constrained random and functional coverage

[Divya2030]

hi, 
  i have to randomise  some bits of 32 bit instruction 
  and some bits will be constant like below format 

+---------+-------+-------+-----+-------+---------+
| 0100000 | XXXXX | XXXXX | 111 | XXXXX | 0110011 |
+---------+-------+-------+-----+-------+---------+

through direct random function we can randomise cant find coverage
 using cocotb-coverage library.

    for i in range(n):
            instruction = random.randint(0,0xFFFFFFFF)
            instruction = instruction & 0x01FF8F80

            instruction = instruction | 0x40007033

how we can find constrained random and coverage using cocotb-coverage?

[mciepluc]

@Divyanirankari You should NOT use constraint solver for such a task. This is a typical scenario of usage of the random number generator, as you provided. Please use random.randint or np.random.randint. In order to combine random generation with contraint solver, please use pre_randomize() or post_randomize() functions.

Please look at the correct usage in the provided example. Note that randomized are fields that have small domains, so constraint solver can easily find the solution. For larger domain - generating the packet payload - a post_randomize() and random.randint is used.

[Divya2030]

@mciepluc Ok will use pre_randomize() and post_randomize(). How to find coverage for this type of scenario.?

[Divya2030]

What will be the bins for this scenario in Coverpoint. ?. I am not able to understand?

[mciepluc]

@Divyanirankari I am sorry but I do not get your question. You as a designer should know what should be included in the functional coverage scope and define the bins accordingly.

[Divya2030]

@mciepluc Ok Thanks