RepoAnalysis_Historical.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"font-size:250%\">Repo Analysis History</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the historical section of the [RepoAnalysis](./RepoAnalysis.ipynb) notebook, which is aimed at developing the methodology to analyze a given set of repositories fast.\n",
    "This notebook includes many of the early approaches and insights of repository analysis. It acts supplementary to the [RepoAnalysis](./RepoAnalysis.ipynb) notebook, which starts at the first big run of the analysis.\n",
    "\n",
    "This notebook is structured as follows: At the beginning, [initial approaches](#Initial-approaches) are described. This especially includes evaluating srcML as analysis tool. In the end, regex is decided as way to go. Following, the insights are aggregated into a [first working](#working) (with acceptable time) set of functions to analyze one or multiple repos. Going on from this, an approach for [mapping from users](#mapuser) to their impact with dictionaries and chainmaps is developed (but later discarded). \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as numpy\n",
    "import matplotlib.pyplot as pyplot\n",
    "from git import Repo \n",
    "from datetime import datetime\n",
    "import matplotlib as matplotlib\n",
    "import subprocess\n",
    "import time\n",
    "from git.db import GitCmdObjectDB\n",
    "from git.db import GitDB"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Initial approaches\n",
    "In my initial approaches, I explored the gitpython an srcml technologies, the first to iterate over commits and get their contents, and the second to analyze the contents.\n",
    "I used the [Scylla](https://github.com/bptlab/scylla/) repo for testing purposes, as it was neither too big, nor too small.\n",
    "\n",
    "For this cell to work, the repo has to be checked out beforehand, e.g. with \n",
    "`Repo.clone_from(url='https://github.com/bptlab/scylla.git', to_path='scylla.git', bare=True)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "repo = Repo.init('scylla.git', bare=True, odbt=GitDB)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring gipython and srcml"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gitpython Classes over time\n",
    "The first gitpython approach already allows to iterate over files of all commits. I used this to implement a simple classes over time metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time used: 10.006792783737183\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f707a81cf40>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "timestamps = list(map(lambda commit: datetime.fromtimestamp(commit.committed_date), repo.iter_commits()))\n",
    "fileCounts = []\n",
    "\n",
    "start = time.time()\n",
    "for commit in repo.iter_commits():\n",
    "    fileCount = 0\n",
    "    for obj in commit.tree.traverse():\n",
    "        if obj.type == 'blob' and obj.name.endswith('.java'):\n",
    "            fileCount = fileCount + 1\n",
    "    fileCounts.append(fileCount)\n",
    "end = time.time()\n",
    "print('Time used: '+str(end - start))\n",
    "    \n",
    "dates = matplotlib.dates.date2num(timestamps)\n",
    "pyplot.figure(figsize=(12, 7))\n",
    "pyplot.plot_date(dates, fileCounts, '-')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gitpython Test classes over time\n",
    "With the same approach, also a heutistic about test classes was possible"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time used: 2.4306929111480713\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7efe74e17670>]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "timestamps = list(map(lambda commit: datetime.fromtimestamp(commit.committed_date), repo.iter_commits()))\n",
    "fileCounts = []\n",
    "\n",
    "start = time.time()\n",
    "for commit in repo.iter_commits():\n",
    "    fileCount = 0\n",
    "    for obj in commit.tree.traverse():\n",
    "        if obj.type == 'blob' and obj.name.endswith('.java') and ('Test' in obj.name):\n",
    "            fileCount = fileCount + 1\n",
    "    fileCounts.append(fileCount)\n",
    "end = time.time()\n",
    "print('Time used: '+str(end - start))\n",
    "\n",
    "dates = matplotlib.dates.date2num(timestamps)\n",
    "pyplot.figure(figsize=(12, 7))\n",
    "pyplot.plot_date(dates, fileCounts, '-')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyze the srcml contents of a single file\n",
    "In the following, I started to explore scrml: From testing in the console, I knew that the following command worked: <br>\n",
    "``` git --git-dir=\"./scylla.git\" show ee00a205a2225ad73b0264cf7ba64be5c7044d0b:src/main/java/de/hpi/bpt/scylla/Scylla.java | xargs -0 srcml -l Java --text ```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"yes\"?>\n",
      "<unit xmlns=\"http://www.srcML.org/srcML/src\" revision=\"0.9.5\" language=\"Java\"><package>package <name><name>de</name><operator>.</operator><name>hpi</name><operator>.</operator><name>bpt</name><operator>.</operator><name>scylla</name></name>;</package>\n",
      "\n",
      "<import>import <name><name>java</name><operator>.</operator><name>util</name><operator>.</operator><name>Arrays</name></name>;</import>\n",
      "\n",
      "<comment type=\"block\" format=\"javadoc\">/**\n",
      " * Scylla is an extensible simulator for business processes in BPMN. &lt;br&gt;\n",
      " * This class initializes and runs the {@link SimulationManager} which simulates the processes based on the given input.\n",
      " * \n",
      " * @author Tsun Yin Wong\n",
      " *\n",
      " */</comment>\n",
      "<class><specifier>public</specifier> class <name>Scylla</name> <block>{\n",
      "    <decl_stmt><decl><specifier>public</specifier> <specifier>static</specifier> <type><specifier>final</specifier> <name>String</name></type> <name>OS</name> <init>= <expr><call><name><name>System</name><operator>.</operator><name>getProperty</name></name><argument_list>(<argument><expr><literal type=\"string\">\"os.name\"</literal></expr></argument>)</argument_list></call></expr></init></decl>;</decl_stmt>\n",
      "    <decl_stmt><decl><specifier>public</specifier> <specifier>static</specifier> <type><specifier>final</specifier> <name>String</name></type> <name>FILEDELIM</name> <init>= <expr><literal type=\"string\">\"/\"</literal></expr></init></decl>;</decl_stmt><comment type=\"line\">//(OS.contains(\"Linux\") || OS.contains(\"Mac OS\")) ? \"/\" : \"\\\\\";</comment>\n",
      "  \n",
      "    <function><specifier>public</specifier> <specifier>static</specifier> <type><name>void</name></type> <name>main</name><parameter_list>(<parameter><decl><type><name><name>String</name><index>[]</index></name></type> <name>args</name></decl></parameter>)</parameter_list> <throws>throws <argument><expr><name>IllegalArgumentException</name></expr></argument></throws> <block>{\n",
      "\n",
      "        <if>if <condition>(<expr><call><name><name>Arrays</name><operator>.</operator><name>stream</name></name><argument_list>(<argument><expr><name>args</name></expr></argument>)</argument_list></call><operator>.</operator><call><name>anyMatch</name><argument_list>(<argument><expr><lambda><parameter_list type=\"pseudo\"><parameter><decl><name>x</name></decl></parameter></parameter_list> -&gt; <block type=\"pseudo\"><expr><call><name><name>x</name><operator>.</operator><name>contains</name></name><argument_list>(<argument><expr><literal type=\"string\">\"--help\"</literal></expr></argument>)</argument_list></call></expr></block></lambda></expr></argument>)</argument_list></call></expr>)</condition><then> <block>{\n",
      "                <expr><call><name><name>System</name><operator>.</operator><name>out</name><operator>.</operator><name>println</name></name><argument_list>(<argument><expr><literal type=\"string\">\"Usage: Scylla --config=&lt;your config file&gt; --bpmn=&lt;your first bpmn file&gt; [--bpmn=&lt;your second bpmn file&gt;] [--bpmn=...] --sim=&lt;your first sim file&gt; [--sim=&lt;your second sim file&gt;] [--sim=...] [--output=&lt;your output path&gt;]\"</literal></expr></argument>)</argument_list></call></expr>;</block></then></if>\n",
      "                <return>return;</return>\n",
      "        }</block></function>\n",
      "\n",
      "        <decl_stmt><decl><type><name>String</name></type> <name>configurationFile</name> <init>= <expr><call><name><name>Arrays</name><operator>.</operator><name>stream</name></name><argument_list>(<argument><expr><name>args</name></expr></argument>)</argument_list></call>\n",
      "                                                <operator>.</operator><call><name>filter</name><argument_list>(<argument><expr><lambda><parameter_list type=\"pseudo\"><parameter><decl><name>x</name></decl></parameter></parameter_list> -&gt; <block type=\"pseudo\"><expr><call><name><name>x</name><operator>.</operator><name>contains</name></name><argument_list>(<argument><expr><literal type=\"string\">\"--config\"</literal></expr></argument>)</argument_list></call></expr></block></lambda></expr></argument>)</argument_list></call>\n",
      "                                                <operator>.</operator><call><name>map</name><argument_list>(<argument><expr><lambda><parameter_list type=\"pseudo\"><parameter><decl><name>s</name></decl></parameter></parameter_list> -&gt; <block>{\n",
      "                                                        <expr><name><name>String</name><index>[]</index></name> <name>splitted</name> <operator>=</operator> <call><name><name>s</name><operator>.</operator><name>split</name></name><argument_list>(<argument><expr><literal type=\"string\">\"=\"</literal></expr></argument>)</argument_list></call></expr></block></lambda></expr></argument>;\n",
      "                                                        return <argument><expr><name><name>splitted</name><index>[<expr><name><name>splitted</name><operator>.</operator><name>length</name></name> <operator>-</operator> <literal type=\"number\">1</literal></expr>]</index></name></expr></argument>;</argument_list></call></expr></init></decl></decl_stmt>\n",
      "                                                }</block></class>)\n",
      "                                                <expr_stmt><expr><operator>.</operator><call><name>findFirst</name><argument_list>()</argument_list></call>\n",
      "                                                <operator>.</operator><call><name>orElseThrow</name><argument_list>(<argument><expr><lambda><parameter_list>()</parameter_list> -&gt; <block type=\"pseudo\"><expr><operator>new</operator> <call><name>IllegalArgumentException</name><argument_list>(<argument><expr><literal type=\"string\">\"You have to provide a configuration file. Usage: --config=&lt;your file path&gt;\"</literal></expr></argument>)</argument_list></call></expr></block></lambda></expr></argument>)</argument_list></call></expr>;</expr_stmt>\n",
      "\n",
      "        <decl_stmt><decl><type><name><name>String</name><index>[]</index></name></type> <name>bpmnFilenames</name> <init>= <expr><call><name><name>Arrays</name><operator>.</operator><name>stream</name></name><argument_list>(<argument><expr><name>args</name></expr></argument>)</argument_list></call>\n",
      "                                                <operator>.</operator><call><name>filter</name><argument_list>(<argument><expr><lambda><parameter_list type=\"pseudo\"><parameter><decl><name>x</name></decl></parameter></parameter_list> -&gt; <block type=\"pseudo\"><expr><call><name><name>x</name><operator>.</operator><name>contains</name></name><argument_list>(<argument><expr><literal type=\"string\">\"--bpmn\"</literal></expr></argument>)</argument_list></call></expr></block></lambda></expr></argument>)</argument_list></call>\n",
      "                                                <operator>.</operator><call><name>map</name><argument_list>(<argument><expr><lambda><parameter_list type=\"pseudo\"><parameter><decl><name>s</name></decl></parameter></parameter_list> -&gt; <block>{\n",
