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Models are used in both Software Engineering (SE) and Artificial Intelligence(AI). SE models may specify the architecture at different levels of abstractionand for addressing different concerns at various stages of the softwaredevelopment life-cycle, from early conceptualization and design, toverification, implementation, testing and evolution. However, AI models mayprovide smart capabilities, such as prediction and decision-making support. Forinstance, in Machine Learning (ML), which is currently the most popularsub-discipline of AI, mathematical models may learn useful patterns in theobserved data and can become capable of making predictions. The goal of thiswork is to create synergy by bringing models in the said communities togetherand proposing a holistic approach to model-driven software development forintelligent systems that require ML. We illustrate how software models canbecome capable of creating and dealing with ML models in a seamless manner. Themain focus is on the domain of the Internet of Things (IoT), where both ML andmodel-driven SE play a key role. In the context of the need to take aCyber-Physical System-of-Systems perspective of the targeted architecture, anintegrated design environment for both SE and ML sub-systems would best supportthe optimization and overall efficiency of the implementation of the resultingsystem. In particular, we implement the proposed approach, called ML-Quadrat,based on ThingML, and validate it using a case study from the IoT domain, aswell as through an empirical user evaluation. It transpires that the proposedapproach is not only feasible, but may also contribute to the performance leapof software development for smart Cyber-Physical Systems (CPS) which areconnected to the IoT, as well as an enhanced user experience of thepractitioners who use the proposed modeling solution.

A Model-Driven Approach to Machine Learning and Software Modeling for the IoT