RQ1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "import numpy as np\n",
    "from itertools import cycle\n",
    "from matplotlib.patches import Patch\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "global bar\n",
    "def textured_box(bar):\n",
    "    # Define some hatches\n",
    "    hatches = cycle(['///','x', '*', 'o','-', '+', '\\\\'])\n",
    "\n",
    "    # Loop over the bars\n",
    "    for i,thisbar in enumerate(bar.artists):\n",
    "        # Set a different hatch for each bar\n",
    "        thisbar.set_hatch(hatches[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "global bar\n",
    "global num_metrics\n",
    "hatches = cycle(['///','x', '*', 'o','-', '+', '\\\\'])\n",
    "def textured_box(bar,num_metrics):\n",
    "    \n",
    "    for i, patch in enumerate(bar.artists):\n",
    "        # Blue bars first, then green bars\n",
    "        if i % num_metrics == 0:\n",
    "            hatch = next(hatches)\n",
    "        patch.set_hatch(hatch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "global bar\n",
    "def textured_box(bar):\n",
    "    #Loop over the bars\n",
    "    hatches = [\"/\", \"o\", \"*\", \"\\\\\"]\n",
    "    for hatch, patch in zip(hatches, ml_trad_boxplot.artists):\n",
    "        patch.set_hatch(hatch)\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ax.patches[5].set_hatch('x')\n",
    "from matplotlib.patches import Patch\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clone density distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "dl_type1 = pd.read_csv('data/clone_data/dl_type1.csv')\n",
    "dl_type2 = pd.read_csv('data/clone_data/dl_type2.csv')\n",
    "dl_type3 = pd.read_csv('data/clone_data/dl_type3.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "dl_type1['clone_type']='Type 1'\n",
    "dl_type2['clone_type']='Type 2'\n",
    "dl_type3['clone_type']='Type 3'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "dl_all_clones = pd.concat([dl_type1,dl_type2])\n",
    "dl_all_clones = pd.concat([dl_all_clones,dl_type3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "trad_type1 = pd.read_csv('data/clone_data/trad_type1.csv')\n",
    "trad_type2 = pd.read_csv('data/clone_data/trad_type2.csv')\n",
    "trad_type3 = pd.read_csv('data/clone_data/trad_type3.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "trad_type1['clone_type']='Type 1'\n",
    "trad_type2['clone_type']='Type 2'\n",
    "trad_type3['clone_type']='Type 3'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "trad_all_clones = pd.concat([trad_type1,trad_type2])\n",
    "trad_all_clones = pd.concat([trad_all_clones,trad_type3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "dl_all_clones['project_type']='Deep Learning'\n",
    "trad_all_clones['project_type']='Traditional'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "repos_size = pd.read_csv(\"data/slocs_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_clones = pd.concat([dl_all_clones,trad_all_clones])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_locc(row):\n",
    "    startline=row['startline']\n",
    "    endline = row['endline']\n",
    "    return endline - startline +1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_clones['locc']=all_clones.apply(get_locc, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "clones_by_type = all_clones.groupby(['repoName','granularity','clone_type','project_type']).agg({'locc':'sum'}).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "repos_clones_size = pd.merge(clones_by_type,repos_size,how='left', on=['repoName'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "repos_clones_size = repos_clones_size[repos_clones_size['total_sloc'] !=0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "repos_clones_size['LOCC / total SLOC'] = repos_clones_size['locc']/repos_clones_size['total_sloc']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "repos_clones_size['LOCC_total_SLOC'] = repos_clones_size['locc']/repos_clones_size['total_sloc']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ml_trad_boxplot = sns.boxplot(x=\"project_type\", y=\"LOCC / total SLOC\", data=repos_clones_size, showfliers=False)\n",
    "#ml_trad_boxplot.set_yscale(\"log\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "traditional = repos_clones_size[repos_clones_size['project_type']=='Traditional']\n",