      "                                                        <expr><name><name>String</name><index>[]</index></name> <name>splitted</name> <operator>=</operator> <call><name><name>s</name><operator>.</operator><name>split</name></name><argument_list>(<argument><expr><literal type=\"string\">\"=\"</literal></expr></argument>)</argument_list></call></expr></block></lambda></expr></argument>;\n",
      "                                                        return <argument><expr><name><name>splitted</name><index>[<expr><name><name>splitted</name><operator>.</operator><name>length</name></name> <operator>-</operator> <literal type=\"number\">1</literal></expr>]</index></name></expr></argument>;</argument_list></call></expr></init></decl></decl_stmt>\n",
      "                                                })\n",
      "                                                <expr_stmt><expr><operator>.</operator><call><name>toArray</name><argument_list>(<argument><expr><name><name>String</name><index>[]</index></name><operator>::</operator><operator>new</operator></expr></argument>)</argument_list></call></expr>;</expr_stmt>\n",
      "\n",
      "        <if>if <condition>(<expr><name><name>bpmnFilenames</name><operator>.</operator><name>length</name></name> <operator>==</operator> <literal type=\"number\">0</literal></expr>)</condition><then> <block>{\n",
      "                <throw>throw <expr><operator>new</operator> <call><name>IllegalArgumentException</name><argument_list>(<argument><expr><literal type=\"string\">\"You have to provide at least one bpmn diagram file. Usage: --bpmn=&lt;your file path&gt;\"</literal></expr></argument>)</argument_list></call></expr>;</throw>\n",
      "        }</block></then></if>\n",
      "\n",
      "        <decl_stmt><decl><type><name><name>String</name><index>[]</index></name></type> <name>simFilenames</name> <init>= <expr><call><name><name>Arrays</name><operator>.</operator><name>stream</name></name><argument_list>(<argument><expr><name>args</name></expr></argument>)</argument_list></call>\n",
      "                                                <operator>.</operator><call><name>filter</name><argument_list>(<argument><expr><lambda><parameter_list type=\"pseudo\"><parameter><decl><name>x</name></decl></parameter></parameter_list> -&gt; <block type=\"pseudo\"><expr><call><name><name>x</name><operator>.</operator><name>contains</name></name><argument_list>(<argument><expr><literal type=\"string\">\"--sim\"</literal></expr></argument>)</argument_list></call></expr></block></lambda></expr></argument>)</argument_list></call>\n",
      "                                                <operator>.</operator><call><name>map</name><argument_list>(<argument><expr><lambda><parameter_list type=\"pseudo\"><parameter><decl><name>s</name></decl></parameter></parameter_list> -&gt; <block>{\n",
      "                                                        <expr><name><name>String</name><index>[]</index></name> <name>splitted</name> <operator>=</operator> <call><name><name>s</name><operator>.</operator><name>split</name></name><argument_list>(<argument><expr><literal type=\"string\">\"=\"</literal></expr></argument>)</argument_list></call></expr></block></lambda></expr></argument>;\n",
      "                                                        return <argument><expr><name><name>splitted</name><index>[<expr><name><name>splitted</name><operator>.</operator><name>length</name></name> <operator>-</operator> <literal type=\"number\">1</literal></expr>]</index></name></expr></argument>;</argument_list></call></expr></init></decl></decl_stmt>\n",
      "                                                })\n",
      "                                                <expr_stmt><expr><operator>.</operator><call><name>toArray</name><argument_list>(<argument><expr><name><name>String</name><index>[]</index></name><operator>::</operator><operator>new</operator></expr></argument>)</argument_list></call></expr>;</expr_stmt>\n",
      "\n",
      "        <if>if <condition>(<expr><name><name>simFilenames</name><operator>.</operator><name>length</name></name> <operator>==</operator> <literal type=\"number\">0</literal></expr>)</condition><then> <block>{\n",
      "                <throw>throw <expr><operator>new</operator> <call><name>IllegalArgumentException</name><argument_list>(<argument><expr><literal type=\"string\">\"You have to provide at least one simulation file. Usage: --sim=&lt;your file path&gt;\"</literal></expr></argument>)</argument_list></call></expr>;</throw>\n",
      "        }</block></then></if>\n",
      "\n",
      "        <decl_stmt><decl><type><name>boolean</name></type> <name>enableBpsLogging</name> <init>= <expr><call><name><name>Arrays</name><operator>.</operator><name>stream</name></name><argument_list>(<argument><expr><name>args</name></expr></argument>)</argument_list></call><operator>.</operator><call><name>anyMatch</name><argument_list>(<argument><expr><lambda><parameter_list type=\"pseudo\"><parameter><decl><name>x</name></decl></parameter></parameter_list> -&gt; <block type=\"pseudo\"><expr><literal type=\"string\">\"--enable-bps-logging\"</literal><operator>.</operator><call><name>equalsIgnoreCase</name><argument_list>(<argument><expr><name>x</name></expr></argument>)</argument_list></call></expr></block></lambda></expr></argument>)</argument_list></call></expr></init></decl>;</decl_stmt>\n",
      "        <decl_stmt><decl><type><name>boolean</name></type> <name>enableDesmojLogging</name> <init>= <expr><call><name><name>Arrays</name><operator>.</operator><name>stream</name></name><argument_list>(<argument><expr><name>args</name></expr></argument>)</argument_list></call><operator>.</operator><call><name>anyMatch</name><argument_list>(<argument><expr><lambda><parameter_list type=\"pseudo\"><parameter><decl><name>x</name></decl></parameter></parameter_list> -&gt; <block type=\"pseudo\"><expr><literal type=\"string\">\"--desmoj-logging\"</literal><operator>.</operator><call><name>equalsIgnoreCase</name><argument_list>(<argument><expr><name>x</name></expr></argument>)</argument_list></call></expr></block></lambda></expr></argument>)</argument_list></call></expr></init></decl>;</decl_stmt>\n",
      "\n",
      "        <decl_stmt><decl><type><name>String</name></type> <name>outputFolder</name> <init>= <expr><call><name><name>Arrays</name><operator>.</operator><name>stream</name></name><argument_list>(<argument><expr><name>args</name></expr></argument>)</argument_list></call>\n",
      "                                        <operator>.</operator><call><name>filter</name><argument_list>(<argument><expr><lambda><parameter_list type=\"pseudo\"><parameter><decl><name>x</name></decl></parameter></parameter_list> -&gt; <block type=\"pseudo\"><expr><call><name><name>x</name><operator>.</operator><name>contains</name></name><argument_list>(<argument><expr><literal type=\"string\">\"--output\"</literal></expr></argument>)</argument_list></call></expr></block></lambda></expr></argument>)</argument_list></call>\n",
      "                                        <operator>.</operator><call><name>map</name><argument_list>(<argument><expr><lambda><parameter_list type=\"pseudo\"><parameter><decl><name>s</name></decl></parameter></parameter_list> -&gt; <block>{\n",
      "                                                <expr><name><name>String</name><index>[]</index></name> <name>splitted</name> <operator>=</operator> <call><name><name>s</name><operator>.</operator><name>split</name></name><argument_list>(<argument><expr><literal type=\"string\">\"=\"</literal></expr></argument>)</argument_list></call></expr></block></lambda></expr></argument>;\n",
      "                                                return <argument><expr><name><name>splitted</name><index>[<expr><name><name>splitted</name><operator>.</operator><name>length</name></name> <operator>-</operator> <literal type=\"number\">1</literal></expr>]</index></name></expr></argument>;</argument_list></call></expr></init></decl></decl_stmt>\n",
      "                                        })\n",
      "                                        <expr_stmt><expr><operator>.</operator><call><name>findFirst</name><argument_list>()</argument_list></call>\n",
      "                                        <operator>.</operator><call><name>orElse</name><argument_list>(<argument><expr><literal type=\"string\">\"sim-out/\"</literal></expr></argument>)</argument_list></call></expr>;</expr_stmt>\n",
      "        \n",
      "        <decl_stmt><decl><type><name>SimulationManager</name></type> <name>manager</name> <init>= <expr><operator>new</operator> <call><name>SimulationManager</name><argument_list>(<argument><expr><name>outputFolder</name></expr></argument>, <argument><expr><name>bpmnFilenames</name></expr></argument>, <argument><expr><name>simFilenames</name></expr></argument>, <argument><expr><name>configurationFile</name></expr></argument>,\n",
      "                <argument><expr><name>enableBpsLogging</name></expr></argument>, <argument><expr><name>enableDesmojLogging</name></expr></argument>)</argument_list></call></expr></init></decl>;</decl_stmt>\n",
      "        <expr_stmt><expr><call><name><name>manager</name><operator>.</operator><name>run</name></name><argument_list>()</argument_list></call></expr>;</expr_stmt>\n",
      "    }\n",
      "    \n",
      "\tpublic <function><specifier>static</specifier> <type><name>String</name></type> <name>normalizePath</name><parameter_list>(<parameter><decl><type><name>String</name></type> <name>path</name></decl></parameter>)</parameter_list> <block>{\n",
      "\t\t<expr_stmt><expr><name>path</name> <operator>=</operator> <call><name><name>path</name><operator>.</operator><name>replace</name></name><argument_list>(<argument><expr><literal type=\"string\">\"\\\\\"</literal></expr></argument>, <argument><expr><name>FILEDELIM</name></expr></argument>)</argument_list></call></expr>;</expr_stmt>\n",
      "\t\t<expr_stmt><expr><name>path</name> <operator>=</operator> <call><name><name>path</name><operator>.</operator><name>replace</name></name><argument_list>(<argument><expr><literal type=\"string\">\"/\"</literal></expr></argument>, <argument><expr><name>FILEDELIM</name></expr></argument>)</argument_list></call></expr>;</expr_stmt>\n",
      "\t\t<return>return <expr><name>path</name></expr>;</return>\n",
      "\t}</block></function>\n",
      "\t\n",
      "\t<function><specifier>public</specifier> <specifier>static</specifier> <type><name><name>String</name><index>[]</index></name></type> <name>normalizePaths</name><parameter_list>(<parameter><decl><type><name><name>String</name><index>[]</index></name></type> <name>paths</name></decl></parameter>)</parameter_list> <block>{\n",
      "\t\t<decl_stmt><decl><type><name><name>String</name><index>[]</index></name></type> <name>normalized</name> <init>= <expr><operator>new</operator> <name><name>String</name><index>[<expr><name><name>paths</name><operator>.</operator><name>length</name></name></expr>]</index></name></expr></init></decl>;</decl_stmt>\n",
      "\t\t<for>for<control>(<init><decl><type><name>int</name></type> <name>i</name> <init>= <expr><literal type=\"number\">0</literal></expr></init></decl>;</init> <condition><expr><name>i</name> <operator>&lt;</operator> <name><name>paths</name><operator>.</operator><name>length</name></name></expr>;</condition> <incr><expr><name>i</name><operator>++</operator></expr></incr>)</control> <block>{\n",
      "\t\t\t<expr_stmt><expr><name><name>normalized</name><index>[<expr><name>i</name></expr>]</index></name> <operator>=</operator> <call><name>normalizePath</name><argument_list>(<argument><expr><name><name>paths</name><index>[<expr><name>i</name></expr>]</index></name></expr></argument>)</argument_list></call></expr>;</expr_stmt>\n",
      "\t\t}</block></for>\n",
      "\t\t<return>return <expr><name>normalized</name></expr>;</return>\n",
      "\t}</block></function>\n",
      "\n",
      "}</unit>\n",
      "\n"
     ]
    }
   ],
   "source": [
    "commit = repo.active_branch.commit\n",
    "for obj in commit.tree.traverse():\n",
    "    if obj.type == 'blob' and obj.name.endswith('.java') and obj.name == 'Scylla.java':\n",
