Phase 0: The AI Code Quality Crisis · 50 min · Python · pytest

The Defect Landscape in Statistics

If you cannot cite the numbers, you cannot argue for the budget.

Real-world application: Engineers who can cite specific statistics (55.8% vulnerability rate, 97.8% detection gap, CWE-1236 at 50.7%) demonstrate they have read the research and can make a data-driven case for AI code testing investment to engineering leadership.

What you will learn

Cite the 55.8% Z3-proven vulnerability rate and explain its methodology
Identify the top CWE categories in AI-generated code (CWE-1236, CWE-916, CWE-20, CWE-117)
Explain the 97.8% detection gap between Z3 formal verification and industry tools
Compare vulnerability rates across programming languages (Python vs Java vs C++)

Introduction

The Defect Landscape in Statistics

Objective

Generate a structured markdown report of AI code vulnerability statistics and use it to build a data-driven argument for AI code testing investment.

If you cannot cite the numbers, you cannot argue for the budget.

Unlock the full lesson

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.

Create a free account — the opening phases of 29 of 35 courses are free, no credit card.