notebooks/ProjectAnalysis/TestAnalysis/04-AnalyzeResults.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import pandas as pd\n",
    "import re\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "from scipy import stats\n",
    "root=\"/home/jovyan/work\"\n",
    "results_path=root+\"/results/\"\n",
    "procesed_results_path=root+\"/notebooks/ProjectAnalysis/TestAnalysis/results/\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AnalyzeResults\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "many4j_projects = pd.read_csv(results_path+'Many4JResults.csv') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Change days to years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "many4j_projects['Age'] = many4j_projects['Age'].map(lambda days: days/365.) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Size Analysis (# commits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def scatterPlot(x_value):\n",
    "    fig, (ax1, ax2, ax3, ax4) = plt.subplots(1, 4, figsize=(15,3))\n",
    "    ax1.scatter(x=many4j_projects[x_value], y=many4j_projects[\"FullyTestability_A\"], label=\"Many4J\")\n",
    "    ax2.scatter(x=many4j_projects[x_value], y=many4j_projects[\"FullyTestability_T\"], label=\"Many4J\")\n",
    "    ax3.scatter(x=many4j_projects[x_value], y=many4j_projects[\"TestabilityRate_A\"], label=\"Many4J\")\n",
    "    ax4.scatter(x=many4j_projects[x_value], y=many4j_projects[\"TestabilityRate_T\"], label=\"Many4J\")\n",
    "\n",
    "    ax1.set_xscale('log')\n",
    "    ax2.set_xscale('log')\n",
    "    ax3.set_xscale('log')\n",
    "    ax4.set_xscale('log')\n",
    "    \n",
    "    ax1.set_title(\"FullyTestability_A\")\n",
    "    ax2.set_title(\"FullyTestability_T\")\n",
    "    ax3.set_title(\"TestabilityRate_A\")\n",
    "    ax4.set_title(\"TestabilityRate_T\")\n",
    "\n",
    "    plt.legend()\n",
    "    plt.legend()\n",
    "    plt.legend()\n",
    "    plt.legend()\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.savefig(\"latex_figures/\"+x_value+\"-scatterplot.pdf\")\n",
    "    plt.show(block=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "alpha = 5e-2\n",
    "def isNormalDistribution(name, data):\n",
    "    k2, p = stats.normaltest(list(data.values))\n",
    "    \n",
    "    if p < alpha: \n",
    "        print(\"{:s} is NOT a normal distribution: p = {:g}\".format(name, p))\n",
    "    else: \n",
    "        print(\"{:s} is a normal distribution: p = {:g}\".format(name, p))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def describeResults(metric,dataset, name, data):\n",
    "    return [\n",
    "        dataset,\n",
    "        name,\n",
    "        len(data.index),\n",
    "        data[metric].mean(),\n",
    "        100*data['Source buildability'].mean(),\n",
    "        100*data['Test buildability_A'].mean(),\n",
    "        100*data['TestabilityRate_A'].mean(),\n",
    "        100*data['TestabilityRate_T'].mean(),\n",
    "        100*data['FullyTestability_A'].mean(),\n",
    "        100*data['FullyTestability_T'].mean(),\n",
    "    ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def describeResultsByQuartile(metric, dataset, df):\n",
    "\n",
    "    q1 = df[metric].quantile(0.25)\n",
    "    short_df = df[ df[metric]< q1 ]\n",
    "    short_summary = describeResults(metric,dataset,\"Short\",short_df)\n",
    "    \n",
    "    q3 = df[metric].quantile(0.75)\n",
    "    medium_df = df.query(\"`%s` >= %d and `%s` < %d\"%(metric,q1,metric, q3))\n",
    "    medium_summary = describeResults(metric,dataset,\"Medium\",medium_df)\n",
    "    \n",
    "    large_df = df[ df[metric] >= q3 ]\n",
    "    large_summary = describeResults(metric,dataset,\"Large\",large_df)\n",
    "    \n",
    "    all_df = describeResults(metric,dataset,\"All\", df)\n",
    "    \n",
    "    summary = [short_summary, medium_summary, large_summary] #, all_df]\n",
    "    \n",
    "    print(dataset)\n",
    "    print(\"Q1: %d\"%q1)\n",
    "    print(\"Q3: %d\"%q3)\n",
    "    \n",
    "    returned_df = pd.DataFrame(summary, columns = [\n",
    "        \"Dataset\",\n",
    "        \"\",\n",
    "        \"# of projects\",\n",
    "        metric+\" Mean\",\n",
    "        \"Source buildability\",\n",
    "        \"Test buildability\",\n",
    "        \"TestabilityRate_A\",\n",
    "        \"TestabilityRate_T\",\n",
    "        \"FullyTestability_A\",\n",
    "        \"FullyTestability_T\",\n",
    "    ]).round(decimals=2)\n",
    "    return returned_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def allDescriptiobByDataset(metric):\n",
    "    return pd.concat([\n",
    "        describeResultsByQuartile(metric, \"Many4j\",many4j_projects)\n",
    "    ], ignore_index=True).set_index(['Dataset', '']).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x300 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatterPlot('Total Commits')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Many4j\n",
      "Q1: 1189\n",
      "Q3: 6331\n"
     ]
    },
    {
     "data": {