    "        content = subprocess.check_output('git --git-dir=\"./scylla.git\" show %s:%s | xargs -0 srcml -l Java --text' % (commit.hexsha, obj.path), shell=True).decode(\"utf-8\")\n",
    "        print(content)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With srcml I could for instance extract all functions and then analyze their length:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "<function xmlns=\"http://www.srcML.org/srcML/src\"><specifier>public</specifier> <specifier>static</specifier> <type><name>void</name></type> <name>main</name><parameter_list>(<parameter><decl><type><name><name>String</name><index>[]</index></name></type> <name>args</name></decl></parameter>)</parameter_list> <throws>throws <argument><expr><name>IllegalArgumentException</name></expr></argument></throws> <block>{\n",
      "\n",
      "        <if>if <condition>(<expr><call><name><name>Arrays</name><operator>.</operator><name>stream</name></name><argument_list>(<argument><expr><name>args</name></expr></argument>)</argument_list></call><operator>.</operator><call><name>anyMatch</name><argument_list>(<argument><expr><lambda><parameter_list type=\"pseudo\"><parameter><decl><name>x</name></decl></parameter></parameter_list> -&gt; <block type=\"pseudo\"><expr><call><name><name>x</name><operator>.</operator><name>contains</name></name><argument_list>(<argument><expr><literal type=\"string\">\"--help\"</literal></expr></argument>)</argument_list></call></expr></block></lambda></expr></argument>)</argument_list></call></expr>)</condition><then> <block>{\n",
      "                <expr><call><name><name>System</name><operator>.</operator><name>out</name><operator>.</operator><name>println</name></name><argument_list>(<argument><expr><literal type=\"string\">\"Usage: Scylla --config=&lt;your config file&gt; --bpmn=&lt;your first bpmn file&gt; [--bpmn=&lt;your second bpmn file&gt;] [--bpmn=...] --sim=&lt;your first sim file&gt; [--sim=&lt;your second sim file&gt;] [--sim=...] [--output=&lt;your output path&gt;]\"</literal></expr></argument>)</argument_list></call></expr>;</block></then></if>\n",
      "                <return>return;</return>\n",
      "        }</block></function>\n",
      "\n",
      "        \n",
      "\n",
      "<function xmlns=\"http://www.srcML.org/srcML/src\"><specifier>static</specifier> <type><name>String</name></type> <name>normalizePath</name><parameter_list>(<parameter><decl><type><name>String</name></type> <name>path</name></decl></parameter>)</parameter_list> <block>{\n",
      "\t\t<expr_stmt><expr><name>path</name> <operator>=</operator> <call><name><name>path</name><operator>.</operator><name>replace</name></name><argument_list>(<argument><expr><literal type=\"string\">\"\\\\\"</literal></expr></argument>, <argument><expr><name>FILEDELIM</name></expr></argument>)</argument_list></call></expr>;</expr_stmt>\n",
      "\t\t<expr_stmt><expr><name>path</name> <operator>=</operator> <call><name><name>path</name><operator>.</operator><name>replace</name></name><argument_list>(<argument><expr><literal type=\"string\">\"/\"</literal></expr></argument>, <argument><expr><name>FILEDELIM</name></expr></argument>)</argument_list></call></expr>;</expr_stmt>\n",
      "\t\t<return>return <expr><name>path</name></expr>;</return>\n",
      "\t}</block></function>\n",
      "\t\n",
      "\t\n",
      "\n",
      "<function xmlns=\"http://www.srcML.org/srcML/src\"><specifier>public</specifier> <specifier>static</specifier> <type><name><name>String</name><index>[]</index></name></type> <name>normalizePaths</name><parameter_list>(<parameter><decl><type><name><name>String</name><index>[]</index></name></type> <name>paths</name></decl></parameter>)</parameter_list> <block>{\n",
      "\t\t<decl_stmt><decl><type><name><name>String</name><index>[]</index></name></type> <name>normalized</name> <init>= <expr><operator>new</operator> <name><name>String</name><index>[<expr><name><name>paths</name><operator>.</operator><name>length</name></name></expr>]</index></name></expr></init></decl>;</decl_stmt>\n",
      "\t\t<for>for<control>(<init><decl><type><name>int</name></type> <name>i</name> <init>= <expr><literal type=\"number\">0</literal></expr></init></decl>;</init> <condition><expr><name>i</name> <operator>&lt;</operator> <name><name>paths</name><operator>.</operator><name>length</name></name></expr>;</condition> <incr><expr><name>i</name><operator>++</operator></expr></incr>)</control> <block>{\n",
      "\t\t\t<expr_stmt><expr><name><name>normalized</name><index>[<expr><name>i</name></expr>]</index></name> <operator>=</operator> <call><name>normalizePath</name><argument_list>(<argument><expr><name><name>paths</name><index>[<expr><name>i</name></expr>]</index></name></expr></argument>)</argument_list></call></expr>;</expr_stmt>\n",
      "\t\t}</block></for>\n",
      "\t\t<return>return <expr><name>normalized</name></expr>;</return>\n",
      "\t}</block></function>\n",
      "\n",
      "}\n",
      "[8, 7, 9]\n",
      "Scylla.java: 0\n"
     ]
    }
   ],
   "source": [
    "content = subprocess.check_output('git --git-dir=\"./scylla.git\" show %s:%s | xargs -0 srcml -l Java --text' % (commit.hexsha, 'src/main/java/de/hpi/bpt/scylla/Scylla.java'), shell=True)\n",
    "tree = etree.fromstring(content)\n",
    "functions = tree.xpath('//src:function', namespaces=namespaces)\n",
    "for x in list(map(lambda func: etree.tostring(func).decode('utf-8'), functions)):\n",
    "    print('')\n",
    "    print(x)\n",
    "print(list(map(lambda func: len(etree.tostring(func).decode('utf-8').split('\\n')), functions)))\n",
    "metric = 0\n",
    "print('Scylla.java: '+str(metric))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyze the srcml contents of all files of a commit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This could also be done fore each file of a commit:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[6, 5, 7]\n",
      "Scylla.java: 6.0\n",
      "[20, 45, 14, 10]\n",
      "ScyllaScripts.java: 22.25\n",
      "[94, 25, 12, 24, 13, 5, 3, 3, 3, 3, 3]\n",
      "SimulationManager.java: 17.09090909090909\n",
      "[5, 28, 3, 3, 3, 3, 3]\n",
      "FocusTraversalOnArray.java: 6.857142857142857\n",
      "[4, 4, 6, 3, 3, 3, 11]\n",
      "ArgumentTests.java: 4.857142857142857\n",
      "[3, 10]\n",
      "SeedProvider.java: 6.5\n",
      "[4, 4, 4, 5, 3]\n",
      "SimulationManagerForTests.java: 4.0\n",
      "[6, 4, 3, 4, 3, 3, 3, 3, 3, 3, 9, 4, 9, 9, 1, 3, 3, 10]\n",
      "SimulationTest.java: 4.611111111111111\n",
      "[]\n",
      "TestSeeds.java: 0\n",
      "[10, 7, 16, 3, 9, 8, 9, 9, 9]\n",
      "TestUtils.java: 8.88888888888889\n",
      "[3]\n",
      "TravisTests.java: 3.0\n",
      "[13, 7, 6, 3, 6, 4, 4]\n",
      "CheckBoxList.java: 6.142857142857143\n",
      "[3, 3, 3, 4]\n",
      "CheckboxListPanel.java: 3.25\n",
      "[9, 9, 3, 3, 3, 3]\n",
      "Console.java: 5.0\n",
      "[5, 3, 3, 3, 3, 5, 3, 26, 3, 3, 3, 3, 8, 5, 37, 18, 28, 7, 9, 4, 8, 3, 6, 3, 5, 18, 6, 4, 3, 6, 4, 3]\n",
      "EditorPane.java: 7.75\n",
      "[9, 3, 7]\n",
      "EditorTabTitlePanel.java: 6.333333333333333\n",
      "[3, 14, 11, 9, 9]\n",
      "ExpandPanel.java: 9.2\n",
      "[10, 10, 5, 14, 5]\n",
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      "[]\n",
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      "DistributionWrapper.java: 3.0\n",
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      "TriangularDistribution.java: 3.0\n",
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      "UniformDistribution.java: 3.0\n",
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      "EventType.java: 4.5\n",
      "[6, 3]\n",
      "GatewayType.java: 4.5\n",
      "[6, 3]\n",
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      "[3, 3, 3]\n",
      "MessageFlow.java: 3.0\n",
      "[6, 3]\n",
      "TaskType.java: 4.5\n",
      "[4]\n",
      "BPMNEndEventPluggable.java: 4.0\n",
      "[4]\n",
      "BPMNIntermediateEventPluggable.java: 4.0\n",
      "[4]\n",
      "BPMNStartEventPluggable.java: 4.0\n",
      "[30]\n",
      "ExternalEventPluggable.java: 30.0\n",
      "[4]\n",
      "GatewayEventPluggable.java: 4.0\n",
      "[]\n",
      "IEventPluggable.java: 0\n",
      "[]\n",
      "IExternalEventPluggable.java: 0\n",
      "[29]\n",
      "ProcessInstanceGenerationEventPluggable.java: 29.0\n",
      "[4]\n",
      "ProcessSimulationStopEventPluggable.java: 4.0\n",
      "[4]\n",
      "ResourceAvailabilityEventPluggable.java: 4.0\n",
      "[27]\n",
      "ScyllaEventPluggable.java: 27.0\n",
      "[4]\n",
      "TaskBeginEventPluggable.java: 4.0\n",
      "[4]\n",
      "TaskCancelEventPluggable.java: 4.0\n",
      "[4]\n",
      "TaskEnableEventPluggable.java: 4.0\n",
      "[4]\n",
      "TaskTerminateEventPluggable.java: 4.0\n",
      "[9]\n",
      "ResourceAssignmentPluggable.java: 9.0\n",
      "[10]\n",
      "ResourceQueueUpdatedPluggable.java: 10.0\n",
      "[]\n",
      "MultipleStartNodesException.java: 0\n",
      "[]\n",
      "NoStartNodeException.java: 0\n",
      "[]\n",
      "NodeNotFoundException.java: 0\n",
      "Time used: 102.90304827690125\n"
     ]
    }
   ],
   "source": [
    "import subprocess\n",
    "from lxml import etree\n",
    "\n",
    "repo = Repo.init('./scylla.git', bare=True, odbt=GitDB)\n",
    "commit = repo.active_branch.commit\n",
    "namespaces = {'src': 'http://www.srcML.org/srcML/src'}\n",
    "\n",
    "start = time.time()\n",
    "for obj in commit.tree.traverse():\n",
    "    if obj.type == 'blob' and obj.name.endswith('.java'):\n",
    "        content = subprocess.check_output('git --git-dir=\"./scylla.git\" show %s:%s | xargs -0 srcml -l Java --text' % (commit.hexsha, obj.path), shell=True)\n",
    "        tree = etree.fromstring(content)\n",
    "        functions = tree.xpath('//src:function/src:block', namespaces=namespaces)\n",
    "        linesPerFunction = list(map(lambda func: len(etree.tostring(func).decode('utf-8').split('\\n')), functions))\n",
    "        print(linesPerFunction)\n",
    "        metric = sum(linesPerFunction) / len(linesPerFunction) if len(linesPerFunction) > 0 else 0\n",
    "        print(obj.name+': '+str(metric))\n",
    "end = time.time()\n",
    "print('Time used: '+str(end - start))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Combine gitpython and srcml"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After knowing what both technologies could do, I combined them:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Naive approach to calculate LoC over time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(Note: When recycling the notebook, the following cell was not reevaluated because of the long run time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-15-ff53b8609d22>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mobj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcommit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtree\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtraverse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'blob'\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mendswith\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'.java'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m             \u001b[0mcontent\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msubprocess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcheck_output\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'git --git-dir=\"./scylla.git\" show %s:%s | xargs -0 srcml -l Java --text'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mcommit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhexsha\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshell\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"utf-8\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m             \u001b[0mlineCount\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlineCount\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcontent\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'\\n'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m     \u001b[0mlineCounts\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlineCount\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/subprocess.py\u001b[0m in \u001b[0;36mcheck_output\u001b[0;34m(timeout, *popenargs, **kwargs)\u001b[0m\n\u001b[1;32m    409\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'input'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m''\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'universal_newlines'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34mb''\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    410\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 411\u001b[0;31m     return run(*popenargs, stdout=PIPE, timeout=timeout, check=True,\n\u001b[0m\u001b[1;32m    412\u001b[0m                **kwargs).stdout\n\u001b[1;32m    413\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/subprocess.