code/ILP-progol-tag-alloc-and-write-nodes.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prolog Experiment: Tag the memory allocation and write nodes.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now run the progol commands below in Blue Crystal, as we need progol 4.4 compiled with GCC 5.4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import tempfile\n",
    "import subprocess\n",
    "import graph_visualisation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Idea:* in [previous experiments](./ILP-joern-ey-into-progol.ipynb) we've only tagged the memory write as a bug. This makes sense, since this is the spot where the program will crash. However, the memory allocation is also pretty important, since this is where the mistake lies. In this notebook we tag both nodes as buggy, like:\n",
    "\n",
    "`bug(MemoryAllocNode, MemoryWriteNode).`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This also has the potential to solve some complexity issues. Previously we've gotten rules which navigate across to the referenced pointer, but are unable to move up the ast edges to the memory allocation node. E.g."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "graph_visualisation.jupyter_display_graph(graph_visualisation.prolog_rule_to_dot(\n",
    "        \"bug(A) :- writeToPointer(A), ancestor(A,B), ancestor(A,C), ast(A,D), ast(A,E), ref(E,F), sizeOf(C), ancestor(D,B), ast(C,B), ast(D,C).\"\n",
    "))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We believe the memory allocation is a parent of `F`, and so we want it to find a rule like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "graph_visualisation.jupyter_display_graph(graph_visualisation.prolog_rule_to_dot(\"\"\"\n",
    "bug(A) :- writeToPointer(A), ancestor(A,C), ancestor(A,E), ref(E,F), sizeOf(C), ancestor(C,B), \n",
    "\tancestor(G,F), ancestor(G,H), alloc(H), ancestor(H,I), not(sizeOf(I)).\n",
    "\"\"\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By directly tagging `A` and `G`, maybe our system will only have to find:\n",
    "  \n",
    "  - the small sub graphs rooted at `A` and `G` respectively\n",
    "  - the reference edge linking them"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the section below we load in the dataset as in the \"Joern-ey into Progol\" notebook. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>testcase_ID</th>\n",
       "      <th>filename</th>\n",
       "      <th>code</th>\n",
       "      <th>flaw</th>\n",
       "      <th>flaw_loc</th>\n",
       "      <th>bug</th>\n",
       "      <th>code_length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>62804</td>\n",
       "      <td>000/062/804/CWE121_Stack_Based_Buffer_Overflow...</td>\n",
       "      <td>/* TEMPLATE GENERATED TESTCASE FILE\\nFilename:...</td>\n",
       "      <td>CWE-121</td>\n",
       "      <td>33</td>\n",
       "      <td>False</td>\n",
       "      <td>1722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>62821</td>\n",
       "      <td>000/062/821/CWE121_Stack_Based_Buffer_Overflow...</td>\n",
       "      <td>/* TEMPLATE GENERATED TESTCASE FILE\\nFilename:...</td>\n",
       "      <td>CWE-121</td>\n",
       "      <td>35</td>\n",
       "      <td>False</td>\n",
       "      <td>1808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>62852</td>\n",
       "      <td>000/062/852/CWE121_Stack_Based_Buffer_Overflow...</td>\n",
       "      <td>/* TEMPLATE GENERATED TESTCASE FILE\\nFilename:...</td>\n",
       "      <td>CWE-121</td>\n",
       "      <td>30</td>\n",
       "      <td>False</td>\n",
       "      <td>1674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>62853</td>\n",