      "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>Dataset</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Many4j</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Short</th>\n",
       "      <th>Medium</th>\n",
       "      <th>Large</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th># of projects</th>\n",
       "      <td>17.00</td>\n",
       "      <td>32.00</td>\n",
       "      <td>17.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total Commits Mean</th>\n",
       "      <td>667.12</td>\n",
       "      <td>3062.06</td>\n",
       "      <td>17544.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Source buildability</th>\n",
       "      <td>59.71</td>\n",
       "      <td>53.54</td>\n",
       "      <td>23.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Test buildability</th>\n",
       "      <td>52.93</td>\n",
       "      <td>46.67</td>\n",
       "      <td>21.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TestabilityRate_A</th>\n",
       "      <td>50.36</td>\n",
       "      <td>41.72</td>\n",
       "      <td>21.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TestabilityRate_T</th>\n",
       "      <td>96.17</td>\n",
       "      <td>90.60</td>\n",
       "      <td>98.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FullyTestability_A</th>\n",
       "      <td>38.54</td>\n",
       "      <td>20.88</td>\n",
       "      <td>8.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FullyTestability_T</th>\n",
       "      <td>74.80</td>\n",
       "      <td>45.49</td>\n",
       "      <td>43.49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Dataset              Many4j                   \n",
       "                      Short   Medium     Large\n",
       "# of projects         17.00    32.00     17.00\n",
       "Total Commits Mean   667.12  3062.06  17544.24\n",
       "Source buildability   59.71    53.54     23.10\n",
       "Test buildability     52.93    46.67     21.23\n",
       "TestabilityRate_A     50.36    41.72     21.10\n",
       "TestabilityRate_T     96.17    90.60     98.81\n",
       "FullyTestability_A    38.54    20.88      8.05\n",
       "FullyTestability_T    74.80    45.49     43.49"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "allDescriptiobByDataset('Total Commits')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Age Analysis (# years)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x300 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatterPlot('Age')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Many4j\n",
      "Q1: 7\n",
      "Q3: 11\n"
     ]
    },
    {
     "data": {
      "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>Dataset</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Many4j</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Short</th>\n",
       "      <th>Medium</th>\n",
       "      <th>Large</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th># of projects</th>\n",
       "      <td>17.00</td>\n",
       "      <td>32.00</td>\n",
       "      <td>17.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age Mean</th>\n",
       "      <td>5.88</td>\n",
       "      <td>9.03</td>\n",
       "      <td>13.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Source buildability</th>\n",
       "      <td>48.97</td>\n",
       "      <td>46.78</td>\n",
       "      <td>45.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Test buildability</th>\n",
       "      <td>41.24</td>\n",
       "      <td>40.84</td>\n",
       "      <td>42.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TestabilityRate_A</th>\n",
       "      <td>37.10</td>\n",
       "      <td>38.68</td>\n",
       "      <td>41.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TestabilityRate_T</th>\n",
       "      <td>89.96</td>\n",
       "      <td>94.77</td>\n",
       "      <td>95.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FullyTestability_A</th>\n",
       "      <td>19.79</td>\n",
       "      <td>23.50</td>\n",
       "      <td>23.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FullyTestability_T</th>\n",
       "      <td>50.02</td>\n",
       "      <td>54.24</td>\n",
       "      <td>50.47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Dataset             Many4j              \n",
       "                     Short Medium  Large\n",
       "# of projects        17.00  32.00  17.00\n",
       "Age Mean              5.88   9.03  13.39\n",
       "Source buildability  48.97  46.78  45.32\n",
       "Test buildability    41.24  40.84  42.77\n",
       "TestabilityRate_A    37.10  38.68  41.19\n",
       "TestabilityRate_T    89.96  94.77  95.72\n",
       "FullyTestability_A   19.79  23.50  23.31\n",
       "FullyTestability_T   50.02  54.24  50.47"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "allDescriptiobByDataset('Age')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. LoC Analysis (# LoC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x300 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatterPlot('LoC')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Many4j\n",
