process/code/update_cve_test.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import psycopg2\n",
    "import pandas as pd\n",
    "import re\n",
    "import shutil\n",
    "import time\n",
    "from psycopg2.extras import DictCursor\n",
    "from contextlib import contextmanager\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "connected with Database successfully\n"
     ]
    }
   ],
   "source": [
    " # https://www.psycopg.org/docs/extras.html?highlight=dictcursor#psycopg2.extras.DictCursor\n",
    " con = psycopg2.connect(database=\"swhgd-popular-4k\", user=\"postgres\", password=\"Simba#@41\")\n",
    " print(\"connected with Database successfully\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "@contextmanager\n",
    "def timed_cursor():\n",
    "    start_time = time.time()\n",
    "    yield con.cursor(cursor_factory=DictCursor)\n",
    "    print(\"--- %s seconds ---\" % (time.time() - start_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_cve(row):\n",
    "    rv = list()\n",
    "    for regex in regexes:\n",
    "        rv.extend(list(set(regex.findall(row['msg']))))\n",
    "    if not rv and regex_sql_inj.search(row['msg']):\n",
    "        rv.append(\"sql_injection\")\n",
    "    return rv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def copy_cwe(row):\n",
    "    if not isinstance(row['cve_data'], str):\n",
    "        return row['cwe_group']\n",
    "    if 'CWE' in row['cve_data']:\n",
    "        return row['cve_data']\n",
    "    return row['cwe_group']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_statistics(df, lang):\n",
    "    cwe_counts = df['cwe_group'].value_counts()\n",
    "    cwe_counts.to_csv(os.path.join(res_dir, f'count_cwe_groups_{lang}.csv'), header=False)\n",
    "\n",
    "    severity_counts = df['severity'].value_counts()\n",
    "    severity_counts.to_csv(os.path.join(res_dir, f'count_severity_{lang}.csv'), header=False)\n",
    "\n",
    "    avg_impacts = df['impact_score'].describe()\n",
    "    avg_impacts.to_csv(os.path.join(res_dir, f'stat_impact_score_{lang}.csv'), header=False)\n",
    "\n",
    "    # Bug Fix -> \"Array must be all same time zone\"\n",
    "    df['commit_date'] = pd.to_datetime(df['commit_date'], utc=True)\n",
    "\n",
    "    stat_yearly = df[['commit_date']]\n",
    "    stat_yearly = stat_yearly.groupby(stat_yearly.commit_date.dt.year).count()\n",
    "    stat_yearly.to_csv(os.path.join(res_dir, f'fixes_per_year_{lang}.csv'), header=False)\n",
    "\n",
    "    cwe_group_year = df[['commit_date', \"cwe_group\"]]\n",
    "    cwe_group_year = cwe_group_year.groupby([cwe_group_year.commit_date.dt.year, 'cwe_group']).count()\n",
    "    cwe_group_year.to_csv(os.path.join(res_dir, f'count_cwe_groups_by_year_{lang}.csv'), header=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_on(lang):\n",
    "    df = None\n",
    "    with timed_cursor() as cur:\n",
    "        query = f\"SELECT * FROM cve_revs_test_{lang}\"\n",
    "        df = pd.read_sql_query(query, con=con)\n",
    "    if df is None:\n",
    "        print(\"Query failed\")\n",
    "        return\n",
    "\n",
    "    # Extract CVE number\n",
    "    df['cve_data'] = df.apply(lambda row: get_cve(row), axis=1)\n",
    "\n",
    "    # Explode revisions with multiple CVE\n",
    "    df = df.explode(\"cve_data\")\n",
    "\n",
    "    # CVEs should be all uppered\n",
    "    df['cve_data'] = df['cve_data'].str.upper()\n",
    "\n",
    "    # Clean unnecessary fields\n",
    "    del df['msg']\n",
    "    # Bug Fix -> There is no column named cve\n",
    "    # del df['cve']\n",
    "\n",
    "    # Drop false rows\n",
    "    df = df[df.cve_data.notnull()]\n",
    "\n",
    "    # JOIN the cve_list to the data\n",
    "    df = df.merge(cve_list, on=\"cve_data\", how=\"left\")\n",
    "    # Bug Fix -> Add published_date,severity,impact_score\n",
    "    df = pd.merge(df,cvss_list[['cve_data','published_date','severity','impact_score']],on='cve_data', how='left')\n",
    "    # Copy CWE if CWE was used in commit message\n",
    "    df['cwe_group'] = df.apply(lambda row: copy_cwe(row), axis=1)\n",
    "\n",
    "    # Save csv\n",
    "    df.to_csv(os.path.join(res_dir, rf'result_{lang}.csv'))\n",
    "\n",
    "    create_statistics(df, lang)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def select_random_rows(table, limit):\n",
    "    sql = f\"select * from {table} order by random() limit {limit};\"\n",
    "    with timed_cursor() as cur:\n",
    "        df = pd.read_sql_query(sql, con=con)\n",
    "        with open(os.path.join(res_dir, f\"sample_from_{table}.txt\"), 'w') as f:\n",
    "            for rec_index, rec in df.iterrows():\n",
    "                f.write(f'------- commit message {rec_index} starts -------\\n')\n",
    "                f.write(rec['msg'])\n",
    "                f.write('\\n------- commit message ends -------\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "--- 0.04899787902832031 seconds ---\n--- 0.0029993057250976562 seconds ---\n"
     ]
    }
   ],
   "source": [
    "res_dir = '../../data/results'\n",
    "\n",
    "regex_cve = re.compile(r\"(CVE-\\d{4}-\\d{4,})\", re.IGNORECASE)\n",
