5 Controlflow_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "014f2f87",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Control flow analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "211fdc54",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "So far, we have considered raw text, produced token streams with lexers, and syntax trees using parsers. The source code statements that we have captured in the abstract syntax tree are executed in sequence; this sequence is known as the _control flow_. In this paper we will consider how to represent and analyse the control flow of a program."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae23a8d3",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "We will consider the control flow only withing individual functions in this lecture, such as the following."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "287eb3ce",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "code = \"\"\"\n",
    "  public int foo(int x) {\n",
    "    int y = 0;\n",
    "\n",
    "    while(x >= 0) {\n",
    "        if(x % 2 == 0)\n",
    "            y += x;\n",
    "        x--;\n",
    "    }\n",
    "\n",
    "    return y;\n",
    "  }\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a8dcca1",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "Similar to the last chapter, we are going to use [javalang](https://github.com/c2nes/javalang/) to extract syntax trees for individual functions. To do this, we re-use the helper function that defines a dummy class wrapped around the method, as the javalang parser expects a valid Java class as input."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6cecac22",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "import javalang"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6f181ac3",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "def parse_method(code):\n",
    "    class_code = \"class Dummy {\\n\" + code + \"\\n}\";\n",
    "    tokens = javalang.tokenizer.tokenize(class_code)\n",
    "    parser = javalang.parser.Parser(tokens)\n",
    "    ast = parser.parse()\n",
    "    _, node = list(ast.filter(javalang.tree.MethodDeclaration))[0]\n",
    "    return node"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "98169c4d",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "tree = parse_method(code)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11ff5d97",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "To recall the last chapter, let's look at the AST for this method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ed4825dc",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "from graphviz import Digraph\n",
    "def print_tree(tree):\n",
    "    unique_id = 1\n",
    "    dot = Digraph()\n",
    "    for path, node in tree:\n",
    "        dot.node(str(id(node)), str(type(node)))\n",
    "        \n",
    "        for child in node.children:\n",
    "            if isinstance(child, javalang.ast.Node):\n",
    "                dot.edge(str(id(node)), str(id(child)))\n",
    "            elif type(child) == str:\n",
    "                strid = str(unique_id)\n",
    "                unique_id = unique_id + 1\n",
    "                dot.node(strid, child)\n",
    "                dot.edge(str(id(node)), strid)\n",
    "            elif type(child) == list:\n",
    "                for lc in child:\n",
    "                    dot.edge(str(id(node)), str(id(lc)))\n",
    "                 \n",
    "    return dot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "20320025",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
       "<!-- Generated by graphviz version 3.0.0 (20220226.1711)\n",
       " -->\n",
       "<!-- Pages: 1 -->\n",
       "<svg width=\"2409pt\" height=\"548pt\"\n",
       " viewBox=\"0.00 0.00 2408.83 548.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 544)\">\n",
       "<polygon fill=\"white\" stroke=\"transparent\" points=\"-4,4 -4,-544 2404.83,-544 2404.83,4 -4,4\"/>\n",
       "<!-- 4472337168 -->\n",
       "<g id=\"node1\" class=\"node\">\n",
       "<title>4472337168</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"635.29\" cy=\"-522\" rx=\"149.47\" ry=\"18\"/>\n",
       "<text text-anchor=\"start\" x=\"528.29\" y=\"-518.3\" font-family=\"Times,serif\" font-size=\"14.00\">class &#39;javalang.tree.MethodDeclaration&#39;</text>\n",
       "</g>\n",
       "<!-- 4472115792 -->\n",
       "<g id=\"node2\" class=\"node\">\n",
       "<title>4472115792</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"118.29\" cy=\"-450\" rx=\"118.08\" ry=\"18\"/>\n",
       "<text text-anchor=\"start\" x=\"35.29\" y=\"-446.3\" font-family=\"Times,serif\" font-size=\"14.00\">class &#39;javalang.tree.BasicType&#39;</text>\n",
       "</g>\n",
       "<!-- 4472337168&#45;&gt;4472115792 -->\n",
       "<g id=\"edge1\" class=\"edge\">\n",
       "<title>4472337168&#45;&gt;4472115792</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M539.86,-508.08C446.96,-495.5 306.57,-476.49 213.69,-463.92\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"214.11,-460.44 203.73,-462.57 213.17,-467.38 214.11,-460.44\"/>\n",
       "</g>\n",
       "<!-- 1 -->\n",
       "<g id=\"node3\" class=\"node\">\n",
       "<title>1</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"281.29\" cy=\"-450\" rx=\"27\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"281.29\" y=\"-446.3\" font-family=\"Times,serif\" font-size=\"14.00\">foo</text>\n",
       "</g>\n",
       "<!-- 4472337168&#45;&gt;1 -->\n",
       "<g id=\"edge2\" class=\"edge\">\n",
       "<title>4472337168&#45;&gt;1</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M523.52,-510.02C461.75,-502.06 384.19,-488.93 317.29,-468 315.36,-467.4 313.4,-466.71 311.45,-465.96\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"312.41,-462.57 301.84,-461.89 309.68,-469.02 312.41,-462.57\"/>\n",
       "</g>\n",
       "<!-- 4472117088 -->\n",
       "<g id=\"node4\" class=\"node\">\n",
       "<title>4472117088</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"469.29\" cy=\"-450\" rx=\"142.97\" ry=\"18\"/>\n",
       "<text text-anchor=\"start\" x=\"367.29\" y=\"-446.3\" font-family=\"Times,serif\" font-size=\"14.00\">class &#39;javalang.tree.FormalParameter&#39;</text>\n",
       "</g>\n",
       "<!-- 4472337168&#45;&gt;4472117088 -->\n",
       "<g id=\"edge3\" class=\"edge\">\n",
       "<title>4472337168&#45;&gt;4472117088</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M596.37,-504.59C572.83,-494.66 542.63,-481.93 517.58,-471.36\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"518.83,-468.09 508.25,-467.43 516.11,-474.54 518.83,-468.09\"/>\n",
       "</g>\n",
       "<!-- 4472117328 -->\n",
       "<g id=\"node5\" class=\"node\">\n",
       "<title>4472117328</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"801.29\" cy=\"-450\" rx=\"170.87\" ry=\"18\"/>\n",
       "<text text-anchor=\"start\" x=\"677.79\" y=\"-446.3\" font-family=\"Times,serif\" font-size=\"14.00\">class &#39;javalang.tree.LocalVariableDeclaration&#39;</text>\n",
       "</g>\n",
       "<!-- 4472337168&#45;&gt;4472117328 -->\n",
       "<g id=\"edge4\" class=\"edge\">\n",
       "<title>4472337168&#45;&gt;4472117328</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M674.21,-504.59C697.66,-494.7 727.73,-482.02 752.73,-471.48\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"754.18,-474.66 762.04,-467.55 751.46,-468.21 754.18,-474.66\"/>\n",
       "</g>\n",
       "<!-- 4472336688 -->\n",
       "<g id=\"node6\" class=\"node\">\n",
       "<title>4472336688</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1227.29\" cy=\"-450\" rx=\"138.38\" ry=\"18\"/>\n",
       "<text text-anchor=\"start\" x=\"1128.79\" y=\"-446.3\" font-family=\"Times,serif\" font-size=\"14.00\">class &#39;javalang.tree.WhileStatement&#39;</text>\n",
       "</g>\n",
       "<!-- 4472337168&#45;&gt;4472336688 -->\n",
       "<g id=\"edge5\" class=\"edge\">\n",
       "<title>4472337168&#45;&gt;4472336688</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M737.73,-508.89C844.12,-496.31 1009.9,-476.7 1118.55,-463.86\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1119.04,-467.32 1128.56,-462.67 1118.22,-460.37 1119.04,-467.32\"/>\n",
       "</g>\n",
       "<!-- 4472336880 -->\n",
       "<g id=\"node7\" class=\"node\">\n",
       "<title>4472336880</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1881.29\" cy=\"-450\" rx=\"140.28\" ry=\"18\"/>\n",
       "<text text-anchor=\"start\" x=\"1781.29\" y=\"-446.3\" font-family=\"Times,serif\" font-size=\"14.00\">class &#39;javalang.tree.ReturnStatement&#39;</text>\n",
       "</g>\n",
       "<!-- 4472337168&#45;&gt;4472336880 -->\n",
       "<g id=\"edge6\" class=\"edge\">\n",
       "<title>4472337168&#45;&gt;4472336880</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M767.45,-513.58C1005.63,-500.19 1504.47,-472.17 1745.37,-458.64\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1745.8,-462.12 1755.59,-458.06 1745.41,-455.13 1745.8,-462.12\"/>\n",
       "</g>\n",
       "<!-- 2 -->\n",
       "<g id=\"node8\" class=\"node\">\n",
       "<title>2</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"118.29\" cy=\"-378\" rx=\"27\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"118.29\" y=\"-374.3\" font-family=\"Times,serif\" font-size=\"14.00\">int</text>\n",
       "</g>\n",
       "<!-- 4472115792&#45;&gt;2 -->\n",
       "<g id=\"edge7\" class=\"edge\">\n",
       "<title>4472115792&#45;&gt;2</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M118.29,-431.7C118.29,-423.98 118.29,-414.71 118.29,-406.11\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"121.79,-406.1 118.29,-396.1 114.79,-406.1 121.79,-406.1\"/>\n",
       "</g>\n",
       "<!-- 4472115840 -->\n",
       "<g id=\"node9\" class=\"node\">\n",
       "<title>4472115840</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"293.29\" cy=\"-378\" rx=\"118.08\" ry=\"18\"/>\n",
       "<text text-anchor=\"start\" x=\"210.29\" y=\"-374.3\" font-family=\"Times,serif\" font-size=\"14.00\">class &#39;javalang.tree.BasicType&#39;</text>\n",
       "</g>\n",
       "<!-- 4472117088&#45;&gt;4472115840 -->\n",
       "<g id=\"edge8\" class=\"edge\">\n",
       "<title>4472117088&#45;&gt;4472115840</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M428.03,-432.59C402.6,-422.47 369.84,-409.44 342.99,-398.77\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"343.97,-395.39 333.39,-394.95 341.38,-401.9 343.97,-395.39\"/>\n",
       "</g>\n",
       "<!-- 3 -->\n",
       "<g id=\"node10\" class=\"node\">\n",
       "<title>3</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"456.29\" cy=\"-378\" rx=\"27\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"456.29\" y=\"-374.3\" font-family=\"Times,serif\" font-size=\"14.00\">x</text>\n",
       "</g>\n",
       "<!-- 4472117088&#45;&gt;3 -->\n",
       "<g id=\"edge9\" class=\"edge\">\n",
       "<title>4472117088&#45;&gt;3</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M466.08,-431.7C464.64,-423.98 462.92,-414.71 461.32,-406.11\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"464.73,-405.3 459.47,-396.1 457.85,-406.58 464.73,-405.3\"/>\n",
       "</g>\n",
       "<!-- 4472117712 -->\n",
       "<g id=\"node12\" class=\"node\">\n",
       "<title>4472117712</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"619.29\" cy=\"-378\" rx=\"118.08\" ry=\"18\"/>\n",
       "<text text-anchor=\"start\" x=\"536.29\" y=\"-374.3\" font-family=\"Times,serif\" font-size=\"14.00\">class &#39;javalang.tree.BasicType&#39;</text>\n",
       "</g>\n",
       "<!-- 4472117328&#45;&gt;4472117712 -->\n",
       "<g id=\"edge11\" class=\"edge\">\n",
       "<title>4472117328&#45;&gt;4472117712</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M758.16,-432.41C731.83,-422.28 698.03,-409.28 670.36,-398.64\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"671.45,-395.31 660.86,-394.99 668.94,-401.84 671.45,-395.31\"/>\n",
       "</g>\n",
       "<!-- 4472118624 -->\n",
       "<g id=\"node13\" class=\"node\">\n",
       "<title>4472118624</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"903.29\" cy=\"-378\" rx=\"147.57\" ry=\"18\"/>\n",
       "<text text-anchor=\"start\" x=\"797.79\" y=\"-374.3\" font-family=\"Times,serif\" font-size=\"14.00\">class &#39;javalang.tree.VariableDeclarator&#39;</text>\n",
       "</g>\n",
       "<!-- 4472117328&#45;&gt;4472118624 -->\n",
       "<g id=\"edge12\" class=\"edge\">\n",
       "<title>4472117328&#45;&gt;4472118624</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M825.98,-432.05C839.26,-422.94 855.82,-411.58 870.2,-401.71\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"872.56,-404.33 878.83,-395.79 868.6,-398.56 872.56,-404.33\"/>\n",
       "</g>\n",
       "<!-- 4472050448 -->\n",
       "<g id=\"node18\" class=\"node\">\n",
       "<title>4472050448</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1227.29\" cy=\"-378\" rx=\"139.98\" ry=\"18\"/>\n",
       "<text text-anchor=\"start\" x=\"1127.79\" y=\"-374.3\" font-family=\"Times,serif\" font-size=\"14.00\">class &#39;javalang.tree.BinaryOperation&#39;</text>\n",
       "</g>\n",
       "<!-- 4472336688&#45;&gt;4472050448 -->\n",
       "<g id=\"edge17\" class=\"edge\">\n",
       "<title>4472336688&#45;&gt;4472050448</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1227.29,-431.7C1227.29,-423.98 1227.29,-414.71 1227.29,-406.11\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1230.79,-406.1 1227.29,-396.1 1223.79,-406.1 1230.79,-406.1\"/>\n",
       "</g>\n",
       "<!-- 4472336544 -->\n",
       "<g id=\"node19\" class=\"node\">\n",
       "<title>4472336544</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1742.29\" cy=\"-378\" rx=\"137.58\" ry=\"18\"/>\n",
       "<text text-anchor=\"start\" x=\"1644.29\" y=\"-374.3\" font-family=\"Times,serif\" font-size=\"14.00\">class &#39;javalang.tree.BlockStatement&#39;</text>\n",
       "</g>\n",
       "<!-- 4472336688&#45;&gt;4472336544 -->\n",
       "<g id=\"edge18\" class=\"edge\">\n",
       "<title>4472336688&#45;&gt;4472336544</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1319.36,-436.49C1409.67,-424.21 1547.04,-405.54 1640.54,-392.83\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1641.14,-396.28 1650.58,-391.47 1640.2,-389.34 1641.14,-396.28\"/>\n",
       "</g>\n",
       "<!-- 4472337024 -->\n",
       "<g id=\"node52\" class=\"node\">\n",
       "<title>4472337024</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"2238.29\" cy=\"-378\" rx=\"147.57\" ry=\"18\"/>\n",
       "<text text-anchor=\"start\" x=\"2132.79\" y=\"-374.3\" font-family=\"Times,serif\" font-size=\"14.00\">class &#39;javalang.tree.MemberReference&#39;</text>\n",
       "</g>\n",
       "<!-- 4472336880&#45;&gt;4472337024 -->\n",
       "<g id=\"edge51\" class=\"edge\">\n",
       "<title>4472336880&#45;&gt;4472337024</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1954.83,-434.58C2012.74,-423.23 2093.5,-407.39 2154.02,-395.52\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"2154.79,-398.94 2163.93,-393.58 2153.44,-392.07 2154.79,-398.94\"/>\n",
       "</g>\n",
       "<!-- 4 -->\n",
       "<g id=\"node11\" class=\"node\">\n",
       "<title>4</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"293.29\" cy=\"-306\" rx=\"27\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"293.29\" y=\"-302.3\" font-family=\"Times,serif\" font-size=\"14.00\">int</text>\n",
       "</g>\n",
       "<!-- 4472115840&#45;&gt;4 -->\n",
       "<g id=\"edge10\" class=\"edge\">\n",
       "<title>4472115840&#45;&gt;4</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M293.29,-359.7C293.29,-351.98 293.29,-342.71 293.29,-334.11\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"296.79,-334.1 293.29,-324.1 289.79,-334.1 296.79,-334.1\"/>\n",
       "</g>\n",
       "<!-- 5 -->\n",
       "<g id=\"node14\" class=\"node\">\n",
       "<title>5</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"619.29\" cy=\"-306\" rx=\"27\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"619.29\" y=\"-302.3\" font-family=\"Times,serif\" font-size=\"14.00\">int</text>\n",
       "</g>\n",
       "<!-- 4472117712&#45;&gt;5 -->\n",
       "<g id=\"edge13\" class=\"edge\">\n",
       "<title>4472117712&#45;&gt;5</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M619.29,-359.7C619.29,-351.98 619.29,-342.71 619.29,-334.11\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"622.79,-334.1 619.29,-324.1 615.79,-334.1 622.79,-334.1\"/>\n",
       "</g>\n",
       "<!-- 6 -->\n",
       "<g id=\"node15\" class=\"node\">\n",
       "<title>6</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"734.29\" cy=\"-306\" rx=\"27\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"734.29\" y=\"-302.3\" font-family=\"Times,serif\" font-size=\"14.00\">y</text>\n",
       "</g>\n",
       "<!-- 4472118624&#45;&gt;6 -->\n",
       "<g id=\"edge14\" class=\"edge\">\n",
       "<title>4472118624&#45;&gt;6</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M860.48,-360.72C834.28,-350.56 800.15,-336.99 770.29,-324 768.48,-323.21 766.63,-322.39 764.77,-321.55\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"766.06,-318.29 755.51,-317.29 763.13,-324.65 766.06,-318.29\"/>\n",
       "</g>\n",
       "<!-- 4472118576 -->\n",
       "<g id=\"node16\" class=\"node\">\n",
       "<title>4472118576</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"884.29\" cy=\"-306\" rx=\"104.78\" ry=\"18\"/>\n",
       "<text text-anchor=\"start\" x=\"811.79\" y=\"-302.3\" font-family=\"Times,serif\" font-size=\"14.00\">class &#39;javalang.tree.Literal&#39;</text>\n",
       "</g>\n",
       "<!-- 4472118624&#45;&gt;4472118576 -->\n",
       "<g id=\"edge15\" class=\"edge\">\n",
