@misc{GieseHenklerHirsch2017,
  author    = {Giese, Holger and Henkler, Stefan and Hirsch, Martin},
  title     = {A multi-paradigm approach supporting the modular execution of reconfigurable hybrid systems},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-402896},
  pages     = {34},
  year      = {2017},
  abstract  = {Advanced mechatronic systems have to integrate existing technologies from mechanical, electrical and software engineering. They must be able to adapt their structure and behavior at runtime by reconfiguration to react flexibly to changes in the environment. Therefore, a tight integration of structural and behavioral models of the different domains is required. This integration results in complex reconfigurable hybrid systems, the execution logic of which cannot be addressed directly with existing standard modeling, simulation, and code-generation techniques. We present in this paper how our component-based approach for reconfigurable mechatronic systems, M ECHATRONIC UML, efficiently handles the complex interplay of discrete behavior and continuous behavior in a modular manner. In addition, its extension to even more flexible reconfiguration cases is presented.},
  language  = {en}
}
@book{DyckGieseLambers2017,
  author    = {Dyck, Johannes and Giese, Holger and Lambers, Leen},
  title     = {Automatic verification of behavior preservation at the transformation level for relational model transformation},
  number    = {112},
  publisher = {Universit{\"a}tsverlag Potsdam},
  address   = {Potsdam},
  isbn      = {978-3-86956-391-6},
  issn      = {1613-5652},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-100279},
  publisher      = {Universit{\"a}t Potsdam},
  pages     = {viii, 112},
  year      = {2017},
  abstract  = {The correctness of model transformations is a crucial element for model-driven engineering of high quality software. In particular, behavior preservation is the most important correctness property avoiding the introduction of semantic errors during the model-driven engineering process. Behavior preservation verification techniques either show that specific properties are preserved, or more generally and complex, they show some kind of behavioral equivalence or refinement between source and target model of the transformation. Both kinds of behavior preservation verification goals have been presented with automatic tool support for the instance level, i.e. for a given source and target model specified by the model transformation. However, up until now there is no automatic verification approach available at the transformation level, i.e. for all source and target models specified by the model transformation. In this report, we extend our results presented in [27] and outline a new sophisticated approach for the automatic verification of behavior preservation captured by bisimulation resp. simulation for model transformations specified by triple graph grammars and semantic definitions given by graph transformation rules. In particular, we show that the behavior preservation problem can be reduced to invariant checking for graph transformation and that the resulting checking problem can be addressed by our own invariant checker even for a complex example where a sequence chart is transformed into communicating automata. We further discuss today's limitations of invariant checking for graph transformation and motivate further lines of future work in this direction.},
  language  = {en}
}
@article{GhahremaniGiese2020,
  author    = {Ghahremani, Sona and Giese, Holger},
  title     = {Evaluation of self-healing systems},
  series = {Computers},
  volume    = {9},
  journal   = {Computers},
  number    = {1},
  publisher = {MDPI},
  address   = {Basel},
  issn      = {2073-431X},
  doi       = {10.3390/computers9010016},
  pages     = {32},
  year      = {2020},
  abstract  = {Evaluating the performance of self-adaptive systems is challenging due to their interactions with often highly dynamic environments. In the specific case of self-healing systems, the performance evaluations of self-healing approaches and their parameter tuning rely on the considered characteristics of failure occurrences and the resulting interactions with the self-healing actions. In this paper, we first study the state-of-the-art for evaluating the performances of self-healing systems by means of a systematic literature review. We provide a classification of different input types for such systems and analyse the limitations of each input type. A main finding is that the employed inputs are often not sophisticated regarding the considered characteristics for failure occurrences. To further study the impact of the identified limitations, we present experiments demonstrating that wrong assumptions regarding the characteristics of the failure occurrences can result in large performance prediction errors, disadvantageous design-time decisions concerning the selection of alternative self-healing approaches, and disadvantageous deployment-time decisions concerning parameter tuning. Furthermore, the experiments indicate that employing multiple alternative input characteristics can help with reducing the risk of premature disadvantageous design-time decisions.},
  language  = {en}
}