AI in Software Testing (TTAI2140)

Overview

AI is beginning to reshape how testing is planned, written, and maintained, and this course helps you build the skills to apply it in ways that actually make your work easier. Whether you are creating test cases, choosing what to run, or reviewing failures, you will learn how to use AI tools to speed things up, reduce repetitive effort, and improve coverage. You will work hands-on with user-friendly AI tools generate test data, build tests from user stories, and support smarter decisions about what to test and when.

You will practice spotting flaky or redundant tests, creating self-healing flows, and using AI to explain what went wrong in a failing run. You will also explore how AI can predict risk based on commit history or past bugs, helping you focus on the areas that matter most. The course will show you how to plug AI into common testing workflows, including CI/CD tools like GitHub Actions, and how to write prompts that give you useful, accurate results. You will get examples, use cases, and guided labs that you can use right away in your own projects.

This expert-led, one-day course is designed for software testers who are new to AI but already familiar with core testing practices. It is about 50 percent hands-on, with labs built around common tasks that testers perform every day. Whether you are working in a manual, automated, or hybrid role, this course will help you start using AI in ways that are practical, helpful, and easy to build on.

Audience Profile

This course is designed for software testers who are new to using AI and want to learn how to apply it confidently in real testing environments. It is ideal for QA professionals, test engineers, SDETs, or manual testers who want to add AI-assisted workflows to their skillset. The course is beginner-friendly when it comes to AI, but assumes a basic understanding of software testing concepts.

At Course Completion

The goal of this course is to help you build practical, hands-on skills for using AI in modern software testing. You will walk away with real experience using AI tools that can support faster, smarter, and more reliable testing in your day-to-day work.

By the end of this course, you will be able to:

Use cutting-edge AI tools to generate realistic and varied test data tailored to your testing goals
Select and prioritize test cases based on code changes, historical results, and risk indicators
Generate UI, API, and functional test cases using plain-language prompts and AI coding tools
Identify flaky or redundant tests and improve stability with self-healing and AI analytics
Use AI to summarize test failures, detect patterns, and support faster triage
Integrate AI-generated tests and insights into existing CI/CD workflows for real-time value

Outline

Please note that this list of topics is based on our standard course offering, evolved from current industry uses and trends. We will work with you to tune this course and level of coverage to target the skills you need most. Course agenda, topics and labs are subject to adjust during live delivery in response to student skill level, interests and participation. The course tools, topics, use cases and hands-on labs can also be adjusted to suit your specific needs, goals or requirements. Please inquire for details and options.

1. Introduction to AI in Software Testing

2. Generating Test Data with AI

3. Selecting Test Cases with AI

4. AI-Enhanced Test Generation

5. Smart Test Execution and Maintenance

6. Defect Prediction using AI

7. Integrating AI into the Testing Workflow

Prerequisites

This course is designed for experienced software testers who are new to using AI and want to learn how to apply it confidently in real testing environments. It is ideal for QA professionals, test engineers, SDETs, or manual testers who want to add AI-assisted workflows to their skillset. The course is beginner-friendly when it comes to AI, but assumes a basic understanding of software testing concepts.