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(input, capture_output, timeout, check, *popenargs, **kwargs)\u001b[0m\n\u001b[1;32m    489\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mPopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mpopenargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mprocess\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    490\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 491\u001b[0;31m             \u001b[0mstdout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstderr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprocess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcommunicate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    492\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mTimeoutExpired\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    493\u001b[0m             \u001b[0mprocess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkill\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/subprocess.py\u001b[0m in \u001b[0;36mcommunicate\u001b[0;34m(self, input, timeout)\u001b[0m\n\u001b[1;32m   1009\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stdin_write\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1010\u001b[0m             \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstdout\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1011\u001b[0;31m                 \u001b[0mstdout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstdout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1012\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstdout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1013\u001b[0m             \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstderr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "timestamps = list(map(lambda commit: datetime.fromtimestamp(commit.committed_date), repo.iter_commits()))\n",
    "lineCounts = []\n",
    "\n",
    "start = time.time()\n",
    "for commit in repo.iter_commits():\n",
    "    lineCount = 0\n",
    "    for obj in commit.tree.traverse():\n",
    "        if obj.type == 'blob' and obj.name.endswith('.java'):\n",
    "            content = subprocess.check_output('git --git-dir=\"./scylla.git\" show %s:%s | xargs -0 srcml -l Java --text' % (commit.hexsha, obj.path), shell=True).decode(\"utf-8\")\n",
    "            lineCount = lineCount + len(content.split('\\n'))\n",
    "    lineCounts.append(lineCount)\n",
    "\n",
    "end = time.time()\n",
    "print('Time used: '+str(end - start))\n",
    "\n",
    "dates = matplotlib.dates.date2num(timestamps)\n",
    "pyplot.figure(figsize=(12, 7))\n",
    "pyplot.plot_date(dates, lineCounts, '-')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### And now for average LoC per File:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "timestamps = list(map(lambda commit: datetime.fromtimestamp(commit.committed_date), repo.iter_commits()))\n",
    "lineCounts = []\n",
    "\n",
    "start = time.time()\n",
    "for commit in repo.iter_commits():\n",
    "    fileCount = 0\n",
    "    lineCount = 0\n",
    "    for obj in commit.tree.traverse():\n",
    "        if obj.type == 'blob' and obj.name.endswith('.java'):\n",
    "            content = subprocess.check_output('git --git-dir=\"./scylla.git\" show %s:%s | xargs -0 -I $ srcml -l Java --text $ | wc -l' % (commit.hexsha, obj.path), shell=True).decode(\"utf-8\")\n",
    "            lineCount = lineCount + int(content)\n",
    "            fileCount = fileCount + 1\n",
    "    if fileCount == 0:\n",
    "        lineCounts.append(0)\n",
    "    else:\n",
    "        lineCounts.append(lineCount / fileCount)\n",
    "\n",
    "end = time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time used: 7572.929245471954\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7efe6d033730>]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Time used: '+str(end - start))\n",
    "dates = matplotlib.dates.date2num(timestamps)\n",
    "pyplot.figure(figsize=(12, 7))\n",
    "pyplot.plot_date(dates, lineCounts, '-')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tuning file traversal speed\n",
    "While the initial srcml approach works, it is incredibly slow. 2h for one not even big repository just is not feasible.\n",
    "\n",
    "Through testing I found some general remarks:\n",
    "* Calculating LoC for one single commit takes already very long!\n",
    "* Removing the xargs to srcml does not change time significally\n",
    "* Checking out improves time; but not enough that I would accept\n",
    "* Using gitpython only to access file contents offers orders of magnitude of improvement and should therefore be further pursued\n",
    "\n",
    "The following cells show different the approaches I tried to tune the speed and which lead me to this conclusions. <br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ScrMl approach\n",
    "The previous appraoch: 27 seconds for one commit (note: when reevaluating the cells for submission, times were much higher, but still comparable)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29976\n",
      "Time used: 102.27309346199036\n"
     ]
    }
   ],
   "source": [
    "start = time.time()\n",
    "commit = repo.active_branch.commit\n",
    "lineCount = 0\n",
    "for obj in commit.tree.traverse():\n",
    "    if obj.type == 'blob' and obj.name.endswith('.java'):\n",
    "        content = subprocess.check_output('git --git-dir=\"./scylla.git\" show %s:%s | xargs -0 srcml -l Java --text' % (commit.hexsha, obj.path), shell=True).decode(\"utf-8\")\n",
    "        lineCount = lineCount + len(content.split('\\n'))\n",
    "print(lineCount)\n",
    "end = time.time()\n",
    "print('Time used: '+str(end - start))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using wc\n",
    "`wc` can be used to get the lines of some output. Maybe this reduces the output that python needs to read from shell? <br>\n",
    "No, it still takes 27 seconds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29705\n",
      "Time used: 101.1365864276886\n"
     ]
    }
   ],
   "source": [
    "start = time.time()\n",
    "commit = repo.active_branch.commit\n",
    "lineCount = 0\n",
    "for obj in commit.tree.traverse():\n",
    "    if obj.type == 'blob' and obj.name.endswith('.java'):\n",
    "        content = subprocess.check_output('git --git-dir=\"./scylla.git\" show %s:%s | xargs -0 -I $ srcml -l Java --text $ | wc -l' % (commit.hexsha, obj.path), shell=True).decode(\"utf-8\")\n",
    "        lineCount = lineCount + int(content)\n",
    "print(lineCount)\n",
    "end = time.time()\n",
    "print('Time used: '+str(end - start))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checkout repos\n",
    "Or let's try checkouted repos ...<br>\n",
    "While checkouted repositories are indeed faster, it is not the order of magnitude that we need"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29152\n",
      "Time used: 65.3673300743103\n"
     ]
    }
   ],
   "source": [
    "start = time.time()\n",
    "commit = repo.active_branch.commit\n",
    "lineCount = 0\n",
    "for obj in commit.tree.traverse():\n",
    "    if obj.type == 'blob' and obj.name.endswith('.java'):\n",
    "        content = subprocess.check_output('git --git-dir=\"./scylla/.git\" show %s:%s | wc -l' % (commit.hexsha, obj.path), shell=True).decode(\"utf-8\")\n",
    "        lineCount = lineCount + int(content)\n",
    "print(lineCount)\n",
    "end = time.time()\n",
    "print('Time used: '+str(end - start))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using pipes\n",
    "Using Toni's first tip (pipes):<br>\n",
    "Is a little bit faster but again not fast enough"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29967\n",
      "Time used: 87.47958111763\n"
     ]
    }
   ],
   "source": [
    "start = time.time()\n",
    "commit = repo.active_branch.commit\n",
    "lineCount = 0\n",
    "for obj in commit.tree.traverse():\n",
    "    if obj.type == 'blob' and obj.name.endswith('.java'):\n",
    "        with subprocess.Popen(['srcml', '-l', 'Java'], stdin=subprocess.PIPE, stdout=subprocess.PIPE) as srcml:\n",
    "            srcml.stdin.write(obj.data_stream.read())\n",
    "            srcml.stdin.close() \n",
    "            content = srcml.stdout.read().decode(\"utf-8\")\n",
    "        lineCount = lineCount + len(content.split('\\n'))\n",
    "print(lineCount)\n",
    "end = time.time()\n",
    "print('Time used: '+str(end - start))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using GitPython only\n",
    "Using Toni's second tip (gitpyhon `blob.data_stream`):<br>\n",
    "This is two orders of magnitude faster! Thanks a lot!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29423\n",
      "Time used: 0.4099154472351074\n"
     ]
    }
   ],
   "source": [
    "start = time.time()\n",
    "commit = repo.active_branch.commit\n",
    "lineCount = 0\n",
    "for obj in commit.tree.traverse():\n",
    "    if obj.type == 'blob' and obj.name.endswith('.java'):\n",
    "        content = obj.data_stream.read().decode(\"utf-8\")\n",
    "        lineCount = lineCount + len(content.split('\\n'))\n",
    "print(lineCount)\n",
    "end = time.time()\n",
    "print('Time used: '+str(end - start))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is even so fast, we can analyze the whole repo faster than a single commit with the old way "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time used: 84.62123703956604\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f7079e24e50>]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "timestamps = list(map(lambda commit: datetime.fromtimestamp(commit.committed_date), repo.iter_commits()))\n",
    "lineCounts = []\n",
    "\n",
    "start = time.time()\n",
    "for commit in repo.iter_commits():\n",
    "    lineCount = 0\n",
    "    for obj in commit.tree.traverse():\n",
    "        if obj.type == 'blob' and obj.name.endswith('.java'):\n",
    "            content = obj.data_stream.read().decode(\"utf-8\")\n",
    "            lineCount = lineCount + len(content.split('\\n'))\n",
    "    lineCounts.append(lineCount)\n",
    "\n",
    "end = time.time()\n",
    "print('Time used: '+str(end - start))\n",
    "\n",
    "dates = matplotlib.dates.date2num(timestamps)\n",
    "pyplot.figure(figsize=(12, 7))\n",
    "pyplot.plot_date(dates, lineCounts, '-')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Replacing ScrML: LoC/Method With Regex "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The preceding chapter has shown that calling a subprocess for every commit is completely unfeasible. \n",
    "This means that scrml cannot be used to analyze the repositories fast.\n",
    "\n",
    "Therefore, an alternative has to be found: Regex, which can work very fast and very precise on the file contents provided by gitpython.\n",
    "The following function shows how regex can be used to analyze the lines of code per method for a repository."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time used: 184.47402715682983\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f707b023130>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import re\n",
    "commit = repo.active_branch.commit\n",
    "classRegex = re.compile(r\"class\")\n",
