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@PHDTHESIS{Kusmenko:835778,
      author       = {Kusmenko, Evgeny},
      othercontributors = {Rumpe, Bernhard and Aßmann, Uwe},
      title        = {{M}odel-driven development methodology and domain-specific
                      languages for the design of artificial intelligence in
                      cyber-physical systems},
      volume       = {49},
      school       = {RWTH Aachen University},
      type         = {Dissertation},
      address      = {Düren},
      publisher    = {Shaker Verlag},
      reportid     = {RWTH-2021-10814},
      isbn         = {978-3-8440-8286-9},
      series       = {Aachener Informatik Berichte Software Engineering},
      pages        = {xiv, 324 Seiten : Illustrationen},
      year         = {2021},
      note         = {Zweitveröffentlicht auf dem Publikationsserver der RWTH
                      Aachen University 2022; Dissertation, RWTH Aachen
                      University, 2021},
      abstract     = {The development of cyber-physical systems poses a multitude
                      of challenges requiring experts from different fields. Such
                      systems cannot be developed successfully without the support
                      of appropriate processes, languages, and tools. Model-driven
                      software engineering is an important approach which helps
                      development teams to cope with the increasing complexity of
                      today's cyber-physical systems. The aim of this thesis is to
                      develop a model-driven engineering methodology with a
                      particular focus on interconnected intelligent
                      cyber-physical systems such as cooperative vehicles. The
                      basis of the proposed methodology is a
                      component-and-connector architecture description language
                      focusing on the decomposition and integration of
                      cyber-physical system software. It features a strong,
                      math-oriented type system abstracting away from the
                      technical realization and incorporating physical units. To
                      facilitate the development of highly-interconnected
                      self-adaptive systems, the language enables its users to
                      model component and connector arrays and supports
                      architectural runtime-reconfiguration. Architectural
                      elements can be altered, added, and removed dynamically upon
                      the occurrence of trigger events. In order to fully cover
                      the development process, the proposed methodology, in
                      addition to structural modeling, provides means for behavior
                      specification and its seamless integration into the
                      components of the architecture. A matrix-oriented scripting
                      language enables the developer to specify algorithms using a
                      syntax close to the mathematical domain. What is more, a
                      dedicated deep learning modeling language is provided for
                      the development and training of neural networks as directed
                      acyclic graphs of neuron layers. The framework supports
                      different learning methods including supervised,
                      reinforcement, and generative adversarial learning, covering
                      a broad range of applications from image and natural
                      language processing to decision making and test data
                      generation. The presented toolchain enables an automated
                      generation of fully functional C++ code together with the
                      corresponding build and training scripts based on the
                      architectural models and behavior specifications. Finally,
                      to facilitate the integration and deployment of the modeled
                      software in distributed environments, we use a tagging
                      approach to model the middleware and to control a middleware
                      generation toolchain.},
      cin          = {121510 / 120000},
      ddc          = {004},
      cid          = {$I:(DE-82)121510_20140620$ / $I:(DE-82)120000_20140620$},
      typ          = {PUB:(DE-HGF)11 / PUB:(DE-HGF)3},
      doi          = {10.18154/RWTH-2021-10814},
      url          = {https://publications.rwth-aachen.de/record/835778},
}