def test_optimises_to_maximum():
with deterministic_PRNG():
def test(data):
data.target_observations["m"] = data.draw_bits(8)
runner = ConjectureRunner(test, settings=TEST_SETTINGS)
runner.cached_test_function([0])
try:
runner.optimise_targets()
except RunIsComplete:
pass
assert runner.best_observed_targets["m"] == 255
def test_optimises_when_last_element_is_empty():
with deterministic_PRNG():
def test(data):
data.target_observations["n"] = data.draw_bits(8)
data.start_example(1)
data.stop_example(1)
runner = ConjectureRunner(test, settings=TEST_SETTINGS)
runner.cached_test_function([250])
try:
runner.optimise_targets()
except RunIsComplete:
pass
assert runner.best_observed_targets["n"] == 255
def test_can_optimise_last_with_following_empty():
with deterministic_PRNG():
def test(data):
for _ in range(100):
data.draw_bits(2)
data.target_observations[""] = data.draw_bits(8)
data.start_example(1)
data.stop_example()
runner = ConjectureRunner(
test, settings=settings(TEST_SETTINGS, max_examples=100)
)
runner.cached_test_function(bytes(101))
with pytest.raises(RunIsComplete):
runner.optimise_targets()
assert runner.best_observed_targets[""] == 255
def test_targeting_can_drive_length_very_high():
with deterministic_PRNG():
def test(data):
count = 0
while data.draw_bits(2) == 3:
count += 1
data.target_observations[""] = min(count, 100)
runner = ConjectureRunner(test, settings=TEST_SETTINGS)
runner.cached_test_function(bytes(10))
try:
runner.optimise_targets()
except RunIsComplete:
pass
assert runner.best_observed_targets[""] == 100
def test_optimiser_when_test_grows_buffer_to_invalid():
with deterministic_PRNG():
def test(data):
m = data.draw_bits(8)
data.target_observations["m"] = m
if m > 100:
data.draw_bits(16)
data.mark_invalid()
runner = ConjectureRunner(test, settings=TEST_SETTINGS)
runner.cached_test_function(bytes(10))
try:
runner.optimise_targets()
except RunIsComplete:
pass
assert runner.best_observed_targets["m"] == 100
def test_does_not_optimise_the_pareto_front_if_interesting():
def test(data):
n = data.draw_bits(8)
data.target_observations[""] = n
if n == 255:
data.mark_interesting()
runner = ConjectureRunner(
test,
settings=settings(max_examples=10000, database=InMemoryExampleDatabase(),),
database_key=b"stuff",
)
runner.cached_test_function([0])
runner.pareto_optimise = None
runner.optimise_targets()
assert runner.interesting_examples
def test_optimises_multiple_targets():
with deterministic_PRNG():
def test(data):
n = data.draw_bits(8)
m = data.draw_bits(8)
if n + m > 256:
data.mark_invalid()
data.target_observations["m"] = m
data.target_observations["n"] = n
data.target_observations["m + n"] = m + n
runner = ConjectureRunner(test, settings=TEST_SETTINGS)
runner.cached_test_function([200, 0])
runner.cached_test_function([0, 200])
try:
runner.optimise_targets()
except RunIsComplete:
pass
assert runner.best_observed_targets["m"] == 255
assert runner.best_observed_targets["n"] == 255
assert runner.best_observed_targets["m + n"] == 256
def test_can_patch_up_examples():
with deterministic_PRNG():
def test(data):
data.start_example(42)
m = data.draw_bits(6)
data.target_observations["m"] = m
for _ in range(m):
data.draw_bits(1)
data.stop_example()
for i in range(4):
if i != data.draw_bits(8):
data.mark_invalid()
runner = ConjectureRunner(test, settings=TEST_SETTINGS)
d = runner.cached_test_function([0, 0, 1, 2, 3, 4])
assert d.status == Status.VALID
try:
runner.optimise_targets()
except RunIsComplete:
pass
assert runner.best_observed_targets["m"] == 63
def test_can_find_endpoints_of_a_range(lower, upper, score_up):
with deterministic_PRNG():
def test(data):
n = data.draw_bits(16)
if n < lower or n > upper:
data.mark_invalid()
if not score_up:
n = -n
data.target_observations["n"] = n
runner = ConjectureRunner(
test, settings=settings(TEST_SETTINGS, max_examples=1000)
)
runner.cached_test_function(int_to_bytes((lower + upper) // 2, 2))
try:
runner.optimise_targets()
except RunIsComplete:
pass
if score_up:
assert runner.best_observed_targets["n"] == upper
else:
assert runner.best_observed_targets["n"] == -lower
def test_does_not_run_optimisation_when_max_examples_is_small():
def test(data):
data.target_observations["n"] = data.draw_bits(16)
with deterministic_PRNG():
runner = ConjectureRunner(
test, settings=settings(TEST_SETTINGS, max_examples=10)
)
runner.optimise_targets = Mock(name="optimise_targets")
try:
runner.generate_new_examples()
except RunIsComplete:
pass
assert runner.optimise_targets.call_count == 0
