abstract = "Software verification may yield spurious failures when
environment assumptions are not accounted for.
Environment assumptions are the expectations that a
system or a component makes about its operational
environment and are often specified in terms of
conditions over the inputs of that system or component.
we propose an approach to automatically infer
environment assumptions for Cyber-Physical Systems
(CPS). Our approach improves the state-of-the-art in
three different ways: First, we learn assumptions for
complex CPS models involving signal and numeric
variables; second, the learned assumptions include
arithmetic expressions defined over multiple variables;
third, we identify the trade-off between soundness and
informativeness of environment assumptions and
demonstrate the flexibility of our approach in
prioritising either of these criteria.
We evaluate our approach using a public domain
benchmark of CPS models from Lockheed Martin and a
component of a satellite control system from LuxSpace,
a satellite system provider. The results show that our
approach outperforms state-of-the-art techniques on
learning assumptions for CPS models, and further, when
applied to our industrial CPS model, our approach is
able to learn assumptions that are sufficiently close
to the assumptions manually developed by engineers to
be of practical value.",