notebooks/Tracking Reengineerings under the Hood.ipynb

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       "   additions  deletions                                               file  \\\n",
       "0         19          0  src/main/java/org/springframework/samples/petc...   \n",
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       "3          0         99  src/main/java/org/springframework/samples/petc...   \n",
       "4         19          0  src/main/java/org/springframework/samples/petc...   \n",
       "\n",
       "             timestamp  \n",
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       "4  2017-12-30 00:38:54  "
      ]
     },
     "execution_count": 1,
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   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "log = pd.read_csv(\"datasets/git_log_refactoring.gz\")\n",
    "log.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 31487 entries, 0 to 31486\n",
      "Data columns (total 4 columns):\n",
      "additions    31487 non-null int64\n",
      "deletions    31487 non-null int64\n",
      "file         31487 non-null object\n",
      "timestamp    31487 non-null object\n",
      "dtypes: int64(2), object(2)\n",
      "memory usage: 984.0+ KB\n"
     ]
    }
   ],
   "source": [
    "log.info()"
   ]
  },
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       "   additions  deletions                                               file  \\\n",
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       "3         39          0  src/main/java/org/springframework/samples/petc...   \n",
       "4          0        139  src/main/java/org/springframework/samples/petc...   \n",
       "\n",
       "            timestamp  \n",
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       "2 2013-05-15 17:36:46  \n",
       "3 2013-05-15 17:36:46  \n",
       "4 2013-05-15 17:36:46  "
      ]
     },
     "execution_count": 3,
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   ],
   "source": [
    "log['timestamp'] = pd.to_datetime(log['timestamp'])\n",
    "log = log.sort_values(by='timestamp').reset_index(drop=True)\n",
    "log.head()"
   ]
  },
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       "     additions  deletions                                               file  \\\n",
       "0          250          0  src/main/java/org/springframework/samples/petc...   \n",
       "24          50          0  src/main/java/org/springframework/samples/petc...   \n",
       "55          78          0  src/main/java/org/springframework/samples/petc...   \n",
       "186        142          0  src/main/java/org/springframework/samples/petc...   \n",
       "243        123          0  src/main/java/org/springframework/samples/petc...   \n",
       "\n",
       "              timestamp  type  \n",
       "0   2013-05-15 03:35:33  jdbc  \n",
       "24  2013-05-16 02:15:44  jdbc  \n",
       "55  2013-05-17 15:12:26  jdbc  \n",
       "186 2013-05-24 05:52:31  jdbc  \n",
       "243 2013-05-28 08:15:35  jdbc  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log.loc[log['file'].str.contains(\"/jdbc/\"), 'type'] = 'jdbc'\n",
    "log.loc[log['file'].str.contains(\"/jpa/\"), 'type'] = 'jpa'\n",
    "log = log.dropna(subset=['type'])\n",
    "log.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "     additions  deletions                                               file  \\\n",
       "0          250          0  src/main/java/org/springframework/samples/petc...   \n",
       "24          50          0  src/main/java/org/springframework/samples/petc...   \n",
       "55          78          0  src/main/java/org/springframework/samples/petc...   \n",
       "186        142          0  src/main/java/org/springframework/samples/petc...   \n",
       "243        123          0  src/main/java/org/springframework/samples/petc...   \n",
       "\n",
       "              timestamp  type  lines  \n",
       "0   2013-05-15 03:35:33  jdbc    250  \n",
       "24  2013-05-16 02:15:44  jdbc     50  \n",
       "55  2013-05-17 15:12:26  jdbc     78  \n",
       "186 2013-05-24 05:52:31  jdbc    142  \n",
       "243 2013-05-28 08:15:35  jdbc    123  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log['lines'] = log['additions'] - log['deletions']\n",
    "log.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "2013-05-28 08:15:35  jdbc    123\n",
       "Name: lines, dtype: int64"
      ]
     },
     "execution_count": 6,
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    }
   ],
   "source": [
    "log_timed = log.groupby(['timestamp', 'type']).lines.sum()\n",
    "log_timed.head()"
   ]
  },
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   "execution_count": 7,
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       "type                 jdbc  jpa\n",
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    "log_progess = log_timed.unstack(fill_value=0).cumsum()\n",
    "log_progess.head()"
   ]
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  {
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   "execution_count": 30,
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from IPython.display import set_matplotlib_formats\n",
    "set_matplotlib_formats('svg')\n",
    "ax = log_progess.plot()\n",
    "ax.set_title(\"Reengineering of the database access technology\")\n",
    "ax.set_xlabel(\"time\")\n",
    "ax.set_ylabel(\"changes\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>type</th>\n",
       "      <th>jdbc</th>\n",
       "      <th>jpa</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>5940</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>12997</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>7424</td>\n",
       "      <td>2655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>3614</td>\n",
       "      <td>4769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>3320</td>\n",
       "      <td>6761</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "type   jdbc   jpa\n",
       "year             \n",
       "2013   5940     0\n",
       "2014  12997     0\n",
       "2015   7424  2655\n",
       "2016   3614  4769\n",
       "2017   3320  6761"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "progress_per_year = log_progess.groupby(log_progess.index.year).last()\n",
    "progress_per_year.index.name = \"year\"\n",
    "progress_per_year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.savefig(\"reengineering.svg\", format=\"svg\")"
   ]
  }
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