  Systems engineering, in particular in the automotive domain, needs to cope
with the massively increasing numbers of requirements that arise during the
development process. To guarantee a high product quality and make sure that
functional safety standards such as ISO26262 are fulfilled, the exploitation of
potentials of model-driven systems engineering in the form of automatic
analyses, consistency checks, and tracing mechanisms is indispensable. However,
the language in which requirements are written, and the tools needed to operate
on them, are highly individual and require domain-specific tailoring. This
hinders automated processing of requirements as well as the linking of
requirements to models. Introducing formal requirement notations in existing
projects leads to the challenge of translating masses of requirements and
process changes on the one hand and to the necessity of the corresponding
training for the requirements engineers.
  In this paper, based on the analysis of an open-source set of automotive
requirements, we derive domain-specific language constructs helping us to avoid
ambiguities in requirements and increase the level of formality. The main
contribution is the adoption and evaluation of few-shot learning with large
pretrained language models for the automated translation of informal
requirements to structured languages such as a requirement DSL. We show that
support sets of less than ten translation examples can suffice to few-shot
train a language model to incorporate keywords and implement syntactic rules
into informal natural language requirements.
