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|
# This file is part of Hypothesis, which may be found at
# https://github.com/HypothesisWorks/hypothesis/
#
# Copyright the Hypothesis Authors.
# Individual contributors are listed in AUTHORS.rst and the git log.
#
# This Source Code Form is subject to the terms of the Mozilla Public License,
# v. 2.0. If a copy of the MPL was not distributed with this file, You can
# obtain one at https://mozilla.org/MPL/2.0/.
import math
from random import Random
import pytest
from hypothesis import HealthCheck, assume, example, given, settings
from hypothesis.internal.conjecture.choice import ChoiceNode
from hypothesis.internal.conjecture.data import Status
from hypothesis.internal.conjecture.datatree import compute_max_children
from hypothesis.internal.conjecture.engine import ConjectureRunner, RunIsComplete
from hypothesis.internal.intervalsets import IntervalSet
from tests.conjecture.common import (
buffer_size_limit,
integer_constr,
nodes,
)
runner_settings = settings(
max_examples=100, database=None, suppress_health_check=list(HealthCheck)
)
def test_optimises_to_maximum():
def test(data):
data.target_observations["m"] = data.draw_integer(0, 2**8 - 1)
runner = ConjectureRunner(test, settings=runner_settings, random=Random(0))
runner.cached_test_function((0,))
try:
runner.optimise_targets()
except RunIsComplete:
pass
assert runner.best_observed_targets["m"] == 255
def test_optimises_multiple_targets():
def test(data):
n = data.draw_integer(0, 2**8 - 1)
m = data.draw_integer(0, 2**8 - 1)
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=runner_settings, random=Random(0))
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_optimises_when_last_element_is_empty():
def test(data):
data.target_observations["n"] = data.draw_integer(0, 2**8 - 1)
data.start_span(label=1)
data.stop_span()
runner = ConjectureRunner(test, settings=runner_settings, random=Random(0))
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():
def test(data):
for _ in range(100):
data.draw_integer(0, 3)
data.target_observations[""] = data.draw_integer(0, 2**8 - 1)
data.start_span(1)
data.stop_span()
runner = ConjectureRunner(
test, settings=settings(runner_settings, max_examples=100), random=Random(0)
)
runner.cached_test_function((0,) * 101)
with pytest.raises(RunIsComplete):
runner.optimise_targets()
assert runner.best_observed_targets[""] == 255
@pytest.mark.parametrize("lower, upper", [(0, 1000), (13, 100), (1000, 2**16 - 1)])
@pytest.mark.parametrize("score_up", [False, True])
def test_can_find_endpoints_of_a_range(lower, upper, score_up):
def test(data):
n = data.draw_integer(0, 2**16 - 1)
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(runner_settings, max_examples=1000), random=Random(0)
)
runner.cached_test_function(((lower + upper) // 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_targeting_can_drive_length_very_high():
def test(data):
count = 0
while data.draw_boolean(0.25):
count += 1
data.target_observations[""] = min(count, 100)
runner = ConjectureRunner(test, settings=runner_settings, random=Random(0))
# extend here to ensure we get a valid (non-overrun) test case. The
# outcome of the test case doesn't really matter as long as we have
# something for the runner to optimize.
runner.cached_test_function([], extend=50)
try:
runner.optimise_targets()
except RunIsComplete:
pass
assert runner.best_observed_targets[""] == 100
def test_optimiser_when_test_grows_buffer_to_invalid():
def test(data):
m = data.draw_integer(0, 2**8 - 1)
data.target_observations["m"] = m
if m > 100:
data.draw_integer(0, 2**16 - 1)
data.mark_invalid()
runner = ConjectureRunner(test, settings=runner_settings, random=Random(0))
runner.cached_test_function((0,) * 10)
try:
runner.optimise_targets()
except RunIsComplete:
pass
assert runner.best_observed_targets["m"] == 100
def test_can_patch_up_examples():
def test(data):
data.start_span(42)
m = data.draw_integer(0, 2**6 - 1)
data.target_observations["m"] = m
for _ in range(m):
data.draw_boolean()
data.stop_span()
for i in range(4):
if i != data.draw_integer(0, 2**8 - 1):
data.mark_invalid()
runner = ConjectureRunner(
test, settings=settings(runner_settings, max_examples=1000), random=Random(0)
)
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_optimiser_when_test_grows_buffer_to_overflow():
def test(data):
m = data.draw_integer(0, 2**8 - 1)
data.target_observations["m"] = m
if m > 100:
data.draw_integer(0, 2**64 - 1)
data.mark_invalid()
runner = ConjectureRunner(test, settings=runner_settings, random=Random(0))
with buffer_size_limit(2):
runner.cached_test_function((0,) * 10)
try:
runner.optimise_targets()
except RunIsComplete:
pass
assert runner.best_observed_targets["m"] == 100
@given(nodes())
@example(
ChoiceNode(
type="bytes",
value=b"\xb1",
constraints={"min_size": 1, "max_size": 1},
was_forced=False,
)
)
@example(
ChoiceNode(
type="string",
value="aaaa",
constraints={
"min_size": 0,
"max_size": 10,
"intervals": IntervalSet.from_string("abcd"),
},
was_forced=False,
)
)
@example(
ChoiceNode(
type="integer", value=1, constraints=integer_constr(0, 200), was_forced=False
)
)
def test_optimising_all_nodes(node):
assume(compute_max_children(node.type, node.constraints) > 50)
size_function = {
"integer": lambda n: n,
"float": lambda f: f if math.isfinite(f) else 0,
"string": lambda s: len(s),
"bytes": lambda b: len(b),
"boolean": lambda b: int(b),
}
def test(data):
v = getattr(data, f"draw_{node.type}")(**node.constraints)
data.target_observations["v"] = size_function[node.type](v)
runner = ConjectureRunner(
test, settings=settings(runner_settings, max_examples=50), random=Random(0)
)
runner.cached_test_function([node.value])
try:
runner.optimise_targets()
except RunIsComplete:
pass
|