    "methodRegex = re.compile(r\"(public|protected|private|static|\\s) +[\\w\\<\\>\\[\\]]+\\s+(\\w+) *\\([^\\)]*\\) *(\\{?|[^;])\")\n",
    "\n",
    "timestamps = []\n",
    "metrics = []\n",
    "\n",
    "start = time.time()\n",
    "for commit in repo.iter_commits():\n",
    "    fileCount = 0\n",
    "    aggregatedMetric = 0\n",
    "    for obj in commit.tree.traverse():\n",
    "        if obj.type == 'blob' and obj.name.endswith('.java'):\n",
    "            fileCount = fileCount + 1\n",
    "            contentWithHeader = obj.data_stream.read().decode(\"utf-8\")\n",
    "            content = contentWithHeader.split('{', 1)[1]\n",
    "            numMethods = len(methodRegex.findall(content))\n",
    "            loc = len(content.split('\\n'))\n",
    "            metric = loc / numMethods if numMethods > 0 else 0\n",
    "            aggregatedMetric = aggregatedMetric + metric\n",
    "    metrics.append(aggregatedMetric/fileCount if fileCount > 0 else 0)\n",
    "    timestamps.append(datetime.fromtimestamp(commit.committed_date))\n",
    "\n",
    "end = time.time()\n",
    "print('Time used: '+str(end - start))\n",
    "\n",
    "metrics.pop()\n",
    "timestamps.pop()\n",
    "\n",
    "dates = matplotlib.dates.date2num(timestamps)\n",
    "pyplot.figure(figsize=(12, 7))\n",
    "pyplot.plot_date(dates, metrics, '-')\n",
    "pyplot.legend(['LoC/M'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "New chapter\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "import matplotlib.pyplot as pyplot\n",
    "import matplotlib\n",
    "import time\n",
    "from datetime import datetime\n",
    "from multiprocessing import Pool\n",
    "import multiprocessing\n",
    "import functools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%aimport repoLibrarian\n",
    "%aimport repoAnalysis\n",
    "%autoreload"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"anchor\" id=\"working\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Developing a first approach to analyze a given set of repos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that analyzing repositories was generally possible, a suitable approach to do this on a large scale for multiple repositories and multiple metrics had to be developed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following steps use the [repoLibrarian.py](./repoLibrarian.py) module for managing the set of analyzed repos. For more information on the module see its source code or the [RepoLibrarian.ipynb](./RepoLibrarian.ipynb) notebook. The development of said module has been heavily influenced by this notebook, and was once even included in this notebook before RepoLibrarian was split apart. While there were many iterations and steps that lead to the [repoAnalysis.py](./repoAnalysis.py) module of today, iterations were usally by in-cell-edits, therefore not all of history could be restory. However, the following cells show the first steps toawrd efficient mechanisms.\n",
    "\n",
    "A local folder of repositories is used at first, to test the developed approaches in a safe environment. Most of the used repositories are listed below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'./repos/'"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "repoLibrarian.setReposFolder('./repos/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['json-iterator/java.git',\n",
       " 'alibaba/arthas.git',\n",
       " 'sayems/jbehave-tutorial.git',\n",
       " 'bptlab/Unicorn.git',\n",
       " 'bptlab/correlation-analysis.git',\n",
       " 'bptlab/scylla.git',\n",
       " 'bptlab/cepta.git',\n",
       " 'bptlab/fcm2cpn.git']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(repoLibrarian.knownRepos())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Deleted repo \"sayems/jbehave-tutorial.git\"\n"
     ]
    }
   ],
   "source": [
    "repoLibrarian.deleteRepo('sayems', 'jbehave-tutorial.git')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Aggregated Knowledge (the intermediate state)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following function aggregates the knowledge from the previous chapters. It runs a function for all files of all commits of a repo, aggregates for each commit and combines with the counted files. Notice the commented lines that remove the first metric values that are zero. This was meant to filter out outliers at the start of projects (when there is usually just a readme) but the idea was later discarded as it was unsure what a metric value of zero would mean generally for all metrics. The `fileCountConsumer` parameter was later, as there were metrics where the aggregation of all files was not to average, but rather to sum. CP437 decoding instead of utf-8 is needed as there are some asian repositories in the set which break the utf-8.\n",
    "\n",
    "Variations/iterations of this functions can be found in the course of this notebook and the final functions in [repoAnalysis.py](./repoAnalysis.py) used for the experiment runs (e.g. the final `calculateDeltaMetrics`) are in the end based on this function.\n",
    "\n",
    "Note: This function was once also located in the [repoAnalysis.py](./repoAnalysis.py) module, but has been moved back to the notebook to collect historical information in the notebooks and keep the modules cleaner."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def timedMetricPerFileForRepo(repoTuple, metricFunction, fileCountConsumer = repoAnalysis.safeDivision):\n",
    "    (user, project) = repoTuple\n",
    "    repo = repoLibrarian.getRepo(user, project)\n",
    "    timestamps = []\n",
    "    metrics = []\n",
    "    try:\n",
    "        start = time.time()\n",
    "        for commit in repo.iter_commits():\n",
    "            fileCount = 0\n",
    "            aggregatedMetric = 0\n",
    "            for obj in commit.tree.traverse():\n",
    "                if obj.type == 'blob' and obj.name.endswith('.java'):\n",
    "                    fileCount = fileCount + 1\n",
    "                    content = obj.data_stream.read().decode(\"CP437\")#.decode(\"utf-8\")\n",
    "                    metric = metricFunction(content)\n",
    "                    aggregatedMetric = aggregatedMetric + metric\n",
    "            metrics.append(fileCountConsumer(aggregatedMetric, fileCount))\n",
    "            timestamps.append(commit.committed_date)\n",
    "\n",
    "        end = time.time()\n",
    "        print('Time used for '+str(repoTuple)+': '+str(end - start))\n",
    "#        while len(metrics) > 0 and metrics[-1] < 1:\n",
    "#            metrics.pop()\n",
    "#            timestamps.pop()\n",
    "        return (timestamps, metrics)\n",
    "    except Exception as e:\n",
    "        print('Failed to analyze '+str(repoTuple)+': '+str(e))\n",
    "        return ([],[])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`simpleRunMetric` was the first function to calculate (and display) metrics for multiple repos. Again the base structure of parallely mapping each repo with a metric suite can be found throughout the following course of the project."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def simpleRunMetric(metricFunction, fileCountConsumer = repoAnalysis.safeDivision):\n",
    "    repos = list(map(lambda repo: (repo.split('/', 1)[0], repo.split('/', 1)[1]), repoLibrarian.knownRepos()))\n",
    "\n",
    "    start = time.time()\n",
    "    with Pool(int(multiprocessing.cpu_count())) as pool:\n",
    "        allMetrics = pool.map(functools.partial(timedMetricPerFileForRepo, metricFunction=metricFunction, fileCountConsumer=fileCountConsumer), repos)\n",
    "    end = time.time()\n",
    "    print('Total Time used: '+str(end - start))\n",
    "    \n",
    "    pyplot.figure(figsize=(12, 7))\n",
    "    for (timestamps, metrics) in allMetrics:\n",
    "        dates = matplotlib.dates.epoch2num(timestamps)\n",
    "        pyplot.plot_date(dates, metrics, '-')\n",
    "    pyplot.legend(repos)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reconstructed historical metric functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Over the course of the following sections, basic metrics functions had been developed. These were however extracted to the [repoAnalysis.py](./repoAnalysis.py) module and revised over and over so the original implementations are not there anymore (while there are definitely strong traces in the module). Therefore I extracted the historical functions from the git history, so the following cells will run. \n",
    "\n",
    "The functions presented basically use the knowledge about using regex that has been shown before. Note the two regexes that are used for lines of code per method and lambda density: Methods are discovered by their method definition (which can be quite diverse in Java). Lambdas are counted when using the `::` or `->` operator. This is more limited, as it does not count usages of function objects when they come from a library or framework are from a class that implements (not extends) a functional interface. However, static type checking is way out of scope of this project."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def safeDivision(a,b):\n",
    "    return a/b if b > 0 else 0\n",
    "\n",
    "def removeHeader(contentWithHeader):\n",
    "    content = contentWithHeader.split('{', 1)\n",
    "    content = content[1] if len(content) > 1 else ''\n",
    "    return content\n",
    "\n",
    "def occurencesOf(regex, content):\n",
    "    return len(regex.findall(content))\n",
    "\n",
    "methodRegex = re.compile(r\"(public|protected|private|static|\\s) +[\\w\\<\\>\\[\\]]+\\s+(\\w+) *\\([^\\)]*\\) *(\\{?|[^;])\")\n",
    "def locmOf(contentWithHeader):\n",
    "    content = removeHeader(contentWithHeader)\n",
    "    numMethods = occurencesOf(methodRegex, content)\n",
    "    loc = len(content.split('\\n'))\n",
    "    locm = safeDivision(loc, numMethods)\n",
    "    return locm\n",
    "\n",
    "lambdaRegex = re.compile(r\"->|::\")\n",
    "def lambdaDensity(content):\n",
    "    numLambdas = occurencesOf(lambdaRegex, content) * 1000\n",
    "    loc = len(content.split('\\n'))\n",
    "    density = numLambdas / loc\n",
    "    return density\n",
    "\n",
    "def loc(content):\n",
    "    return len(content.split('\\n'))\n",
    "\n",
    "def identity(x,*y):\n",
    "    return x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory runs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To gain an understanding of the (new layer of) data, I ran the metrics on a small test set of repos. There were some insights derived from this:\n",
    "- Repos tend to have an initial phase where metric deviate strongly from their \"usual\" flow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time used for ('bptlab', 'fcm2cpn.git'): 0.4013631343841553\n",
      "Time used for ('bptlab', 'correlation-analysis.git'): 3.382075071334839\n",
      "Time used for ('bptlab', 'scylla.git'): 28.26970410346985\n",
      "Time used for ('json-iterator', 'java.git'): 29.891782999038696\n",
      "Time used for ('bptlab', 'cepta.git'): 33.18532967567444\n",
      "Time used for ('bptlab', 'Unicorn.git'): 38.575215578079224\n",
      "Time used for ('alibaba', 'arthas.git'): 160.6042914390564\n",
      "Total Time used: 163.90643429756165\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "simpleRunMetric(locmOf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time used for ('bptlab', 'fcm2cpn.git'): 0.32689738273620605\n",
      "Time used for ('bptlab', 'correlation-analysis.git'): 1.6104001998901367\n",
      "Time used for ('bptlab', 'scylla.git'): 8.252939701080322\n",
      "Time used for ('json-iterator', 'java.git'): 8.504035711288452\n",
      "Time used for ('bptlab', 'cepta.git'): 21.032324075698853\n",
      "Time used for ('bptlab', 'Unicorn.git'): 21.757805824279785\n",
      "Time used for ('alibaba', 'arthas.git'): 44.89477014541626\n",
      "Total Time used: 48.273399353027344\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "simpleRunMetric(lambdaDensity)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: The following metric run is special in multiple aspects: Firstly, the run uses the loc metric, which does not aggregate files by taking the average.\n",
