notebooks/RQ1_NumberOfCalls.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys, os\n",
    "sys.path.append(os.path.dirname(os.path.abspath(os.path.dirname(''))))\n",
    "\n",
    "import pandas as pd\n",
    "from lib.repo_interface import get_repo_interface\n",
    "from lib import name_utils\n",
    "import json\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.style.use('../style/style-formal.mplstyle')\n",
    "\n",
    "class Dataset:\n",
    "    def __init__(self, name, short_name, formal_name):\n",
    "        self.name = name\n",
    "        self.short_name = short_name\n",
    "        self.formal_name = formal_name\n",
    "    \n",
    "    def get_FL_results(self):\n",
    "        with open(f\"../combined_fl_results/{self.short_name}_gpt3_results_R5_full.json\", 'r') as f:\n",
    "            fl_results = json.load(f) \n",
    "        return fl_results\n",
    "\n",
    "    def get_valid_bugs(self):\n",
    "        validity_data = f\"../data/{self.name}_bug_validity.csv\"\n",
    "\n",
    "        bugs = pd.read_csv(validity_data)\n",
    "        valid_bugs = bugs[bugs.validity == \"ok\"].bug.tolist()\n",
    "        return valid_bugs\n",
    "\n",
    "def called_in_test(ri):\n",
    "    cit_info = {m: list() for m in ri._buggy_methods}\n",
    "    short_names = {m: name_utils.get_method_name(m) for m in ri._buggy_methods}\n",
    "\n",
    "    for test in ri.failing_test_signatures:\n",
    "        snippet = ri.get_test_snippet(test)\n",
    "        for m in cit_info:\n",
    "            if \".\"+short_names[m]+\"(\" in snippet:\n",
    "                cit_info[m].append(test)\n",
    "    return cit_info\n",
    "\n",
    "def exists_in_trace(ri):\n",
    "    eit_info = {m: list() for m in ri._buggy_methods}\n",
    "    short_names = {m: name_utils.get_method_name(m) for m in ri._buggy_methods}\n",
    "    for test in ri.failing_test_signatures:\n",
    "        fail_info = ri.get_fail_info(test)\n",
    "        for m in eit_info:\n",
    "            if \".\"+short_names[m]+\"(\" in fail_info:\n",
    "                eit_info[m].append(test)\n",
    "    return eit_info\n",
    "\n",
    "DATASETS = [\n",
    "    Dataset(\"bugsinpy\", \"bip\", \"BugsInPy\"),\n",
    "    Dataset(\"defects4j\", \"d4j\", \"Defects4J\")\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "meta_info_rows = []\n",
    "method_info_rows = []\n",
    "for dataset in DATASETS:\n",
    "    valid_bugs = dataset.get_valid_bugs()\n",
    "    for bug in valid_bugs:\n",
    "        ri = get_repo_interface(bug)\n",
    "        cit = called_in_test(ri)\n",
    "        eit = exists_in_trace(ri)\n",
    "        method_analysis = {}\n",
    "        for method in cit:\n",
    "            method_info_rows.append(\n",
    "                [dataset.name, bug.split(\"_\")[0], bug, method, len(cit[method]) > 0, len(eit[method]) > 0]\n",
    "            )\n",
    "        meta_info_rows.append(\n",
    "            [dataset.name, bug.split(\"_\")[0], bug, len(ri._fail_info), len(ri._buggy_methods)])\n",
    "\n",
    "df = pd.DataFrame(data=meta_info_rows,\n",
    "            columns=[\"dataset\", \"project\", \"bug_name\", \"num_failing_tests\", \"num_buggy_methods\"])\n",
    "method_info_df = pd.DataFrame(data=method_info_rows,\n",
    "            columns=[\"dataset\", \"project\", \"bug_name\", \"method\", \"called_in_test\", \"exists_in_trace\"])\n",
    "method_info_df[\"(called in test, exists in trace)\"] = method_info_df[['called_in_test', 'exists_in_trace']].apply(tuple, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.groupby([\"dataset\", \"project\"]).agg({\n",
    "    \"bug_name\": \"count\",\n",
    "    \"num_failing_tests\": \"mean\",\n",
    "    \"num_buggy_methods\": \"mean\"\n",
    "}).reset_index().set_index([\"dataset\", \"project\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_call_patterns(dataset, bug, rep=5, model=\"gpt-3.5-turbo-0613\", skip_error_run=True):\n",
    "    result_paths = [\n",
    "        f\"../results/{dataset}_autofl_{r}/{model}/XFL-{bug}.json\"\n",
    "        for r in range(1, rep+1)\n",
    "    ]\n",
    "    call_patterns = []\n",
    "    for rpath in result_paths:\n",
    "        with open(rpath, 'r') as f:\n",
    "            result = json.load(f)\n",
    "\n",
    "            if skip_error_run and \"openai.error.InvalidRequestError\" in result[\"buggy_methods\"]:\n",
    "                continue\n",
    "            \n",
    "            call_patterns.append([])\n",
    "\n",
    "            for m in result[\"messages\"]:\n",
    "                if m[\"role\"] == \"assistant\" and \"function_call\" in m:\n",
    "                     call_patterns[-1].append((m[\"function_call\"][\"name\"], json.loads(m[\"function_call\"][\"arguments\"])))\n",
    "    return call_patterns\n",
    "\n",
    "call_pattern_rows = []\n",
    "for dataset in DATASETS:\n",
    "    for bug in dataset.get_valid_bugs():\n",
