@inproceedings{45661,
  abstract     = {{Effect chain modelling is a method for creating information
models for impact analyses of changes in system elements. For
the estimation of change propagation, dependencies between
requirements must be detected. The high number of require-
ment dependencies in the engineering of complex technical
systems results in the need for automation. In a study, it was
shown that transformer models (BERT) are suitable for the
automated dependency analysis of requirements. However,
there are currently deficits in the applicability of the models
for different projects without an extensive and heterogeneous
training database. This paper investigates how active learning
can be used to train BERT models (active-BERT) in order to
increase the performance of the models for classifying requi-
rement dependencies of projects with heterogeneous require-
ments. The results show that the performance of the models
increases significantly through active learning. Through active-
BERT, engineers are enabled to model effect chains efficiently
and to handle requirement changes effectively.}},
  author       = {{Gräßler, Iris and Preuß, Daniel}},
  booktitle    = {{Stuttgarter Symposium für Produktentwicklung SSP 2023}},
  editor       = {{Hölzle, Katharina and Kreimeyer, Matthias and Roth, Daniel and Maier, Thomas and Riedel, Oliver}},
  issn         = {{2364-4885}},
  location     = {{Stuttgart}},
  publisher    = {{Fraunhofer IAO}},
  title        = {{{Automatisierte Abhängigkeitsanalyse von Anforderungen zur Wirkkettenmodellierung}}},
  year         = {{2023}},
}