       "      <td>000/062/853/CWE121_Stack_Based_Buffer_Overflow...</td>\n",
       "      <td>/* TEMPLATE GENERATED TESTCASE FILE\\nFilename:...</td>\n",
       "      <td>CWE-121</td>\n",
       "      <td>33</td>\n",
       "      <td>False</td>\n",
       "      <td>2396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>62854</td>\n",
       "      <td>000/062/854/CWE121_Stack_Based_Buffer_Overflow...</td>\n",
       "      <td>/* TEMPLATE GENERATED TESTCASE FILE\\nFilename:...</td>\n",
       "      <td>CWE-121</td>\n",
       "      <td>33</td>\n",
       "      <td>False</td>\n",
       "      <td>2414</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   testcase_ID                                           filename  \\\n",
       "0        62804  000/062/804/CWE121_Stack_Based_Buffer_Overflow...   \n",
       "1        62821  000/062/821/CWE121_Stack_Based_Buffer_Overflow...   \n",
       "2        62852  000/062/852/CWE121_Stack_Based_Buffer_Overflow...   \n",
       "3        62853  000/062/853/CWE121_Stack_Based_Buffer_Overflow...   \n",
       "4        62854  000/062/854/CWE121_Stack_Based_Buffer_Overflow...   \n",
       "\n",
       "                                                code     flaw  flaw_loc  \\\n",
       "0  /* TEMPLATE GENERATED TESTCASE FILE\\nFilename:...  CWE-121        33   \n",
       "1  /* TEMPLATE GENERATED TESTCASE FILE\\nFilename:...  CWE-121        35   \n",
       "2  /* TEMPLATE GENERATED TESTCASE FILE\\nFilename:...  CWE-121        30   \n",
       "3  /* TEMPLATE GENERATED TESTCASE FILE\\nFilename:...  CWE-121        33   \n",
       "4  /* TEMPLATE GENERATED TESTCASE FILE\\nFilename:...  CWE-121        33   \n",
       "\n",
       "     bug  code_length  \n",
       "0  False         1722  \n",
       "1  False         1808  \n",
       "2  False         1674  \n",
       "3  False         2396  \n",
       "4  False         2414  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ilp_data = pd.read_csv(\"../data/ilp_dataset.csv.gz\")\n",
    "ilp_data = ilp_data.drop(\"Unnamed: 0\", axis=1)\n",
    "ilp_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>testcase_ID</th>\n",
       "      <th>flaw</th>\n",
       "      <th>bug</th>\n",
       "      <th>code_length</th>\n",
       "      <th>tree</th>\n",
       "      <th>source_map</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-232086</td>\n",
       "      <td>CWE-122</td>\n",
       "      <td>True</td>\n",
       "      <td>1625</td>\n",
       "      <td>% START: Generated Prolog\\n% NODE PROPERTIES \\...</td>\n",
       "      <td>% CODE\\nsource_code(bad_232086_id_1_f_l_c_, \"p...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-232012</td>\n",
       "      <td>CWE-122</td>\n",
       "      <td>True</td>\n",
       "      <td>1619</td>\n",
       "      <td>% START: Generated Prolog\\n% NODE PROPERTIES \\...</td>\n",
       "      <td>% CODE\\nsource_code(bad_232012_id_0_f_l_c_, \"p...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-62917</td>\n",
       "      <td>CWE-121</td>\n",
       "      <td>True</td>\n",
       "      <td>1622</td>\n",
       "      <td>% START: Generated Prolog\\n% NODE PROPERTIES \\...</td>\n",
       "      <td>% CODE\\nsource_code(bad_62917_id_0_f_l_c_, \"p1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-62916</td>\n",
       "      <td>CWE-121</td>\n",
       "      <td>True</td>\n",
       "      <td>1649</td>\n",
       "      <td>% START: Generated Prolog\\n% NODE PROPERTIES \\...</td>\n",
       "      <td>% CODE\\nsource_code(bad_62916_id_1_f_l_c_, \"p1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-62915</td>\n",
       "      <td>CWE-121</td>\n",
       "      <td>True</td>\n",
