130 lines
4.3 KiB
Python
130 lines
4.3 KiB
Python
import math
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import os
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import re
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import subprocess
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import sys
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from math import sqrt
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from statistics import mean, variance
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from typing import List, Dict
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import matplotlib.pyplot as plt
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import pandas as pd
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import seaborn as sns
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from scipy.stats import wilcoxon
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from tqdm import tqdm
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ROOT_DIR = os.path.dirname(__file__)
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IN_SOURCE_DIR = os.path.join(ROOT_DIR, "benchmark")
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IN_TEST_DIR = os.path.join(ROOT_DIR, "tests")
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IN_FUZZER_TEST_DIR = os.path.join(ROOT_DIR, "fuzzer_tests")
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OUT_DIR = os.path.join(ROOT_DIR, "out")
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MUT_PY_PATH = os.path.join(ROOT_DIR, 'env37', 'bin', 'mut.py')
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REPS: int = 10
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def cohen_d(d1: List[float], d2: List[float]) -> float:
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pooled_sd = sqrt(((len(d1) - 1) * variance(d1) + (len(d2) - 1) * variance(d2)) /
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(len(d1) + len(d2) - 2))
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if pooled_sd == 0:
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return math.inf
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return (mean(d1) - mean(d2)) / pooled_sd
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def effect_size(eff: float) -> str:
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if eff <= 0.01:
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return 'Very small'
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elif eff <= 0.2:
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return 'Small'
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elif eff <= 0.5:
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return 'Medium'
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elif eff <= 0.8:
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return 'Large'
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elif eff <= 1.2:
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return 'Very large'
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else:
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return 'Huge'
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def compute_stats(df_gen: pd.DataFrame, df_fuz: pd.DataFrame, output_file: str, avg_output_file: str, stat_csv: str):
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combined_df = pd.concat([df_gen, df_fuz], keys=["genetic", "fuzzer"]).reset_index()
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combined_df.columns = ['source', *combined_df.columns[1:]]
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del combined_df[combined_df.columns[1]]
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plt.figure(figsize=(18, 8))
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sns.set(style="whitegrid")
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sns.boxplot(data=combined_df, x="file", y="score", hue="source")
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plt.yticks(range(0, 101, 10))
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plt.savefig(output_file)
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plt.figure(figsize=(18, 8))
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df_avg = combined_df.groupby(['file', 'source']).mean().reset_index()
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sns.set(style="whitegrid")
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sns.barplot(data=df_avg, x="file", y="score", hue="source")
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plt.yticks(range(0, 101, 10))
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plt.savefig(avg_output_file)
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df_avg = df_avg.pivot(index='file', columns='source', values='score').rename_axis(None, axis=1)
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df_avg['cohen-d'] = [math.nan] * len(df_avg.index)
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df_avg['interpretation'] = [math.nan] * len(df_avg.index)
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df_avg['wilcoxon'] = [math.nan] * len(df_avg.index)
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for f in combined_df['file'].drop_duplicates():
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list_gen = df_gen.loc[(df_gen.file == f), 'score'].tolist()
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list_fuz = df_fuz.loc[(df_fuz.file == f), 'score'].tolist()
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df_avg.loc[f, 'cohen-d'] = cohen_d(list_gen, list_fuz)
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df_avg.loc[f, 'interpretation'] = effect_size(df_avg.loc[f, 'cohen-d'])
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df_avg.loc[f, 'wilcoxon'] = wilcoxon(list_gen, list_fuz, zero_method='zsplit').pvalue
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df_avg.to_csv(stat_csv)
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def run_mutpy(test_path: str, source_path: str) -> float:
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output = subprocess.check_output(
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[sys.executable,
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MUT_PY_PATH,
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'-t', source_path,
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'-u', test_path,
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'--hom-strategy', 'RANDOM',
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'--percentage', '75']).decode('utf-8')
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score = re.search('Mutation score \\[.*]: (\\d+\\.\\d+)%', output).group(1)
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return float(score)
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def mutate_suite(out_file: str, in_test_dir: str, to_test: List[str]):
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scores: List[Dict[str, any]] = []
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if os.path.isfile(out_file): # do not re-generate if file exists
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return pd.read_csv(out_file, index_col=0)
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for filename in tqdm(to_test, desc=f"mut.py [{os.path.basename(out_file)}]"):
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source_path = os.path.join(IN_SOURCE_DIR, f"{filename}.py")
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test_path = os.path.join(in_test_dir, f"test_{filename}.py")
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scores.append({
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'file': filename,
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'score': run_mutpy(test_path, source_path)
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})
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df = pd.DataFrame.from_records(scores)
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df.to_csv(out_file)
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return df
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def main():
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files = [os.path.splitext(f) for f in os.listdir(IN_SOURCE_DIR)]
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to_test = [file[0] for file in files if file[1] == ".py"]
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to_test = [e for t in to_test for e in ([t] * REPS)]
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df_gen = mutate_suite(os.path.join(OUT_DIR, 'mutation_results_genetic.csv'), IN_TEST_DIR, to_test)
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df_fuz = mutate_suite(os.path.join(OUT_DIR, 'mutation_results_fuzzer.csv'), IN_FUZZER_TEST_DIR, to_test)
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compute_stats(df_gen, df_fuz,
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os.path.join(OUT_DIR, "mutation_scores.png"),
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os.path.join(OUT_DIR, "mutation_scores_mean.png"),
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os.path.join(OUT_DIR, "stats.csv"))
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if __name__ == "__main__":
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main()
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