def __init__(
self,
test_function,
settings=None,
random=None,
database_key=None,
ignore_limits=False,
):
self._test_function = test_function
self.settings = settings or Settings()
self.shrinks = 0
self.finish_shrinking_deadline = None
self.call_count = 0
self.valid_examples = 0
self.random = random or Random(getrandbits(128))
self.database_key = database_key
self.ignore_limits = ignore_limits
# Global dict of per-phase statistics, and a list of per-call stats
# which transfer to the global dict at the end of each phase.
self.statistics = {}
self.stats_per_test_case = []
self.events_to_strings = WeakKeyDictionary()
self.interesting_examples = {}
# We use call_count because there may be few possible valid_examples.
self.first_bug_found_at = None
self.last_bug_found_at = None
self.shrunk_examples = set()
self.health_check_state = None
self.tree = DataTree()
self.best_observed_targets = defaultdict(lambda: NO_SCORE)
self.best_examples_of_observed_targets = {}
# We keep the pareto front in the example database if we have one. This
# is only marginally useful at present, but speeds up local development
# because it means that large targets will be quickly surfaced in your
# testing.
if self.database_key is not None and self.settings.database is not None:
self.pareto_front = ParetoFront(self.random)
self.pareto_front.on_evict(self.on_pareto_evict)
else:
self.pareto_front = None
# We want to be able to get the ConjectureData object that results
# from running a buffer without recalculating, especially during
# shrinking where we need to know about the structure of the
# executed test case.
self.__data_cache = LRUReusedCache(CACHE_SIZE)
# We ensure that the test has this much stack space remaining, no matter
# the size of the stack when called, to de-flake RecursionErrors (#2494).
self.__recursion_limit = sys.getrecursionlimit()
self.__pending_call_explanation = None
def __init__(self,
test_function,
settings=None,
random=None,
database_key=None):
self._test_function = test_function
self.settings = settings or Settings()
self.shrinks = 0
self.call_count = 0
self.event_call_counts = Counter()
self.valid_examples = 0
self.random = random or Random(getrandbits(128))
self.database_key = database_key
self.status_runtimes = {}
self.all_drawtimes = []
self.all_runtimes = []
self.events_to_strings = WeakKeyDictionary()
self.interesting_examples = {}
# We use call_count because there may be few possible valid_examples.
self.first_bug_found_at = None
self.last_bug_found_at = None
self.shrunk_examples = set()
self.health_check_state = None
self.tree = DataTree()
self.best_observed_targets = defaultdict(lambda: NO_SCORE)
self.best_examples_of_observed_targets = {}
# We keep the pareto front in the example database if we have one. This
# is only marginally useful at present, but speeds up local development
# because it means that large targets will be quickly surfaced in your
# testing.
if self.database_key is not None and self.settings.database is not None:
self.pareto_front = ParetoFront(self.random)
self.pareto_front.on_evict(self.on_pareto_evict)
else:
self.pareto_front = None
# We want to be able to get the ConjectureData object that results
# from running a buffer without recalculating, especially during
# shrinking where we need to know about the structure of the
# executed test case.
self.__data_cache = LRUReusedCache(CACHE_SIZE)
class ConjectureRunner(object):
def __init__(self, test_function, settings=None, random=None, database_key=None):
self._test_function = test_function
self.settings = settings or Settings()
self.shrinks = 0
self.call_count = 0
self.event_call_counts = Counter()
self.valid_examples = 0
self.random = random or Random(getrandbits(128))
self.database_key = database_key
self.status_runtimes = {}
self.all_drawtimes = []
self.all_runtimes = []
self.events_to_strings = WeakKeyDictionary()
self.interesting_examples = {}
# We use call_count because there may be few possible valid_examples.
self.first_bug_found_at = None
self.last_bug_found_at = None
self.shrunk_examples = set()
self.health_check_state = None
self.tree = DataTree()
self.best_observed_targets = defaultdict(lambda: NO_SCORE)