       "      <td>1638</td>\n",
       "      <td>% START: Generated Prolog\\n% NODE PROPERTIES \\...</td>\n",
       "      <td>% CODE\\nsource_code(bad_62915_id_0_f_l_c_, \"p1...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   testcase_ID     flaw   bug  code_length  \\\n",
       "0      -232086  CWE-122  True         1625   \n",
       "1      -232012  CWE-122  True         1619   \n",
       "2       -62917  CWE-121  True         1622   \n",
       "3       -62916  CWE-121  True         1649   \n",
       "4       -62915  CWE-121  True         1638   \n",
       "\n",
       "                                                tree  \\\n",
       "0  % START: Generated Prolog\\n% NODE PROPERTIES \\...   \n",
       "1  % START: Generated Prolog\\n% NODE PROPERTIES \\...   \n",
       "2  % START: Generated Prolog\\n% NODE PROPERTIES \\...   \n",
       "3  % START: Generated Prolog\\n% NODE PROPERTIES \\...   \n",
       "4  % START: Generated Prolog\\n% NODE PROPERTIES \\...   \n",
       "\n",
       "                                          source_map  \n",
       "0  % CODE\\nsource_code(bad_232086_id_1_f_l_c_, \"p...  \n",
       "1  % CODE\\nsource_code(bad_232012_id_0_f_l_c_, \"p...  \n",
       "2  % CODE\\nsource_code(bad_62917_id_0_f_l_c_, \"p1...  \n",
       "3  % CODE\\nsource_code(bad_62916_id_1_f_l_c_, \"p1...  \n",
       "4  % CODE\\nsource_code(bad_62915_id_0_f_l_c_, \"p1...  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prolog = pd.read_csv(\"../data/ilp_prolog_data.csv.gz\")\n",
    "prolog = prolog.drop(\"Unnamed: 0\", axis=1)\n",
    "prolog.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py:190: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._setitem_with_indexer(indexer, value)\n"
     ]
    }
   ],
   "source": [
    "juliet = pd.read_csv(\"../data/buffer_overflow_data.csv.gz\")\n",
    "juliet.drop(\"Unnamed: 0\", axis=1)\n",
    "\n",
    "prolog['source_code'] = ''\n",
    "prolog['flaw_loc'] = 0\n",
    "prolog['bad_code'] = ''\n",
    "\n",
    "for i in range(len(prolog)):\n",
    "    label = prolog.iloc[i].testcase_ID == juliet.testcase_ID\n",
    "    prolog['source_code'].iloc[i] = juliet.loc[label].iloc[0].code\n",
    "    prolog['flaw_loc'].iloc[i] = juliet.loc[label].iloc[0].flaw_loc\n",
    "    \n",
    "for i in range(len(prolog)):\n",
    "    loc =  prolog.iloc[i].flaw_loc\n",
    "    testcase_ID = prolog.iloc[i].testcase_ID\n",
    "    if testcase_ID < 0:\n",
    "        prolog['bad_code'].iloc[i] = prolog.iloc[i].source_code.split('\\n')[loc-1].strip()[0:-1]\n",
    "    else:\n",
    "        prolog['bad_code'].iloc[i] = prolog.loc[prolog.testcase_ID == -testcase_ID].iloc[0].bad_code\n",
    "        \n",
    "good_examples = prolog[prolog['bug'] == False]\n",
    "bad_examples = prolog[prolog['bug'] == True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_node_ids(tree):\n",
    "    nodes = set()\n",
    "    \n",
    "    in_ast_section = False\n",
    "    \n",
    "    for line in tree.split('\\n'):\n",
    "        line = line.strip()\n",
    "        \n",
    "        if line == '% AST':\n",
    "            in_ast_section = True\n",
    "        \n",
    "        if line == '% CFG':\n",
    "            in_ast_section = False\n",
    "            \n",
    "        if line == '% REF':\n",
    "            in_ast_section = False\n",
    "        \n",
    "        if in_ast_section and not line.startswith(\"%\"):\n",
    "            parent, child = line[4:-2].split(\", \")\n",
    "            nodes.add(parent)\n",
    "            nodes.add(child)\n",
    "            \n",
    "    return nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# good nodes_g\n",
