notebooks/SWOT analysis for spotting worthless code.ipynb

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    "# Introduction\n",
    "In this short blog post, I want to show you an idea where you take some very detailed datasets from a software project and transform it into a representation where management can reason about. For this,\n",
    "\n",
    "* we take the utilization of a web application measured by the code coverage during user interactions\n",
    "* we estimate the investments in the software by the number of changes\n",
    "* we aggregate the data to something management can understand\n",
    "* we visualize the results in a [SWOT (Strengths, Weaknesses, Opportunities, Threats) matrix](https://en.wikipedia.org/wiki/SWOT_analysis)\n",
    "\n",
    "This little picture summarizes the main ideas where the data comes from:\n",
    "\n",
    "![](resources/worthless_code.png)\n",
    "\n",
    "\n",
    "Let's get some insights!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "slide_type": "skip"
    }
   },
   "source": [
    "## Utilization of a production system\n",
    "A proxy for utilization, we've taken the code coverage of a web application during the live operation with the user. We've collected that data by attaching the JaCoCo agent to the Java process (for details on how to do that see my blog post [\"Visualizing Production Coverage with JaCoCo, Pandas and D3\"](https://www.feststelltaste.de/visualizing-production-coverage-with-jacoco-pandas-and-d3/)).\n",
    "\n",
    "The result is a dataset with covered lines per class. We read that into a Pandas DataFrame."
   ]
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       "      <th>PACKAGE</th>\n",
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       "      <th>LINE_MISSED</th>\n",
       "      <th>LINE_COVERED</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>org.springframework.samples.petclinic</td>\n",
       "      <td>PetclinicInitializer</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>org.springframework.samples.petclinic.model</td>\n",
       "      <td>NamedEntity</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>org.springframework.samples.petclinic.model</td>\n",
       "      <td>Specialty</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>org.springframework.samples.petclinic.model</td>\n",
       "      <td>PetType</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>org.springframework.samples.petclinic.model</td>\n",
       "      <td>Vets</td>\n",
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      "text/plain": [
       "                                       PACKAGE                 CLASS  \\\n",
       "0        org.springframework.samples.petclinic  PetclinicInitializer   \n",
       "1  org.springframework.samples.petclinic.model           NamedEntity   \n",
       "2  org.springframework.samples.petclinic.model             Specialty   \n",
       "3  org.springframework.samples.petclinic.model               PetType   \n",
       "4  org.springframework.samples.petclinic.model                  Vets   \n",
       "\n",
       "   LINE_MISSED  LINE_COVERED  \n",
       "0            0            24  \n",
       "1            1             4  \n",
       "2            0             1  \n",
       "3            0             1  \n",
       "4            4             0  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "coverage = pd.read_csv(\"datasets/jacoco_production_coverage_spring_petclinic.csv\")\n",
    "coverage.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "slide_type": "skip"
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   "source": [
    "We need to calculate some additional variables for our analysis later on: The lines of code per class and the ratio of the covered lines of code to all lines of code per class."
   ]
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   "cell_type": "code",
   "execution_count": 2,
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       "      <th></th>\n",
       "      <th>PACKAGE</th>\n",
       "      <th>CLASS</th>\n",
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       "      <th>lines</th>\n",
       "      <th>covered</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>org.springframework.samples.petclinic</td>\n",
       "      <td>PetclinicInitializer</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>org.springframework.samples.petclinic.model</td>\n",
       "      <td>NamedEntity</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>org.springframework.samples.petclinic.model</td>\n",
       "      <td>Specialty</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>org.springframework.samples.petclinic.model</td>\n",
       "      <td>PetType</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>org.springframework.samples.petclinic.model</td>\n",
       "      <td>Vets</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
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      "text/plain": [
       "                                       PACKAGE                 CLASS  \\\n",
       "0        org.springframework.samples.petclinic  PetclinicInitializer   \n",
       "1  org.springframework.samples.petclinic.model           NamedEntity   \n",
       "2  org.springframework.samples.petclinic.model             Specialty   \n",
       "3  org.springframework.samples.petclinic.model               PetType   \n",
       "4  org.springframework.samples.petclinic.model                  Vets   \n",
       "\n",
       "   LINE_MISSED  LINE_COVERED  lines  covered  \n",
       "0            0            24     24      1.0  \n",
       "1            1             4      5      0.8  \n",
       "2            0             1      1      1.0  \n",
       "3            0             1      1      1.0  \n",
       "4            4             0      4      0.0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coverage['lines'] = coverage.LINE_MISSED + coverage.LINE_COVERED\n",
    "coverage['covered'] = coverage.LINE_COVERED / coverage.lines\n",
    "coverage.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "Next, we create a unique identifier for connecting the coverage data to the number of changes per class. We choose the fully qualified name (\"fqn\") of a class as key for this, set this variable as an index and only take the columns that we need later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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     "slide_type": "skip"
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     "data": {
