def run(self) -> List[Individual]:
gen = 0
while self.budget_manager.is_budget_available():
logger.log_progress(
f"\n---> Starting generation {str(gen)} "
f"at {str(self.budget_manager.get_time_budget_used())}")
new_individuals = self.population_generator.generate(
self.sampling_size, gen=gen)
if new_individuals is None:
# Timeout occurred
break
success = self.parallel_evaluator.evaluate(new_individuals)
if not success:
# Timeout occurred
break
# update logbook
self.parallel_evaluator.test_suite_evaluator.update_logbook(
gen, new_individuals)
history = RequiredFeature('history').request()
history.update(self.population)
# select best individuals between current population and new one
self.population[:] = self.toolbox.selectBest(
self.population + new_individuals, self.population_size)
gen += 1
return self.population
def set_empty_fitness(self, individual: IndividualMultiObjective) -> None:
individual.fitness.values = (0, sys.maxsize, 0)
individual.evaluation_finish_timestamp = time.time()
individual.evaluation_elapsed_time = 0
hall_of_fame = RequiredFeature('hall_of_fame').request()
hall_of_fame.update([individual])
def evaluate(
self, device: Device,
individual: IndividualMultiObjective) -> IndividualMultiObjective:
assert not individual.fitness.valid
coverage_fetcher = RequiredFeature('coverage_fetcher').request()
start_time = time.time()
device.mark_work_start()
script_path, suite_lengths = self.dump_individual_to_files(individual)
coverage, unique_crashes, scripts_crash_status = coverage_fetcher.get_suite_coverage(
script_path, device, individual.generation,
individual.index_in_generation)
# remove from suite lengths the scripts that did NOT cause a crash
for script, had_crash in scripts_crash_status.items():
if not had_crash:
suite_lengths.pop(script, None)
# 1st obj: coverage, 2nd: average seq length of the suite, 3rd: #crashes
if suite_lengths:
length = numpy.mean(list(suite_lengths.values()))
else:
length = sys.maxsize
crashes = len(unique_crashes)
individual.fitness.values = (coverage, length, crashes)
finish_time = time.time()
individual.evaluation_finish_timestamp = finish_time
individual.evaluation_elapsed_time = finish_time - start_time
individual.length = length
individual.crashes = crashes
hall_of_fame = RequiredFeature('hall_of_fame').request()
hall_of_fame.update([individual])
budget_manager = RequiredFeature('budget_manager').request()
budget_manager.increase_evaluations_used()
device.mark_work_stop()
return individual
def evaluate(
self, device: Device, individual: IndividualSingleObjective
) -> IndividualSingleObjective:
assert not individual.fitness.valid
coverage_fetcher = RequiredFeature('coverage_fetcher').request()
start_time = time.time()
device.mark_work_start()
script_path, suite_lengths = self.dump_individual_to_files(individual)
coverage, unique_crashes, scripts_crash_status = coverage_fetcher.get_suite_coverage(
script_path, device, individual.generation,
individual.index_in_generation)
# remove from suite lengths the scripts that did NOT cause a crash
for script, had_crash in scripts_crash_status.items():
if not had_crash:
suite_lengths.pop(script, None)
individual.fitness.values = (coverage, )
finish_time = time.time()
individual.evaluation_finish_timestamp = finish_time
individual.evaluation_elapsed_time = finish_time - start_time
# Save crashes and length info outside fitness, to avoid messing up with the "wvalues" and "dominates" function
# inside Deap's Fitness base class.
if suite_lengths:
individual.length = numpy.mean(list(suite_lengths.values()))
else:
individual.length = sys.maxsize
individual.crashes = len(unique_crashes)
hall_of_fame = RequiredFeature('hall_of_fame').request()
hall_of_fame.update([individual])
budget_manager = RequiredFeature('budget_manager').request()
budget_manager.increase_evaluations_used()
device.mark_work_stop()
return individual
def initPopulation(self) -> bool:
verbose_level: bool = RequiredFeature('verbose_level').request()
if verbose_level > 0:
logger.log_progress(
f"\n---> Starting to generate initial population "
f"at {str(self.budget_manager.get_time_budget_used())}")
self.population = self.population_generator.generate(
self.population_size, gen=0)
if self.population is None or len(
self.population) < self.population_size:
logger.log_progress(
"\nFailed to initialise population with proper size, exiting setup"
)
return False
if verbose_level > 0:
logger.log_progress(
f"\n---> Starting to evaluate initial population "
f"at {str(self.budget_manager.get_time_budget_used())}")
success = self.parallel_evaluator.evaluate(self.population)
if not success:
logger.log_progress(
"\nBudget ran out during parallel evaluation, exiting setup")
return False
self.parallel_evaluator.test_suite_evaluator.update_logbook(
0, self.population)
history = RequiredFeature('history').request()
history.update(self.population)
if verbose_level > 0:
logger.log_progress(
f"\n---> Finished creating initial population "
f"at {str(self.budget_manager.get_time_budget_used())}")
return True