    "regex_cwe = re.compile(r\"(CWE-[\\d]{1,4})\", re.IGNORECASE)\n",
    "regex_nvd = re.compile(r\"(NVD .+)\", re.IGNORECASE)\n",
    "regex_sql_inj = re.compile(r\"sql ?injection\", re.IGNORECASE)\n",
    "\n",
    "regexes = [regex_cve, regex_cwe, regex_nvd]\n",
    "\n",
    "cve_list = pd.read_csv(os.path.join(\"../../data/cve_parsed\", \"cve_related_problems.csv\"), sep=\"\\t\")\n",
    "# Bug Fix -> Rename CVE to cve_data\n",
    "cve_list.rename(columns={\"CVE\": \"cve_data\", \"Problem\": \"cwe_group\"}, inplace=True)\n",
    "\n",
    "# CVSS Scores\n",
    "# Bug Fix -> Add published_date,severity,impact_score -> This Data wasn't available previously \n",
    "cvss_list = pd.read_csv(os.path.join(\"../../data/cve_parsed\", \"cve_cvss_scores.csv\"), sep=\"\\t\")\n",
    "# Bug Fix -> Rename Columns - CVE, \"Published Date\", \"Impact Score\"\n",
    "cvss_list.rename(columns={\"CVE\": \"cve_data\", \"Published Date\": \"published_date\", \"Impact Score\": \"impact_score\"}, inplace=True)\n",
    "\n",
    "if os.path.exists(res_dir):\n",
    "    shutil.rmtree(res_dir)\n",
    "os.makedirs(res_dir)\n",
    "run_on(\"js\")\n",
    "run_on(\"py\")\n",
    "\n",
    "# select_random_rows(\"cve_revs_py\", 348)\n",
    "# select_random_rows(\"cve_revs_js\", 352)"
   ]
  },
  {
   "source": [
    "## Statistics 1 \n",
    "\n",
    "Count Delta(Time span between published_date of CWE & commit_date of CWE)\n",
    "\n",
    "**RQ2** - Reaction Times to Security Issues:\n",
    "\n",
    "Here we can observe that it takes average 128 days for python and 49 days for JavaScript comminities to fix a vulnerablity.\n",
    "We observe opposit result comparing to research paper's result. \n",
    "\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "JS: 49.36274509803922\nPython: 128.56891326489742\n"
     ]
    },
    {
     "output_type": "display_data",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# stat1\n",
    "js_result_csv = pd.read_csv( os.path.join(\"../../data/results\", \"result_js.csv\"),index_col=0).dropna(subset=['published_date'])\n",
    "py_result_csv = pd.read_csv( os.path.join(\"../../data/results\", \"result_py.csv\"),index_col=0).dropna(subset=['published_date'])\n",
    "\n",
    "js_result_csv[['published_date','commit_date']] = js_result_csv[['published_date','commit_date']].apply(pd.to_datetime,utc=True)\n",
    "js_result_csv['js_delta'] = ((js_result_csv['commit_date'] - js_result_csv['published_date']).dt.days).apply(lambda val: val if val>0 else 0)\n",
    "js_stat_1 = js_result_csv.query('js_delta >= 0').drop(columns=['impact_score']).groupby(js_result_csv['commit_date'].dt.year).agg('mean')\n",
    "\n",
    "py_result_csv[['published_date','commit_date']] = py_result_csv[['published_date','commit_date']].apply(pd.to_datetime,utc=True)\n",
    "py_result_csv['py_delta'] = ((py_result_csv['commit_date'] - py_result_csv['published_date']).dt.days).apply(lambda val: val if val>0 else 0)\n",
    "py_stat_1 = py_result_csv.query('py_delta >= 0').drop(columns=['impact_score']).groupby(py_result_csv['commit_date'].dt.year).agg('mean')\n",
    "\n",
    "stat_1 = pd.concat([js_stat_1, py_stat_1], axis=1).query('js_delta > 0 or py_delta > 0')\n",
    "stat_1_ax = stat_1.plot(kind='bar', title='Average number of days between mitigation commit date and CVE publish date grouped by years', figsize=(20,10))\n",
    "stat_1_ax.set(xlabel=\"Year\", ylabel=\"Days\")\n",
    "print(\"JS: \"+str(stat_1['js_delta'].mean()))\n",
    "print(\"Python: \"+str(stat_1['py_delta'].mean()))"
   ]
  },
  {
   "source": [
    "## Statistics 2\n",
    "Count JS CWEs, Py CWEs & Common CWEs.\n",
    "\n",
    "**RQ1** - Typical Security Issue Types:\n",
    "\n",
    "The CWEs having at least 10 references in either of the analyzed languages are as follows:  \n",
    "- 'CWE-119'\n",
    "- 'CWE-189' \n",
    "- 'CWE-20'\n",
    "- 'CWE-399'\n",
    "- 'CWE-601'\n",
    "\n",
    "Interestingly, except for CWE-399 & (CWE-254, CWE-79)(not found 10 references of both) that is the type of the vulnerabilities mitigated in both languages, each of the CWE groups can be attributed to either JavaScript or Python projects.\n",
    "\n",
    "We observe different result comparing to research paper's result. \n",
    "\n",
    "Original Logic on stat2.py had one bug:\n",
    "```\n",
    "next(js_r)\n",
    "next(py_r)\n",
    "```\n",
    "Above code will skip 1 line from csv files while reading, however there are no headers in `count_cwe_groups_js.csv` or `count_cwe_groups_py.csv` "
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "JavaScript CVEs: 8\nPython CVEs: 18\nCommon CVEs: 23\nThe CWEs having at least 10 references in either of the analyzed languages are as follows\n['CWE-399', 'CWE-601', 'CWE-20', 'CWE-189', 'CWE-119']\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "          js    py\n",
       "CWE-399  1.0  30.0\n",
       "CWE-601  NaN  30.0\n",
       "CWE-20   NaN  20.0\n",
       "CWE-189  NaN  18.0\n",
