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"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "ca65c59e",
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"source": [
"import pandas as pd\n",
"import os\n",
"import glob\n",
"import re\n",
"\n",
"def strip_comments_and_cwe(code):\n",
" \"\"\"Strip comments and CWE-related variable names from code.\"\"\"\n",
" code = re.sub(r'/\\*.*?\\*/', '', code, flags=re.DOTALL)\n",
" code = re.sub(r'//.*?\\n', '\\n', code)\n",
" code = re.sub(r'\\bCWE\\d{3}_\\w+', 'var', code)\n",
" code = re.sub(r'\\n\\s*\\n', '\\n', code).strip()\n",
" return code\n",
"\n",
"def extract_none_samples_from_juliet(juliet_dir):\n",
" \"\"\"Extract 'good' (non-vulnerable) samples from Juliet dataset and label as 'none'.\"\"\"\n",
" cwes = ['CWE121', 'CWE78', 'CWE122', 'CWE190', 'CWE191']\n",
" good_samples = []\n",
" \n",
" for cwe in cwes:\n",
" cwe_dir = os.path.join(juliet_dir, f'{cwe}*')\n",
" cwe_dirs = glob.glob(cwe_dir)\n",
" for dir_path in cwe_dirs:\n",
" good_files = glob.glob(os.path.join(dir_path, '*good*.c'))\n",
" for file_path in good_files:\n",
" try:\n",
" with open(file_path, 'r', encoding='utf-8', errors='ignore') as f:\n",
" code = f.read()\n",
" good_samples.append({\n",
" 'cwe': 'none',\n",
" 'code': code,\n",
" 'file': os.path.basename(file_path)\n",
" })\n",
" except Exception as e:\n",
" print(f\"Error reading {file_path}: {e}\")\n",
" \n",
" return pd.DataFrame(good_samples)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ef098685",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Juliet directory exists: True\n",
"\n",
"Looking for CWE121 directories: ['C:\\\\Users\\\\MartyNattakit\\\\Desktop\\\\Datasets\\\\2022-08-11-juliet-c-cplusplus-v1-3-1-with-extra-support\\\\cwe121_results.txt']\n",
"Good files in C:\\Users\\MartyNattakit\\Desktop\\Datasets\\2022-08-11-juliet-c-cplusplus-v1-3-1-with-extra-support\\cwe121_results.txt: []\n",
"\n",
"Looking for CWE78 directories: []\n",
"\n",
"Looking for CWE122 directories: []\n",
"\n",
"Looking for CWE190 directories: []\n",
"\n",
"Looking for CWE191 directories: []\n"
]
}
],
"source": [
"import os\n",
"import glob\n",
"\n",
"# Updated Juliet path\n",
"juliet_dir = r\"C:\\Users\\MartyNattakit\\Desktop\\Datasets\\2022-08-11-juliet-c-cplusplus-v1-3-1-with-extra-support\"\n",
"\n",
"# Check if directory exists\n",
"print(f\"Juliet directory exists: {os.path.exists(juliet_dir)}\")\n",
"\n",
"# Check for CWE directories\n",
"cwes = ['CWE121', 'CWE78', 'CWE122', 'CWE190', 'CWE191']\n",
"for cwe in cwes:\n",
" cwe_dir = os.path.join(juliet_dir, f'{cwe}*')\n",
" cwe_dirs = glob.glob(cwe_dir)\n",
" print(f\"\\nLooking for {cwe} directories: {cwe_dirs}\")\n",
" for dir_path in cwe_dirs:\n",
" good_files = glob.glob(os.path.join(dir_path, '*good*.c'))\n",
" print(f\"Good files in {dir_path}: {good_files}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b95cd6d2",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Loaded original dataset with 2000 samples.\n",
"Original CWE Distribution:\n",
" cwe\n",
"CWE121 400\n",
"CWE78 400\n",
"CWE190 400\n",
"CWE191 400\n",
"CWE122 400\n",
"Name: count, dtype: int64\n",
"Extracted 0 'none' samples from Juliet.\n",
"No 'none' samples found in Juliet. Adding synthetic 'none' samples instead...\n",
"Updated dataset saved as C:\\Users\\MartyNattakit\\Desktop\\CodeSentinel\\cwe_top5_sampled_with_juliet_none.csv with 2400 samples.\n",
"Final CWE Distribution:\n",
" cwe\n",
"CWE121 400\n",
"CWE78 400\n",
"CWE190 400\n",
"CWE191 400\n",
"CWE122 400\n",
"none 400\n",
"Name: count, dtype: int64\n",
"Unique CWE labels: ['CWE121' 'CWE78' 'CWE190' 'CWE191' 'CWE122' 'none']\n"
]
}
],
"source": [
"#Prepare and Save Dataset\n",
"original_csv_path = r\"C:\\Users\\MartyNattakit\\Desktop\\CodeSentinel\\cwe_top5_sampled.csv\"\n",
"juliet_dir = r\"C:\\Users\\MartyNattakit\\Desktop\\Datasets\\2022-08-11-juliet-c-cplusplus-v1-3-1-with-extra-support\"\n",
"output_csv_path = r\"C:\\Users\\MartyNattakit\\Desktop\\CodeSentinel\\cwe_top5_sampled_with_juliet_none.csv\"\n",
"\n",
"# Load the original dataset\n",
"full_df = pd.read_csv(original_csv_path)\n",
"print(f\"Loaded original dataset with {len(full_df)} samples.\")\n",
"print(\"Original CWE Distribution:\\n\", full_df['cwe'].value_counts())\n",
"\n",
"# Extract 'none' samples from Juliet\n",
"none_df = extract_none_samples_from_juliet(juliet_dir)\n",
"print(f\"Extracted {len(none_df)} 'none' samples from Juliet.\")\n",
"\n",
"# Fallback: If no 'none' samples extracted, add synthetic ones\n",
"if len(none_df) == 0:\n",
" print(\"No 'none' samples found in Juliet. Adding synthetic 'none' samples instead...\")\n",
" none_samples = pd.DataFrame({\n",
" 'cwe': ['none'] * 400,\n",
" 'code': [\n",
" 'int main() { printf(\"Hello, World!\"); return 0; }',\n",
" 'void func() { int x = 5; printf(\"%d\", x); }',\n",
" 'int add(int a, int b) { return a + b; }',\n",
" 'void loop() { for(int i = 0; i < 10; i++) { printf(\".\"); } }',\n",
" 'int main() { char str[] = \"test\"; puts(str); return 0; }'\n",
" ] * 80,\n",
" 'file': ['synthetic_none_' + str(i) + '.c' for i in range(400)]\n",
" })\n",
" none_df = none_samples\n",
"\n",
"# Combine with original dataset\n",
"full_df = pd.concat([full_df, none_df], ignore_index=True)\n",
"\n",
"# Clean the code\n",
"full_df['code'] = full_df['code'].apply(strip_comments_and_cwe)\n",
"\n",
"# Save the updated dataset\n",
"full_df.to_csv(output_csv_path, index=False)\n",
"print(f\"Updated dataset saved as {output_csv_path} with {len(full_df)} samples.\")\n",
"print(\"Final CWE Distribution:\\n\", full_df['cwe'].value_counts())\n",
"print(\"Unique CWE labels:\", full_df['cwe'].unique())"
]
}
],
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