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The "Hype Cycle" (HC) and the "Technology Adoption Lifecycle" (TALC) models plotted together (from: http://setandbma.wordpress.com/2012/05/28/technology-adoption-shift/), with the current position of Model Driven Engineering indicated by a thick blue line. 

The "Hype Cycle" (HC) and the "Technology Adoption Lifecycle" (TALC) models plotted together (from: http://setandbma.wordpress.com/2012/05/28/technology-adoption-shift/), with the current position of Model Driven Engineering indicated by a thick blue line. 

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Model Driven Engineering (MDE) is an approach to software development where models play a central role in all software engineering processes. Conceived to provide significant gains in productivity, portability, maintainability and interoperability, MDE is now starting to be effectively used in industry. Thus, companies are beginning to evaluate the...

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... Such low-code AI-enhanced, ML-enabled systems (also called smart software), give rise to unique software engineering challenges [6], [7], [8], e.g., AI elements are hard to specify [9], architect, test and verify [10]. Organizations must adapt to leverage them [11], [12]. Additional complexity arises from all the potential interactions between the AI components and the "traditional" ones (since we need to specify how they collaborate, test that they behave consistently and analyze their interdependencies). ...
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... These studies show somehow contradictory findings. The work by Liebel et al. [41] shows that modelling can be suitable for embedded systems, and, given that one understands the consequences of adopting modelling, the study by Vallecillo et al. [42] convinces us that modelling is ready for industry. A different point of view is suggested by other stud-ies. ...
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... Rigorous experiments such as those of (Panach et al. 2021) have shown that software quality increases with the MBSE approach. Team productivity has also been found to improve with the systematical use of models (Vallecillo 2015). ...
... In a survey presented in (Störrle 2017), more than 70 per cent of the respondents said they only used models as communication artefacts, mostly during the first stages of development (as depicted on the right of Figure 1). The multiple reasons for this are accurately summarised in (Vallecillo 2015). Some of them are listed in Section 3 as weaknesses and threats. ...
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Since the early days of Model-driven Engineering (MDE), our community has been discussing the reasons why MDE had not quickly became mainstream. It is now clear the answer is a mix of technical and social factors, but among the former, the lack of maturity of MDE tools is often mentioned. The goal of this paper is to explore the question of whether this lack of maturity is actually true. We do so by comparing the maturity of over a hundred modeling and non-modeling projects living together in the Eclipse ecosystem. In both cases, we use the word project to refer to a variety of tools, libraries and other artefacts to build and manipulate software components, either at the model or code level. Our maturity model is based on code-centric and community metrics that we evaluate on the repository data for both kinds of projects. Their incubation status is also considered in the assessment. Results show that there are indeed differences between modeling and non-modeling projects, though less than we expected when setting up the study. Moreover, while the incubation status clearly separates non-modeling projects, the same is not true for modeling projects which seem to remain much more stable across their lifespan. We believe our results help to have a better perspective on maturity of modeling support nowadays and provide ideas for further analysis towards their improvement.