@inproceedings{OberhauserMoser2023,
  author    = {Roy Oberhauser and Sandro Moser},
  title     = {Design Pattern Detection in Code},
  series = {Proceedings of the Eighteenth International Conference on Software Engineering Advances},
  url       = {https://nbn-resolving.org/urn:nbn:de:bsz:944-opus4-32541},
  pages     = {122 -- 129},
  year      = {2023},
  abstract  = {Software design patterns and the abstractions they offer can support developers and maintainers with program code comprehension. Yet manually-created pattern documentation within code or code-related assets, such as documents or models, can be unreliable, incomplete, and labor-intensive. While various Design Pattern Detection (DPD) techniques have been proposed, industrial adoption of automated DPD remains limited. This paper contributes a hybrid DPD solution approach that leverages a Bayesian network integrating developer expertise via rule-based micropatterns with our machine learning subsystem that utilizes graph embeddings. The prototype shows its feasibility, and the evaluation using three design patterns shows its potential for detecting both design patterns and variations.},
  language  = {en}
}