    "nodes_g = set()\n",
    "\n",
    "for i in range(len(good_examples)):\n",
    "    good_nodes = extract_node_ids(good_examples.iloc[i].tree)\n",
    "    nodes_g = set.union(nodes_g,good_nodes)\n",
    "\n",
    "    \n",
    "# bad nodes_b\n",
    "nodes_b = set()\n",
    "\n",
    "for i in range(len(bad_examples)):\n",
    "    bad_nodes = extract_node_ids(bad_examples.iloc[i].tree)\n",
    "    nodes_b = set.union(nodes_b,bad_nodes)\n",
    "\n",
    "nodes = set.union(nodes_g,nodes_b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_bug_id(good_examples, i = 0):\n",
    "    tree = good_examples.source_map.iloc[i]\n",
    "    parents = []\n",
    "    children = []\n",
    "    in_code_section = False\n",
    "    temp = 0\n",
    "    for line in tree.split('\\n'):\n",
    "        line = line.strip()\n",
    "        \n",
    "        if line == '% CODE':\n",
    "            in_code_section = True\n",
    "        \n",
    "        if in_code_section and not line.startswith(\"%\"):\n",
    "            try:\n",
    "                #import pdb; pdb.set_trace()\n",
    "                parent, child = line[12:-3].split(', \"')\n",
    "                parents.append(parent)\n",
    "                children.append(child)\n",
    "            except:\n",
    "                temp +=1\n",
    "    zipbObj = zip(children, parents)\n",
    "    bug_node = dict(zipbObj)\n",
    "    \n",
    "    loc = good_examples.iloc[i].flaw_loc\n",
    "    source_code = good_examples.iloc[i].bad_code\n",
    "\n",
    "    return bug_node[source_code]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "bad_node = []\n",
    "good_node = []\n",
    "for i in range(len(bad_examples)):\n",
    "    bad_node.append(find_bug_id(bad_examples,i))\n",
    "    good_node.append(find_bug_id(good_examples,i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['(good_62821, good_62821_id_101_f_loop_18_c_l_60_c_11_)',\n",
       " '(good_62861, good_62861_id_161_f_memcpy_10_c_l_37_c_12_)',\n",
       " '(good_62913, good_62913_id_192_f_memmove_14_c_l_34_c_16_)',\n",
       " '(bad_62853, bad_62853_id_17_f_l_c_)',\n",
       " '(good_62865, good_62865_id_87_f_l_c_)']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "node_and_tree = []\n",
    "for node in nodes:\n",
    "    start, end = node.split('_id')\n",
    "    node_and_tree.append('(' +start+ ', ' +node+ ')')\n",
    "\n",
    "node_and_tree[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "node_tree_types = [\n",
    "    'node_in_tree'+ node_tree +'.' for node_tree in node_and_tree\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "node_types = [\n",
    "    'node('+ node_id +').' for node_id in nodes\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_tree_ids(tree):\n",
    "    line = tree.split('\\n')[2]\n",
    "    line = line.strip()\n",
    "    line = line.split('(')[1]\n",
    "    tree_id = line.split('_id')[0]\n",
    "\n",
    "    return tree_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "tree_ids = []\n",
    "for tree in prolog.tree:\n",
    "    tree_ids.append(extract_tree_ids(tree))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "trees = [\n",
    "    'tree('+ tree_label +').' for tree_label in tree_ids\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_progol_script(header, positive_examples, negative_examples, testcases=None):\n",
    "    script_template = \"\"\"\n",
    "{header}\n",
    "\n",
    "%% Trees\n",
    "{trees}\n",
    "\n",
    "%% Types\n",
    "{types}\n",
    "\n",
    "%% background knowledge\n",
    "{bg_knowledge}\n",
    "\n",
    "%% learning task\n",
    "%% positive examples\n",