      "Q1: 8393\n",
      "Q3: 148922\n"
     ]
    },
    {
     "data": {
      "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>Dataset</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Many4j</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Short</th>\n",
       "      <th>Medium</th>\n",
       "      <th>Large</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th># of projects</th>\n",
       "      <td>17.00</td>\n",
       "      <td>32.00</td>\n",
       "      <td>17.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LoC Mean</th>\n",
       "      <td>3657.41</td>\n",
       "      <td>45434.97</td>\n",
       "      <td>722791.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Source buildability</th>\n",
       "      <td>56.22</td>\n",
       "      <td>52.92</td>\n",
       "      <td>27.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Test buildability</th>\n",
       "      <td>47.85</td>\n",
       "      <td>47.24</td>\n",
       "      <td>25.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TestabilityRate_A</th>\n",
       "      <td>45.24</td>\n",
       "      <td>42.31</td>\n",
       "      <td>25.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TestabilityRate_T</th>\n",
       "      <td>95.81</td>\n",
       "      <td>90.79</td>\n",
       "      <td>98.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FullyTestability_A</th>\n",
       "      <td>32.23</td>\n",
       "      <td>24.79</td>\n",
       "      <td>7.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FullyTestability_T</th>\n",
       "      <td>71.20</td>\n",
       "      <td>50.00</td>\n",
       "      <td>38.61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Dataset               Many4j                     \n",
       "                       Short    Medium      Large\n",
       "# of projects          17.00     32.00      17.00\n",
       "LoC Mean             3657.41  45434.97  722791.59\n",
       "Source buildability    56.22     52.92      27.75\n",
       "Test buildability      47.85     47.24      25.25\n",
       "TestabilityRate_A      45.24     42.31      25.11\n",
       "TestabilityRate_T      95.81     90.79      98.81\n",
       "FullyTestability_A     32.23     24.79       7.00\n",
       "FullyTestability_T     71.20     50.00      38.61"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "allDescriptiobByDataset('LoC')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. All Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def allDescription():\n",
    "    return pd.concat([\n",
    "        describeResultsByQuartile('Total Commits', 'Total Commits',many4j_projects),\n",
    "        describeResultsByQuartile('Age', 'Age',many4j_projects),\n",
    "        describeResultsByQuartile('LoC', 'LoC',many4j_projects)\n",
    "    ], ignore_index=True).set_index(['Dataset', '']).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Commits\n",
      "Q1: 1189\n",
      "Q3: 6331\n",
      "Age\n",
      "Q1: 7\n",
      "Q3: 11\n",
      "LoC\n",
      "Q1: 8393\n",
      "Q3: 148922\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>Dataset</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Total Commits</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Age</th>\n",
       "      <th colspan=\"3\" halign=\"left\">LoC</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Short</th>\n",
       "      <th>Medium</th>\n",
       "      <th>Large</th>\n",
       "      <th>Short</th>\n",
       "      <th>Medium</th>\n",
       "      <th>Large</th>\n",
       "      <th>Short</th>\n",
       "      <th>Medium</th>\n",
       "      <th>Large</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th># of projects</th>\n",
       "      <td>17.00</td>\n",
       "      <td>32.00</td>\n",
       "      <td>17.00</td>\n",
       "      <td>17.00</td>\n",
       "      <td>32.00</td>\n",
       "      <td>17.00</td>\n",
       "      <td>17.00</td>\n",
       "      <td>32.00</td>\n",
       "      <td>17.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total Commits Mean</th>\n",
       "      <td>667.12</td>\n",
       "      <td>3062.06</td>\n",
       "      <td>17544.24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Source buildability</th>\n",
       "      <td>59.71</td>\n",
       "      <td>53.54</td>\n",
       "      <td>23.10</td>\n",
       "      <td>48.97</td>\n",
       "      <td>46.78</td>\n",
       "      <td>45.32</td>\n",
       "      <td>56.22</td>\n",
       "      <td>52.92</td>\n",
       "      <td>27.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Test buildability</th>\n",
       "      <td>52.93</td>\n",
       "      <td>46.67</td>\n",
       "      <td>21.23</td>\n",
       "      <td>41.24</td>\n",
       "      <td>40.84</td>\n",
       "      <td>42.77</td>\n",
       "      <td>47.85</td>\n",
       "      <td>47.24</td>\n",
       "      <td>25.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TestabilityRate_A</th>\n",