       "<title>4472118624&#45;&gt;4472118576</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M898.59,-359.7C896.48,-351.9 893.93,-342.51 891.57,-333.83\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"894.93,-332.84 888.93,-324.1 888.17,-334.67 894.93,-332.84\"/>\n",
       "</g>\n",
       "<!-- 7 -->\n",
       "<g id=\"node17\" class=\"node\">\n",
       "<title>7</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"884.29\" cy=\"-234\" rx=\"27\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"884.29\" y=\"-230.3\" font-family=\"Times,serif\" font-size=\"14.00\">0</text>\n",
       "</g>\n",
       "<!-- 4472118576&#45;&gt;7 -->\n",
       "<g id=\"edge16\" class=\"edge\">\n",
       "<title>4472118576&#45;&gt;7</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M884.29,-287.7C884.29,-279.98 884.29,-270.71 884.29,-262.11\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"887.79,-262.1 884.29,-252.1 880.79,-262.1 887.79,-262.1\"/>\n",
       "</g>\n",
       "<!-- 8 -->\n",
       "<g id=\"node20\" class=\"node\">\n",
       "<title>8</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1034.29\" cy=\"-306\" rx=\"27\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"1034.29\" y=\"-302.3\" font-family=\"Times,serif\" font-size=\"14.00\">&gt;=</text>\n",
       "</g>\n",
       "<!-- 4472050448&#45;&gt;8 -->\n",
       "<g id=\"edge19\" class=\"edge\">\n",
       "<title>4472050448&#45;&gt;8</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1175.48,-361.17C1144.61,-351.33 1104.84,-337.94 1070.29,-324 1068.46,-323.26 1066.6,-322.48 1064.72,-321.67\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1065.98,-318.4 1055.43,-317.48 1063.11,-324.78 1065.98,-318.4\"/>\n",
       "</g>\n",
       "<!-- 4472117376 -->\n",
       "<g id=\"node21\" class=\"node\">\n",
       "<title>4472117376</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1227.29\" cy=\"-306\" rx=\"147.57\" ry=\"18\"/>\n",
       "<text text-anchor=\"start\" x=\"1121.79\" y=\"-302.3\" font-family=\"Times,serif\" font-size=\"14.00\">class &#39;javalang.tree.MemberReference&#39;</text>\n",
       "</g>\n",
       "<!-- 4472050448&#45;&gt;4472117376 -->\n",
       "<g id=\"edge20\" class=\"edge\">\n",
       "<title>4472050448&#45;&gt;4472117376</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1227.29,-359.7C1227.29,-351.98 1227.29,-342.71 1227.29,-334.11\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1230.79,-334.1 1227.29,-324.1 1223.79,-334.1 1230.79,-334.1\"/>\n",
       "</g>\n",
       "<!-- 4472117616 -->\n",
       "<g id=\"node22\" class=\"node\">\n",
       "<title>4472117616</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1497.29\" cy=\"-306\" rx=\"104.78\" ry=\"18\"/>\n",
       "<text text-anchor=\"start\" x=\"1424.79\" y=\"-302.3\" font-family=\"Times,serif\" font-size=\"14.00\">class &#39;javalang.tree.Literal&#39;</text>\n",
       "</g>\n",
       "<!-- 4472050448&#45;&gt;4472117616 -->\n",
       "<g id=\"edge21\" class=\"edge\">\n",
       "<title>4472050448&#45;&gt;4472117616</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1286.54,-361.64C1329.55,-350.49 1387.72,-335.41 1432.05,-323.91\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1433.06,-327.27 1441.86,-321.37 1431.3,-320.49 1433.06,-327.27\"/>\n",
       "</g>\n",
       "<!-- 4472051216 -->\n",
       "<g id=\"node26\" class=\"node\">\n",
       "<title>4472051216</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1742.29\" cy=\"-306\" rx=\"122.38\" ry=\"18\"/>\n",
       "<text text-anchor=\"start\" x=\"1656.29\" y=\"-302.3\" font-family=\"Times,serif\" font-size=\"14.00\">class &#39;javalang.tree.IfStatement&#39;</text>\n",
       "</g>\n",
       "<!-- 4472336544&#45;&gt;4472051216 -->\n",
       "<g id=\"edge25\" class=\"edge\">\n",
       "<title>4472336544&#45;&gt;4472051216</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1742.29,-359.7C1742.29,-351.98 1742.29,-342.71 1742.29,-334.11\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1745.79,-334.1 1742.29,-324.1 1738.79,-334.1 1745.79,-334.1\"/>\n",
       "</g>\n",
       "<!-- 4472336832 -->\n",
       "<g id=\"node27\" class=\"node\">\n",
       "<title>4472336832</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"2038.29\" cy=\"-306\" rx=\"155.17\" ry=\"18\"/>\n",
       "<text text-anchor=\"start\" x=\"1926.79\" y=\"-302.3\" font-family=\"Times,serif\" font-size=\"14.00\">class &#39;javalang.tree.StatementExpression&#39;</text>\n",
       "</g>\n",
       "<!-- 4472336544&#45;&gt;4472336832 -->\n",
       "<g id=\"edge26\" class=\"edge\">\n",
       "<title>4472336544&#45;&gt;4472336832</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1805.79,-361.98C1852.04,-351.05 1914.84,-336.19 1963.51,-324.68\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1964.44,-328.06 1973.36,-322.35 1962.82,-321.25 1964.44,-328.06\"/>\n",
       "</g>\n",
       "<!-- 9 -->\n",
       "<g id=\"node23\" class=\"node\">\n",
       "<title>9</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1191.29\" cy=\"-234\" rx=\"27\" ry=\"18\"/>\n",
       "</g>\n",
       "<!-- 4472117376&#45;&gt;9 -->\n",
       "<g id=\"edge22\" class=\"edge\">\n",
       "<title>4472117376&#45;&gt;9</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1218.39,-287.7C1214.13,-279.4 1208.94,-269.31 1204.24,-260.19\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1207.28,-258.44 1199.59,-251.14 1201.05,-261.64 1207.28,-258.44\"/>\n",
       "</g>\n",
       "<!-- 10 -->\n",
       "<g id=\"node24\" class=\"node\">\n",
       "<title>10</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1263.29\" cy=\"-234\" rx=\"27\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"1263.29\" y=\"-230.3\" font-family=\"Times,serif\" font-size=\"14.00\">x</text>\n",
       "</g>\n",
       "<!-- 4472117376&#45;&gt;10 -->\n",
       "<g id=\"edge23\" class=\"edge\">\n",
       "<title>4472117376&#45;&gt;10</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1236.19,-287.7C1240.45,-279.4 1245.64,-269.31 1250.34,-260.19\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1253.53,-261.64 1254.99,-251.14 1247.3,-258.44 1253.53,-261.64\"/>\n",
       "</g>\n",
       "<!-- 11 -->\n",
       "<g id=\"node25\" class=\"node\">\n",
       "<title>11</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1434.29\" cy=\"-234\" rx=\"27\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"1434.29\" y=\"-230.3\" font-family=\"Times,serif\" font-size=\"14.00\">0</text>\n",
       "</g>\n",
       "<!-- 4472117616&#45;&gt;11 -->\n",
       "<g id=\"edge24\" class=\"edge\">\n",
       "<title>4472117616&#45;&gt;11</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1482.04,-288.05C1473.76,-278.86 1463.43,-267.38 1454.49,-257.45\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1456.98,-254.98 1447.69,-249.89 1451.78,-259.66 1456.98,-254.98\"/>\n",
       "</g>\n",
       "<!-- 4472051120 -->\n",
       "<g id=\"node28\" class=\"node\">\n",
       "<title>4472051120</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1619.29\" cy=\"-234\" rx=\"139.98\" ry=\"18\"/>\n",
       "<text text-anchor=\"start\" x=\"1519.79\" y=\"-230.3\" font-family=\"Times,serif\" font-size=\"14.00\">class &#39;javalang.tree.BinaryOperation&#39;</text>\n",
       "</g>\n",
       "<!-- 4472051216&#45;&gt;4472051120 -->\n",
       "<g id=\"edge27\" class=\"edge\">\n",
       "<title>4472051216&#45;&gt;4472051120</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1713.14,-288.41C1696.56,-278.98 1675.61,-267.05 1657.7,-256.86\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1659.08,-253.62 1648.66,-251.71 1655.62,-259.7 1659.08,-253.62\"/>\n",
       "</g>\n",
       "<!-- 4472051168 -->\n",
       "<g id=\"node29\" class=\"node\">\n",
       "<title>4472051168</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1932.29\" cy=\"-234\" rx=\"155.17\" ry=\"18\"/>\n",
       "<text text-anchor=\"start\" x=\"1820.79\" y=\"-230.3\" font-family=\"Times,serif\" font-size=\"14.00\">class &#39;javalang.tree.StatementExpression&#39;</text>\n",
       "</g>\n",
       "<!-- 4472051216&#45;&gt;4472051168 -->\n",
       "<g id=\"edge28\" class=\"edge\">\n",
       "<title>4472051216&#45;&gt;4472051168</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1785.4,-289.12C1812.95,-278.97 1848.88,-265.73 1878.32,-254.88\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1879.85,-258.05 1888.03,-251.31 1877.43,-251.48 1879.85,-258.05\"/>\n",
       "</g>\n",
       "<!-- 4472336784 -->\n",
       "<g id=\"node48\" class=\"node\">\n",
       "<title>4472336784</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"2253.29\" cy=\"-234\" rx=\"147.57\" ry=\"18\"/>\n",
       "<text text-anchor=\"start\" x=\"2147.79\" y=\"-230.3\" font-family=\"Times,serif\" font-size=\"14.00\">class &#39;javalang.tree.MemberReference&#39;</text>\n",
       "</g>\n",
       "<!-- 4472336832&#45;&gt;4472336784 -->\n",
       "<g id=\"edge47\" class=\"edge\">\n",
       "<title>4472336832&#45;&gt;4472336784</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M2087.88,-288.85C2119.6,-278.53 2160.9,-265.08 2194.3,-254.21\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"2195.52,-257.49 2203.94,-251.07 2193.35,-250.83 2195.52,-257.49\"/>\n",
       "</g>\n",
       "<!-- 12 -->\n",
       "<g id=\"node30\" class=\"node\">\n",
       "<title>12</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1218.29\" cy=\"-162\" rx=\"27\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"1218.29\" y=\"-158.3\" font-family=\"Times,serif\" font-size=\"14.00\">==</text>\n",
       "</g>\n",
       "<!-- 4472051120&#45;&gt;12 -->\n",
       "<g id=\"edge29\" class=\"edge\">\n",
       "<title>4472051120&#45;&gt;12</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1517.17,-221.6C1426.79,-210.89 1302.03,-194.39 1254.29,-180 1252.35,-179.42 1250.39,-178.74 1248.43,-178.01\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1249.39,-174.62 1238.81,-173.97 1246.68,-181.07 1249.39,-174.62\"/>\n",
       "</g>\n",
       "<!-- 4472051840 -->\n",
       "<g id=\"node31\" class=\"node\">\n",
       "<title>4472051840</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1403.29\" cy=\"-162\" rx=\"139.98\" ry=\"18\"/>\n",
       "<text text-anchor=\"start\" x=\"1303.79\" y=\"-158.3\" font-family=\"Times,serif\" font-size=\"14.00\">class &#39;javalang.tree.BinaryOperation&#39;</text>\n",
       "</g>\n",
       "<!-- 4472051120&#45;&gt;4472051840 -->\n",
       "<g id=\"edge30\" class=\"edge\">\n",
       "<title>4472051120&#45;&gt;4472051840</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1570.28,-217.12C1538.22,-206.73 1496.15,-193.09 1462.26,-182.11\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1463.07,-178.7 1452.48,-178.94 1460.92,-185.35 1463.07,-178.7\"/>\n",
       "</g>\n",
       "<!-- 4472050976 -->\n",
       "<g id=\"node32\" class=\"node\">\n",
       "<title>4472050976</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1665.29\" cy=\"-162\" rx=\"104.78\" ry=\"18\"/>\n",
       "<text text-anchor=\"start\" x=\"1592.79\" y=\"-158.3\" font-family=\"Times,serif\" font-size=\"14.00\">class &#39;javalang.tree.Literal&#39;</text>\n",
       "</g>\n",
       "<!-- 4472051120&#45;&gt;4472050976 -->\n",
       "<g id=\"edge31\" class=\"edge\">\n",
       "<title>4472051120&#45;&gt;4472050976</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1630.42,-216.05C1635.82,-207.84 1642.43,-197.79 1648.43,-188.66\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1651.37,-190.55 1653.94,-180.28 1645.52,-186.71 1651.37,-190.55\"/>\n",
       "</g>\n",
       "<!-- 4472050592 -->\n",
       "<g id=\"node40\" class=\"node\">\n",
       "<title>4472050592</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1944.29\" cy=\"-162\" rx=\"123.48\" ry=\"18\"/>\n",
       "<text text-anchor=\"start\" x=\"1857.29\" y=\"-158.3\" font-family=\"Times,serif\" font-size=\"14.00\">class &#39;javalang.tree.Assignment&#39;</text>\n",
       "</g>\n",
       "<!-- 4472051168&#45;&gt;4472050592 -->\n",
       "<g id=\"edge39\" class=\"edge\">\n",
       "<title>4472051168&#45;&gt;4472050592</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1935.26,-215.7C1936.58,-207.98 1938.17,-198.71 1939.64,-190.11\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1943.12,-190.55 1941.36,-180.1 1936.22,-189.37 1943.12,-190.55\"/>\n",
       "</g>\n",
       "<!-- 13 -->\n",
       "<g id=\"node33\" class=\"node\">\n",
       "<title>13</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1052.29\" cy=\"-90\" rx=\"27\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"1052.29\" y=\"-86.3\" font-family=\"Times,serif\" font-size=\"14.00\">%</text>\n",
       "</g>\n",
       "<!-- 4472051840&#45;&gt;13 -->\n",
       "<g id=\"edge32\" class=\"edge\">\n",
       "<title>4472051840&#45;&gt;13</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1311.38,-148.39C1233.72,-137.2 1128.84,-120.72 1088.29,-108 1086.36,-107.39 1084.4,-106.71 1082.45,-105.96\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1083.41,-102.57 1072.84,-101.89 1080.68,-109.01 1083.41,-102.57\"/>\n",
       "</g>\n",
       "<!-- 4472049920 -->\n",
       "<g id=\"node34\" class=\"node\">\n",
       "<title>4472049920</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1245.29\" cy=\"-90\" rx=\"147.57\" ry=\"18\"/>\n",
       "<text text-anchor=\"start\" x=\"1139.79\" y=\"-86.3\" font-family=\"Times,serif\" font-size=\"14.00\">class &#39;javalang.tree.MemberReference&#39;</text>\n",
       "</g>\n",
       "<!-- 4472051840&#45;&gt;4472049920 -->\n",
       "<g id=\"edge33\" class=\"edge\">\n",
       "<title>4472051840&#45;&gt;4472049920</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1366.25,-144.59C1343.94,-134.71 1315.35,-122.04 1291.57,-111.5\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1292.94,-108.28 1282.37,-107.43 1290.1,-114.68 1292.94,-108.28\"/>\n",
       "</g>\n",
       "<!-- 4472050400 -->\n",
       "<g id=\"node35\" class=\"node\">\n",
       "<title>4472050400</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1515.29\" cy=\"-90\" rx=\"104.78\" ry=\"18\"/>\n",
       "<text text-anchor=\"start\" x=\"1442.79\" y=\"-86.3\" font-family=\"Times,serif\" font-size=\"14.00\">class &#39;javalang.tree.Literal&#39;</text>\n",
       "</g>\n",
       "<!-- 4472051840&#45;&gt;4472050400 -->\n",
       "<g id=\"edge34\" class=\"edge\">\n",
       "<title>4472051840&#45;&gt;4472050400</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1430.12,-144.23C1445.08,-134.88 1463.89,-123.12 1480.03,-113.04\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1482.06,-115.9 1488.68,-107.63 1478.35,-109.96 1482.06,-115.9\"/>\n",
       "</g>\n",
       "<!-- 17 -->\n",
       "<g id=\"node39\" class=\"node\">\n",
       "<title>17</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1665.29\" cy=\"-90\" rx=\"27\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"1665.29\" y=\"-86.3\" font-family=\"Times,serif\" font-size=\"14.00\">0</text>\n",
       "</g>\n",
       "<!-- 4472050976&#45;&gt;17 -->\n",
       "<g id=\"edge38\" class=\"edge\">\n",
       "<title>4472050976&#45;&gt;17</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1665.29,-143.7C1665.29,-135.98 1665.29,-126.71 1665.29,-118.11\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1668.79,-118.1 1665.29,-108.1 1661.79,-118.1 1668.79,-118.1\"/>\n",
       "</g>\n",
       "<!-- 14 -->\n",
       "<g id=\"node36\" class=\"node\">\n",
       "<title>14</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1209.29\" cy=\"-18\" rx=\"27\" ry=\"18\"/>\n",
       "</g>\n",
       "<!-- 4472049920&#45;&gt;14 -->\n",
       "<g id=\"edge35\" class=\"edge\">\n",
       "<title>4472049920&#45;&gt;14</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1236.39,-71.7C1232.13,-63.4 1226.94,-53.31 1222.24,-44.19\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1225.28,-42.44 1217.59,-35.14 1219.05,-45.64 1225.28,-42.44\"/>\n",
       "</g>\n",
       "<!-- 15 -->\n",
       "<g id=\"node37\" class=\"node\">\n",
       "<title>15</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1281.29\" cy=\"-18\" rx=\"27\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"1281.29\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">x</text>\n",
       "</g>\n",
       "<!-- 4472049920&#45;&gt;15 -->\n",
       "<g id=\"edge36\" class=\"edge\">\n",
       "<title>4472049920&#45;&gt;15</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1254.19,-71.7C1258.45,-63.4 1263.64,-53.31 1268.34,-44.19\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1271.53,-45.64 1272.99,-35.14 1265.3,-42.44 1271.53,-45.64\"/>\n",
       "</g>\n",
       "<!-- 16 -->\n",
       "<g id=\"node38\" class=\"node\">\n",
       "<title>16</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1515.29\" cy=\"-18\" rx=\"27\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"1515.29\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">2</text>\n",
       "</g>\n",
       "<!-- 4472050400&#45;&gt;16 -->\n",
       "<g id=\"edge37\" class=\"edge\">\n",
       "<title>4472050400&#45;&gt;16</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1515.29,-71.7C1515.29,-63.98 1515.29,-54.71 1515.29,-46.11\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1518.79,-46.1 1515.29,-36.1 1511.79,-46.1 1518.79,-46.1\"/>\n",
       "</g>\n",
       "<!-- 4472050304 -->\n",
       "<g id=\"node41\" class=\"node\">\n",
       "<title>4472050304</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1858.29\" cy=\"-90\" rx=\"147.57\" ry=\"18\"/>\n",
       "<text text-anchor=\"start\" x=\"1752.79\" y=\"-86.3\" font-family=\"Times,serif\" font-size=\"14.00\">class &#39;javalang.tree.MemberReference&#39;</text>\n",
       "</g>\n",
