Opleiding: AI in Software Testing [M-AIST]

OVERVIEW

Master the future of quality assurance with AI-powered testing.

You’ll learn how to harness large language models (LLMs), such as ChatGPT and GitHub Copilot, to generate, analyze, and maintain test cases with greater speed and precision.

Through a progressive series of labs, you’ll explore real-world techniques for AI-assisted test creation, legacy code analysis, code coverage improvement, exploratory testing, synthetic data generation, and much more. You’ll also tackle the unique challenges of testing AI systems themselves, manage flaky tests, and integrate AI-generated tests into CI/CD pipelines. Ethical considerations and model limitations are addressed throughout to ensure responsible AI adoption.

Updated June 2026

OBJECTIVES

By the end of the course, you'll have built a fully AI-enhanced testing workflow, from test generation to reporting, and gained the skills to apply AI effectively and confidently in your software projects.

• Boost Testing Productivity
• • Learn to rapidly generate comprehensive test cases using AI tools, saving hours of manual work.
• Improve Test Coverage
• • Use AI to identify untested logic paths, edge cases, and blind spots that manual testing often misses.
• Master AI-Powered QA Tools
• • Gain hands-on experience with tools like ChatGPT, GitHub Copilot, Applitools, and Launchable.
• Level-Up Prompt Engineering Skills
• • Learn how to craft effective AI prompts for generating tests, refactoring code, and diagnosing issues.
• Tackle Real-World Scenarios
• • Practice testing legacy code, flaky tests, and AI/ML systems with realistic lab exercises.
• Streamline CI/CD Workflows
• • Integrate AI-generated tests into automated pipelines using GitHub Actions for seamless QA delivery.
• Write Maintainable, Clean Test Code
• • Identify and fix test smells using AI suggestions to improve readability and reduce duplication.
• Generate Documentation Effortlessly
• • Use LLMs to create readable test descriptions, test plans, and QA reports from your code.
• Work Smarter with AI, Not Blindly
• • Understand the limitations, risks, and ethics of AI in testing—so you can use it wisely and responsibly.
• Capstone Project for Real-World Readiness
• • Apply everything you've learned in a guided, full-cycle project testing a complete application.

AUDIENCE

This hands-on course introduces software testers, QA professionals, and developers to the practical use of artificial intelligence in modern testing workflows.

CERTIFICATION

None

NEXT STEP

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CONTENT

Module 1: Foundations of AI in Testing

• Introduction to AI in Software Testing
• Benefits and use cases of AI for QA
• Overview of AI tools: GitHub Copilot, ChatGPT, Applitools, Launchable, and more
• Understanding zero-shot and few-shot prompting

Module 2: AI-Driven Test Case Generation

• Writing effective prompts for test creation
• Generating unit and edge case tests using LLMs
• Prompt patterns and strategies for maximizing test relevance
• Evaluating and refining AI-generated test cases

Module 3: AI-Assisted Code Coverage and Refactoring

• Measuring code coverage (line, branch, function)
• Using AI to detect gaps in coverage
• Refactoring verbose or redundant tests
• Mutation testing overview

Module 4: Testing Legacy Code with AI

• Understanding undocumented code with LLMs
• Generating regression tests for legacy behavior
• Using AI to reverse-engineer and protect critical functionality

Module 5: Exploratory and Edge Case Testing

• Defining exploratory testing and its value
• Generating edge cases with AI (fuzzing, boundary tests)
• Handling complex or malformed input scenarios

Module 6: Generating Synthetic Test Data

• Creating structured and unstructured data using AI
• Valid vs. invalid input generation
• Risks: hallucinations, unrealistic data, format constraints

Module 7: Detecting and Fixing Test Smells

• Common anti-patterns in test code
• Using AI to clean up, rename, and restructure tests
• Improving maintainability and test intent clarity

Module 8: Testing AI and Machine Learning Systems

• Unique challenges in testing non-deterministic output
• Output validation via heuristics, type checks, and human-in-the-loop
• Designing robust, behavior-focused test cases

Module 9: Test Maintenance and Flaky Tests

• Identifying causes of flaky tests (async, timing, randomness)
• Diagnosing issues with AI analysis of logs and failures
• Stabilizing tests with mocks, retries, and dependency control

Module 10: CI/CD Integration

• Incorporating AI-generated tests into CI workflows
• Using GitHub Actions for automated test runs
• Reviewing and tagging AI-generated content
• Managing regression lifecycles and metrics

Module 11: Documentation and Reporting with AI

• Auto-generating test documentation and summaries
• Writing JSDoc-style comments and QA reports
• Using LLMs for stakeholder-friendly communication

Module 12: Limitations, Ethics, and Trust

• Understanding hallucinations, overconfidence, and logic gaps
• Mitigating risk with prompt design and human oversight
• Intellectual property and authorship concerns in AI-generated code

Module 13: Capstone Project

• Apply AI techniques to a full-stack JavaScript application
• Generate, refactor, document, and integrate tests
• Demonstrate your complete AI-enhanced testing workflow in CI/CD