@misc{8312,
  author       = {{Bäumer, Frederik Simon and Geierhos, Michaela}},
  booktitle    = {{encyclopedia.pub}},
  keywords     = {{OTF Computing, Natural Language Processing, Requirements Engineering}},
  publisher    = {{MDPI}},
  title        = {{{Requirements Engineering in OTF-Computing}}},
  year         = {{2019}},
}

@inproceedings{2332,
  abstract     = {{Ein wichtiges Element der Digitalen Transformation ist die Digitalisierung der Prozesse in Unternehmen. Eine Herausforderung besteht hierbei in der systematischen Erkennung von Digitalisierungspotenzialen in Prozessen. Bestehende Ansätze benötigen Experten, welche Potenziale über ihre Erfahrung oder zeitaufwendig mithilfe von Musterkatalogen identifizieren.
In diesem Artikel werden verschiedene Digitalisierungspotenziale klassifiziert und Muster für ein zukünftiges musterbasiertes Analyseverfahren zur automatisierten Identifikation von Digitalisierungspotenzialen in BPMN-Diagrammen beschrieben. Im Vergleich zu bestehenden Ansätzen erlaubt es Experten die Identifizierung von Digitalisierungspotenzialen effizienter und effektiver durchzuführen.}},
  author       = {{Rittmeier, Florian and Engels, Gregor and Teetz, Alexander}},
  booktitle    = {{Joint Proceedings of the Workshops at Modellierung 2018 co-located with Modellierung 2018, Braunschweig, Germany, February 21, 2018.}},
  keywords     = {{Digitalisierungspotenziale, BPI, Digitale Transformation, Information Flow-Modellierung, Patterns, Requirements Engineering}},
  pages        = {{215----221}},
  publisher    = {{CEUR-WS.org}},
  title        = {{{Digitalisierungspotenziale in Geschäftsprozessen effizient und effektiv erkennen (Effective and Efficient Identification of Digitalization Potentials in Business Processes)}}},
  volume       = {{2060}},
  year         = {{2018}},
}

@inproceedings{97,
  abstract     = {{Bridging the gap between informal, imprecise, and vague user requirements descriptions and precise formalized specifications is the main task of requirements engineering. Techniques such as interviews or story telling are used when requirements engineers try to identify a user's needs. The requirements specification process is typically done in a dialogue between users, domain experts, and requirements engineers. In our research, we aim at automating the specification of requirements. The idea is to distinguish between untrained users and trained users, and to exploit domain knowledge learned from previous runs of our system. We let untrained users provide unstructured natural language descriptions, while we allow trained users to provide examples of behavioral descriptions. In both cases, our goal is to synthesize formal requirements models similar to statecharts. From requirements specification processes with trained users, behavioral ontologies are learned which are later used to support the requirements specification process for untrained users. Our research method is original in combining natural language processing and search-based techniques for the synthesis of requirements specifications. Our work is embedded in a larger project that aims at automating the whole software development and deployment process in envisioned future software service markets.}},
  author       = {{van Rooijen, Lorijn and Bäumer, Frederik Simon and Platenius, Marie Christin and Geierhos, Michaela and Hamann, Heiko and Engels, Gregor}},
  booktitle    = {{2017 IEEE 25th International Requirements Engineering Conference Workshops (REW)}},
  isbn         = {{978-1-5386-3489-9}},
  keywords     = {{Software, Unified modeling language, Requirements engineering, Ontologies, Search problems, Natural languages}},
  location     = {{Lisbon, Portugal}},
  pages        = {{379--385}},
  publisher    = {{IEEE}},
  title        = {{{From User Demand to Software Service: Using Machine Learning to Automate the Requirements Specification Process}}},
  doi          = {{10.1109/REW.2017.26}},
  year         = {{2017}},
}

@misc{33312,
  abstract     = {{Mechatronic systems are used more than ever in human life. They can be found in a very wide range of domain contexts, from household appliances, and cars, to medical equipment. Mechatronic systems, as a kind of embedded systems, are the tight integration of mechanical and electrical engineering, which embed software systems. Information security of mechatronic systems has not received much attention yet. However, wherever data exists, cyber attacks threaten mechatronic systems.

The thesis focuses on the early design stages of the development of mechatronic systems. Model sequence diagrams (MSDs) are used to model requirements with real-time and safety properties. In this thesis, MSDs are extended such that security properties for example authenticity and privacy can be modeled and analyzed automatically.}},
  author       = {{Schwichtenberg, Bahar}},
  keywords     = {{Software Architecture, Requirements Engineering, Embedded Systems}},
  title        = {{{Early Prediction of Security Properties for Mechatronic Systems}}},
  year         = {{2015}},
}