    "dl = repos_clones_size[repos_clones_size['project_type']=='Deep Learning']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MannwhitneyuResult(statistic=25987.0, pvalue=5.146640278957621e-16)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.mannwhitneyu(traditional.LOCC_total_SLOC,dl.LOCC_total_SLOC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MannwhitneyuResult(statistic=25987.0, pvalue=1.0293280557915242e-15)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.mannwhitneyu(traditional.LOCC_total_SLOC,dl.LOCC_total_SLOC, alternative='two-sided')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MannwhitneyuResult(statistic=58498.0, pvalue=1.0293280557915242e-15)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.mannwhitneyu(dl.LOCC_total_SLOC,traditional.LOCC_total_SLOC, alternative='two-sided')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def runs(lst):\n",
    "    \"Iterator, chunks repeated values\"\n",
    "    for j,two in enumerate(lst):\n",
    "        if j == 0:\n",
    "            one,i = two,0\n",
    "        if one!=two:\n",
    "            yield j - i,one\n",
    "            i = j\n",
    "        one=two\n",
    "    yield j - i + 1,two\n",
    "def cliffsDelta(lst1,lst2): \n",
    "    \"Returns true if there are more than 'dull' differences\"\n",
    "    m, n = len(lst1), len(lst2)\n",
    "    lst2 = sorted(lst2)\n",
    "    j = more = less = 0\n",
    "    for repeats,x in runs(sorted(lst1)):\n",
    "        while j <= (n - 1) and lst2[j] <  x: \n",
    "            j += 1\n",
    "        more += j*repeats\n",
    "        while j <= (n - 1) and lst2[j] == x: \n",
    "            j += 1\n",
    "        less += (n - j)*repeats\n",
    "    d= (more - less) / (m*n) \n",
    "    return abs(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7479079126472155"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cliffsDelta(traditional.LOCC_total_SLOC,dl.LOCC_total_SLOC)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## By clone type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Clone occurrences by project type and by clone type')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ml_trad_boxplot = sns.boxplot(x=\"project_type\", y=\"LOCC / total SLOC\", data=repos_clones_size, showfliers=False, hue='clone_type')\n",
    "ml_trad_boxplot.set_yscale(\"log\")\n",
    "ml_trad_boxplot.set(xlabel='Project Type', ylabel='log(LOCC / total SLOC)')\n",
    "plt.legend(loc='best')\n",
    "plt.title('Clone occurrences by project type and by clone type')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "trad_type1 = traditional[traditional['clone_type']=='Type 1']\n",
    "trad_type2 = traditional[traditional['clone_type']=='Type 2']\n",
    "trad_type3 = traditional[traditional['clone_type']=='Type 3']\n",
    "\n",
    "dl_type1 = dl[dl['clone_type']=='Type 1']\n",
    "dl_type2 = dl[dl['clone_type']=='Type 2']\n",
    "dl_type3 = dl[dl['clone_type']=='Type 3']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MannwhitneyuResult(statistic=1627.0, pvalue=3.2953094551306856e-12)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.mannwhitneyu(trad_type1.LOCC_total_SLOC,dl_type1.LOCC_total_SLOC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MannwhitneyuResult(statistic=1812.0, pvalue=9.38365512744147e-10)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.mannwhitneyu(trad_type2.LOCC_total_SLOC,dl_type2.LOCC_total_SLOC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MannwhitneyuResult(statistic=4186.0, pvalue=2.7205326923789545e-06)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.mannwhitneyu(trad_type3.LOCC_total_SLOC,dl_type3.LOCC_total_SLOC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7832254853160776"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cliffsDelta(trad_type1.LOCC_total_SLOC,dl_type1.LOCC_total_SLOC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5262745098039215"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cliffsDelta(trad_type2.LOCC_total_SLOC,dl_type2.LOCC_total_SLOC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6464646464646465"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cliffsDelta(trad_type3.LOCC_total_SLOC,dl_type3.LOCC_total_SLOC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_granularity(row):\n",