    "Secondly, it runs on a special set of repositories that were determined by the very first set of polyglots (see [DataExplorer](./DataExplorer.ipynb) for information on how polyglots are determined). This is relevant because it showed how different the method at that point in time performed in different repos: Some runs took under a second, some took nearly 45min. It also showed the large difference in size that could occur which makes repos harder to compare - an insight which strengthend the ideas of calculating metrics diff based and plotting them over lifetime.\n",
    "\n",
    "The cell has been taken as is (and not reevaluated) as the results are hard to reproduce. However, the list of analyzed repos can be found in the outputs so it is theoretically possible."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time used for ('Elena-Zhao', 'Guimi.git'): 0.1284182071685791\n",
      "Time used for ('Elena-Zhao', 'Mini-Chatter.git'): 0.10931038856506348\n",
      "Time used for ('pleonex', 'ChatRMI.git'): 0.1066889762878418\n",
      "Time used for ('craigslist206', 'huffman.git'): 0.16383004188537598\n",
      "Time used for ('Elena-Zhao', 'Weibao.git'): 0.24792146682739258\n",
      "Time used for ('pleonex', 'CocoKiller.git'): 0.2406604290008545\n",
      "Time used for ('miken22', '304-Project.git'): 0.24741840362548828\n",
      "Time used for ('bptlab', 'fcm2cpn.git'): 0.2866392135620117\n",
      "Time used for ('pleonex', 'NiKate-Origins.git'): 0.39778637886047363\n",
      "Time used for ('JTReed', 'Porygon.git'): 0.21101975440979004\n",
      "Failed to analyze ('braliar', 'jabref.git'): Reference at 'refs/heads/master' does not exist\n",
      "Time used for ('tyler124', 'Monitordroid.git'): 0.23861050605773926\n",
      "Time used for ('GourdErwa', 'jasper_sample.git'): 0.2540762424468994\n",
      "Time used for ('JTReed', 'Porygon2.git'): 0.4825553894042969\n",
      "Time used for ('shengnwen', 'WeiBaoSSE.git'): 0.5534257888793945\n",
      "Time used for ('zzjove', 'MOMA.git'): 1.1395726203918457\n",
      "Time used for ('allantsai123', 'COSC310project.git'): 0.9791743755340576\n",
      "Time used for ('YashArora0606', 'SignInSystem.git'): 5.023577690124512\n",
      "Time used for ('tainarareis', 'Urutau.git'): 5.614899396896362\n",
      "Time used for ('italopaiva', 'EuVou.git'): 8.872634172439575\n",
      "Time used for ('bptlab', 'scylla.git'): 23.644248962402344\n",
      "Time used for ('boceckts', 'jabref.git'): 2368.3593599796295\n",
      "Time used for ('ayanai1', 'jabref.git'): 2524.6302893161774\n",
      "Time used for ('obraliar', 'jabref.git'): 2615.0065610408783\n",
      "Time used for ('Braunch', 'jabref.git'): 2618.8515598773956\n",
      "Time used for ('mairdl', 'jabref.git'): 2641.888402938843\n",
      "Total Time used: 2643.4202370643616\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "simpleRunMetric(loc, identity)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another insight from the first \"real repository set\" run was that there are forks which have duplicate data. At that time, the problem was solved by deleting all duplicates except the newest. However, the mechanism has not been generalized as forks could not only be advancements of their source, but also be parallely developed. Instead, later, when evaluating the results, duplicates are eliminated by taking distinct commit shas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Delete all the duplicates except the newest\n",
    "for (user, repo) in repoLibrarian.managedRepos():\n",
    "    if repo.endswith('jabref.git') and not 'mairdl' in user:\n",
    "        deleteRepo(user, repo)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"anchor\" id=\"mapuser\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploring approaches to map from user commits to metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As the focus of the project was to analyze developer code, not repositories, the analysis results need to be attributed to developers. In the following cells, I evaluated some approaches to do so. <br>\n",
    "Note: Again, these are only the that could be reconstructed, many approaches have been tried and discard immediately."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### For each project: map from commit sha to metrics "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first problem to solve is to calculate metric deltas of commits to their parents instead of just looking at the project metrics at a given commit.<br>\n",
    "The following approach uses an ordered dict of commit hexsha to commit data tuple to allow this: As each commit saves the hexsha of its parent, this can later be resolved. The `Wrap` class wraps the dict because of weird behavior with `Pool.map` (some data types cannot be pickled and thus must be wrapped, OrderedDict is one of them).\n",
    "\n",
    "Apart from this changes, `averageMetricPerCommitForRepo` is a direct successor to the `timedMetricPerFileForRepo` from above. The function has been steadily improved, the following cell shows a progressed version that has been adapted to run in this \"history\" context."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import collections\n",
    "\n",
    "class Wrap:\n",
    "    def __init__(self, data):\n",
    "        self.data = data\n",
    "\n",
    "def averageMetricPerCommitForRepo(repoTuple, metricFunction, fileCountConsumer = repoAnalysis.safeDivision):\n",
    "    (user, project) = repoTuple\n",
    "    repo = repoLibrarian.getRepo(user, project)\n",
    "    try:\n",
    "        start = time.time()\n",
    "        commitToMetric = collections.OrderedDict()\n",
    "        for commit in repo.iter_commits():\n",
    "            fileCount = 0\n",
    "            aggregatedMetric = 0\n",
    "            totalLoc = 0\n",
    "            for obj in commit.tree.traverse():\n",
    "                if repoLibrarian.isJavaFile(obj):\n",
    "                    fileCount = fileCount + 1\n",
    "                    content = obj.data_stream.read().decode(\"CP437\")#.decode(\"utf-8\")\n",
    "                    loc = repoAnalysis.loc(content)\n",
    "                    metric = metricFunction(content) #, loc=loc) Named parameters have been introduced later when metric functions were upgraded, removed in revision for compliance to the old code\n",
    "                    aggregatedMetric = aggregatedMetric + metric\n",
    "                    totalLoc = totalLoc + loc\n",
    "            consumedMetric = fileCountConsumer(aggregatedMetric, fileCount)\n",
    "            parent = commit.parents[-1].hexsha if len(commit.parents) == 1 else None\n",
    "            commitToMetric[commit.hexsha] = (commit.committed_date, consumedMetric, parent, totalLoc)\n",
    "        end = time.time()\n",
    "        print('Time used for '+str(repoTuple)+': '+str(end - start))\n",
    "    except Exception as e:\n",
    "        print('Failed to analyze '+str(repoTuple)+': '+str(e))\n",
    "    return Wrap(commitToMetric)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate deltas of commits to parents"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The map can then be used to determine for each commit the delta to it's parent commit:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def deltasOf(metricPerCommit):\n",
    "    deltas = collections.OrderedDict()\n",
    "    for (sha, metric) in metricPerCommit.items():\n",
    "        if(metric[2] != None): #There is a unique parent -> No merges or orphans(initial commit)\n",
    "            parentMetric = metricPerCommit[metric[2]]\n",
    "            deltas[sha] = (metric[0], (metric[1] - parentMetric[1]), metric[2])\n",
    "    return deltas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To see that the delta function works, I applied it to my favourite example repository. I also extracted the function for plotting repos for this purpose."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time used for ('bptlab', 'scylla'): 28.080988883972168\n"
     ]
    }
   ],
   "source": [
    "data = averageMetricPerCommitForRepo(('bptlab', 'scylla'), locmOf).data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotByDate(data):\n",
    "    timestamps = map(lambda tuple: tuple[0], data)\n",
    "    metrics = map(lambda tuple: tuple[1], data)\n",
    "    dates = matplotlib.dates.epoch2num(list(timestamps))\n",
    "    pyplot.plot_date(dates, list(metrics), '-')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting the data shows that it looks sane. Note that the deltas do not 100% match the absolute changes, this is due to the fact that many of the bigger deflections result from merges where there is no delta value present; one could also say that  the continuity of the graphs is misleading here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f4730541160>"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pyplot.figure(figsize=(12, 7))\n",
    "deltas = deltasOf(data)\n",
    "plotByDate(data.values())\n",
    "plotByDate(deltas.values())\n",
    "pyplot.legend(['Absolute metrics', 'Delta metrics'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot by lifecycle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An interesting approach to compare repositories (and later developers) using a time component is the lifecycle approach. For this, time of an event is set in relation to the first and last event of a series. This makes is possible to compare curves of repositories/developers that started and ended at completely different dates. When additionally using deltas, it is also possible to quantitatively compate them.\n",
    "\n",
    "A simple additional plot method has been created to implement this approach:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotByLifecycle(data):\n",
    "    if len(data) <= 1:\n",
    "        pyplot.plot([], [])\n",
    "        return\n",
    "    data = list(sorted(data, key=lambda tupl: -tupl[0]))\n",
    "    timestamps = map(lambda tupl: tupl[0], data)\n",
    "    metrics = map(lambda tupl: tupl[1], data)\n",
    "    maxDate = next(iter(data))[0]\n",
    "    minDate = next(reversed(data))[0]\n",
    "    interval = maxDate - minDate\n",
    "    dates = list(map(lambda stamp : (stamp - minDate) / interval, timestamps))\n",
    "    pyplot.plot(dates, list(metrics), '-')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To test the sanity of this approach, it has been applied to the example repo. We expect the plot to look exactly like the one above, which it does."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pyplot.figure(figsize=(12, 7))\n",
    "plotByLifecycle(data.values())\n",
    "plotByLifecycle(deltas.values())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create and analyze basic set of repositories to analyzed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To test developed approaches, a first set of polyglot user repositories (and some more) was selected. \"Back then\" every 1221th user was selected as base for the polyglot selection (`MOD(id, 1221) = 5`) which lead to 12 polyglots. \n",
    "\n",
    "I wanted to see if I could map from a commit sha to metrics even when multiple repositories were analyzed (which is needed as one developer usually works on multiple repositories)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`basicJavaRepos` again is a reconstructed value; I usually switched between the test repo set and the first set of projects of polyglot users."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Could not download repo \"alstonlo/Chat-Software-Assignment\n",
      "Failed to download ('alstonlo', 'Chat-Software-Assignment'): Cmd('git') failed due to: exit code(128)\n",
      "  cmdline: git clone --bare -v https://github.com/alstonlo/Chat-Software-Assignment.git ./repos/alstonlo/Chat-Software-Assignment.git\n",
      "  stderr: 'Cloning into bare repository './repos/alstonlo/Chat-Software-Assignment.git'...\n",