       "      <td>50.36</td>\n",
       "      <td>41.72</td>\n",
       "      <td>21.10</td>\n",
       "      <td>37.10</td>\n",
       "      <td>38.68</td>\n",
       "      <td>41.19</td>\n",
       "      <td>45.24</td>\n",
       "      <td>42.31</td>\n",
       "      <td>25.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TestabilityRate_T</th>\n",
       "      <td>96.17</td>\n",
       "      <td>90.60</td>\n",
       "      <td>98.81</td>\n",
       "      <td>89.96</td>\n",
       "      <td>94.77</td>\n",
       "      <td>95.72</td>\n",
       "      <td>95.81</td>\n",
       "      <td>90.79</td>\n",
       "      <td>98.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FullyTestability_A</th>\n",
       "      <td>38.54</td>\n",
       "      <td>20.88</td>\n",
       "      <td>8.05</td>\n",
       "      <td>19.79</td>\n",
       "      <td>23.50</td>\n",
       "      <td>23.31</td>\n",
       "      <td>32.23</td>\n",
       "      <td>24.79</td>\n",
       "      <td>7.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FullyTestability_T</th>\n",
       "      <td>74.80</td>\n",
       "      <td>45.49</td>\n",
       "      <td>43.49</td>\n",
       "      <td>50.02</td>\n",
       "      <td>54.24</td>\n",
       "      <td>50.47</td>\n",
       "      <td>71.20</td>\n",
       "      <td>50.00</td>\n",
       "      <td>38.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age Mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.88</td>\n",
       "      <td>9.03</td>\n",
       "      <td>13.39</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LoC Mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3657.41</td>\n",
       "      <td>45434.97</td>\n",
       "      <td>722791.59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Dataset             Total Commits                       Age                \\\n",
       "                            Short   Medium     Large  Short Medium  Large   \n",
       "# of projects               17.00    32.00     17.00  17.00  32.00  17.00   \n",
       "Total Commits Mean         667.12  3062.06  17544.24    NaN    NaN    NaN   \n",
       "Source buildability         59.71    53.54     23.10  48.97  46.78  45.32   \n",
       "Test buildability           52.93    46.67     21.23  41.24  40.84  42.77   \n",
       "TestabilityRate_A           50.36    41.72     21.10  37.10  38.68  41.19   \n",
       "TestabilityRate_T           96.17    90.60     98.81  89.96  94.77  95.72   \n",
       "FullyTestability_A          38.54    20.88      8.05  19.79  23.50  23.31   \n",
       "FullyTestability_T          74.80    45.49     43.49  50.02  54.24  50.47   \n",
       "Age Mean                      NaN      NaN       NaN   5.88   9.03  13.39   \n",
       "LoC Mean                      NaN      NaN       NaN    NaN    NaN    NaN   \n",
       "\n",
       "Dataset                  LoC                       \n",
       "                       Short    Medium      Large  \n",
       "# of projects          17.00     32.00      17.00  \n",
       "Total Commits Mean       NaN       NaN        NaN  \n",
       "Source buildability    56.22     52.92      27.75  \n",
       "Test buildability      47.85     47.24      25.25  \n",
       "TestabilityRate_A      45.24     42.31      25.11  \n",
       "TestabilityRate_T      95.81     90.79      98.81  \n",
       "FullyTestability_A     32.23     24.79       7.00  \n",
       "FullyTestability_T     71.20     50.00      38.61  \n",
       "Age Mean                 NaN       NaN        NaN  \n",
       "LoC Mean             3657.41  45434.97  722791.59  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "allDescription()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.1 Test normality distribution of testability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TestabilityRate_A is NOT a normal distribution: p = 0.0218404\n",
      "TestabilityRate_T is NOT a normal distribution: p = 1.48062e-18\n",
      "FullyTestability_A is NOT a normal distribution: p = 0.00180761\n",
      "FullyTestability_T is NOT a normal distribution: p = 1.19995e-33\n"
     ]
    }
   ],
   "source": [
    "isNormalDistribution(\"TestabilityRate_A\", many4j_projects['TestabilityRate_A'])\n",
    "isNormalDistribution(\"TestabilityRate_T\", many4j_projects['TestabilityRate_T'])\n",
    "isNormalDistribution(\"FullyTestability_A\", many4j_projects['FullyTestability_A'])\n",
    "isNormalDistribution(\"FullyTestability_T\", many4j_projects['FullyTestability_T'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.2 Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>Total Commits corr</th>\n",
       "      <th>Total Commits pvalue</th>\n",
       "      <th>Age corr</th>\n",
       "      <th>Age pvalue</th>\n",
       "      <th>LoC corr</th>\n",
       "      <th>LoC pvalue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TestabilityRate_A</th>\n",
       "      <td>-0.414</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.048</td>\n",
       "      <td>0.705</td>\n",