       "<!-- 4472050592&#45;&gt;4472050304 -->\n",
       "<g id=\"edge40\" class=\"edge\">\n",
       "<title>4472050592&#45;&gt;4472050304</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1923.47,-144.05C1912.57,-135.18 1899.04,-124.17 1887.14,-114.48\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1889.18,-111.63 1879.21,-108.03 1884.76,-117.06 1889.18,-111.63\"/>\n",
       "</g>\n",
       "<!-- 4472051888 -->\n",
       "<g id=\"node42\" class=\"node\">\n",
       "<title>4472051888</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"2171.29\" cy=\"-90\" rx=\"147.57\" ry=\"18\"/>\n",
       "<text text-anchor=\"start\" x=\"2065.79\" y=\"-86.3\" font-family=\"Times,serif\" font-size=\"14.00\">class &#39;javalang.tree.MemberReference&#39;</text>\n",
       "</g>\n",
       "<!-- 4472050592&#45;&gt;4472051888 -->\n",
       "<g id=\"edge41\" class=\"edge\">\n",
       "<title>4472050592&#45;&gt;4472051888</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1994.67,-145.46C2028.58,-135.01 2073.5,-121.15 2109.56,-110.04\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"2110.95,-113.27 2119.48,-106.98 2108.89,-106.58 2110.95,-113.27\"/>\n",
       "</g>\n",
       "<!-- 18 -->\n",
       "<g id=\"node43\" class=\"node\">\n",
       "<title>18</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"2364.29\" cy=\"-90\" rx=\"27\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"2364.29\" y=\"-86.3\" font-family=\"Times,serif\" font-size=\"14.00\">+=</text>\n",
       "</g>\n",
       "<!-- 4472050592&#45;&gt;18 -->\n",
       "<g id=\"edge42\" class=\"edge\">\n",
       "<title>4472050592&#45;&gt;18</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M2035.9,-149.84C2140.53,-136.85 2300.88,-116.16 2328.29,-108 2330.23,-107.42 2332.19,-106.76 2334.15,-106.03\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"2335.9,-109.09 2343.77,-102 2333.2,-102.63 2335.9,-109.09\"/>\n",
       "</g>\n",
       "<!-- 19 -->\n",
       "<g id=\"node44\" class=\"node\">\n",
       "<title>19</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1822.29\" cy=\"-18\" rx=\"27\" ry=\"18\"/>\n",
       "</g>\n",
       "<!-- 4472050304&#45;&gt;19 -->\n",
       "<g id=\"edge43\" class=\"edge\">\n",
       "<title>4472050304&#45;&gt;19</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1849.39,-71.7C1845.13,-63.4 1839.94,-53.31 1835.24,-44.19\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1838.28,-42.44 1830.59,-35.14 1832.05,-45.64 1838.28,-42.44\"/>\n",
       "</g>\n",
       "<!-- 20 -->\n",
       "<g id=\"node45\" class=\"node\">\n",
       "<title>20</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1894.29\" cy=\"-18\" rx=\"27\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"1894.29\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">y</text>\n",
       "</g>\n",
       "<!-- 4472050304&#45;&gt;20 -->\n",
       "<g id=\"edge44\" class=\"edge\">\n",
       "<title>4472050304&#45;&gt;20</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1867.19,-71.7C1871.45,-63.4 1876.64,-53.31 1881.34,-44.19\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1884.53,-45.64 1885.99,-35.14 1878.3,-42.44 1884.53,-45.64\"/>\n",
       "</g>\n",
       "<!-- 21 -->\n",
       "<g id=\"node46\" class=\"node\">\n",
       "<title>21</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"2135.29\" cy=\"-18\" rx=\"27\" ry=\"18\"/>\n",
       "</g>\n",
       "<!-- 4472051888&#45;&gt;21 -->\n",
       "<g id=\"edge45\" class=\"edge\">\n",
       "<title>4472051888&#45;&gt;21</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M2162.39,-71.7C2158.13,-63.4 2152.94,-53.31 2148.24,-44.19\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"2151.28,-42.44 2143.59,-35.14 2145.05,-45.64 2151.28,-42.44\"/>\n",
       "</g>\n",
       "<!-- 22 -->\n",
       "<g id=\"node47\" class=\"node\">\n",
       "<title>22</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"2207.29\" cy=\"-18\" rx=\"27\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"2207.29\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">x</text>\n",
       "</g>\n",
       "<!-- 4472051888&#45;&gt;22 -->\n",
       "<g id=\"edge46\" class=\"edge\">\n",
       "<title>4472051888&#45;&gt;22</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M2180.19,-71.7C2184.45,-63.4 2189.64,-53.31 2194.34,-44.19\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"2197.53,-45.64 2198.99,-35.14 2191.3,-42.44 2197.53,-45.64\"/>\n",
       "</g>\n",
       "<!-- 4472302576 -->\n",
       "<g id=\"node49\" class=\"node\">\n",
       "<title>4472302576</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"2153.29\" cy=\"-162\" rx=\"54.69\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"2153.29\" y=\"-158.3\" font-family=\"Times,serif\" font-size=\"14.00\">4472302576</text>\n",
       "</g>\n",
       "<!-- 4472336784&#45;&gt;4472302576 -->\n",
       "<g id=\"edge48\" class=\"edge\">\n",
       "<title>4472336784&#45;&gt;4472302576</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M2229.34,-216.23C2215.72,-206.7 2198.52,-194.66 2183.92,-184.44\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"2185.88,-181.54 2175.68,-178.67 2181.86,-187.27 2185.88,-181.54\"/>\n",
       "</g>\n",
       "<!-- 23 -->\n",
       "<g id=\"node50\" class=\"node\">\n",
       "<title>23</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"2253.29\" cy=\"-162\" rx=\"27\" ry=\"18\"/>\n",
       "</g>\n",
       "<!-- 4472336784&#45;&gt;23 -->\n",
       "<g id=\"edge49\" class=\"edge\">\n",
       "<title>4472336784&#45;&gt;23</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M2253.29,-215.7C2253.29,-207.98 2253.29,-198.71 2253.29,-190.11\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"2256.79,-190.1 2253.29,-180.1 2249.79,-190.1 2256.79,-190.1\"/>\n",
       "</g>\n",
       "<!-- 24 -->\n",
       "<g id=\"node51\" class=\"node\">\n",
       "<title>24</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"2325.29\" cy=\"-162\" rx=\"27\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"2325.29\" y=\"-158.3\" font-family=\"Times,serif\" font-size=\"14.00\">x</text>\n",
       "</g>\n",
       "<!-- 4472336784&#45;&gt;24 -->\n",
       "<g id=\"edge50\" class=\"edge\">\n",
       "<title>4472336784&#45;&gt;24</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M2270.72,-216.05C2280.5,-206.55 2292.79,-194.59 2303.24,-184.44\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"2305.72,-186.9 2310.45,-177.42 2300.84,-181.89 2305.72,-186.9\"/>\n",
       "</g>\n",
       "<!-- 25 -->\n",
       "<g id=\"node53\" class=\"node\">\n",
       "<title>25</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"2238.29\" cy=\"-306\" rx=\"27\" ry=\"18\"/>\n",
       "</g>\n",
       "<!-- 4472337024&#45;&gt;25 -->\n",
       "<g id=\"edge52\" class=\"edge\">\n",
       "<title>4472337024&#45;&gt;25</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M2238.29,-359.7C2238.29,-351.98 2238.29,-342.71 2238.29,-334.11\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"2241.79,-334.1 2238.29,-324.1 2234.79,-334.1 2241.79,-334.1\"/>\n",
       "</g>\n",
       "<!-- 26 -->\n",
       "<g id=\"node54\" class=\"node\">\n",
       "<title>26</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"2310.29\" cy=\"-306\" rx=\"27\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"2310.29\" y=\"-302.3\" font-family=\"Times,serif\" font-size=\"14.00\">y</text>\n",
       "</g>\n",
       "<!-- 4472337024&#45;&gt;26 -->\n",
       "<g id=\"edge53\" class=\"edge\">\n",
       "<title>4472337024&#45;&gt;26</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M2255.72,-360.05C2265.5,-350.55 2277.79,-338.59 2288.24,-328.44\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"2290.72,-330.9 2295.45,-321.42 2285.84,-325.89 2290.72,-330.9\"/>\n",
       "</g>\n",
       "</g>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<graphviz.graphs.Digraph at 0x10a92f940>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print_tree(tree)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78ddb534",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Control flow graph"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92c3aa8d",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "The Control Flow Graph (CFG) models the flow of control in a program. A CFG is a directed graph $G = (N, E)$ where nodes $N$ correspond to _statements_ in the program, and edges $E$ represent where the control can flow. AST nodes that are children of statements (e.g. parameters) are not included in the CFG. (You are familiar with the relevant types of nodes since these are the roots of the statement trees you're extracting as part of the first assignment). By definition, our CFG will always contains two dedicated nodes representing an entry and exit node."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca0c3a8b",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "For reasons of convenience, we'll be using an existing graph library that provides some of the infrastructure we need when applying algorithms on the graph. The library we'll be using is [NetworkX](https://networkx.org/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "12921cac",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "import networkx as nx"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e98070d4",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "One of the reasons we're using NetworkX is that it can render graphs directly in our Jupyter notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f82024d",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "The first thing we need to do is define a class that represents a node in the CFG. We will store the AST node representing the statement as an attribute of a CFG node (could also use the statement tree), and add some code to produce readable labels for the CFG nodes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d8c15a75",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "class CFGNode:\n",
    "    def __init__(self, ast_node):\n",
    "        self.ast_node = ast_node\n",
    "    \n",
    "    def __str__(self):\n",
    "        if type(self.ast_node) == str:\n",
    "            return self.ast_node\n",
    "        return str(self.ast_node.position.line - 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b1546b4",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "A control flow graph consists of a graph and dedicated start and end nodes. Since we are also going to create other graphs besides the CFG, which will only differ in the edges they contain, we define a general helper class `ProgramGraph` together with some wrapper functions to access the underlying NetworkX graph datastructure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8f41f764",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "class ProgramGraph:\n",
    "    def __init__(self, graph, start, end):\n",
    "        self.graph = graph\n",
    "        self.start = start\n",
    "        self.end   = end\n",
    "        \n",
    "    def nodes(self):\n",
    "        return self.graph.nodes()\n",
    "    \n",
    "    def edges(self):\n",
    "        return self.graph.edges()\n",
    "    \n",
    "    def successors(self, node):\n",
    "        return self.graph.successors(node)\n",
    "    \n",
    "    def predecessors(self, node):\n",
    "        return self.graph.predecessors(node)\n",
    "    \n",
    "    def is_branch(self, node):\n",
    "        return self.graph.out_degree(node) > 1\n",
    "    \n",
    "    def is_merge(self, node):\n",
    "        return self.graph.in_degree(node) > 1\n",
    "    \n",
    "    def plot(self):\n",
    "        pos = nx.nx_agraph.graphviz_layout(self.graph, prog=\"dot\")\n",
    "        nx.draw_networkx_nodes(self.graph, pos=pos, node_size=800, node_color='#FFFFFF', edgecolors='#000000')\n",
    "        nx.draw_networkx_edges(self.graph, pos=pos, connectionstyle=\"arc\", arrowsize=20)\n",
    "        nx.draw_networkx_labels(self.graph, pos=pos)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d425e663",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "The process of building a CFG consists of traversing the nodes of the AST, and creating CFG nodes whenever we encounter statements, as well as the relevant edges where we need them. We'll create a class `CFGBuilder` that helps us construct a CFG."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1098fbf2",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "class CFGBuilder:\n",
    "    def __init__(self, method_declaration):\n",
    "        # create graph\n",
    "        self.graph = nx.DiGraph()\n",
    "        \n",
    "        # create entry/end node\n",
    "        self.start = CFGNode(\"Start\")\n",
    "        self.end   = CFGNode(\"End\")\n",
    "        self.graph.add_node(self.start)\n",
    "        self.graph.add_node(self.end)\n",
    "        \n",
    "        # set entry as current target\n",
    "        self.frontier = [ self.start ]\n",
    "        \n",
    "        for node in method_declaration.body:\n",
    "            self.add_node(node)\n",
    "            \n",
    "        # Add edges from last nodes to end node\n",
    "        for parent in self.frontier:\n",
    "            self.graph.add_edge(parent, self.end)\n",
    "\n",
    "    def create_graph(self):\n",
    "        return ProgramGraph(self.graph, self.start, self.end)\n",
    "    \n",
    "    def add_node(self, node):\n",
    "        # TODO: implement\n",
    "        pass "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6968d35",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "We now just need to fill the method `add_node` with content. For sequential control flow, edges are added simply between successor statements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ad73094f",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "code = \"\"\"\n",
    "  public void foo(int x) {\n",
    "    int y = x + 1;\n",
    "    System.out.println(\"y = \" + y);\n",
    "  }\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfb5b959",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "Let's extend the `CFGBuilder` to handle this case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b27f4628",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "class CFGBuilder(CFGBuilder):\n",
    "\n",
    "    def add_node(self, node):\n",
    "        cfg_node = CFGNode(node)\n",
    "        self.graph.add_node(cfg_node)\n",
    "        for parent in self.frontier:\n",
    "            self.graph.add_edge(parent, cfg_node)\n",
    "        self.frontier = [ cfg_node ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "96fd9e30",
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tree = parse_method(code)\n",
    "cfg = CFGBuilder(tree).create_graph()\n",
    "cfg.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d45be09",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "Code isn't always sequential, so we need to consider different types of programming constructs that we might encounter; for example, we might encounter `BlockStatements`: A block statement is a single node in the AST, but it contains children as subnodes. If we don't treat blocks special, we will only get a single node in the CFG."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "dc1828e0",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "code = \"\"\"\n",
    "  public void foo(int x) {\n",
    "    {\n",
    "      int y = x + 1;\n",
    "      System.out.println(\"y = \" + y);\n",
    "    }\n",
    "  }\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "98e1bf29",
   "metadata": {
    "scrolled": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tree = parse_method(code)\n",
    "cfg = CFGBuilder(tree).create_graph()\n",
    "cfg.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66ac6a08",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "To fix this issue, we need to override our `add_node` method to handle block statements differently. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7a9cf1d7",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "class CFGBuilder(CFGBuilder):\n",
    "    def add_block_node(self, block_node):\n",
    "        for node in block_node.statements:\n",
    "            self.add_node(node)            \n",
    "\n",
    "    def add_statement_node(self, node):\n",
    "        cfg_node = CFGNode(node)\n",
    "        self.graph.add_node(cfg_node)\n",
    "        for parent in self.frontier:\n",
    "            self.graph.add_edge(parent, cfg_node)\n",
    "        self.frontier = [ cfg_node ]           \n",
    "\n",
    "    def add_node(self, node):\n",
    "        if type(node) == javalang.tree.StatementExpression or type(node) == javalang.tree.LocalVariableDeclaration:\n",
    "            self.add_statement_node(node)            \n",
    "        elif type(node) == javalang.tree.BlockStatement:\n",
    "            self.add_block_node(node)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a0e32d7a",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tree = parse_method(code)\n",
    "cfg = CFGBuilder(tree).create_graph()\n",
    "cfg.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36989c56",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "As you can imagine, we will need to extend the `add_node` function to delegate for all possible types of AST nodes. To avoid creating a large if-else construct, and because javalang does not provide a visitor interface, we'll use Python's single dispatch to create overloaded methods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b32a1a8c",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "from functools import singledispatchmethod"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a3896d2c",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "class CFGBuilder(CFGBuilder):\n",