self.best_examples_of_observed_targets = {}
# We keep the pareto front in the example database if we have one. This
# is only marginally useful at present, but speeds up local development
# because it means that large targets will be quickly surfaced in your
# testing.
if self.database_key is not None and self.settings.database is not None:
self.pareto_front = ParetoFront(self.random)
self.pareto_front.on_evict(self.on_pareto_evict)
else:
self.pareto_front = None
# We want to be able to get the ConjectureData object that results
# from running a buffer without recalculating, especially during
# shrinking where we need to know about the structure of the
# executed test case.
self.__data_cache = LRUReusedCache(CACHE_SIZE)
@property
def should_optimise(self):
return Phase.target in self.settings.phases
def __tree_is_exhausted(self):
return self.tree.is_exhausted
def __stoppable_test_function(self, data):
"""Run ``self._test_function``, but convert a ``StopTest`` exception
into a normal return.
"""
try:
self._test_function(data)
except StopTest as e:
if e.testcounter == data.testcounter:
# This StopTest has successfully stopped its test, and can now
# be discarded.
pass
else:
# This StopTest was raised by a different ConjectureData. We
# need to re-raise it so that it will eventually reach the
# correct engine.
raise
def test_function(self, data):
assert isinstance(data.observer, TreeRecordingObserver)
self.call_count += 1
interrupted = False
try:
self.__stoppable_test_function(data)
except KeyboardInterrupt:
interrupted = True
raise
except BaseException:
self.save_buffer(data.buffer)
raise
finally:
# No branch, because if we're interrupted we always raise
# the KeyboardInterrupt, never continue to the code below.
if not interrupted: # pragma: no branch
data.freeze()
self.note_details(data)
self.debug_data(data)
if self.pareto_front is not None and self.pareto_front.add(data.as_result()):
self.save_buffer(data.buffer, sub_key=b"pareto")
assert len(data.buffer) <= BUFFER_SIZE
if data.status >= Status.VALID:
for k, v in data.target_observations.items():
self.best_observed_targets[k] = max(self.best_observed_targets[k], v)
if k not in self.best_examples_of_observed_targets:
self.best_examples_of_observed_targets[k] = data.as_result()
continue
existing_example = self.best_examples_of_observed_targets[k]
existing_score = existing_example.target_observations[k]
if v < existing_score:
continue
if v > existing_score or sort_key(data.buffer) < sort_key(
existing_example.buffer
):
self.best_examples_of_observed_targets[k] = data.as_result()
if data.status == Status.VALID:
self.valid_examples += 1
if data.status == Status.INTERESTING:
key = data.interesting_origin
changed = False
try:
existing = self.interesting_examples[key]
except KeyError:
changed = True
self.last_bug_found_at = self.call_count
if self.first_bug_found_at is None:
self.first_bug_found_at = self.call_count
else:
if sort_key(data.buffer) < sort_key(existing.buffer):
self.shrinks += 1
self.downgrade_buffer(existing.buffer)
self.__data_cache.unpin(existing.buffer)
changed = True
if changed:
self.save_buffer(data.buffer)
self.interesting_examples[key] = data.as_result()
self.__data_cache.pin(data.buffer)
self.shrunk_examples.discard(key)
if self.shrinks >= MAX_SHRINKS:
self.exit_with(ExitReason.max_shrinks)
if not self.interesting_examples:
# Note that this logic is reproduced to end the generation phase when
# we have interesting examples. Update that too if you change this!
# (The doubled implementation is because here we exit the engine entirely,
# while in the other case below we just want to move on to shrinking.)
if self.valid_examples >= self.settings.max_examples:
self.exit_with(ExitReason.max_examples)
if self.call_count >= max(
self.settings.max_examples * 10,
# We have a high-ish default max iterations, so that tests
# don't become flaky when max_examples is too low.
1000,
):
self.exit_with(ExitReason.max_iterations)
if self.__tree_is_exhausted():
self.exit_with(ExitReason.finished)
self.record_for_health_check(data)
def on_pareto_evict(self, data):
self.settings.database.delete(self.pareto_key, data.buffer)
def generate_novel_prefix(self):
"""Uses the tree to proactively generate a starting sequence of bytes
that we haven't explored yet for this test.
When this method is called, we assume that there must be at
least one novel prefix left to find. If there were not, then the
test run should have already stopped due to tree exhaustion.