       "CWE-119  NaN  13.0"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>js</th>\n      <th>py</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>CWE-399</th>\n      <td>1.0</td>\n      <td>30.0</td>\n    </tr>\n    <tr>\n      <th>CWE-601</th>\n      <td>NaN</td>\n      <td>30.0</td>\n    </tr>\n    <tr>\n      <th>CWE-20</th>\n      <td>NaN</td>\n      <td>20.0</td>\n    </tr>\n    <tr>\n      <th>CWE-189</th>\n      <td>NaN</td>\n      <td>18.0</td>\n    </tr>\n    <tr>\n      <th>CWE-119</th>\n      <td>NaN</td>\n      <td>13.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
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     "metadata": {},
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# Stat2\n",
    "js_count_cwe_groups_csv = pd.read_csv( os.path.join(\"../../data/results\", \"count_cwe_groups_js.csv\"), header=None, names=['CWE','js'], index_col='CWE')\n",
    "py_count_cwe_groups_csv = pd.read_csv( os.path.join(\"../../data/results\", \"count_cwe_groups_py.csv\"), header=None, names=['CWE','py'], index_col='CWE')\n",
    "\n",
    "# Excluding Columns 'NVD-CWE-noinfo' & 'NVD-CWE-Other'\n",
    "js_count_cwe_groups_csv = js_count_cwe_groups_csv.filter(regex=r'CWE-.[\\d]+', axis=0)\n",
    "py_count_cwe_groups_csv = py_count_cwe_groups_csv.filter(regex=r'CWE-.[\\d]+', axis=0)\n",
    "\n",
    "# ax = js_csv.plot(kind='bar',figsize=(20,10))\n",
    "# py_csv.plot(ax=ax, kind='bar')\n",
    "\n",
    "stat_2 = pd.concat([js_count_cwe_groups_csv, py_count_cwe_groups_csv], axis=1, sort=False)\n",
    "js_count = len(stat_2['js'].dropna())\n",
    "py_count = len(stat_2['py'].dropna())\n",
    "print(\"JavaScript CVEs: \"+ str(js_count))\n",
    "print(\"Python CVEs: \"+ str(py_count))\n",
    "print(\"Common CVEs: \"+ str(len(stat_2)))\n",
    "stat_2_ax = stat_2.plot(kind='bar', figsize=(20,10))\n",
    "stat_2_ax.set(xlabel=\"Number of security issues found with the given CWE types\", ylabel=\"No. of Vulneability migitation\")\n",
    "stat_2 = stat_2.query('js > 10 or py >10')\n",
    "# stat_2.plot(kind='bar', figsize=(20,10))\n",
    "print('The CWEs having at least 10 references in either of the analyzed languages are as follows')\n",
    "print(stat_2.index.to_list())\n",
    "stat_2\n",
    "# plt.show()"
   ]
  },
  {
   "source": [
    "## Statistics 3\n",
    "Count CWEs (per year) which are present in list given below if delta is >0: \n",
    " \n",
    "'CWE-399', 'CWE-601', 'CWE-20', 'CWE-189', 'CWE-119'"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "         js_delta    py_delta\n",
       "CWE-399     396.0    0.000000\n",
       "CWE-119       NaN  104.923077\n",
       "CWE-189       NaN  221.444444\n",
       "CWE-20        NaN  112.300000\n",
       "CWE-601       NaN    0.000000"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>js_delta</th>\n      <th>py_delta</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>CWE-399</th>\n      <td>396.0</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>CWE-119</th>\n      <td>NaN</td>\n      <td>104.923077</td>\n    </tr>\n    <tr>\n      <th>CWE-189</th>\n      <td>NaN</td>\n      <td>221.444444</td>\n    </tr>\n    <tr>\n      <th>CWE-20</th>\n      <td>NaN</td>\n      <td>112.300000</td>\n    </tr>\n    <tr>\n      <th>CWE-601</th>\n      <td>NaN</td>\n      <td>0.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 12
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 1440x720 with 1 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# stat3.py\n",
    "CWE = [\n",
    "        \"CWE-399\", \n",
    "        \"CWE-601\", \n",
    "        \"CWE-20\", \n",
    "        \"CWE-189\", \n",
    "        \"CWE-119\"\n",
    "      ]\n",
    "\n",
    "js_stat_3 = js_result_csv.query('js_delta >= 0 and cwe_group in @CWE').drop(columns=['impact_score']).groupby(js_result_csv['cwe_group']).agg('mean')\n",
    "py_stat_3 = py_result_csv.query('py_delta >= 0 and cwe_group in @CWE').drop(columns=['impact_score']).groupby(py_result_csv['cwe_group']).agg('mean')\n",
    "\n",
    "stat_3 = pd.concat([js_stat_3, py_stat_3], axis=1, sort=False)\n",
    "stat_3_ax = stat_3.plot(kind='bar', figsize=(20,10))\n",
    "stat_3_ax.set(xlabel=\"CWEs\", ylabel=\"Days\")\n",
    "stat_3"
   ]
  },
  {
   "source": [
    "## Statistics 4\n",
    "Count CWEs (per year) which are present in list given below:  \n",
    "'CWE-399', 'CWE-601', 'CWE-20', 'CWE-189', 'CWE-119'\n",
    "\n",
    "Original Logic on stat4.py had one bug:\n",
    "```\n",
    "next(js_reader)\n",
    "next(py_reader)\n",
    "```\n",
    "Above code will skip 1 line from csv files while reading, however there are no headers in `count_cwe_groups_by_year_js.csv` or `count_cwe_groups_by_year_py.csv` "
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "              js_count  py_count\n",
       "CWE     year                    \n",
       "CWE-119 2008       NaN       2.0\n",
       "        2010       NaN      10.0\n",
       "        2017       NaN       1.0\n",
       "CWE-189 2008       NaN      18.0\n",
       "CWE-20  2012       NaN      10.0\n",
       "        2013       NaN       8.0\n",
       "        2014       NaN       2.0\n",
       "CWE-399 2012       NaN      24.0\n",
       "        2013       NaN       6.0\n",
       "        2018       1.0       NaN\n",
       "CWE-601 2016       NaN      10.0\n",
       "        2017       NaN      12.0\n",
