You could approach this a number of ways.

First, if you can ensure the seed of the generator, then that could be used on a case by case basis. But this tricky, since it introduces a subtle dependency on the implementation of biasedcoin.

Another option is to split the function/process into a stochastic and deterministic part. Test the deterministic part thoroughly. biasedcoin is too trivial for this, but it works nicely in more complicated setups.

For biasedcoin, I would probably go with a statistical approach. Given a certain bias, the expected value and distribution is known. I would simply test that several trials of the function lie within some bound.