"""
return self.tree.generate_novel_prefix(self.random)
def record_for_health_check(self, data):
# Once we've actually found a bug, there's no point in trying to run
# health checks - they'll just mask the actually important information.
if data.status == Status.INTERESTING:
self.health_check_state = None
state = self.health_check_state
if state is None:
return
state.draw_times.extend(data.draw_times)
if data.status == Status.VALID:
state.valid_examples += 1
elif data.status == Status.INVALID:
state.invalid_examples += 1
else:
assert data.status == Status.OVERRUN
state.overrun_examples += 1
max_valid_draws = 10
max_invalid_draws = 50
max_overrun_draws = 20
assert state.valid_examples <= max_valid_draws
if state.valid_examples == max_valid_draws:
self.health_check_state = None
return
if state.overrun_examples == max_overrun_draws:
fail_health_check(
self.settings,
(
"Examples routinely exceeded the max allowable size. "
"(%d examples overran while generating %d valid ones)"
". Generating examples this large will usually lead to"
" bad results. You could try setting max_size parameters "
"on your collections and turning "
"max_leaves down on recursive() calls."
)
% (state.overrun_examples, state.valid_examples),
HealthCheck.data_too_large,
)
if state.invalid_examples == max_invalid_draws:
fail_health_check(
self.settings,
(
"It looks like your strategy is filtering out a lot "
"of data. Health check found %d filtered examples but "
"only %d good ones. This will make your tests much "
"slower, and also will probably distort the data "
"generation quite a lot. You should adapt your "
"strategy to filter less. This can also be caused by "
"a low max_leaves parameter in recursive() calls"
)
% (state.invalid_examples, state.valid_examples),
HealthCheck.filter_too_much,
)
draw_time = sum(state.draw_times)
if draw_time > 1.0:
fail_health_check(
self.settings,
(
"Data generation is extremely slow: Only produced "
"%d valid examples in %.2f seconds (%d invalid ones "
"and %d exceeded maximum size). Try decreasing "
"size of the data you're generating (with e.g."
"max_size or max_leaves parameters)."
)
% (
state.valid_examples,
draw_time,
state.invalid_examples,
state.overrun_examples,
),
HealthCheck.too_slow,
)
def save_buffer(self, buffer, sub_key=None):
if self.settings.database is not None:
key = self.sub_key(sub_key)
if key is None:
return
self.settings.database.save(key, hbytes(buffer))
def downgrade_buffer(self, buffer):
if self.settings.database is not None and self.database_key is not None:
self.settings.database.move(self.database_key, self.secondary_key, buffer)
def sub_key(self, sub_key):
if self.database_key is None:
return None
if sub_key is None:
return self.database_key
return b".".join((self.database_key, sub_key))
@property
def secondary_key(self):
return self.sub_key(b"secondary")
@property
def pareto_key(self):
return self.sub_key(b"pareto")
def note_details(self, data):
self.__data_cache[data.buffer] = data.as_result()
runtime = max(data.finish_time - data.start_time, 0.0)
self.all_runtimes.append(runtime)
self.all_drawtimes.extend(data.draw_times)
self.status_runtimes.setdefault(data.status, []).append(runtime)
for event in set(map(self.event_to_string, data.events)):
self.event_call_counts[event] += 1
def debug(self, message):
if self.settings.verbosity >= Verbosity.debug:
base_report(message)
@property
def report_debug_info(self):
return self.settings.verbosity >= Verbosity.debug
def debug_data(self, data):
if not self.report_debug_info:
return
stack = [[]]
def go(ex):
if ex.length == 0:
return
if len(ex.children) == 0:
stack[-1].append(int_from_bytes(data.buffer[ex.start : ex.end]))
else:
node = []
stack.append(node)
for v in ex.children:
go(v)
stack.pop()
if len(node) == 1:
stack[-1].extend(node)
else:
stack[-1].append(node)
go(data.examples[0])
assert len(stack) == 1
status = repr(data.status)
if data.status == Status.INTERESTING:
status = "%s (%r)" % (status, data.interesting_origin)
self.debug(
"%d bytes %r -> %s, %s" % (data.index, stack[0], status, data.output)
)
def run(self):
with local_settings(self.settings):
try:
self._run()
except RunIsComplete:
pass
for v in self.interesting_examples.values():
self.debug_data(v)
self.debug(
u"Run complete after %d examples (%d valid) and %d shrinks"
% (self.call_count, self.valid_examples, self.shrinks)
)
@property
def database(self):
if self.database_key is None:
return None
return self.settings.database
def has_existing_examples(self):
return self.database is not None and Phase.reuse in self.settings.phases
def reuse_existing_examples(self):
"""If appropriate (we have a database and have been told to use it),
try to reload existing examples from the database.