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       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>lines</th>\n",
       "      <th>covered</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fqn</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>org.springframework.samples.petclinic.PetclinicInitializer</th>\n",
       "      <td>24</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>org.springframework.samples.petclinic.model.NamedEntity</th>\n",
       "      <td>5</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>org.springframework.samples.petclinic.model.Specialty</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>org.springframework.samples.petclinic.model.PetType</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>org.springframework.samples.petclinic.model.Vets</th>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "                                                    lines  covered\n",
       "fqn                                                               \n",
       "org.springframework.samples.petclinic.Petclinic...     24      1.0\n",
       "org.springframework.samples.petclinic.model.Nam...      5      0.8\n",
       "org.springframework.samples.petclinic.model.Spe...      1      1.0\n",
       "org.springframework.samples.petclinic.model.Pet...      1      1.0\n",
       "org.springframework.samples.petclinic.model.Vets        4      0.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coverage['fqn'] = coverage.PACKAGE + \".\" + coverage.CLASS\n",
    "coverage_per_class = coverage.set_index('fqn')[['lines', 'covered']]\n",
    "coverage_per_class.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "# Investments in code\n",
    "As a proxy for the investments we took into our software, we choose the number of changes per file. For this, we need a log of our version control system. With Git, a simple numstat log does the job. We can get a list for each change of a file including the added and deleted lines with the command\n",
    "```\n",
    "git log --no-merges --no-renames --numstat --pretty=format:\"\"\n",
    "```\n",
    "We kind of hacked the git log pretty format option, but it does the job. We read that dataset into a DataFrame as well, using the tabular as a separator and specifying some column names manually."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>additions</th>\n",
       "      <th>deletions</th>\n",
       "      <th>path</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
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       "      <td>src/test/java/org/springframework/samples/petc...</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25</td>\n",
       "      <td>7</td>\n",
       "      <td>src/test/java/org/springframework/samples/petc...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21</td>\n",
       "      <td>9</td>\n",
       "      <td>src/test/java/org/springframework/samples/petc...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "      <td>src/test/java/org/springframework/samples/petc...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>src/test/java/org/springframework/samples/petc...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   additions  deletions                                               path\n",
       "0          4          5  src/test/java/org/springframework/samples/petc...\n",
       "1         25          7  src/test/java/org/springframework/samples/petc...\n",
       "2         21          9  src/test/java/org/springframework/samples/petc...\n",
       "3         23          3  src/test/java/org/springframework/samples/petc...\n",
       "4         10          6  src/test/java/org/springframework/samples/petc..."
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "changes = pd.read_csv(\n",
    "    \"datasets/git_log_numstat_spring_petclinic.log\",\n",
    "    sep=\"\\t\",\n",
    "    names=['additions', 'deletions', 'path'])\n",
    "changes.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As with the coverage data, we need a unique identifier per class. We can create that based on the path of each file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'org.springframework.samples.petclinic.web.CrashControllerTests'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "changes['fqn'] = changes.path.str.extract(\n",
    "    \"/java/(.*)\\.java\",\n",
    "    expand=True)[0]\n",
    "changes['fqn'] = changes.fqn.str.replace(\"/\", \".\")\n",
    "changes['fqn'][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "Different to the coverage dataset, we have multiple entries per file (for each change of that file). We need to group the changes by the `fqn` of each file or class respectively. We just take the path column that holds the number of changes for each class and renames that column to `changes`. The result is a Series of the number of changes per file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fqn\n",
       "org.springframework.samples.petclinic.AbstractClinicTests             5\n",
       "org.springframework.samples.petclinic.AbstractOwnerRepositoryTests    6\n",
       "org.springframework.samples.petclinic.AbstractPetRepositoryTests      6\n",
       "org.springframework.samples.petclinic.AbstractVetRepositoryTests      4\n",
       "org.springframework.samples.petclinic.AbstractVisitRepositoryTests    5\n",
       "Name: changes, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "changes_per_file = changes.groupby('fqn').path.count()\n",
    "changes_per_file.name = \"changes\"\n",
    "changes_per_file.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Joining of the datasets\n",
    "Next, we can join the two different datasets with the DataFrames' `join` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\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>lines</th>\n",
       "      <th>covered</th>\n",
       "      <th>changes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fqn</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>org.springframework.samples.petclinic.PetclinicInitializer</th>\n",
       "      <td>24</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>org.springframework.samples.petclinic.model.NamedEntity</th>\n",
       "      <td>5</td>\n",
       "      <td>0.8</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>org.springframework.samples.petclinic.model.Specialty</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>org.springframework.samples.petclinic.model.PetType</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>org.springframework.samples.petclinic.model.Vets</th>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    lines  covered  changes\n",
