Finding more bugs with less work
David R. MacIver
from hypothesis import given
import hypothesis.strategies as st
def mean(xs):
return sum(xs) / len(xs)
@given(st.lists(st.floats()))
def test_mean(xs):
mean(xs)
ZeroDivisionError: division by zero
Falsifying example: test_mean(xs=[])
from hypothesis import given
import hypothesis.strategies as st
def mean(xs):
return sum(xs) / (1 + len(xs))
@given(st.lists(st.floats()))
def test_mean(xs):
mean(xs)
from hypothesis import given
import hypothesis.strategies as st
def mean(xs):
return sum(xs) / (1 + len(xs))
@given(st.lists(st.floats()))
def test_mean(xs):
m = mean(xs)
if xs:
assert min(xs) <= m <= max(xs)
assert 1.0 <= 0.5
where 1.0 = min([1.0])
and 0.5 = mean([1.0])
Falsifying example: test_mean(xs=[1.0])
from hypothesis import given
import hypothesis.strategies as st
def mean(xs):
return sum(xs) / len(xs)
@given(st.lists(st.floats(), min_size=1))
def test_mean(xs):
assert min(xs) <= mean(xs) <= max(xs)
assert nan <= nan
where nan = min([nan])
and nan = mean([nan])
Falsifying example: test_mean(xs=[nan])
import math
from hypothesis import given, assume
import hypothesis.strategies as st
def mean(xs):
return sum(xs) / len(xs)
@given(st.lists(st.floats(), min_size=1))
def test_mean(xs):
assume(not any(math.isnan(x) for x in xs))
assert min(xs) <= mean(xs) <= max(xs)
assert -inf <= nan
where -inf = min([-inf, inf])
and nan = mean([-inf, inf])
Falsifying example: test_mean(xs=[-inf, inf])
import math
from hypothesis import given, assume
import hypothesis.strategies as st
def mean(xs):
return sum(xs) / len(xs)
@given(st.lists(st.floats(), min_size=1))
def test_mean(xs):
assume(not any(math.isnan(x) or math.isinf(x) for x in xs))
assert min(xs) <= mean(xs) <= max(xs)
assert inf <= 8.98846567431158e+307
where inf = mean(
[8.988465674311579e+307, 8.98846567431158e+307])
and 8.98846567431158e+307 = max(
[8.988465674311579e+307, 8.98846567431158e+307])
Falsifying example: test_mean(
xs=[8.988465674311579e+307, 8.98846567431158e+307])
import math
from hypothesis import given, assume
import hypothesis.strategies as st
def mean(xs):
return sum(x / len(xs) for x in xs)
@given(st.lists(st.floats(), min_size=1))
def test_mean(xs):
assume(not any(math.isnan(x) or math.isinf(x) for x in xs))
assert min(xs) <= mean(xs) <= max(xs)
xs = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
assert 1.0 <= 0.9999999999999999
where 1.0 = min([1.0, 1.0, 1.0, 1.0, 1.0, 1.0])
and 0.9999999999999999 = mean([1.0, 1.0, 1.0, 1.0, 1.0, 1.0])
Falsifying example: test_mean(xs=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0])
import math
from hypothesis import given, assume
import hypothesis.strategies as st
def mean(xs):
return np.array(xs).mean()
@given(st.lists(st.floats(), min_size=1))
def test_mean(xs):
assume(not any(math.isnan(x) or math.isinf(x) for x in xs))
assert min(xs) <= mean(xs) <= max(xs)
assert inf <= 8.98846567431158e+307
where inf = mean(
[8.988465674311579e+307, 8.98846567431158e+307])
and 8.98846567431158e+307 = max(
[8.988465674311579e+307, 8.98846567431158e+307])
Falsifying example: test_mean(
xs=[8.988465674311579e+307, 8.98846567431158e+307])
