A Model-Driven Engineering Approach to Machine Learning and Software Modeling

by   Armin Moin, et al.

Models are used in both the Software Engineering (SE) and the Artificial Intelligence (AI) communities. In the former case, models of software, which may specify the software system architecture on different levels of abstraction could be used in various stages of the Software Development Life-Cycle (SDLC), from early conceptualization and design, to verification, implementation, testing and evolution. However, in the latter case, i.e., AI, models may provide smart capabilities, such as prediction and decision making support. For instance, in Machine Learning (ML), which is the most popular sub-discipline of AI at the present time, mathematical models may learn useful patterns in the observed data instances and can become capable of making better predictions or recommendations in the future. The goal of this work is to create synergy by bringing models in the said communities together and proposing a holistic approach. We illustrate how software models can become capable of producing or dealing with data analytics and ML models. The main focus is on the Internet of Things (IoT) and smart Cyber-Physical Systems (CPS) use cases, where both ML and model-driven (model-based) SE play a key role. In particular, we implement the proposed approach in an open source prototype and validate it using two use cases from the IoT/CPS domain.


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