def modify_experiment_data_if_requested( # pylint: disable=too-many-arguments
experiment_df, experiment_names, benchmarks, fuzzers,
label_by_experiment, end_time, merge_with_clobber):
"""Helper function that returns a copy of |experiment_df| that is modified
based on the other parameters. These parameters come from values specified
by the user on the command line (or callers to generate_report)."""
if benchmarks:
# Filter benchmarks if requested.
experiment_df = data_utils.filter_benchmarks(experiment_df, benchmarks)
if fuzzers is not None:
# Filter fuzzers if requested.
experiment_df = data_utils.filter_fuzzers(experiment_df, fuzzers)
if label_by_experiment:
# Label each fuzzer by the experiment it came from to easily compare the
# same fuzzer accross multiple experiments.
experiment_df = data_utils.label_fuzzers_by_experiment(experiment_df)
if end_time is not None:
# Cut off the experiment at a specific time if requested.
experiment_df = data_utils.filter_max_time(experiment_df, end_time)
if merge_with_clobber:
# Merge with clobber if requested.
experiment_df = data_utils.clobber_experiments_data(
experiment_df, experiment_names)
return experiment_df
def test_clobber_experiments_data():
"""Tests that clobber experiments data clobbers stale snapshots from earlier
experiments."""
df = pd.concat(
create_experiment_data('experiment-%d' % experiment_num)
for experiment_num in range(3))
df.reset_index(inplace=True)
to_drop = df[(df.experiment == 'experiment-2') & (df.benchmark == 'libpng')
& (df.fuzzer == 'afl')].index
df.drop(to_drop, inplace=True)
experiments = list(df['experiment'].drop_duplicates().values)
df = data_utils.clobber_experiments_data(df, experiments)
columns = ['experiment', 'benchmark', 'fuzzer']
expected_result = pd.DataFrame([
['experiment-2', 'libpng', 'libfuzzer'],
['experiment-2', 'libxml', 'afl'],
['experiment-2', 'libxml', 'libfuzzer'],
['experiment-1', 'libpng', 'afl'],
],
columns=columns)
expected_result.sort_index(inplace=True)
assert (
df[columns].drop_duplicates().values == expected_result.values).all()
def generate_report(experiment_names,
report_directory,
report_name=None,
label_by_experiment=False,
benchmarks=None,
fuzzers=None,
report_type='default',
quick=False,
log_scale=False,
from_cached_data=False,
in_progress=False,
end_time=None,
merge_with_clobber=False):
"""Generate report helper."""
report_name = report_name or experiment_names[0]
filesystem.create_directory(report_directory)
data_path = os.path.join(report_directory, 'data.csv.gz')
if from_cached_data and os.path.exists(data_path):
experiment_df = pd.read_csv(data_path)
else:
experiment_df = queries.get_experiment_data(experiment_names)
# Save the raw data along with the report.
experiment_df.to_csv(data_path)
data_utils.validate_data(experiment_df)
if benchmarks is not None:
experiment_df = data_utils.filter_benchmarks(experiment_df, benchmarks)
if fuzzers is not None:
experiment_df = data_utils.filter_fuzzers(experiment_df, fuzzers)
if label_by_experiment:
experiment_df = data_utils.label_fuzzers_by_experiment(experiment_df)
if end_time is not None:
experiment_df = data_utils.filter_max_time(experiment_df, end_time)
if merge_with_clobber:
experiment_df = data_utils.clobber_experiments_data(
experiment_df, experiment_names)
fuzzer_names = experiment_df.fuzzer.unique()
plotter = plotting.Plotter(fuzzer_names, quick, log_scale)
experiment_ctx = experiment_results.ExperimentResults(
experiment_df, report_directory, plotter, experiment_name=report_name)
template = report_type + '.html'
detailed_report = rendering.render_report(experiment_ctx, template,
in_progress)
filesystem.write(os.path.join(report_directory, 'index.html'),
detailed_report)
def generate_report(experiment_names,
report_directory,
report_name=None,
label_by_experiment=False,
benchmarks=None,
fuzzers=None,
report_type='default',
quick=False,
log_scale=False,
from_cached_data=False,
in_progress=False,
end_time=None,
merge_with_clobber=False,
merge_with_clobber_nonprivate=False,
coverage_report=False):
"""Generate report helper."""
if merge_with_clobber_nonprivate:
experiment_names = (
queries.add_nonprivate_experiments_for_merge_with_clobber(
experiment_names))
main_experiment_name = experiment_names[0]
report_name = report_name or main_experiment_name
filesystem.create_directory(report_directory)
data_path = os.path.join(report_directory, 'data.csv.gz')
if from_cached_data and os.path.exists(data_path):
experiment_df = pd.read_csv(data_path)
description = "from cached data"
else:
experiment_df = queries.get_experiment_data(experiment_names)
description = queries.get_experiment_description(main_experiment_name)
data_utils.validate_data(experiment_df)
if benchmarks is not None:
experiment_df = data_utils.filter_benchmarks(experiment_df, benchmarks)
if fuzzers is not None:
experiment_df = data_utils.filter_fuzzers(experiment_df, fuzzers)
if label_by_experiment:
experiment_df = data_utils.label_fuzzers_by_experiment(experiment_df)
if end_time is not None:
experiment_df = data_utils.filter_max_time(experiment_df, end_time)
if merge_with_clobber or merge_with_clobber_nonprivate:
experiment_df = data_utils.clobber_experiments_data(
experiment_df, experiment_names)
# Save the filtered raw data along with the report if not using cached data
# or if the data does not exist.
if not from_cached_data or not os.path.exists(data_path):
experiment_df.to_csv(data_path)
# Load the coverage json summary file.
coverage_dict = {}
if coverage_report:
coverage_dict = coverage_data_utils.get_covered_regions_dict(
experiment_df)
fuzzer_names = experiment_df.fuzzer.unique()
plotter = plotting.Plotter(fuzzer_names, quick, log_scale)
experiment_ctx = experiment_results.ExperimentResults(
experiment_df,
coverage_dict,
report_directory,
plotter,
experiment_name=report_name)
template = report_type + '.html'
detailed_report = rendering.render_report(experiment_ctx, template,
in_progress, coverage_report,
description)
filesystem.write(os.path.join(report_directory, 'index.html'),
detailed_report)
