One of them would be a fuzz testing. You start with a predefined 'corpus' of inputs which are then modified by the fuzzer to reach yet not covered code paths. In the process it discovers many bugs and crashes.

The input fuzzing process is rarely purely random. There are advanced techniques that allow the fuzzer to link input data to conditions of not covered branches.

It is quite useful mechanism for checking inputs, formats, behaviour patterns (if you have two solutions but one model, one simple that works 100% but is slowish and one more complex but very fast).

See: https://github.com/dvyukov/go-fuzz#trophies and http://lcamtuf.coredump.cx/afl/