    "    @singledispatchmethod\n",
    "    def add_node(self, node):\n",
    "        pass # Ignore, but could also throw NotImplementedError\n",
    "    \n",
    "    @add_node.register        \n",
    "    def add_block_node(self, block_node: javalang.tree.BlockStatement):\n",
    "        for node in block_node.statements:\n",
    "            self.add_node(node)            \n",
    "    \n",
    "    @add_node.register\n",
    "    def add_statement_node(self, node: javalang.tree.StatementExpression):\n",
    "        cfg_node = CFGNode(node)\n",
    "        self.graph.add_node(cfg_node)\n",
    "        for parent in self.frontier:\n",
    "            self.graph.add_edge(parent, cfg_node)\n",
    "        self.frontier = [ cfg_node ]\n",
    "        \n",
    "    @add_node.register\n",
    "    def add_declaration_node(self, node: javalang.tree.LocalVariableDeclaration):\n",
    "        self.add_statement_node(node)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "48e1a759",
   "metadata": {
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tree = parse_method(code)\n",
    "cfg = CFGBuilder(tree).create_graph()\n",
    "cfg.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6c0ee03",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "Next let's consider branching statements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "992a736d",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "code = \"\"\"\n",
    "  public void foo(int x) {\n",
    "    {\n",
    "      int y = x + 1;\n",
    "      if (y == 42) {\n",
    "          System.out.println(\"y = the answer\");\n",
    "      }\n",
    "    }\n",
    "  }\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bacc0139",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "Our CFG builder doesn't know about branching yet, so it will treat an if statement like an individual statement."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7c2d442e",
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tree = parse_method(code)\n",
    "cfg = CFGBuilder(tree).create_graph()\n",
    "cfg.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bd3114b",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "Thus, we need to add a method that handles if nodes. Whenever we add such a new method, the Python single dispatch requires us to also add the `add_node` method again, so expect to see this again and again (but still better adding new branches to a large if-construct, I think)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4f872fb5",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "class CFGBuilder(CFGBuilder):\n",
    "    \n",
    "    @singledispatchmethod\n",
    "    def add_node(cls, arg):\n",
    "        super().add_node(arg)\n",
    "    \n",
    "    @add_node.register\n",
    "    def add_if_node(self, if_node: javalang.tree.IfStatement):\n",
    "        cfg_node = CFGNode(if_node)\n",
    "        self.graph.add_node(cfg_node)\n",
    "\n",
    "        for parent in self.frontier:\n",
    "            self.graph.add_edge(parent, cfg_node)\n",
    "        self.frontier = [cfg_node]\n",
    "        self.add_node(if_node.then_statement)\n",
    "        \n",
    "        if if_node.else_statement:\n",
    "            current_frontier = self.frontier[:]\n",
    "            self.frontier = [cfg_node]\n",
    "            self.add_node(if_node.else_statement)\n",
    "            self.frontier.extend(current_frontier)\n",
    "        else:\n",
    "            self.frontier.append(cfg_node)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "85459640",
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAADnCAYAAAC9roUQAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAAAnv0lEQVR4nO3df1CTd54H8HfAEAMo6MoBgujiD0jk9uCAiDxua8tSWne7h7rjHoGlbjyZnerO3bSAunu6rXNTC3Tcu7lqO8xKe06E9uaGY6drdxiW2W1LXCmi3A0KakXU+CMrFZBgII/kuT865IxEDZAnIfh+zXSAkOd5vnE6b79+n+/z+SgkSQIREflGkL8HQET0NGHoEhH5EEOXiMiHGLpERD7E0CUi8qE5j/vlokWLpGXLlvloKEREs0N7e3ufJElR7n732NBdtmwZTp06Jc+oiIhmKYVCceVRv+PyAhGRDzF0iYh86LHLC74kiiIsFgvsdjtCQkIQHR0NpVLp72EREXmV30JXkiSYTCbU1dWhra0NZ8+eRWRkJFQqFUZHRzEwMIDVq1cjMzMTBQUFEAQBCoXCX8MlIvIKny8vSJIEo9GI1NRUGAwGJCQk4ODBg7BYLLh+/Tp6enpw/fp1WCwWHDx4EAkJCTAYDEhNTYXRaARrRRBRIFM8LsQyMjIkb+5eMJvNKCkpwa1bt1BRUYGcnBwEBT059x0OB5qbm1FeXo7Y2FhUV1cjPj7ea+MiIvImhULRLklShrvf+Wym29LSgvT0dKxduxatra3Izc31KHABICgoCLm5ufjyyy+RlZWF9PR0mEwmmUdMROR9PlnTbWlpwcaNG2E0GpGXlzfl8yiVSuzbtw9r1qxBfn4+GhoaIAiCF0dKRCQv2We6ZrMZmzdvnnbgPigvLw9GoxGbNm2C2Wz2yjmJiHxB1tCVJAnbt2/Hzp07vRa44/Ly8rBjxw6UlJTw5hoRBQxZQ/fYsWOwWCzYvXu3LOffs2cPbt68idraWlnOT0TkbbKFriRJqKqqQkVFhWwPOSiVSlRWVqKyspKzXSIKCLKFrslkwsjICHJycuS6BAAgJycHNpsNJ06ckPU6RETeIFvo1tXV4fnnn8e6desQERGBhQsXQhAEtLW14cMPP8S6deumdf7e3l4oFAo4HA4YDAYuMRBRQJBty9jJkydx4cIFVFdXY8uWLbDb7fjiiy+gUqmmfe779++7/CwIAkpLS6d9XiIiucky0xVFEWfPnkVwcDAKCgoQHBwMtVqNF154AUqlEj/72c/w5z//GeHh4YiMjAQAHD9+HGlpaZg/fz6WLFmCN954w3m+8VntkSNHkJCQgOeffx7PPPMMACAyMhIvvfQS/ud//geiKMrxcYiIvEaW0LVYLFiwYAGCg4Pxyiuv4Pe//z36+/sBABqNBu+//z7Wrl0Lq9WKgYEBAEBYWBiOHj2KgYEBHD9+HO+99x4aGhpczvvZZ5+hq6sLjY2N+PzzzwEAAwMDsFqtWLhwISwWixwfh4jIa2QJXbvdDrVajZaWFigUCmzfvh1RUVH44Q9/+MhgXL9+Pf76r/8aQUFB+M53voOCggJ89tlnLu954403EBYWBrVaPeF4lUoFu90ux8chIvIaWUI3JCQEo6Oj0Gg0+PDDD2E2m9HZ2YkbN27gn/7pn9we09raiueeew5RUVGIiIjA+++/j76+Ppf3LFmy5JHXHB0dRUhIiDc/BhGR18kSutHR0c5/9o9LTk7G1q1b0dnZ6bYurl6vxw9/+ENcu3YNg4OD+NnPfjZh7+2Dxz34/dDQEAYHBxEdHS3DpyEi8h5ZQlepVCIxMRG7d+921ka4du0a6urqkJWVhejoaJjNZpflgKGhISxcuBBz587Fl19++cQtYFFRUQgKCkJPTw86OjqQkpLCThNENOPJtmXsb//2b/H555/jv//7vzEwMIDIyEj84Ac/QFVVFebOnYvVq1cjJiYGQUFB6Ovrw+HDh/H6669j586dePbZZ7FlyxbnTTZ3QkND8ctf/hKCIMBqtWLDhg1yfRQiIq+RrYh5S0sLDAYDuru7Pa6bOxUOhwPJycn44IMPWOaRiGYEvxQxFwQBarUazc3Ncl0CANDc3IzQ0FBkZ2fLeh0iIm+QLXQVCgXKysqwa9cu2R5aEEUR5eXlKCsrY9NKIgoIspZ2LCwsRExMDN5++21Zzn/gwAEsXrwYer1elvMTEXmbrO16FAoFqqurkZ6eDp1O59VC5o2NjTh06BBOnz7NWS4RBQzZ2/XEx8ejvr4eRUVFaGxs9Mo5GxsbUVRUhPr6esTFxXnlnEREvuCTbsCCIKChoQHFxcXYv3//lNd4RVHEm2++ieLiYjalJKKA5LMW7IIgoL29HSdPnoROp0NTUxMcDodHxzocDjQ1NUGn06G1tRXt7e0MXCIKSD5pwT4uPj4ex48fR21tLUpLS2Gz2WAwGCAIAtLS0hAeHu58r9VqxZkzZ2AymVBTUwO1Wo3y8nLo9Xqu4RJRwJLt4YgnkSQJJ06cQG1tLU6dOoXOzk5ERERAFEUolUoMDg4iJSUFGRkZ0Ov1yM7OZtgSUUB43MMRPp3pPkihUEAQBOcygSiKsFgsWLJkCa5du4bo6GjWUiCiWcdvofswpVKJ+Ph4AHB+JSKabXx2I42IiBi6REQ+xdAlIvIhhi4RkQ8xdImIfIihS0TkQwxdIiIfYugSEfkQQ5eIyIf8/kSa2WzG/fv3XV7r7e11fr9o0SKXQjhERIHMr6F76dIlrFixAqGhoS7FbFJSUgAAo6Oj2LhxI/7zP//TX0MkIvIqvy4vLF++HBkZGbh37x6Gh4cxPDwMAM7vQ0JC8Oqrr/pziEREXuX3Nd2qqiqEhYW5/Z1Go8H69et9OyAiIhn5PXTXr18PjUYz4XW1Wo133nnHDyMiIpKP39Z0JUmCyWRCXV0dbDYblEolwsLCoFQqIYoi7t27h/LycmRmZqKgoACCILCIOREFPJ/PdCVJgtFoRGpqKgwGAxISEvD+++/jzp076O/vx1/+8hf09/fj66+/xsGDB5GQkACDwYDU1FQYjUY8rtMFEdFM59N2PWazGSUlJbh16xYqKiqQk5ODoKAn577D4UBzczPKy8sRGxuL6upqFjonohnrce16fDbTbWlpQXp6OtauXYvW1lbk5uZ6FLgAEBQUhNzcXHz55ZfIyspCeno6TCaTzCMmIvI+n6zptrS0YOPGjTAajcjLy5vyeZRKJfbt24c1a9YgPz8fDQ0NbMVORAFF9pmu2WzG5s2bpx24D8rLy4PRaMSmTZtgNpu9ck4iIl+QNXQlScL27duxc+dOrwXuuLy8POzYsQMlJSW8uUZEAUPW0D127BgsFgt2794ty/n37NmDmzdvora2VpbzExF5m2yhK0kSqqqqUFFRAaVSKcs1lEolKisrUVlZydkuEQUE2ULXZDJhZGQEOTk5kzru4sWLmDt3LoqKijx6f05ODmw2G06cODGVYRIR+ZRsoVtXVweDweDxtrBxO3bsQGZmpsfvDwoKgsFg4BIDEQUE2UK3ra1t0tu5PvroI0RGRk56diwIArz5EAcRkVxkCV1RFHH27FmkpqZ6fMzdu3exb98+HDx4cNLXS0tLQ2dnJ0RRnPSxRES+JEvoWiwWREZGTqrjw969e7Ft27YpPd4bHh6OiIgIWCyWSR9LRORLsjyRZrfboVKpPH5/R0cH/vCHP+DMmTNTvqZKpYLdbp/y8UREviBL6IaEhGB0dNTj9//pT39Cb28vEhISAABWqxVjY2M4d+4cTp8+7dE5RkdHERISMqXxEhH5iixVxkRRRGRkJCwWi0dLDPfu3cPdu3edP7/zzjvo7e3Fe++9h6ioqCcePzQ0hJiYGAwMDMi2J5iIyFOPqzImy0xXqVRi9erV6OjowLp16574/tDQUISGhjp/Dg8Px9y5cz0KXOCb5YmUlBQGLhHNeLJVGcvMzITJZPIodB/2xhtvTOr9JpMJGRlu/1IhIppRZNunW1BQgCNHjsDhcMh1CQDfFDivqamBXq+X9TpERN4gW+gKggC1Wo3m5ma5LgEAaG5uRmhoKLKzs2W9DhGRN8gWugqFAmVlZdi1a5dsDy2Ioojy8nKUlZWxaSURBQRZSzsWFhYiJiYGb7/9tiznP3DgABYvXsylBSIKGLK261EoFKiurkZ6ejp0Op1XC5k3Njbi0KFDOH36NGe5RBQwZG/XEx8fj/r6ehQVFaGxsdEr52xsbERRURHq6+sRFxfnlXMSEfmCT7oBC4KAhoYGFBcXY//+/VNe4xVFEW+++SaKi4vZlJKIApLPWrALgoD29nacPHkSOp0OTU1NHm8nczgcaGpqgk6nQ2trK9rb2xm4RBSQfNKCfVx8fDyOHz+O2tpalJaWwmazwWAwQBAEpKWluTwybLVacebMGZhMJtTU1ECtVqO8vBx6vZ5ruEQUsGSpveAJSZJw4sQJ1NbW4tSpU+js7ERERAREUYRSqcTg4CBSUlKQkZEBvV6P7Oxshi0RBQSf117whEKhgCAIzmUCURRhsViwZMkSXLt2DdHR0aylQESzjt9C92FKpdJZwHwqhcyJiAKBz26kERERQ5eIyKcYukREPsTQJSLyIYYuEZEPMXSJiHyIoUtE5EMMXSIiH2LoEhH5kF+fSJMkCf/7v/+L+/fvu7ze3t7u/D4+Ph7R0dG+HhoRkSz8GrqXL19Gamoq5s2bh6Cg/5905+TkAABsNhvy8/Px8ccf+2uIRERe5dflhcTERDzzzDMYGhrC4OAgBgcHAcD5/Zw5c/Daa6/5c4hERF7l9zXdqqoqhIaGuv1deno61qxZ4+MRERHJx++hq9PpkJExseykWq1GVVWVH0ZERCQfv63pSpIEk8mEuro69Pf3Q6lUIiwsDEqlEqIo4t69e/j5z3+OzMxMFBQUQBAEFjEnooDn85muJEkwGo1ITU2FwWBAQkICDh8+jDt37qC/vx9/+ctf0N/fj6+//hoHDx5EQkICDAYDUlNTYTQa8bhOF0REM51P2/WYzWaUlJTg1q1bqKioQE5OjsuuhUdxOBxobm5GeXk5YmNjUV1dzULnRDRjPa5dj89mui0tLUhPT8fatWvR2tqK3NxcjwIXAIKCgpCbm4svv/wSWVlZSE9Ph8lkknnERETe55M13ZaWFmzcuBFGoxF5eXlTPo9SqcS+ffuwZs0a5Ofno6Ghga3YiSigyD7TNZvN2Lx587QD90F5eXkwGo3YtGkTzGazV85JROQLsoauJEnYvn07du7c6bXAHZeXl4cdO3agpKSEN9eIKGDIGrrHjh2DxWLB7t27ZTn/nj17cPPmTdTW1spyfiIib5MtdCVJQlVVFSoqKqBUKmW5hlKpRGVlJSorKznbJaKAIFvomkwmjIyMOIvXPMn69esxd+5chIeHIzw8HElJSR4dl5OTA5vNhhMnTkxnuEREPiFb6NbV1cFgMHi8LQwA3n33XVitVlitVpw/f96jY4KCgmAwGLjEQEQBQbbQbWtr89l2LkEQ4M2HOIiI5CJL6IqiiLNnzyI1NXVSx+3ZsweLFi2CIAj405/+5PFxaWlp6OzshCiKkxsoEZGPyRK6FosFkZGRCA8P9/iYiooK9PT04Pr16ygpKcHLL7+MS5cueXRseHg4IiIiYLFYpjpkIiKfkCV07XY7VCrVpI5Zs2YN5s2bB5VKhVdeeQWCIODTTz/1+HiVSgW73T7ZoRIR+ZQsoRsSEoLR0dFpnUOhUExqG9jIyIhsW9OIiLxFltoL0dHRGBgYgNVq9WiJYWBgAK2trXj22WcxZ84cfPzxx/j888/xb//2bx5db2hoCF9//TU0Gg20Wi20Wq3ze41Gg29/+9sIDg6e7sciIpo2WUJXqVRi9erV6OjowLp16574flEU8c///M/o7u5GcHAwkpOT0dDQgFWrVnl0vY6ODqSlpaGxsRFdXV04d+4curq68N5776GrqwsWiwUrV650CWOtVosVK1YgJCRkuh+XiMhjslUZy8zMhMlk8ih0o6Ki0NbWNuVrmUwmZGRkIDIyEmvXrsXatWtdfj88PIzu7m5nIB87dgznzp3D1atXsWzZMpdZsVarRVJS0iP7thERTYdsRcxbWlpgMBjQ3d09qQckJsvhcCA5ORkffPDBpPcFj46O4uLFi84wHp8hX7x4EbGxsROWKTQaDSIiImT6JEQ0WzyuiLlsM11BEKBWq9Hc3Izc3Fy5LoPm5maEhoYiOzt70seqVCqkpKQgJSXF5fX79+/j8uXLzhD+4x//iEOHDqG7uxuRkZETZsYajQZRUVHe+khENIvJ2q7HaDTi4MGDaG1tlWVngSiK0Ol0KC0tRWFhodfP/zCHw4Fr1665rBuPz5DnzJnj9kbe4sWL2VCT6CnzuJmurKErSRK+//3vY+3atdi7d++Uz/Mo+/fvR2trK373u9/5NdgkSYLFYnEJ4vGvNpvNuTTxYCAvW7ZM1mUXIvIfv4Uu8E3niPT0dBw9etSrhcwbGxtRXFyM06dPIy4uzmvn9bY7d+5MCOKuri709fVh1apVE3ZULF++nPuNiQKcX0MX+GZ3QX5+vtda9jQ2NqKoqCige6QNDQ257KgY/2o2m5GYmDhh3XjVqlVQq9X+HjYRecDvoQt8E7ybNm3Cjh07sGfPninN5kRRxFtvvYXDhw+jvr4+YAP3cUZGRnDhwoUJM+NLly4hLi7O7Y6KefPm+XvYRPSAGRG6wDdLDSUlJbh58yYqKyuRk5Pj0bqmw+FAc3MzysvLERsbi+rqasTHx3ttXIFAFEX09PRMuIF3/vx5LFy40O2TeN/61rf8PWyip9KMCV3gm5tOtbW1qKyshM1mg8FggCAISEtLc3lk2Gq14syZMzCZTKipqYFarUZ5eTn0ej13AzzA4XDgypUrbndUzJ07d8IyhVarRUxMDP8MiWQ0o0J3nCRJOHHiBGpra3Hq1Cl0dnYiIiICoihCqVRicHAQKSkpyMjIgF6vR3Z2NoNiEiRJws2bN93uqLDb7RNmxVqtFgkJCdxRQeQFMzJ0HyaKIiwWC5YsWYJr164hOjqad/Fl0tfX53ZHRX9/P5KSkiYE8vLlyzFnjmzP0RDNOgERuuMmW9KRvOfu3bvo6uqaEMg3btzA8uXLJ6wbr1q1atJ1k4meBgxdmhabzYbz589PqFHR09ODhISECQ9+JCcnT6prCNFsw9AlWYiiiK+++mrCDbwLFy4gKirK7brxggUL/D1sItkxdMmnxsbG0Nvb63bdOCwszG3BoOjoaN4opVmDoUszgiRJuH79+oQgPnv2LCRJcjszXrJkCcOYAg5Dl2Y0SZJw+/ZttzPju3fvIjk5eUIgJyYmsgUTzVgMXQpYAwMDbndUWCwWrFixYsKDHytXrmQLJvI7hi7NOsPDw253VPT29rptwZScnMwWTOQzDF16aoyOjjp3VDw4M7548SJiYmLcFgyKjIz097BplmHo0lNvvAWTu3XjiIiIR7Zg4k08mgqGLtEjOBwOmM1mtzUqgoKC3LZgiouLYxjTYzF0iSZpvAWTu5nx8PCwSwum8a/Lli3jjgoC4KduwESBTKFQICYmBjExMXjuuedcfjfegmk8iP/4xz/i3LlzuH37ttsWTCtWrGDxJnLiTJfIS6xWK7q7uyfMjK9eveq2BVNSUhJbMM1SXF4g8qORkRFcvHhxwo6KS5cuYfHixW53VMyfP9/fww544+Vi7XY7QkJCfFoulqFLNAPdv38fly5dmrDXuLu7GwsWLJiwTKHRaLBo0SJ/D3vGkiQJJpMJdXV1aGtrw9mzZxEZGQmVSoXR0VEMDAxg9erVyMzMREFBAQRBkO2GKEOXKIA4HA5cvXrV7Y6KkJAQty2YYmNjn9odFZIk4dixY6iqqoLNZsO2bdsgCAJSU1MntADr6OiAyWTCkSNHoFarUVZWhsLCQq//2TF0iWaB8RZM7nZUjIyMuC0YtHTp0lndgmm82e2tW7dQUVExY5rdMnSJZrnxFkwPB/KdO3ce2YIp0HdUtLS0YPPmzdi5cyd27949pc8jiiIOHDiAQ4cOob6+HoIgeGVsDF2ip9Tdu3fd7qi4fv06EhMT3bZgmjt3rr+H/UQtLS3YuHEjjEYj8vLypn2+xsZGFBUVoaGhwSvBy9AlIhc2mw0XLlyYsKOip6cHS5YscduCad68ef4eNoBvlhTS09Nx9OhRrwTuuMbGRhQXF6O9vX3aSw0MXSLyyHgLpoeXKc6fP49Fixa53VGxcOFCWcbS398Pm82GxYsXO1+TJAkbNmxAdnY29u7d6/Vr7t+/HydPnsTx48endXONoUtE0zI2NoYrV6643VERGhrqtmBQTEzMtIJrz549+PWvf43q6moUFxcDAIxGIw4ePIjW1lZZ1qRFUYROp0NpaSkKCwunfB6GLhHJYrwF08NBfO7cOYyNjT2yBZMnOwy+973vobm5GaGhoXjxxRdRU1ODZ555Bu+88w5yc3Nl+0xNTU0oLS1FR0fHlP/SYOgSkc/dvn3b7cx4cHDwkS2Y5sz5/3IwcXFxuHHjBgBApVIhLCwMCxYswIULFzzeBvfRRx/hzTffxNWrVxETE4MPP/wQ3/3udx97jMPhQHJyMj744IMp31RjwRsi8rmoqCg8++yzePbZZ11eHxwcdAni6upqdHV14ebNm84WTKtWrcKtW7ecx4yOjgIASktLPQ7cpqYm7Nq1Cx9//DF0Oh1u3rzp0XFBQUEwGAyora312hayBzF0icinIiIikJWVhaysLJfX7927h/Pnz+PcuXP47LPPJvzTXqVSPXGW+qBf/epX2Ldvn/M6cXFxHh8rCAJKS0s9fv9kzN5HVYgooISGhiItLQ2FhYV44YUXJvS0s9lsSE1N9ehcY2NjOHXqFG7fvo0VK1YgPj4eO3fuhM1m8+j4tLQ0dHZ2QhTFyX6MJ2LoEtGMc/bsWQwPDzt/DgkJQVhYmEsthcexWCwQRRH/9V//hS+++AIdHR04c+YM/uVf/sWj48PDwxEREQGLxTKl8T8OQ5eIZpy2tjYEBwdDpVIhJSUF//iP/4iIiAiPjx+vU/zzn/8csbGxWLRoEV577TV8+umnHp9DpVLBbrdPeuxPwtAlohnnxz/+Md5991309vbi9OnTeOmllzAyMuLx8QsWLEB8fLzLuvBkt3+Njo4iJCRkUsd4gjfSiGhGsNlsOH/+PLq6unDhwgV0dXXhX//1X9HT04P4+Hj09/fDarV6vMTw05/+FP/+7/+OF198EUqlEr/+9a/xgx/8wKNjh4aGMDg4iOjo6Ol8JLcYukTkU0NDQ27LU5rNZixfvtz5iPGPfvQjlyI8Op0OHR0dWLdunUfX2bt3L/r6+pzHb9myBb/85S89OrajowMpKSmyPPXG0CUiWXz99dduH454uNykwWDwqNxkZmYmTCaTx6GrVCpx+PBhHD58eNJjN5lMyMhw+2zDtDF0iWjKHi6s/mC4jo6Oujx1lpubO63C6gUFBTAYDCgrK5O1MLvD4UBNTQ0++OADWc7P0CWiJ3qwhdDDs9eHWwht3rwZGo0Gixcv9mobHEEQoFar0dzcLGvthfF6D9nZ2bKcn6FLRE7jzTIfDtaHm2XqdDps3brVp80yFQoFysrKsGvXLqxfv162KmPl5eUoKyuTr2klC94QPX1GRkacOwTctYV3V8R8Mvtk5SJJEr7//e9j7dq1stXTbW1txe9+9zvZ6ulypks0i1mtVrfteq5evYrExERnuObn5+MXv/gFVq1aNeHx25lEoVCguroa6enp0Ol0Xu8ccejQIZw+fVrWzsoMXaJZ4M6dOy6NKcfD9fbt21i1apVzxlpcXAyNRoMVK1bIsvHfF+Lj41FfX4/8/HxZeqRNpjDOVDB0iQKEJEmwWCxudwoMDw+77BR47rnnoNVqsWzZMgQHB/t76F4nCAIaGhqwadMm7NixA3v27JlyN+C33noLhw8f9lpTyidh6BLNMJIk4dq1a253CigUCpe11r/7u7+DVqtFXFycrP8knokEQUB7eztKSkqg0+lQWVmJnJwcj7aTORwONDc3o7y8HLGxsV5pRukp3kgj8pOxsTH09PRMCNauri7Mnz/fbRPIqKiopy5cn0SSJNTW1qKyshI2mw0GgwGCICAtLc3lkWGr1YozZ87AZDKhpqYGarUa5eXl0Ov1Xv8zZbseIj8aHR3FxYsXJ+wUuHjxImJiYiYEq0ajQWRkpL+HHXAkScKJEydQW1uLU6dOobOzExEREVCpVBgdHcXg4CBSUlKQkZEBvV6P7Oxs+baFMXSJ5Dc8POzsfPBgwPb29mLp0qUuwarVapGUlISwsDB/D3vWEkURFosFdrsdISEhiI6OlmVvrzvcMkbkRQMDAy47Bca/3rp1CytXrnQGq16vh0ajwcqVK6FSqfw97KeOUqn02TrtZDB0idyQJAm3b99221r87t27zmUArVaLkpISaLVafPvb33bpZkvkDv8PoaeaJEm4fv36hGDt6urC2NiYy5LAhg0boNVqER8fL2vBFZrdGLr0VBgbG0Nvb6/bnQKhoaHOYP3Od76Dv//7v4dGo0F0dDR3CpDXMXRpVrHb7fjqq68mLAtcuHABUVFRziWBdevWYfv27dBoNFi4cKG/h01PEYYuBaTx1i4Pz1x7enqwZMkS57LAiy++iNdeew3Jycket3khkhNDl2a0u3fvut0pcOPGDWdrF41Ggy1btkCj0ThbsxDNVAxdmhH6+vrc7hTo7+9HcnKyc1nAYDBAq9UiMTHRZ3suibyJoUs+M97axd1OAbvd7tJ94IUXXoBGo5lyaxeimYqhS17ncDhw5coVt00JVSqVy1NZP/rRj6DRaBAbG8udAvRU8Gvojo6O4pNPPsH9+/ddXv/oo4+c32s0GvzN3/yNr4dGHhBFEZcuXZoQrOfPn8fChQudwZqVlYWf/vSnPm3tQjRT+bX2gtlsRkJCAkJDQ53/hBwaGsK8efMAfNNSpKCgAP/xH/8h2xjoycZbuzw8c7106RLi4uJclgW0Wi2Sk5Mxf/58fw+byG9mbO2F+Ph4bNy4Eb/97W8xNjbmfH1oaAgAoFar8Ytf/MJfw3vqWK1WtzsFrl27hsTERGewbtq0CRqNBklJSVCr1f4eNlFA8fua7ltvvYVPP/3UJXQBIDg4GC+++CKSkpL8NLLZa7y1y8M3tPr6+pCUlOScsRYXF0Or1WLFihXcKUDkJX4P3aSkJGzYsAENDQ1wOBzO15VKJQ4cOODHkQW28dYu7roP3Lt3z2VJ4Pnnn4dWq8XSpUtnZWsXopnEb6ErSRJMJhPq6upw6dIlBAcHY/78+VAqlRBFETabDT/5yU+QmZmJgoICCILAu9tuOBwOZ2uXh2evwcHBLmutT3NrF6KZwuehK0kSjh07hqqqKthsNmzbtg0FBQVITU2d0Fqjo6MDJpMJBoMBarUaZWVlKCwsfCoD4/79++jp6ZkQrN3d3YiIiHAGa0ZGBn7yk59Aq9UiKirK38Mmoof4dPeC2WxGSUkJbt26hYqKiik3kauurp6RxYm9Yby1y8Mz1/HWLg93H0hOTmZrF6IZZka062lpacHmzZuxc+dO7N69e8rtkg8cOIBDhw6hvr7eJ+2S5TI8PIzu7u4JM9crV65g2bJlLsE6vlOArV2IAoPfQ7elpQUbN26E0WhEXl7etM/X2NiIoqIin/Wpn47x1i4P7xSwWCxYtWrVhD2uK1euREhIiL+HTUTT4NfQNZvNSE9Px9GjR70SuOMaGxtRXFzs0371jzLe2sVdTYGhoSEkJydPmLmytQvR7OW30JUkCRs2bEB2djb27t075fM8yv79+3Hy5EkcP37ceXPNYrFgYGBAlv29kiTBbDa73SkgSZJLsI5/z9YuRE8fvz2RduzYMVgsFuzevVuW8+/Zswc6nQ61tbUoLCzEJ598Ar1ej5SUFPz5z3+e8nkfbO3y8E6BsLAwZ7CmpqaioKAAWq0Wf/VXf/VU7qogosmRbaYrSRJSU1PxzjvvIDc3d6rjm+CNN97AV199BaPRCABoamrC66+/jjVr1uDYsWOw2WxYsGAB7ty588Rzjbd2eXjmOt7a5eGdAhqNBgsWLPDaZyGi2ckvM12TyYSRkRHk5OQAAJYtWwaLxeLyxNPWrVvx7rvvTus6OTk5uHPnDo4ePQq73Q7gm9oNd+/edRZduXfvHs6fPz9hSeDy5ctISEhw6fb6+uuvs7ULEclGttCtq6uDwWBwWc/85JNP8L3vfc+r1wkKCsKrr76K/fv3O1+bM2cOduzYgf7+fpw7dw43b97EihUrnDPWLVu2OHcKsLULEfmSbKHb1taGgoKCJ77vww8/xG9+8xtkZWXhyJEjiIyMxOHDh/HSSy8BAC5fvoytW7fi9OnTyMrKcnuD7Lvf/S5UKhVGR0cBfPP0ls1mwz/8wz9Ao9Fg+fLl3ClARDOCLLfVRVHE2bNnkZqa6tH7W1tbkZSUhL6+PpSXl2Pbtm0YX2vW6/VIT09HX18f9u7d67a2blpaGmw2m/Pn+/fvIyEhAfn5+UhKSmLgEtGMIUsaWSwWREZGTlgXzc/PdwnAqqoqKJVKLF26FNu3bwcAvPLKK3j11VdhsVhgt9vR1taGP/zhD1CpVHjmmWfw8ssvT7heeHg4QkNDMTg46HxNzuLrRERTJctM1263Q6VSTXi9oaEBAwMDzv/GgzYmJsb5ntDQUADfFLy5ceMGFixY4PL469KlS91ec968ecjLy0NoaCjUajUuXLjgzY9EROQVssx0Q0JCnOur0xEbG4v+/n4MDw87g/fq1atu98OOjY3hN7/5DaKjo/HFF1/g8uXL074+EZG3yTLTjY6OxsDAAKxW67TOs3TpUmRkZOBXv/oV7HY7Wlpa8Mknn0x439DQEAYHBxEdHQ2lUonnn38e27Ztm9a1iYjkIEvoKpVKrF69Gh0dHS6vv/zyywgPD3f+t3Hjxieeq7a2Fq2trVi4cCHefPNNFBcXT3hPR0cHUlJS2FKGiGY82W7rZ2ZmwmQyYd26dQCA3t7eR75369atLj8/+JRcYmIivvjii8dey2QyISPD7cMfREQzimyVWAoKCnDkyBGXvmdycDgcqKmpgV6vl/U6RETeIFvoCoIAtVqN5uZmuS4BAGhubkZoaCiys7NlvQ4RkTfIFroKhQJlZWXYtWsXRFGU5RqiKKK8vBxlZWWs8EVEAUHWQq+FhYWIiYnB22+/Lcv5Dxw4gMWLF3NpgYgChqzPxyoUClRXVyM9PR06nc7rnSMOHTqE06dPc5ZLRAFD9pYG8fHxqK+vR1FRERobG71yzvEeafX19YiLi/PKOYmIfMEnfWQEQUBDQwOKi4uxf//+Ka/xiqLo3KsbCE0piYge5rPmXYIgoL29HSdPnoROp0NTU5PH28kcDgeampqg0+nQ2tqK9vZ2Bi4RBSSf1jyMj4/H8ePHUVtbi9LSUthsNhgMBgiCgLS0NJeqZFarFWfOnIHJZEJNTQ3UajXKy8uh1+u5hktEAUv2FuyPIkkSTpw4gdraWpw6dQqdnZ2IiIhwFiMfHBxESkoKMjIyoNfrkZ2dzbAlooDgt27Aj6NQKCAIgnOZQBRFZw3dkJAQZ/EaIqLZZMa0VFAqlYiPj/f3MIiIZOWzG2lERMTQJSLyqcfeSFMoFLcBXPHdcIiIZoWlkiRFufvFY0OXiIi8i8sLREQ+xNAlIvIhhi4RkQ8xdImIfIihS0TkQ/8HKdKGIweI6lQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tree = parse_method(code)\n",
    "cfg = CFGBuilder(tree).create_graph()\n",
    "cfg.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ef208b2",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "Let's also consider the case where there is an else-branch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6f45898d",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "code = \"\"\"\n",