      "fatal: could not read Username for 'https://github.com': No such device or address\n",
      "'\n",
      "('MarioLizana', 'RadioControlSED')\n",
      "('craigslist206', 'huffman')\n",
      "('pleonex', 'ChatRMI')\n",
      "('pleonex', 'CocoKiller')\n",
      "('pleonex', 'NiKate-Origins')\n",
      "('pleonex', 'locaviewer')\n",
      "('miken22', '304-Project')\n",
      "('NeebalLearningPvtLtd', 'InventoryManagementSystem')\n",
      "('zzjove', 'MOMA')\n",
      "('tainarareis', 'Urutau')\n",
      "('italopaiva', 'EuVou')\n",
      "('ShutUpPaulo', 'TecProg_2016-01')\n",
      "('andrevctr12', 'PAA_HUFFMAN')\n",
      "('Elena-Zhao', 'Weibao')\n",
      "('Elena-Zhao', 'MOMA')\n",
      "('Elena-Zhao', 'Database-Auto-troubleshooting')\n",
      "('Elena-Zhao', 'Guimi')\n",
      "('Elena-Zhao', 'Mini-Chatter')\n",
      "('alstonlo', 'Bongo-Cat-Attacc')\n",
      "('dataspy', 'surprise-theory')\n",
      "('ieeeugrsb', 'ieeextreme8')\n",
      "('bptlab', 'scylla')\n",
      "('bptlab', 'fcm2cpn')\n",
      "('allantsai123', 'COSC310project')\n",
      "('JTReed', 'Porygon2')\n",
      "('JTReed', 'Porygon')\n",
      "('shengnwen', 'WeiBaoSSE')\n",
      "('lucasBrilhante', 'campus-party-mobile')\n",
      "('lucasBrilhante', 'das-framework-teste')\n",
      "('mairdl', 'jabref')\n",
      "('tyler124', 'Monitordroid')\n",
      "('YashArora0606', 'SignInSystem')\n",
      "('GourdErwa', 'jasper_sample')\n",
      "('igorribeiroduarte', 'formulasTesouroDireto')\n"
     ]
    }
   ],
   "source": [
    "basicJavaRepos = list(filter(lambda tupl: repoLibrarian.isJavaRepo(*tupl), [\n",
    "    ('MarioLizana', 'RadioControlSED'),\n",
    "    ('craigslist206', 'huffman'),\n",
    "    ('pleonex', 'ChatRMI'),\n",
    "    ('pleonex', 'CocoKiller'),\n",
    "    ('pleonex', 'NiKate-Origins'),\n",
    "    ('pleonex', 'locaviewer'),\n",
    "    ('miken22', '304-Project'),\n",
    "    ('NeebalLearningPvtLtd', 'InventoryManagementSystem'),\n",
    "    ('zzjove', 'MOMA'),\n",
    "    ('tainarareis', 'Urutau'),\n",
    "    ('italopaiva', 'EuVou'),\n",
    "    ('ShutUpPaulo', 'TecProg_2016-01'),\n",
    "    ('andrevctr12', 'PAA_HUFFMAN'),\n",
    "    ('Elena-Zhao', 'Weibao'),\n",
    "    ('Elena-Zhao', 'MOMA'),\n",
    "    ('Elena-Zhao', 'Database-Auto-troubleshooting'),\n",
    "    ('Elena-Zhao', 'Guimi'),\n",
    "    ('Elena-Zhao', 'Mini-Chatter'),\n",
    "    ('alstonlo', 'Chat-Software-Assignment'),\n",
    "    ('alstonlo', 'Bongo-Cat-Attacc'),\n",
    "    ('dataspy', 'surprise-theory'),\n",
    "    ('ieeeugrsb', 'ieeextreme8'),\n",
    "    ('bptlab', 'scylla'),\n",
    "    ('bptlab', 'fcm2cpn'),\n",
    "    ('allantsai123', 'COSC310project'),\n",
    "    ('JTReed', 'Porygon2'),\n",
    "    ('JTReed', 'Porygon'),\n",
    "    ('shengnwen', 'WeiBaoSSE'),\n",
    "    ('lucasBrilhante', 'campus-party-mobile'),\n",
    "    ('lucasBrilhante', 'das-framework-teste'),\n",
    "    ('mairdl', 'jabref'),\n",
    "    ('tyler124', 'Monitordroid'),\n",
    "    ('YashArora0606', 'SignInSystem'),\n",
    "    ('GourdErwa', 'jasper_sample'),\n",
    "    ('igorribeiroduarte', 'formulasTesouroDireto')\n",
    "]))\n",
    "for x in basicJavaRepos: print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To deal with the pickling errors of `Pool.map` I tried more ways, such as using ThreadPools instead (this would avoid the functools partial, which is needed because I use lambdas). However I stuck to `Pool.map` for parallelism."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time used for ('Elena-Zhao', 'Database-Auto-troubleshooting'): 0.3693199157714844\n",
      "Time used for ('MarioLizana', 'RadioControlSED'): 0.3744783401489258\n",
      "Time used for ('pleonex', 'ChatRMI'): 0.4095268249511719\n",
      "Time used for ('Elena-Zhao', 'MOMA'): 0.41657161712646484\n",
      "Time used for ('craigslist206', 'huffman'): 0.4591677188873291\n",
      "Time used for ('miken22', '304-Project'): 0.48020148277282715\n",
      "Time used for ('andrevctr12', 'PAA_HUFFMAN'): 0.46935009956359863\n",
      "Time used for ('pleonex', 'CocoKiller'): 0.5340681076049805\n",
      "Time used for ('Elena-Zhao', 'Weibao'): 0.6766207218170166\n",
      "Time used for ('Elena-Zhao', 'Mini-Chatter'): 0.3139381408691406\n",
      "Time used for ('pleonex', 'NiKate-Origins'): 0.8306424617767334\n",
      "Time used for ('Elena-Zhao', 'Guimi'): 0.36624956130981445\n",
      "Time used for ('NeebalLearningPvtLtd', 'InventoryManagementSystem'): 1.0844483375549316\n",
      "Time used for ('ieeeugrsb', 'ieeextreme8'): 0.5296401977539062\n",
      "Time used for ('bptlab', 'fcm2cpn'): 0.5534877777099609\n",
      "Time used for ('JTReed', 'Porygon'): 0.3426940441131592\n",
      "Time used for ('dataspy', 'surprise-theory'): 0.7817707061767578\n",
      "Time used for ('lucasBrilhante', 'das-framework-teste'): 0.2967820167541504\n",
      "Time used for ('zzjove', 'MOMA'): 1.5183439254760742\n",
      "Time used for ('lucasBrilhante', 'campus-party-mobile'): 0.8304378986358643\n",
      "Time used for ('tyler124', 'Monitordroid'): 0.6334531307220459\n",
      "Time used for ('GourdErwa', 'jasper_sample'): 0.5223710536956787\n",
      "Time used for ('JTReed', 'Porygon2'): 1.206420660018921\n",
      "Time used for ('igorribeiroduarte', 'formulasTesouroDireto'): 0.4269404411315918\n",
      "Time used for ('shengnwen', 'WeiBaoSSE'): 1.1521782875061035\n",
      "Time used for ('allantsai123', 'COSC310project'): 2.48500657081604\n",
      "Time used for ('alstonlo', 'Bongo-Cat-Attacc'): 3.4298598766326904\n",
      "Time used for ('tainarareis', 'Urutau'): 4.012904167175293\n",
      "Time used for ('YashArora0606', 'SignInSystem'): 4.303943634033203\n",
      "Time used for ('italopaiva', 'EuVou'): 25.149054288864136\n",
      "Time used for ('bptlab', 'scylla'): 29.116821765899658\n",
      "Time used for ('ShutUpPaulo', 'TecProg_2016-01'): 48.73932957649231\n",
      "Time used for ('pleonex', 'locaviewer'): 172.28586053848267\n",
      "Time used for ('mairdl', 'jabref'): 4068.063727617264\n",
      "Total Time used: 4070.6688606739044\n"
     ]
    }
   ],
   "source": [
    "# from multiprocessing.pool import ThreadPool\n",
    "# import consumers\n",
    "\n",
    "start = time.time()\n",
    "with Pool(int(multiprocessing.cpu_count()/4)) as pool:\n",
    "    #allMetrics = pool.map(lambda repo: averageMetricPerCommitForRepo(repoTuple=repo, metricFunction=locmOf), basicJavaRepos)\n",
    "    allMetrics = pool.map(functools.partial(averageMetricPerCommitForRepo, metricFunction=locmOf), basicJavaRepos)\n",
    "allMetrics = list(map(lambda wrap: wrap.data, allMetrics))\n",
    "end = time.time()\n",
    "print('Total Time used: '+str(end - start))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting the data we can see that there seems to be a \"habitable zone\" for loc per method (between approx. 5 and 20), where many repos lie. The number of repos more or less encourages this assumption."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f472bfc8ca0>"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# reposToMetrics = dict(zip(repos, allMetrics))\n",
    "\n",
    "pyplot.figure(figsize=(20, 10))\n",
    "for data in allMetrics:\n",
    "    plotByLifecycle(\n",
    "        #deltasOf(\n",
    "            data.values()\n",
    "        #)\n",
    "    )\n",
    "\n",
    "pyplot.legend(basicJavaRepos, bbox_to_anchor=(1.5, 1.0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting deltas does not lead to any significant insights, except that looks like for most repos deltas are more extreme at the beginning (which is natural, as the influence of one commit on the overall code base is higher)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f472bf4d730>"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pyplot.figure(figsize=(20, 10))\n",
    "for data in allMetrics:\n",
    "    plotByLifecycle(\n",
    "        deltasOf(\n",
    "            dict(data)\n",
    "        ).values()\n",
    "    )\n",
    "\n",
    "pyplot.legend(basicJavaRepos, bbox_to_anchor=(1.5, 1.0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example user"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Additionally, an example user has been selected from the early set of polyglots. As this data is not valid anymore, this again is just reconstructed:5080586"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "testUser = '1979246'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time used: 0.2890956401824951\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 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>url</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>https://api.github.com/repos/Elena-Zhao/Guimi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>https://api.github.com/repos/Elena-Zhao/Mini-C...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>https://api.github.com/repos/Elena-Zhao/Weibao</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>https://api.github.com/repos/shengnwen/WeiBaoSSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>https://api.github.com/repos/zzjove/MOMA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 url\n",
       "0      https://api.github.com/repos/Elena-Zhao/Guimi\n",
       "1  https://api.github.com/repos/Elena-Zhao/Mini-C...\n",
       "2     https://api.github.com/repos/Elena-Zhao/Weibao\n",
       "3   https://api.github.com/repos/shengnwen/WeiBaoSSE\n",
       "4           https://api.github.com/repos/zzjove/MOMA"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time used: 1814.089327096939\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 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>sha</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00a71b51c7e9799e4ff6f2f4ab47d5f7d7d510d4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>00c1d1e85de1c8f37701a47cd5e05795298821ea</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01001912335b140a37eb1e8b68d6f25b2667ed9f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0116fc48d6b71e4cbb809cf9ff4ff53a13a2064c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>018a42d66fdb8a52265b4814aa90b9739fb87388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>367</th>\n",
       "      <td>f91757af4750d9ce2e8b6df8b429a7523cce2df2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>368</th>\n",
       "      <td>fa1a39cb69b3032d179d6c5704795d1017e3184b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>369</th>\n",
       "      <td>fcfa524c1304bc804552c7966e66f80f54569eed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>370</th>\n",
       "      <td>fdb007b351cd2878f9865a122572b3a48a49da02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>371</th>\n",
       "      <td>fe534d1a2ea5b78f6deb28703ecc58a5e19cf264</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>372 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          sha\n",
       "0    00a71b51c7e9799e4ff6f2f4ab47d5f7d7d510d4\n",
       "1    00c1d1e85de1c8f37701a47cd5e05795298821ea\n",
       "2    01001912335b140a37eb1e8b68d6f25b2667ed9f\n",
       "3    0116fc48d6b71e4cbb809cf9ff4ff53a13a2064c\n",
       "4    018a42d66fdb8a52265b4814aa90b9739fb87388\n",
       "..                                        ...\n",
       "367  f91757af4750d9ce2e8b6df8b429a7523cce2df2\n",
       "368  fa1a39cb69b3032d179d6c5704795d1017e3184b\n",
       "369  fcfa524c1304bc804552c7966e66f80f54569eed\n",
       "370  fdb007b351cd2878f9865a122572b3a48a49da02\n",
       "371  fe534d1a2ea5b78f6deb28703ecc58a5e19cf264\n",
       "\n",
       "[372 rows x 1 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import dbUtils\n",
    "testUserRepos = dbUtils.runQuery('''\n",
    "    SELECT DISTINCT url\n",
    "    FROM ght.project_members, ght.projects\n",
    "    WHERE user_id = {}\n",
    "    AND repo_id = id\n",
    "    AND language = 'Java'\n",
    "'''.format(testUser), mute=True)\n",
    "display(testUserRepos)\n",
    "\n",
    "testUserCommits = dbUtils.runQuery('''\n",
    "    SELECT DISTINCT sha\n",
    "    FROM ght.commits\n",
    "    WHERE author_id = {}\n",
    "'''.format(testUser), mute=True)\n",