       "        2018       NaN       8.0"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th></th>\n      <th>js_count</th>\n      <th>py_count</th>\n    </tr>\n    <tr>\n      <th>CWE</th>\n      <th>year</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th rowspan=\"3\" valign=\"top\">CWE-119</th>\n      <th>2008</th>\n      <td>NaN</td>\n      <td>2.0</td>\n    </tr>\n    <tr>\n      <th>2010</th>\n      <td>NaN</td>\n      <td>10.0</td>\n    </tr>\n    <tr>\n      <th>2017</th>\n      <td>NaN</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>CWE-189</th>\n      <th>2008</th>\n      <td>NaN</td>\n      <td>18.0</td>\n    </tr>\n    <tr>\n      <th rowspan=\"3\" valign=\"top\">CWE-20</th>\n      <th>2012</th>\n      <td>NaN</td>\n      <td>10.0</td>\n    </tr>\n    <tr>\n      <th>2013</th>\n      <td>NaN</td>\n      <td>8.0</td>\n    </tr>\n    <tr>\n      <th>2014</th>\n      <td>NaN</td>\n      <td>2.0</td>\n    </tr>\n    <tr>\n      <th rowspan=\"3\" valign=\"top\">CWE-399</th>\n      <th>2012</th>\n      <td>NaN</td>\n      <td>24.0</td>\n    </tr>\n    <tr>\n      <th>2013</th>\n      <td>NaN</td>\n      <td>6.0</td>\n    </tr>\n    <tr>\n      <th>2018</th>\n      <td>1.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th rowspan=\"3\" valign=\"top\">CWE-601</th>\n      <th>2016</th>\n      <td>NaN</td>\n      <td>10.0</td>\n    </tr>\n    <tr>\n      <th>2017</th>\n      <td>NaN</td>\n      <td>12.0</td>\n    </tr>\n    <tr>\n      <th>2018</th>\n      <td>NaN</td>\n      <td>8.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 13
    },
    {
     "output_type": "display_data",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# stat4\n",
    "CWE = [\n",
    "        \"CWE-399\", \n",
    "        \"CWE-601\", \n",
    "        \"CWE-20\", \n",
    "        \"CWE-189\", \n",
    "        \"CWE-119\"\n",
    "      ]\n",
    "js_cwe_groups_by_year_csv = pd.read_csv( os.path.join(\"../../data/results\", \"count_cwe_groups_by_year_js.csv\"), header=None, names=['year','CWE','js_count'], index_col='year')\n",
    "py_cwe_groups_by_year_csv = pd.read_csv( os.path.join(\"../../data/results\", \"count_cwe_groups_by_year_py.csv\"), header=None, names=['year','CWE','py_count'], index_col='year')\n",
    "\n",
    "js_stat_4 = js_cwe_groups_by_year_csv.query('CWE in @CWE').groupby(['CWE','year']).sum()\n",
    "py_stat_4 = py_cwe_groups_by_year_csv.query('CWE in @CWE').groupby(['CWE','year']).sum()\n",
    "\n",
    "stat_4 = pd.concat([js_stat_4, py_stat_4], axis=1)\n",
    "stat_4_ax = stat_4.plot(kind='bar', figsize=(20,10))\n",
    "stat_4_ax.set(xlabel=\"CWE & Year\", ylabel=\"Vulneability Count\")\n",
    "stat_4"
   ]
  },
  {
   "source": [
    "## Statistics 5\n",
    "Count CWE & Vulnerability per year for python and javascript Projects\n",
    "\n",
    "Original Logic on stat5.py was buggy:\n",
    "\n",
    "```\n",
    "if year not in yearly:\n",
    "    yearly[year] = count\n",
    "yearly[year] += count\n",
    "```\n",
    "Above code will add count 2 times if year is not found"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "              js_count  py_count\n",
       "CWE     year                    \n",
       "CWE-119 2008       NaN       2.0\n",
       "        2010       NaN      10.0\n",
       "        2017       NaN       1.0\n",
       "CWE-134 2016       1.0       NaN\n",
       "CWE-185 2018       NaN       9.0\n",
       "CWE-189 2008       NaN      18.0\n",
       "CWE-190 2017       NaN       4.0\n",
       "CWE-20  2012       NaN      10.0\n",
       "        2013       NaN       8.0\n",
       "        2014       NaN       2.0\n",
       "CWE-200 2015       NaN       2.0\n",
       "        2016       NaN       2.0\n",
       "        2018       NaN       6.0\n",
       "CWE-254 2016       NaN       4.0\n",
       "        2018       1.0       2.0\n",
       "CWE-264 2016       NaN       3.0\n",
       "CWE-284 2016       NaN       2.0\n",
       "CWE-295 2018       NaN       4.0\n",
       "CWE-310 2017       NaN       4.0\n",
       "CWE-326 2014       NaN       4.0\n",
       "CWE-399 2012       NaN      24.0\n",
       "        2013       NaN       6.0\n",
       "        2018       1.0       NaN\n",
       "CWE-400 2018       2.0       NaN\n",
       "CWE-471 2018       1.0       NaN\n",
       "CWE-601 2016       NaN      10.0\n",
       "        2017       NaN      12.0\n",
       "        2018       NaN       8.0\n",
       "CWE-693 2017       NaN       1.0\n",
       "CWE-77  2016       2.0       NaN\n",
       "CWE-78  2018       8.0       NaN\n",
       "CWE-79  2016       NaN       7.0\n",
       "        2017       NaN       3.0\n",
       "        2018       3.0       NaN\n",
       "CWE-798 2016       NaN       6.0\n",