    "        call_patterns = get_call_patterns(dataset.short_name, bug, skip_error_run=True)\n",
    "        if call_patterns:\n",
    "            for s in call_patterns:\n",
    "                call_pattern_rows.append([bug, len(s)])\n",
    "\n",
    "call_pattern_df = pd.DataFrame(data=call_pattern_rows, columns=[\"bug_name\", \"call_seq_len\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>called_in_test</th>\n",
       "      <th>exists_in_trace</th>\n",
       "      <th>call_seq_len</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bug_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PySnooper_3</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>5.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ansible_1</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>2.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ansible_14</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>4.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ansible_15</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ansible_16</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>youtube-dl_5</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>youtube-dl_6</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>youtube-dl_7</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>youtube-dl_8</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>youtube-dl_9</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>418 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              called_in_test  exists_in_trace  call_seq_len\n",
       "bug_name                                                   \n",
       "PySnooper_3            False             True           5.6\n",
       "ansible_1               True             True           2.8\n",
       "ansible_14              True            False           4.6\n",
       "ansible_15             False            False           4.4\n",
       "ansible_16              True            False           6.0\n",
       "...                      ...              ...           ...\n",
       "youtube-dl_5           False            False           5.2\n",
       "youtube-dl_6           False            False           5.0\n",
       "youtube-dl_7           False            False           5.2\n",
       "youtube-dl_8           False             True           5.0\n",
       "youtube-dl_9           False            False           5.0\n",
       "\n",
       "[418 rows x 3 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Called in Test, Exists in Trace)\n",
      "(False, False)    5.131699\n",
      "(False, True)     4.969858\n",
      "(True, False)     5.070062\n",
      "(True, True)      4.504938\n",
      "Name: call_seq_len, dtype: float64\n",
      "F_onewayResult(statistic=2.702415941039649, pvalue=0.0452486326444566)\n",
      "Test Multiple Comparison ttest_ind \n",
      "FWER=0.05 method=bonf\n",
      "alphacSidak=0.01, alphacBonf=0.008\n",
      "============================================================\n",
      "    group1         group2      stat   pval  pval_corr reject\n",
      "------------------------------------------------------------\n",
      "(False, False) (False, True)  0.6835 0.4948       1.0  False\n",
      "(False, False) (True, False)  0.2728 0.7852       1.0  False\n",
      "(False, False)  (True, True)  2.8654 0.0045    0.0267   True\n",
      " (False, True) (True, False) -0.3262  0.745       1.0  False\n",
      " (False, True)  (True, True)  1.6785 0.0964    0.5785  False\n",
      " (True, False)  (True, True)  2.0297 0.0449    0.2693  False\n",
      "------------------------------------------------------------\n",
      "('(False, False)', '(True, True)', 2.8654, 0.0045, 0.0267, True)\n",
      "3 0 9.5\n",
      "9.5 6.6000000000000005\n"
     ]
    },
    {
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",
      "text/plain": [
       "<Figure size 1500x750 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>called_in_test</th>\n",
       "      <th>exists_in_trace</th>\n",
       "      <th>call_seq_len</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bug_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Chart_1</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>5.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chart_10</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>3.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chart_11</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>4.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chart_12</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>3.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chart_13</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>4.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time_5</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time_6</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>5.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time_7</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time_8</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time_9</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1.60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>345 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          called_in_test  exists_in_trace  call_seq_len\n",