If there are a lot we don't try all of them. We always try the
smallest example in the database (which is guaranteed to be the
last failure) and the largest (which is usually the seed example
which the last failure came from but we don't enforce that). We
then take a random sampling of the remainder and try those. Any
examples that are no longer interesting are cleared out.
"""
if self.has_existing_examples():
self.debug("Reusing examples from database")
# We have to do some careful juggling here. We have two database
# corpora: The primary and secondary. The primary corpus is a
# small set of minimized examples each of which has at one point
# demonstrated a distinct bug. We want to retry all of these.
# We also have a secondary corpus of examples that have at some
# point demonstrated interestingness (currently only ones that
# were previously non-minimal examples of a bug, but this will
# likely expand in future). These are a good source of potentially
# interesting examples, but there are a lot of them, so we down
# sample the secondary corpus to a more manageable size.
corpus = sorted(
self.settings.database.fetch(self.database_key), key=sort_key
)
desired_size = max(2, ceil(0.1 * self.settings.max_examples))
if len(corpus) < desired_size:
extra_corpus = list(self.settings.database.fetch(self.secondary_key))
shortfall = desired_size - len(corpus)
if len(extra_corpus) <= shortfall:
extra = extra_corpus
else:
extra = self.random.sample(extra_corpus, shortfall)
extra.sort(key=sort_key)
corpus.extend(extra)
for existing in corpus:
data = self.cached_test_function(existing)
if data.status != Status.INTERESTING:
self.settings.database.delete(self.database_key, existing)
self.settings.database.delete(self.secondary_key, existing)
# If we've not found any interesting examples so far we try some of
# the pareto front from the last run.
if len(corpus) < desired_size and not self.interesting_examples:
desired_extra = desired_size - len(corpus)
pareto_corpus = list(self.settings.database.fetch(self.pareto_key))
if len(pareto_corpus) > desired_extra:
pareto_corpus = self.random.sample(pareto_corpus, desired_extra)
pareto_corpus.sort(key=sort_key)
for existing in pareto_corpus:
data = self.cached_test_function(existing)
if data not in self.pareto_front:
self.settings.database.delete(self.pareto_key, existing)
if data.status == Status.INTERESTING:
break
def exit_with(self, reason):
self.debug("exit_with(%s)" % (reason.name,))
self.exit_reason = reason
raise RunIsComplete()
def generate_new_examples(self):
if Phase.generate not in self.settings.phases:
return
if self.interesting_examples:
# The example database has failing examples from a previous run,
# so we'd rather report that they're still failing ASAP than take
# the time to look for additional failures.
return
self.debug("Generating new examples")
zero_data = self.cached_test_function(hbytes(BUFFER_SIZE))
if zero_data.status > Status.OVERRUN:
self.__data_cache.pin(zero_data.buffer)
if zero_data.status == Status.OVERRUN or (
zero_data.status == Status.VALID and len(zero_data.buffer) * 2 > BUFFER_SIZE
):
fail_health_check(
self.settings,
"The smallest natural example for your test is extremely "
"large. This makes it difficult for Hypothesis to generate "
"good examples, especially when trying to reduce failing ones "
"at the end. Consider reducing the size of your data if it is "
"of a fixed size. You could also fix this by improving how "
"your data shrinks (see https://hypothesis.readthedocs.io/en/"
"latest/data.html#shrinking for details), or by introducing "
"default values inside your strategy. e.g. could you replace "
"some arguments with their defaults by using "
"one_of(none(), some_complex_strategy)?",
HealthCheck.large_base_example,
)
self.health_check_state = HealthCheckState()
def should_generate_more():