       "CWE-909 2018       NaN       8.0"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th></th>\n      <th>js_count</th>\n      <th>py_count</th>\n    </tr>\n    <tr>\n      <th>CWE</th>\n      <th>year</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th rowspan=\"3\" valign=\"top\">CWE-119</th>\n      <th>2008</th>\n      <td>NaN</td>\n      <td>2.0</td>\n    </tr>\n    <tr>\n      <th>2010</th>\n      <td>NaN</td>\n      <td>10.0</td>\n    </tr>\n    <tr>\n      <th>2017</th>\n      <td>NaN</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>CWE-134</th>\n      <th>2016</th>\n      <td>1.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>CWE-185</th>\n      <th>2018</th>\n      <td>NaN</td>\n      <td>9.0</td>\n    </tr>\n    <tr>\n      <th>CWE-189</th>\n      <th>2008</th>\n      <td>NaN</td>\n      <td>18.0</td>\n    </tr>\n    <tr>\n      <th>CWE-190</th>\n      <th>2017</th>\n      <td>NaN</td>\n      <td>4.0</td>\n    </tr>\n    <tr>\n      <th rowspan=\"3\" valign=\"top\">CWE-20</th>\n      <th>2012</th>\n      <td>NaN</td>\n      <td>10.0</td>\n    </tr>\n    <tr>\n      <th>2013</th>\n      <td>NaN</td>\n      <td>8.0</td>\n    </tr>\n    <tr>\n      <th>2014</th>\n      <td>NaN</td>\n      <td>2.0</td>\n    </tr>\n    <tr>\n      <th rowspan=\"3\" valign=\"top\">CWE-200</th>\n      <th>2015</th>\n      <td>NaN</td>\n      <td>2.0</td>\n    </tr>\n    <tr>\n      <th>2016</th>\n      <td>NaN</td>\n      <td>2.0</td>\n    </tr>\n    <tr>\n      <th>2018</th>\n      <td>NaN</td>\n      <td>6.0</td>\n    </tr>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">CWE-254</th>\n      <th>2016</th>\n      <td>NaN</td>\n      <td>4.0</td>\n    </tr>\n    <tr>\n      <th>2018</th>\n      <td>1.0</td>\n      <td>2.0</td>\n    </tr>\n    <tr>\n      <th>CWE-264</th>\n      <th>2016</th>\n      <td>NaN</td>\n      <td>3.0</td>\n    </tr>\n    <tr>\n      <th>CWE-284</th>\n      <th>2016</th>\n      <td>NaN</td>\n      <td>2.0</td>\n    </tr>\n    <tr>\n      <th>CWE-295</th>\n      <th>2018</th>\n      <td>NaN</td>\n      <td>4.0</td>\n    </tr>\n    <tr>\n      <th>CWE-310</th>\n      <th>2017</th>\n      <td>NaN</td>\n      <td>4.0</td>\n    </tr>\n    <tr>\n      <th>CWE-326</th>\n      <th>2014</th>\n      <td>NaN</td>\n      <td>4.0</td>\n    </tr>\n    <tr>\n      <th rowspan=\"3\" valign=\"top\">CWE-399</th>\n      <th>2012</th>\n      <td>NaN</td>\n      <td>24.0</td>\n    </tr>\n    <tr>\n      <th>2013</th>\n      <td>NaN</td>\n      <td>6.0</td>\n    </tr>\n    <tr>\n      <th>2018</th>\n      <td>1.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>CWE-400</th>\n      <th>2018</th>\n      <td>2.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>CWE-471</th>\n      <th>2018</th>\n      <td>1.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th rowspan=\"3\" valign=\"top\">CWE-601</th>\n      <th>2016</th>\n      <td>NaN</td>\n      <td>10.0</td>\n    </tr>\n    <tr>\n      <th>2017</th>\n      <td>NaN</td>\n      <td>12.0</td>\n    </tr>\n    <tr>\n      <th>2018</th>\n      <td>NaN</td>\n      <td>8.0</td>\n    </tr>\n    <tr>\n      <th>CWE-693</th>\n      <th>2017</th>\n      <td>NaN</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>CWE-77</th>\n      <th>2016</th>\n      <td>2.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>CWE-78</th>\n      <th>2018</th>\n      <td>8.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th rowspan=\"3\" valign=\"top\">CWE-79</th>\n      <th>2016</th>\n      <td>NaN</td>\n      <td>7.0</td>\n    </tr>\n    <tr>\n      <th>2017</th>\n      <td>NaN</td>\n      <td>3.0</td>\n    </tr>\n    <tr>\n      <th>2018</th>\n      <td>3.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>CWE-798</th>\n      <th>2016</th>\n      <td>NaN</td>\n      <td>6.0</td>\n    </tr>\n    <tr>\n      <th>CWE-909</th>\n      <th>2018</th>\n      <td>NaN</td>\n      <td>8.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 15
    },
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932.099219)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-40\"/>\r\n       <use x=\"39.013672\" xlink:href=\"#DejaVuSans-67\"/>\r\n       <use x=\"108.837891\" xlink:href=\"#DejaVuSans-87\"/>\r\n       <use x=\"207.714844\" xlink:href=\"#DejaVuSans-69\"/>\r\n       <use x=\"270.898438\" xlink:href=\"#DejaVuSans-45\"/>\r\n       <use x=\"306.982422\" xlink:href=\"#DejaVuSans-49\"/>\r\n       <use x=\"370.605469\" xlink:href=\"#DejaVuSans-56\"/>\r\n       <use x=\"434.228516\" xlink:href=\"#DejaVuSans-57\"/>\r\n       <use x=\"497.851562\" xlink:href=\"#DejaVuSans-44\"/>\r\n       <use x=\"529.638672\" xlink:href=\"#DejaVuSans-32\"/>\r\n       <use x=\"561.425781\" xlink:href=\"#DejaVuSans-50\"/>\r\n       <use x=\"625.048828\" xlink:href=\"#DejaVuSans-48\"/>\r\n       <use x=\"688.671875\" xlink:href=\"#DejaVuSans-48\"/>\r\n       <use x=\"752.294922\" xlink:href=\"#DejaVuSans-56\"/>\r\n       <use x=\"815.917969\" xlink:href=\"#DejaVuSans-41\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"ytick_7\">\r\n     <g id=\"line2d_13\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"113.371875\" xlink:href=\"#m59e50d3d58\" y=\"898.1\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_13\">\r\n      <!