    "{positive_examples}\n",
    "\n",
    "%% negative examples\n",
    "{negative_examples}\n",
    "\n",
    "    \"\"\"\n",
    "    script = script_template.format(\n",
    "        header = header,\n",
    "        trees =  '\\n'.join(trees),\n",
    "        types = '\\n'.join(node_tree_types + node_types),\n",
    "        bg_knowledge = '\\n'.join(prolog['tree']),\n",
    "        positive_examples='\\n'.join(positive_examples),\n",
    "        negative_examples='\\n'.join(negative_examples),\n",
    "    )\n",
    "    \n",
    "    # keep all testcases unless a testcases set is given in fn call\n",
    "    if testcases:\n",
    "        # remove all lines not related to this testcase\n",
    "        script = '\\n'.join([\n",
    "            line for line in script.split(\"\\n\")  if any(\n",
    "                str(testcase) in line for testcase in testcases\n",
    "            ) or not (\"good_\" in line or \"bad_\" in line)\n",
    "        ])\n",
    "    \n",
    "    return script"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we try to locate the buggy nodes. For each test case this will be:\n",
    "  1. Location where the memory is _allocated_.\n",
    "  2. Location where the memory is _written to_.\n",
    "  \n",
    "We already have code above to find the latter. Can we find the first as well?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "bad_write_nodes = bad_node\n",
    "good_write_nodes = good_node"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['bad_232086_id_145_f_memmove_01_c_l_30_c_8_',\n",
       " 'bad_232012_id_145_f_memcpy_01_c_l_30_c_8_',\n",
       " 'bad_62917_id_121_f_memmove_18_c_l_31_c_8_',\n",
       " 'bad_62916_id_133_f_memmove_17_c_l_33_c_8_',\n",
       " 'bad_62915_id_121_f_memmove_16_c_l_33_c_8_',\n",
       " 'bad_62913_id_127_f_memmove_14_c_l_32_c_8_',\n",
       " 'bad_62909_id_121_f_memmove_10_c_l_32_c_8_',\n",
       " 'bad_62902_id_127_f_memmove_03_c_l_32_c_8_',\n",
       " 'bad_62901_id_121_f_memmove_02_c_l_32_c_8_',\n",
       " 'bad_62900_id_121_f_memmove_01_c_l_29_c_8_',\n",
       " 'bad_62869_id_120_f_memcpy_18_c_l_31_c_8_',\n",
       " 'bad_62868_id_133_f_memcpy_17_c_l_33_c_8_',\n",
       " 'bad_62867_id_120_f_memcpy_16_c_l_33_c_8_',\n",
       " 'bad_62865_id_127_f_memcpy_14_c_l_32_c_8_',\n",
       " 'bad_62861_id_120_f_memcpy_10_c_l_32_c_8_',\n",
       " 'bad_62854_id_128_f_memcpy_03_c_l_32_c_8_',\n",
       " 'bad_62853_id_120_f_memcpy_02_c_l_32_c_8_',\n",
       " 'bad_62852_id_121_f_memcpy_01_c_l_29_c_8_',\n",
       " 'bad_62821_id_110_f_loop_18_c_l_34_c_12_',\n",
       " 'bad_62804_id_110_f_loop_01_c_l_32_c_12_']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bad_write_nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# this code is horrible sorry. if you need to figure out what it does speak to Dan.\n",
    "\n",
    "alloc_source_maps = [\n",
    "    line\n",
    "    for source_map in prolog['source_map']\n",
    "    for line in source_map.split(\"\\n\")\n",
    "    if 'data =' in line.lower() and 'alloc' in line.lower()\n",
    "]\n",
    "\n",
    "bad_alloc_nodes = [\n",
    "    source_map.strip(\"source_code(\").split(\",\")[0]\n",
    "    for source_map in alloc_source_maps\n",
    "    if 'bad' in source_map\n",
    "]\n",
    "\n",
    "good_alloc_nodes = [\n",
    "    source_map.strip(\"source_code(\").split(\",\")[0]\n",
    "    for source_map in alloc_source_maps\n",
    "    if 'good' in source_map\n",
    "]\n",
    "\n",
    "positive_examples = []\n",
    "for write_node in bad_write_nodes:\n",
    "    # find the alloc node which has a line number before the write\n",
    "    write_line = int(write_node.split(\"_\")[-4])\n",
