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"source": [
"# DebugTraj-50K — Daily Coding Companion\n",
"\n",
"**This notebook shows how YOU can use this dataset in your daily coding life.**\n",
"\n",
"No ML experience needed. Every example is practical and copy-paste ready.\n",
"\n",
"---\n",
"### What you will learn:\n",
"1. How long will my bug take to fix?\n",
"2. Am I debugging the right way?\n",
"3. What should I do next when stuck?\n",
"4. Which language is hardest to debug?\n",
"5. Build your own personal debugging report\n",
"6. Get smarter search suggestions while debugging\n",
"7. Know when to ask for help\n"
]
},
{
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Dataset loaded successfully!\n",
"Sessions : 50,000\n",
"Events : 665,364\n"
]
}
],
"source": [
"# Setup — run this first\n",
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"import warnings\n",
"warnings.filterwarnings('ignore')\n",
"\n",
"sns.set_theme(style='darkgrid')\n",
"\n",
"BASE = '/kaggle/input/datasets/abhisheksingh016/debugtraj-50k/'\n",
"\n",
"sessions = pd.read_csv(BASE + 'code_debugging_sessions_fixed.csv')\n",
"events = pd.read_csv(BASE + 'code_debugging_events_fixed.csv')\n",
"\n",
"print('Dataset loaded successfully!')\n",
"print(f'Sessions : {len(sessions):,}')\n",
"print(f'Events : {len(events):,}')"
]
},
{
"cell_type": "markdown",
"id": "6e1c9d46",
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"source": [
"---\n",
"## Example 1: How Long Will My Bug Take to Fix?\n",
"\n",
"**Scenario:** You just hit a bug. You want to know — realistically — how long it will take.\n",
"\n",
"Just fill in your details below and run the cell."
]
},
{
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},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Language : Python\n",
"Experience : Mid-level (2-5 years)\n",
"Severity : 3/5\n",
"Based on : 4,127 similar sessions\n",
"\n",
"Expected fix time : 4 minutes\n",
"Fast scenario : 2 minutes (top 25%)\n",
"Slow scenario : 5 minutes (bottom 25%)\n",
"Chance of fixing : 88.3%\n",
"\n",
"Verdict: Quick fix — should be done soon.\n"
]
}
],
"source": [
"# --- FILL IN YOUR DETAILS ---\n",
"my_language = 'Python' # Python, JavaScript, Java, TypeScript, C++, Go, C#, Rust\n",
"my_experience = 'Mid-level (2-5 years)' # Junior (0-2 years) / Mid-level (2-5 years) / Senior (5-10 years) / Staff/Principal (10+ years)\n",
"my_error = 'AttributeError' # e.g. TypeError, NullPointerException, SyntaxError\n",
"my_severity = 3 # 1=minor, 2=low, 3=medium, 4=high, 5=critical\n",
"# ----------------------------\n",
"\n",
"# Filter similar sessions from dataset\n",
"similar = sessions[\n",
" (sessions['programming_language'] == my_language) &\n",
" (sessions['experience_level'] == my_experience) &\n",
" (sessions['error_severity'] == my_severity)\n",
"]\n",
"\n",
"if len(similar) == 0:\n",
" # Relax filter if no exact match\n",
" similar = sessions[\n",
" (sessions['programming_language'] == my_language) &\n",
" (sessions['experience_level'] == my_experience)\n",
" ]\n",
"\n",
"avg_time = similar['resolution_time_minutes'].mean()\n",
"fast_time = similar['resolution_time_minutes'].quantile(0.25)\n",
"slow_time = similar['resolution_time_minutes'].quantile(0.75)\n",
"success = (similar['outcome'] == 'fixed').mean() * 100\n",
"\n",
"print(f'Language : {my_language}')\n",
"print(f'Experience : {my_experience}')\n",
"print(f'Severity : {my_severity}/5')\n",
"print(f'Based on : {len(similar):,} similar sessions')\n",
"print()\n",
"print(f'Expected fix time : {avg_time:.0f} minutes')\n",
"print(f'Fast scenario : {fast_time:.0f} minutes (top 25%)')\n",
"print(f'Slow scenario : {slow_time:.0f} minutes (bottom 25%)')\n",
"print(f'Chance of fixing : {success:.1f}%')\n",
"print()\n",
"if avg_time < 10:\n",
" print('Verdict: Quick fix — should be done soon.')\n",
"elif avg_time < 30:\n",
" print('Verdict: Moderate bug — take it step by step.')\n",
"else:\n",
" print('Verdict: Complex bug — consider asking a colleague.')"
]
},
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"source": [
"---\n",
"## Example 2: Am I Debugging the Right Way?\n",
"\n",
"**Scenario:** You want to compare your debugging habits with developers at your level.\n",
"\n",
"Fill in what you did during your last debugging session."