    "    granularity=row['granularity']\n",
    "    if 'block' in granularity:\n",
    "        return 'Block'\n",
    "    elif 'function' in granularity:\n",
    "        return 'Function'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "repos_clones_size['granularity']=repos_clones_size.apply(update_granularity, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "repos_clones_size_block = repos_clones_size[repos_clones_size['granularity']=='Block']\n",
    "repos_clones_size_fct = repos_clones_size[repos_clones_size['granularity']=='Function']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "traditional_block = repos_clones_size_block[repos_clones_size_block['project_type']=='Traditional']\n",
    "dl_block = repos_clones_size_block[repos_clones_size_block['project_type']=='Deep Learning']\n",
    "traditional_fct = repos_clones_size_fct[repos_clones_size_fct['project_type']=='Traditional']\n",
    "dl_fct = repos_clones_size_fct[repos_clones_size_fct['project_type']=='Deep Learning']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "trad_type1_fct = traditional_fct[traditional_fct['clone_type']=='Type 1']\n",
    "trad_type2_fct = traditional_fct[traditional_fct['clone_type']=='Type 2']\n",
    "trad_type3_fct = traditional_fct[traditional_fct['clone_type']=='Type 3']\n",
    "\n",
    "dl_type1_fct =dl_fct[dl_fct['clone_type']=='Type 1']\n",
    "dl_type2_fct = dl_fct[dl_fct['clone_type']=='Type 2']\n",
    "dl_type3_fct = dl_fct[dl_fct['clone_type']=='Type 3']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "trad_type1_block = traditional_block[traditional_block['clone_type']=='Type 1']\n",
    "trad_type2_block = traditional_block[traditional_block['clone_type']=='Type 2']\n",
    "trad_type3_block = traditional_block[traditional_block['clone_type']=='Type 3']\n",
    "\n",
    "dl_type1_block = dl_block[dl_block['clone_type']=='Type 1']\n",
    "dl_type2_block = dl_block[dl_block['clone_type']=='Type 2']\n",
    "dl_type3_block = dl_block[dl_block['clone_type']=='Type 3']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 482.4x482.4 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "sns.set(rc={'figure.figsize':(6.7,6.7)})\n",
    "sns.set(font_scale=1.5)\n",
    "sns.set_style('white')\n",
    "sns.set_style('ticks', {'axes.edgecolor': '0',  \n",
    "                        'xtick.color': '0',\n",
    "                        'ytick.color': '0'})\n",
    "# Define some hatches\n",
    "hatches = cycle(['///','x', 'o'])\n",
    "\n",
    "\n",
    "# Boxplot\n",
    "ax = sns.boxplot(x=\"project_type\", y=\"LOCC / total SLOC\", data=repos_clones_size_fct,\n",
    "                 showfliers=False, hue='clone_type',color='black',medianprops=dict(color='black'), \n",
    "                  boxprops = dict(facecolor='white',edgecolor='black' ))\n",
    "ax.set_yscale(\"log\")\n",
    "ax.set(xlabel='Project Type', ylabel='log(LOCC / total SLOC)')\n",
    "#plt.legend(loc='best')\n",
    "plt.title('(a) Function Granularity')\n",
    "patches=[]\n",
    "for i, patch in enumerate(ax.artists):\n",
    "    # Boxes from left to right\n",
    "    hatch = next(hatches)\n",
    "    patch.set_hatch(hatch)\n",
    "    if patch not in patches:\n",
    "        patches.append(patch)\n",
    "\n",
    "type1_patch = Patch(fc='white',edgecolor='black', label='Type 1', hatch='///')\n",
    "type2_patch = Patch( fc='white',edgecolor='black',label='Type 2',     hatch ='x')\n",
    "type3_patch = Patch(fc='white',edgecolor='black', label='Type 3',     hatch ='o')\n",
    "legend=plt.legend(handles=[type1_patch, type2_patch, type3_patch],loc='best')\n",
    "frame = legend.get_frame()\n",
    "plt.show()\n",
    "fig = ax.get_figure()\n",