-- (CWE-190, 2017) -->\r\n      <g transform=\"translate(20.878125 901.899219)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-40\"/>\r\n       <use x=\"39.013672\" xlink:href=\"#DejaVuSans-67\"/>\r\n       <use x=\"108.837891\" xlink:href=\"#DejaVuSans-87\"/>\r\n       <use x=\"207.714844\" xlink:href=\"#DejaVuSans-69\"/>\r\n       <use x=\"270.898438\" xlink:href=\"#DejaVuSans-45\"/>\r\n       <use x=\"306.982422\" xlink:href=\"#DejaVuSans-49\"/>\r\n       <use x=\"370.605469\" xlink:href=\"#DejaVuSans-57\"/>\r\n       <use x=\"434.228516\" xlink:href=\"#DejaVuSans-48\"/>\r\n       <use x=\"497.851562\" xlink:href=\"#DejaVuSans-44\"/>\r\n       <use x=\"529.638672\" xlink:href=\"#DejaVuSans-32\"/>\r\n       <use x=\"561.425781\" xlink:href=\"#DejaVuSans-50\"/>\r\n       <use x=\"625.048828\" xlink:href=\"#DejaVuSans-48\"/>\r\n       <use x=\"688.671875\" xlink:href=\"#DejaVuSans-49\"/>\r\n       <use x=\"752.294922\" xlink:href=\"#DejaVuSans-55\"/>\r\n       <use x=\"815.917969\" xlink:href=\"#DejaVuSans-41\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"ytick_8\">\r\n     <g id=\"line2d_14\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"113.371875\" xlink:href=\"#m59e50d3d58\" y=\"867.9\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_14\">\r\n      <!-- (CWE-20, 2012) -->\r\n      <g transform=\"translate(27.240625 871.699219)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-40\"/>\r\n       <use x=\"39.013672\" xlink:href=\"#DejaVuSans-67\"/>\r\n       <use x=\"108.837891\" xlink:href=\"#DejaVuSans-87\"/>\r\n       <use x=\"207.714844\" xlink:href=\"#DejaVuSans-69\"/>\r\n       <use x=\"270.898438\" xlink:href=\"#DejaVuSans-45\"/>\r\n      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xlink:href=\"#DejaVuSans-40\"/>\r\n       <use x=\"39.013672\" xlink:href=\"#DejaVuSans-67\"/>\r\n       <use x=\"108.837891\" xlink:href=\"#DejaVuSans-87\"/>\r\n       <use x=\"207.714844\" xlink:href=\"#DejaVuSans-69\"/>\r\n       <use x=\"270.898438\" xlink:href=\"#DejaVuSans-45\"/>\r\n       <use x=\"306.982422\" xlink:href=\"#DejaVuSans-50\"/>\r\n       <use x=\"370.605469\" xlink:href=\"#DejaVuSans-48\"/>\r\n       <use x=\"434.228516\" xlink:href=\"#DejaVuSans-44\"/>\r\n       <use x=\"466.015625\" xlink:href=\"#DejaVuSans-32\"/>\r\n       <use x=\"497.802734\" xlink:href=\"#DejaVuSans-50\"/>\r\n       <use x=\"561.425781\" xlink:href=\"#DejaVuSans-48\"/>\r\n       <use x=\"625.048828\" xlink:href=\"#DejaVuSans-49\"/>\r\n       <use x=\"688.671875\" xlink:href=\"#DejaVuSans-51\"/>\r\n       <use x=\"752.294922\" xlink:href=\"#DejaVuSans-41\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"ytick_10\">\r\n     <g id=\"line2d_16\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"113.371875\" xlink:href=\"#m59e50d3d58\" y=\"807.5\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_16\">\r\n      <!-- (CWE-20, 2014) -->\r\n      <g transform=\"translate(27.240625 811.299219)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-40\"/>\r\n       <use x=\"39.013672\" xlink:href=\"#DejaVuSans-67\"/>\r\n       <use x=\"108.837891\" xlink:href=\"#DejaVuSans-87\"/>\r\n       <use x=\"207.714844\" xlink:href=\"#DejaVuSans-69\"/>\r\n       <use x=\"270.898438\" xlink:href=\"#DejaVuSans-45\"/>\r\n       <use x=\"306.982422\" xlink:href=\"#DejaVuSans-50\"/>\r\n       <use x=\"370.605469\" xlink:href=\"#DejaVuSans-48\"/>\r\n       <use x=\"434.228516\" xlink:href=\"#DejaVuSans-44\"/>\r\n       <use x=\"466.015625\" xlink:href=\"#DejaVuSans-32\"/>\r\n       <use x=\"497.802734\" xlink:href=\"#DejaVuSans-50\"/>\r\n       <use x=\"561.425781\" xlink:href=\"#DejaVuSans-48\"/>\r\n       <use x=\"625.048828\" xlink:href=\"#DejaVuSans-49\"/>\r\n       <use x=\"688.671875\" xlink:href=\"#DejaVuSans-52\"/>\r\n       <use x=\"752.294922\" xlink:href=\"#DejaVuSans-41\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"ytick_11\">\r\n     <g id=\"line2d_17\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"113.371875\" xlink:href=\"#m59e50d3d58\" y=\"777.3\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_17\">\r\n      <!-- (CWE-200, 2015) -->\r\n      <g transform=\"translate(20.878125 781.099219)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-40\"/>\r\n       <use x=\"39.013672\" xlink:href=\"#DejaVuSans-67\"/>\r\n       <use x=\"108.837891\" xlink:href=\"#DejaVuSans-87\"/>\r\n       <use x=\"207.714844\" xlink:href=\"#DejaVuSans-69\"/>\r\n       <use x=\"270.898438\" xlink:href=\"#DejaVuSans-45\"/>\r\n       <use x=\"306.982422\" xlink:href=\"#DejaVuSans-50\"/>\r\n       <use x=\"370.605469\" xlink:href=\"#DejaVuSans-48\"/>\r\n       <use x=\"434.228516\" xlink:href=\"#DejaVuSans-48\"/>\r\n       <use x=\"497.851562\" xlink:href=\"#DejaVuSans-44\"/>\r\n       <use x=\"529.638672\" xlink:href=\"#DejaVuSans-32\"/>\r\n       <use x=\"561.425781\" xlink:href=\"#DejaVuSans-50\"/>\r\n       <use x=\"625.048828\" xlink:href=\"#DejaVuSans-48\"/>\r\n       <use x=\"688.671875\" xlink:href=\"#DejaVuSans-49\"/>\r\n       <use x=\"752.294922\" xlink:href=\"#DejaVuSans-53\"/>\r\n       <use x=\"815.917969\" xlink:href=\"#DejaVuSans-41\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"ytick_12\">\r\n     <g id=\"line2d_18\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"113.371875\" xlink:href=\"#m59e50d3d58\" y=\"747.1\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_18\">\r\n      <!-- (CWE-200, 2016) -->\r\n      <g transform=\"translate(20.878125 750.899219)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-40\"/>\r\n       <use x=\"39.013672\" xlink:href=\"#DejaVuSans-67\"/>\r\n    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x=\"113.371875\" xlink:href=\"#m59e50d3d58\" y=\"716.9\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_19\">\r\n      <!