]
},
{
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Comparing you vs other Mid-level (2-5 years) developers:\n",
"\n",
"Metric You Avg Peer Verdict\n",
"-------------------------------------------------------\n",
"Web Searches 5.0 3.2 Above avg\n",
"Compile Attempts 8.0 6.0 Above avg\n",
"Files Changed 2.0 3.0 Below avg\n",
"Lines Changed 15.0 11.4 Above avg\n",
"\n",
"AI tool usage among peers: 23.2%\n",
"You used AI — same as most developers at your level.\n"
]
}
],
"source": [
"# --- FILL IN YOUR LAST SESSION ---\n",
"my_experience = 'Mid-level (2-5 years)'\n",
"my_searches = 5 # how many times did you Google?\n",
"my_compiles = 8 # how many times did you compile/run?\n",
"my_files_changed = 2 # how many files did you modify?\n",
"my_lines_changed = 15 # how many lines did you change?\n",
"my_used_ai = True # did you use ChatGPT/Copilot?\n",
"# ---------------------------------\n",
"\n",
"peers = sessions[sessions['experience_level'] == my_experience]\n",
"\n",
"metrics = {\n",
" 'Web Searches' : (my_searches, peers['num_web_searches'].mean()),\n",
" 'Compile Attempts': (my_compiles, peers['compile_attempts'].mean()),\n",
" 'Files Changed' : (my_files_changed, peers['files_modified'].mean()),\n",
" 'Lines Changed' : (my_lines_changed, peers['lines_changed'].mean()),\n",
"}\n",
"\n",
"print(f'Comparing you vs other {my_experience} developers:')\n",
"print()\n",
"print(f'{\"Metric\":<20} {\"You\":>8} {\"Avg Peer\":>10} {\"Verdict\":>12}')\n",
"print('-' * 55)\n",
"for metric, (mine, avg) in metrics.items():\n",
" diff = ((mine - avg) / avg) * 100\n",
" if abs(diff) < 20:\n",
" verdict = 'Normal'\n",
" elif diff > 0:\n",
" verdict = 'Above avg'\n",
" else:\n",
" verdict = 'Below avg'\n",
" print(f'{metric:<20} {mine:>8.1f} {avg:>10.1f} {verdict:>12}')\n",
"\n",
"print()\n",
"ai_pct = peers['ai_assistant_used'].mean() * 100\n",
"print(f'AI tool usage among peers: {ai_pct:.1f}%')\n",
"if my_used_ai:\n",
" print('You used AI — same as most developers at your level.')\n",
"else:\n",
" print(f'You did not use AI — {ai_pct:.0f}% of your peers do.')"
]
},
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},
"source": [
"---\n",
"## Example 3: What Should I Do Next When Stuck?\n",
"\n",
"**Scenario:** You have been debugging for a while. What do most developers do in this situation?"
]
},
{
"cell_type": "code",
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"You have been debugging for 20 minutes in Python.\n",
"Searches: 4 | Compile attempts: 6\n",
"Found 3,384 similar sessions in dataset.\n",
"\n",
"Top strategies that WORKED in similar situations:\n",
"\n",
" 1. Index bounds validation (11% of cases)\n",
" 2. Exception handling wrapped (10% of cases)\n",
" 3. Variable initialization fixed (10% of cases)\n",
" 4. Type cast / conversion applied (10% of cases)\n",
" 5. Null/undefined check added (10% of cases)\n",
"\n",
"Tip: Most developers (89%) solved this independently. Keep going!\n"
]
}
],
"source": [
"# --- FILL IN YOUR CURRENT SITUATION ---\n",
"my_language = 'Python'\n",
"my_experience = 'Junior (0-2 years)'\n",
"minutes_so_far = 20 # how many minutes have you been debugging?\n",
"searches_done = 4 # how many searches have you done?\n",
"compiles_done = 6 # how many compile/run attempts?\n",
"# ---------------------------------------\n",
"\n",
"# Find sessions with similar progress\n",
"similar = sessions[\n",
" (sessions['programming_language'] == my_language) &\n",
" (sessions['num_web_searches'] >= searches_done - 1) &\n",
" (sessions['num_web_searches'] <= searches_done + 2) &\n",
" (sessions['compile_attempts'] >= compiles_done - 1)\n",
"]\n",
"\n",
"print(f'You have been debugging for {minutes_so_far} minutes in {my_language}.')\n",
"print(f'Searches: {searches_done} | Compile attempts: {compiles_done}')\n",
"print(f'Found {len(similar):,} similar sessions in dataset.')\n",
"print()\n",
"\n",
"# What strategy worked for similar sessions?\n",
"strategies = similar[similar['outcome']=='fixed']['fix_strategy'].value_counts().head(5)\n",
"print('Top strategies that WORKED in similar situations:')\n",
"print()\n",
"for i, (strategy, count) in enumerate(strategies.items(), 1):\n",
" pct = count / len(similar) * 100\n",
" print(f' {i}. {strategy} ({pct:.0f}% of cases)')\n",
"\n",
"print()\n",
"\n",
"# Should they ask for help?\n",
"ask_rate = similar['asked_colleague'].mean() * 100\n",
"if ask_rate > 40:\n",
" print(f'Tip: {ask_rate:.0f}% of developers in this situation asked a colleague. Consider it!')\n",
"else:\n",
" print(f'Tip: Most developers ({100-ask_rate:.0f}%) solved this independently. Keep going!')\n",
"\n",
"# Break suggestion\n",
"break_rate = similar[similar['resolution_time_minutes'] > minutes_so_far]['took_break'].mean() * 100\n",
"if break_rate > 35:\n",
" print(f'Tip: {break_rate:.0f}% took a short break and then fixed it. Try stepping away for 5 minutes.')"