    "fig.savefig(\"dl_trad_clone_type_fct.pdf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MannwhitneyuResult(statistic=344.0, pvalue=3.1256716143260985e-06)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.mannwhitneyu(trad_type1_fct.LOCC_total_SLOC,dl_type1_fct.LOCC_total_SLOC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5845410628019324"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cliffsDelta(trad_type1_fct.LOCC_total_SLOC,dl_type1_fct.LOCC_total_SLOC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MannwhitneyuResult(statistic=670.0, pvalue=3.382972751334123e-05)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.mannwhitneyu(trad_type2_fct.LOCC_total_SLOC,dl_type2_fct.LOCC_total_SLOC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.46314102564102566"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cliffsDelta(trad_type2_fct.LOCC_total_SLOC,dl_type2_fct.LOCC_total_SLOC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MannwhitneyuResult(statistic=1085.0, pvalue=0.001766510851886626)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.mannwhitneyu(trad_type3_fct.LOCC_total_SLOC,dl_type3_fct.LOCC_total_SLOC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.31889516635279347"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cliffsDelta(trad_type3_fct.LOCC_total_SLOC,dl_type3_fct.LOCC_total_SLOC)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### blocks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MannwhitneyuResult(statistic=458.0, pvalue=7.60737673214229e-08)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.mannwhitneyu(trad_type1_block.LOCC_total_SLOC,dl_type1_block.LOCC_total_SLOC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.617056856187291"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cliffsDelta(trad_type1_block.LOCC_total_SLOC,dl_type1_block.LOCC_total_SLOC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MannwhitneyuResult(statistic=282.0, pvalue=4.172758725088073e-06)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.mannwhitneyu(trad_type2_block.LOCC_total_SLOC,dl_type2_block.LOCC_total_SLOC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5988620199146515"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cliffsDelta(trad_type2_block.LOCC_total_SLOC,dl_type2_block.LOCC_total_SLOC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MannwhitneyuResult(statistic=1016.0, pvalue=0.0002872876666620591)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.mannwhitneyu(trad_type3_block.LOCC_total_SLOC,dl_type3_block.LOCC_total_SLOC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.37438423645320196"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cliffsDelta(trad_type3_block.LOCC_total_SLOC,dl_type3_block.LOCC_total_SLOC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 482.4x482.4 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(6.7,6.7)})\n",
    "sns.set(font_scale=1.5)\n",
    "sns.set_style('white')\n",
    "sns.set_style('ticks', {'axes.edgecolor': '0',  \n",
    "                        'xtick.color': '0',\n",
    "                        'ytick.color': '0'})\n",
    "# Define some hatches\n",
    "hatches = cycle(['///','x', 'o'])\n",
    "\n",
    "\n",
    "# Boxplot\n",
    "ax = sns.boxplot(x=\"project_type\", y=\"LOCC / total SLOC\", data=repos_clones_size_fct, showfliers=False, hue='clone_type',color='black',medianprops=dict(color='black'), \n",
    "                  boxprops = dict(facecolor='white',edgecolor='black' ))\n",
    "ax.set_yscale(\"log\")\n",
    "ax.set(xlabel='Project Type', ylabel='log(LOCC / total SLOC)')\n",
    "plt.legend(loc='best')\n",
    "plt.title('(a) Function Granularity')\n",
    "patches=[]\n",
    "for i, patch in enumerate(ax.artists):\n",
    "    # Boxes from left to right\n",
    "    hatch = next(hatches)\n",
    "    patch.set_hatch(hatch)\n",
    "    if patch not in patches:\n",
    "        patches.append(patch)\n",
    "\n",
    "type1_patch = Patch(fc='white',edgecolor='black', label='Type 1', hatch='///')\n",
    "type2_patch = Patch( fc='white',edgecolor='black',label='Type 2',     hatch ='x')\n",
    "type3_patch = Patch(fc='white',edgecolor='black', label='Type 3',     hatch ='o')\n",
    "legend=plt.legend(handles=[type1_patch, type2_patch, type3_patch],loc='best')\n",
    "frame = legend.get_frame()\n",
    "plt.show()\n",
    "fig = ax.get_figure()\n",
    "fig.savefig(\"dl_trad_clone_type_fct.pdf\", bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 482.4x482.4 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(6.7,6.7)})\n",
    "sns.set(font_scale=1.5)\n",
    "sns.set_style('white')\n",
    "sns.set_style('ticks', {'axes.edgecolor': '0',  \n",
    "                        'xtick.color': '0',\n",
    "                        'ytick.color': '0'})\n",
    "# Define some hatches\n",