       "      <td>-0.340</td>\n",
       "      <td>0.005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TestabilityRate_T</th>\n",
       "      <td>0.083</td>\n",
       "      <td>0.505</td>\n",
       "      <td>0.177</td>\n",
       "      <td>0.156</td>\n",
       "      <td>0.126</td>\n",
       "      <td>0.315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FullyTestability_A</th>\n",
       "      <td>-0.548</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.070</td>\n",
       "      <td>0.574</td>\n",
       "      <td>-0.508</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FullyTestability_T</th>\n",
       "      <td>-0.346</td>\n",
       "      <td>0.004</td>\n",
       "      <td>-0.086</td>\n",
       "      <td>0.492</td>\n",
       "      <td>-0.351</td>\n",
       "      <td>0.004</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Total Commits corr  Total Commits pvalue  Age corr  \\\n",
       "TestabilityRate_A               -0.414                 0.001     0.048   \n",
       "TestabilityRate_T                0.083                 0.505     0.177   \n",
       "FullyTestability_A              -0.548                 0.000    -0.070   \n",
       "FullyTestability_T              -0.346                 0.004    -0.086   \n",
       "\n",
       "                    Age pvalue  LoC corr  LoC pvalue  \n",
       "TestabilityRate_A        0.705    -0.340       0.005  \n",
       "TestabilityRate_T        0.156     0.126       0.315  \n",
       "FullyTestability_A       0.574    -0.508       0.000  \n",
       "FullyTestability_T       0.492    -0.351       0.004  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testabilities = [\n",
    "    'TestabilityRate_A', \n",
    "    'TestabilityRate_T',\n",
    "    'FullyTestability_A', \n",
    "    'FullyTestability_T',\n",
    "]\n",
    "metrics = [\n",
    "    'Total Commits',\n",
    "    'Age',\n",
    "    'LoC'\n",
    "]\n",
    "values = [\n",
    "    'corr',\n",
    "    'pvalue'\n",
    "]\n",
    "results = {}\n",
    "\n",
    "for metric in metrics:\n",
    "    results[metric+\" corr\"] = {}\n",
    "    results[metric+\" pvalue\"] = {}\n",
    "    for testability in testabilities:\n",
    "        corr, pvalue = stats.spearmanr(many4j_projects[testability], many4j_projects[metric])\n",
    "        for v in values:\n",
    "            results[metric+\" corr\"][testability] = round(corr,3)\n",
    "            results[metric+\" pvalue\"][testability] = round(pvalue,3)\n",
    "\n",
    "pd.DataFrame(results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Testability per projects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getProjectsByMetric(dataset, metric, percent, comp):\n",
    "    if comp == 'greater':\n",
    "        return dataset[ dataset[metric] >= percent ]\n",
    "    else:\n",
    "        return dataset[ dataset[metric] <= percent ]\n",
    "\n",
    "def getProjectsByTestability(dataset, comp):\n",
    "    return [ \n",
    "        len(getProjectsByMetric(dataset, \"FullyTestability_A\", 0.99, comp).index),\n",
    "        len(getProjectsByMetric(dataset, \"FullyTestability_T\", 0.99, comp).index),\n",
    "        len(getProjectsByMetric(dataset, \"TestabilityRate_A\", 0.99, comp).index),\n",
    "        len(getProjectsByMetric(dataset, \"TestabilityRate_T\", 0.99, comp).index),\n",
    "    ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.1 Best projects per Testability (count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Dataset</th>\n",
       "      <td>Many4J</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FullyTestability_A</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FullyTestability_T</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TestabilityRate_A</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TestabilityRate_T</th>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         0\n",
       "Dataset             Many4J\n",
       "FullyTestability_A       0\n",
       "FullyTestability_T       8\n",
       "TestabilityRate_A        0\n",
       "TestabilityRate_T       43"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "many4j_best = getProjectsByTestability(many4j_projects, \"greater\")\n",
    "pd.DataFrame([\n",
    "    [\"Many4J\"] + many4j_best\n",
    "], columns = [\n",
    "        \"Dataset\",\n",
    "        \"FullyTestability_A\",\n",
    "        \"FullyTestability_T\",\n",
    "        \"TestabilityRate_A\",\n",
    "        \"TestabilityRate_T\"\n",
    "]).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def topProjectsBy(dataset, metric):\n",
    "    return (dataset.sort_values(by=metric, ascending=False).round(decimals=2).head(6)\n",
    "            #[['Project', 'Dataset', 'Age', 'LoC', 'Total Commits']]\n",
    "            #.set_index(['Project'])\n",
    "           )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.2 Best projects per Testability (Details)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Best Projects (FullyTestability C)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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       "        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>41</th>\n",