    "    \n",
    "    testcase_id = '_'.join(write_node.split(\"_\")[0:2])\n",
    "    \n",
    "    potential_alloc_nodes = [node for node in bad_alloc_nodes if testcase_id in node]\n",
    "    for alloc_node in potential_alloc_nodes:\n",
    "        alloc_line = int(alloc_node.split(\"_\")[-4])\n",
    "        \n",
    "        if alloc_line <= write_line:\n",
    "            positive_examples.append(\n",
    "                \"bug({},{}).\".format(alloc_node, write_node)\n",
    "            )\n",
    "            \n",
    "negative_examples = []\n",
    "for write_node in good_write_nodes:\n",
    "        \n",
    "    # find the alloc node which has a line number before the write\n",
    "    write_line = int(write_node.split(\"_\")[-4])\n",
    "    \n",
    "    testcase_id = '_'.join(write_node.split(\"_\")[0:2])\n",
    "    \n",
    "    potential_alloc_nodes = [node for node in good_alloc_nodes if testcase_id in node]\n",
    "    for alloc_node in potential_alloc_nodes:\n",
    "        alloc_line = int(alloc_node.split(\"_\")[-4])\n",
    "        \n",
    "        if alloc_line <= write_line:\n",
    "            negative_examples.append(\n",
    "                \":- bug({},{}).\".format(alloc_node, write_node)\n",
    "            )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we experiment with settings and different rule structures to see if it can learn a rule:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CProgol Version 5.0\n",
      "\n",
      "[:- set(c,5000)? - Time taken 0.00s]\n",
      "[:- set(i,5000)? - Time taken 0.00s]\n",
      "[:- set(h,10000)? - Time taken 0.00s]\n",
      "[The cover flag has been turned ON]\n",
      "[:- set(cover)? - Time taken 0.00s]\n",
      "[:- set(inflate,2000)? - Time taken 0.00s]\n",
      "[:- modeh(100,bug(+node,+node))? - Time taken 0.00s]\n",
      "[:- modeb(10,runs_before(+node,+node))? - Time taken 0.00s]\n",
      "[:- modeb(2,ancestor(+node,-node))? - Time taken 0.00s]\n",
      "[:- modeb(2,ancestor(-node,+node))? - Time taken 0.00s]\n",
      "[:- modeb(100,ref(+node,-node))? - Time taken 0.00s]\n",
      "[:- modeb(100,ref(-node,+node))? - Time taken 0.00s]\n",
      "[:- modeb(100,ref(+node,+node))? - Time taken 0.00s]\n",
      "[:- modeb(100,assignment(+node))? - Time taken 0.00s]\n",
      "[:- modeb(100,sizeOf(+node))? - Time taken 0.00s]\n",
      "[:- modeb(100,alloc(+node))? - Time taken 0.00s]\n",
      "[:- modeb(100,writeToPointer(+node))? - Time taken 0.00s]\n",
      "[:- modeb(100,alloc_doesnt_check_sizeOf(+node))? - Time taken 0.00s]\n",
      "[:- modeb(100,pointer(+node))? - Time taken 0.00s]\n",
      "[:- modeb(100,sizeOfInt(+node))? - Time taken 0.00s]\n",
      "[:- modeb(100,array10(+node))? - Time taken 0.00s]\n",
      "[:- modeb(100,voidPointer(+node))? - Time taken 0.00s]\n",
      "[Testing for contradictions]\n",
      "[No contradictions found]\n",
      "[Generalising bug/2]\n",
      "[Generalising bug(bad_232086_id_168_f_memmove_01_c_l_25_c_4_,bad_232086_id_145_f_memmove_01_c_l_30_c_8_).]\n",
      "[Generalising <bug(bad_232086_id_168_f_memmove_01_c_l_25_c_4_,bad_232086_id_145_f_memmove_01_c_l_30_c_8_).>]\n",
      "^C\n"
     ]
    }
   ],
   "source": [
    "header = \"\"\"\n",
    "\n",
    "% Settings\n",
    "\n",
    "%:-randomseed?\n",
    ":- set(c,3)?\n",
    ":- set(i,3)?\n",
    ":- set(h,2000)?\n",
    ":- set(cover)?\n",
    ":- set(inflate,1500)?\n",
    "\n",
    "% Mode declarations\n",
    "\n",
    ":- modeh(*, bug(+node,+node))?\n",
    "\n",
    "%:- modeb(*, same_tree(+node,+node))?\n",
    "%:- modeb(*, same_tree(+node,-node))?\n",
    "%:- modeb(*, same_tree(-node,+node))?\n",
