def setUp(self):
"""Put data in the local ndb table the tests to query from and set
bandit selection environment variable."""
test_helpers.patch_environ(self)
data = []
strategy1 = data_types.FuzzStrategyProbability()
strategy1.strategy_name = 'corpus_mutations_ml_rnn,corpus_subset,'
strategy1.probability = 0.33
strategy1.engine = 'afl'
data.append(strategy1)
strategy2 = data_types.FuzzStrategyProbability()
strategy2.strategy_name = ('corpus_mutations_radamsa,corpus_subset,')
strategy2.probability = 0.34
strategy2.engine = 'afl'
data.append(strategy2)
strategy3 = data_types.FuzzStrategyProbability()
strategy3.strategy_name = ('corpus_subset,')
strategy3.probability = 0.33
strategy3.engine = 'afl'
data.append(strategy3)
ndb.put_multi(data)
distribution = fuzz_task.get_strategy_distribution_from_ndb()
environment.set_value('USE_BANDIT_STRATEGY_SELECTION', True)
environment.set_value('STRATEGY_SELECTION_DISTRIBUTION', distribution)
def setUp(self):
"""Put data in the local ndb table the tests to query from and set
bandit selection environment variable."""
test_helpers.patch_environ(self)
data = []
strategy1 = data_types.FuzzStrategyProbability()
strategy1.strategy_name = 'fork,corpus_subset,recommended_dict,'
strategy1.probability_medium_temperature = 0.33
strategy1.probability_high_temperature = 0.33
strategy1.probability_low_temperature = 0.33
data.append(strategy1)
strategy2 = data_types.FuzzStrategyProbability()
strategy2.strategy_name = ('random_max_len,corpus_mutations_ml_rnn,'
'value_profile,recommended_dict,')
strategy2.probability_medium_temperature = 0.34
strategy2.probability_high_temperature = 0.34
strategy2.probability_low_temperature = 0.34
data.append(strategy2)
strategy3 = data_types.FuzzStrategyProbability()
strategy3.strategy_name = ('corpus_mutations_radamsa,'
'random_max_len,corpus_subset,')
strategy3.probability_medium_temperature = 0.33
strategy3.probability_high_temperature = 0.33
strategy3.probability_low_temperature = 0.33
data.append(strategy3)
ndb.put_multi(data)
distribution = fuzz_task.get_strategy_distribution_from_ndb()
environment.set_value('USE_BANDIT_STRATEGY_SELECTION', True)
environment.set_value('STRATEGY_SELECTION_DISTRIBUTION', distribution)
def setUp(self):
"""Put data in the local ndb table the tests to query from and set
bandit selection environment variable."""
test_helpers.patch_environ(self)
os.environ['USE_BANDIT_STRATEGY_SELECTION'] = 'True'
data = []
strategy1 = data_types.FuzzStrategyProbability()
strategy1.strategy_name = 'fork,corpus_subset,recommended_dict,'
strategy1.probability = 0.33
data.append(strategy1)
strategy2 = data_types.FuzzStrategyProbability()
strategy2.strategy_name = ('random_max_len,corpus_mutations_ml_rnn,'
'value_profile,recommended_dict,')
strategy2.probability = .34
data.append(strategy2)
strategy3 = data_types.FuzzStrategyProbability()
strategy3.strategy_name = ('corpus_mutations_radamsa,'
'random_max_len,corpus_subset,')
strategy3.probability = .33
data.append(strategy3)
ndb.put_multi(data)
def _query_and_upload_strategy_probabilities(engine):
"""Uploads queried data into datastore.
Calls query functions and uploads query results
to datastore to use as new probabilities. Probabilities
are based on new_edges feature."""
strategy_data = []
data = _query_multi_armed_bandit_probabilities(engine)
logs.log('Queried distribution for {}.'.format(engine.name))
# TODO(mukundv): Update once we choose a temperature parameter for final
# implementation.
for row in data:
curr_strategy = data_types.FuzzStrategyProbability()
curr_strategy.strategy_name = str(row['strategy'])
curr_strategy.probability = float(row['bandit_weight'])
curr_strategy.engine = engine.name
strategy_data.append(curr_strategy)
query = data_types.FuzzStrategyProbability.query(
data_types.FuzzStrategyProbability.engine == engine.name)
ndb_utils.delete_multi(
[entity.key for entity in ndb_utils.get_all_from_query(query)])
ndb_utils.put_multi(strategy_data)
logs.log('Uploaded queried distribution to ndb for {}'.format(engine.name))
_store_probabilities_in_bigquery(engine, data)
logs.log('Uploaded queried distribution to BigQuery for {}'.format(
engine.name))
def _query_and_upload_strategy_probabilities():
"""Uploads queried data into datastore.