    "  public void foo(int x) {\n",
    "    {\n",
    "      if (y == 42) {\n",
    "          System.out.println(\"y = the answer\");\n",
    "      } else {\n",
    "          y = 3;\n",
    "      }\n",
    "    }\n",
    "  }\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "bb59e473",
   "metadata": {
    "scrolled": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tree = parse_method(code)\n",
    "cfg = CFGBuilder(tree).create_graph()\n",
    "cfg.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "508c1890",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "Next up, while loops."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ef52aff2",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "code = \"\"\"\n",
    "  public void foo(int x) {\n",
    "     int y = 0;\n",
    "     while (y < x) {\n",
    "       System.out.println(\"Hallo\");\n",
    "       y += 1;\n",
    "     }\n",
    "  }\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "42cc2c4f",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "class CFGBuilder(CFGBuilder):\n",
    "\n",
    "    @singledispatchmethod\n",
    "    def add_node(cls, arg):\n",
    "        super().add_node(arg)\n",
    "    \n",
    "    @add_node.register\n",
    "    def add_while_node(self, while_node: javalang.tree.WhileStatement):\n",
    "        cfg_node = CFGNode(while_node)\n",
    "        self.graph.add_node(cfg_node)\n",
    "        for parent in self.frontier:\n",
    "            self.graph.add_edge(parent, cfg_node)\n",
    "        self.frontier = [cfg_node]\n",
    "        self.add_node(while_node.body)\n",
    "        for parent in self.frontier:\n",
    "            self.graph.add_edge(parent, cfg_node)\n",
    "        self.frontier = [cfg_node]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "9f9168cd",
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAADnCAYAAAC9roUQAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAAAtDUlEQVR4nO3de1zT570H8E+CAQIoQcGIIlpbLxRKk3Hnx6wrZUyrVu3WDWTYxstssXWbXMrLasWdHhQdo3XWlq3qeiK4tWVUaxUpp6sQ5Kq4A17a6bTES6gVEBBIIM/5o8ccUwED/H5J0O/79fJVTPJc8mqfTx+e3+/3PCLGGAghhFiH2NYdIISQBwmFLiGEWBGFLiGEWBGFLiGEWBGFLiGEWNGogd709PRkU6dOtVJXCCHk/lBbW3udMebV13sDhu7UqVNRU1MjTK8IIeQ+JRKJLvX3Hi0vEEKIFVHoEkKIFQ24vEAIsR8GgwE6nQ56vR6Ojo6Qy+WQSCS27hYZJApdQuwUYwwajQb5+fmorq5GQ0MDZDIZnJyc0N3djZaWFvj7+yMkJARxcXHgOA4ikcjW3Sb3QMsLhNgZxhjUajUUCgVUKhV8fX2RnZ0NnU6Hy5cv48KFC7h8+TJ0Oh2ys7Ph6+sLlUoFhUIBtVoN2k/FvokG+hcUHBzM6O4FQqxHq9Vi1apVuHbtGrZu3Yro6GiIxfeeGxmNRpSUlCA1NRXe3t7Izc2Fj4+PFXpM+iISiWoZY8F9vUczXULsRFlZGYKCghAREYHKykrExMRYFLgAIBaLERMTg6qqKoSHhyMoKAgajUbgHpOhoDVdQuxAWVkZFi9eDLVajdjY2CHXI5FIsHHjRoSFhWHRokUoLCwEx3E89pQMF810CbExrVaLZ599dtiBe6fY2Fio1WosWbIEWq2WlzoJPyh0CbEhxhhWrlyJNWvW8Ba4t8XGxiIpKQmrVq2ii2t2hEKXEBvat28fdDodXn31VUHqT09Px9WrV5GXlydI/WTwKHQJsRHGGLZt24atW7cK9pCDRCJBVlYWsrKyaLZrJyh0CbERjUaDrq4uREdHC9pOdHQ0Ojs7UV5eLmg7xDIUuoTYSH5+Pp588klERUXB3d0dY8eOBcdxqK6uxt69exEVFTWs+i9evAiRSASj0QiVSkVLDHaCbhkjxEYqKirw5ZdfIjc3F8899xz0ej1KS0vh5OQ07Lp7enrM/s5xHJKTk4ddLxk+mukSYgMGgwENDQ1wcHBAXFwcHBwcIJVK8eMf/xgSiQSrV6/G8ePH4ebmBplMBgA4dOgQlEolxowZg8mTJ2PTpk2m+m7Pat977z34+vriySefxOzZswEAMpkMc+fOxalTp2AwGGzwbcmdKHQJsQGdTgcPDw84ODhg2bJlOHz4MJqbmwEAfn5+eOeddxAREYH29na0tLQAAFxdXfH++++jpaUFhw4dwq5du1BYWGhW7xdffIEzZ86gqKgIx44dAwC0tLSgvb0dY8eOhU6ns+bXJH2g0CXEBvR6PaRSKcrKyiASibBy5Up4eXlh4cKF/QbjnDlz8Nhjj0EsFiMwMBBxcXH44osvzD6zadMmuLq6QiqV3lXeyckJer1ekO9DLEehS4gNODo6oru7G35+fti7dy+0Wi3q6+tx5coV/PrXv+6zTGVlJX70ox/By8sL7u7ueOedd3D9+nWzz0yePLnfNru7u+Ho6Mjn1yBDQKFLiA3I5XLTr/23zZo1C88//zzq6+v73Bc3Pj4eCxcuRGNjI1pbW7F69eq77r29s9ydP7e1taG1tRVyuVyAb0MGg0KXEBuQSCSYNm0aXn31VdPeCI2NjcjPz0d4eDjkcjm0Wq3ZckBbWxvGjh0LZ2dnVFVV3fMWMC8vL4jFYly4cAF1dXUICAigkybsAN0yRoiN/OAHP8CxY8fw97//HS0tLZDJZJg/fz62bdsGZ2dn+Pv7Y8KECRCLxbh+/TrefvttrFu3DmvWrMETTzyB5557znSRrS8uLi5Yv349OI5De3s75s2bZ70vR/pFm5gTYiNlZWVQqVQ4e/asxfvmDoXRaMSsWbOwZ88e2ubRSmgTc0LsEMdxkEqlKCkpEbSdkpISuLi4IDIyUtB2iGUodAmxEZFIhJSUFKSlpQn20ILBYEBqaipSUlLo0Eo7QaFLiA0tXboUEyZMwJYtWwSpPzMzExMnTkR8fLwg9ZPBowtphNiQSCRCbm4ugoKCEBoayutG5kVFRdi5cydOnDhBs1w7QjNdQmzMx8cHBQUFSEhIQFFRES91FhUVISEhAQUFBZg0aRIvdRJ+UOgSYgc4jkNhYSESExOxefPmIa/xGgwGZGRkIDExkQ6ltFMUuoTYCY7jUFtbi4qKCoSGhqK4uBhGo9GiskajEcXFxQgNDUVlZSVqa2spcO0UrekSYkd8fHxw6NAh5OXlITk5GZ2dnVCpVOA4DkqlEm5ubqbPtre34+TJk9BoNNi9ezekUilSU1MRHx9Pa7h2jB6OIMROMcZQXl6OvLw81NTUoL6+Hu7u7jAYDJBIJGhtbUVAQACCg4MRHx+PyMhICls7MdDDETTTJcROiUQicBxnWiYwGAzQ6XSYPHkyGhsbIZfLaS+FEYhCl5ARQiKRwMfHBwBM/yQjD11II4QQK6LQJYQQK6LQJYQQK6LQJYQQK6LQJYQQK6LQJYQQK6LQJYQQK6LQJYQQK6LQJYQQK6In0gixc9988w06OjrMXrt48aLpZzc3N3h6elq5V2SoKHQJsXOzZs1CR0cHRo36/+EaEBAAAOjp6cHYsWNx5coVW3WPDBItLxBi51asWAGRSISOjg7TjPf2z2KxGL/61a9s3EMyGBS6hNi5tLQ0iMV9D1UHBwf85je/sXKPyHBQ6BJi58aOHYs1a9bAycnJ7HVnZ2ckJydjzJgxNuoZGQpa0yXETjHGoNFokJ+fj4qKChiNRshkMkgkEhgMBnR0dODgwYNoampCXFwcOI6jTcxHAJrpEmJnGGNQq9VQKBRQqVTw9fXFm2++iRs3bqC5uRlNTU1obm7GjRs3kJOTA19fX6hUKigUCqjVagx0GgyxPTquhxA7otVqsWrVKly7dg1bt25FdHR0v+u5dzIajSgpKUFqaiq8vb2Rm5tLG53b0EDH9dBMlxA7UVZWhqCgIERERKCyshIxMTEWBS4AiMVixMTEoKqqCuHh4QgKCoJGoxG4x2QoaE2XEDtQVlaGxYsXQ61WIzY2dsj1SCQSbNy4EWFhYVi0aBEKCwvpKHY7QzNdQmxMq9Xi2WefHXbg3ik2NhZqtRpLliyBVqvlpU7CDwpdQmyIMYaVK1dizZo1vAXubbGxsUhKSsKqVavo4pododAlxIb27dsHnU6HV199VZD609PTcfXqVeTl5QlSPxk8Cl1CbIQxhm3btmHr1q2QSCSCtCGRSJCVlYWsrCya7doJCl1CbESj0aCrqwvR0dEWl0lISIC3tzfGjBmDGTNm4M9//vM9y0RHR6OzsxPl5eXD6S7hCYUuITaSn58PlUpl8W1hwHfLBRcvXsTNmzdx4MABvPbaa6itrR2wjFgshkqloiUGO0GhS4iNVFdXD/p2Ln9/f9MeDCKRCCKRCOfPn79nOY7jQA862QcKXUJswGAwoKGhAQqFYtBlX3rpJbi4uGDWrFnw9vbGvHnz7llGqVSivr4eBoNhCL0lfKLQJcQGdDodZDIZ3NzcBl327bffRltbG0pLS7FkyZK7dh/ri5ubG9zd3aHT6YbSXcIjCl1CbECv11sUlv1xcHBAVFQUtFotdu3aZVEZJycn6PX6IbdJ+EGhS4gNODo6oru7e9j19PT0WLSmCwDd3d1wdHQcdptkeCh0CbEBuVyOlpYWtLe3W1ymqakJ+/fvR3t7O3p7e1FUVIT8/HyLbjlra2tDa2sr5HL5cLpNeEChS4gNSCQS+Pv7o66uzuIyIpEIu3btgo+PDzw8PJCcnIycnBwsXLjwnmXr6uoQEBAg2EMYxHK0yxghNhISEgKNRoOoqCiLPu/l5YUvvvhiSG1pNBoEB/e5vSuxMprpEmIjcXFxeO+992A0GgVtx2g0Yvfu3YiPjxe0HWIZCl1CbITjOEilUpSUlAjaTklJCVxcXBAZGSloO8QyFLqE2IhIJEJKSgrS0tIEe2jBYDAgNTUVKSkpdGilnaDQJcSGli5digkTJmDLli2C1J+ZmYmJEyfS0oIdoQtphNiQSCRCbm4ugoKCEBoayutG5kVFRdi5cydOnDhBs1w7QjNdQmzMx8cHBQUFSEhIQFFRES91FhUVISEhAQUFBZg0aRIvdRJ+UOgSYgc4jkNhYSESExOxefPmIa/xGgwGZGRkIDExkQ6ltFMUuoTYCY7jUFtbi4qKCoSGhqK4uNji28mMRiOKi4sRGhqKyspK1NbWUuDaKVrTJcSO+Pj44NChQ8jLy0NycjI6OzuhUqnAcRyUSqXZrmTt7e04efIkNBoNdu/eDalUitTUVMTHx9Marh0TDXRuUnBwMKONjwmxDcYYysvLkZeXh5qaGtTX18Pd3R0GgwESiQStra0ICAhAcHAw4uPjERkZSWFrJ0QiUS1jrM9HAGmmS4idEolE4DjOtExgMBig0+kwefJkNDY2Qi6X014KIxCFLiEjhEQigY+PDwCY/klGHrqQRgghVkShSwghVkShSwghVkShSwghVkShSwghVkShSwghVkShSwghVkShSwghVkShSwghVkRPpBFi5/71r3+htbXV7LXa2lrTzx4eHpg2bZq1u0WGiEKXEDsXHh6OW7duwdHR0fRadHQ0AECv10Mmk+HKlSu26h4ZJFpeIMTOrV27FgDQ2tpqmvHe/lkkEuE3v/mNLbtHBolClxA79+tf/xoODg59vieRSJCUlGTlHpHhoNAlxM6NHj0aqampcHZ2NntdKpVi/fr1cHFxsVHPyFDQmi4hdooxBo1Gg/z8fFRWVqK3txcymQwSiQQGgwEdHR3461//iosXLyIuLg4cx9Em5iMAzXQJsTOMMajVaigUCqhUKvj6+iInJwc3btxAc3Mzmpqa0NzcjBs3biAnJwe+vr5QqVRQKBRQq9UY6DQYYnt0XA8hdkSr1WLVqlW4du0atm7diujoaIjF954bGY1GlJSUIDU1Fd7e3sjNzaWNzm1ooON6aKZLiJ0oKytDUFAQIiIiUFlZiZiYGIsCFwDEYjFiYmJQVVWF8PBwBAUFQaPRCNxjMhS0pkuIHSgrK8PixYuhVqsRGxs75HokEgk2btyIsLAwLFq0CIWFhXQUu52hmS4hNqbVavHss88OO3DvFBsbC7VajSVLlkCr1fJSJ+EHhS4hNsQYw8qVK7FmzRreAve22NhYJCUlYdWqVXRxzY5Q6BJiQ/v27YNOp8Orr74qSP3p6em4evUq8vLyBKmfDB6FLiE2whjDtm3bsHXrVkgkEkHakEgkyMrKQlZWFs127QSFLiE2otFo0NXVZdq8xlJfffUVnJ2dkZCQYNHno6Oj0dnZifLy8qF0k/CMQpcQG8nPz4dKpbL4trDbkpKSEBISYvHnxWIxVCoVLTHYCQpdQmykurp60Ldz7d+/HzKZbNCzY47jQA862QcKXUJswGAwoKGhAQqFwuIyN2/exMaNG5GdnT3o9pRKJerr62EwGAZdlvCLQpcQG9DpdJDJZHBzc7O4zIYNG7B8+fIhPd7r5uYGd3d36HS6QZcl/KIn0gixAb1eDycnJ4s/X1dXh88++wwnT54ccptOTk7Q6/VDLk/4QaFLiA04Ojqiu7vb4s//4x//wMWLF+Hr6wsAaG9vR29vL06fPo0TJ05YVEd3d7fZkT/ENih0CbGR5uZmtLe3W7TEsGrVKvziF78w/X379u24ePEidu3aZVFbbW1taG1thVwuH3J/CT8odAkRWE9PD/75z3+ivLwcx48fx/Hjx9Hc3AyZTIa6ujpERUXdsw4XFxezEyLc3Nzg7OwMLy8vi/pQV1eHgIAAwR7CIJaj0CWEZ998840pXI8fP47a2lr4+voiIiICTz75JF577TXMnDkTL7/8MjQajUWh+32bNm0a1Oc1Gg2Cg/vc3pVYGW1iTsgw9PT0oL6+3ixkm5qaEBYWhsjISERERCAsLAwymeyusmVlZVCpVDh79uygH5AYDKPRiFmzZmHPnj20zaOVDLSJOc10CRmEb7/9FhUVFaalgpqaGkyaNAkRERH44Q9/iNTUVPj5+fV7eu+dOI6DVCpFSUkJYmJiBOtzSUkJXFxcEBkZKVgbxHIUuoT0o7e3Fw0NDWaz2KtXryI0NBQRERFITk5GeHg4xo4dO6T6RSIRUlJSkJaWhjlz5giy3mowGJCamoqUlBQ6tNJO0PICIf+nubkZFRUVpoCtqqqCXC43LRNERETA39/folmspRhjePrppxEREYENGzbwVu9tGRkZqKqqwieffEKha0W0vEDI9xiNRpw5cwbHjx83LRVotVqEhIQgIiICa9euRXh4ODw9PQXth0gkQm5uLoKCghAaGsrrRuZFRUXYunUrVqxYAZ1OhwkTJvBWNxk6mumSB0JLSwsqKytNs9jKykp4enqaZrARERF47LHHMGqUbeYhGo0GixYt4u3InqKiIixevBidnZ1wcnKCSCTCvHnzkJOTg8mTJ/PQYzKQgWa6FLrkvmM0GnHu3DmztdiLFy8iKCjItFQQHh6O8ePH27qrZjQaDZYsWYKkpCSkp6cPaY3XYDAgIyMD2dnZ6OzsNHtv1KhRKCkpwezZs/nqMukHhS65r928eRNVVVWmpYLKykrIZDKzWWxgYOCIeDBAq9Vi1apVuHr1KrKyshAdHW3R7WRGoxElJSVYu3YtLl26hFu3bpnec3BwwOjRo3HkyBGEhYUJ2X3yfyh0yX2DMYYvv/zSbBZ74cIFKJVKs5AdyeuXjDHk5eUhKysLnZ2dUKlU4DgOSqXS7JHh9vZ2nDx5EhqNBrt374ZUKsWcOXOwZ88etLW1AfhuzdjT0xO1tbW0rGBFFLpkxGpvbzfNYm//cXNzQ0REhGmp4PHHH78vN3JhjKG8vBx5eXmoqalBfX093N3dYTAYIJFI0NraioCAAAQHByM+Ph6RkZFoamrClClT0N3dDRcXFyiVSnz55ZfYt2+foPcCE3MUumREYIzh/PnzZncUfPXVV1AoFGaz2IkTJ9q6qzZhMBig0+kwefJkNDY2Qi6X97lkMnHiRHz77bd48cUXkZ2djbKyMvzsZz/DF198gVmzZtmg5w8eumWM2KWOjg5UV1ebZrAVFRVwcnIyheuyZcugVCoHte/s/UwikZg2MB9oI/Pk5GR4eHjghRdeAADMnj0bW7ZswYIFC1BRUYFx48ZZpb+kbzTTJVbBGMO///1vs2WCs2fP4rHHHjN7+GAopyI8aEQi0ZCOU09NTUVVVRWOHj16Xy7H2BNaXiBW19nZiZqaGrOlglGjRpktE/zgBz+As7Ozrbs64gw1dHt7e7FkyRJ4eXnhT3/6Ez2hJiAKXSIoxhguXbpkNos9ffo0/P39zULW19eXBjoPhhq6wHcXJqOiovDLX/4S69at47ln5DZa0yW86urqQm1trVnIGo1G0zLBc889h6CgIEilUlt3lXyPm5sbDhw4gIiICMyYMQMLFiywdZceOBS65J4aGxvNlgnq6+vh5+eHiIgIPPvss9i+fTumTp1Ks9gRwtfXFwUFBZg/fz5KSkoQGBho6y49UCh0iZnu7m6cOHHCbBar1+tNSwRZWVkIDg6Gq6urrbtKhiEsLAw7duzAwoULUVlZSWenWRGF7gPu8uXLZgF76tQpzJgxA5GRkXjmmWewZcsWTJs2jWax96Ff/OIXOHv2LBYtWoTPP/+cLmpaCV1Ie4Do9XrU1dWZLRXcunXL7GJXSEiIRafTEtsZzoW072OMIS4uDmKxGPv27aP/ufKE7l54QF29etVsFltXV4eHH37YFLCRkZF45JFHaKCNMHyGLvDd7X1z5szB/PnzBdlI/UFEdy88AAwGA06dOmUWsq2trQgPD0dkZCQyMjIQGhqK0aNH27qrZJCqqqrQ3Nxs9lpRUZHp53Hjxg3rpF+pVIrCwkKEh4dj5syZeO6554ZcF7k3mumOUE1NTaZwLS8vx4kTJ/DQQw+ZLRXMmDFD0FNmiXXI5XK0tbWZniJrbW2Fu7s7gO+WjMaNG4fGxsZht1NXV4eYmBh8+umnCAkJGXZ9DzKa6Y5wPT09+Oc//2k2i71x4wbCwsIQERGB1157DWFhYaaBSO4vqamp2LhxI1pbW02v3f7Z1dUV6enpvLSjUCjw5z//GYsXL0ZFRQU9ki0QmunaoevXr5sFbE1NDSZPnmy2neGsWbNoFvuA6OzshLe3t1no3jZu3DhcvnyZ102BsrKysH//fpSWltKtgUM0Ima6t7et0+v1cHR07HfbuvtNb28v6uvrzZYKmpqaTLPYtLQ0hIWFwcPDw9ZdJTYilUqxYcMGbNiwwewIHhcXF2zevJn3XdhSUlJw9uxZJCQk4KOPPqL/ufPMZjNdxhg0Gg3y8/NRXV2NhoYGyGQyODk5obu7Gy0tLfD390dISAji4uLAcdx9cZX922+/NTvmu7q6GhMnTjRbi3300Ud5PeabjEx3jpGqqiqcOnUKrq6ukEgkMBgM6OjowOOPP47Q0FDex4her8dTTz0FjuOQmZnJS50PErua6TLGsG/fPmzbtg2dnZ1Yvnw54uLioFAo7jqKpK6uDhqNBiqVClKpFCkpKVi6dOmICd/e3l6cPn3abKngypUrCAkJQWRkJNatW4ewsDDa35SYsYcx4ujoiIKCAoSFhWHWrFlYtmzZcL8WuY0x1u+foKAgxqfGxkY2d+5cplQq2dGjR1lvb69F5Xp7e9nRo0eZQqFgc+fOZY2Njbz2iy83btxghw8fZhs3bmRPPfUUGzNmDJs+fTpLTExku3btYnV1daynp8fW3SR2zN7GSENDA/Py8mKlpaW81PegAFDD+slVq4VuaWkpGz9+PNu8eTPT6/VDqkOv17OMjAw2fvx4VlZWxlvfhqK3t5c1NDSwP/3pT0ylUjE/Pz/m5ubG5syZw9LT09mBAwdYU1OTTftIRhZ7HSNHjhxhEyZMYOfPn+elvgeBzUO3tLSUeXp6siNHjvBS35EjR5inp6dVg7elpYUVFRWxTZs2sdjYWCaTydi0adPY0qVL2c6dO9mJEyeYwWCwWn/I/cXex8iOHTvYo48+ylpaWnip735n09BtbGxk48eP5+0/ptuOHDnCxo8ff9evUefPn7f4V7L+9Pb2sjNnzrDdu3ezlStXMn9/f+bq6spmz57N0tLSWGFhIbt27dqw2iDkNmuPkaF66aWX2E9+8hOaXFhgoNAV9O4FxhjmzZuHyMhIQZ7p3rx5MyoqKnDo0CEAwJtvvol169ahsLBwUJszt7W1oaqqyrQJTEVFBdzd3c3uKHj88ccfiFvYiHVZc4wM9+JaT08P5s2bBz8/P7z55ps89fD+NNDdC4LOdP/rv/6LKZXKIa9P3Yter2cKhYLt3buXLV26lLm4uDCRSMSSk5P7LWM0Gtm5c+fY3r172a9+9SsWGBjIXF1dGcdxLDk5mRUUFLArV64I0l9Cvs9aY0StVvNSX3NzM5s5cybbtWsXL/Xdr2CL5QWj0cgCAwPZ0aNHh9f773n99dfZ0qVLTX8/evQoGzt2LHN2dmYAGACmUChM77e1tbH//u//Zm+88QabP38+GzduHPP19WU///nPWU5ODqusrGTd3d289pEQSwg1Rr7v6NGjLDAwkBmNRl7q++qrr5hcLmfFxcW81Hc/Gih0BXvURKPRoKurC9HR0QCAqVOnQiqVws3NzfRnzZo1w24nOjoaTk5O6OrqMr1WX1+P1atXQ6lUQi6XY/369fj222+xbNkynDp1CpcuXcL+/fuxdu1ahIaG0nHUxCa+P0YsMWfOHDg7O5vG0MyZM+9ZJjo6Gp2dnSgvLx9Od00eeeQR7N+/H/Hx8Th37hwvdT5IBHs4Ij8/HyqVyuwRwoMHD+Kpp57itR2xWIw1a9Zg8+bN6O7uNr3u4uKCXbt2QalU8v6YJCF86GuMWOKPf/wjVqxYYfHnxWIxVCoV8vLywHHcYLvZpzlz5iAzMxPz589HRUUFPeAzCILNdKurqy36F7x3715ERUUhOTkZHh4eeOihh3D48GHT+//+97/xxBNPYPTo0YiJicH169fvquOHP/yhWbCOGjUK06ZNQ3h4OAUusVuWjhE+cBwHvh/pX758OZ555hn89Kc/hV6v57Xu+5kgoWswGNDQ0ACFQmHR5ysrKzFz5kxcv34dqampWL58uWln/Pj4eAQFBeH69evYsGED/vKXv9xVXqlUmm0E0tXVhc8++4yX70KIEAY7Ru6Unp4OT09PcByHf/zjHxaVUSqVqK+vh8FgGHR7A9m6dSvc3NyQlJTE62kW97X+FnvZMC6kNTY2sokTJ5q9NmXKFObq6src3d1Nf3Jzc9mePXvYww8/bPpcR0cHA8CuXr3KLl26xBwcHFh7e7vp/bi4OLMLabfdvkDm6OjIpFIpGz9+/JD6Tog19DVGLFFRUcFu3rzJurq62N69e5mbmxv717/+ZVFZb29vQR6hv3nzJgsMDGS///3vea97pIK1L6Tp9fo+f60vLCxES0uL6c/KlSsBABMmTDB9xsXFBcB3m3lcuXIFHh4eZnt6Tpkypc82R48ejc8//xzXrl3D/v37sWnTJh6/ESH86m+M3EtYWBhGjx4NJycnLFu2DBzH4dNPP7WorJOTkyDLAKNHj8aBAwewfft2fPLJJ7zXf78R5EKao6Oj2UWtofL29kZzczM6OjpMwfv111/3eZN3d3c3HB0d4eHhgYULFw67bUKExNcYGcwhlbfHiBCmTJmCgoICLFiwACUlJQgMDBSknfuBIDNduVyOlpYWtLe3D6ueKVOmIDg4GK+//jr0ej3Kyspw8ODBuz7X1taG1tZWyOXyYbVHiLUMZYy0tLSgqKgIXV1d6Onpwb59+3Ds2DH85Cc/uWdZa4yR8PBwvPnmm1i4cCF0Op1g7Yx0goSuRCKBv78/6urqzF5fsGCB2X26ixcvvmddeXl5qKysxNixY5GRkYHExMS7PlNXV4eAgAB6TJeMGP2NkYEYDAa89tpr8PLygqenJ3bs2IHCwkLMmDHjnmWtNUbi4+ORmJiIxYsXm907T/6fYPfphoSEQKPRICoqCgBw8eLFfj/7/PPPm/39zl+Xpk2bhtLS0gHb0mg0wzqCmhBb+P4YuRcvLy9UV1cPqS1rjpFNmzbh7NmzWL58OdRq9Yg5dMBaBLtPNy4uDu+99x6MRqNQTQAAjEYjdu/ejfj4eEHbIYRv9+sYEYvF2Lt3L7788ku88cYbVmlzJBEsdDmOg1QqRUlJiVBNAABKSkrg4uKCyMhIQdshhG/38xhxcXHBgQMHkJubiw8++MBq7Y4EgoWuSCRCSkoK0tLSeL8h+zaDwYDU1FSkpKTQrzBkxLHWGHnllVdw48YNlJWVCdJGf7y9vfHxxx/jpZde4v1puJFM0LOVly5digkTJmDLli2C1J+ZmYmJEyfS0gIZsawxRqZNm4bt27dj6dKlSEhIwNWrVwVpqy9KpRK5ublYtGgRtFqt1dq1a/09NcFGyMkRWq2W13oJsTZrjZG2tjaWnp7Oxo0bx7Zv3y7YHr59yczMZEql0uzp0vsZbH1GWllZmV2f/0SIrVlzjJw7d47FxsYyPz8/9tlnn/HS3r0YjUaWmJjIlixZMuzjtEYCm4cuY9/9RzV+/HiWkZExrJNON23aZBenARPCN2uOEaPRyAoLC9nUqVPZz372M/b1118PtdsW6+rqYlFRUSw9PV3wtmxtoNAVdE33ThzHoba2FhUVFQgNDUVxcbHFt8oYjUYUFxcjNDQUlZWVqK2ttdqWeIRYizXHiEgkwjPPPIPTp0/j0UcfhVKpRGZmJi+PJvfHyckJBQUF2L9/P95//33B2rF7/aUx43mme5vRaGRqtZoFBgay6dOns8zMTHbs2DHW1tZm9rm2tjZ27NgxlpmZyaZPn84CAwOZWq3m7cgRQuyVLcbI+fPn2cKFC9n06dPZ4cOH+foqfaqvr2deXl6stLRU0HZsCbY6DfheYV9eXo68vDzU1NSgvr4e7u7ucHJyQnd3N1pbWxEQEIDg4GDEx8cjMjKSbgsjDxRbjJFPP/0Ur7zyCgICAvCHP/wBDz30EE/fxtyRI0fwwgsvoLy8XLA2bGmg04BtFrrfZzAYoNPpoNfr4ejoCLlcTnspEHIHa42Rrq4uZGdnIzs7Gy+//DJSU1MhlUp5b+ett95Cbm4uysvLMWbMGN7rt6UREbqEEPvy9ddfY926daitrUVOTg4WLFjA62+bjDG89NJLuHTpEg4cOIBRowTbCsbqBgpdq11II4SMLL6+vvjggw/w7rvvIi0tDU8//TS++uor3uoXiUR46623oNfrkZyczFu99o5ClxAyoJiYGJw6dQpPPvkkIiIisH79enR0dPBSt0QiwQcffIDDhw/j3Xff5aVOe0ehSwi5J0dHRyQnJ+PUqVO4ePEiHn30UXz44Ye8HEbp4eGBTz75BBs3bhR88x97QKFLCLHYpEmTsG/fPrz//vvIyMjAj3/8Y5w5c2bY9U6fPh1//etfER8fj3PnzvHQU/tFoUsIGbQnnngCJ0+exIIFCzB79mykpKSgra1tWHXOmTMHb7zxBhYsWIAbN27w1FP7Q6FLCBmSUaNG4ZVXXkF9fT2uX78OPz8/5OXlDWvJYcWKFViwYAF++tOfCrbdpa1R6BJChkUul2PPnj3429/+hu3bt2POnDn4n//5nyHXl5WVBVdXVyQlJfGyZmxvKHQJIbyIjIxEdXU14uLiEB0djbVr16KlpWXQ9Tg4OCAvLw8VFRXIycnhvZ+2RqFLCOGNg4MDVq9ejdOnT6Orqwt+fn7Yu3fvoM+BGz16NA4ePIht27bh0KFDAvXWNih0CSG88/T0xLvvvosDBw5g165diIqKwokTJwZVx5QpU/DRRx/h+eefH9Zyhb2h0CWECCYkJATHjx/H8uXLMW/ePLz44ouDujMhIiICOTk5WLhwIZqamgTsqfVQ6BJCBCUWi7F8+XKcOXMGo0aNgp+fH3Jzc9Hb22tR+dtnuy1atAhdXV0C91Z4FLqEEKvw8PDAjh07UFRUhPfffx/h4eGorKy0qGxGRgYmTZqEFStWjPg7Gih0CSFWpVAoUFpaildeeQWLFy/G8uXL8c033wxYRiwW4y9/+QvOnTuH//zP/7SoHYPBAK1WiwsXLkCr1drNfb8UuoQQqxOJRPjlL3+Js2fPQiaTwd/fH3/84x/R09PTbxmJRIKPP/4Y77zzDj788MO73meMoaysDElJSQgNDYVMJkNYWBieeuophIWFQSaTITQ0FElJSSgrK7PZjJlClxBiM2PGjMHvf/97fP755ygoKEBwcDDKysru+lx7ezt8fX1RVFSEjz/+GC+++CJu7/XNGINarYZCoYBKpYKvry+ys7Oh0+lw+fJlXLhwAZcvX4ZOp0N2djZ8fX2hUqmgUCigVqutH779nePDBDojjRBC+mI0Gtn+/fuZj48PS0hIYFeuXDG999vf/pY5OjoyNzc3du3aNfbRRx+xSZMmsaqqKjZ37lymVCrZ0aNHLT7evbe3lx09epQpFAo2d+5c1tjYyOt3gT2cBkwIIQMRiUT4+c9/jjNnzsDHxwePPfYYsrOz0dDQgF27dkGv16O7uxsrV67EkiVL8PTTT+NHP/qR6YJcTEwMxGLLIk0sFiMmJgZVVVUIDw9HUFAQNBqNwN/w//SXxoxmuoQQGzp79iyLiYlhUqmUiUQiBoABYC4uLiwzM5N5enqyI0eO8NLWkSNHmKenJysrK+OlPtBMlxAy0sycOROrV6+G0Wg0W3e9desWfve730GtViM2NpaXtmJjY6FWq7FkyRJotVpe6uwPhS4hxC7dunULL774Irq7u81ed3Fxwbp163gL3NtiY2ORlJSEVatWCXpxjUKXEGKXfve736G1tfWu1ydNmoQNGzYI0mZ6ejquXr2KvLw8QeoHKHQJIXbq2rVr8PT0xOjRoyEWizFq1CiMGTMGO3fuhEQiEaRNiUSCrKwsZGVlCTbbpdAlhNilPXv2QKvV4ubNm+jp6cHBgwchl8sRHR1tcR379++Hn58fXF1d8fDDD6O0tPSeZaKjo9HZ2Yny8vLhdL9fFLqEELsnEolw8OBBLF++3OLbwoqLi5GWloY9e/agra0Nx44dw7Rp0+5ZTiwWQ6VSCbbEQKFLCBkRqqurwXGcxZ9//fXXsXHjRoSHh0MsFmPSpEmYNGmSRWU5jjM98cY3Cl1CiN0zGAxoaGiAQqGw6PO9vb2oqanBN998g0ceeQQ+Pj5Ys2YNOjs7LSqvVCpRX18vyCY5FLqEELun0+kgk8ng5uZm8ecNBgM+/PBDlJaWoq6uDidPnsR//Md/WFTezc0N7u7u0Ol0w+l2nyh0CSF2T6/Xw8nJyeLPS6VSAMDLL78Mb29veHp64re//S0+/fRTi+twcnKCXq8fdF/vhUKXEGL3HB0d73pIYiAeHh7w8fGBSCQyvXbnz5bo7u6Go6PjoMpYgkKXEGL35HI5Wlpa0N7ebnGZF154ATt27EBTUxOam5vxhz/8AfPnz7eobFtbG1pbWyGXy4fa5X5R6BJC7J5EIoG/vz/q6uosLrNhwwaEhIRgxowZ8PPzg1KpxPr16y0qW1dXh4CAAEEewqDQJYSMCCEhIYPaflEikeDtt99GS0sLrl27hrfeegvOzs4WldVoNAgODh5qVwdEoUsIGRHi4uLw3nvvwWg0CtqO0WjE7t27ER8fL0j9FLqEkBGB4zhIpVKUlJQI2k5JSQlcXFwQGRkpSP0UuoSQEUEkEiElJQVpaWmCnexrMBiQmpqKlJSUQd/tYCkKXULIiLF06VJMmDABW7ZsEaT+zMxMTJw4UbClBQAYJVjNhBDCM5FIhNzcXAQFBSE0NJTXjcyLioqwc+dOnDhxQrBZLkAzXULICOPj44OCggIkJCSgqKiIlzqLioqQkJCAgoICizfFGSoKXULIiMNxHAoLC5GYmIjNmzcPeY3XYDAgIyMDiYmJKCwsHNQuZkNFoUsIGZE4jkNtbS0qKioQGhqK4uJii28nMxqNKC4uRmhoKCorK1FbW2uVwAVoTZcQMoL5+Pjg0KFDyMvLQ3JyMjo7O6FSqcBxHJRKpdmuZO3t7Th58iQ0Gg12794NqVSK1NRUxMfHC7qG+32igc4BCg4OZkJt5EsIIXxijKG8vBx5eXmoqalBfX093N3d4eTkhO7ubrS2tiIgIADBwcGIj49HZGSkYGErEolqGWN9PtJGM11CyH1BJBKB4zjTMoHBYIBOp4Ner4ejoyPkcrlgB1oOBoUuIeS+JJFI4OPjY+tu3IUupBFCiBVR6BJCiBUNeCFNJBJ9A+CS9bpDCCH3hSmMMa++3hgwdAkhhPCLlhcIIcSKKHQJIcSKKHQJIcSKKHQJIcSKKHQJIcSK/hdrwrDa5Q6bTAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tree = parse_method(code)\n",
    "cfg = CFGBuilder(tree).create_graph()\n",
    "cfg.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86ed7850",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "Do-while loops."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3d9f0d42",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "code = \"\"\"\n",