]
},
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"source": [
"---\n",
"## Example 4: Which Language is Hardest to Debug?\n",
"\n",
"**Scenario:** You are choosing between two languages for your next project. See which one is easier to debug."
]
},
{
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"name": "stdout",
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"text": [
"Language Debugging Difficulty Ranking (easiest to hardest):\n",
"\n",
"Rank Language Avg Time Fix Rate Searches Compiles\n",
"------------------------------------------------------------\n",
"1 Python 3.9m 88.4% 2.6 5.2\n",
"2 JavaScript 3.9m 88.6% 2.5 5.0\n",
"3 TypeScript 4.4m 88.5% 2.6 5.3\n",
"4 C# 5.9m 89.9% 3.7 7.3\n",
"5 Java 6.0m 89.2% 3.3 6.8\n",
"6 Go 6.2m 88.1% 3.7 7.4\n",
"7 C++ 10.3m 88.7% 4.3 8.6\n",
"8 Rust 11.4m 88.7% 4.1 8.1\n",
"\n"
]
},
{
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7duyQdLW4l5ZPSlen9Vq6dKmWLl2qpk2bKjk5WaNGjdKVK1fk7++vESNGaMGCBerbt6+kq0XM559/Plvv4UbXJp07dzYGzKTPQdMvLOvOHLRGjRp69tlnNWvWLC1evFgLFy7Uk08+KUlKTk6+5jXWkSNH1K9fP82aNcsotl64cEHr1q2TdHVE98svv2wUZtu0aWOMaM7sLq+ceOyxxzRlyhTNmzdPS5Ys0YwZM3THHXdIuvqZT/u3nN1rI1euPYHsYuQs4KPSFyvSiq+tW7fWd999J+nqlAdVq1aVJHXo0MEo+qxfv15RUVGKi4vTsWPHJF39Eg0ICJDdbndKCCZNmqTbbrtN0tWk5O2333Zb/OmTtLTiXPrEpEuXLkbhp0ePHtq8ebOxT2YLnj3xxBNG0hUaGqpHHnlE0tXbVLI7DUPa69I8+OCDxq1olSpV0j333HPd/YcOHWqMDihUqJCR2GXX0KFD1aNHD0nS9u3bjZGLhw4d0q233qqvvvrKSITuvvtuY3G16OhoNWnSxLRbcaKiovTUU09Jkr799ltj4Y127doZhciPP/5Ymzdvls1mU3x8vKpUqaLPPvvMKPC1bt3aSAC7dOmizz77TMnJyfrkk080fvx4Wa1Z+9vjCy+8oIYNG0q6uqjE2LFjJV3tr9jYWNWuXVs333yz/v77b33++ecaO3asAgICjNGtJUqUUJ06ddzUMlLdunU1e/ZsLVy4UMePH8/QR+lHXqfXs2dPY2TKwYMHNXnyZElX/y1IVwuiaaNSq1WrZoxwadSokbZt25aj2/HSK1SokKpWraolS5Zoz549SkpKks1mM56/ePGi/vrrL1WpUiXbn521a9c67Z9W0O/atavxx6S1a9eqbdu2Lr0HAACupWDBgnr55ZcVGxur8+fPG3f1lCpVSs8888wNXx8QEKDXXntNw4YNc7oN/cSJE1q1apVWrVqlMWPGqFevXjmKb82aNcbPo0ePVvfu3Y3HaTmn3W43CmaSNHnyZMXExEiSGjdurIceekiSdPbsWW3ZskXS1Xw87fW33nqrGjVqpC+//FJnz57V1q1b1bp1a+MOnYCAAFWoUEE1atRQoUKF1KFDhwxtIF0tHpcvX16RkZEKDg7Wfffdl+33m9ZmacqVK6dnn31Wd955p9N+jRo10qxZs7R9+3adPn06wzRUv/76q6Kjo1W7dm2nW/ErVKjgNPfwhg0bdObMGUlSw4YNjeeaN2+uzz77zPiD+JkzZxQREZGl93Cja5MyZcqoYcOG+vbbb/X999/r9OnTKlq0qHEtFhgYqLvvvjtL58qKWrVqafv27VqxYoWOHDmS4W6ma+Wgd911l1HEvXTpkkaMGCHp/3LQ3377TcePH5d09d/TpEmTFBAQoKZNm+qXX37JdJqM7Kpfv77mz5+vuLg4JSQkOE1l4HA49Ntvv6lUqVLZvjZy5doTyC6Ks4APOn/+vPGFERYWpvr160uSWrVqpRdeeEE2m02fffaZnnzySVksFtWtW1elS5fW8ePH9cUXX2jUqFH6/PPPjeOlTWlw+vRp46/kwcHBRmFWkttXl/3nn3+MnwsXLixJTqNzZ8+erdmzZ2d43V9//ZXp8dIvDpD+5/j4eDkcjmwvwpD+9p30x6tUqZKKFCmSYf7Ta+0fFRWVrfOmlzYSWpLTrXRpxexrnbNIkSKqVKmSMXra0/4bS5rq1asbP6efaiJtpHb6/v9vUp7m3LlzOnnypNNtglmNJf3IkvRt16VLF02ePNm4EKlUqZJxi1zbtm2zXAi+EZvNpkceeeS6/fLf+ZfTpP+3kL7t0v4tpJ87K20kgXR1lEuNGjVcLs5+8cUXxlQp13Kjf5fX+uyk7/f/zoud5sCBA9kNGQCQD2W2IFixYsWy9No6deoY0xukeeGFF4w8NSuv//zzz7V+/Xpt2rRJ27dvd7pjZPLkybrvvvtyNPVU+u/KtFF9/5WQkGCcLzAw0Pjj9H8dPnzYKGCdOnVKDz74YKb7pX33du7cWZ9++qmSk5ONP7iWKlVKderU0cMPP2zkV2