       "      <th>46</th>\n",
       "      <th>40</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Project</th>\n",
       "      <td>jsoup</td>\n",
       "      <td>spark</td>\n",
       "      <td>retrofit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dataset</th>\n",
       "      <td>ManySStub4J</td>\n",
       "      <td>ManySStub4J</td>\n",
       "      <td>ManySStub4J</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>11.12</td>\n",
       "      <td>9.55</td>\n",
       "      <td>10.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LoC</th>\n",
       "      <td>17831.0</td>\n",
       "      <td>8655.0</td>\n",
       "      <td>22003.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total Commits</th>\n",
       "      <td>1442</td>\n",
       "      <td>1062</td>\n",
       "      <td>1865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Source buildable commits</th>\n",
       "      <td>1410</td>\n",
       "      <td>1051</td>\n",
       "      <td>1431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Source buildability</th>\n",
       "      <td>0.98</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Test buildable commits</th>\n",
       "      <td>1403</td>\n",
       "      <td>986</td>\n",
       "      <td>1431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Test buildability_A</th>\n",
       "      <td>0.97</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Test buildability_S</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.94</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fully Testable commits</th>\n",
       "      <td>1349</td>\n",
       "      <td>921</td>\n",
       "      <td>1411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FullyTestability_A</th>\n",
       "      <td>0.94</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FullyTestability_T</th>\n",
       "      <td>0.96</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TestabilityRate_A</th>\n",
       "      <td>0.97</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TestabilityRate_A_std</th>\n",
       "      <td>0.17</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TestabilityRate_T</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TestabilityRate_T_std</th>\n",
       "      <td>0.06</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   41           46           40\n",
       "Project                         jsoup        spark     retrofit\n",
       "Dataset                   ManySStub4J  ManySStub4J  ManySStub4J\n",
       "Age                             11.12         9.55        10.48\n",
       "LoC                           17831.0       8655.0      22003.0\n",
       "Total Commits                    1442         1062         1865\n",
       "Source buildable commits         1410         1051         1431\n",
       "Source buildability              0.98         0.99         0.77\n",
       "Test buildable commits           1403          986         1431\n",
       "Test buildability_A              0.97         0.93         0.77\n",
       "Test buildability_S               1.0         0.94          1.0\n",
       "Fully Testable commits           1349          921         1411\n",
       "FullyTestability_A               0.94         0.87         0.76\n",
       "FullyTestability_T               0.96         0.93         0.99\n",
       "TestabilityRate_A                0.97         0.92         0.77\n",
       "TestabilityRate_A_std            0.17         0.26         0.42\n",
       "TestabilityRate_T                 1.0         0.99          1.0\n",
       "TestabilityRate_T_std            0.06         0.05          0.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "topProjectsBy(many4j_projects,'FullyTestability_A').head(3).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Best Projects (FullyTestability T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th>65</th>\n",
       "      <th>52</th>\n",
       "      <th>47</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Project</th>\n",
       "      <td>camel</td>\n",
       "      <td>jfinal</td>\n",
       "      <td>javapoet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dataset</th>\n",
       "      <td>ManySStub4J</td>\n",
       "      <td>ManySStub4J</td>\n",
       "      <td>ManySStub4J</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>14.28</td>\n",
       "      <td>8.78</td>\n",
       "      <td>8.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LoC</th>\n",
       "      <td>1124256.0</td>\n",
       "      <td>4977.0</td>\n",
       "      <td>8306.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total Commits</th>\n",
       "      <td>53286</td>\n",
       "      <td>473</td>\n",
       "      <td>846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Source buildable commits</th>\n",
       "      <td>21</td>\n",
       "      <td>360</td>\n",
