  In this paper we present a novel algorithm for automatic performance testing
that uses an online variant of the Generative Adversarial Network (GAN) to
optimize the test generation process. The objective of the proposed approach is
to generate, for a given test budget, a test suite containing a high number of
tests revealing performance defects. This is achieved using a GAN to generate
the tests and predict their outcome. This GAN is trained online while
generating and executing the tests. The proposed approach does not require a
prior training set or model of the system under test. We provide an initial
evaluation the algorithm using an example test system, and compare the obtained
results with other possible approaches.
  We consider that the presented algorithm serves as a proof of concept and we
hope that it can spark a research discussion on the application of GANs to test
generation.