    "  public void foo(int x) {\n",
    "     int y = 0;\n",
    "     do {\n",
    "       System.out.println(\"Hallo\");\n",
    "       y += 1;\n",
    "     } while (y < x);\n",
    "  }\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "760ebca2",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "class CFGBuilder(CFGBuilder):\n",
    "\n",
    "    @singledispatchmethod\n",
    "    def add_node(cls, arg):\n",
    "        super().add_node(arg)\n",
    "    \n",
    "    @add_node.register\n",
    "    def add_do_node(self, do_node:javalang.tree.DoStatement):\n",
    "\n",
    "        top = self.frontier[0]\n",
    "        self.add_node(do_node.body)\n",
    "        \n",
    "        cfg_node = CFGNode(do_node)\n",
    "        self.graph.add_node(cfg_node)\n",
    "        for parent in self.frontier:\n",
    "            self.graph.add_edge(parent, cfg_node)\n",
    "        \n",
    "        head = list(self.graph.successors(top))[0]\n",
    "        self.graph.add_edge(cfg_node, head)\n",
    "        self.frontier = [cfg_node]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "838d3836",
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tree = parse_method(code)\n",
    "cfg = CFGBuilder(tree).create_graph()\n",
    "cfg.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43617bbe",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "For `for`-loops we will not split up the loop header into three nodes (initialisation, condition, increment) to keep CFGs simple, but adding more nodes here would be straightforward."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "4eebeb99",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "code = \"\"\"\n",
    "  public void foo(int x) {\n",
    "     for(int i = 0; i < 10; i++) {\n",
    "       System.out.println(\"Hallo\");\n",
    "       System.out.println(\"Hallo\");\n",
    "     }\n",
    "  }\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "68015fbf",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "class CFGBuilder(CFGBuilder):\n",
    "    @singledispatchmethod\n",
    "    def add_node(cls, arg):\n",
    "        super().add_node(arg)\n",
    "    \n",
    "    @add_node.register\n",
    "    def add_for_node(self, for_node: javalang.tree.ForStatement):\n",
    "        cfg_node = CFGNode(for_node)\n",
    "        self.graph.add_node(cfg_node)\n",
    "        for parent in self.frontier:\n",
    "            self.graph.add_edge(parent, cfg_node)\n",
    "        self.frontier = [ cfg_node ]\n",
    "        self.add_node(for_node.body)\n",
    "        for loop_end in self.frontier:\n",
    "            self.graph.add_edge(loop_end, cfg_node)\n",
    "        self.frontier = [ cfg_node ]   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "6e85332c",
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tree = parse_method(code)\n",
    "cfg = CFGBuilder(tree).create_graph()\n",
    "cfg.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d016cbac",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "Return statements need to be linked directly to end."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "4af597ae",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "code = \"\"\"\n",
    "  public boolean foo(int x) {\n",
    "     if (x == 42) {\n",
    "       return true;\n",
    "     } else if (x == 0) {\n",
    "       return false;\n",
    "     }\n",
    "     System.out.println(\"Hallo\");\n",
    "     return false;\n",
    "  }\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "04b43c2e",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "class CFGBuilder(CFGBuilder):\n",
    "    \n",
    "    @singledispatchmethod\n",
    "    def add_node(cls, arg):\n",
    "        super().add_node(arg)\n",
    "    \n",
    "    @add_node.register\n",
    "    def add_return_node(self, return_node: javalang.tree.ReturnStatement):\n",
    "        cfg_node = CFGNode(return_node)\n",
    "        self.graph.add_node(cfg_node)\n",
    "        for parent in self.frontier:\n",
    "            self.graph.add_edge(parent, cfg_node)\n",
    "        self.graph.add_edge(cfg_node, self.end)\n",
    "        self.frontier = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "fbbc1c97",
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tree = parse_method(code)\n",
    "cfg = CFGBuilder(tree).create_graph()\n",
    "cfg.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fca340c0",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "Switch-statements:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "68ab8c53",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "code = \"\"\"\n",
    "  public int foo(int x) {\n",
    "     switch (x) {\n",
    "       case 1:\n",
    "         return true;\n",
    "       case 2:\n",
    "         return false;\n",
    "       default:\n",
    "         return false;\n",
    "     }\n",
    "  }\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "40392af3",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "class CFGBuilder(CFGBuilder):\n",
    "    @singledispatchmethod\n",
    "    def add_node(cls, arg):\n",
    "        super().add_node(arg)\n",
    "    \n",
    "    @add_node.register\n",
    "    def add_switch_node(self, switch_node: javalang.tree.SwitchStatement):\n",
    "        cfg_node = CFGNode(switch_node)\n",
    "        self.graph.add_node(cfg_node)\n",
    "        for parent in self.frontier:\n",
    "            self.graph.add_edge(parent, cfg_node)\n",
    "        self.frontier = []\n",
    "        break_cases = []\n",
    "        for case in switch_node.cases:\n",
    "            self.frontier.append(cfg_node)\n",
    "            for statement in case.statements:\n",
    "                self.add_node(statement)\n",
    "            if isinstance(case.statements[-1], javalang.tree.BreakStatement):\n",
    "                # If last statement is a break, add edge to the end\n",
    "                break_cases.extend(self.frontier)\n",
    "                self.frontier = []\n",
    "        self.frontier.append(cfg_node)\n",
    "        self.frontier.extend(break_cases)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "f8ebb023",
   "metadata": {
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tree = parse_method(code)\n",
    "cfg = CFGBuilder(tree).create_graph()\n",
    "cfg.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80e29dfb",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "We can observe how the CFG differs depending on whether we forget (or purposely omit) a `break` statement in a switch-case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "f6031f6d",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "code = \"\"\"\n",
    "  public int foo(int x) {\n",
    "     switch (x) {\n",
    "       case 1:\n",
    "         System.out.println(\"Hallo\");\n",
    "         break;\n",
    "       case 2:\n",
    "         System.out.println(\"Hallo\");\n",
    "         break;\n",
    "       case 3:\n",
    "         System.out.println(\"Hallo\");\n",
    "         break;\n",
    "     }\n",
    "     return false;\n",
    "  }\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "02c9be0e",
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tree = parse_method(code)\n",
    "cfg = CFGBuilder(tree).create_graph()\n",
    "cfg.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c9fc353",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "Here's the version with a missing `break`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "b4932603",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "code = \"\"\"\n",
    "  public int foo(int x) {\n",
    "     switch (x) {\n",
    "       case 1:\n",
    "         System.out.println(\"Hallo\");\n",
    "         break;\n",
    "       case 2:\n",
    "         System.out.println(\"Hallo\");\n",
    "         // Fall through\n",
    "       case 3:\n",
    "         System.out.println(\"Hallo\");\n",
    "         break;\n",
    "     }\n",
    "     return false;\n",
    "  }\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "7b79cc66",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tree = parse_method(code)\n",
    "cfg = CFGBuilder(tree).create_graph()\n",
    "cfg.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e0d3552",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "There are still several aspects of Java programs we have not covered yet, such as:\n",
    "- `break` statements in loops\n",
    "- `continue` statements\n",
    "- `try`/`catch` statements\n",
    "- Exceptional control flow\n",
    "- `goto` statements\n",
    "\n",
    "However, the functionality we have now is sufficient to cover the examples we need."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cc43b34",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Basic Blocks"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "468a02fd",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "A _basic block_ is a sequence of statements that must execute consecutively. Basic blocks can be used to reduce the number of nodes in a control flow graph. The control flow enters the first statement in the block and leaves the last statement, and no intermediate statements can exit the program or jump to another statement, thus we can generally also apply control flow analysis on basic blocks rather than individual statements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "695c2a5c",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "from dataclasses import dataclass\n",
    "\n",
    "class BasicBlock:\n",
    "    def __init__(self):        \n",
    "        self.cfg_nodes = []\n",
    "        \n",
    "    def __str__(self):\n",
    "        return \",\".join([str(node) for node in self.cfg_nodes])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "073b48b9",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "The first step to create basic blocks is to identify _leaders_. The following nodes in the CFG are leaders:\n",
    "\n",
    "- The entry node\n",
    "- Every node that is the target of a branch\n",
    "- Every node that is the target of an implicit goto (e.g., header of a while or for loop). We can identify these nodes because they will have more than one incoming edge."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "4232de3b",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "def get_leaders(cfg):\n",
    "    leaders = set()\n",
    "    leaders.add(cfg.start)\n",
    "    leaders.add(cfg.end) #todo: check\n",
    "    \n",
    "    for node in cfg.nodes():\n",
    "        if cfg.is_branch(node):\n",
    "            for successor in cfg.successors(node):\n",
    "                leaders.add(successor)\n",
    "        if cfg.is_merge(node):\n",
    "            leaders.add(node)\n",
    "\n",
    "    return leaders"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f3a8389",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "Each leader starts a basic block that covers the sequence of nodes until the next leader. Thus, to calculate basic blocks we do a search starting from leader blocks up to the next leader blocks, and add all these blocks to our BasicBlock datastructure. We also maintain a mapping of CFG nodes to basic blocks, such that we can then connect the basic blocks with control flow edges."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "ca4f96e1",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "def convert_bb(cfg):\n",
    "    bbcfg = nx.DiGraph()\n",
    "    \n",
    "    leaders = get_leaders(cfg)\n",
    "    bb_mapping = {}\n",
    "    \n",
    "    for leader in leaders:\n",
    "        bb = BasicBlock()\n",
    "        queue = [leader]\n",
    "        while queue:\n",
    "            element = queue.pop()\n",
    "            if element not in bb.cfg_nodes and element not in queue:\n",
    "                bb.cfg_nodes.append(element)\n",
    "                bb_mapping[element] = bb\n",
    "                \n",
    "                if not cfg.is_branch(element):\n",
    "                    for successor in cfg.successors(element):\n",
    "                        if successor not in leaders:\n",
    "                            queue.append(successor)\n",
    "        bbcfg.add_node(bb)\n",
    "    \n",
    "    for s, t in cfg.edges():\n",
    "        bbfrom = bb_mapping[s]\n",
    "        bbto   = bb_mapping[t]\n",
    "        if bbfrom != bbto:\n",
    "            bbcfg.add_edge(bbfrom, bbto)\n",
    "\n",
    "    return ProgramGraph(bbcfg, bb_mapping[cfg.start], bb_mapping[cfg.end])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2143a6e",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "Let's consider an example that contains some sequences of statements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "7923d356",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "code = \"\"\"\n",
    "  public void foo(int x) {\n",
    "     int y = 0;\n",
    "     int z = x + 1;\n",
    "     while (y < x) {\n",
    "       System.out.println(\"Hallo\");\n",
    "       y += 1;\n",
    "     }\n",
    "  }\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfa196da",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "First, here's the original CFG."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "0cee3b65",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tree = parse_method(code)\n",
    "cfg = CFGBuilder(tree).create_graph()\n",
    "cfg.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aec008a2",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "Here's the version with basic blocks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "515da161",
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bbcfg = convert_bb(cfg)\n",
    "bbcfg.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cdef857",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "Note that the two edges between node 5 and the merged node 6,7 overlap."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5be58508",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Dominance"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ed8b127",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "Node $a$ dominates node $b$ if every path from entry to $b$ must pass through $a$.\n",
    "\n",
    "Node $a$ dominates $b$ if and only if:\n",
    "- $a = b$ or\n",
    "- $a$ is the unique predecessor to $b$ or\n",
    "- $a$ is the unique predecessor to any of $b$’s predecessors.\n",
    "\n",
    "In other words, the entry node dominates all other nodes; every node dominates itself; and the entry node to a loop dominates all other nodes in the loop."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ad0fd06",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "The calculation of dominators is based on a fixpoint algorithm which updates a map $D$ of dominator nodes for each node in the CFG until no more changes are found:\n",
    "1. We already know that the entry node will only be dominated by itself, so the map is initialised for entry accordingly.\n",
    "2. For each other node the map is initialised with all other nodes to start with, and the sets are iteratively reduced.\n",
    "3. In every iteration of the algorithm, the dominator set is updated for each node except the entry node. For node $n$ the set is updated to $n \\cup (\\bigcap D(p))$ for all immediate predecessors $p$ of n, where $D$ defines the currently stored for $p$.\n",
    "4. If the dominator set for any of the nodes was changed, the algorithm repeats step 3. If no set was changed, a fixpoint has been reached."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "e5c3e316",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "class ProgramGraph(ProgramGraph):\n",
    "    def dominators(self):\n",
    "        entry = self.start\n",
    "        remaining_nodes = [node for node in self.nodes() if node != entry]\n",
    "\n",
    "        # D(entry) <- {entry}\n",
    "        dominators = {}\n",
    "        dominators[entry] = [entry]\n",
    "\n",
    "        # D(node \\ {entry}) <- nodes\n",
    "        for node in remaining_nodes:\n",
    "            dominators[node] = set(self.nodes())\n",
    "\n",
    "        changed = True\n",
    "        while changed:\n",
    "            changed = False\n",
    "\n",
    "            for node in remaining_nodes:\n",
    "                current_dominators = dominators[node]\n",
    "\n",
    "                # new_dominators = node \\cup intersection(dominators for all predecessors)\n",
    "                new_dominators = set(self.nodes())\n",
    "\n",
    "                for p in self.predecessors(node):\n",
    "                    new_dominators = new_dominators.intersection(dominators[p])\n",
    "\n",
    "                new_dominators.add(node)\n",
    "\n",
    "                if new_dominators.symmetric_difference(current_dominators):\n",
    "                    dominators[node] = new_dominators\n",
    "                    changed = True\n",
    "\n",
    "        return dominators"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a17dc71",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "Let's see this algorithm in action. First, here's the CFG that is the starting point."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "4a558134",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tree = parse_method(code)\n",
    "cfg = CFGBuilder(tree).create_graph()\n",
    "cfg.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "37764411",
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start: Start\n",
      "End: 5,End,Start,4,3\n",
      "3: 3,Start\n",
      "4: 4,3,Start\n",
