We believe cognification could drastically improve the benefits and reduce the costs of adopting MDSE, and thus boost its adoption.
At the practical level, cognification comprises tools that go from artificial intelligence (machine learning, deep learning, as well as human cognitive capabilities, exploited through online activities, crowdsourcing, gamification and so on.
Opportunities (and challenges) for MDE
Here is a set of MDSE tasks and tools whose benefits can be especially boosted thanks to cognification.
- A modeling bot playing the role of virtual assistant in the modeling tasks
- A model inferencer able to deduce a common schema behind a set of unstructured data coming from the software process
- A code generator able to learn the style and best practices of a company
- A real-time model reviewer able to give continuous quality feedback
- A morphing modeling tool, able to adapt its interface at run-time
- A semantic reasoning platform able to map modeled concepts to existing ontologies
- A data fusion engine that is able to perform semantic integration and impact analysis of design-time models with runtime data
- A tool for collaboration between domain experts and modeling designers
Obviously, we are aware that some research initiatives aiming at cognifying specific tasks in Software Engineering exist (including some activities of ours). But what we claim here is a change in magnitude of their coverage, integration, and impact in the short-term future.
If you want to get a more detailed description, you can go through the detailed post by Jordi Cabot that reports the whole content of the paper.