@misc{BrandGiese2019,
  author    = {Brand, Thomas and Giese, Holger},
  title     = {Generic adaptive monitoring based on executed architecture runtime model queries and events},
  series = {IEEE Xplore},
  journal   = {IEEE Xplore},
  publisher = {IEEE},
  address   = {New York},
  isbn      = {978-1-7281-2731-6},
  issn      = {1949-3673},
  doi       = {10.1109/SASO.2019.00012},
  pages     = {17 -- 22},
  year      = {2019},
  abstract  = {Monitoring is a key functionality for automated decision making as it is performed by self-adaptive systems, too. Effective monitoring provides the relevant information on time. This can be achieved with exhaustive monitoring causing a high overhead consumption of economical and ecological resources. In contrast, our generic adaptive monitoring approach supports effectiveness with increased efficiency. Also, it adapts to changes regarding the information demand and the monitored system without additional configuration and software implementation effort. The approach observes the executions of runtime model queries and processes change events to determine the currently required monitoring configuration. In this paper we explicate different possibilities to use the approach and evaluate their characteristics regarding the phenomenon detection time and the monitoring effort. Our approach allows balancing between those two characteristics. This makes it an interesting option for the monitoring function of self-adaptive systems because for them usually very short-lived phenomena are not relevant.},
  language  = {en}
}
@misc{GhahremaniGiese2019,
  author    = {Ghahremani, Sona and Giese, Holger},
  title     = {Performance evaluation for self-healing systems},
  series = {2019 IEEE 4th International Workshops on Foundations and Applications of Self* Systems (FAS*W)},
  journal   = {2019 IEEE 4th International Workshops on Foundations and Applications of Self* Systems (FAS*W)},
  publisher = {IEEE},
  address   = {New York},
  isbn      = {978-1-7281-2406-3},
  doi       = {10.1109/FAS-W.2019.00039},
  pages     = {116 -- 119},
  year      = {2019},
  abstract  = {Evaluating the performance of self-adaptive systems (SAS) is challenging due to their complexity and interaction with the often highly dynamic environment. In the context of self-healing systems (SHS), employing simulators has been shown to be the most dominant means for performance evaluation. Simulating a SHS also requires realistic fault injection scenarios. We study the state of the practice for evaluating the performance of SHS by means of a systematic literature review. We present the current practice and point out that a more thorough and careful treatment in evaluating the performance of SHS is required.},
  language  = {en}
}
@article{DyckGieseLambers2019,
  author    = {Dyck, Johannes and Giese, Holger and Lambers, Leen},
  title     = {Automatic verification of behavior preservation at the transformation level for relational model transformation},
  series = {Software and systems modeling},
  volume    = {18},
  journal   = {Software and systems modeling},
  number    = {5},
  publisher = {Springer},
  address   = {Heidelberg},
  issn      = {1619-1366},
  doi       = {10.1007/s10270-018-00706-9},
  pages     = {2937 -- 2972},
  year      = {2019},
  abstract  = {The correctness of model transformations is a crucial element for model-driven engineering of high-quality software. In particular, behavior preservation is an important correctness property avoiding the introduction of semantic errors during the model-driven engineering process. Behavior preservation verification techniques show some kind of behavioral equivalence or refinement between source and target model of the transformation. Automatic tool support is available for verifying behavior preservation at the instance level, i.e., for a given source and target model specified by the model transformation. However, until now there is no sound and automatic verification approach available at the transformation level, i.e., for all source and target models. In this article, we extend our results presented in earlier work (Giese and Lambers, in: Ehrig et al (eds) Graph transformations, Springer, Berlin, 2012) and outline a new transformation-level approach for the sound and automatic verification of behavior preservation captured by bisimulation resp.simulation for outplace model transformations specified by triple graph grammars and semantic definitions given by graph transformation rules. In particular, we first show how behavior preservation can be modeled in a symbolic manner at the transformation level and then describe that transformation-level verification of behavior preservation can be reduced to invariant checking of suitable conditions for graph transformations. We demonstrate that the resulting checking problem can be addressed by our own invariant checker for an example of a transformation between sequence charts and communicating automata.},
  language  = {en}
}
@book{GieseMaximovaSakizloglouetal.2019,
  author    = {Giese, Holger and Maximova, Maria and Sakizloglou, Lucas and Schneider, Sven},
  title     = {Metric temporal graph logic over typed attributed graphs},
  number    = {127},
  publisher = {Universit{\"a}tsverlag Potsdam},
  address   = {Potsdam},
  isbn      = {978-3-86956-463-0},
  issn      = {1613-5652},
  doi       = {10.25932/publishup-42752},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-427522},
  publisher      = {Universit{\"a}t Potsdam},
  pages     = {34},
  year      = {2019},
  abstract  = {Graph repair, restoring consistency of a graph, plays a prominent role in several areas of computer science and beyond: For example, in model-driven engineering, the abstract syntax of models is usually encoded using graphs. Flexible edit operations temporarily create inconsistent graphs not representing a valid model, thus requiring graph repair. Similarly, in graph databases—managing the storage and manipulation of graph data—updates may cause that a given database does not satisfy some integrity constraints, requiring also graph repair. We present a logic-based incremental approach to graph repair, generating a sound and complete (upon termination) overview of least-changing repairs. In our context, we formalize consistency by so-called graph conditions being equivalent to first-order logic on graphs. We present two kind of repair algorithms: State-based repair restores consistency independent of the graph update history, whereas deltabased (or incremental) repair takes this history explicitly into account. Technically, our algorithms rely on an existing model generation algorithm for graph conditions implemented in AutoGraph. Moreover, the delta-based approach uses the new concept of satisfaction (ST) trees for encoding if and how a graph satisfies a graph condition. We then demonstrate how to manipulate these STs incrementally with respect to a graph update.},
  language  = {en}
}