  We present the OGAN algorithm for automatic requirement falsification of
cyber-physical systems. System inputs and output are represented as piecewise
constant signals over time while requirements are expressed in signal temporal
logic. OGAN can find inputs that are counterexamples for the safety of a system
revealing design, software, or hardware defects before the system is taken into
operation. The OGAN algorithm works by training a generative machine learning
model to produce such counterexamples. It executes tests atomically and does
not require any previous model of the system under test. We evaluate OGAN using
the ARCH-COMP benchmark problems, and the experimental results show that
generative models are a viable method for requirement falsification. OGAN can
be applied to new systems with little effort, has few requirements for the
system under test, and exhibits state-of-the-art CPS falsification efficiency
and effectiveness.