      "5: 4,3,5,Start\n",
      "6: 6,5,Start,4,3\n",
      "7: 5,7,3,Start,4,6\n"
     ]
    }
   ],
   "source": [
    "for node, dominators in cfg.dominators().items():\n",
    "    dom = \",\".join([str(d) for d in dominators])\n",
    "    print(f\"{node}: {dom}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09421aa8",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "For example, the end node is not dominated by nodes 6 and 7, since there exists a path from start to end that does not include these two nodes."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fb86bc4",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "The _strict_ dominators for a CFG node $n$ are all of its dominators except $n$ itself."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "855432ca",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "class ProgramGraph(ProgramGraph):\n",
    "    def strict_dominators(self):\n",
    "        dominators = self.dominators()\n",
    "        for node, dom in dominators.items():\n",
    "            dom.remove(node)\n",
    "        return dominators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "d9458a4b",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "# Recreate CFG object since the method was added\n",
    "cfg = CFGBuilder(tree).create_graph()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "73b38bc5",
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start: \n",
      "End: 4,5,Start,3\n",
      "3: Start\n",
      "4: 3,Start\n",
      "5: 3,4,Start\n",
      "6: 4,5,Start,3\n",
      "7: 4,5,Start,6,3\n"
     ]
    }
   ],
   "source": [
    "for node, dominators in cfg.strict_dominators().items():\n",
    "    dom = \",\".join([str(d) for d in dominators])\n",
    "    print(f\"{node}: {dom}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58444426",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "The immediate dominator for a node $n$ is the last dominator of $n$ on any path from the entry to $n$. To calculate the immediate dominator for node $n$, we iterate over the strict dominators $s$ of $n$, and remove the dominators $D(s)$ from $D(n)$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "6eb8cfee",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Immediate dominator of 3: Start\n",
      "Immediate dominator of 4: 3\n",
      "Immediate dominator of 5: 4\n",
      "Immediate dominator of End: 5\n",
      "Immediate dominator of 6: 5\n",
      "Immediate dominator of 7: 6\n"
     ]
    }
   ],
   "source": [
    "dominator_map = cfg.strict_dominators()\n",
    "queue = [ cfg.start ]\n",
    "while queue:\n",
    "    current = queue.pop()\n",
    "    for node in cfg.nodes():            \n",
    "        dominators = dominator_map[node]\n",
    "            \n",
    "        if current in dominators:\n",
    "            dominators.remove(current)\n",
    "            if not dominators:\n",
    "                print(f\"Immediate dominator of {node}: {current}\")\n",
    "                queue.append(node)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64d2144c",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "Given the dominance information, we can create the _dominator tree_, which is a graph (tree) that contains the same nodes as the CFG, but a node’s parent is its immediate dominator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "7bad12d0",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "def dominator_tree(cfg):\n",
    "    dt = nx.DiGraph()\n",
    "    dt.add_nodes_from(cfg.nodes())\n",
    "    dominator_map = cfg.strict_dominators()\n",
    "    queue = [ cfg.start ]\n",
    "    while queue:\n",
    "        current = queue.pop()\n",
    "        for node in cfg.nodes():            \n",
    "            dominators = dominator_map[node]\n",
    "            \n",
    "            if current in dominators:\n",
    "                dominators.remove(current)\n",
    "                if not dominators:\n",
    "                    dt.add_edge(current, node)\n",
    "                    queue.append(node)\n",
    "\n",
    "    return ProgramGraph(dt, cfg.start, cfg.end)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "1ebde388",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "dt = dominator_tree(cfg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "5d1500a7",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "889b6ed2",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Post-Dominance"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d69ef17",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "Node $b$ post-dominates $a$ if every path from $a$ to the exit node passes through $b$. The calculation of the post-dominance information uses the identical algorithm as the dominance calculation, except that, since the relation is inverted, the entire CFG is reversed before applying the algorithm. A graph is reversed by (1) inverting the direction of all its edges, and (2) swapping start and end nodes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "4f2e6034",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "class ProgramGraph(ProgramGraph):\n",
    "    def reverse(self):\n",
    "        reverse_cfg = self.graph.reverse()\n",
    "        return ProgramGraph(reverse_cfg, self.end, self.start)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71ebffcc",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "Here's a simple example to invert."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "e0b72453",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "code = \"\"\"\n",
    "  public boolean foo(int x) {\n",
    "     if (x == 42) {\n",
    "       return true;\n",
    "     } else if (x == 0) {\n",
    "       return false;\n",
    "     }\n",
    "     System.out.println(\"Hallo\");\n",
    "     return false;\n",
    "  }\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddb9b030",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "First we consider the original CFG."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "f46f120d",
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tree = parse_method(code)\n",
    "cfg = CFGBuilder(tree).create_graph()\n",
    "cfg.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1553fe0d",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "...and now the reversed CFG."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "4035a171",
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rcfg = cfg.reverse()\n",
    "rcfg.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3180bba8",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "The post-dominators can now be immediately calculated by reversing the CFG, and then calculating the dominators on the resulting reversed CFG."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "cb9b1eaa",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "class ProgramGraph(ProgramGraph):\n",
    "    def post_dominators(self):\n",
    "        rcfg = self.reverse()\n",
    "        return rcfg.dominators()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "eb7341eb",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "cfg = CFGBuilder(tree).create_graph()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "129fc8c3",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "End: End\n",
      "Start: 3,End,Start\n",
      "3: 3,End\n",
      "4: End,4\n",
      "5: End,5\n",
      "6: End,6\n",
      "8: End,9,8\n",
      "9: End,9\n"
     ]
    }
   ],
   "source": [
    "for node, postdominators in cfg.post_dominators().items():\n",
    "    dom = \",\".join([str(d) for d in postdominators])\n",
    "    print(f\"{node}: {dom}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54b6994b",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "Similarly to the dominator tree, we can also define a postdominator-tree, in which each node has its immediate post-dominator as a parent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "664a8aa6",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "def postdominator_tree(cfg):\n",
    "    rcfg = cfg.reverse()\n",
    "    return dominator_tree(rcfg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "cd6e96ef",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pdt = postdominator_tree(cfg)\n",
    "pdt.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "301b6c2b",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Control Dependence"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0abd127",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "Intuitively, a statement $b$ in the program is control dependent on statement $a$, if $a$ directly determines whether $b$ will be executed or not.\n",
    "\n",
    "We define $b$ to be control dependent on $a$ if:\n",
    "- There exists a directed path from $a$ to $b$ in the control flow graph where all nodes (excl. $a$) are post-dominated by $b$.\n",
    "- $a$ is not post-dominated by $b$.\n",
    "- For $b$ to be control dependent on $a$, $a$ must have two exits, one of which goes through $b$."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0fe7ccd",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "The algorithm to calculate control dependencies works as follows:\n",
    "\n",
    "1. Create a new graph that contains the same node as the CFG (and DT and PDT).\n",
    "2. Find $S$, a set of edges $(a,b)$ in the CFG such that $b$ is not an ancestor of $a$ in the post-dominator tree.\n",
    "3. For each edge $(a,b) \\in S$, find $L$, the least common ancestor $L$ of $a$ and $b$ in the post-dominator tree.\n",
    "4. Consider each edge $(a,b) \\in S$ and its corresponding $L$. Traverse backwards in the post-dominator tree from $b$ to $L$, marking each node visited; mark $L$ only if $L = a$.\n",
    "5. Statements representing all marked nodes are control dependent on $a$; add edges to $a$ from all marked nodes.\n",
    "6. All remaining nodes that are not connected in the end are only dependent on entry\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a7f093d",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "We thus need a helper function that gives us the least common ancestor of two nodes in a graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "e5b45a3b",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "class ProgramGraph(ProgramGraph):\n",
    "    def transitive_successors(self, node):\n",
    "        return nx.nodes(nx.dfs_tree(self.graph, node))\n",
    "    \n",
    "    def least_common_ancestor(self, node1, node2):\n",
    "        current = node1\n",
    "        successors = self.transitive_successors(current)\n",
    "\n",
    "        while not (node1 in successors) or not (node2 in successors):\n",
    "            current = list(self.graph.predecessors(current))[0]\n",
    "            successors = self.transitive_successors(current)\n",
    "        \n",
    "        return current  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eeb3f705",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "The first step of the algorithm requires a function to help us determine if one node is an ancestor of another node. We determine this using reachability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "510f9ca5",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "class ProgramGraph(ProgramGraph):\n",
    "    def is_reachable(self, source, target):\n",
    "        successors = self.transitive_successors(source)\n",
    "        return target in successors"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53fd637d",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "Whenever we have found control dependencies we add edges, and in the end some nodes will not be connected since they are executed uncondintionally. To find these nodes, we can look for nodes without incoming edges."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "4a46dbf9",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "class ProgramGraph(ProgramGraph):\n",
    "    def in_degree(self, node):\n",
    "        return self.graph.in_degree(node)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea8ce6ef",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "We will just connect all unconditionally executed nodes to the start node."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6936dbd8",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "The actual algorithm just puts together these steps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "591c42e7",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "def control_dependence_graph(cfg):\n",
    "    pdt = postdominator_tree(cfg)\n",
    "    cdg = nx.DiGraph()\n",
    "    cdg.add_nodes_from(cfg.nodes())\n",
    "    \n",
    "    # 1. Find S, a set of edges (a,b) in the CFG such that\n",
    "    # b is not an ancestor of a in the post-dominator tree.\n",
    "    edges = set()\n",
    "    for a, b in cfg.edges():\n",
    "        if not pdt.is_reachable(b, a):\n",
    "            edges.add((a, b))\n",
    "\n",
    "    for a, b in edges:\n",
    "        # 2. For each edge (a,b) in S, find L, the least common ancestor\n",
    "        # of a and b in the post-dominator tree.\n",
    "        lca = pdt.least_common_ancestor(a, b)\n",
    "\n",
    "        # Traverse backwards in the post-dominator tree from b to L,\n",
    "        # marking each node visited; mark L only if L = a.\n",
    "        # Statements representing all marked nodes are control dependent on a.\n",
    "        current = b\n",
    "        while current != lca:\n",
    "            cdg.add_edge(a, current)\n",
    "            current = list(pdt.predecessors(current))[0]\n",
    "\n",
    "        if a == lca:\n",
    "            cdg.add_edge(a, lca)\n",
    "\n",
    "    # Any nodes not connected at this point are only dependent on entry\n",
    "    entry = cfg.start\n",
    "    for node in cdg.nodes():\n",
    "        if node != entry and cdg.in_degree(node) == 0:\n",
    "            cdg.add_edge(entry, node)\n",
    "\n",
    "    return ProgramGraph(cdg, cfg.start, cfg.end)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4609b204",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "We will now look at all the relevant intermediate graphs and the resulting control dependence graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "5a55edd3",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cfg = CFGBuilder(tree).create_graph()\n",
    "cfg.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "b7620c50",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt = dominator_tree(cfg)\n",
    "dt.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "ff9c0eea",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pdt = postdominator_tree(cfg)\n",
    "pdt.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "5ccb78d3",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cdg = control_dependence_graph(cfg)\n",
    "cdg.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d17de3e7",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "Finally, we consider a slightly trickier example which is also included on the lecture slides, to see that the control dependence graph is actually not a tree."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "5d845cb8",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "code = \"\"\"\n",
    "public boolean testMe(int x, int y) {\n",
    "    if (x <= y) {\n",
    "        if (x == y) {\n",
    "            System.out.println(\"Some output\");\n",
    "        }\n",
    "        if (x > 0) {\n",
    "            if (y == 17) {\n",
    "               return true;\n",
    "            }\n",
    "        }\n",
    "    }\n",
    "    return false;\n",
    "}\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "2c721509",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tree = parse_method(code)\n",
    "cfg = CFGBuilder(tree).create_graph()\n",
    "cfg.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "cf2be26d",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt = dominator_tree(cfg)\n",
