def test_relearning_does_not_change_generation():
learner = LStar(lambda s: len(s) >= 3)
prev = learner.generation
learner.learn(bytes(3))
assert prev != learner.generation
prev = learner.generation
learner.learn(bytes(3))
assert prev == learner.generation
def test_can_learn_dead_nodes():
learner = LStar(lambda s: len(s) == 4 and max(s) <= 1)
learner.learn(bytes(4))
assert learner.dfa.matches(bytes(4))
assert learner.dfa.matches(bytes([1] * 4))
assert learner.dfa.matches(bytes([1] * 4))
learner.learn([2, 0, 0, 0])
# Need a length 5 string to distinguish this from
# something that just loops back to zero.
learner.learn([2, 0, 0, 0, 0])
dfa = learner.dfa
assert dfa.is_dead(dfa.transition(dfa.start, 2))
assert dfa.is_dead(dfa.transition(dfa.start, 3))
def test_iteration_with_dead_nodes():
learner = LStar(lambda s: len(s) == 3 and max(s) <= 1 and s[1] == 0)
learner.learn([1, 0, 1])
learner.learn([1, 1, 1])
learner.learn([0, 1, 1])
learner.learn([1, 1, 0])
learner.learn([1, 1, 1, 0, 1])
dfa = learner.dfa
i = dfa.transition(dfa.start, 1)
assert not dfa.is_dead(i)
assert dfa.is_dead(dfa.transition(i, 2))
assert list(learner.dfa.all_matching_strings()) == [
bytes([0, 0, 0]),
bytes([0, 0, 1]),
bytes([1, 0, 0]),
bytes([1, 0, 1]),
]
def test_learning_is_just_checking_when_fully_explored():
count = [0]
def accept(s):
count[0] += 1
return len(s) <= 5 and all(c == 0 for c in s)
learner = LStar(accept)
for c in [0, 1]:
for n in range(10):
learner.learn(bytes([c]) * n)
assert list(learner.dfa.all_matching_strings()) == [bytes(n) for n in range(6)]
(prev,) = count
learner.learn([2] * 11)
calls = count[0] - prev
assert calls == 1
def learner_for(strategy):
"""Returns an LStar learner that predicts whether a buffer
corresponds to a discard free choice sequence leading to
a valid value for this strategy."""
try:
return LEARNERS[strategy]
except KeyError:
pass
def test_function(data):
data.draw(strategy)
data.mark_interesting()
runner = ConjectureRunner(
test_function,
settings=settings(
database=None,
verbosity=Verbosity.quiet,
suppress_health_check=HealthCheck.all(),
),
random=Random(0),
ignore_limits=True,
)
def predicate(s):
result = runner.cached_test_function(s)
if result.status < Status.VALID:
return False
if result.has_discards:
return False
return result.buffer == s
learner = LStar(predicate)
runner.run()
(v,) = runner.interesting_examples.values()
# We make sure the learner has properly learned small examples.
# This is all fairly ad hoc but is mostly designed to get it
# to understand what the smallest example is and avoid any
# loops at the beginning of the DFA that don't really exist.
learner.learn(v.buffer)
for n in [1, 2, 3]:
for _ in range(5):
learner.learn(uniform(runner.random, n) + v.buffer)
prev = -1
while learner.generation != prev:
prev = learner.generation
for _ in range(10):
s = uniform(runner.random, len(v.buffer)) + bytes(BUFFER_SIZE)
learner.learn(s)
data = runner.cached_test_function(s)
if data.status >= Status.VALID:
learner.learn(data.buffer)
LEARNERS[strategy] = learner
return learner
def learn_a_new_dfa(runner, u, v, predicate):
"""Given two buffers ``u`` and ``v```, learn a DFA that will
allow the shrinker to normalise them better. ``u`` and ``v``
should not currently shrink to the same test case when calling
this function."""
from hypothesis.internal.conjecture.shrinker import dfa_replacement, sort_key
assert predicate(runner.cached_test_function(u))
assert predicate(runner.cached_test_function(v))
u_shrunk = fully_shrink(runner, u, predicate)
v_shrunk = fully_shrink(runner, v, predicate)
u, v = sorted((u_shrunk.buffer, v_shrunk.buffer), key=sort_key)
assert u != v
assert not v.startswith(u)
