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Testing AI-Written Code

Verify, test, and ship code from AI coding assistants with confidence

10 phases. 56 lessons. 56 labs. 4 projects. AI-generated code has a 55.8% vulnerability rate (Z3-proven), 1.7x more defects than human PRs, and 19.7% hallucinated package recommendations. This course teaches the full verification stack: test-driven AI development, static analysis, property-based testing, mutation testing, formal verification with Z3, supply chain defense against slopsquatting, CI/CD quality gates with dual-agent test generation, intent verification, and production monitoring. You build real testing pipelines, run real mutation analysis, execute real security scans, and graduate with a portfolio that proves you can verify AI-generated code.

10 phases · 56 lessons · 56 labs · 4 projects

Take ML & AI Engineering first — this course builds on it.

Outcomes you will have by the end

• 4 GitHub repos with testing artifacts — TDD verification, mutation testing pipeline, CI/CD quality gate system, and end-to-end testing system — each with documentation and demo evaluations.
• A working mutation testing CI pipeline — mutmut configured in GitHub Actions, >80% mutation score, surviving mutant analysis report.
• A complete CI/CD quality gate system — 7-job GitHub Actions pipeline with dual-agent test generation, 7-category quality scoring, intent verification, and dependency scanning.
• A comprehensive prompt library — 15+ prompts for testing, review, security, and continuous execution, tested against real AI-generated code.
• A security analysis report — SAST + Z3 formal verification findings against an AI-generated codebase, with severity-ranked vulnerabilities and remediation guidance.
• Verified AI Code Testing Engineer certificate — Issued by DeVenture Academy, tied to completion record. Lists specific testing skills, phases, and projects.

What you will be able to do

TDD for AI Code · Property-Based Testing · Mutation Testing · Formal Verification (Z3) · SAST/Semgrep · Slopsquatting Defense · CI/CD Quality Gates · Intent Verification

Every phase, every lesson, every project

• The AI Code Quality Crisis (5 lessons) — free — Defect patterns, 55.8% vulnerability rate, CWE concentration, trust trap, verification pyramid, environment setup
  • Why AI Code Fails Differently
  • The Defect Landscape in Statistics
  • The Trust Trap
  • The Testing Mindset Shift
  • Setting Up Your Testing Environment
• Test-Driven AI Development (5 lessons) — free — TDD as primary defense, spec-first development, AI TDD loop, behavioral contracts, Project 1
  • TDD as the Primary Defense Against AI Code Defects
  • Spec-First Development with AI Assistants
  • The AI TDD Loop: Spec, Test, Generate, Verify, Iterate
  • Behavioral Contracts: Constraining AI Code Generation
  • Project 1: TDD Verification Pipeline for AI-Generated Code
• Static Analysis & Automated Gates (6 lessons) — Linting, SAST, AI review tools, hook-based verification, automated gauntlet, quality gates
  • Linting & Static Analysis Basics
  • SAST for AI Code
  • AI-Powered Code Review Tools
  • Hook-Based Real-Time Verification
  • Building the Automated Gauntlet
  • Configuring Quality Gates
• Property-Based Testing for AI Code (6 lessons) — PBT vs example-based, Hypothesis, deriving properties, Anthropic agentic PBT, PBT for AI code, stateful testing
  • Introduction to Property-Based Testing
  • Hypothesis Python PBT
  • Deriving Properties from Code
  • The Anthropic Agentic PBT Approach
  • PBT for AI-Generated Code
  • Advanced PBT: Stateful Testing
• Mutation Testing & Behavioral Verification (5 lessons) — Why coverage lies, mutation tools, interpreting scores, AI-specific patterns, Project 2
  • Why Coverage Lies
  • Mutation Testing Tools
  • Interpreting Mutation Scores
  • AI-Specific Mutation Patterns
  • Project 2: Mutation Testing Pipeline
• Security Testing AI-Generated Code (6 lessons) — 55.8% vulnerability problem, CWE patterns, Z3 formal verification, generation-review asymmetry, security prompts, security gates
  • The 55% Vulnerability Problem
  • CWE Patterns in AI Code
  • Formal Verification with Z3
  • The Generation-Review Asymmetry
  • Security Prompt Patterns
  • Building Security Gates
• Supply Chain Security & Slopsquatting (5 lessons) — Slopsquatting attack vector, hallucinated package detection, dependency allowlisting, SBOM, lockfile enforcement
  • The Slopsquatting Attack Vector
  • Hallucinated Package Detection
  • Dependency Verification & Allowlisting
  • SBOM for AI Code
  • Lockfile Enforcement & Hash Verification
• CI/CD Pipelines for AI Code (6 lessons) — Inner-loop validation, GitHub Actions quality gates, agent-CI/CD pattern, multi-agent test generation, intent verification, Project 3
  • Pipeline Architecture for AI Code
  • GitHub Actions for AI Code
  • Deployment Gates & Release Quality
  • Parallel Gate Execution
  • AI Code Review Automation
  • Project: Building a Complete CI/CD Pipeline
• Testing Framework & Continuous Execution (7 lessons) — Verification pyramid, reusable framework, continuous workflows, prompt library (testing), prompt library (review), master checklist
  • The Verification Pyramid for AI Code
  • Building a Reusable Testing Framework
  • Continuous Testing Workflows
  • Prompt Library: Testing Prompts
  • Master Checklist: AI Code Quality
  • Prompt Library: Review Prompts
  • Integrating the Framework into Your Workflow
• Capstone: End-to-End AI Code Testing System (5 lessons) — Production monitoring, incident response, AI code debt strategy, portfolio building, Project 4
  • Monitoring AI Code in Production
  • AI Code Incident Response
  • The AI Code Debt Strategy
  • Building Your Testing Portfolio
  • Project: End-to-End Testing System

