  Although machine learning (ML) has been successful in automating various
software engineering needs, software testing still remains a highly challenging
topic. In this paper, we aim to improve the generative testing of software by
directly augmenting the random number generator (RNG) with a deep reinforcement
learning (RL) agent using an efficient, automatically extractable state
representation of the software under test. Using the Cosmos SDK as the testbed,
we show that the proposed DeepRNG framework provides a statistically
significant improvement to the testing of the highly complex software library
with over 350,000 lines of code. The source code of the DeepRNG framework is
publicly available online.