       "bug_name                                               \n",
       "Chart_1             True            False          5.80\n",
       "Chart_10            True            False          3.40\n",
       "Chart_11            True            False          4.40\n",
       "Chart_12           False            False          3.60\n",
       "Chart_13           False             True          4.75\n",
       "...                  ...              ...           ...\n",
       "Time_5              True             True          5.00\n",
       "Time_6              True             True          5.80\n",
       "Time_7              True             True          4.00\n",
       "Time_8              True             True          3.00\n",
       "Time_9              True            False          1.60\n",
       "\n",
       "[345 rows x 3 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "from scipy.stats import ttest_ind\n",
    "from statsmodels.sandbox.stats.multicomp import MultiComparison\n",
    "\n",
    "for dataset in DATASETS:\n",
    "    anal_df = method_info_df[method_info_df.dataset == dataset.name].groupby(\"bug_name\")[[\"called_in_test\", \"exists_in_trace\"]].any().join(call_pattern_df.groupby(\"bug_name\").call_seq_len.mean())\n",
    "    display(anal_df)    \n",
    "    # combination\n",
    "    bug_type_col = \"(Called in Test, Exists in Trace)\"\n",
    "    anal_df[bug_type_col] = anal_df[['called_in_test', 'exists_in_trace']].apply(tuple, axis=1).astype(str)\n",
    "\n",
    "    fl_results = dataset.get_FL_results()\n",
    "    anal_df = anal_df.join(pd.DataFrame(fl_results[\"confidence\"], index=[\"confidence\"]).T)\n",
    "\n",
    "    bug_counts = anal_df.reset_index().groupby(bug_type_col).bug_name.count().to_dict()\n",
    "\n",
    "    # plt.subplot(1, 2, 1)\n",
    "    plt.figure(figsize=(5, 2.5))\n",
    "    type_order = anal_df.groupby(bug_type_col).call_seq_len.median().sort_values().index.tolist()\n",
    "    ax = sns.boxplot(data=anal_df, x=\"call_seq_len\", y=bug_type_col,\n",
    "                order=type_order, orient=\"h\", fliersize=1)\n",
    "    ax.set_yticklabels([f\"{t}\\n{bug_counts[t]} bugs\" for t in type_order])\n",
    "    ax.set_zorder(100)\n",
    "    ax.set_axisbelow(True)\n",
    "\n",
    "    print(anal_df.groupby(bug_type_col).call_seq_len.mean())\n",
    "    plt.xlabel(\"Avg. Number of Function Calls Made\")\n",
    "\n",
    "    from scipy.stats import f_oneway\n",
    "\n",
    "    print(f_oneway(*anal_df[~anal_df.call_seq_len.isna()].groupby(bug_type_col)[\"call_seq_len\"].apply(list).tolist()))\n",
    "\n",
    "    comp = MultiComparison(anal_df[~anal_df.call_seq_len.isna()][\"call_seq_len\"], anal_df[~anal_df.call_seq_len.isna()][bug_type_col])\n",
    "    result = comp.allpairtest(ttest_ind, method='bonf')\n",
    "    print(result[0])\n",
    "    max_value = 8.2 if dataset.name == \"defects4j\" else 9.5\n",
    "    annot_count = 0\n",
    "\n",
    "    annot_color=\"grey\"\n",
    "    for ttest_result in reversed(result[2]):\n",
    "        group1,group2,stat,pval,pval_corr,reject = ttest_result\n",
    "        if pval < 0.008:\n",
    "            print(ttest_result)\n",
    "            y_pos = (type_order.index(group1) + type_order.index(group2))/2 + 0.05\n",
    "            x_pos = max_value+annot_count*max_value*0.13\n",
    "            print(type_order.index(group1),type_order.index(group2), x_pos)\n",
    "            arrow_width = max([type_order.index(group1),type_order.index(group2)]) - min([type_order.index(group1),type_order.index(group2)])\n",
    "            arrow_width = 2.2 * arrow_width\n",
    "\n",
    "            print(max_value,arrow_width)\n",
    "            plt.annotate(f'p<0.008', xy=(x_pos, y_pos),\n",
    "                         xytext=(0, 0),\n",
    "                         fontsize=8,\n",
    "                         textcoords='offset pixels',\n",
    "                         xycoords=\"data\", va='bottom', ha=\"center\",\n",
    "                         bbox=dict(boxstyle='square', fc='white', color='grey'),\n",
    "                         arrowprops=dict(\n",
    "                             arrowstyle=f']-, widthA={arrow_width}, lengthA=1, angleA=90', lw=1.5, color=annot_color), color=annot_color)\n",
    "            \n",
    "            annot_count += 1\n",
    "\n",
    "    plt.xlim((0.4, 10.9))\n",
    "\n",
    "    plt.savefig(f\"./figures/RQ1_call_pattern_hor_{dataset.name}.pdf\", bbox_inches=\"tight\")\n",
    "    plt.show()\n"
   ]
  }
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