The technologies you will use

pytest · Hypothesis · mutmut · Semgrep · Z3 SMT Solver · Ruff · GitHub Copilot Review · Claude Code · CodeRabbit · Verdict CI · CodeLoop · Socket.dev

Roles this course prepares you for

• AI Code Testing Engineer ($130k-$190k) — Tests, verifies, and validates AI-generated code before production. Designs test suites, runs mutation testing, performs security analysis, builds CI/CD quality gates.
• AI QA Engineer ($120k-$290k) — Designs evaluation frameworks for AI-generated code. Builds test infrastructure, defines quality bars, runs adversarial testing, owns production observability.
• SDET for AI Code ($140k-$220k) — Blends test automation with AI code expertise. Builds automated test generation pipelines, configures mutation testing in CI, designs contract tests.
• AI Security Testing Engineer ($150k-$230k) — Specializes in security testing of AI-generated code. Runs SAST, formal verification with Z3, slopsquatting defense, builds security gates.
• AI Code Quality Architect ($170k-$280k) — Designs overall testing strategy for teams using AI coding assistants. Defines verification pyramid, selects tools, sets quality thresholds. Senior role.
• DevSecOps for AI Code ($150k-$220k) — Integrates security and quality gates into AI development pipelines. CI/CD security, supply chain scanning, automated adversarial testing.

What AI code testing actually is

AI code testing blends traditional software testing (unit tests, integration tests, regression tests) with AI-specific verification techniques (property-based testing, mutation testing, formal verification, intent verification). It is not traditional QA with an AI slide added — the failure modes are structurally different: hallucinated APIs, systematic CWE patterns, slopsquatting, and the generation-review asymmetry.

What you do every day

You write Hypothesis property tests for AI-generated functions, run mutmut to find untested behavioral paths, execute Semgrep scans on AI PRs, build CI/CD quality gates with dual-agent test generation, perform intent verification on PRs, and respond when AI-generated code fails in production. You write security reports and quality scorecards that get read by engineering leadership.

Why companies are hiring for this now

AI-assisted developers produce commits at 3-4x the rate of their peers but introduce security findings at 10x the rate. 55.8% of AI-generated code contains Z3-proven vulnerabilities. 19.7% of AI-recommended packages do not exist. Every team shipping AI-generated code now needs engineers who can verify it. AI QA engineer job postings grew rapidly in 2025-2026, and AI testing engineers command a 30-50% premium over traditional QA.

What this course is not

It is not a general software testing course with a few AI slides. It is not a theory course — you will run real mutation testing, real Z3 verification, real Semgrep scans, and build real CI/CD pipelines. And it does not pretend the field has a finished playbook: you will learn the frameworks that exist (Hypothesis, mutmut, Z3, Verdict CI) and the reasoning skills to handle AI code failure patterns that show up in your job next month.

Common questions

What background do I need?

Python proficiency and basic understanding of AI coding assistants (Claude Code, Cursor, or GitHub Copilot). No prior testing experience required — Phase 0 teaches the fundamentals from scratch. Knowing pytest basics helps but is not required.

Is this standalone or does it require another course?

Fully standalone. If you already know Python and use AI coding assistants, you can start here directly. The ML & AI Engineering course provides broader AI engineering context but is not a prerequisite.

How is this different from testing content in other courses?

No other course on this platform teaches testing specifically for AI-generated code. This course goes 10x deeper: 56 lessons dedicated entirely to the verification stack for AI code — TDD adapted for AI agents, property-based testing with Hypothesis, mutation testing, Z3 formal verification, slopsquatting defense, CI/CD quality gates with dual-agent test generation, and intent verification.

How long does this course take?

120-160 hours of structured content. Most engineers complete it in 4-6 months at 8-10 hours per week. Phases 0-1 (free) can be completed in about a week.

Do I need a GPU or special hardware?

No. Every lab runs on CPU. The Z3 formal verification labs use small code artifacts specifically so they run locally. CI/CD labs use GitHub Actions (free tier sufficient).

What specific jobs does this course prepare me for?

AI Code Testing Engineer ($130k-$190k), AI QA Engineer ($120k-$290k), SDET for AI Code ($140k-$220k), AI Security Testing Engineer ($150k-$230k), AI Code Quality Architect ($170k-$280k), and DevSecOps for AI Code ($150k-$220k). AI QA engineers command a 30-50% premium over traditional QA at senior levels.

Do I need to use a specific AI coding assistant?

No. The course is tool-agnostic. Labs demonstrate patterns in Claude Code, Cursor, and GitHub Copilot, but the testing methodologies apply to any AI coding assistant. You will learn to test code regardless of which tool generated it.

Is this field established enough to have a reliable curriculum?

The field is emerging rapidly, driven by the 55.8% vulnerability rate in AI-generated code and the 3-4x commit velocity of AI-assisted developers. This course is built from peer-reviewed research (Z3 formal verification studies, USENIX slopsquatting research, Anthropic agentic PBT) and the tools testing teams actually use in production (Hypothesis, mutmut, Semgrep, Z3, Verdict CI, CodeLoop). You will also learn to reason from first principles, because you will encounter AI code failure patterns that no course has documented yet.

Key terms in this course

Agent

Continue your learning path

ML & AI Engineering · AI Security & Red Teaming · Forward Deployed AI Engineering

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