On the Need for Data-Based Model-Driven Engineering

Alexandra Mazak, Sabine Sint, Manuel Wimmer

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

n order to deal with the increasing complexity of modern systems such as in software-intensive environments, models are used in many research fields as abstract descriptions of reality. On the one side, a model serves as an abstraction for a specific purpose, as a kind of “blueprint” of a system, describing a system’s structure and desired behavior in the design phase. On the other side, there are so-called runtime models providing real abstractions of systems during runtime, e.g., to monitor runtime behavior. Today, we recognize a discrepancy between the early snapshots and their real world correspondents. To overcome this discrepancy, we propose to fully integrate models from the very beginning within the lifecycle of a system. As a first step in this direction, we introduce a data-based model-driven engineering approach where we provide a unifying framework to combine downstream information from the model-driven engineering process with upstream information gathered during a system’s operation at runtime, by explicitly considering also a timing component. We present this temporal model framework step-by-step by selected use cases with increasing complexity.
Original languageEnglish
Title of host publicationSecurity and Quality in Cyber-Physical Systems Engineering
Place of PublicationHeidelberg
PublisherSpringer
Pages103-127
Number of pages24
ISBN (Electronic)9783030253127
ISBN (Print)978-3-030-25311-0
DOIs
Publication statusPublished - Nov 2019

Fields of science

  • 102006 Computer supported cooperative work (CSCW)
  • 102015 Information systems
  • 102016 IT security
  • 102020 Medical informatics
  • 102022 Software development
  • 102027 Web engineering
  • 102034 Cyber-physical systems
  • 509026 Digitalisation research
  • 502032 Quality management
  • 502050 Business informatics
  • 503015 Subject didactics of technical sciences

JKU Focus areas

  • Digital Transformation

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