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"""
Compute success rate for each model on the BookWriter-A2A 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-A2A"
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 the 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 1 chapter file
result["success"] = result["has_chapters_folder"] and result["chapter_count"] > 0
return result
def analyze_model_results():
"""Analyze execution results for each model."""
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 all session subfolders
for session_dir in sorted(test_results_dir.iterdir()):
if not session_dir.is_dir():
continue
# Check success
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 chapters (over 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 a summary."""
print("\n" + "=" * 80)
print(f"Model Execution Summary - {PROJECT_NAME}")
print("=" * 80 + "\n")
print("Success criteria: chapters folder exists AND at least 1 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: {avg_chapters:.1f}")
print()
else:
print(f"📊 {model}")
print(f" 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]: # only show 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 failed sessions")
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✅ Saved detailed results to: {output_path}")
def save_csv_summary(results, output_file="success_rate.csv"):
"""Save a 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"✅ Saved CSV summary to: {output_path}")
if __name__ == "__main__":
print(f"Starting success-rate analysis - {PROJECT_NAME}")
print(f"Success criteria: chapters folder exists AND at least 1 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✅ Completed!")
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