  This doctoral dissertation proposes a novel approach to enhance the
development of smart services for the Internet of Things (IoT) and smart
Cyber-Physical Systems (CPS). The proposed approach offers abstraction and
automation to the software engineering processes, as well as the Data Analytics
(DA) and Machine Learning (ML) practices. This is realized in an integrated and
seamless manner. We implement and validate the proposed approach by extending
an open source modeling tool, called ThingML. ThingML is a domain-specific
language and modeling tool with code generation for the IoT/CPS domain. Neither
ThingML nor any other IoT/CPS modeling tool supports DA/ML at the modeling
level. Therefore, as the primary contribution of the doctoral dissertation, we
add the necessary syntax and semantics concerning DA/ML methods and techniques
to the modeling language of ThingML. Moreover, we support the APIs of several
ML libraries and frameworks for the automated generation of the source code of
the target software in Python and Java. Our approach enables
platform-independent, as well as platform-specific models. Further, we assist
in carrying out semiautomated DA/ML tasks by offering Automated ML (AutoML), in
the background (in expert mode), and through model-checking constraints and
hints at design-time. Finally, we consider three use case scenarios from the
domains of network security, smart energy systems and energy exchange markets.
