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"""
Compute the success rate for each model on the RecruitmentAssistant-H_A2A task.
Success criteria: the reports folder exists and contains multiple (>=2) .md files.
"""
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 = "RecruitmentAssistant-H_A2A"
def check_reports_success(reports_dir: Path) -> dict:
"""
Check whether the reports folder meets the success criteria.
Success criteria:
1. reports folder exists
2. contains multiple (>=2) .md files
Returns:
{
'success': bool,
'reports_exists': bool,
'md_count': int,
'md_files': list
}
"""
result = {"success": False, "reports_exists": False, "md_count": 0, "md_files": []}
# Check whether the reports folder exists
if not reports_dir.exists() or not reports_dir.is_dir():
return result
result["reports_exists"] = True
# Count .md files
md_files = sorted([f.name for f in reports_dir.glob("*.md")])
result["md_count"] = len(md_files)
result["md_files"] = md_files
# Success: at least 2 .md files
result["success"] = result["md_count"] >= 2
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 does not exist for model {model}: {test_results_dir}"
)
continue
model_stats = {
"success_count": 0,
"failure_count": 0,
"total_count": 0,
"sessions": [],
}
# Iterate over all session subfolders
for session_dir in sorted(test_results_dir.iterdir()):
if not session_dir.is_dir():
continue
reports_dir = session_dir / "reports"
# Check reports folder and .md files
check_result = check_reports_success(reports_dir)
model_stats["total_count"] += 1
if check_result["success"]:
model_stats["success_count"] += 1
else:
model_stats["failure_count"] += 1
model_stats["sessions"].append(
{
"session": session_dir.name,
"success": check_result["success"],
"reports_exists": check_result["reports_exists"],
"md_count": check_result["md_count"],
"md_files": check_result["md_files"],
}
)
results[model] = model_stats
return results
def print_summary(results):
"""Print summary statistics."""
print("\n" + "=" * 80)
print(f"Model execution results summary - {PROJECT_NAME}")
print("=" * 80 + "\n")
for model, stats in results.items():
total = stats["total_count"]
success = stats["success_count"]
failure = stats["failure_count"]
if total > 0:
success_rate = (success / total) * 100
print(f"📊 {model}")
print(f" Total: {total}")
print(
f" Success: {success} (reports exists and has >=2 .md files, {success_rate:.1f}%)"
)
print(f" Failure: {failure} ({100-success_rate:.1f}%)")
print()
else:
print(f"📊 {model}")
print(" No data")
print()
print("=" * 80)
def print_detailed_stats(results):
"""Print detailed statistics."""
print("\n" + "=" * 80)
print("Detailed statistics")
print("=" * 80 + "\n")
for model, stats in results.items():
if stats["total_count"] == 0:
continue
print(f"### {model}")
print()
# Distribution of .md file counts
md_count_dist = defaultdict(int)
for session in stats["sessions"]:
md_count_dist[session["md_count"]] += 1
print(".md file count distribution:")
for count in sorted(md_count_dist.keys()):
sessions = md_count_dist[count]
percentage = (sessions / stats["total_count"]) * 100
print(f" {count} files: {sessions} ({percentage:.1f}%)")
# List failed sessions
failed_sessions = [s for s in stats["sessions"] if not s["success"]]
if failed_sessions:
print(f"\nFailed sessions ({len(failed_sessions)}):")
for session in failed_sessions[:5]: # show only the first 5
reason = (
"reports folder does not exist"
if not session["reports_exists"]
else f"only {session['md_count']} .md files"
)
print(f" - {session['session']}: {reason}")
if len(failed_sessions) > 5:
print(f" ... and {len(failed_sessions) - 5} more failed sessions")
print()
print("=" * 80)
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(%)"])
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
writer.writerow([model, total, success, failure, f"{success_rate:.2f}"])
print(f"✅ CSV summary saved to: {output_path}")
if __name__ == "__main__":
print(f"Starting success-rate analysis - {PROJECT_NAME}")
print(
"Success criteria: reports folder exists and contains multiple (>=2) .md files\n"
)
results = analyze_model_results()
print_summary(results)
print_detailed_stats(results)
save_detailed_results(results)
save_csv_summary(results)
print("\n✅ Done!")
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