Phase 3: Property-Based Testing for AI Code · 55 min · Python · Hypothesis · pytest

PBT for AI-Generated Code

The AI writes the code. The AI writes the properties. Hypothesis finds the contradictions.

Real-world application: Engineers who can implement the dual-mode workflow (AI writes code + AI writes properties + Hypothesis finds contradictions) demonstrate the ability to create self-improving verification loops for AI-generated code.

What you will learn

Implement the dual-mode workflow: AI writes code + AI writes properties + Hypothesis finds contradictions
Generate properties from function specifications and type hints
Verify AI-generated functions using PBT as a verification layer
Handle the case where AI code and AI properties disagree -- which is wrong?

Introduction

PBT for AI-Generated Code

Objective

Run the dual-mode workflow simulation on 3 AI-generated functions (normalize, reverse, flatten). Observe how the producer AI writes code, the verifier AI writes properties, Hypothesis finds contradictions, and the human classifies them as code bugs or property errors.

The AI writes the code. The AI writes the properties. Hypothesis finds the contradictions.

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You've read the first 2 sections. The rest of this lesson covers The Problem, The Concept, Build, Check Yourself, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.

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