l/VP3nn3/UvXt3WSwWlStXTg0aNNAjjzziNHI3u44fP+6UX0hXc/wePXro/Pnz13zdtfKq/0o/NcLXX39tLI6bnsPh0F9//ZXl4mxWrk26du2qb7/9Vqmpqfr000/VuXNn/fTTT5Kkpk2bZvnfXlaMHDnSaRDOf2UlB73R9UjVqlWdpgKrVauWy8XZuLg4Pfzww9dd/yGn10auXHsC2cW0BoAP2rBhgy5fvizp6l+/q1WrpsjISDVo0MAYzRYfH2/cQmWxWNS+fXtje1xcnDGlQdmyZRUdHZ3hHLm5ouz58+eNW/MlZWs+otTUVKc5h3Ii/Yg/ScbcoteS3bZwV9ulT97TrzScfu4td5/THdInkukLm4UKFcp0/8zez/Wk/QEhu67VRp06dTLad+3atU5TGqR9btxhx44dRlJYvHhxvfrqq1q6dKnTVA3Xaov0/xb8/Pyuu39W/i2k3ycrn4f0F6qdO3fWggULtHTpUjVq1MjYntnCXe76d3mtuWoBAEgvswXB0kbDZcV/5y9Nn69mRWhoqLp166a33npL33//vd5++21jKqdLly4ZhZ5rfQ+njd50lcVicfk7OO27NyYmRsuXL9f999+vqlWrKjg4WCdOnNDHH3+s2NhYoyDWrVs3zZ07V/fee69uu+02BQQE6PDhw1qxYoXTehZZMXjwYP3yyy969dVXZbValZqaqpdfftlpLt2PPvrIKMxGRUVp5syZWrp0qdMda9nNMW/E3flIy5YtjYLn2rVrtXXrVuM6x5056LFjx4zCbMGCBTV+/HgtWbLEaW5WT+Wg6WUlB12+fLlRmG3evLnefvttLV261GlEdm7moO649gQkirOAT/rkk0+ytF/6qQ/S33r0xhtvGIlU+/btjS+vokWLGgsaXLx40Wm0mjtuSUnz5ptvGn/hLFiwoDECIH3y/Morr+j333/P8N+uXbsynZQ9Li4u05/LlCljvL+0ouHZs2eNL/mjR49m+hfRtHnHpKsLK6X566+/Moz8u97+rs4xdj3XOmdiYqJX/pU3ff8PHjz4mv1fqVKlLB8zfbuk/3eRfkGH4sWLG3Mub9q0yfh8pd265y7pR4t36NBB9913n9Mtda5I/37Sv+fU1FSnx2nSF9D//fdf4+dt27ZlWvxOH/vYsWPVqFEjRUdHO21Pk93PTvp+X7x4cab9/uWXX2Z4HQAA7vTee+/p22+/lfR/Rahp06Zl6e6Ns2fPZsiVrVarmjZt6pS3pBWjrvU9vHXr1kyPn/67Mm1Kgv8KDw837li6fPmyMdXZf5UvX97IjcuXL689e/Zk+N799ddfjVvCHQ6HoqKi9OKLL+qjjz7Sjh07jCnDkpOTjVGmDodDTZo00WuvvaaPP/5YO3fu1MMPPyzp6gjd7ObEgYGBuu+++4winM1m0/Tp043nT548afw8YMAAtWzZUrVr177meg/pi3X/LealH9XbqVOna+agjRs3znL8Wbk2CQwMNK7R4uLi9M4770i6uj7HtUZI50T6fK1x48Z64IEHVLduXbcsdJU+B92zZ49TwfVa14/ZuSZL388jR45U06ZNVbt2bafPTZrsXhu5cu0JZBfTGgA+JiEhwUi2QkJCNHLkSKfnU1JSNHHiRElXF9p55plnZLVajUURfv/9dyPxlP5vSgPpahLZvHlzY76qp556SoMGDdLx48eztdrpfx06dEg///yzTp48qXXr1jndUjN06FAjkezQoYNxnldeeUWJiYmKjIxUUlKSDh8+rG+//VY33XST04qcaaZMmSJ/f38FBwc7jURMW2lWuvqF/dtvv+nSpUt64oknVKdOHS1btizDX22lq3/JXrZsmaSriwiUKlVKpUuXzrCaZ/r905LladOmqUCBAgoODjbmLMsNd911lyZNmiSHw6EvvvhCM2fOVLVq1bR48WLT5pt1RZs2bTR58mRduXJFb7/9tiwWi2rVqqVLly7p6NGj+uGHH3T58mUtXLgwy8ccN26cRo4cqcuXL2vq1KnG9vT/LqSrt+Jt3LhRly5d0m+//SYpZ4swJCYmZtrnbdq0MRYKka7O/XznnXcqMTHRmD/WFY0aNVJwcLCSk5MVFxenl156STExMVq7dm2mUxqEhoYqLCxMZ8+e1aFDhzRu3DhVqlRJ8+fPz/T4ZcqUMW79mjZtmmJiYrRmzZpMFxvL7menQ4cOxmjl//3vf3rsscd0880368yZM/r777+1ZcsWNWnSRIMHD85J0wAAcEPx8fF67bXXJF39zhs/frwGDBigy5cva/To0Vq+fLnTqMH/SkxMVPfu3VWrVi21bNnSGDn6ww8/GKM9AwMDFRkZKenqnWtWq1V2u10//PCDpkyZopCQkGuu79CxY0djcaVXXnlFp0+fVo0aNfTPP//o/fff14oVK2S1WtW+fXtjrv8nnnhCgwYNUqVKlXTkyBFt3LhRc+fONRYC3rJliw4fPqyBAweqa9euCgkJ0bFjx7Rnzx59+eWXeu+991S2bFlNmDBBp06dUsOGDVW6dGn5+flp27ZtRmxpxbWhQ4cqJCREd955p0qVKiWbzeY0j2lORx/27dtXH330kRwOhzZu3KgDBw7olltuccqrlixZooCAAO3evVsrV67M9Djpp9r64osvVLZsWfn7+6tmzZpq2LChIiIidObMGa1evVpFihRRw4YNZbfbFR8frx07dmjfvn3ZWqA0K9cm0tUcNG0Ea9oCZi1btjRGXGdHZjlow4YNdcsttxiPf/jhB61bt05Wq9UpN86patWqGdPnnTx5Uv/73//UsWNHbd269ZrF2exck6Xv5zlz5qhTp076+uuvjQXy0svutZEr155AdlGcBXzM+vXrjUnQY2JijMn901uzZo327t2rU6dO6ccff1SDBg0kXf0C+v333439qlWr5vRlLUnDhw/X119/raSkJP322296/PHHJcko7OZEZnP4WCw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"text/plain": [
