#atom

Testing components without exposing their internal implementation details

Core Idea: Black box testing evaluates component functionality without examining internal structure, but LLMs have inherent difficulty adhering to this principle since they tend to analyze implementation details.

Key Elements

• Black box testing focuses on testing functionality without knowledge of internal implementation
• LLMs struggle with this approach because:
  • Implementation files are often loaded into their context
  • Agents are tuned to examine implementations to understand interfaces
  • Models (like Sonnet 3.7) aim to make code consistent, often eliminating test redundancies that are actually valuable
• This tendency can lead to tests that mirror implementation bugs rather than catching them
• Information hiding boundaries need to be explicitly defined for LLMs

Improvement Strategies

• Mask or summarize implementation details when loading files into context
• Explicitly instruct models to maintain intentional redundancy in tests
• Define clear information hiding boundaries for the AI architect
• Create separate contexts for testing versus implementation
• Maintain independent test expectations rather than deriving them from implementation

Connections

• Related Concepts: Preparatory Refactoring (both involve thinking about code structure), Respect the Spec (maintaining important boundaries)
• Broader Context: Software Testing Principles (fundamental approaches to verification)
• Applications: LLM Test Generation (creating effective tests with AI)

References

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

#testing #software-development #ai-limitations #information-hiding

Connections:

Sources:

• From: AI Blindspots