# End the generation phase where we would have ended it if no bugs had
# been found. This reproduces the exit logic in `self.test_function`,
# but with the important distinction that this clause will move on to
# the shrinking phase having found one or more bugs, while the other
# will exit having found zero bugs.
if (
self.valid_examples >= self.settings.max_examples
or self.call_count >= max(self.settings.max_examples * 10, 1000)
or (
self.best_examples_of_observed_targets
and self.valid_examples * 2 >= self.settings.max_examples
and self.should_optimise
)
): # pragma: no cover
return False
# If we haven't found a bug, keep looking - if we hit any limits on
# the number of tests to run that will raise an exception and stop
# the run.
if not self.interesting_examples:
return True
# If we've found a bug and won't report more than one, stop looking.
elif not self.settings.report_multiple_bugs:
return False
assert self.first_bug_found_at <= self.last_bug_found_at <= self.call_count
# Otherwise, keep searching for between ten and 'a heuristic' calls.
# We cap 'calls after first bug' so errors are reported reasonably
# soon even for tests that are allowed to run for a very long time,
# or sooner if the latest half of our test effort has been fruitless.
return self.call_count < MIN_TEST_CALLS or self.call_count < min(
self.first_bug_found_at + 1000, self.last_bug_found_at * 2
)
# We attempt to use the size of the minimal generated test case starting
# from a given novel prefix as a guideline to generate smaller test
# cases for an initial period, by restriscting ourselves to test cases
# that are not much larger than it.
#
# Calculating the actual minimal generated test case is hard, so we
# take a best guess that zero extending a prefix produces the minimal
# test case starting with that prefix (this is true for our built in
# strategies). This is only a reasonable thing to do if the resulting
# test case is valid. If we regularly run into situations where it is
# not valid then this strategy is a waste of time, so we want to
# abandon it early. In order to do this we track how many times in a
# row it has failed to work, and abort small test case generation when
# it has failed too many times in a row.
consecutive_zero_extend_is_invalid = 0
while should_generate_more():
prefix = self.generate_novel_prefix()
assert len(prefix) <= BUFFER_SIZE
# We control growth during initial example generation, for two
# reasons:
#
# * It gives us an opportunity to find small examples early, which
# gives us a fast path for easy to find bugs.
# * It avoids low probability events where we might end up
# generating very large examples during health checks, which
# on slower machines can trigger HealthCheck.too_slow.
#
# The heuristic we use is that we attempt to estimate the smallest
# extension of this prefix, and limit the size to no more than
# an order of magnitude larger than that. If we fail to estimate
# the size accurately, we skip over this prefix and try again.
#
# We need to tune the example size based on the initial prefix,
# because any fixed size might be too small, and any size based
# on the strategy in general can fall afoul of strategies that
# have very different sizes for different prefixes.
small_example_cap = clamp(10, self.settings.max_examples // 10, 50)
if (
self.valid_examples <= small_example_cap
and self.call_count <= 5 * small_example_cap
and not self.interesting_examples
and consecutive_zero_extend_is_invalid < 5
):
minimal_example = self.cached_test_function(
prefix + hbytes(BUFFER_SIZE - len(prefix))
)
if minimal_example.status < Status.VALID:
consecutive_zero_extend_is_invalid += 1
continue
consecutive_zero_extend_is_invalid = 0
minimal_extension = len(minimal_example.buffer) - len(prefix)
max_length = min(len(prefix) + minimal_extension * 10, BUFFER_SIZE)
# We could end up in a situation where even though the prefix was
# novel when we generated it, because we've now tried zero extending
# it not all possible continuations of it will be novel. In order to
# avoid making redundant test calls, we rerun it in simulation mode
# first. If this has a predictable result, then we don't bother
# running the test function for real here. If however we encounter
# some novel behaviour, we try again with the real test function,
# starting from the new novel prefix that has discovered.
try:
trial_data = self.new_conjecture_data(
prefix=prefix, max_length=max_length
)
self.tree.simulate_test_function(trial_data)
continue
except PreviouslyUnseenBehaviour:
pass
# If the simulation entered part of the tree that has been killed,
# we don't want to run this.
if trial_data.observer.killed:
continue
# We might have hit the cap on number of examples we should
# run when calculating the minimal example.
if not should_generate_more():
break
prefix = trial_data.buffer
else:
max_length = BUFFER_SIZE
data = self.new_conjecture_data(prefix=prefix, max_length=max_length)
self.test_function(data)