"<Figure size 1400x500 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Language comparison\n",
"lang_stats = sessions.groupby('programming_language').agg(\n",
" avg_time = ('resolution_time_minutes', 'mean'),\n",
" success_rate= ('outcome', lambda x: (x=='fixed').mean() * 100),\n",
" avg_searches= ('num_web_searches', 'mean'),\n",
" avg_compiles= ('compile_attempts', 'mean'),\n",
" total_sessions = ('session_id', 'count')\n",
").round(1).sort_values('avg_time')\n",
"\n",
"print('Language Debugging Difficulty Ranking (easiest to hardest):')\n",
"print()\n",
"print(f'{\"Rank\":<5} {\"Language\":<14} {\"Avg Time\":>9} {\"Fix Rate\":>9} {\"Searches\":>9} {\"Compiles\":>9}')\n",
"print('-' * 60)\n",
"for rank, (lang, row) in enumerate(lang_stats.iterrows(), 1):\n",
" print(f'{rank:<5} {lang:<14} {row[\"avg_time\"]:>8.1f}m {row[\"success_rate\"]:>8.1f}% {row[\"avg_searches\"]:>9.1f} {row[\"avg_compiles\"]:>9.1f}')\n",
"\n",
"print()\n",
"\n",
"# Visual chart\n",
"fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n",
"\n",
"lang_stats['avg_time'].plot(kind='barh', ax=axes[0],\n",
" color=sns.color_palette('RdYlGn_r', len(lang_stats)))\n",
"axes[0].set_title('Avg Debugging Time by Language', fontweight='bold')\n",
"axes[0].set_xlabel('Minutes')\n",
"\n",
"lang_stats['success_rate'].plot(kind='barh', ax=axes[1],\n",
" color=sns.color_palette('RdYlGn', len(lang_stats)))\n",
"axes[1].set_title('Fix Success Rate by Language', fontweight='bold')\n",
"axes[1].set_xlabel('% Fixed')\n",
"\n",
"plt.tight_layout()\n",
"plt.savefig('language_difficulty.png', dpi=150, bbox_inches='tight')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "0faff775",
"metadata": {
"papermill": {
"duration": 0.005075,
"end_time": "2026-06-27T15:07:19.902042+00:00",
"exception": false,
"start_time": "2026-06-27T15:07:19.896967+00:00",
"status": "completed"
},
"tags": []
},
"source": [
"---\n",
"## Example 5: Build Your Personal Debugging Report\n",
"\n",
"**Scenario:** End of the week. You want to see how your debugging sessions compare to the dataset."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "82e22622",
"metadata": {
"execution": {
"iopub.execute_input": "2026-06-27T15:07:19.913144Z",
"iopub.status.busy": "2026-06-27T15:07:19.912760Z",
"iopub.status.idle": "2026-06-27T15:07:19.946025Z",
"shell.execute_reply": "2026-06-27T15:07:19.944719Z"
},
"papermill": {
"duration": 0.042051,
"end_time": "2026-06-27T15:07:19.948570+00:00",
"exception": false,
"start_time": "2026-06-27T15:07:19.906519+00:00",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"=== YOUR WEEKLY DEBUGGING REPORT ===\n",
"\n",
"Metric You Peers Status\n",
"-------------------------------------------------------\n",
"Avg Fix Time (min) 30.0 5.3 Needs work\n",
"Fix Rate (%) 80.0 88.6 Normal\n",
"Avg Searches 5.0 3.2 Needs work\n",
"Avg Compiles 7.0 6.0 Normal\n",
"\n",
"Your sessions this week : 5\n",