# We would like to avoid using LStar on large strings as its
# behaviour can be quadratic or worse. In order to help achieve
# this we peel off a common prefix and suffix of the two final
# results and just learn the internal bit where they differ.
#
# This potentially reduces the length quite far if there's
# just one tricky bit of control flow we're struggling to
# reduce inside a strategy somewhere and the rest of the
# test function reduces fine.
if v.endswith(u):
prefix = b""
suffix = u
u_core = b""
assert len(u) > 0
v_core = v[:-len(u)]
else:
i = 0
while u[i] == v[i]:
i += 1
prefix = u[:i]
assert u.startswith(prefix)
assert v.startswith(prefix)
i = 1
while u[-i] == v[-i]:
i += 1
suffix = u[max(len(prefix), len(u) + 1 - i):]
assert u.endswith(suffix)
assert v.endswith(suffix)
u_core = u[len(prefix):len(u) - len(suffix)]
v_core = v[len(prefix):len(v) - len(suffix)]
assert u == prefix + u_core + suffix, (list(u), list(v))
assert v == prefix + v_core + suffix, (list(u), list(v))
better = runner.cached_test_function(u)
worse = runner.cached_test_function(v)
allow_discards = worse.has_discards or better.has_discards
def is_valid_core(s):
if not (len(u_core) <= len(s) <= len(v_core)):
return False
buf = prefix + s + suffix
result = runner.cached_test_function(buf)
return (
predicate(result)
# Because we're often using this to learn strategies
# rather than entire complex test functions, it's
# important that our replacements are precise and
# don't leave the rest of the test case in a weird
# state.
and result.buffer == buf
# Because the shrinker is good at removing discarded
# data, unless we need discards to allow one or both
# of u and v to result in valid shrinks, we don't
# count attempts that have them as valid. This will
# cause us to match fewer strings, which will make
# the resulting shrink pass more efficient when run
# on test functions it wasn't really intended for.
and (allow_discards or not result.has_discards))
assert sort_key(u_core) < sort_key(v_core)
assert is_valid_core(u_core)
assert is_valid_core(v_core)
learner = LStar(is_valid_core)
prev = -1
while learner.generation != prev:
prev = learner.generation
learner.learn(u_core)
learner.learn(v_core)
# L* has a tendency to learn DFAs which wrap around to
# the beginning. We don't want to it to do that unless
# it's accurate, so we use these as examples to show
# check going around the DFA twice.
learner.learn(u_core * 2)
learner.learn(v_core * 2)
if learner.dfa.max_length(learner.dfa.start) > len(v_core):
# The language we learn is finite and bounded above
# by the length of v_core. This is important in order
# to keep our shrink passes reasonably efficient -
# otherwise they can match far too much. So whenever
# we learn a DFA that could match a string longer
# than len(v_core) we fix it by finding the first
# string longer than v_core and learning that as
# a correction.
x = next(
learner.dfa.all_matching_strings(min_length=len(v_core) + 1))
assert not is_valid_core(x)
learner.learn(x)
assert not learner.dfa.matches(x)
assert learner.generation != prev
else:
# We mostly care about getting the right answer on the
# minimal test case, but because we're doing this offline
# anyway we might as well spend a little more time trying
# small examples to make sure the learner gets them right.
for x in islice(learner.dfa.all_matching_strings(), 100):
if not is_valid_core(x):
learner.learn(x)
assert learner.generation != prev
break
# We've now successfully learned a DFA that works for shrinking
# our failed normalisation further. Canonicalise it into a concrete
# DFA so we can save it for later.
new_dfa = learner.dfa.canonicalise()
assert math.isfinite(new_dfa.max_length(new_dfa.start))
shrinker = runner.new_shrinker(runner.cached_test_function(v), predicate)
assert (len(prefix),
len(v) - len(suffix)) in shrinker.matching_regions(new_dfa)
name = "tmp-dfa-" + repr(new_dfa)
shrinker.extra_dfas[name] = new_dfa
shrinker.fixate_shrink_passes([dfa_replacement(name)])
assert sort_key(shrinker.buffer) < sort_key(v)
return new_dfa
def test_iterates_over_learned_strings():
upper_bound = bytes([1, 2])
learner = LStar(lambda s: len(s) == 2 and max(s) <= 5 and s <= upper_bound)
learner.learn(upper_bound)
prev = -1
while learner.generation != prev:
prev = learner.generation
learner.learn([1, 2, 0])
learner.learn([6, 1, 2])
learner.learn([1, 3])
for i in range(7):
learner.learn([0, i])
learner.learn([1, i])
learner.learn([2, 0])
learner.learn([2, 0, 0, 0])
learner.learn([2, 0, 0])
learner.learn([0, 6, 0, 0])
learner.learn([1, 3, 0, 0])
learner.learn([1, 6, 0, 0])
learner.learn([0, 0, 0, 0, 0])
dfa = learner.dfa
n = 9
matches = list(itertools.islice(dfa.all_matching_strings(), n + 1))
for m in matches:
assert learner.member(m), list(m)
assert len(matches) == n
def test_cannot_reuse_dfa():
x = LStar(lambda x: len(x) == 3)
dfa = x.dfa
x.learn(bytes(3))
with pytest.raises(InvalidState):
dfa.start
def test_iterates_over_learned_strings():
upper_bound = bytes([1, 2])
learner = LStar(lambda s: len(s) == 2 and max(s) <= 5 and s <= upper_bound)
learner.learn(upper_bound)
learner.learn([1, 2, 0])
learner.learn([6, 1, 2])
learner.learn([1, 3])
learner.learn([0, 5])
learner.learn([0, 6])
learner.learn([2, 0])
learner.learn([2, 0, 0, 0])
learner.learn([2, 0, 0])
dfa = learner.dfa
n = 9
matches = list(itertools.islice(dfa.all_matching_strings(), n + 1))
assert len(matches) == n
