  Automotive software testing continues to rely largely upon expensive field
tests to ensure quality because alternatives like simulation-based testing are
relatively immature. As a step towards lowering reliance on field tests, we
present SilGAN, a deep generative model that eases specification, stimulus
generation, and automation of automotive software-in-the-loop testing. The
model is trained using data recorded from vehicles in the field. Upon training,
the model uses a concise specification for a driving scenario to generate
realistic vehicle state transitions that can occur during such a scenario. Such
authentic emulation of internal vehicle behavior can be used for rapid,
systematic and inexpensive testing of vehicle control software. In addition, by
presenting a targeted method for searching through the information learned by
the model, we show how a test objective like code coverage can be automated.
The data driven end-to-end testing pipeline that we present vastly expands the
scope and credibility of automotive simulation-based testing. This reduces time
to market while helping maintain required standards of quality.