# A thing that is often useful but rarely happens by accident is
# to generate the same value at multiple different points in the
# test case.
#
# Rather than make this the responsibility of individual strategies
# we implement a small mutator that just takes parts of the test
# case with the same label and tries replacing one of them with a
# copy of the other and tries running it. If we've made a good
# guess about what to put where, this will run a similar generated
# test case with more duplication.
if (
# An OVERRUN doesn't have enough information about the test
# case to mutate, so we just skip those.
data.status >= Status.INVALID
# This has a tendency to trigger some weird edge cases during
# generation so we don't let it run until we're done with the
# health checks.
and self.health_check_state is None
):
initial_calls = self.call_count
failed_mutations = 0
while (
should_generate_more()
# We implement fairly conservative checks for how long we
# we should run mutation for, as it's generally not obvious
# how helpful it is for any given test case.
and self.call_count <= initial_calls + 5
and failed_mutations <= 5
):
groups = defaultdict(list)
for ex in data.examples:
groups[ex.label, ex.depth].append(ex)
groups = [v for v in groups.values() if len(v) > 1]
if not groups:
break
group = self.random.choice(groups)
ex1, ex2 = sorted(
self.random.sample(group, 2), key=lambda i: i.index
)
assert ex1.end <= ex2.start
replacements = [data.buffer[e.start : e.end] for e in [ex1, ex2]]
replacement = self.random.choice(replacements)
try:
# We attempt to replace both the the examples with
# whichever choice we made. Note that this might end
# up messing up and getting the example boundaries
# wrong - labels matching are only a best guess as to
# whether the two are equivalent - but it doesn't
# really matter. It may not achieve the desired result
# but it's still a perfectly acceptable choice sequence.
# to try.
new_data = self.cached_test_function(
data.buffer[: ex1.start]
+ replacement
+ data.buffer[ex1.end : ex2.start]
+ replacement
+ data.buffer[ex2.end :],
# We set error_on_discard so that we don't end up
# entering parts of the tree we consider redundant
# and not worth exploring.
error_on_discard=True,
extend=BUFFER_SIZE,
)
except ContainsDiscard:
failed_mutations += 1
continue
if (
new_data.status >= data.status
and data.buffer != new_data.buffer
and all(
k in new_data.target_observations
and new_data.target_observations[k] >= v
for k, v in data.target_observations.items()
)
):
data = new_data
failed_mutations = 0
else:
failed_mutations += 1
def optimise_targets(self):
"""If any target observations have been made, attempt to optimise them
all."""
if not self.should_optimise:
return
while True:
prev_calls = self.call_count
for target, data in list(self.best_examples_of_observed_targets.items()):
self.new_optimiser(data, target).run()
# This shows up as a missing branch for some reason but is definitely
# covered if you add an assertion to check.
if self.interesting_examples or (prev_calls == self.call_count):
break
def _run(self):
self.reuse_existing_examples()
self.generate_new_examples()
self.optimise_targets()
self.shrink_interesting_examples()
self.exit_with(ExitReason.finished)
def new_conjecture_data(self, prefix, max_length=BUFFER_SIZE, observer=None):
return ConjectureData(
prefix=prefix,
max_length=max_length,
random=self.random,
observer=observer or self.tree.new_observer(),
)
def new_conjecture_data_for_buffer(self, buffer):
return ConjectureData.for_buffer(buffer, observer=self.tree.new_observer())
def shrink_interesting_examples(self):
"""If we've found interesting examples, try to replace each of them
with a minimal interesting example with the same interesting_origin.
We may find one or more examples with a new interesting_origin
during the shrink process. If so we shrink these too.