    "%:- modeb(*, same_tree(-node,-node))?\n",
    "\n",
    ":- modeb(10, runs_before(+node,+node))?\n",
    "%:- modeb(1, runs_before(+node,-node))?\n",
    ":- modeb(2, ancestor(+node, -node))?\n",
    ":- modeb(2, ancestor(-node, +node))?\n",
    "%:- modeb(5, ast(+node,-node))?\n",
    "%:- modeb(10, cfg(+node, -node))?\n",
    "\n",
    ":- modeb(*, ref(+node,-node))?\n",
    ":- modeb(*, ref(-node,+node))?\n",
    ":- modeb(*, ref(+node,+node))?\n",
    "%:- modeb(*, ref(-node,-node))?\n",
    "\n",
    ":- modeb(*, assignment(+node))?\n",
    "%:- modeb(*, compMemberAccess(+node))?\n",
    ":- modeb(*, sizeOf(+node))?\n",
    ":- modeb(*, alloc(+node))?\n",
    ":- modeb(*, writeToPointer(+node))?\n",
    "\n",
    ":- modeb(*, alloc_doesnt_check_sizeOf(+node))?\n",
    "\n",
    ":- modeb(*, pointer(+node))?\n",
    ":- modeb(*, sizeOfInt(+node))?\n",
    ":- modeb(*, array10(+node))?\n",
    ":- modeb(*, voidPointer(+node))?\n",
    "\n",
    "writeToPointer(A) :- ast(A,B), assignment(A), compMemberAccess(B). \n",
    "\n",
    "ancestor(A,C) :- ast(A,B), ancestor(B,C).\n",
    "ancestor(A,C) :- ast(A,C).\n",
    "\n",
    "runs_before(A,C) :- cfg(A,B), runs_before(B,C).\n",
    "runs_before(A,C) :- cfg(A,C).\n",
    "\n",
    "contains_sizeOf_call(A) :- ancestor(A, B), sizeof(B).\n",
    "alloc_doesnt_check_sizeOf(A) :- alloc(A), not(contains_sizeOf_call(A)).\n",
    "\n",
    "same_tree(A,B) :- node_in_tree(T,A), node_in_tree(T,B).\n",
    "\"\"\"\n",
    "script = make_progol_script(\n",
    "    header,\n",
    "    positive_examples,\n",
    "    negative_examples,\n",
    ")\n",
    "with open(\"/tmp/progol_input.pl\", \"w\") as f:\n",
    "    f.write(script)\n",
    "!/progol/source/progol /tmp/progol_input.pl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"../code/ILP-progol-input.pl\", \"w\") as f:\n",
    "    f.write(script)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below are generated with settings:\n",
    "\n",
    ":- set(c,3)?\n",
    "\n",
    ":- set(i,3)?\n",
    "\n",
    ":- set(h,2000)?\n",
    "\n",
    ":- set(cover)?\n",
    "\n",
    ":- set(inflate,1500)?\n",
    "\n",
    "and rules from commit 0246295"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "graph_visualisation.jupyter_display_graph(graph_visualisation.prolog_rule_to_dot(\n",
    "        \"\"\"bug(A,B) :- ancestor(A,C), ancestor(A,D), ancestor(B,E), ancestor(B,\n",
    "\tF), ancestor(B,G), cfg(A,H), cfg(B,I), assignment(A),\n",
    "\twriteToPointer(B), cfg(C,D), cfg(D,J), cfg(E,F), cfg(F,\n",
    "\tK), cfg(G,E), cfg(H,L), cfg(I,M), ref(H,N), ref(I,O),\n",
    "\tsizeOf(F), alloc(D), alloc_doesnt_check_sizeOf(D), pointer(I),\n",
    "\tsizeOfInt(H), array10(H), ancestor(K,E), ancestor(J,\n",
    "\tC), cfg(K,B), cfg(M,P), cfg(J,A), cfg(L,Q), ref(Q,O),\n",
    "\tassignment(L), pointer(O), sizeOfInt(N), array10(N).\"\"\"\n",
    "))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "graph_visualisation.jupyter_display_graph(graph_visualisation.prolog_rule_to_dot(\n",
    "        \"\"\"bug(A,B) :- ancestor(A,C), ancestor(A,D), ancestor(B,E), ancestor(B,\n",
    "\tF), ancestor(B,G), cfg(A,H), cfg(B,I), assignment(A),\n",
    "\twriteToPointer(B), cfg(E,F), cfg(F,J), cfg(G,E), cfg(C,\n",
    "\tD), cfg(D,K), cfg(I,L), cfg(H,M), ref(I,N), ref(H,N),\n",
    "\tsizeOf(F), alloc(D), alloc_doesnt_check_sizeOf(D), pointer(I),\n",
    "\tpointer(H), ancestor(K,C), ancestor(J,E), cfg(K,A), \n",
    "\tcfg(M,O), cfg(L,P), cfg(J,B), pointer(N).\"\"\"\n",
    "))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below is using the same as above with adding in ast(1,+,-) and ast(1,-,+)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "graph_visualisation.jupyter_display_graph(graph_visualisation.prolog_rule_to_dot(\n",
