  Search Based Software Testing (SBST) is a popular automated testing technique
which uses a feedback mechanism to search for faults in software. Despite its
popularity, it has fundamental challenges related to the design, construction
and interpretation of the feedback. Neural Networks (NN) have been hugely
popular in recent years for a wide range of tasks. We believe that they can
address many of the issues inherent to common SBST approaches. Unfortunately,
NNs require large and representative training datasets.
  In this work we present an SBST framework based on a deconvolutional
generative neural network. Not only does it retain the beneficial qualities
that make NNs appropriate for SBST tasks, it also produces its own training
data which circumvents the problem of acquiring a training dataset that limits
the use of NNs.
  We demonstrate through a series of experiments that this architecture is
possible and practical. It generates diverse, sensible program inputs, while
exploring the space of program behaviours. It also creates a meaningful
ordering over program behaviours and is able to find crashing executions. This
is all done without any prior knowledge of the program.
  We believe this proof of concept opens new directions for future work at the
intersection of SBST and neural networks.
