def test_cross(self):
stim = FieldCompositeModel("stim", True)
f = FieldScalarModel("a", 16, False, True)
stim.add_field(f)
f2 = FieldScalarModel("b", 16, False, True)
stim.add_field(f2)
cg = CovergroupModel("cg")
cp = CoverpointModel(ExprFieldRefModel(f), "cp1",
CoverageOptionsModel())
cg.add_coverpoint(cp)
bn = CoverpointBinArrayModel("cp", 0, 1, 16)
cp.add_bin_model(bn)
cp2 = CoverpointModel(ExprFieldRefModel(f2), "cp2",
CoverageOptionsModel())
cg.add_coverpoint(cp2)
bn = CoverpointBinArrayModel("cp", 0, 1, 16)
cp2.add_bin_model(bn)
cr = CoverpointCrossModel("aXb",
CoverageOptionsModel())
cr.add_coverpoint(cp)
cr.add_coverpoint(cp2)
cg.add_coverpoint(cr)
gen = GeneratorModel("top")
gen.add_field(stim)
gen.add_covergroup(cg)
gen.finalize()
# Need a special randomizer to deal with generators
r = Randomizer()
count = 0
for i in range(1000):
r.do_randomize([gen])
cg.sample()
count += 1
cov = cg.get_coverage()
print("Coverage: (" + str(i) + ") " + str(cov))
if cov == 100:
break
self.assertEqual(cg.get_coverage(), 100)
# Ensure that we converge relatively quickly
self.assertLessEqual(count, (256+16+16))
def test_coverpoint_bins(self):
stim = FieldCompositeModel("stim", True)
f = FieldScalarModel("a", 16, False, True)
stim.add_field(f)
f2 = FieldScalarModel("b", 16, False, True)
stim.add_field(f2)
cg = CovergroupModel("cg")
cp = CoverpointModel(ExprFieldRefModel(f), "cp1",
CoverageOptionsModel())
cg.add_coverpoint(cp)
cp.add_bin_model(CoverpointBinArrayModel("bn1", 0, 1, 16))
cp.add_bin_model(CoverpointBinCollectionModel.mk_collection("bn2", RangelistModel([
[17,65535-16-1]
]), 16))
cp.add_bin_model(CoverpointBinArrayModel("bn3", 0, 65535-16, 65535))
cp2 = CoverpointModel(ExprFieldRefModel(f2), "cp2",
CoverageOptionsModel())
cg.add_coverpoint(cp2)
bn = CoverpointBinArrayModel("cp", 0, 1, 16)
cp2.add_bin_model(bn)
gen = GeneratorModel("top")
gen.add_field(stim)
gen.add_covergroup(cg)
gen.finalize()
# Need a special randomizer to deal with generators
r = Randomizer()
count = 0
for i in range(1000):
r.do_randomize([gen])
cg.sample()
count += 1
cov = cg.get_coverage()
if cov == 100:
break
self.assertEqual(cg.get_coverage(), 100)
# Ensure that we converge relatively quickly
self.assertLessEqual(count, 64)
def test_smoke(self):
stim = FieldCompositeModel("stim", True)
f = FieldScalarModel("a", 16, False, True)
stim.add_field(f)
f2 = FieldScalarModel("b", 16, False, True)
stim.add_field(f2)
cg = CovergroupModel("cg")
cp = CoverpointModel(ExprFieldRefModel(f), "cp1",
CoverageOptionsModel())
cg.add_coverpoint(cp)
bn = CoverpointBinArrayModel("cp", 1, 16)
cp.add_bin_model(bn)
cp2 = CoverpointModel(ExprFieldRefModel(f2), "cp2",
CoverageOptionsModel())
cg.add_coverpoint(cp2)
bn = CoverpointBinArrayModel("cp", 1, 16)
cp2.add_bin_model(bn)
gen = GeneratorModel("top")
gen.add_field(stim)
gen.add_covergroup(cg)
gen.finalize()
# Need a special randomizer to deal with generators
r = Randomizer(RandState(0))
randstate = RandState(0)
count = 0
for i in range(1000):
r.do_randomize(randstate, SourceInfo("", -1), [gen])
cg.sample()
count += 1
cov = cg.get_coverage()
if cov == 100:
break
self.assertEqual(cg.get_coverage(), 100)
# Ensure that we converge relatively quickly
self.assertLessEqual(count, 32)