#atom

Systematic hypothesis testing for solving software problems

Core Idea: Effective debugging requires systematically examining assumptions and identifying mismatches between expectations and reality, rather than randomly trying solutions based on intuition.

Key Elements

• Contrasts two fundamental debugging approaches:
  • Random experimentation based on intuition ("vibes")
  • Systematic examination of assumptions (scientific method)
• Non-reasoning AI models typically use a "guess and check" approach
  • One-shot solutions without systematic analysis
  • In agent loops, may randomly try fixes until success or endless loops
• Scientific debugging provides better long-term understanding
  • Identifies root causes rather than symptoms
  • Builds knowledge for future problem-solving
  • Reduces reliance on trial and error

Implementation Strategies

• Use reasoning models for debugging complex issues
• Explicitly state hypotheses about system behavior
• Design experiments to test specific assumptions
• Document findings and insights, even when wrong
• Maintain a mental model of the system while using AI assistance
• Guide AI by sharing root cause analysis rather than requesting blind fixes

Model Considerations

• Reasoning models (like Grok 3, DeepSeek-R1) perform better for debugging
• Non-reasoning models attempt one-shot solutions with limited analysis
• Agent loops can create destructive cycles when models fail to understand root causes
• Human-AI collaboration often produces better results than either alone

Connections

• Related Concepts: Stop Digging (knowing when to change approach), Bulldozer Method (persistent problem-solving)
• Broader Context: Scientific Method (systematic knowledge acquisition)
• Applications: Root Cause Analysis (identifying fundamental issues)

References

1. Edward Z. Yang (2025). "AI Blindspots" collection, March 2025.

#debugging #scientific-method #problem-solving #software-development

Connections:

Sources:

• From: AI Blindspots