"Fixed : 4 / 5\n",
"\n",
"Overall: Strong week! You resolved most bugs efficiently.\n"
]
}
],
"source": [
"# --- FILL IN YOUR WEEK ---\n",
"my_sessions = [\n",
" {'language': 'Python', 'minutes': 15, 'fixed': True, 'searches': 3, 'compiles': 4},\n",
" {'language': 'JavaScript', 'minutes': 45, 'fixed': True, 'searches': 7, 'compiles': 9},\n",
" {'language': 'Python', 'minutes': 8, 'fixed': True, 'searches': 1, 'compiles': 2},\n",
" {'language': 'TypeScript', 'minutes': 60, 'fixed': False, 'searches': 10,'compiles': 15},\n",
" {'language': 'Python', 'minutes': 22, 'fixed': True, 'searches': 4, 'compiles': 5},\n",
"]\n",
"my_experience = 'Mid-level (2-5 years)'\n",
"# -------------------------\n",
"\n",
"my_df = pd.DataFrame(my_sessions)\n",
"\n",
"my_avg_time = my_df['minutes'].mean()\n",
"my_fix_rate = my_df['fixed'].mean() * 100\n",
"my_avg_search = my_df['searches'].mean()\n",
"my_avg_compile = my_df['compiles'].mean()\n",
"\n",
"peers = sessions[sessions['experience_level'] == my_experience]\n",
"p_avg_time = peers['resolution_time_minutes'].mean()\n",
"p_fix_rate = (peers['outcome']=='fixed').mean() * 100\n",
"p_avg_search = peers['num_web_searches'].mean()\n",
"p_avg_compile = peers['compile_attempts'].mean()\n",
"\n",
"print('=== YOUR WEEKLY DEBUGGING REPORT ===')\n",
"print()\n",
"print(f'{\"Metric\":<22} {\"You\":>8} {\"Peers\":>8} {\"Status\":>12}')\n",
"print('-' * 55)\n",
"\n",
"def status(mine, peer, lower_is_better=True):\n",
" if lower_is_better:\n",
" return 'Great' if mine < peer * 0.9 else 'Normal' if mine < peer * 1.2 else 'Needs work'\n",
" else:\n",
" return 'Great' if mine > peer * 1.1 else 'Normal' if mine > peer * 0.8 else 'Needs work'\n",
"\n",
"rows = [\n",
" ('Avg Fix Time (min)', my_avg_time, p_avg_time, True),\n",
" ('Fix Rate (%)', my_fix_rate, p_fix_rate, False),\n",
" ('Avg Searches', my_avg_search, p_avg_search, True),\n",
" ('Avg Compiles', my_avg_compile, p_avg_compile, True),\n",
"]\n",
"\n",
"for label, mine, peer, lib in rows:\n",
" print(f'{label:<22} {mine:>8.1f} {peer:>8.1f} {status(mine, peer, lib):>12}')\n",
"\n",
"print()\n",
"print(f'Your sessions this week : {len(my_df)}')\n",
"print(f'Fixed : {my_df[\"fixed\"].sum()} / {len(my_df)}')\n",
"print()\n",
"if my_fix_rate >= 80:\n",
" print('Overall: Strong week! You resolved most bugs efficiently.')\n",
"elif my_fix_rate >= 60:\n",
" print('Overall: Decent week. A few tough ones — normal for your level.')\n",
"else:\n",
" print('Overall: Tough week. Consider reviewing debugging strategies.')"
]
},
{
"cell_type": "markdown",
"id": "c6d4120b",
"metadata": {
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"exception": false,
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"status": "completed"
},
"tags": []
},
"source": [
"---\n",
"## Example 6: Smarter Search Suggestions While Debugging\n",
"\n",
"**Scenario:** You are stuck on an error. What do developers actually search for in this situation?"
