  Generation-based fuzz testing can uncover various bugs and security
vulnerabilities. However, compared to mutation-based fuzz testing, it takes
much longer to develop a well-balanced generator that produces good test cases
and decides where to break the underlying structure to exercise new code paths.
We propose a novel approach to combine a trained test case generator deep
learning model with a double deep Q-network (DDQN) for the first time. The DDQN
guides test case creation based on a code coverage signal. Our approach
improves the code coverage performance of the underlying generator model by up
to 18.5\% for the Firefox HTML rendering engine compared to the baseline
grammar based fuzzer.