       "fqn                                                                        \n",
       "org.springframework.samples.petclinic.Petclinic...     24      1.0      1.0\n",
       "org.springframework.samples.petclinic.model.Nam...      5      0.8      3.0\n",
       "org.springframework.samples.petclinic.model.Spe...      1      1.0      2.0\n",
       "org.springframework.samples.petclinic.model.Pet...      1      1.0      4.0\n",
       "org.springframework.samples.petclinic.model.Vets        4      0.0      5.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analysis = coverage_per_class.join(changes_per_file)\n",
    "analysis.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating a higher technical view\n",
    "OK, now to the key idea of this analysis. We need to raise the very fine-granular data on class level to a more higher-level where we can reason about in a better way. For this, we derive higher-level perspectives based on the `fqn` of the classes. For technical components, we can do this in our case based on the package name  because the last part of the package name contains the name of the technical component. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "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>lines</th>\n",
       "      <th>covered</th>\n",
       "      <th>changes</th>\n",
       "      <th>tech</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fqn</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>org.springframework.samples.petclinic.PetclinicInitializer</th>\n",
       "      <td>24</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>petclinic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>org.springframework.samples.petclinic.model.NamedEntity</th>\n",
       "      <td>5</td>\n",
       "      <td>0.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>model</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>org.springframework.samples.petclinic.model.Specialty</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>model</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>org.springframework.samples.petclinic.model.PetType</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>model</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>org.springframework.samples.petclinic.model.Vets</th>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>model</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    lines  covered  changes  \\\n",
       "fqn                                                                           \n",
       "org.springframework.samples.petclinic.Petclinic...     24      1.0      1.0   \n",
       "org.springframework.samples.petclinic.model.Nam...      5      0.8      3.0   \n",
       "org.springframework.samples.petclinic.model.Spe...      1      1.0      2.0   \n",
       "org.springframework.samples.petclinic.model.Pet...      1      1.0      4.0   \n",
       "org.springframework.samples.petclinic.model.Vets        4      0.0      5.0   \n",
       "\n",
       "                                                         tech  \n",
       "fqn                                                            \n",
       "org.springframework.samples.petclinic.Petclinic...  petclinic  \n",
       "org.springframework.samples.petclinic.model.Nam...      model  \n",
       "org.springframework.samples.petclinic.model.Spe...      model  \n",
       "org.springframework.samples.petclinic.model.Pet...      model  \n",
       "org.springframework.samples.petclinic.model.Vets        model  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analysis['tech'] = analysis.index.str.split(\".\").str[-2]\n",
    "analysis.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now aggregate our fine-granular data to a higher-level representation by grouping the data accordingly to the technical components. For the `agg` function, we provide a dictionary with the aggregation methods for each column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "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>lines</th>\n",
       "      <th>covered</th>\n",
       "      <th>changes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tech</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>jdbc</th>\n",
       "      <td>150</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>93.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jpa</th>\n",
       "      <td>28</td>\n",
       "      <td>0.691558</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <td>108</td>\n",
       "      <td>0.739048</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petclinic</th>\n",
       "      <td>24</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>service</th>\n",
       "      <td>18</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           lines   covered  changes\n",
       "tech                               \n",
       "jdbc         150  0.000000     93.0\n",
       "jpa           28  0.691558     39.0\n",
       "model        108  0.739048     44.0\n",
       "petclinic     24  1.000000      1.0\n",
       "service       18  0.888889     12.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tech_insights = analysis.groupby('tech').agg({\n",
    "    \"lines\" : \"sum\",\n",
    "    \"covered\": \"mean\",\n",
    "    \"changes\" : \"sum\"\n",
    "    })\n",
    "tech_insights.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the help of a (self-written) little visualization, we can now create a SWOT matrix from our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22af527bda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from lib.ausi import portfolio\n",
    "portfolio.plot_diagram(tech_insights, \"changes\", \"covered\", \"lines\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Discussion**  \n",
    "* In the lower right, we can see that we changed the `jdbc` component very often (aka invested much money) but the component isn't used at all. A clear failure of investments (OK, in this case, it's an alternative implementation of the database access, but we could also delete this code completely without any side effects).\n",
    "* Above, we see that we also invested heavily in the `web` component, but this isn't used as much as it should be (in relation to the changes). Maybe we can find code in this component that isn't needed anymore.\n",