]
},
{
"cell_type": "code",
"execution_count": 7,
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"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Error: AttributeError in Python\n",
"Found 2,655 matching sessions in dataset\n",
"\n",
"What developers searched/used to fix this error:\n",
"\n",
" no external search ########## 32%\n",
" exact error message #### 14%\n",
" similar code example #### 14%\n",
" colleague/Slack #### 14%\n",
" AI assistant (ChatGPT/Copilot) #### 14%\n",
" library documentation #### 14%\n",
" language-specific forum #### 13%\n",
" GitHub issue search #### 13%\n",
"\n",
"Most common fix strategy : Variable initialization fixed\n",
"Average fix time : 4 minutes\n",
"Fix success rate : 88.4%\n"
]
}
],
"source": [
"# --- FILL IN YOUR ERROR ---\n",
"my_language = 'Python'\n",
"my_error = 'AttributeError'\n",
"# --------------------------\n",
"\n",
"similar = sessions[\n",
" (sessions['programming_language'] == my_language) &\n",
" (sessions['error_type'] == my_error)\n",
"]\n",
"\n",
"print(f'Error: {my_error} in {my_language}')\n",
"print(f'Found {len(similar):,} matching sessions in dataset')\n",
"print()\n",
"\n",
"# What external resources did developers use?\n",
"all_resources = []\n",
"for r in similar['external_resources_used'].dropna():\n",
" all_resources.extend([x.strip() for x in str(r).split('|')])\n",
"\n",
"resource_counts = pd.Series(all_resources).value_counts().head(8)\n",
"print('What developers searched/used to fix this error:')\n",
"print()\n",
"for resource, count in resource_counts.items():\n",
" pct = count / len(similar) * 100\n",
" bar = '#' * int(pct / 3)\n",
" print(f' {resource:<35} {bar} {pct:.0f}%')\n",
"\n",
"print()\n",
"\n",
"# Most common fix strategy\n",
"top_fix = similar[similar['outcome']=='fixed']['fix_strategy'].value_counts().index[0]\n",
"fix_rate = (similar['outcome']=='fixed').mean() * 100\n",
"avg_time = similar['resolution_time_minutes'].mean()\n",
"\n",
"print(f'Most common fix strategy : {top_fix}')\n",
"print(f'Average fix time : {avg_time:.0f} minutes')\n",
"print(f'Fix success rate : {fix_rate:.1f}%')"
]
},
{
"cell_type": "markdown",
"id": "ba2bf48d",
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"tags": []
},
"source": [
"---\n",
"## Example 7: Know When to Ask for Help\n",
"\n",
"**Scenario:** You have been stuck for a while. Should you keep trying or ask a colleague?\n",
"\n",
"This tool tells you based on real data from 50,000 sessions."
]
},
{
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"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"=== SHOULD YOU ASK FOR HELP? ===\n",
"\n",
"You have spent 35 min on a severity-4 JavaScript bug.\n",
"Searches: 8 | Compiles: 12 | AI used: True\n",
"\n",
"In 0 similar situations from the dataset:\n",
" Fixed it alone : nan%\n",
" Fixed after asking : nan%\n",
" Escalated to senior : nan%\n",
" Abandoned : nan%\n",
"\n",
"Avg extra time needed : nan more minutes\n",
"Developers who asked : nan%\n",
"\n",
"VERDICT: Keep going. Most developers solve this independently.\n",
"Extra tip: You have exhausted common resources. A fresh pair of eyes will help.\n"
]
}
],
"source": [
"# --- FILL IN YOUR SITUATION ---\n",
"my_language = 'JavaScript'\n",
"my_experience = 'Junior (0-2 years)'\n",
"my_severity = 4\n",
"minutes_spent = 35\n",
"searches_done = 8\n",
"compiles_done = 12\n",
"tried_ai = True\n",
"# ------------------------------\n",
"\n",
"similar = sessions[\n",
" (sessions['programming_language'] == my_language) &\n",
" (sessions['experience_level'] == my_experience) &\n",
" (sessions['error_severity'] == my_severity)\n",
"]\n",
"\n",
"if len(similar) < 50:\n",
" similar = sessions[\n",
" (sessions['experience_level'] == my_experience) &\n",
" (sessions['error_severity'] == my_severity)\n",
" ]\n",
"\n",
"# Sessions that went longer than you and what happened\n",
"longer = similar[similar['resolution_time_minutes'] >= minutes_spent]\n",
"\n",
"fixed_alone = ((longer['outcome']=='fixed') & (~longer['asked_colleague'])).mean() * 100\n",
"fixed_with_help= ((longer['outcome']=='fixed') & (longer['asked_colleague'])).mean() * 100\n",
"escalated = (longer['outcome']=='escalated').mean() * 100\n",
"abandoned = (longer['outcome']=='abandoned').mean() * 100\n",
"ask_rate = longer['asked_colleague'].mean() * 100\n",
"avg_extra_time = longer['resolution_time_minutes'].mean() - minutes_spent\n",
"\n",
"print('=== SHOULD YOU ASK FOR HELP? ===')\n",
"print()\n",
"print(f'You have spent {minutes_spent} min on a severity-{my_severity} {my_language} bug.')\n",
"print(f'Searches: {searches_done} | Compiles: {compiles_done} | AI used: {tried_ai}')\n",
"print()\n",
"print(f'In {len(longer):,} similar situations from the dataset:')\n",
"print(f' Fixed it alone : {fixed_alone:.0f}%')\n",
"print(f' Fixed after asking : {fixed_with_help:.0f}%')\n",
"print(f' Escalated to senior : {escalated:.0f}%')\n",
"print(f' Abandoned : {abandoned:.0f}%')\n",
"print()\n",
"print(f'Avg extra time needed : {avg_extra_time:.0f} more minutes')\n",
"print(f'Developers who asked : {ask_rate:.0f}%')\n",
"print()\n",
"\n",
"# Decision\n",
"if ask_rate > 50 and fixed_alone < 30:\n",
" print('VERDICT: Ask for help NOW. Data shows most people need it at this point.')\n",
"elif ask_rate > 30:\n",
" print('VERDICT: Consider asking. About half of developers do at this stage.')\n",
"else:\n",
" print('VERDICT: Keep going. Most developers solve this independently.')\n",
"\n",
"if searches_done >= 8 and tried_ai:\n",
" print('Extra tip: You have exhausted common resources. A fresh pair of eyes will help.')"
