Common UML and modeling errors (II): n-ary associations
Do you understand how to read the cardinalities in a ternary association? Here we explain it to you
A new book on low-code is now available!
I’m thrilled to announce that I’ve just published a new low-code book. A pragmatic book that aims to answer all your low-code questions.
Tree of Thoughts Framework for LLM-Based Domain Modeling
This article explores how the Tree of Thoughts (ToT) framework can be adapted to enhance LLM-based domain modeling. It also introduces a Domain-Specific Language (DSL) tailored to support and streamline this approach.
Teaching Object Constraint Language (OCL)? Why [not]? Your input is needed!
Launching a collective effort to increase the teaching of OCL in modeling courses.
Akwatype – Early data modeling for all your teams
There are very few tools that focus on data modeling. Akwatype is your exception, helping you to structure your data modeling to communicate, describe your data flows and feed your development team
JointJS: JavaScript diagramming toolkit
JointJS is a JavaScript diagramming library based on SVG, used to develop visually rich, browser-native applications with ready-to-use shapes, automatic layouts, drag & drop functionality, and more than 40 UI components
A Metascience Study of the Adoption of Low-Code terminology in Modeling Publications
Is low-code changing the way that researchers publish in the modeling field?
Towards Data Interoperability for the Digital Product Passport Ecosystem
Our proposal for a formalization of the Digital Product Passport concept and a code generator to accelerate the development of applications around the DPP ecosystem.
Software Product Lines to automate the Combination of Machine Learning Models
We propose a Software Product Line approach for expressing and generating combinations of LLMs, e.g. using a Mixture of Experts technique
Multi-Cloud Deployment with the BESSER low-code platform
Model, generate and deploy multi-cloud applications with the BESSER low-code tool
Automating Bias Testing of Large Language Models
Large Language Models (LLMs) are being quickly integrated in a myriad of software applications. This may introduce a number of biases, such as gender, age or ethnicity, in the behavior of such applications. To face this challenge, we explore the automatic generation of tests to assess the potential biases of an LLM.
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