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Functional Coverage and Constrained Randomization Extensions for Cocotb

Project description

cocotb-coverage

Functional Coverage and Constrained Randomization Extensions for Cocotb

Documentation Status

This package allows you to use constrained randomization and functional coverage techniques known from CRV (constrained random verification) and MDV (metric-driven verification) methodologies, available in SystemVerilog or e. Such extensions enable the implementation of an advanced verification environment for complex projects.

The implemented functionality is intended to be easily understandable by SystemVerilog users and provides significant extensions compared to Hardware Verification Languages.

References:

Simple example below:

# point represented by x and y coordinates in range (-10,10)
class Point(crv.Randomized):

    def __init__(self, x, y):
        crv.Randomized.__init__(self)
        self.x = x
        self.y = y

        self.add_rand("x", list(range(-10, 10)))
        self.add_rand("y", list(range(-10, 10)))
        # constraining the space so that x < y
        self.add_constraint(lambda x, y: x < y)

...

# create an arbitrary point
p = Point(0,0)

for _ in range (10):
    
    # cover example arithmetic properties
    @CoverPoint("top.x_negative", xf = lambda point : point.x < 0, bins = [True, False])
    @CoverPoint("top.y_negative", xf = lambda point : point.y < 0, bins = [True, False])
    @CoverPoint("top.xy_equal", xf = lambda point : point.x == point.y, bins = [True, False])
    @CoverCross("top.cross", items = ["top.x_negative", "top.y_negative"])
    def plot_point(point):
        ...
    
    p.randomize()  # randomize object
    plot_point(p)  # call a function which will sample the coverage
              

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