HARTMANN software group

AI in Software Testing Training in St. Louis

Enroll in or hire us to teach our AI in Software Testing class in St. Louis, Missouri by calling us @303.377.6176. Like all HSG classes, AI in Software Testing may be offered either onsite or via instructor led virtual training. Consider looking at our public training schedule to see if it is scheduled: Public Training Classes
Provided there are enough attendees, AI in Software Testing may be taught at one of our local training facilities.
We offer private customized training for groups of 3 or more attendees.
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Course Description

  

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.

Course Length: 2 Days
Course Tuition: $790 (US)

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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