@book{BarkowskyGiese2023,
  author    = {Barkowsky, Matthias and Giese, Holger},
  title     = {Triple graph grammars for multi-version models},
  number    = {155},
  isbn      = {978-3-86956-556-9},
  issn      = {1613-5652},
  doi       = {10.25932/publishup-57399},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-573994},
  publisher      = {Universit{\"a}t Potsdam},
  pages     = {28 -- 28},
  year      = {2023},
  abstract  = {Like conventional software projects, projects in model-driven software engineering require adequate management of multiple versions of development artifacts, importantly allowing living with temporary inconsistencies. In the case of model-driven software engineering, employed versioning approaches also have to handle situations where different artifacts, that is, different models, are linked via automatic model transformations. In this report, we propose a technique for jointly handling the transformation of multiple versions of a source model into corresponding versions of a target model, which enables the use of a more compact representation that may afford improved execution time of both the transformation and further analysis operations. Our approach is based on the well-known formalism of triple graph grammars and a previously introduced encoding of model version histories called multi-version models. In addition to showing the correctness of our approach with respect to the standard semantics of triple graph grammars, we conduct an empirical evaluation that demonstrates the potential benefit regarding execution time performance.},
  language  = {en}
}
@book{BarkowskyGiese2023,
  author    = {Barkowsky, Matthias and Giese, Holger},
  title     = {Modular and incremental global model management with extended generalized discrimination networks},
  number    = {154},
  isbn      = {978-3-86956-555-2},
  issn      = {1613-5652},
  doi       = {10.25932/publishup-57396},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-573965},
  publisher      = {Universit{\"a}t Potsdam},
  pages     = {63 -- 63},
  year      = {2023},
  abstract  = {Complex projects developed under the model-driven engineering paradigm nowadays often involve several interrelated models, which are automatically processed via a multitude of model operations. Modular and incremental construction and execution of such networks of models and model operations are required to accommodate efficient development with potentially large-scale models. The underlying problem is also called Global Model Management. In this report, we propose an approach to modular and incremental Global Model Management via an extension to the existing technique of Generalized Discrimination Networks (GDNs). In addition to further generalizing the notion of query operations employed in GDNs, we adapt the previously query-only mechanism to operations with side effects to integrate model transformation and model synchronization. We provide incremental algorithms for the execution of the resulting extended Generalized Discrimination Networks (eGDNs), as well as a prototypical implementation for a number of example eGDN operations. Based on this prototypical implementation, we experiment with an application scenario from the software development domain to empirically evaluate our approach with respect to scalability and conceptually demonstrate its applicability in a typical scenario. Initial results confirm that the presented approach can indeed be employed to realize efficient Global Model Management in the considered scenario.},
  language  = {en}
}