Course Format and Delivery

Delivery Method: LiveOnline
Schedule: 3 sessions of 4.5 hour
Cost: $1,795 USD

All sessions are delivered live by an expert instructor in a fully interactive online environment.

*20% off for group bookings when booking 3 or more attendees from the same organization on the same course dates in the same transaction.

About this course

Generative AI can assist in practical test automation work such as generating test scenarios, producing automated test code, reviewing existing tests, and helping design automation approaches. This three-day course introduces how to apply generative AI across the test automation lifecycle while ensuring that all outputs are validated through human review, execution, and technical judgment.

The course is hands-on and uses a coherent case study implemented in multiple architectural styles to demonstrate how to adapt automation approaches. Participants work with AI-generated test scenarios and automated tests, evaluate their quality, correct errors, and refine prompts and test structures. The course also covers how automated tests are organized, executed, and integrated into development and delivery workflows.

What you will learn

• Use generative AI to produce and refine test scenarios for functional, boundary, and error conditions in a defined application.
• Evaluate AI-generated test scenarios and test code for correctness, coverage, and alignment with specified requirements.
• Generate and execute automated unit and UI tests using a test automation framework.
• Identify and correct errors in AI-generated test code through debugging and test execution feedback.
• Apply appropriate test design techniques, including assertions, test structure, and locator selection for UI automation.
• Distinguish between unit, integration, API, and UI tests and select appropriate automation approaches for each.
• Incorporate automated tests into a basic continuous integration workflow and interpret test results for decision-making.

Topics Covered

Day 1: Foundations and Code-Level Automation

Module 1: Defining Test Automation Goals

Identify the business and technical reasons for adopting test automation, including faster feedback, improved regression coverage, reduced repetitive work, and better release confidence. Use generative AI to examine current testing activities and define specific, measurable objectives for the course.

Module 2: Generating and Evaluating Test Scenarios with AI

Use generative AI to create functional, negative, boundary, and error-handling scenarios for a simple application. Compare results from different prompts or AI tools and evaluate the scenarios for correctness, completeness, redundancy, unsupported assumptions, and suitability for automation.

Module 3: Application Architecture and Test Automation

Compare several implementations of the same application, such as a single-page application, structured JavaScript application, server-based application, and authenticated multipage application. Examine how architecture affects test layers, automation interfaces, test data, environment requirements, and the balance among unit, API, integration, and UI testing.

Module 4: Generating and Reviewing Unit Tests

Use generative AI to create executable tests for isolated business-logic functions within a simple application. Explore why some application scenarios cannot be tested effectively at the unit level and practice reviewing, running, debugging, and correcting AI-generated test code.

Day 2: Building Maintainable UI Automation

Module 5: Structuring Maintainable Automated Tests

Learn common structures and conventions for automated tests, including Arrange-Act-Assert, setup and teardown, fixtures, test isolation, naming, reusable functions, and clear assertions. Reorganize AI-generated tests so that the code communicates test intent and can be maintained as the application changes.

Module 6: Evaluating AI-Generated UI Tests

Examine complete browser-based tests generated with a framework such as Playwright or Selenium. Assess the tests for behavioral correctness, unnecessary steps, synchronization problems, weak assertions, duplicated logic, hidden assumptions, and alignment with the original test scenarios.

Module 7: Designing Reliable Locator Strategies

Explore how browser automation identifies and interacts with page elements and why poorly chosen locators create fragile tests. Use generative AI to propose locator expressions, then compare those suggestions against accessibility, semantic meaning, uniqueness, readability, and resistance to routine UI changes.

Module 8: Generating, Executing, and Improving Complete UI Tests

Combine reviewed scenarios, application knowledge, locator strategies, test data, actions, and assertions to create complete automated UI tests. Execute the generated tests, analyze failures, distinguish application defects from automation defects, and refine both the prompts and the resulting code.

Day 3: Scaling Test Automation

Module 9: Assessing Automation Candidates and Readiness

Evaluate which tests and application areas are suitable for automation based on risk, frequency, stability, business value, technical feasibility, and maintenance cost. Assess whether the organization’s people, processes, environments, data, architecture, and tools can support reliable automation.

Module 10: Developing a Whole-Application Automation Strategy

Use generative AI to develop a layered test automation strategy for an application rather than a collection of individual scripts. Define coverage at the unit, component, API, integration, and UI levels, along with priorities, ownership, tool choices, test data requirements, and measures of effectiveness.

Module 11: Integrating Automated Tests into Build and Deployment Processes

Examine how automated tests fit into continuous integration, delivery, and deployment workflows. Use generative AI to outline execution stages, triggers, parallelization, reporting, failure handling, release gates, environment preparation, and responsibilities for investigating failed tests.

Module 12: Extending Automation and Creating an Adoption Roadmap

Explore how generative AI can assist with nonfunctional testing areas such as performance, accessibility, security, compatibility, reliability, and resilience. Conclude the course by creating a prioritized implementation roadmap that identifies initial projects, governance requirements, capability gaps, ownership, success measures, and next steps.