       "      <td>398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Source buildability</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Test buildable commits</th>\n",
       "      <td>20</td>\n",
       "      <td>79</td>\n",
       "      <td>396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Test buildability_A</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Test buildability_S</th>\n",
       "      <td>0.95</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fully Testable commits</th>\n",
       "      <td>20</td>\n",
       "      <td>79</td>\n",
       "      <td>396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FullyTestability_A</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FullyTestability_T</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TestabilityRate_A</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TestabilityRate_A_std</th>\n",
       "      <td>0.02</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TestabilityRate_T</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TestabilityRate_T_std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   65           52           47\n",
       "Project                         camel       jfinal     javapoet\n",
       "Dataset                   ManySStub4J  ManySStub4J  ManySStub4J\n",
       "Age                             14.28         8.78         8.88\n",
       "LoC                         1124256.0       4977.0       8306.0\n",
       "Total Commits                   53286          473          846\n",
       "Source buildable commits           21          360          398\n",
       "Source buildability               0.0         0.76         0.47\n",
       "Test buildable commits             20           79          396\n",
       "Test buildability_A               0.0         0.17         0.47\n",
       "Test buildability_S              0.95         0.22         0.99\n",
       "Fully Testable commits             20           79          396\n",
       "FullyTestability_A                0.0         0.17         0.47\n",
       "FullyTestability_T                1.0          1.0          1.0\n",
       "TestabilityRate_A                 0.0         0.17         0.47\n",
       "TestabilityRate_A_std            0.02         0.37          0.5\n",
       "TestabilityRate_T                 1.0          1.0          1.0\n",
       "TestabilityRate_T_std             0.0          0.0          0.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "topProjectsBy(many4j_projects,'FullyTestability_T').head(3).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Best Projects (TestabilityRate A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>58</th>\n",
       "      <th>41</th>\n",
       "      <th>36</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Project</th>\n",
       "      <td>DiskLruCache</td>\n",
       "      <td>jsoup</td>\n",
       "      <td>elastic-job</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dataset</th>\n",
       "      <td>ManySStub4J</td>\n",
       "      <td>ManySStub4J</td>\n",
       "      <td>ManySStub4J</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>4.45</td>\n",
       "      <td>11.12</td>\n",
       "      <td>6.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LoC</th>\n",
       "      <td>1436.0</td>\n",
       "      <td>17831.0</td>\n",
       "      <td>25310.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total Commits</th>\n",
       "      <td>136</td>\n",
       "      <td>1442</td>\n",
       "      <td>2160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Source buildable commits</th>\n",
       "      <td>136</td>\n",
       "      <td>1410</td>\n",
       "      <td>2032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Source buildability</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Test buildable commits</th>\n",
       "      <td>133</td>\n",
       "      <td>1403</td>\n",
       "      <td>2027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Test buildability_A</th>\n",
       "      <td>0.98</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Test buildability_S</th>\n",
       "      <td>0.98</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fully Testable commits</th>\n",
       "      <td>96</td>\n",
       "      <td>1349</td>\n",
       "      <td>1158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FullyTestability_A</th>\n",
       "      <td>0.71</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FullyTestability_T</th>\n",
       "      <td>0.72</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TestabilityRate_A</th>\n",
       "      <td>0.97</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TestabilityRate_A_std</th>\n",
       "      <td>0.15</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TestabilityRate_T</th>\n",
       "      <td>0.99</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TestabilityRate_T_std</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    58           41           36\n",
       "Project                   DiskLruCache        jsoup  elastic-job\n",