    "hatches = cycle(['///','x', 'o'])\n",
    "\n",
    "\n",
    "# Boxplot\n",
    "ax = sns.boxplot(x=\"project_type\", y=\"LOCC / total SLOC\", data=repos_clones_size_block, showfliers=False, hue='clone_type',color='black',medianprops=dict(color='black'), \n",
    "                  boxprops = dict(facecolor='white',edgecolor='black' ))\n",
    "ax.set_yscale(\"log\")\n",
    "ax.set(xlabel='Project Type', ylabel='log(LOCC / total SLOC)')\n",
    "plt.legend(loc='best')\n",
    "plt.title('(b) Block Granularity')\n",
    "patches=[]\n",
    "for i, patch in enumerate(ax.artists):\n",
    "    # Boxes from left to right\n",
    "    hatch = next(hatches)\n",
    "    patch.set_hatch(hatch)\n",
    "    if patch not in patches:\n",
    "        patches.append(patch)\n",
    "\n",
    "type1_patch = Patch(fc='white',edgecolor='black', label='Type 1', hatch='///')\n",
    "type2_patch = Patch( fc='white',edgecolor='black',label='Type 2',     hatch ='x')\n",
    "type3_patch = Patch(fc='white',edgecolor='black', label='Type 3',     hatch ='o')\n",
    "legend=plt.legend(handles=[type1_patch, type2_patch, type3_patch],loc='best')\n",
    "frame = legend.get_frame()\n",
    "plt.show()\n",
    "fig = ax.get_figure()\n",
    "fig.savefig(\"dl_trad_clone_type_block.pdf\", bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '(a) Block Granularity')"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ml_trad_boxplot = sns.boxplot(x=\"project_type\", y=\"LOCC / total SLOC\", data=repos_clones_size_block, showfliers=False, hue='clone_type')\n",
    "ml_trad_boxplot.set_yscale(\"log\")\n",
    "ml_trad_boxplot.set(xlabel='Project Type', ylabel='log(LOCC / total SLOC)')\n",
    "plt.legend(loc='best')\n",
    "plt.title('(a) Block Granularity')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'itertools.cycle' object is not subscriptable",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-62-5c0f3d113130>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlegend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'best'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'(a) Function Granularity'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[0mtextured_box\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mml_trad_boxplot\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      7\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-61-e79d42d9212d>\u001b[0m in \u001b[0;36mtextured_box\u001b[1;34m(bar)\u001b[0m\n\u001b[0;32m      7\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mthisbar\u001b[0m \u001b[1;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbar\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0martists\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m         \u001b[1;31m# Set a different hatch for each bar\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m         \u001b[0mthisbar\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_hatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhatches\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: 'itertools.cycle' object is not subscriptable"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ml_trad_boxplot = sns.boxplot(x=\"project_type\", y=\"LOCC / total SLOC\", data=repos_clones_size_fct, showfliers=False, hue='clone_type')\n",
    "ml_trad_boxplot.set_yscale(\"log\")\n",
    "ml_trad_boxplot.set(xlabel='Project Type', ylabel='log(LOCC / total SLOC)')\n",
    "plt.legend(loc='best')\n",
    "plt.title('(a) Function Granularity')\n",
    "textured_box(ml_trad_boxplot)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 482.4x482.4 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "sns.set(rc={'figure.figsize':(6.7,6.7)})\n",
    "sns.set(font_scale=1.5)\n",
    "sns.set_style('white')\n",
    "sns.set_style('ticks', {'axes.edgecolor': '0',  \n",
    "                        'xtick.color': '0',\n",
    "                        'ytick.color': '0'})\n",
    "# Define some hatches\n",
    "hatches = cycle(['///','x', 'o'])\n",
    "\n",
    "\n",
    "# Boxplot\n",
    "ax = sns.boxplot(x=\"project_type\", y=\"LOCC / total SLOC\", data=repos_clones_size_fct, showfliers=False,color='black',medianprops=dict(color='black'), \n",
    "                  boxprops = dict(facecolor='white',edgecolor='black' ))\n",
    "#ax.set_yscale(\"log\")\n",
