def test_can_reduce_poison_from_any_subtree(size, seed):
"""This test validates that we can minimize to any leaf node of a binary
tree, regardless of where in the tree the leaf is."""
random = Random(seed)
# Initially we create the minimal tree of size n, regardless of whether it
# is poisoned (which it won't be - the poison event essentially never
# happens when drawing uniformly at random).
# Choose p so that the expected size of the tree is equal to the desired
# size.
p = 1.0 / (2.0 - 1.0 / size)
strat = PoisonedTree(p)
def test_function(data):
v = data.draw(strat)
if len(v) >= size:
data.mark_interesting()
runner = ConjectureRunner(test_function,
random=random,
settings=settings(TEST_SETTINGS,
buffer_size=LOTS))
while not runner.interesting_examples:
runner.test_function(
runner.new_conjecture_data(lambda data, n: uniform(random, n)))
runner.shrink_interesting_examples()
data, = runner.interesting_examples.values()
assert len(ConjectureData.for_buffer(data.buffer).draw(strat)) == size
starts = [b.start for b in data.blocks if b.length == 2]
assert len(starts) % 2 == 0
for i in hrange(0, len(starts), 2):
# Now for each leaf position in the tree we try inserting a poison
# value artificially. Additionally, we add a marker to the end that
# must be preserved. The marker means that we are not allow to rely on
# discarding the end of the buffer to get the desired shrink.
u = starts[i]
marker = hbytes([1, 2, 3, 4])
def test_function_with_poison(data):
v = data.draw(strat)
m = data.draw_bytes(len(marker))
if POISON in v and m == marker:
data.mark_interesting()
runner = ConjectureRunner(test_function_with_poison,
random=random,
settings=TEST_SETTINGS)
runner.cached_test_function(data.buffer[:u] + hbytes([255]) * 4 +
data.buffer[u + 4:] + marker)
assert runner.interesting_examples
runner.shrink_interesting_examples()
shrunk, = runner.interesting_examples.values()
assert ConjectureData.for_buffer(
shrunk.buffer).draw(strat) == (POISON, )
def test_can_reduce_poison_from_any_subtree(size, seed):
"""This test validates that we can minimize to any leaf node of a binary
tree, regardless of where in the tree the leaf is."""
random = Random(seed)
# Initially we create the minimal tree of size n, regardless of whether it
# is poisoned (which it won't be - the poison event essentially never
# happens when drawing uniformly at random).
# Choose p so that the expected size of the tree is equal to the desired
# size.
p = 1.0 / (2.0 - 1.0 / size)
strat = PoisonedTree(p)
def test_function(data):
v = data.draw(strat)
if len(v) >= size:
data.mark_interesting()
runner = ConjectureRunner(
test_function, random=random, settings=settings(TEST_SETTINGS, buffer_size=LOTS)
)
while not runner.interesting_examples:
runner.test_function(
runner.new_conjecture_data(lambda data, n: uniform(random, n))
)
runner.shrink_interesting_examples()
data, = runner.interesting_examples.values()
assert len(ConjectureData.for_buffer(data.buffer).draw(strat)) == size
starts = [b.start for b in data.blocks if b.length == 2]
assert len(starts) % 2 == 0
for i in hrange(0, len(starts), 2):
# Now for each leaf position in the tree we try inserting a poison
# value artificially. Additionally, we add a marker to the end that
# must be preserved. The marker means that we are not allow to rely on
# discarding the end of the buffer to get the desired shrink.
u = starts[i]
marker = hbytes([1, 2, 3, 4])
def test_function_with_poison(data):
v = data.draw(strat)
m = data.draw_bytes(len(marker))
if POISON in v and m == marker:
data.mark_interesting()
runner = ConjectureRunner(
test_function_with_poison, random=random, settings=TEST_SETTINGS
)
runner.cached_test_function(
data.buffer[:u] + hbytes([255]) * 4 + data.buffer[u + 4 :] + marker
)
assert runner.interesting_examples
runner.shrink_interesting_examples()
shrunk, = runner.interesting_examples.values()
assert ConjectureData.for_buffer(shrunk.buffer).draw(strat) == (POISON,)
