File size: 7,957 Bytes
8c10cf2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 | #!/usr/bin/env python3
"""
Compute the success rate of each model on the BookWriter-MCP task.
Success criteria:
1. A chapters folder exists
2. The chapters folder contains at least one .md file
3. The session root contains a main book .md file
"""
import json
import os
from pathlib import Path
from collections import defaultdict
# Base path and model list
BASE_DIR = Path("/Users/wzr/TOSEM-2025/RESULTS")
MODELS = [
"DeepSeek-R1",
"DeepSeek-V3-1",
"GPT-4o-mini",
"GPT-5",
"Gemini-2.5-flash",
"Gemini-2.5-flash-nothinking",
"Qwen3-235b",
]
PROJECT_NAME = "BookWriter-MCP"
def check_session_success(session_dir: Path) -> dict:
"""
Check whether a single session is successful.
Returns:
{
"success": bool,
"has_chapters_folder": bool,
"has_main_md": bool,
"chapter_count": int,
"chapter_files": list,
"main_md_file": str or None,
}
"""
result = {
"success": False,
"has_chapters_folder": False,
"has_main_md": False,
"chapter_count": 0,
"chapter_files": [],
"main_md_file": None,
}
# Check chapters folder
chapters_dir = session_dir / "chapters"
if chapters_dir.exists() and chapters_dir.is_dir():
result["has_chapters_folder"] = True
# Count .md files in chapters
chapter_files = sorted(chapters_dir.glob("*.md"))
result["chapter_count"] = len(chapter_files)
result["chapter_files"] = [f.name for f in chapter_files]
# Check main book .md file in session root (exclude execution_path.md)
md_files = [f for f in session_dir.glob("*.md") if f.name != "execution_path.md"]
if md_files:
result["has_main_md"] = True
result["main_md_file"] = md_files[0].name
# Success criteria: chapters folder exists AND at least one chapter file
result["success"] = result["has_chapters_folder"] and result["chapter_count"] > 0
return result
def analyze_model_results():
"""Analyze execution results for all models."""
results = {}
for model in MODELS:
test_results_dir = BASE_DIR / model / PROJECT_NAME / "test_results"
if not test_results_dir.exists():
print(
f"⚠️ test_results directory not found for model {model}: {test_results_dir}"
)
continue
model_stats = {
"success_count": 0,
"failure_count": 0,
"total_count": 0,
"avg_chapters": 0,
"sessions": [],
}
total_chapters = 0
# Iterate over all session subdirectories
for session_dir in sorted(test_results_dir.iterdir()):
if not session_dir.is_dir():
continue
# Check success status
check_result = check_session_success(session_dir)
model_stats["total_count"] += 1
if check_result["success"]:
model_stats["success_count"] += 1
total_chapters += check_result["chapter_count"]
else:
model_stats["failure_count"] += 1
model_stats["sessions"].append(
{
"session": session_dir.name,
"success": check_result["success"],
"has_chapters_folder": check_result["has_chapters_folder"],
"has_main_md": check_result["has_main_md"],
"chapter_count": check_result["chapter_count"],
"chapter_files": check_result["chapter_files"],
"main_md_file": check_result["main_md_file"],
}
)
# Compute average chapter count among successful sessions
if model_stats["success_count"] > 0:
model_stats["avg_chapters"] = total_chapters / model_stats["success_count"]
results[model] = model_stats
return results
def print_summary(results):
"""Print summary statistics."""
print("\n" + "=" * 80)
print(f"Model execution summary - {PROJECT_NAME}")
print("=" * 80 + "\n")
print(
"Success criteria: chapters folder exists AND at least one chapter .md file\n"
)
for model, stats in results.items():
total = stats["total_count"]
success = stats["success_count"]
failure = stats["failure_count"]
avg_chapters = stats["avg_chapters"]
if total > 0:
success_rate = (success / total) * 100
print(f"📊 {model}")
print(f" Total: {total}")
print(f" Success: {success} ({success_rate:.1f}%)")
print(f" Failure: {failure} ({100-success_rate:.1f}%)")
if success > 0:
print(f" Avg chapters (success only): {avg_chapters:.1f}")
print()
else:
print(f"📊 {model}")
print(" No data")
print()
print("=" * 80)
def print_failure_details(results):
"""Print details for failed sessions."""
print("\n" + "=" * 80)
print("Failure details")
print("=" * 80 + "\n")
for model, stats in results.items():
failures = [s for s in stats["sessions"] if not s["success"]]
if failures:
print(f"📊 {model} - {len(failures)} failed sessions:")
for session in failures[:5]: # show only the first 5
print(f"\n 🔴 {session['session']}")
print(
f" - chapters folder: {'✅' if session['has_chapters_folder'] else '❌'}"
)
print(
f" - main .md file: {'✅' if session['has_main_md'] else '❌'} {session['main_md_file'] or ''}"
)
print(f" - chapter count: {session['chapter_count']}")
if session["chapter_files"]:
print(
f" - chapter files: {', '.join(session['chapter_files'][:3])}"
)
if len(failures) > 5:
print(f"\n ... and {len(failures) - 5} more failures")
print()
def save_detailed_results(results, output_file="success_detailed_results.json"):
"""Save detailed results to a JSON file."""
output_path = Path(__file__).parent / output_file
with open(output_path, "w", encoding="utf-8") as f:
json.dump(results, f, indent=2, ensure_ascii=False)
print(f"\n✅ Detailed results saved to: {output_path}")
def save_csv_summary(results, output_file="success_rate.csv"):
"""Save summary to a CSV file."""
import csv
output_path = Path(__file__).parent / output_file
with open(output_path, "w", newline="", encoding="utf-8") as f:
writer = csv.writer(f)
writer.writerow(
["Model", "Total", "Success", "Failure", "Success_Rate(%)", "Avg_Chapters"]
)
for model, stats in results.items():
total = stats["total_count"]
success = stats["success_count"]
failure = stats["failure_count"]
success_rate = (success / total * 100) if total > 0 else 0
avg_chapters = stats["avg_chapters"]
writer.writerow(
[
model,
total,
success,
failure,
f"{success_rate:.2f}",
f"{avg_chapters:.2f}",
]
)
print(f"✅ CSV summary saved to: {output_path}")
if __name__ == "__main__":
print(f"Starting success-rate evaluation - {PROJECT_NAME}")
print(
"Success criteria: chapters folder exists AND at least one chapter .md file\n"
)
results = analyze_model_results()
print_summary(results)
print_failure_details(results)
save_detailed_results(results)
save_csv_summary(results)
print("\n✅ Done")
|