{
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction\n",
    "In my [previous blog post](https://www.feststelltaste.de/checking-the-modularization-of-software-systems-by-analyzing-co-changing-source-code-files/), we looked at the similarity within and across modules by only looking at the change data of each source code file.\n",
    "\n",
    "In this analysis, we use same data analysis approach, but visualize the result in a 3D scatter plot."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Wrangling\n",
    "We just repeat the stuff explained in the mentioned blog post. The only difference is, that we are going from a 2D representation of the distance matrix to a 3D representation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
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       "      <th>backend/src/main/java/at/dropover/comment/boundary/ChangeCommentRequestModel.java</th>\n",
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       "                                                           x         y  \\\n",
       "file                                                                     \n",
       "backend/src/main/java/at/dropover/comment/bound...  0.204507 -0.500405   \n",
       "backend/src/main/java/at/dropover/comment/bound...  0.420250 -0.287899   \n",
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       "backend/src/main/java/at/dropover/comment/bound...  0.176830 -0.580814   \n",
       "\n",
       "                                                           z   module  \n",
       "file                                                                   \n",
       "backend/src/main/java/at/dropover/comment/bound...  0.328621  comment  \n",
       "backend/src/main/java/at/dropover/comment/bound...  0.239667  comment  \n",
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     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.metrics.pairwise import cosine_distances\n",
    "from sklearn.manifold import MDS\n",
    "import numpy as np\n",
    "from matplotlib import cm\n",
    "from matplotlib.colors import rgb2hex\n",
    "import ipyvolume as ipv\n",
    "\n",
    "# read, filter and prepare data\n",
    "git_log = pd.read_csv(\"https://git.io/Jez2h\")\n",
    "prod_code = git_log.copy()\n",
    "prod_code = prod_code[prod_code.file.str.endswith(\".java\")]\n",
    "prod_code = prod_code[prod_code.file.str.startswith(\"backend/src/main\")]\n",
    "prod_code = prod_code[~prod_code.file.str.endswith(\"package-info.java\")]\n",
    "prod_code['hit'] = 1\n",
    "\n",
    "# pivot table to get a change vector per file\n",
    "commit_matrix = prod_code.reset_index().pivot_table(\n",
    "    index='file',\n",
    "    columns='sha',\n",
    "    values='hit',\n",
    "    fill_value=0)\n",
    "commit_matrix.iloc[0:5,50:55]\n",
    "\n",
    "# calculate distance between files based on changes\n",
    "dissimilarity_matrix = cosine_distances(commit_matrix)\n",
    "\n",
    "# break down matrix to 3D representation\n",
    "model = MDS(dissimilarity='precomputed', random_state=0, n_components=3)\n",
    "dissimilarity_3d = model.fit_transform(dissimilarity_matrix)\n",
    "\n",
    "# extract module names\n",
    "dissimilarity_3d_df = pd.DataFrame(\n",
    "    dissimilarity_3d,\n",
    "    index=commit_matrix.index,\n",
    "    columns=[\"x\", \"y\", \"z\"])\n",
    "dissimilarity_3d_df['module'] = dissimilarity_3d_df.index.str.split(\"/\").str[6]\n",
    "dissimilarity_3d_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization\n",
    "So this part is new: We brew a color for each module."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
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       "      <th>backend/src/main/java/at/dropover/comment/boundary/AddCommentRequestModel.java</th>\n",
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       "      <td>[0.6196078431372549, 0.00392156862745098, 0.25...</td>\n",
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       "    <tr>\n",
       "      <th>backend/src/main/java/at/dropover/comment/boundary/GetCommentResponseModel.java</th>\n",
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       "      <td>-0.580814</td>\n",
       "      <td>0.123574</td>\n",
       "      <td>comment</td>\n",
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       "                                                           x         y  \\\n",
       "file                                                                     \n",
       "backend/src/main/java/at/dropover/comment/bound...  0.204507 -0.500405   \n",
       "backend/src/main/java/at/dropover/comment/bound...  0.420250 -0.287899   \n",
       "backend/src/main/java/at/dropover/comment/bound...  0.384984 -0.418287   \n",
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       "backend/src/main/java/at/dropover/comment/bound...  0.176830 -0.580814   \n",
       "\n",
       "                                                           z   module  \\\n",
       "file                                                                    \n",
       "backend/src/main/java/at/dropover/comment/bound...  0.328621  comment   \n",
       "backend/src/main/java/at/dropover/comment/bound...  0.239667  comment   \n",
       "backend/src/main/java/at/dropover/comment/bound...  0.185055  comment   \n",
       "backend/src/main/java/at/dropover/comment/bound...  0.299084  comment   \n",
       "backend/src/main/java/at/dropover/comment/bound...  0.123574  comment   \n",
       "\n",
       "                                                                                                color  \n",
       "file                                                                                                   \n",
       "backend/src/main/java/at/dropover/comment/bound...  [0.6196078431372549, 0.00392156862745098, 0.25...  \n",
       "backend/src/main/java/at/dropover/comment/bound...  [0.6196078431372549, 0.00392156862745098, 0.25...  \n",
       "backend/src/main/java/at/dropover/comment/bound...  [0.6196078431372549, 0.00392156862745098, 0.25...  \n",
       "backend/src/main/java/at/dropover/comment/bound...  [0.6196078431372549, 0.00392156862745098, 0.25...  \n",
       "backend/src/main/java/at/dropover/comment/bound...  [0.6196078431372549, 0.00392156862745098, 0.25...  "
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modules = dissimilarity_3d_df[['module']].drop_duplicates()\n",
    "rgb_colors = [x for x in cm.Spectral(np.linspace(0,1,len(modules)))]\n",
    "modules['color'] = rgb_colors\n",
    "modules = modules.set_index(\"module\", drop=True)\n",
    "dissimilarity_3d_df['color'] = dissimilarity_3d_df['module'].map(modules['color'].to_dict())\n",
    "dissimilarity_3d_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And then, we visualize this data with [ipyvolume](https://ipyvolume.readthedocs.io/en/latest/index.html#)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1bd327b383cc46a8a12aecb8c6afdd6b",
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       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(Figure(camera=PerspectiveCamera(fov=46.0, position=(0.0, 0.0, 2.0), quaternion=(0.0, 0.0, 0.0, …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = dissimilarity_3d_df['x']\n",
    "y = dissimilarity_3d_df['y']\n",
    "z = dissimilarity_3d_df['z']\n",
    "color = dissimilarity_3d_df['color'].values.tolist()\n",
    "ipv.quickscatter(x, y, z, color=color, size=7, marker=\"sphere\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Summary\n",
    "That's it! A nice 3D representation of our little software program.\n",
    "\n",
    "\n",
    "\n",
    "We see some spheres with the same color near to each other. These modules the were change together in the first place. But there are also some mixed up areas. The reasons for this are explained [here](https://www.feststelltaste.de/checking-the-modularization-of-software-systems-by-analyzing-co-changing-source-code-files/#Modules-%22comment%22,-%22framework%22-and-%22site%22)."
   ]
  }
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