Self-Debugging AI: A Comprehensive Analysis of Claude 4.1 Sonnet's Code Generation and Error Resolution Capabilities

This paper presents a novel meta-experimental approach to analyzing the debugging capabilities of large language models (LLMs), specifically Claude 3 Opus. Through a carefully designed experiment where the AI system first generates intentionally buggy code and subsequently debugs it without prior knowledge, we document and analyze the systematic debugging methodology employed by modern AI systems. Our experiment involved a Python-based Task Management System containing 12 distinct bug categories, ranging from syntax errors to complex runtime issues. The AI successfully identified and resolved all bugs using a methodical, error-driven approach that mirrors human debugging strategies. Key findings include the AI’s ability to: (1) prioritize syntax errors before runtime issues, (2) leverage Python’s error messages effectively, (3) implement comprehensive fixes with proper error handling, and (4) validate solutions through automated testing. This research contributes to understanding AI’s role in automated software debugging and has implications for the future of AI-assisted software development, code review processes, and programming education.