    "display(testUserCommits)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using a dict of repositories to map from user commits to metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One of first tested approaches was to use a dictionary of repositories from which then dictionaries of commits could be accessed: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "reposToMetrics = dict(zip(basicJavaRepos, allMetrics))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While the approach works in general, it is hard to: get all repos of a user, then get the metrics for each repo, then map them to deltas and then filter for authorship again - all in python code for each user. (The following cell does not filter by authorship).\n",
    "\n",
    "Plotting the results also foreshadows the problem that values can get very extreme for one author - making them hard to interpret and motivating the need to aggregate in any way (for instance lifecycle phases)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "all = []\n",
    "for url in testUserRepos['url']:\n",
    "    split = url.split('/')\n",
    "    user = split[-2]\n",
    "    project = split[-1]\n",
    "    key = (user, project)\n",
    "    if key in reposToMetrics:\n",
    "        metrics = reposToMetrics[key]\n",
    "        deltas = deltasOf(metrics)\n",
    "        all.extend(deltas.values())\n",
    "        #all.extend(metrics.items())\n",
    "    else:\n",
    "        print('Repo unavailable: '+str(key))\n",
    "all.sort(key = lambda tup: tup[0], reverse=True)\n",
    "plotByLifecycle(all)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using chainmap to map from user commits to metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another one of the tested approaches uses chainmaps over all repositories in order to quickly access data for one commit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Time used: 0.00019025802612304688\n"
     ]
    }
   ],
   "source": [
    "start = time.time()\n",
    "chainMap = collections.ChainMap(*allMetrics)\n",
    "end = time.time()\n",
    "print('Total Time used: '+str(end - start))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The chainmap can successfully be used to get metrics for a given commit (although this approach has later been discarded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1391951934, 17.90714285714286, '02328f470caaf73eb99264465385c57d70b0bb87', 368)\n"
     ]
    }
   ],
   "source": [
    "sha = next(iter(next(iter(allMetrics))))\n",
    "print(chainMap.get(sha, default=None))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "all = []\n",
    "for url in testUserRepos['url']:\n",
    "    split = url.split('/')\n",
    "    user = split[-2]\n",
    "    project = split[-1]\n",
    "    key = (user, project)\n",
    "    if key in reposToMetrics:\n",
    "        metrics = reposToMetrics[key]\n",
    "        deltas = deltasOf(metrics)\n",
    "        all.extend(deltas.values())\n",
    "        #all.extend(metrics.items())\n",
    "    else:\n",
    "        print('Repo unavailable: '+str(key))\n",
    "all.sort(key = lambda tup: tup[0], reverse=True)\n",
    "plotByLifecycle(all)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Shortcomings however include that all data has to be in memory and that further working with is (e.g. joining with developer data) is quite tedious."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Further Progress from this point"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Much has happened between this point and the first iteration run (which marks the beginning of [RepoAnalysis](./RepoAnalysis.ipynb) notebook). Some developments also occured parallely. However, the results of this phase were mostly extracted to the [repoAnalysis](./repoAnalysis.py) module and later overridden, so there are not many cells left. For the sake of continuity and completeness, this chapter tries to recap the improvements made."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using a table as output format"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After experimenting with multiple output formats, persisting result data to a table seemed the best choice. There are several reasons for that:\n",
    "* Persistency: When data is on disk, it does not need to be in memory which is both more friendly to other users of the system and more safe, as the data is not lost when a notebook is restarted\n",
    "* Querying: SQL is a perfect domain language for running analytic queries on the dataset\n",
    "* Easy joins: The table allows to easily join the data with developer data or parent commit data which is very good"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lab for new metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The set of available metrics shown before only consists of loc per method, loc in general, and lambda density. As this is quite a limited set of metrics, this section of the notebook was assigned for the development of new metrics."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As test code the following source was taken: (It includes nearly all possible combinations for comments and strings):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = '''\n",
    "/**\n",
    "Cool javadoc that shows how cool the class is\n",
    "*/\n",
    "public class Foo extends Bar {//Hidden comment\n",
    "    /**Documentation for a*/\n",
    "    public static Object b = \"//No, no comment\";\n",
    "    //Docu for b\n",
    "    public String/*Mean inline comment*/ b;\n",
    "    /* Another\n",
    "    Multiline*/\n",
    "\n",
    "    public static void main(String[] args) {\n",
    "        //Comment\n",
    "        //Comment\n",
    "        System.out.println(\"Actual code goes here /*This is not a comment ...\")\n",
    "        System.out.println(\"... and it doesn't end here*/\")\n",
    "\n",
    "    }\n",
    "}\n",
    "'''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### String Literal Removal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "String literals are problematic for regex-based metrics, as code inside it should not count towards them. Therefore, a regex to remove string literals (or replace them with a more harmless one to prevent invalid syntx) hat been developed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "stringRemover = re.compile(r\"\\\".*?\\\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Demonstration:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "/**\n",
      "Cool javadoc that shows how cool the class is\n",
      "*/\n",
      "public class Foo extends Bar {//Hidden comment\n",
      "    /**Documentation for a*/\n",
      "    public static Object b = \"...\";\n",
      "    //Docu for b\n",
      "    public String/*Mean inline comment*/ b;\n",
      "    /* Another\n",
      "    Multiline*/\n",
      "\n",
      "    public static void main(String[] args) {\n",
      "        //Comment\n",
      "        //Comment\n",
      "        System.out.println(\"...\")\n",
      "        System.out.println(\"...\")\n",
      "\n",
      "    }\n",
      "}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(stringRemover.sub(\"\\\"...\\\"\", x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comment finder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly challenging, for the same reasons, are comments. They are additionally interesting in order to determine the number of comment lines as a metric. Comments in Java can be oneline (which starts with `//` and always counts as one comment line because there can never be two in one line) or multiline (between `/*` and `*/`, which counts as many lines that there are in, so just split along line breaks)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "commentRegex = re.compile(r\"//.*?\\n|/\\*.*?\\*/\", re.S)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to work good, this regex needs to be combined with the string literal regex. Demonstration:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['/**\\nCool javadoc that shows how cool the class is\\n*/', '//Hidden comment\\n', '/**Documentation for a*/', '//Docu for b\\n', '/*Mean inline comment*/', '/* Another\\n    Multiline*/', '//Comment\\n', '//Comment\\n']\n",
      "11\n"
     ]
    }
   ],
   "source": [
    "matches = commentRegex.findall(stringRemover.sub(\"\\\"...\\\"\", x))\n",
    "print(matches)\n",
    "# Trailing newlines indicate that comment ends with the newline (indicating a oneline comment) and are therefore stripped\n",
    "print(sum(map(lambda x: len(x.rstrip().split(\"\\n\")), matches)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Indent"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another interesting metric to calculate is the average indent that code has: Firstly it can be an indicator for code complexity (more indent usually means deeper nesting. Secondly, Python has a special way of dealing with indent, which is a good motivation to analyze this aspect.\n",
    "\n",
    "The tricky part about indent is that there are several ways of doing this: While tabs are unambiguous for all environments (opinion of the author), there are several different numbers of spaces that can be used instead. [This](https://blog.overops.com/tabs-vs-spaces-how-they-write-java-in-google-twitter-mozilla-and-pied-piper/) blog suggests that \"the majority use spaces in different formations, *preferring 4 spaces* over 2\". So 4 spaces are assumed as one tab. This might warp results for developers that only use two spaces, and be a confounding factor if the usage of two or four spaces changes between the experiment groups. However, less than [a quarter](https://ukupat.github.io/tabs-or-spaces/) actually use two spaces on github, and other factors such as IDE and workplace also influence the indent.\n",
    "\n",
    "The following non-regex method counts the number of trailing whitespace of each row, counting tabs as four. We expect an average of 1, as there are five lines and five indents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n"
     ]
    }
   ],
   "source": [
    "y = '''\n",
    "    indent_1\n",
    "\t    indent_2\n",
    "        indent_2\n",
    "'''\n",
    "indents = list(map(lambda line: len(line[:(len(line)-len(line.lstrip()))].replace('\\t', '    ')) / 4, y.split('\\n')))\n",
    "avg = sum(indents)/len(indents)\n",
    "print(avg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Further metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As additional metrics a reflection heuristic and snake case usage were added, as both were expected to potentially differ for the polyglot users. The underlying regexes can be found at the [repoAnalysis](./repoAnalysis.py) module. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculating absolute metrics instead of per loc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One major change that was done in the approach was to calculate all metrics as occurences first and then calculating loc separately. This is based on the insight that saving densities to table only leads to data loss while we can still efficiently calculate them afterward from the table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generalizing metric calculation on metric suites"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The \"state of the art\" metric calculation function at that point in time, `averageMetricPerCommitForRepo`, did only accept one single metric function. Of course all the previously developed metrics should be applied to all commits. With regard to the fact that data has been planned to be put into a table, the new `calculateMetrics` function (a version of which can still be found in the [repoAnalysis](./repoAnalysis.py) module for historical purposes) returns data as pandas dataframe (which has proven to be efficient and intuitive). Metrics functions are called each with all of full, header-free, and string-and-comment-free file contents. This abolishes the need to calculate these for multiple metrics improving run time a bit."
   ]
  }
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