-- (CWE-200, 2018) -->\r\n      <g transform=\"translate(20.878125 720.699219)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-40\"/>\r\n       <use x=\"39.013672\" xlink:href=\"#DejaVuSans-67\"/>\r\n       <use x=\"108.837891\" xlink:href=\"#DejaVuSans-87\"/>\r\n       <use x=\"207.714844\" xlink:href=\"#DejaVuSans-69\"/>\r\n       <use x=\"270.898438\" xlink:href=\"#DejaVuSans-45\"/>\r\n       <use x=\"306.982422\" xlink:href=\"#DejaVuSans-50\"/>\r\n       <use x=\"370.605469\" xlink:href=\"#DejaVuSans-48\"/>\r\n       <use x=\"434.228516\" xlink:href=\"#DejaVuSans-48\"/>\r\n       <use x=\"497.851562\" xlink:href=\"#DejaVuSans-44\"/>\r\n       <use x=\"529.638672\" xlink:href=\"#DejaVuSans-32\"/>\r\n       <use x=\"561.425781\" xlink:href=\"#DejaVuSans-50\"/>\r\n       <use x=\"625.048828\" xlink:href=\"#DejaVuSans-48\"/>\r\n       <use x=\"688.671875\" xlink:href=\"#DejaVuSans-49\"/>\r\n       <use x=\"752.294922\" xlink:href=\"#DejaVuSans-56\"/>\r\n       <use x=\"815.917969\" xlink:href=\"#DejaVuSans-41\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"ytick_14\">\r\n     <g id=\"line2d_20\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"113.371875\" xlink:href=\"#m59e50d3d58\" y=\"686.7\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_20\">\r\n      <!-- (CWE-254, 2016) -->\r\n      <g transform=\"translate(20.878125 690.499219)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-40\"/>\r\n       <use x=\"39.013672\" xlink:href=\"#DejaVuSans-67\"/>\r\n       <use x=\"108.837891\" xlink:href=\"#DejaVuSans-87\"/>\r\n       <use x=\"207.714844\" xlink:href=\"#DejaVuSans-69\"/>\r\n       <use x=\"270.898438\" xlink:href=\"#DejaVuSans-45\"/>\r\n       <use x=\"306.982422\" xlink:href=\"#DejaVuSans-50\"/>\r\n       <use x=\"370.605469\" xlink:href=\"#DejaVuSans-53\"/>\r\n       <use x=\"434.228516\" xlink:href=\"#DejaVuSans-52\"/>\r\n       <use x=\"497.851562\" xlink:href=\"#DejaVuSans-44\"/>\r\n       <use x=\"529.638672\" xlink:href=\"#DejaVuSans-32\"/>\r\n       <use x=\"561.425781\" xlink:href=\"#DejaVuSans-50\"/>\r\n       <use x=\"625.048828\" xlink:href=\"#DejaVuSans-48\"/>\r\n       <use x=\"688.671875\" xlink:href=\"#DejaVuSans-49\"/>\r\n       <use x=\"752.294922\" xlink:href=\"#DejaVuSans-54\"/>\r\n       <use x=\"815.917969\" xlink:href=\"#DejaVuSans-41\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"ytick_15\">\r\n     <g id=\"line2d_21\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"113.371875\" xlink:href=\"#m59e50d3d58\" y=\"656.5\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_21\">\r\n      <!-- (CWE-254, 2018) -->\r\n      <g transform=\"translate(20.878125 660.299219)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-40\"/>\r\n       <use x=\"39.013672\" xlink:href=\"#DejaVuSans-67\"/>\r\n       <use x=\"108.837891\" xlink:href=\"#DejaVuSans-87\"/>\r\n       <use x=\"207.714844\" xlink:href=\"#DejaVuSans-69\"/>\r\n       <use x=\"270.898438\" xlink:href=\"#DejaVuSans-45\"/>\r\n       <use x=\"306.982422\" xlink:href=\"#DejaVuSans-50\"/>\r\n       <use x=\"370.605469\" xlink:href=\"#DejaVuSans-53\"/>\r\n       <use x=\"434.228516\" xlink:href=\"#DejaVuSans-52\"/>\r\n       <use x=\"497.851562\" xlink:href=\"#DejaVuSans-44\"/>\r\n       <use x=\"529.638672\" xlink:href=\"#DejaVuSans-32\"/>\r\n       <use x=\"561.425781\" xlink:href=\"#DejaVuSans-50\"/>\r\n       <use x=\"625.048828\" xlink:href=\"#DejaVuSans-48\"/>\r\n       <use x=\"688.671875\" xlink:href=\"#DejaVuSans-49\"/>\r\n       <use x=\"752.294922\" xlink:href=\"#DejaVuSans-56\"/>\r\n       <use x=\"815.917969\" xlink:href=\"#DejaVuSans-41\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"ytick_16\">\r\n     <g id=\"line2d_22\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"113.371875\" xlink:href=\"#m59e50d3d58\" y=\"626.3\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_22\">\r\n      <!-- (CWE-264, 2016) -->\r\n      <g transform=\"translate(20.878125 630.099219)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-40\"/>\r\n       <use x=\"39.013672\" xlink:href=\"#DejaVuSans-67\"/>\r\n       <use x=\"108.837891\" xlink:href=\"#DejaVuSans-87\"/>\r\n       <use x=\"207.714844\" xlink:href=\"#DejaVuSans-69\"/>\r\n       <use x=\"270.898438\" xlink:href=\"#DejaVuSans-45\"/>\r\n       <use x=\"306.982422\" xlink:href=\"#DejaVuSans-50\"/>\r\n       <use x=\"370.605469\" xlink:href=\"#DejaVuSans-54\"/>\r\n       <use x=\"434.228516\" xlink:href=\"#DejaVuSans-52\"/>\r\n       <use x=\"497.851562\" xlink:href=\"#DejaVuSans-44\"/>\r\n       <use x=\"529.638672\" xlink:href=\"#DejaVuSans-32\"/>\r\n       <use x=\"561.425781\" xlink:href=\"#DejaVuSans-50\"/>\r\n       <use x=\"625.048828\" xlink:href=\"#DejaVuSans-48\"/>\r\n       <use x=\"688.671875\" xlink:href=\"#DejaVuSans-49\"/>\r\n       <use x=\"752.294922\" xlink:href=\"#DejaVuSans-54\"/>\r\n       <use x=\"815.917969\" xlink:href=\"#DejaVuSans-41\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"ytick_17\">\r\n     <g id=\"line2d_23\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"113.371875\" xlink:href=\"#m59e50d3d58\" y=\"596.1\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_23\">\r\n      <!