    "dt.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "866acd82",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAADnCAYAAAC9roUQAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAAA3m0lEQVR4nO3dd1iT5/oH8G/QMAIKKoRRxFUcBSJIwIF1FBU9bdUKaBFXsVqcrdW6rbNi1dr2aJ0doogTa4cLtQ7AulAErK27AloUFQUVAsn9+8OfHCkBAyR5k3B/rovrOifkfZ4738bbxzd5n1dERGCMMaYfZkIXwBhjNQk3XcYY0yNuuowxpkfcdBljTI+46TLGmB7VruiX9vb21LhxYz2VwhhjpiE5OTmHiBzU/a7Cptu4cWOcOXNGN1UxxpiJEolEf5f3Oz69wBhjesRNlzHG9KjC0wuMGaKioiJkZ2dDoVDA3Nwcjo6OEIvFQpfFmEa46TKDR0RISkrC5s2bcfr0aVy4cAF2dnawsLBAYWEhcnNz4eHhAT8/P4SFhSEgIAAikUjoshlTi08vMINFRIiJiYG3tzciIiLg5uaGZcuWITs7G1lZWbh27RqysrKQnZ2NZcuWwc3NDREREfD29kZMTAx4XxFmiEQVvTHlcjnxtxeYEDIzMzFy5Ej8888/+PzzzxEYGAgzs5evEVQqFQ4dOoTJkyfD2dkZa9euhaurqx4qZux/RCJRMhHJ1f2OV7rM4CQmJsLX1xft27fHyZMn0b17d40aLgCYmZmhe/fuOHXqFNq1awdfX18kJSXpuGLGNMfndJlBSUxMxDvvvIOYmBgEBQVVeRyxWIxPP/0Ubdu2Rd++fbFr1y4EBARosVLGqoZXusxgZGZmIjg4uNoN90VBQUGIiYlBv379kJmZqZUxGasObrrMIBARRowYgbFjx2qt4T4XFBSEMWPGYOTIkfzhGhMcN11mEDZt2oTs7GxMnTpVJ+NPmzYNt2/fRmxsrE7GZ0xT3HSZ4IgIS5Ysweeff66zixzEYjEWL16MxYsX82qXCYqbLhNcUlISCgoKEBgYqLUx58yZg0GDBpV6LDAwEE+fPsXx48e1Ng9jlcVNlwlu8+bNiIiIgJmZGRo3bgwrKyvY2NiU/IwdO1Yr85iZmSEiIoJPMTBBcdNlgjt9+nSpr3P98ssvyM/PL/lZsWKF1uYKCAjg7UqZoLjpMkEVFRXhwoUL8Pb2rvB569evR8eOHTFp0iTUq1cPTZo0wd69e0t+f/36dXTu3Bl16tRB9+7dkZOTo3YcHx8fpKeno6ioSJsvgzGNcdNlgsrOzoadnR1sbGxe+tyTJ0+iRYsWyMnJweTJkzF8+PCSD8UGDhwIX19f5OTkYNasWYiOjlY7ho2NDWxtbZGdna3V18GYpviKNCYohUIBCwuLUo/17dsXtWv/7625ZMkSiMViNGrUCCNGjAAADB06FKNHjy7Z4vH06dM4ePAgLCws0KlTJ7z99tvlzmlhYQGFQqGbF8TYS/BKlwnK3NwchYWFpR7btWsXcnNzS36eN1onJ6eS50gkEgBAfn4+bt26hXr16sHa2rrk940aNSp3zsLCQpibm2vzZTCmMW66TFCOjo7Izc1Ffn5+lcdwdnbGgwcP8Pjx45LHbt68qfa5eXl5ePjwIRwdHas8H2PVwU2XCUosFsPDwwMpKSlVHqNRo0aQy+WYPXs2FAoFEhMT8csvv6h9bkpKCjw9PflOE0ww3HSZ4Pz8/Eptv/j222+X+p7uO++889IxYmNjcfLkSdSvXx9z587FkCFD1D4vKSkJcrnabU4Z0wvexJwJLjExEREREfjzzz813je3KlQqFVq2bIkffviBt3lkOsWbmDODFhAQACsrKxw6dEin8xw6dAgSiQQdOnTQ6TyMVYSbLhOcQqFAkyZNMHbsWJ1dtFBUVIRPPvkEn3zyCd+0kgmKmy4T1O+//w4fHx8QEVxdXbFo0SKdzLNgwQL8/fffqFWrFu8yxgTFTZcJIj8/Hx999BH69euHuXPnYteuXYiOjsaKFSuwf/9+rc61f/9+rF69GtHR0Zg/fz769u2LrKwsrc7BmKa46TK9O3DgALy8vPDgwQOkp6cjNDQUIpEIrq6u2LlzJwYNGqS1xrt//34MGjQIO3fuRO/evXH27Fn4+PjA29sb69at41Uv0z8iKvfH19eXGNOW+/fvU0REBLm5udGePXvUPkepVFK7du3I1taW5s6dSwqFokpzKRQKmjFjBkmlUkpMTCzz+9TUVJLL5dS1a1e6cuVKleZgrDwAzlA5fZVXukwvfvzxR3h6esLKygrp6eno1auX2udFRUWhVq1aOHfuHE6cOAF/f38cOHAAKpVKo3lUKhUOHDgAf39/bN26FW3atFH7bQUvLy/8/vvvePPNN9G2bVssW7YMSqWyWq+RMY2U142JV7pMC/755x8KDQ0ld3d3OnbsWIXPPXDgADk7O1NmZiYREalUKoqJiSGZTEbu7u4UFRVFx44do7y8vFLH5eXl0bFjxygqKorc3d1JJpNRTEwMPX36lPz8/Gjp0qUVznv58mXq3Lkz+fv7U1paWvVeMGNU8UqXmy7TCZVKRdHR0SSVSmnq1Kn05MmTCp+fkZFBTk5OdOjQIbVjJSYm0ujRo8nf358kEgk5OzuTvb09OTs7k0QiIX9/fxo9ejQlJiaSSqUqOfbGjRsklUpf2vCVSiWtWbOG7O3tac6cOVRYWFi1F84YcdNlenbjxg0KCgoib29vSk5OfunzCwsLqX379vTZZ59pNL5CoaCMjAwCQBkZGS8977tnzx5ycXGh27dvv3TsjIwMeuutt8jT05NOnjypUT2M/VtFTZfP6TKtUalU+Oabb+Dr64tOnTrh1KlTaNOmzUuPmzx5Mho0aKDx7dfFYjFcXV0BAK6uri/dvKZXr14YPnw43n33XRQXF1f4XFdXV/z888+YPn06evfujUmTJuHJkyca1cWYJrjpMq3466+/0LlzZ8TGxiIxMRHTp0/XaCevbdu24eeff8aGDRt0uu/C7NmzYW5ujpkzZ770uSKRCGFhYUhLS8Pt27chk8lw+PBhndXGapjylsDEpxeYBhQKBUVFRVGDBg3ov//9LymVSo2PvXjxItnb29OZM2eqNPezt6/m7ty5Qw0bNqSffvqpUsf9/PPP5OrqSiNHjqTc3NxKHctqJvDpBaYL586dQ9u2bfHbb7/hzJkzGDdunMar1cePHyMkJASfffYZfH19dVzpMw4ODti6dSvef/99XL16VePj3n77baSnp0MkEsHT07PcvXoZ00h53Zh4pcvK8fTpU5o2bRo5ODjQ+vXrS31bQBMqlYrCw8NpyJAhlT72RajkSve5r7/+mry9vV/6jQp1fvvtN2rWrBmFhYXRnTt3qjQ/M33glS7TlqSkJHh7e+PSpUtITU3F0KFDK71r1+rVq5GamopVq1YJsuPXuHHj0Lx5c4wbN67Sx3bt2hWpqal45ZVX4OXlhdjYWL6UmFVOed2YeKXLXvDo0SMaO3YsOTs7044dO6o8zqlTp8jBwYEuXbpU7ZpQxZUu0bPX07JlS/r++++rPMapU6fIy8uL3nzzTbp582aVx2GmB7zSZdWxf/9+eHl5IT8/H+np6QgODq7SOPfu3UNoaChWr14Nd3d3LVdZOXXq1EFcXBwmT55c5fuz+fn54cyZM/D390ebNm2wZs0ajS9XZjVYed2YeKVb4927d4+GDh1KjRo1ov3791drLKVSST179qSJEydqqbrqrXSfi42NpWbNmtGDBw+qNU56ejq1bduWOnfurJVVPDNu4JUuq6y4uDh4enrC1tYW6enp6NGjR7XGW7BgAfLz8xEVFaWlCrUjLCwMPXv2xHvvvVetc7MeHh5ISkpCnz590L59eyxduvSlF2KwGqq8bky80q2Rbt26Rf369aOWLVuq3RKxKvbv30/Ozs6UlZWllfGegxZWukREBQUF5O/vT4sXL9bKeFevXqU33niD5HI5nT9/XitjMuMCXumylyEi/PDDD2jdujVatWqFc+fOaeWOuRkZGRgyZAhiY2Ph4uKihUq1z8LCAtu3b8cXX3yBo0ePVnu8pk2b4uDBg4iMjERgYCA+/fRTFBYWaqFSZgq46TLcuHEDQUFBWL58OeLj47FgwQJYWlpWe1yFQoHQ0FB89NFH6NKlS/UL1SE3NzdER0dj4MCBuH37drXHE4lEGD58OM6fP4/U1FS0adMGJ06c0EKlzNhx063BlEol/vvf/0IulyMwMBCnTp2Ct7e31safNGkSpFIpJk+erLUxdSkoKAgjRozQaGMcTbm4uODHH3/E7Nmz8c4772DChAl4/PixVsZmxombbg118eJFvP7669i+fTuSkpIwZcoU1K5dW2vjb9myBbt370Z0dLRON7LRtlmzZsHS0hLTp0/X2pgikQj9+/dHeno6cnJy4OXlhUOHDmltfGZkyjvZS/xBmklSKBS0YMECatCgAX3zzTeV2qBGU3/88QfZ29vT2bNntT72i6ClD9L+7e7du+Tm5kY//vijTsbfvXs3NWzYkIYPH17tr6oxwwT+II0BQHJyMuRyORITE3H27FmMHj1a66vQ/Px8BAcHY9GiRfDx8dHq2Ppib2+Pbdu2YeTIkbhy5YrWx//Pf/6D9PR0mJubw8PDA7t27dL6HMyAldeNiVe6JuPJkyc0ZcoUkkqltGHDhmptMlMRlUpFYWFh9N577+lk/H+Djla6z61YsYJat25dpY1xNHX06FFyd3en0NBQ+ueff3Q2D9Mv8Eq35jp27Bhat26N69evIzU1FYMHD9bZJjMrV67EH3/8gW+++UYn4+vb6NGj8dprr2HMmDE6m6NTp044f/48mjZtCplMho0bN/IGOqauvG5MvNI1ag8fPqTRo0eTi4uLzs5Nvuj3338nBwcHunz5ss7neg46XukSPbvTcKtWrejbb7/V+Vxnzpyh1q1bU69evejvv//W+XxMd8Ar3Zplz5498PLyQmFhIdLT09G3b1+dzpeTk4MBAwZg7dq1ePXVV3U6l77Z2NggLi4OU6dOxblz53Q6l6+vL06fPo2AgAD4+vpi5cqVvIGOKSqvGxOvdI3O3bt3adCgQdSkSRM6ePCgXuYsLi6mHj160CeffKKX+V4EPax0n9uyZQs1bdpUb982+OOPP6h9+/b0+uuv019//aWXOZn2gFe6po2IsG3bNnh5ecHe3h5paWkIDAzUy9zz589HQUEBFi5cqJf5hDJgwAC8+eabGDJkiF5Wn61atUJCQgJCQkLQoUMHfP7557yBjqkorxsTr3SNQlZWFvXp04dee+01+v333/U69969e8nFxYVu3bql13mfgx5XukREhYWF1K5dO4qKitLrvNeuXaPu3btTmzZt6Ny5c3qdm1UNeKVreogI3333Hby9vSGTyXD27Fm0a9dOb/PfvHkTw4YNw+bNm+Hs7Ky3eYVkbm6Obdu24auvvtLrLdmbNGmC/fv3Y9y4cejRowdmzJiBgoICvc3PtIubrhG6du0aunXrhlWrVuHgwYOYN28eLCws9DZ/YWEhQkNDMXHiRHTq1Elv8xqChg0bYuPGjQgPD8etW7f0Nq9IJMKwYcNw/vx5XLx4ET4+Pjh+/Lje5mfaw03XiCiVSnz55Zfw9/dHr169cOLECchkMr3XMXHiRLi4uGDSpEl6n9sQdO/eHZGRkRgwYACKior0OrezszN27tyJBQsWICQkBOPHj0d+fr5ea2DVw03XSFy4cAEBAQH46aefcOLECUyaNEmrG9RoKjY2Fvv27cMPP/wgyJ18DcXMmTNhY2ODadOmCTJ/cHAw0tPT8ejRI3h5eSE+Pl6QOlgVlHeyl/iDNINQWFhIc+fOJXt7e1q9erVONqjRVHp6Otnb21NKSopgNbwIev4g7d9ycnKoUaNGFBcXJ2gd+/bto0aNGtGwYcPo3r17gtbCngF/kGacTp8+DblcjlOnTuHcuXP44IMPBNsmMS8vD8HBwVi8eDFat24tSA2GpkGDBti+fTsiIyNx+fJlweoICgpCWloabGxs4Onpibi4OMFqYRoorxsTr3QF8/jxY5o0aRI5OjrSpk2bdLZBjaZUKhX179+fhg8fLmgd/waBV7rPrVy5kry8vOjx48dCl0IJCQnUokULCg4Optu3bwtdTo0FXukajyNHjkAmkyErKwtpaWkYOHCg4OdOly9fjkuXLmH58uWC1mGoIiMjIZPJMHr0aME3q+nYsSNSUlLQvHlzyGQyrF+/XvCa2L+U142JV7p6lZubSyNHjiRXV1f6+eefhS6nxPHjx8nBwYGuXr0qdCllwEBWukRE+fn55OHhQWvXrhW6lBJnz54lb29v6tGjB12/fl3ocmoU8ErXsP3666/w9PQEAKSnp+Ptt98WuKJn7t69iwEDBuDbb79F06ZNhS7HoFlbWyMuLg7Tp0/H2bNnhS4HAODj44NTp06hS5cukMvlWL58OW+gYwjK68bEK12du3PnDoWFhVGzZs3ot99+E7qcUoqLi6lbt240depUoUspFwxopfvctm3bqEmTJgb3LYKLFy9SQEAABQQE0MWLF4Uux+SBV7qGhYiwefNmeHl5wcXFBampqejatavQZZUyd+5cKJVKzJ8/X+hSjEpoaCh69+6tt41xNNWyZUscO3YMYWFh6NixIxYuXKj3CzvY/yuvGxOvdHUiIyOD3nrrLfL09KSTJ08KXY5ae/bsoVdeecXgbx8DA1zpEj37bnX79u3ps88+E7oUtW7cuEFBQUHk7e1NycnJQpdjksArXeGpVCqsXbsWPj4+kMvlSE5Ohr+/v9BllfH333+XbGTj6OgodDlG6fnGOMuXLzfIW603atQIe/fuxYQJE9CrVy9MmzYNT58+FbqsGoObrh5cuXIFgYGB+O6773D48GHMnj0b5ubmQpdVRmFhIUJCQjB58mS8/vrrQpdj1FxdXbFx40YMGjQIWVlZQpdThkgkwpAhQ5CamoorV67A29sbiYmJQpdVI3DT1aHi4mIsXboU7dq1Q+/evXH8+PGSbykYogkTJsDNzQ0ff/yx0KWYhG7dumHs2LGCbIyjKUdHR2zfvh1RUVEYMGAAxo4di7y8PKHLMmncdHUkLS0NHTp0wJ49e3Dy5ElMmDABtWrVErqscsXExODgwYP4/vvvBb8Yw5RMmzYNdnZ2mDJlitClVKhfv35IT0/H06dP4enpib179wpdkukq72Qv8QdpVVJQUECffvop2dvb07p16wS/hFcTaWlpZG9vT+fPnxe6lEqBgX6Q9m/37t2jxo0b0/bt24UuRSMHDhygxo0b0+DBgyknJ0focowS+IM0/Th58iR8fX2RkpKClJQUvP/++wa/anz06BGCg4PxxRdfCLI3b01Qv359bN++HaNGjcKlS5eELuelunXrhrS0NNSvXx+enp7Yvn07X0qsTeV1Y+KVrsby8/NpwoQJ5OTkRFu2bDGK1S3Rs41sQkJCaOTIkUKXUiUwkpXuc6tXryZPT0/Kz88XuhSNHT9+nFq1akV9+/YV7F54xgi80tWdQ4cOwcvLC3fv3kVaWhoGDBhg8Kvb577++mtcu3YNX3/9tdCl1AgjR46Ej48PRo0aZTQrx/bt2+PcuXPw9PRE69at8f333xtN7QarvG5MvNKt0IMHD+j999+nhg0b0q+//ip0OZWWmJhIUqmUrl27JnQpVQYjW+kSPdu209PTk1avXi10KZWWkpJCvr6+1K1bN6N+3+gDeKWrXT/99BM8PT0hFouRnp6ON998U+iSKuXOnTt499138f3336NJkyZCl1OjSCQSxMXFYebMmThz5ozQ5VRK69atceLECfTo0QN+fn74+uuvoVQqhS7L+JTXjYlXumVkZ2fTgAED6NVXX6UjR44IXU6VFBcXU2BgIE2fPl3oUqoNRrjSfW779u3UqFEjo/12wF9//UWdOnWidu3a0YULF4Qux+CAV7rVQ0SIiYmBl5cXGjVqhNTUVHTu3Fnosqpk9uzZICLMmzdP6FJqtJCQEPTr1w+DBw82qI1xNNW8eXMcPnwYQ4cORefOnTF//nwoFAqhyzIO5XVj4pUuERH9/fff1KtXL5LJZHT69Gmhy6mWX3/9lVxdXSk7O1voUrQCRrzSJSJSKBQUEBBA8+fPF7qUarl586bJ/BnRFlSw0uWmWw6lUkkrV64ke3t7mj9/PikUCqFLqpZr166RVCqlxMREoUvRGmNvukREWVlZ5OzsTAcOHBC6lGpRqVQUExNDUqmUPvnkE3ry5InQJQmqoqbLpxfUuHTpErp06YINGzbg6NGjmDlzJsRisdBlVVlBQQFCQkIwdepUBAQECF0Oe4GLiws2bdqEwYMHIzMzU+hyqkwkEiE8PBxpaWm4efMmZDIZjh49KnRZBomb7guKi4uxePFidOjQAcHBwUhMTMRrr70mdFnV9uGHH6Jp06b46KOPhC6FqdG1a1eMHz8e/fv3N/rzolKpFFu2bMHSpUsRHh6OUaNG4dGjR0KXZVC46f6/8+fPo23btjhw4ABOnz6NDz/80KA3qNHUhg0bcOTIEXz33XdGc9FGTTRlyhQ0aNAAkydPFroUrejTpw/S09OhVCrh6emJ3bt3C12S4SjvvAPVkHO6T58+pRkzZpCDgwN9//33RnMJryZSU1PJ3t6e0tLShC5FJ2AC53RfdP/+fWrSpAlt3bpV6FK06tChQ9S0aVMKDw+nu3fvCl2OXqCCc7q1hW76Qjp+/DiGDx+OVq1a4fz583B2dha6JK15+PAhgoOD8eWXXxr0Hr6aysjIwN27d8s8/vzOu9bW1mjRooW+y9KqevXqYceOHQgKCoJMJkPLli2FLkkr3njjDaSmpuLTTz+Fp6cnvvrqK6O6XF7ryuvGZMIr3by8PBo/fjw5OzvT9u3bTWp1S/Tsk+R+/fpRZGSk0KVojUwmI0tLS6pbt27JDwCqW7cuWVtbk7m5ORUWFgpdplasXbuWPDw8jGpjHE2dOHGCXnvtNerduzdlZmYKXY7OgL+98D8HDhyAl5cXcnNzkZaWhpCQEKP+G3fHjh3o2LEjbt++XfLYl19+iZs3b+Krr74SrjAtmzhxImrXro1Hjx6V/ADPtqYsKipCRESEQd4CqSref/99yOVyfPDBByWbyzx58gTh4eFGf863bdu2OHv2LHx8fODt7Y1169bVvA10yuvGZGIr3fv379N7771Hbm5utHfvXqHL0ZrBgweTSCQiOzs7OnLkCCUkJJBUKqXr168LXZpWFRUVkYuLCwEo82NlZWVy2w4+fvyYZDIZrVy5ki5fvkzNmjUjsVhM7u7uQpemNampqeTn50ddu3alK1euCF2OVsEYLo5QKBSUkZFBV69epYyMDK1ejBAXF0cuLi40duxYevTokdbG1TVNMmnevHlJ87G0tKQ6deoY5a5nmoiOjiYbG5tSDVcsFtOIESOELk0nLl26RHXq1CErKysSiUQlr1fd+0CXf350qbi4mJYuXUoNGjSgL774goqLi7U2tpCZGGTTValUlJCQQKNHjyY/Pz+SSCTk4uJCTZo0IRcXF5JIJOTn50ejR4+mhISEcs+7pqen059//qn2d7dv36bg4GBq0aIFJSQk6Oy1aEtlMyksLCSxWFymCXXv3p0ePHgg9MvRmue5REZGUv369UksFpOdnR05ODiQnZ2dxu8VY1JUVETjx48nc3PzUv9969SpQ+fPn9fanx9DcfnyZerSpQv5+/uX+22bnJycCjeaMqRMDKrpqlQq2rhxI8lkMnJ3d6dFixZRQkIC5eXllXpeXl4eJSQk0KJFi8jd3Z1kMhlt3LixVFCFhYX0yiuv0CuvvEJPnz4tNcf69evJwcGBpk2bVup3hqiqmbRs2ZIsLS3L/HPb3NycnJ2d6dy5c8K8IC3R5nvFmNy6dYt8fX1JIpGU+W9rbW1NI0aMMMlMlEolrVmzhuzt7WnOnDllPhgNCgoic3NzunnzZqnHDfF9YjBNNyMjg3r16kU+Pj4UHx9PSqVSo+OUSiXFx8eTt7c39erVizIyMoiIaOHChSSRSMjKyoomTpxIREQ3btygoKAg8vb2puTkZK3WrwvVzcTd3V3tH87atWtT69atdVu8Dmn7vWJMRowYQbVq1VJ7/loikdBrr71m0plkZGTQW2+9RZ6ennTy5EkierYVpkQiodq1a1Pv3r1LPdcQ3ycG0XSff8Azb968Kp9bUSgUNHfuXJJKpbRr165SzcbKyoomT55MDRo0oIULFxrFOS1tZTJz5kyysrIqWQnZ2dnRkiVLjPYrR9p+rxjbJj/FxcW0adMmatSoUalz2FZWVjRr1qwakYlKpaLY2FhydHSk0aNHk62tbam/eI4ePWrQ7xPBm25CQgLZ29vTvn37tDLevn37qE6dOmVWA5aWlpSamqqVOXRNF5lIJBKaMGGCwZ9OqYgucrG3tzf4JqOOUqmknTt3UsOGDUkikdTITO7cuUNubm5lVvwuLi4G/T4RtOlmZGSQVCrVWjjP7du3r2R19+LfgPPmzdPqPLqgy0ykUqnB//OxPJxLWTU9k71796o9fWZlZWXQmQjWdFUqFfXs2VNnjXDmzJll/oNYWVnRxYsXdTKfNug6k7lz51KvXr0M+gMTdTiXsmp6Jo8ePSIHBwe157VnzZqlkzm1lYlgTXfjxo3k4+Ojs/OrCoWC3N3dS3149PwrU4ZKH5l4e3tTTEyMTsbXFc6lrJqeyaxZs6hWrVplvjbn7u5u8JkI0nRVKhXJZDKKj4+vVvEvEx8fT/b29hQREUFRUVG0adOmcr+3KzR9ZiKTyQx2BfNvnEtZnMmzr85t376dli1bRmPGjKHu3btTvXr1jCITQZpuQkICNW/eXOOvcISHh5OTkxPVqVOH3N3dad26dRodp1Qqyd3d3Sg+FKhsJtbW1qV+zMzMaOzYsS89zpgyIap8LtevX6devXqRnZ0dOTo60pgxY6ioqOilxxlTLpXN5I8//qCuXbtS3bp1qVmzZrRz506NjjOlTJYvX06+vr5kbm5OQ4cOLXn8woUL5OvrS3Z2dmRnZ0eBgYEV3sFYG5lU1HR1tuHN5s2bERERATMzzaaYNm0abty4gUePHuHnn3/GzJkzkZyc/NLjzMzMEBERgdjY2OqWrHOVzSQ/P7/k559//oGVlRVCQ0NfepwxZQJUPpfRo0dDKpXi9u3bSElJwdGjR7Fy5cqXHmdMuVQmk+LiYvTp0wdvvfUW7t+/j7Vr12LQoEG4dOnSS481pUxcXFwwc+ZMRERElHl8x44duH//PnJyctC7d2+8++675c6j60x01nRPnz5dqftxeXh4wMLCAsCz+y2JRCJcvXpVo2MDAgJw5syZKtWpT5XN5EVxcXGQSqV4/fXXNXq+sWQCVD6X69evo3///rC0tISTkxN69uyJCxcuaHSsseRSmUz+/PNP3Lp1CxMmTECtWrXwxhtvICAgABs3btToeFPJpF+/fujbty8aNGhQ6nE7Ozs0btwYIpEIRIRatWrhypUrFc6l00zKWwJTNU4vKBQKkkgkZS7De5lRo0aVfA3Mx8dH4+Pz8vJIIpEY9AURVc3kua5du9Ls2bM1fr4xZEJUtVxWr15NgwcPpsePH1NmZiZ5eHho/M9pY8ilspmkpaWRtbV1qXOQ3bp1o759+2p0vKllMmPGjFKnF56ztbWlWrVqkUgkeult76ubCfR9eiE7Oxt2dnawsbGp1HErV65EXl4eEhIS0K9fv5KV78vY2NjA1tYW2dnZVSlXL6qaCQD8/fffOHr0KIYOHarxMcaQCVC1XDp16oQLFy6gbt26cHV1hVwuR9++fTU61hhyqWwmLVq0gFQqxZIlS1BUVIT4+HgcPXoUT5480eh4U8xEndzcXDx8+BArVqyAj49Phc/VZSY6aboKhULjhvlvtWrVQseOHZGZmYlVq1ZpfJyFhYVB30m1Opls3LgRHTt2RJMmTSp1nKFnAlQ+F5VKhZ49e6Jfv354/PgxcnJy8ODBA0yZMkXjMQw9l8pmIhaLsWvXLuzevRtOTk744osv0L9/f7i6umo8hqllUh5ra2tERkZiyJAhuHPnToXP1VUmOmm65ubmKCwsrNYYxcXFGp/TBYDCwkKDvnNAdTLZsGFDpVa5zxl6JkDlc7l//z5u3ryJsWPHwsLCAg0aNMB7772HPXv2aDyGoedSlfeKTCbD0aNHce/ePezfvx/Xrl2Dv7+/xsebYiblUalUePLkCbKysip8nq4y0UnTdXR0RG5uLvLz8zV6/p07d7Blyxbk5+dDqVRi//792Lx5MwIDAzU6Pi8vDw8fPoSjo2N1ytapymby3PHjx5GVlaXRtxZeZAyZAJXPxd7eHk2aNMGqVatQXFyM3NxcREdHQyaTaXS8MeRSlfdKamoqCgoK8OTJEyxduhS3b9/GsGHDNDrWVDIpLi5GQUEBlEollEolCgoKUFxcjAMHDuDcuXNQKpV49OgRPv74Y9SrVw+tWrUqdyxdZqKTpisWi+Hh4YGUlBSNni8SibBq1Sq4urqiXr16mDRpEr766iv07t1bo+NTUlLg6ekJsVhcjap1q7KZPBcdHY1+/fqhTp06lTrOGDIBqpbLzp07sW/fPjg4OODVV1+FWCzGl19+qdGxxpBLVTLZuHEjnJ2dIZVKcejQIRw4cEDjf46bSiYLFiyAlZUVFi1ahJiYGFhZWWHBggXIzc1FWFgYbG1t0axZM1y9ehX79u2DpaVluWPpMhOd3YLdz88PSUlJ6Nix40uf6+DggKNHj1Z5rqSkJMjl8iofry+VyeS5NWvWVGkuY8kEqHwu3t7eOHLkSJXmMpZcKpvJkiVLsGTJkirNZSqZzJkzB3PmzFH7u8r+S1GnmZT3tQbSwhVp7u7uGl9RU1XGdkUNZ1IW51IWZ1KWMWUCIa5ICwgIgJWVFQ4dOqSrKQAAhw4dgkQiQYcOHXQ6jzZwJupxLmVxJmWZTCbldWMykl3GDHmXJHU4E/U4l7I4k7KMJRMIsdIFgPDwcDg5OWHRokU6GT8qKgouLi4YOHCgTsbXBc5EPc6lLM6kLJPIpLxuTEZw5wipVEqZmZlaHVcfOBP1OJeyOJOyjCETCH2PtMTERIO+n5EQOBP1OJeyOJOyDD0TwZsu0bOQpFIpzZ07t1p37pwzZ45R3M1UE9rKZNasWWRtbU0HDx7UcoXC4PdKWZxJWYaciUE0XaL/3aPe29vboO5RLyRtZdKnTx8aNmyYQd4BoCr4vVIWZ1KWoWZiME2X6NltSGJiYkgmk5G7uztFRUXRsWPHymzZlpeXR8eOHaOoqChyd3cnmUxGMTExJtNUXqSNTPLy8qhVq1Ya33HDGPB7pSzOpCxDzKSipit69nv15HI56WojXyLC8ePHERsbizNnziA9PR22trYoKiqCWCzGw4cP4enpCblcjoEDB6JDhw4QiUQ6qcVQVDeTixcvolOnTti/fz/atGkj4CvRrvJysbCwQGFhIb9XOBMAhpWJSCRKJiK1l7QJ1nT/raioCNnZ2WjYsCEyMjLg6Oho0NeC60NVMtm6dSumT5+OM2fOoF69enqqVL+e56JQKGBubs7vFXAm6giZiVE03eee31KD/U9lMxk/fjxu3LiBXbt2aXzfMcaY9lTUdPlPpAlaunQp7t69i8WLFwtdCmPsX3S2yxgTjrm5ObZt2wZ/f3+0bdsWXbt2Fbokxtj/45WuiWrYsCE2bNiA8PBw3Lp1S+hyGGP/j5uuCevevTtGjRqFAQMGoKioSOhyGGPgpmvyZsyYgTp16mDatGlCl8IYAzddk2dmZoaNGzdix44d2Llzp9DlMFbjcdOtARo0aIDt27cjMjISly5dErocxmo0bro1hJ+fH+bOnYuQkBA8efJE6HIYq7G46dYgkZGRkMlkGDVqFF+AwphAuOnWICKRCGvWrEFycjLWrVsndDmM1Uh8cUQNY21tjbi4OHTs2BG+vr7w9fUVuiTGahRe6dZALVq0wMqVKxEaGor79+8LXQ5jNQo33RoqNDQUffr0wZAhQ6BSqYQuh7Eag5tuDbZ48WI8ePBAZ3dWZYyVxed0azCxWIxt27ZBLpejbdu2CAwMFLokxkwer3RruFdeeQUxMTEYNGgQsrKyhC6HMZPHTZchMDAQY8eO5Y1xGNMDbroMADBt2jTY2dlhypQpQpfCmEnjpssAPNsYZ8OGDfjxxx+xY8cOocthzGRx02Ul6tevj+3bt2PUqFH466+/hC6HMZPETZeVIpfL8dlnnyE4OBiPHz8WuhzGTA43XVbGiBEj4Ovri8jISN4YhzEt46bLyhCJRFi1ahXOnz+PNWvWCF0OYyaFL45gakkkEuzYsQMdO3aEXC6HXC4XuiTGTAKvdFm5mjdvjtWrVyMkJAT37t0TuhzGTAI3XVahfv36ITg4GIMHD+aNcRjTAm667KUWLVqEvLw8LFy4UOhSGDN63HTZS4nFYmzduhUrV67EwYMHhS6HMaPGTZdpxMXFBZs2bcLgwYORmZkpdDmMGS1uukxjXbt2xYcffoj+/ftDoVAIXQ5jRombLquUyZMnw97eHpMnTxa6FMaMEjddVilmZmaIjo7GL7/8gq1btwpdDmNGh5suq7R69ephx44dGDt2LP7880+hy2HMqHDTZVXi4+ODqKgoBAcHIz8/X+hyGDMa3HRZlQ0fPhz+/v744IMPeGMcxjTETZdVmUgkwjfffIP09HSsWrVK6HIYMwq84Q2rFolEgri4OHTo0AFyuRz+/v5Cl8SYQeOVLqu2V199FWvWrEH//v15YxzGXoKbLtOKd955B6GhoRg0aBBvjMNYBbjpMq2JiorCkydPsGDBAqFLYcxgcdNlWlO7dm1s2bIFa9asQXx8vNDlMGaQuOkyrXJ2dkZsbCyGDh2KjIwMocthzOBw02Va17lzZ0yYMAGhoaG8MQ5j/8JNl+nEJ598AicnJ0ycOFHoUhgzKNx0mU6IRCKsX78ee/fuxZYtW4QuhzGDwU2X6YydnR127NiBcePG4Y8//hC6HMYMAjddplPe3t74/PPPERISwhvjMAZuukwPIiIi0L59e4wYMYI3xmE1HjddphcrVqzAn3/+iW+++UboUhgTFG94w/TCysoKcXFxaN++PeRyOdq1ayd0SYwJgle6TG+aNm2KdevWYcCAAcjJyRG6HMYEwU2X6VXv3r0RFhaG8PBwKJVKocthTO+46TK9W7BgARQKBebPny90KYzpHTddpne1a9fG5s2b8e2332Lfvn1Cl8OYXgn6QVphYSF++eUXFBcXl3r8xSuYWrVqhdatW+u7NMEQEXbv3l3mO60vZuLs7IzOnTvruzStcnJywubNmxEaGoqTJ0+iUaNGQpfEmF6IKvrepFwupzNnzuhs8szMTLi5uUEikcDM7NmiOy8vD3Xq1AEAFBQUICwsDNHR0TqrwdCoVCrUq1cPCoUCYrEYQOlMCgsL0bJlS5w/f17IMrVm6dKl2LZtGxISEmBhYVHyuFKphEgkKnlfMGZMRCJRMhHJ1f1O0He0q6sr3nnnHRQUFCAvLw95eXkAUPK/a9eujenTpwtZot6ZmZlhypQpEIlEajMxNzfHnDlzhC1SiyZOnAhXV1d8/PHHJY+lpaWhcePGWLFihYCVMaYbgi8jFi5cWLKie1GtWrXQs2dPtGjRQoCqhDV+/HjUrq3+zI+TkxP69u2r34J0SCQS4YcffkB8fDxiY2MRHR2Ndu3aITMzEwcPHhS6PMa0TvCm26JFC/znP/8p889IsViMqKgogaoSlo2NDaZOnQpLS8tSj1tbW2Px4sUQiUQCVaYbtra2iI2NRUREBCIjI/HkyRMAQHJystrnFxUVITMzE9euXUNmZiaKior0WS5j1SLYB2lEhKSkJGzevBlXr15FrVq1ULduXYjFYhQVFeHp06cYPHgw/Pz8EBYWhoCAAJNrNv/2YiYnT56EUqmEnZ1dSSaPHz9GVFQUDh48aFKZ3LhxA0OGDIFKpSrVQO/cuYO8vDzY2NiU5HL69GlcuHABdnZ2sLCwQGFhIXJzc+Hh4VGj3ivMeOl9pUtEiImJgbe3NyIiIuDm5oYVK1bg/v37ePDgAe7cuYMHDx4gJycHy5Ytg5ubGyIiIuDt7Y2YmBiT3DBFXSZfffVVmUzu379vcpns3bsXMpkMly5dKrNitbKywuLFi0vlsmzZMmRnZyMrKwvXrl1DVlYWsrOzTS4XZsKIqNwfX19f0qaMjAzq1asX+fj4UHx8PCmVSo2OUyqVFB8fT97e3tSrVy/KyMjQal1CqumZBAUFkaWlJQEo82NjY0Oenp41Mhdm3ACcoXL6qt6abkJCAkmlUpo3bx4pFIoqjaFQKGju3LkklUopMTFRa7UJhTMhUqlUtHfvXvLy8iJra+uShmtlZUWzZs2qsbkw4yZ4001ISCB7e3vat2+fVsbbt28f2dvbG/UfJs6kNJVKRYcPH6ZWrVqRRCLhXJhRE7TpZmRkkFQq1dofouf27dtHUqnUKP/5yJmox7kwU1FR09XpB2lEhBEjRmDs2LEICgrS6thBQUEYM2YMRo4caVQfmHAm6nEurMYorxuTFla6GzduJB8fnyqfl3sZhUJB3t7eFBMTo5PxdYEzUY9zYaYEQpxeUKlUJJPJKD4+vnrVv0R8fDzJZDJSqVQ6nUcbOBP1OBdmaipqujo7vZCUlIQHDx5g9uzZsLW1Rf369REQEIDTp09j/fr16NixY7XGv3HjBkQiETp37oynT5/i+PHjWqpcd5KSklBQUIDAwMBKHXf58mVYWlpi0KBBGj0/MDDQaDIBKp9Lly5dYGlpCRsbG9jY2Gh8qbix5cJMk86abnR0NO7evYtx48bh/v37yMrKwuzZs0vtJFVVL24FaWZmhoiICMTGxlZ7XF3bvHkzIiIiKr1z1pgxY+Dn56fx840pE6BquaxYsQL5+fnIz8/HX3/9pdExxpYLM006a7rHjx9H7dq1ERYWhlq1asHKygo9evSAWCxGZGQkfv/9d9jY2MDOzg4AsHv3bvj4+KBu3bpo2LBhqZ20nq9qv/vuO7i5ueGNN95Ap06dAAB2dnaYN28eDh8+rKuXojWnT59GQEBApY7ZsmUL7OzsKr06DggIgC635dSmquRSVcaUCzNR5Z13oGqc01UoFGRlZUX16tWjIUOG0J49e+j+/fslv//hhx8oICCg1DGHDx+m1NRUUiqVdP78eZJKpfTjjz8SEdH169cJAA0ePJjy8/PpyZMnJY8VFRVRXl4eSSQSnX0Iow0KhYIkEgnl5eVpfMzDhw/J3d2dMjIyaPbs2RQeHq7xscaQCVHVcuncuTPZ29tTgwYNqEOHDnT48GGNjzWWXJhxg77P6WZnZ6NevXpISkqCSCTCiBEj4ODggN69eyM7O1vtMV26dIGXlxfMzMwgk8kQFhaGo0ePlnrOnDlzYG1tDSsrq1KP29jYwNbWttyxDUF2djbs7OxgY2Oj8TGzZs3C8OHD4erqWun5jCEToGq5fP755yX7LowcORJvv/02rl69qtGxxpILM106aboKhQIWFhZo1aoV1q9fj8zMTKSnp+PWrVv46KOP1B5z8uRJdO3aFQ4ODrC1tcXq1avL3Ka7YcOG5c5pYWEBhUKhzZehVc8z0VRKSgoOHjyICRMmVHlOQ88EqHwuANC2bVvUqVMHFhYWGDp0KAICArBnzx6NjzeGXJjp0knTNTc3R2FhYanHWrZsiWHDhiE9PV3ttnsDBw5E7969kZGRgYcPHyIyMrLMF9lfPO7fYxQWFsLc3FyLr0K71GVSkSNHjuDGjRtwc3ODk5MTli5diri4OLRp00bjMQw9E6DyuagjEokqddGDMeTCTJdOmq6joyPu37+PhQsXIjMzEwCQkZGBzZs3o127dnB0dERmZmap1UZeXh7q168PS0tLnDp16qWfMDs4OMDMzAzXrl1DXl4eHj58CEdHR128HK1wdHREbm5umRtOlmfkyJG4evUqUlJSkJKSgsjISLz55pvYv3+/RscbQyZA5XPJzc3F/v37UVBQgOLiYmzatAnHjh1Dz549NTreWHJhpksnTVcsFqNFixY4ePAg2rZtC2tra7Rr1w6enp744osv8MYbb8DDwwNOTk6wt7cHAKxcuRKffvop6tSpg3nz5qF///4VziGRSDBjxgwEBATAxcUFjRs3VnvbH0MhFovh4eGBlJQUjZ4vkUjg5ORU8mNjYwNLS0s4ODhodHxKSgo8PT0NOhOg8rkUFRVh5syZcHBwgL29PZYvX45du3ahefPmGh1vLLkw06WzO0cEBATAzc0Nv/32m9rf7969u9T/DwkJQUhIiNrnNm7cWO0/H+fNm4d58+Zh0aJFyMjIqH7ROubn54ekpKQqXRhS2ZtRJiUlQS5XezNSg1OZXBwcHHD69Okqz2VMuTATVd7XGqialwEnJCSQu7u7xptPV5VSqSR3d3ej2LqPM1GPc2GmBkJcBhwQEAArKyscOnRIV1MAAA4dOgSJRIIOHTrodB5t4EzU41xYjVJeNybeZUwnOBP1OBdmSiDUfrrh4eFwcnLCokWLdDJ+VFQUXFxcMHDgQJ2MrwuciXqcC6sxyuvGZCR3jsjMzNTquPrAmajHuTBTAaHvkZaYmMj3A/sXzkQ9zoWZAsGbLtGzP0xSqZTmzp1brTu8zpkzx2Tu8MqZqMe5MGNXUdPV6TndFwUEBCA5ORknTpyAv78/Dhw4AJVKpdGxKpUKBw4cgL+/P06ePInk5GS9bQWoS5yJepwLM2nldWPS8kr3OZVKRTExMSSTycjd3Z2ioqLo2LFjZbb2y8vLo2PHjlFUVBS5u7uTTCajmJgYk7zVCmeiHufCjBUqWOmKqIKNQuRyOelqw2ciwvHjxxEbG4szZ84gPT0dtra2sLCwQGFhIR4+fAhPT0/I5XIMHDgQHTp0ULtRjinhTNTjXJixEYlEyUSk9tJHwZruvxUVFSE7OxsKhQLm5uZwdHSs8dfHcybqcS7M0FXUdHW290JlicXiKm3Wbco4E/U4F2bM9PZBGmOMMW66jDGmVxWe0xWJRHcB/K2/chhjzCQ0IiK1m19X2HQZY4xpF59eYIwxPeKmyxhjesRNlzHG9IibLmOM6RE3XcYY06P/AxypWQY47tpgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pdt = postdominator_tree(cfg)\n",
    "pdt.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "57f1bf55",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cdg = control_dependence_graph(cfg)\n",
    "cdg.plot()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}