    "#ax.set(xlabel='Project Type', ylabel='log(LOCC / total SLOC)')\n",
    "#plt.legend(loc='best')\n",
    "plt.title('(a) Function Granularity')\n",
    "patches=[]\n",
    "for i, patch in enumerate(ax.artists):\n",
    "    # Boxes from left to right\n",
    "    hatch = next(hatches)\n",
    "    patch.set_hatch(hatch)\n",
    "    if patch not in patches:\n",
    "        patches.append(patch)\n",
    "\n",
    "#type1_patch = Patch(fc='white',edgecolor='black', label='Type 1', hatch='///')\n",
    "#type2_patch = Patch( fc='white',edgecolor='black',label='Type 2',     hatch ='x')\n",
    "#type3_patch = Patch(fc='white',edgecolor='black', label='Type 3',     hatch ='o')\n",
    "#legend=plt.legend(handles=[type1_patch, type2_patch, type3_patch],loc='best')\n",
    "#frame = legend.get_frame()\n",
    "plt.show()\n",
    "fig = ax.get_figure()\n",
    "fig.savefig(\"dl_trad_fct.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 482.4x482.4 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"whitegrid\", color_codes=True)\n",
    "\n",
    "ml_trad_boxplot = sns.boxplot(x=\"project_type\", y=\"LOCC / total SLOC\", data=repos_clones_size_fct, showfliers=False)\n",
    "#ml_trad_boxplot.set_yscale(\"log\")\n",
    "\n",
    "plt.title('(a) Function Granularity')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ml_trad_boxplot.patches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 482.4x482.4 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "sns.set(rc={'figure.figsize':(6.7,6.7)})\n",
    "sns.set(font_scale=1.5)\n",
    "sns.set_style('white')\n",
    "sns.set_style('ticks', {'axes.edgecolor': '0',  \n",
    "                        'xtick.color': '0',\n",
    "                        'ytick.color': '0'})\n",
    "# Define some hatches\n",
    "hatches = cycle(['///','x', 'o'])\n",
    "\n",
    "\n",
    "# Boxplot\n",
    "ax = sns.boxplot(x=\"project_type\", y=\"LOCC / total SLOC\", data=repos_clones_size_block, showfliers=False,color='black',medianprops=dict(color='black'), \n",
    "                  boxprops = dict(facecolor='white',edgecolor='black' ))\n",
    "#ax.set_yscale(\"log\")\n",
    "#ax.set(xlabel='Project Type', ylabel='log(LOCC / total SLOC)')\n",
    "#plt.legend(loc='best')\n",
    "plt.title('(b) Block Granularity')\n",
    "patches=[]\n",
    "for i, patch in enumerate(ax.artists):\n",
    "    # Boxes from left to right\n",
    "    hatch = next(hatches)\n",
    "    patch.set_hatch(hatch)\n",
    "    if patch not in patches:\n",
    "        patches.append(patch)\n",
    "\n",
    "#type1_patch = Patch(fc='white',edgecolor='black', label='Type 1', hatch='///')\n",
    "#type2_patch = Patch( fc='white',edgecolor='black',label='Type 2',     hatch ='x')\n",
    "#type3_patch = Patch(fc='white',edgecolor='black', label='Type 3',     hatch ='o')\n",
    "#legend=plt.legend(handles=[type1_patch, type2_patch, type3_patch],loc='best')\n",
    "#frame = legend.get_frame()\n",
    "plt.show()\n",
    "fig = ax.get_figure()\n",
    "fig.savefig(\"dl_trad_block.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 482.4x482.4 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ml_trad_boxplot = sns.boxplot(x=\"project_type\", y=\"LOCC / total SLOC\", data=repos_clones_size_block, showfliers=False)\n",
    "#ml_trad_boxplot.set_yscale(\"log\")\n",
    "plt.title('(a) Block Granularity')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MannwhitneyuResult(statistic=5936.0, pvalue=3.0068434096511597e-10)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.mannwhitneyu(traditional_block.LOCC_total_SLOC,dl_block.LOCC_total_SLOC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5269298075053471"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cliffsDelta(traditional_block.LOCC_total_SLOC,dl_block.LOCC_total_SLOC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MannwhitneyuResult(statistic=7111.0, pvalue=1.7841482512278643e-07)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.mannwhitneyu(traditional_fct.LOCC_total_SLOC,dl_fct.LOCC_total_SLOC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.80374780762485"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cliffsDelta(traditional_fct.LOCC_total_SLOC,dl_fct.LOCC_total_SLOC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}