Belief Uncertainty in Software Models
We propose to assign a degree of belief to model statements, which is expressed by a probability (called credence, in statistical terms) that represents a quantification of such a subjective degree of belief. We discuss how it can be represented using current modeling notations, and how to operate with it in order to make informed decisions.
A PHP2UML model transformation for PHP reverse engineering
A model-based reverse engineering process to generate UML diagrams from PHP code, relying on Modisco and the Eclipse PHP project
Automatic generation of User Interfaces from BPMN Models
A new approach to partially generate graphical user interfaces from business process models based on a set of common BPMN patterns.
Continuous Model-Driven Engineering
Keep reading to see how we managed to combine modeling and continuous delivery and how this combination is beneficial for both continuous integration / deployment and model-driven tools
Margaret Hamilton – A model-driven engineering pioneer
Margaret Hamilton is not only a software engineering pioneer but a model-driven one as well. Her work on the Universal Systems Language already embedded many of the key modeling concepts we use nowadays (platform-independence, code-generation,…).
Pricing model of low-code tools does not work for indie developers
Low-code tools should be the perfect match for solopreneurs trying to release their software products ASAP. But low-code tools are too expensive for them. We explore potential alternatives.
A Model-driven Migration Approach among Cloud Providers
ARGON helps you to define your cloud infrastructure provisioning (via its own DSL) and, based on that, automate the migration process from one cloud provider to the other
JSONSchema To UML: Tool to Generate UML diagrams from JSON Schema Definitions
More and more companies define their data sources using JSONSchemas. This tool helps you to better understand JSONSchema specifications by visualizing them as UML class diagrams.
10 Tools for Modeling AI Components – Machine Learning without the code
A selection of visual modeling environments for data science, machine learning and smart apps. The fastest way to start building your own AI components and ML models
Model-Based Systems Engineering – Adoption Challenges (Survey Results)
The work presented here is a first step in the direction of defining, understanding and measuring the challenges associated with the adoption of model-based systems engineering.
Smart modeling tools – AI to help you model better
List of smart modeling tools, i.e. tools that include an AI assistant to help modelers write better models faster.
Modeling: all you need to know
