In reply to cgales:
In my understanding, constrained random stimulation covers the all gamut of possibilities from full random(few constraints only to get legal transactions) to almost directed(very constrained) stimulation.
What I mean here is changing the level of control on the randomization, starting from a full random and tuning/constraining taking into account current coverage results, while still not being in a directed mode.
I believe that you mean using a stimulation similar to a randc approach, where each value is generated only once.
But, in the case the covergroup contains coverpoint on parameters that don’t directly depends on stimuli, like for instance measurements of an internal state vector of your DUT, then I think you need to measure the coverage results and tell your sequence how to reach the uncovered states (i.e. giving it the relation between stimuli and state of DUT).
In this case, if you rely on pure random stimulation, it probably won’t be very efficient. And you can’t just say to random constraint solver : please generate the transactions that will cover all the state vectors value in my DUT.
What do you think ?