Calls query functions and uploads query results
to datastore to use as new probabilities. Probabilities
are based on new_edges feature."""
strategy_data = []
data = _query_multi_armed_bandit_probabilities()
# TODO(mukundv): Update once we choose a temperature parameter for final
# implementation.
for row in data:
curr_strategy = data_types.FuzzStrategyProbability()
curr_strategy.strategy_name = str(row['strategy'])
curr_strategy.probability_high_temperature = float(
row['bandit_weight_high_temperature'])
curr_strategy.probability_low_temperature = float(
row['bandit_weight_low_temperature'])
curr_strategy.probability_medium_temperature = float(
row['bandit_weight_medium_temperature'])
strategy_data.append(curr_strategy)
ndb.delete_multi([
entity.key for entity in ndb_utils.get_all_from_model(
data_types.FuzzStrategyProbability)
])
ndb.put_multi(strategy_data)
_store_probabilities_in_bigquery(data)
def setUp(self):
"""Put data in the local ndb table the tests to query from."""
test_helpers.patch_environ(self)
test_helpers.patch(
self, ['bot.fuzzers.engine_common.decide_with_probability'])
self.mock.decide_with_probability.return_value = True
os.environ['USE_BANDIT_STRATEGY_SELECTION'] = 'True'
data = []
strategy1 = data_types.FuzzStrategyProbability()
strategy1.strategy_name = ('random_max_len,corpus_mutations_ml_rnn,'
'value_profile,recommended_dict,')
strategy1.probability = 1
data.append(strategy1)
ndb.put_multi(data)
def setUp(self):
"""Put data in the local ndb table the tests to query from."""
test_helpers.patch_environ(self)
test_helpers.patch(
self, ['bot.fuzzers.engine_common.decide_with_probability'])
self.mock.decide_with_probability.return_value = True
data = []
strategy1 = data_types.FuzzStrategyProbability()
strategy1.strategy_name = 'corpus_mutations_ml_rnn,corpus_subset,'
strategy1.probability = 1
strategy1.engine = 'afl'
data.append(strategy1)
ndb.put_multi(data)
distribution = fuzz_task.get_strategy_distribution_from_ndb()
environment.set_value('USE_BANDIT_STRATEGY_SELECTION', True)
environment.set_value('STRATEGY_SELECTION_DISTRIBUTION', distribution)
def _query_and_upload_strategy_probabilities():
"""Uploads queried data into datastore.
Calls query functions and uploads query results
to datastore to use as new probabilities. Probabilities
are based on new_edges feature."""
strategy_data = []
data = _query_multi_armed_bandit_probabilities()
for row in data:
curr_strategy = data_types.FuzzStrategyProbability()
curr_strategy.strategy_name = str(row['strategy'])
curr_strategy.probability = float(row['bandit_weight'])
strategy_data.append(curr_strategy)
ndb.delete_multi([
entity.key for entity in ndb_utils.get_all_from_model(
data_types.FuzzStrategyProbability)
])
ndb.put_multi(strategy_data)
_store_probabilities_in_bigquery(data)
def setUp(self):
"""Put data in the local ndb table the tests to query from."""
test_helpers.patch_environ(self)
test_helpers.patch(
self, ['bot.fuzzers.engine_common.decide_with_probability'])
self.mock.decide_with_probability.return_value = True
data = []
strategy1 = data_types.FuzzStrategyProbability()
strategy1.strategy_name = ('random_max_len,corpus_mutations_ml_rnn,'
'value_profile,recommended_dict,')
strategy1.probability_medium_temperature = 1
strategy1.probability_high_temperature = 1
strategy1.probability_low_temperature = 1
data.append(strategy1)
ndb.put_multi(data)
distribution = fuzz_task.get_strategy_distribution_from_ndb()
environment.set_value('USE_BANDIT_STRATEGY_SELECTION', True)
environment.set_value('STRATEGY_SELECTION_DISTRIBUTION', distribution)
