updated table in report
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5 changed files with 26 additions and 21 deletions
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@ -57,6 +57,8 @@ def compute_stats(df_gen: pd.DataFrame, df_fuz: pd.DataFrame, output_file: str,
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combined_df.columns = ['source', *combined_df.columns[1:]]
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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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del combined_df[combined_df.columns[1]]
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combined_df = combined_df.sort_values(['source', 'file'])
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plt.figure(figsize=(10, 6))
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plt.figure(figsize=(10, 6))
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sns.set(style="whitegrid")
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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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sns.boxplot(data=combined_df, x="file", y="score", hue="source")
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@ -66,7 +68,7 @@ def compute_stats(df_gen: pd.DataFrame, df_fuz: pd.DataFrame, output_file: str,
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plt.savefig(output_file)
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plt.savefig(output_file)
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plt.figure(figsize=(10, 6))
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plt.figure(figsize=(10, 6))
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df_avg = combined_df.groupby(['file', 'source']).mean().reset_index()
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df_avg = combined_df.groupby(['file', 'source']).mean().reset_index().sort_values(['source', 'file'])
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sns.set(style="whitegrid")
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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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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.yticks(range(0, 101, 10))
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@ -112,20 +112,23 @@
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\centering
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\centering
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\begin{tabular}{lrrp{3.5cm}r}
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\begin{tabular}{lrrp{3.5cm}r}
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\toprule
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\toprule
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\textbf{File} & \textbf{$E(\text{Fuzzer})$} & \textbf{$E(\text{Genetic})$} & \hfill \textbf{Cohen's $d$} & \textbf{Wilcoxon $p$} \\ \midrule
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\textbf{File} & \textbf{$E(\text{Fuzzer})$} & \textbf{$E(\text{Genetic})$} & \textbf{Cohen's $|d|$} & \textbf{Wilcoxon $p$} \\
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anagram\_check & 23.16 & 18.51 & -0.5690 \hfill (Large) & 0.0526 \\
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\midrule
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caesar\_cipher & 60.18 & 62.39 & 0.4672 \hfill (Medium) & 0.3590 \\
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check\_armstrong & 58.07 & 93.50 & 2.0757 \hfill Huge & 0.0020 \\
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check\_armstrong & 89.54 & 89.18 & -0.1427 \hfill (Small) & 0.6250 \\
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railfence\_cipher & 88.41 & 87.44 & 0.8844 \hfill Very large & 0.1011 \\
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common\_divisor\_count & 71.21 & 72.26 & 0.2596 \hfill (Medium) & 0.5566 \\
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longest\_substring & 77.41 & 76.98 & 0.0771 \hfill Small & 0.7589 \\
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exponentiation & 68.99 & 68.48 & -0.0990 \hfill (Small) & 0.7695 \\
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common\_divisor\_count & 76.17 & 72.76 & 0.7471 \hfill Large & 0.1258 \\
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gcd & 50.80 & 44.68 & -1.0306 \hfill (Very large) & 0.0665 \\
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zellers\_birthday & 68.09 & 71.75 & 1.4701 \hfill Huge & 0.0039 \\
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longest\_substring & 83.96 & 83.01 & -0.1547 \hfill (Small) & 0.8457 \\
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exponentiation & 69.44 & 67.14 & 0.3342 \hfill Medium & 0.7108 \\
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rabin\_karp & 66.15 & 64.51 & -0.4597 \hfill (Medium) & 0.3081 \\
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caesar\_cipher & 60.59 & 61.20 & 0.3549 \hfill Medium & 0.2955 \\
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railfence\_cipher & 90.28 & 89.62 & -0.3514 \hfill (Medium) & 0.3750 \\
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gcd & 59.15 & 55.66 & 0.5016 \hfill Large & 0.1627 \\
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zellers\_birthday & 69.10 & 67.86 & -0.5598 \hfill (Large) & 0.1851 \\ \bottomrule
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rabin\_karp & 27.90 & 47.55 & 2.3688 \hfill Huge & 0.0078 \\
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anagram\_check & 23.10 & 7.70 & $\infty$ \hfill Huge & 0.0020 \\
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\bottomrule
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\end{tabular}
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\end{tabular}
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\caption{Statistical comparison between fuzzer and genetic algorithm test case generation in terms of mutation
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\caption{Statistical comparison between fuzzer and genetic algorithm test case generation in terms of mutation
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score as reported by \textit{mut.py} over 10 runs. The table reports run means, the wilcoxon paired test p-value
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score as reported by \textit{mut.py} over 10 runs, sorted by genetic mutation score. The table reports run
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and the Cohen's $d$ effect size for each file in the benchmark.}\label{tab:stats}
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means, the wilcoxon paired test p-value and the Cohen's $d$ effect size for each file in the
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benchmark.}\label{tab:stats}
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\end{table}
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\end{table}
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\end{document}
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\end{document}
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