paper, we present
the Yekta low-code framework that allows to generate models in a simple
way through the application of metaheuristic algorithms.
The methodology under the term model-based software engineering (MBSE)
gained importance already around 20 years ago, after the publication of the
MDA initiative by the OMG. This development methodology continues to
evolve, giving rise to recent proposals such as low-code or no-code. Something
that has not changed, as recent surveys point out, is the need for powerful
testing approaches and tools for these new methodologies. In MBSE, test
inputs are models, so it is key to have frameworks for model generation.
However, the main shortcomings of existing model-generation frameworks
are their performance limitations and the need for domain-specific knowledge,
which seriously hampers their industrial adoption. In this paper, we present
the Yekta low-code framework that allows to generate models in a simple
way through the application of metaheuristic algorithms.
| Original language | English |
|---|---|
| Journal | SoftwareX |
| Publication status | Published - 26 Aug 2024 |