A Generic LSTM-Based Neural Network Architecture to infer Heterogeneous Model Transformations
We propose to take advantage of the advances in Artificial Intelligence and, in particular, Long Short-Term Memory Neural Networks (LSTM), to automatically infer heterogeneous model transformations from sets of input-output model pairs.
Dc4MT: a Data-centric Approach for Model Transformations
Need to transform very large models? Take a look at this new approach built on top of Apache Spark
MoWebA Mobile: modeling and generating code for mobile applications’ data layer
The development of mobile apps requires dealing with limited resources like network connectivity. MoWebA offers a model-based solution to this problem.
Model-driven engineering for Industrial IoT architectures
Industrial IoT (IIoT) architectures are typically distributed and asynchronous with event-driven communication, e.g. based on the publication (and corresponding subscription) of messages. These asynchronous architectures enhance scalability and tolerance to changes,...
Lightweight Software Modeling with JetUML
JetUML lets you quickly sketch informal UML diagrams that you can easily integrate in other documents. Use the full power of UML only when you really need it.
Open PhD and Postdoc positions
Now announcing new PhD and Postdoc opportunities to join our team
Model-driven Security for Cyber-Physical Systems
Model-driven development has become a leading paradigm for developing CPS because it enables the developer to verify safety requirements in early development phases and refine the models into an implementation preserving the verified requirements.
AI-enhanced Model-driven engineering – Sosym Journal Special Issue
After witnessing the growing interest in the combination of AI and Model-driven engineering (see for instance our MDE Intelligence workshops at Models but similar workshops have popped up in other conferences, like ER , as well), we are now organizing the special...
Incremental verification of software models
If we want software verification techniques to be more adopted in the industry, we need to move to incremental verification approaches, where we reuse previous analysis to avoid reevaluating the whole model every time it is modified.
Temporal Models on Time Series Databases
We explore the benefits of using time-series databases to store models data. Thanks to this we can now query historical model information and perform advanced time-series analysis on it, especially useful when dealing with run-time models.
Low-code vs model-driven: are they the same?
I don’t see any significant technical novelty in the low-code movement. But I do not see this as being negative. More the opposite. Read why I think the low-code movement represents a great opportunity for the modeling community.
Modeling: all you need to know
