Modelling is a useful technique for shaping, exploring, documenting, understanding and communicating artefacts of many kinds, and not only software. ConML is a conceptual modelling language for non-experts in information technologies and especially suited to serve non-software modeling domains.
A few ideas on where modeling (and modeling tools) should go next, including alternative interfaces, benefitting from AI advances and evolving towards a more personalized notion of modeling
Specification of sequence diagrams is not easy. They are more complex than they appear to be and support provided by most UML modeling tools is often limited (e.g. no advanced checking).
The journey to create, and then make fully open source, a low-code development platform. Empowering citizens to develop the services they need.
Our new Chabot DSL, based on state machine semantics, facilitates the creation of bots with complex conversational flows and maximizes the reuse of bot parts in future projects
It's undeniable that Artificial Intelligence (AI) has become part of everyone's life. It is used by companies to exploit the information they collect to improve the products or services they offer and, wanted or unwanted, it is present in almost every device around...
In a study that was published at MoDELS conference in 2018 , I reported the results of observing the development efforts of two Software Engineering projects that used Model-based approaches to develop software for two self-driving rovers. I found that most of the...
This article introduces a conceptual reference framework – the Models and Data (MODA) framework – to support a data-centric and model-driven approach for the integration of heterogeneous models and their respective data for the entire life-cycle of socio-technical systems.
A Body of Knowledge is a fundamental part of any professional discipline because it captures the knowledge that is inherent, sometimes tacit, and often explicit in a professional domain. We propose the MBEBoK as a BoK for the modeling discipline
Second edition of our workshop exploring the mutual benefits of combining model-driven engineering and security techniques.
A Machine Learning approach to determine the conformance of unstructured models against a set of potential metamodels