       "Dataset                    ManySStub4J  ManySStub4J  ManySStub4J\n",
       "Age                               4.45        11.12         6.44\n",
       "LoC                             1436.0      17831.0      25310.0\n",
       "Total Commits                      136         1442         2160\n",
       "Source buildable commits           136         1410         2032\n",
       "Source buildability                1.0         0.98         0.94\n",
       "Test buildable commits             133         1403         2027\n",
       "Test buildability_A               0.98         0.97         0.94\n",
       "Test buildability_S               0.98          1.0          1.0\n",
       "Fully Testable commits              96         1349         1158\n",
       "FullyTestability_A                0.71         0.94         0.54\n",
       "FullyTestability_T                0.72         0.96         0.57\n",
       "TestabilityRate_A                 0.97         0.97         0.94\n",
       "TestabilityRate_A_std             0.15         0.17         0.24\n",
       "TestabilityRate_T                 0.99          1.0          1.0\n",
       "TestabilityRate_T_std             0.01         0.06         0.01"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "topProjectsBy(many4j_projects,'TestabilityRate_A').head(3).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Best Projects (TestabilityRate T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>65</th>\n",
       "      <th>47</th>\n",
       "      <th>59</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Project</th>\n",
       "      <td>camel</td>\n",
       "      <td>javapoet</td>\n",
       "      <td>otto</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dataset</th>\n",
       "      <td>ManySStub4J</td>\n",
       "      <td>ManySStub4J</td>\n",
       "      <td>ManySStub4J</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>14.28</td>\n",
       "      <td>8.88</td>\n",
       "      <td>5.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LoC</th>\n",
       "      <td>1124256.0</td>\n",
       "      <td>8306.0</td>\n",
       "      <td>1344.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total Commits</th>\n",
       "      <td>53286</td>\n",
       "      <td>846</td>\n",
       "      <td>205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Source buildable commits</th>\n",
       "      <td>21</td>\n",
       "      <td>398</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Source buildability</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Test buildable commits</th>\n",
       "      <td>20</td>\n",
       "      <td>396</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Test buildability_A</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Test buildability_S</th>\n",
       "      <td>0.95</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fully Testable commits</th>\n",
       "      <td>20</td>\n",
       "      <td>396</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FullyTestability_A</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FullyTestability_T</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TestabilityRate_A</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TestabilityRate_A_std</th>\n",
       "      <td>0.02</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TestabilityRate_T</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TestabilityRate_T_std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                                   65           47           59\n",
       "Project                         camel     javapoet         otto\n",
       "Dataset                   ManySStub4J  ManySStub4J  ManySStub4J\n",
       "Age                             14.28         8.88         5.85\n",
       "LoC                         1124256.0       8306.0       1344.0\n",
       "Total Commits                   53286          846          205\n",
       "Source buildable commits           21          398           18\n",
       "Source buildability               0.0         0.47         0.09\n",
       "Test buildable commits             20          396           18\n",
       "Test buildability_A               0.0         0.47         0.09\n",
       "Test buildability_S              0.95         0.99          1.0\n",
       "Fully Testable commits             20          396           18\n",
       "FullyTestability_A                0.0         0.47         0.09\n",
       "FullyTestability_T                1.0          1.0          1.0\n",
       "TestabilityRate_A                 0.0         0.47         0.09\n",
       "TestabilityRate_A_std            0.02          0.5         0.28\n",
       "TestabilityRate_T                 1.0          1.0          1.0\n",
       "TestabilityRate_T_std             0.0          0.0          0.0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "topProjectsBy(many4j_projects,'TestabilityRate_T').head(3).T"
   ]
  }
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