Phase 4: Mutation Testing & Behavioral Verification · 90 min · mutmut · cosmic-ray · pytest

Project 2: Mutation Testing Pipeline

Build the pipeline. Enforce the score. Ship with confidence.

Hiring signal: Engineers who can build a mutation testing CI pipeline that enforces >80% mutation score demonstrate the ability to create automated quality gates for AI-generated code.

What you will learn

Build a mutation testing CI pipeline for an AI-generated codebase
Enforce >80% mutation score as a CI gate
Document all surviving mutants with kill/equivalent/timeout classification
Generate a mutation report artifact for review
Configure the pipeline to run both standard and AI-specific mutations

The Problem

You've learned why coverage lies (Lesson 1), how to use mutation testing tools (Lesson 2), how to interpret mutation scores (Lesson 3), and how to design AI-specific mutation operators (Lesson 4). Now it's time to build a complete mutation testing CI pipeline for an AI-generated codebase.

Project deliverable: Pipeline config + mutation report

Project Requirements

Pipeline config: A CI configuration that: - Runs the test suite - Runs standard mutations (mutmut or cosmic-ray) - Runs AI-specific mutations (cosmic-ray with custom mutators) - Enforces >80% mutation score for BOTH standard and AI-specific - Fails the build if either score drops below 80% - Generates a mutation report artifact

Mutation report: A document that: - Lists all mutants with their status (killed/surviving/equivalent/timeout) - Classifies each surviving mutant as killable, equivalent, or timeout - Shows the mutation score for both standard and AI-specific mutations - Documents the triage process for surviving mutants - Includes the tests written to kill surviving mutants

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers Pipeline Architecture, Build It — plus a hands-on lab, quiz, and project artifact.

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