"""
if Phase.shrink not in self.settings.phases or not self.interesting_examples:
return
self.debug("Shrinking interesting examples")
for prev_data in sorted(
self.interesting_examples.values(), key=lambda d: sort_key(d.buffer)
):
assert prev_data.status == Status.INTERESTING
data = self.new_conjecture_data_for_buffer(prev_data.buffer)
self.test_function(data)
if data.status != Status.INTERESTING:
self.exit_with(ExitReason.flaky)
self.clear_secondary_key()
while len(self.shrunk_examples) < len(self.interesting_examples):
target, example = min(
(
(k, v)
for k, v in self.interesting_examples.items()
if k not in self.shrunk_examples
),
key=lambda kv: (sort_key(kv[1].buffer), sort_key(repr(kv[0]))),
)
self.debug("Shrinking %r" % (target,))
if not self.settings.report_multiple_bugs:
# If multi-bug reporting is disabled, we shrink our currently-minimal
# failure, allowing 'slips' to any bug with a smaller minimal example.
self.shrink(example, lambda d: d.status == Status.INTERESTING)
return
def predicate(d):
if d.status < Status.INTERESTING:
return False
return d.interesting_origin == target
self.shrink(example, predicate)
self.shrunk_examples.add(target)
def clear_secondary_key(self):
if self.has_existing_examples():
# If we have any smaller examples in the secondary corpus, now is
# a good time to try them to see if they work as shrinks. They
# probably won't, but it's worth a shot and gives us a good
# opportunity to clear out the database.
# It's not worth trying the primary corpus because we already
# tried all of those in the initial phase.
corpus = sorted(
self.settings.database.fetch(self.secondary_key), key=sort_key
)
for c in corpus:
primary = {v.buffer for v in self.interesting_examples.values()}
cap = max(map(sort_key, primary))
if sort_key(c) > cap:
break
else:
self.cached_test_function(c)
# We unconditionally remove c from the secondary key as it
# is either now primary or worse than our primary example
# of this reason for interestingness.
self.settings.database.delete(self.secondary_key, c)
def shrink(self, example, predicate):
s = self.new_shrinker(example, predicate)
s.shrink()
return s.shrink_target
def new_shrinker(self, example, predicate):
return Shrinker(self, example, predicate)
def new_optimiser(self, example, target):
from hypothesis.internal.conjecture.optimiser import Optimiser
return Optimiser(self, example, target)
def cached_test_function(self, buffer, error_on_discard=False, extend=0):
"""Checks the tree to see if we've tested this buffer, and returns the
previous result if we have.
Otherwise we call through to ``test_function``, and return a
fresh result.
If ``error_on_discard`` is set to True this will raise ``ContainsDiscard``
in preference to running the actual test function. This is to allow us
to skip test cases we expect to be redundant in some cases. Note that
it may be the case that we don't raise ``ContainsDiscard`` even if the
result has discards if we cannot determine from previous runs whether
it will have a discard.
"""
buffer = hbytes(buffer)[:BUFFER_SIZE]
max_length = min(BUFFER_SIZE, len(buffer) + extend)
def check_result(result):
assert result is Overrun or (
isinstance(result, ConjectureResult) and result.status != Status.OVERRUN
)
return result
try:
return check_result(self.__data_cache[buffer])
except KeyError:
pass
if error_on_discard:
class DiscardObserver(DataObserver):
def kill_branch(self):
raise ContainsDiscard()
observer = DiscardObserver()
else:
observer = DataObserver()
dummy_data = self.new_conjecture_data(
prefix=buffer, max_length=max_length, observer=observer
)
try:
self.tree.simulate_test_function(dummy_data)
except PreviouslyUnseenBehaviour:
pass
else:
if dummy_data.status > Status.OVERRUN:
dummy_data.freeze()
try:
return self.__data_cache[dummy_data.buffer]
except KeyError:
pass
else:
self.__data_cache[buffer] = Overrun
return Overrun
# We didn't find a match in the tree, so we need to run the test
# function normally. Note that test_function will automatically
# add this to the tree so we don't need to update the cache.
result = None
data = self.new_conjecture_data(
prefix=max((buffer, dummy_data.buffer), key=len), max_length=max_length,
)
self.test_function(data)
result = check_result(data.as_result())
self.__data_cache[buffer] = result
return result
def event_to_string(self, event):
if isinstance(event, str):
return event
try:
return self.events_to_strings[event]
except KeyError:
pass
result = str(event)
self.events_to_strings[event] = result
return result