    "* The `petclinic` (a base component), `service`, `jpa` and `model` components in the upper left are used very often with low changes (aka investments). This is good!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Creating a domain  view\n",
    "Next, we want to create a view that non-technical people can understand. For this, we use the naming schema of the classes to identify which business domain each class belongs to."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "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>lines</th>\n",
       "      <th>covered</th>\n",
       "      <th>changes</th>\n",
       "      <th>tech</th>\n",
       "      <th>domain</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fqn</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>org.springframework.samples.petclinic.PetclinicInitializer</th>\n",
       "      <td>24</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>petclinic</td>\n",
       "      <td>Pet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>org.springframework.samples.petclinic.model.NamedEntity</th>\n",
       "      <td>5</td>\n",
       "      <td>0.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>model</td>\n",
       "      <td>Other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>org.springframework.samples.petclinic.model.Specialty</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>model</td>\n",
       "      <td>Specialty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>org.springframework.samples.petclinic.model.PetType</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>model</td>\n",
       "      <td>Pet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>org.springframework.samples.petclinic.model.Vets</th>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>model</td>\n",
       "      <td>Vet</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    lines  covered  changes  \\\n",
       "fqn                                                                           \n",
       "org.springframework.samples.petclinic.Petclinic...     24      1.0      1.0   \n",
       "org.springframework.samples.petclinic.model.Nam...      5      0.8      3.0   \n",
       "org.springframework.samples.petclinic.model.Spe...      1      1.0      2.0   \n",
       "org.springframework.samples.petclinic.model.Pet...      1      1.0      4.0   \n",
       "org.springframework.samples.petclinic.model.Vets        4      0.0      5.0   \n",
       "\n",
       "                                                         tech     domain  \n",
       "fqn                                                                       \n",
       "org.springframework.samples.petclinic.Petclinic...  petclinic        Pet  \n",
       "org.springframework.samples.petclinic.model.Nam...      model      Other  \n",
       "org.springframework.samples.petclinic.model.Spe...      model  Specialty  \n",
       "org.springframework.samples.petclinic.model.Pet...      model        Pet  \n",
       "org.springframework.samples.petclinic.model.Vets        model        Vet  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analysis['domain'] = \"Other\"\n",
    "\n",
    "domains = [\"Owner\", \"Pet\", \"Visit\", \"Vet\", \"Specialty\", \"Clinic\"]\n",
    "for domain in domains:\n",
    "    analysis.loc[analysis.index.str.contains(domain), 'domain'] = domain\n",
    "\n",
    "analysis.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Like with the technical components, we group the business aspects accordingly but also translate the technical terms into non-technical ones."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "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>Size</th>\n",
       "      <th>Utilization</th>\n",
       "      <th>Investment</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>domain</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Clinic</th>\n",
       "      <td>18</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>57</td>\n",
       "      <td>0.480208</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Owner</th>\n",
       "      <td>130</td>\n",
       "      <td>0.549495</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pet</th>\n",
       "      <td>153</td>\n",
       "      <td>0.594156</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Specialty</th>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Size  Utilization  Investment\n",
       "domain                                  \n",
       "Clinic       18     0.888889        12.0\n",
       "Other        57     0.480208        32.0\n",
       "Owner       130     0.549495        59.0\n",
       "Pet         153     0.594156        81.0\n",
       "Specialty     1     1.000000         2.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "domain_insights = analysis.groupby('domain').agg({\n",
    "    \"lines\" : \"sum\",\n",
    "    \"covered\": \"mean\",\n",
    "    \"changes\" : \"sum\" \n",
    "    })\n",
    "domain_insights = domain_insights.rename(columns=\n",
    "   {\"lines\": \"Size\", \"covered\" : \"Utilization\", \"changes\" : \"Investment\"})\n",
    "domain_insights.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Again, we plot a SWOT matrix with the data in the DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22af4b7fcf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "portfolio.plot_diagram(domain_insights, \"Investment\", \"Utilization\", \"Size\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "**Discussion**\n",
    "* The investments into the classes around `Clinic` in the upper left of the matrix were worthwhile.\n",
    "* We have to have a look at the trend of `Pet` and `Owner` related classes because albeit we've invested heavily in this parts, they aren't used so much.\n",
    "* For the `Visit` and `Vet` components in the lower right, we should take a look at if those components are worthwhile further improvements or candidates for removal.\n",
    "* The `Other` component has good chances to be improved the right way in the near future by only developing the parts further that are really needed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Conclusion\n",
    "OK, we've seen how we can take some very fine-granular dataset, combine them and bring them to a higher-level perspective. We've also visualized the results of our analysis with a SWOT matrix to quickly spot parts of our software system that are good or bad investments.\n",
    "\n",
    "\n",
    "What do you think about this approach? Do you like the idea? Or is this complete nonsense? Please leave me a comment!"
   ]
  }
 ],
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