data/processed/RQ1/GameBuilder/README.md

GameBuilder Evaluation Scripts

This directory contains evaluation scripts for analyzing the GameBuilder project results across different LLM models.

Scripts

1. analyze_retry_patterns.py

Analyzes RETRY patterns in GameBuilder project execution paths.

Features:

• Extracts error information from execution paths
• Counts RETRY attempts and locations
• Calculates error rates and RETRY rates per model
• Generates detailed statistics by agent and node type

Output:

• retry_analysis.json - Detailed JSON results
• retry_summary.csv - Summary statistics
• error_by_agent.csv - Error statistics by agent

Usage:

python3 analyze_retry_patterns.py

2. evaluate_success.py

Calculates success rates for each model in GameBuilder tasks.

Features:

• Analyzes validation results from test sessions
• Computes success/failure rates per model
• Tracks individual session outcomes

Output:

• success-finish_detailed_results.json - Detailed results
• success-finish_rate.csv - Success rate summary

Usage:

python3 evaluate_success.py

3. evaluate_trajectory.py

Evaluates trajectory metrics for GameBuilder project.

Features:

• Parses execution paths and extracts trajectories
• Evaluates 6 trajectory metrics:
  • Exact match
  • In-order match
  • Any-order match
  • Precision
  • Recall
  • Single-tool use
• Calculates path diversity and entropy

Output:

• CSV file with evaluation results

Usage:

python3 evaluate_trajectory.py --config reference_trajectory.yaml --output evaluation_results.csv

Arguments:

• --config - Reference trajectory config file (YAML format)
• --base-dir - RESULTS directory path (defaults to two levels up)
• --output - Output CSV file path
• --format - Output format (csv only)

Configuration

reference_trajectory.yaml

Defines the reference trajectory (ground truth) for evaluation.

Key sections:

• project_name - Project identifier (GameBuilder)
• extract_types - Node types to extract (SPAN, Chain, AGENT, LLM, Tool)
• reference_trajectory - Expected execution sequence
• target_tools - Tools to track for single-tool use metric (Python Code Validator)
• models - List of models to evaluate

Project Overview

GameBuilder is a code generation project where:

1. Senior Software Engineer generates game code
2. Software Quality Control Engineer reviews and validates the code
3. Chief Software Quality Control Engineer performs final evaluation

The reference trajectory represents the ideal execution path with:

• 5 LLM calls (1 for code generation, 2 for review, 2 for evaluation)
• 2 tool calls (Python Code Validator used by both QA agents)

Models Evaluated

• GPT-5
• GPT-4o-mini
• DeepSeek-V3-1
• DeepSeek-R1
• Gemini-2.5-flash
• Gemini-2.5-flash-nothinking
• Qwen3-235b

Requirements

• Python 3.7+
• pandas
• PyYAML

Notes

• All scripts automatically process test results from the configured base directory
• Results are saved in the same directory as the scripts
• Execution paths are parsed from execution_path.md files in test session directories
• Validation results are read from validation_result.json files
• The Python Code Validator tool is the primary tool tracked for this project