  Methodologies for development of complex systems and models include external
reviews by domain and technology experts. Among others, such reviews can
uncover undocumented built-in assumptions that may be critical for correct and
safe operation or constrain applicability. Since such assumptions may still
escape human-centered processes like reviews, agile development, and risk
analyses, here, we contribute toward making this process more methodical and
automatable. We first present a blueprint for a taxonomy and formalization of
the problem. We then show that a variety of digital artifacts of the system or
model can be automatically checked against extensive reference knowledge. Since
mimicking the breadth and depth of knowledge and skills of experts may appear
unattainable, we illustrate the basic feasibility of automation with
rudimentary experiments using OpenAI's ChatGPT. We believe that systematic
handling of this aspect of system engineering can contribute significantly to
the quality and safety of complex systems and models, and to the efficiency of
development projects. We dedicate this work to Werner Damm, whose contributions
to modeling and model-based development, in industry and academia, with a
special focus on safety, helped establish a solid foundation to our discipline
and to the work of many scientists and professionals, including, naturally, the
approaches and techniques described here.