]
},
{
"cell_type": "markdown",
"id": "68b1512c",
"metadata": {
"papermill": {
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"status": "completed"
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"tags": []
},
"source": [
"---\n",
"## Bonus: Your Error Type Lookup Table\n",
"\n",
"Quick reference — paste any error type and get instant stats."
]
},
{
"cell_type": "code",
"execution_count": 9,
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"status": "completed"
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"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"=== AttributeError ===\n",
"Total sessions : 2,655\n",
"Avg fix time : 4.2 min\n",
"Fix success rate : 88.4%\n",
"Avg severity : 3.0/5\n",
"Most common in : Python\n",
"Top fix strategy : Variable initialization fixed\n",
"\n",
"Fix time by experience level:\n",
"experience_level\n",
"Junior (0-2 years) 5.0\n",
"Mid-level (2-5 years) 4.4\n",
"Senior (5-10 years) 3.7\n",
"Staff/Principal (10+ years) 2.7\n",
"\n",
"=== TypeError ===\n",
"Total sessions : 4,835\n",
"Avg fix time : 4.4 min\n",
"Fix success rate : 88.9%\n",
"Avg severity : 3.0/5\n",
"Most common in : JavaScript\n",
"Top fix strategy : Variable initialization fixed\n",
"\n",
"Fix time by experience level:\n",
"experience_level\n",
"Junior (0-2 years) 5.2\n",
"Mid-level (2-5 years) 4.4\n",
"Senior (5-10 years) 3.7\n",
"Staff/Principal (10+ years) 2.8\n"
]
}
],
"source": [
"# Quick lookup for any error\n",
"def error_lookup(error_type):\n",
" data = sessions[sessions['error_type'] == error_type]\n",
" if len(data) == 0:\n",
" print(f'Error type \"{error_type}\" not found in dataset.')\n",
" print('Available errors:', sorted(sessions['error_type'].unique())[:20])\n",
" return\n",
"\n",
" print(f'=== {error_type} ===')\n",
" print(f'Total sessions : {len(data):,}')\n",
" print(f'Avg fix time : {data[\"resolution_time_minutes\"].mean():.1f} min')\n",
" print(f'Fix success rate : {(data[\"outcome\"]==\"fixed\").mean()*100:.1f}%')\n",
" print(f'Avg severity : {data[\"error_severity\"].mean():.1f}/5')\n",
" print(f'Most common in : {data[\"programming_language\"].value_counts().index[0]}')\n",
" print(f'Top fix strategy : {data[data[\"outcome\"]==\"fixed\"][\"fix_strategy\"].value_counts().index[0]}')\n",
" print()\n",
" print('Fix time by experience level:')\n",
" print(data.groupby('experience_level')['resolution_time_minutes'].mean().round(1).to_string())\n",
"\n",
"# --- Change this to any error you want ---\n",
"error_lookup('AttributeError')\n",
"print()\n",
"error_lookup('TypeError')"
]
},
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"id": "3148bef8",
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"source": [
"---\n",
"## Summary — Your Daily Debugging Toolkit\n",
"\n",
"| Example | Daily Use Case |\n",
"|---------|---------------|\n",
"| 1 | Estimate how long your current bug will take |\n",
"| 2 | Compare your habits with peer developers |\n",
"| 3 | Get next-step suggestions when stuck |\n",
"| 4 | Choose the easiest language for your project |\n",
"| 5 | Generate your weekly debugging performance report |\n",
"| 6 | Get smarter search suggestions for your error |\n",
"| 7 | Decide when it is time to ask for help |\n",
"\n",
"---\n",
"**Dataset:** DebugTraj-50K by Abhishek Singh | BGIEM Jabalpur | 2026\n",
"\n",
"If this helped you, please upvote the dataset!"
]
}
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|