-- (CWE-284, 2016) -->\r\n      <g transform=\"translate(20.878125 599.899219)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-40\"/>\r\n       <use x=\"39.013672\" xlink:href=\"#DejaVuSans-67\"/>\r\n       <use x=\"108.837891\" xlink:href=\"#DejaVuSans-87\"/>\r\n       <use x=\"207.714844\" xlink:href=\"#DejaVuSans-69\"/>\r\n       <use x=\"270.898438\" xlink:href=\"#DejaVuSans-45\"/>\r\n       <use x=\"306.982422\" xlink:href=\"#DejaVuSans-50\"/>\r\n       <use 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xlink:href=\"#DejaVuSans-48\"/>\r\n       <use x=\"688.671875\" xlink:href=\"#DejaVuSans-49\"/>\r\n       <use x=\"752.294922\" xlink:href=\"#DejaVuSans-55\"/>\r\n       <use x=\"815.917969\" xlink:href=\"#DejaVuSans-41\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"ytick_20\">\r\n     <g id=\"line2d_26\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"113.371875\" xlink:href=\"#m59e50d3d58\" y=\"505.5\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_26\">\r\n      <!-- (CWE-326, 2014) -->\r\n      <g transform=\"translate(20.878125 509.299219)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-40\"/>\r\n       <use x=\"39.013672\" xlink:href=\"#DejaVuSans-67\"/>\r\n       <use x=\"108.837891\" xlink:href=\"#DejaVuSans-87\"/>\r\n       <use x=\"207.714844\" xlink:href=\"#DejaVuSans-69\"/>\r\n       <use x=\"270.898438\" xlink:href=\"#DejaVuSans-45\"/>\r\n       <use x=\"306.982422\" xlink:href=\"#DejaVuSans-51\"/>\r\n       <use 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xlink:href=\"#DejaVuSans-48\"/>\r\n       <use x=\"688.671875\" xlink:href=\"#DejaVuSans-49\"/>\r\n       <use x=\"752.294922\" xlink:href=\"#DejaVuSans-51\"/>\r\n       <use x=\"815.917969\" xlink:href=\"#DejaVuSans-41\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"ytick_23\">\r\n     <g id=\"line2d_29\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"113.371875\" xlink:href=\"#m59e50d3d58\" y=\"414.9\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_29\">\r\n      <!-- (CWE-399, 2018) -->\r\n      <g transform=\"translate(20.878125 418.699219)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-40\"/>\r\n       <use x=\"39.013672\" xlink:href=\"#DejaVuSans-67\"/>\r\n       <use x=\"108.837891\" xlink:href=\"#DejaVuSans-87\"/>\r\n       <use x=\"207.714844\" xlink:href=\"#DejaVuSans-69\"/>\r\n       <use x=\"270.898438\" xlink:href=\"#DejaVuSans-45\"/>\r\n       <use x=\"306.982422\" xlink:href=\"#DejaVuSans-51\"/>\r\n       <use x=\"370.605469\" xlink:href=\"#DejaVuSans-57\"/>\r\n       <use x=\"434.228516\" xlink:href=\"#DejaVuSans-57\"/>\r\n       <use x=\"497.851562\" xlink:href=\"#DejaVuSans-44\"/>\r\n       <use x=\"529.638672\" xlink:href=\"#DejaVuSans-32\"/>\r\n       <use x=\"561.425781\" xlink:href=\"#DejaVuSans-50\"/>\r\n       <use x=\"625.048828\" xlink:href=\"#DejaVuSans-48\"/>\r\n       <use x=\"688.671875\" xlink:href=\"#DejaVuSans-49\"/>\r\n       <use x=\"752.294922\" xlink:href=\"#DejaVuSans-56\"/>\r\n       <use x=\"815.917969\" xlink:href=\"#DejaVuSans-41\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"ytick_24\">\r\n     <g id=\"line2d_30\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"113.371875\" xlink:href=\"#m59e50d3d58\" y=\"384.7\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_30\">\r\n      <!-- (CWE-400, 2018) -->\r\n      <g transform=\"translate(20.878125 388.499219)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-40\"/>\r\n   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xlink:href=\"#DejaVuSans-48\"/>\r\n       <use x=\"688.671875\" xlink:href=\"#DejaVuSans-49\"/>\r\n       <use x=\"752.294922\" xlink:href=\"#DejaVuSans-56\"/>\r\n       <use x=\"815.917969\" xlink:href=\"#DejaVuSans-41\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"ytick_26\">\r\n     <g id=\"line2d_32\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"113.371875\" xlink:href=\"#m59e50d3d58\" y=\"324.3\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_32\">\r\n      <!-- (CWE-601, 2016) -->\r\n      <g transform=\"translate(20.878125 328.099219)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-40\"/>\r\n       <use x=\"39.013672\" xlink:href=\"#DejaVuSans-67\"/>\r\n       <use x=\"108.837891\" xlink:href=\"#DejaVuSans-87\"/>\r\n       <use x=\"207.714844\" xlink:href=\"#DejaVuSans-69\"/>\r\n       <use x=\"270.898438\" xlink:href=\"#DejaVuSans-45\"/>\r\n       <use x=\"306.982422\" xlink:href=\"#DejaVuSans-54\"/>\r\n       <use 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# Stat5\n",
    "js_stat_5 = js_cwe_groups_by_year_csv.groupby(['CWE','year']).sum()\n",
    "py_stat_5 = py_cwe_groups_by_year_csv.groupby(['CWE','year']).sum()\n",
    "\n",
    "js_stat_5['js_count'].groupby('year').sum().to_csv(os.path.join(\"../../data/results\", \"yearly_commits_js.csv\"), header=False)\n",
    "py_stat_5['py_count'].groupby('year').sum().to_csv(os.path.join(\"../../data/results\", \"yearly_commits_py.csv\"), header=False)\n",
    "\n",
    "stat_5 = pd.concat([js_stat_5, py_stat_5], axis=1).filter(regex=r'CWE-.[\\d]+', axis=0)\n",
    "stat_5.plot(kind='barh', figsize=(20,20))\n",
    "stat_5"
   ]
  }
 ]
}