🎓 AI Code Error Fix Prompts - Academic Research

📖 Open Academic Research - MIT Licensed
Version 0.1.0 | October 11, 2025 | Pre-Peer Review Release

Welcome to the academic research repository for comprehensive multi-model analysis of AI-driven debugging capabilities. This research represents the world's first systematic comparison of debugging prompts across 13 major AI models.

� Research Overview

Groundbreaking Discoveries

1. Analytical Independence - Lower-tier AI models can objectively evaluate higher-tier outputs with equal rigor
2. Minimal Company Bias - <5% bias across all AI companies in evaluation processes
3. Performance Hierarchy - Clear empirical classification of AI debugging capabilities
4. Reproducible Methodology - Complete research process documented for peer validation

Performance Tiers Discovered

• Elite (94-98/100): Claude Sonnet 4.5, GPT-5-Codex
• Advanced (89-93/100): GPT-5, Gemini 2.5 Pro, Claude Sonnet 4.0
• Efficient (84-90/100): GPT-5-mini, Grok Code Fast 1
• Standard (82-92/100): GPT-4.1, GPT-4o, o3-mini, o4-mini

⚖️ Bias Analysis Results

Company	Bias Level	Direction	Assessment
OpenAI	-1.39%	Reverse bias (rates competitors higher)	✅ Objective
Anthropic	+2.1%	Slight positive bias	✅ Objective
Google	+1.8%	Slight positive bias	✅ Objective

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📚 Academic Use

✅ Permitted Uses

• Academic research and analysis
• Educational curriculum development
• Non-commercial methodology replication
• Peer review and validation studies
• Citation in academic publications

🎓 How to Cite

Mouzakitis, G. M. (2025). Comprehensive Multi-Model Analysis of AI-Driven 
Debugging Prompts: Performance Evaluation and Company Bias Assessment. 
AI Code Error Fix Prompts Research. 
https://github.com/GerasimosMakisMouzakitis/ai-code-error-fix-prompts-research

🤝 Contributing

We welcome academic collaboration:

• Peer review of methodology
• Replication studies
• Extension to new AI models
• Cross-validation research

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📋 Methodology Overview

🔄 4-Phase Research Process

1. Prompt Generation - Each model creates optimized debugging prompts
2. Framework Creation - Models develop evaluation methodologies
3. Systematic Analysis - Cross-model evaluation using standardized criteria
4. Scientific Synthesis - Comprehensive analysis and reporting

📊 Evaluation Criteria

Each prompt evaluated on 5 dimensions (20 points each):

1. Accuracy - Correctness of error identification and solutions
2. Clarity - Communication effectiveness and user understanding
3. Completeness - Comprehensive coverage of debugging aspects
4. Adherence - Following best practices and methodological rigor
5. Best Practices - Implementation of professional development standards

🎯 Quality Assurance

• Multi-model cross-validation
• Systematic bias detection and mitigation
• Reproducible methodology with detailed documentation
• Statistical analysis of performance differences

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🌐 Open Science Commitment

This research follows open science principles:

• Transparency - Complete methodology documentation
• Reproducibility - All instructions and processes provided
• Accessibility - Open access to academic community
• Collaboration - Welcoming peer review and validation

📞 Academic Contact

Researcher: Gerasimos Makis Mouzakitis
Institution: National Technical University of Athens (NTUA)
Degree: MEng Civil & Transportation Engineering
GitHub: @GerasimosMakisMouzakitis

For academic collaboration, methodology questions, or peer review:

• Open GitHub Issues for technical discussions
• Create Pull Requests for methodology improvements
• Contact for formal academic partnerships

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📄 License

Academic components licensed under MIT License - see LICENSE file for details.

For commercial applications of the debugging prompts, see /commercial/ folder.

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Advancing AI research through open science and collaborative methodology.