Cognifying model-driven software engineering

Jul 21, 2017Download as pptx, pdf1 like3,749 views

Cognification is the application of knowledge to boost the performance and impact of a process. We believe cognification could be a revolution in the way software is built.

Cognifying model-driven
engineering
Jordi Cabot, Robert Clarisó, Marco Brambilla, Sébastien Gerard
@softmodeling – modeling-languages.com July 2017

Cognification:
The application of knowledge
to boost the performance
and impact of a process

During the industrial
revolution, every machine got
an electrified version
The next revolution is the
cognification of everything via
cheap access to specialized
AIs

Best chess
player in the
world is a
centaur (team
of human +
machine
playing
together)

Cognifying model-driven software engineering

Cognification
ML
Crowdsourcing
Ontologies
Big Data
…

…one little problemCognification
ML
Crowdsourcing
Ontologies
Big Data
…
Big Data for models /
modeling artefacts is not
yet a reality

Let’s work
together
jordi.cabot@
icrea.cat
@softmodeling
modeling-
languages.com

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Model-driven engineering for Industrial IoT architecturesJordi Cabot

The document describes an AsyncAPI specification for a model-driven architecture for an industrial IoT system. The specification defines an API for a monitoring channel ("iotbox/{id}/monitor") that publishes and subscribes status messages about IoT boxes. The message formats are defined under "components" including a "statusMessage" format that references nested "lineInfo" and "pressInfo" schemas defining the payload data structure.

Smart modeling of smart softwareJordi Cabot

Jordi Cabot discusses modeling approaches for smart software development. He argues that modeling can help generate smart software faster by automating parts of the process and using better models. His research focuses on domain-specific languages for modeling different aspects of smart systems, like interfaces, datasets, development processes, and more. The goal is to provide guidance and structure to help multidisciplinary teams successfully develop complex AI-based applications.

Modeling should be an independent scientific disciplineJordi Cabot

This document proposes that modeling should become an independent scientific discipline to better realize its full potential. Currently, modeling is seen primarily as a tool within software engineering, but it is applicable across many domains. An independent modeling discipline could bring together experts from different fields, develop a common body of knowledge and terminology, and help modeling gain more recognition and resources. Some initial steps suggested include making modeling tools more usable and accessible across domains, identifying economic benefits to promote adoption, and facilitating interdisciplinary publishing and education around modeling concepts and applications. The overarching goal is for modeling to serve all domains through a transdisciplinary approach.

¿Quién va a desarrollar las Apps del futuro? (aviso: no serán los programador...Jordi Cabot

How to sustain a tool building community-driven effortJordi Cabot

This document discusses key dimensions for sustaining a tool building community-driven effort based on experiences developing modeling tools. It covers onboarding users and contributors, governance models, community health analysis using graph techniques, and optimization strategies. The document advocates an entrepreneurial path for tool development by releasing prototypes as open source software and improving them for real use cases to build a community and offer commercial services.

The Software Challenges of Building Smart Chatbots - ICSE'21Jordi Cabot

Chatbots are popular solutions assisting humans in multiple fields, such as customer support or e-learning. However, building such applications has become a complex task requiring a high-level of expertise in a variety of technical domains. Chatbots need to integrate (AI-based) NLU components, but also connect to internal/external services, deploy on various platforms, etc. The briefing will first cover the current landscape of chatbot frameworks. Then, we’ll get our hands dirty and create a few bots of increasing difficulty playing with aspects like entity recognition, sentiment analysis, event processing, or testing. By the end of the session, attendees will have all the keys to understand the main steps and obstacles to building a good chatbot.

Ingeniería del Software dirigida por modelos -Versión para incrédulosJordi Cabot

Chatbot Tutorial - Create your first bot with Xatkit Jordi Cabot

Création facile de chatbots - Créez votre chatbot en 20 minutes avec une plat...Jordi Cabot

An LSTM-Based Neural Network Architecture for Model TransformationsJordi Cabot

WAPIml: Towards a Modeling Infrastructure for Web APIsJordi Cabot

UMLtoNoSQL : From UML domain models to NoSQL DatabasesJordi Cabot

Multi-Platform Chatbot Modeling and Deployment with the Xatkit FrameworkJordi Cabot

Model-driven Round-trip Engineering of REST APIsJordi Cabot

This document outlines an approach to model-driven engineering of REST APIs. It proposes three main contributions: APIDiscoverer, which uses examples to automatically discover and enrich OpenAPI specifications; APITester, which generates test cases from OpenAPI specifications; and APIComposer, which enables composition of REST APIs based on semantic matching between OpenAPI and OData models. Future work is discussed to improve coverage of APIDiscoverer, support additional features in APITester, and extend matching strategies for APIComposer.