def test_5(self):
self.mock.uniform.return_value = 4.1
self.assertEqual('A5', utils.random_weighted_choice(self.list).data)
self.mock.uniform.return_value = 7
self.assertEqual('A5', utils.random_weighted_choice(self.list).data)
self.mock.uniform.return_value = 9
self.assertEqual('A5', utils.random_weighted_choice(self.list).data)
def test_3(self):
self.mock.uniform.return_value = 1.1
self.assertEqual('A3', utils.random_weighted_choice(self.list).data)
self.mock.uniform.return_value = 2
self.assertEqual('A3', utils.random_weighted_choice(self.list).data)
self.mock.uniform.return_value = 4
self.assertEqual('A3', utils.random_weighted_choice(self.list).data)
def test_2(self):
self.mock.uniform.return_value = 0
self.assertEqual('A2', utils.random_weighted_choice(self.list).data)
self.mock.uniform.return_value = 0.4
self.assertEqual('A2', utils.random_weighted_choice(self.list).data)
self.mock.uniform.return_value = 1
self.assertEqual('A2', utils.random_weighted_choice(self.list).data)
def generate_weighted_strategy_pool(strategy_list, use_generator, engine_name):
"""Generate a strategy pool based on probability distribution from multi armed
bandit experimentation."""
# If weighted strategy selection is enabled, there will be a distribution
# stored in the environment.
distribution = environment.get_value('STRATEGY_SELECTION_DISTRIBUTION')
selection_method = environment.get_value('STRATEGY_SELECTION_METHOD',
default_value='default')
# Otherwise if weighted strategy selection is not enabled (strategy selection
# method is default) or if we cannot query properly, generate strategy
# pool according to default parameters. We pass the combined list of
# multi-armed bandit strategies and manual strategies for consideration in
# the default strategy selection process.
if not distribution or selection_method == 'default':
return generate_default_strategy_pool(strategy_list, use_generator)
# Change the distribution to a list of named tuples rather than a list of
# dictionaries so that we can use the random_weighted_choice function. Filter
# out probability entries from other engines.
distribution_tuples = [
StrategyCombination(strategy_name=elem['strategy_name'],
probability=elem['probability'])
for elem in distribution if elem['engine'] == engine_name
]
if not distribution_tuples:
logs.log_warn(
'Tried to generate a weighted strategy pool, but do not have '
'strategy probabilities for %s fuzzing engine.' % engine_name)
return generate_default_strategy_pool(strategy_list, use_generator)
strategy_selection = utils.random_weighted_choice(distribution_tuples,
'probability')
strategy_name = strategy_selection.strategy_name
chosen_strategies = strategy_name.split(',')
pool = StrategyPool()
for strategy_tuple in strategy_list:
if strategy_tuple.name in chosen_strategies:
pool.add_strategy(strategy_tuple)
# We consider certain strategies separately as those are only supported by a
# small number of fuzz targets and should be used heavily when available.
for value in [
strategy_entry for strategy_entry in strategy_list
if strategy_entry.manually_enable
]:
if do_strategy(value):
pool.add_strategy(value)
logs.log('Strategy pool was generated according to weighted distribution. '
'Chosen strategies: ' + ', '.join(pool.strategy_names))
return pool
def select_fuzz_target(targets, target_weights):
"""Select a fuzz target from a list of potential targets."""
assert targets
weighted_targets = []
for target in targets:
weight = target_weights.get(target, 1.0)
weighted_targets.append(WeightedTarget(target, weight))
return utils.random_weighted_choice(weighted_targets).target
def get_fuzz_task_payload(platform=None):
"""Select a fuzzer that can run on this platform."""
if not platform:
queue_override = environment.get_value('QUEUE_OVERRIDE')
platform = queue_override if queue_override else environment.platform()
if environment.is_local_development():
query = data_types.FuzzerJob.query()
query = query.filter(data_types.FuzzerJobs.platform == platform)
mappings = list(ndb_utils.get_all_from_query(query))
else:
query = data_types.FuzzerJobs.query()
query = query.filter(data_types.FuzzerJobs.platform == platform)
mappings = []
for entity in query:
mappings.extend(entity.fuzzer_jobs)
if not mappings:
return None, None
selection = utils.random_weighted_choice(mappings,
weight_attribute='actual_weight')
return selection.fuzzer, selection.job
def test_4(self):
self.mock.uniform.return_value = 4.1
self.assertNotEqual('A4', utils.random_weighted_choice(self.list).data)