Phase 3: Property-Based Testing for AI Code · 55 min · Claude Code · Hypothesis · pytest
The Anthropic Agentic PBT Approach
The AI reads the code. The AI writes the properties. Hypothesis finds the bugs.
Real-world application: Engineers who can apply the Anthropic agentic PBT 5-step process demonstrate the ability to leverage AI for scalable property generation -- the frontier of AI-assisted testing.
What you will learn
- Apply the 5-step agentic PBT process: read, propose, write, run, reflect
- Understand the 56% valid bug discovery rate across 100 Python packages
- Use Claude Code's /hypothesis command for automated property generation
- Evaluate agent-generated properties for quality and coverage
Introduction
The Anthropic Agentic PBT Approach
Objective
Run the agentic PBT simulation on 3 AI-generated functions (chunk_list, interleave, round_to_decimal). Observe the 5-step process (read, propose, write, run, reflect) and the valid bug vs false positive classification.
The AI reads the code. The AI writes the properties. Hypothesis finds the bugs.
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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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