    "        \"\"\"bug(A,B) :- runs_before(A,B), ancestor(A,C), ancestor(B,D), ancestor(E,\n",
    "\tA), ancestor(E,B), assignment(A), writeToPointer(B), \n",
    "\truns_before(A,D), runs_before(D,B), runs_before(C,A),\n",
    "\truns_before(C,B), runs_before(C,D), ancestor(E,D), ancestor(E,\n",
    "\tC), ancestor(E,F), ancestor(G,F), ancestor(H,G), voidPointer(G),\n",
    "\tancestor(E,H), ancestor(H,F), ref(H,I).\"\"\"\n",
    "))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Changing 1 to 2 in the ancestor mode declaration, i.e.\n",
    "\n",
    "```\n",
    ":- modeb(2, ancestor(-node, +node))?\n",
    ":- modeb(2, ancestor(+node, -node))?\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "header = \"\"\"\n",
    "\n",
    "% Settings\n",
    "\n",
    "%:-randomseed?\n",
    ":- set(c,5000)?\n",
    ":- set(i,5000)?\n",
    ":- set(h,10000)?\n",
    "%:- set(r,5000)?\n",
    ":- set(cover)?\n",
    ":- set(inflate,2000)?\n",
    "%:- set(posonly)?\n",
    "%:- set(memoing)?\n",
    "%:- set(nodes,20000)?\n",
    "%:- set(reductive)?\n",
    "%:- set(splitting)?\n",
    "%:- generalise(bug/20)?\n",
    "\n",
    "% Mode declarations\n",
    "\n",
    ":- modeh(*, bug(+node,+node))?\n",
    "\n",
    "%:- modeb(*, same_tree(+node,+node))?\n",
    "%:- modeb(*, same_tree(+node,-node))?\n",
    "%:- modeb(*, same_tree(-node,+node))?\n",
    "%:- modeb(*, same_tree(-node,-node))?\n",
    "\n",
    ":- modeb(10, runs_before(+node,+node))?\n",
    "%:- modeb(1, runs_before(+node,-node))?\n",
    ":- modeb(2, ancestor(+node, -node))?\n",
    ":- modeb(2, ancestor(-node, +node))?\n",
    "%:- modeb(5, ast(+node,-node))?\n",
    "%:- modeb(10, cfg(+node, -node))?\n",
    "\n",
    ":- modeb(*, ref(+node,-node))?\n",
    ":- modeb(*, ref(-node,+node))?\n",
    ":- modeb(*, ref(+node,+node))?\n",
    "%:- modeb(*, ref(-node,-node))?\n",
    "\n",
    ":- modeb(*, assignment(+node))?\n",
    "%:- modeb(*, compMemberAccess(+node))?\n",
    ":- modeb(*, sizeOf(+node))?\n",
    ":- modeb(*, alloc(+node))?\n",
    ":- modeb(*, writeToPointer(+node))?\n",
    "\n",
    ":- modeb(*, alloc_doesnt_check_sizeOf(+node))?\n",
    "\n",
    ":- modeb(*, pointer(+node))?\n",
    ":- modeb(*, sizeOfInt(+node))?\n",
    ":- modeb(*, array10(+node))?\n",
    ":- modeb(*, voidPointer(+node))?\n",
    "\n",
    "writeToPointer(A) :- ast(A,B), assignment(A), compMemberAccess(B). \n",
    "\n",
    "ancestor(A,C) :- ast(A,B), ancestor(B,C).\n",
    "ancestor(A,C) :- ast(A,C).\n",
    "\n",
    "runs_before(A,C) :- cfg(A,B), runs_before(B,C).\n",
    "runs_before(A,C) :- cfg(A,C).\n",
    "\n",
    "contains_sizeOf_call(A) :- ancestor(A, B), sizeof(B).\n",
    "alloc_doesnt_check_sizeOf(A) :- alloc(A), not(contains_sizeOf_call(A)).\n",
    "\n",
    "same_tree(A,B) :- node_in_tree(T,A), node_in_tree(T,B).\n",
    "\n",
    "\"\"\"\n",
    "script = make_progol_script(\n",
    "    header,\n",
    "    positive_examples,\n",
    "    negative_examples,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We the above, obtained the rule below (using the [ILP-progol-input.pl](./ILP-progol-input.pl) script):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "graph_visualisation.jupyter_display_graph(graph_visualisation.prolog_rule_to_dot(\n",
    "\"\"\"bug(A,B) :- ancestor(A,C), ancestor(B,D), assignment(A), writeToPointer(B), alloc_doesnt_check_sizeOf(C), sizeOfInt(D).\"\"\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "FINAL RULE! Condensed down to one rule, covering all positive examples (and no negatives)."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}