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Keep shoppingComplete AI-Driven Unit Testing for Developers
Role Play
6 students
Complete AI-Driven Unit Testing for Developers
Autonomous and Agentic Test Generation with Diffblue Cover, Claude Code, Cursor and Copilot
Last updated 7/2026
English
What you'll learn
- How to generate complete test suites using Diffblue Cover, and your AI coding assistant of choice (Claude Code, Cursor, or Copilot)
- Learn how to apply AI-driven TDD in practice
- How to identify and avoid the most common AI-driven testing pitfalls
- Learn about AI-driven testing best practices that make your AI-generated tests trustworthy
- How to choose the right AI testing tool for your stack
- How to build an AI testing workflow that integrates into pull request pipelines
- How to make test workflows reusable in Claude Code, with slash commands, agent skills, and CLAUDE. md
- How to make test workflows reusable in Cursor, with project commands, agent skills, and AGENTS. md
- How to make test workflows reusable in Copilot, with prompt files, agent skills, and AGENTS. md
Explore related topics
Course content
6 sections • 23 lectures • 3h 37m total length
Requirements
- Basic programming experience in at least one language — JavaScript/TypeScript, Python or Java is recommended
- Familiarity with the concept of unit testing — knowing what a test is and roughly why we write them is enough; no expert knowledge required
- No prior experience with AI coding tools is required — all tools are introduced from scratch
Description
Most developers now use AI to write tests. Far fewer know how to tell a good AI-generated test from a dangerous one.
That gap is what this course closes. You'll go beyond "prompt it and hope" and build the judgment to generate test suites that genuinely protect your code — across the three most widely used languages in the industry today: JavaScript/TypeScript, Java, and Python.
You'll work hands-on with four of the most important tools in AI testing:
Diffblue Cover — autonomous JUnit generation for Java, no prompting required
Claude Code, Cursor, and GitHub Copilot — agentic assistants that generate tests in your editor, and reusable workflows that make those results repeatable across your team
But tools are only half of it. The heart of this course is critical review. You'll see how AI can quietly turn an existing bug into a passing test, how a suite with 100% line coverage can assert nothing meaningful, and why deleting a failing test is almost always the wrong move. You'll learn to apply AI-driven TDD — where the AI writes failing tests from a spec, then writes the code to pass them, making it structurally impossible to encode a bug.
Each language section is designed so that at least one maps directly to your stack — and if it doesn't, you'll learn how to port the patterns to any language you prefer. You'll also practice the human side in a role-play scenario: advising your manager on adopting AI test generation responsibly.
By the end, you'll have a complete, review-first workflow for AI-generated testing that you can bring to your team on day one — shipping faster and with more confidence.
Who this course is for:
- Software developers and engineers at any level who want to write more tests in less time without sacrificing quality
- Team leads and senior engineers who are evaluating AI testing tools for their organization and need a structured comparison
- QA engineers and SDETs who want to understand how AI tools are changing the developer testing landscape and stay ahead of the shift
- Developers who currently skip writing tests because they're too time-consuming — this course is specifically designed to remove that barrier