Instructor-led training

AI in Software Testing

AI training from Hartmann Software Group, delivered onsite or live online.

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What you'll learn

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

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

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.

Prerequisites

Basic JavaScript Knowledge. Familiarity with Node.js and npm. Introductory Testing Experience. Comfort Using the Command Line. Basic Git/GitHub Skills (for CI/CD labs). Access to Required Tools.

Course Outline

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

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