demos/Production Coverage Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Context\n",
    "\n",
    "John Doe said in meeting..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%less data/jacoco.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GROUP</th>\n",
       "      <th>PACKAGE</th>\n",
       "      <th>CLASS</th>\n",
       "      <th>INSTRUCTION_MISSED</th>\n",
       "      <th>INSTRUCTION_COVERED</th>\n",
       "      <th>BRANCH_MISSED</th>\n",
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       "      <th>METHOD_COVERED</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>JaCoCo Coverage Report</td>\n",
       "      <td>org.springframework.samples.petclinic</td>\n",
       "      <td>PetclinicInitializer</td>\n",
       "      <td>0</td>\n",
       "      <td>124</td>\n",
       "      <td>0</td>\n",
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       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>JaCoCo Coverage Report</td>\n",
       "      <td>org.springframework.samples.petclinic.model</td>\n",
       "      <td>NamedEntity</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>JaCoCo Coverage Report</td>\n",
       "      <td>org.springframework.samples.petclinic.model</td>\n",
       "      <td>Specialty</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>JaCoCo Coverage Report</td>\n",
       "      <td>org.springframework.samples.petclinic.model</td>\n",
       "      <td>PetType</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>JaCoCo Coverage Report</td>\n",
       "      <td>org.springframework.samples.petclinic.model</td>\n",
       "      <td>Vets</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>3</td>\n",
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       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    GROUP                                      PACKAGE  \\\n",
       "0  JaCoCo Coverage Report        org.springframework.samples.petclinic   \n",
       "1  JaCoCo Coverage Report  org.springframework.samples.petclinic.model   \n",
       "2  JaCoCo Coverage Report  org.springframework.samples.petclinic.model   \n",
       "3  JaCoCo Coverage Report  org.springframework.samples.petclinic.model   \n",
       "4  JaCoCo Coverage Report  org.springframework.samples.petclinic.model   \n",
       "\n",
       "                  CLASS  INSTRUCTION_MISSED  INSTRUCTION_COVERED  \\\n",
       "0  PetclinicInitializer                   0                  124   \n",
       "1           NamedEntity                   3                   10   \n",
       "2             Specialty                   0                    3   \n",
       "3               PetType                   0                    3   \n",
       "4                  Vets                  14                    0   \n",
       "\n",
       "   BRANCH_MISSED  BRANCH_COVERED  LINE_MISSED  LINE_COVERED  \\\n",
       "0              0               0            0            24   \n",
       "1              0               0            1             4   \n",
       "2              0               0            0             1   \n",
       "3              0               0            0             1   \n",
       "4              2               0            4             0   \n",
       "\n",
       "   COMPLEXITY_MISSED  COMPLEXITY_COVERED  METHOD_MISSED  METHOD_COVERED  \n",
       "0                  0                   8              0               8  \n",
       "1                  1                   3              1               3  \n",
       "2                  0                   1              0               1  \n",
       "3                  0                   1              0               1  \n",
       "4                  3                   0              2               0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "coverage = pd.read_csv('./data/jacoco.csv') \n",
    "coverage.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x14112723a58>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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XLwT2zOWRwKDsD8D6knpREq7dgWWUBPYYSX2BZyPiRUm7AmdlvA9JWlZpe2ptTNNA4Dxg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\noF+ub+rY7k7+4RERCyTVjsWOlGMzM8f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Mn6CCWSVtAxwFXFu3unI5V6JyWSV1BQ4E/gcg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\nJReGZmZmZgbA/wMCIzIhefrQ0wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14112723048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "coverage.groupby('PACKAGE')['LINE_COVERED'].sum().plot(kind='barh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion\n",
    "\n",
    "Das Package ...jdbc sollte ausgebaut werden"
   ]
  }
 ],
 "metadata": {
  "hide_input": false,
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}