Introduction

Last updated 17 days ago

Introduction

Generative AI for Software Testing

Course Overview

This 4-session course (3 hours per session) is designed to equip software testers with the knowledge and practical skills to integrate Generative AI into the testing workflow. By the end of the course, participants will understand how to leverage AI for test case generation, test data creation, automation, and intelligent test execution.

Course Outcomes

By the end of the course, participants will: ✅ Understand how Generative AI enhances software testing ✅ Generate high-quality test cases using AI models ✅ Use AI to create diverse and realistic test data ✅ Automate UI and API testing with AI-driven scripts ✅ Build AI-powered testing workflows for intelligent execution

Session 1: Introduction to Generative AI in Testing

Overview of Generative AI

What is Generative AI?
Difference between traditional AI and Generative AI
Overview of LLMs (Large Language Models) in software testing
How LLMs are trained and their limitations (tokenization, context windows, hallucinations)

Popular AI Models and Their Usage

(ChatGPT, Codex, Copilot)

Setting Up AI Tools for Testing

Hands-on: Setting up API access for OpenAI (GPT-4, GPT-3.5)
Exploring AI-powered test tools (ChatGPT, Copilot, etc.)
Configuring an AI-powered testing environment

Session 2: AI-Assisted Test Case Generation

Using Generative AI to Generate Test Cases

Understanding prompt engineering for test cases
Creating structured and contextual prompts
Using Retrieval-Augmented Generation (RAG) for contextualized test cases
Hands-on: Writing prompts to generate test cases for different features

Refining AI-Generated Test Cases

Evaluating AI-suggested test cases for effectiveness
Improving AI responses using:
- Context embedding
- Prompt structuring
- Domain-specific tuning
Hands-on: Refining and modifying generated test cases

Automation Readiness of AI-Generated Test Cases

Converting test cases into automation scripts
Identifying reusable test components
Hands-on: Generating test cases and converting them into automation scripts (e.g., Playwright)

Session 3: AI-Powered Test Data Generation

Introduction to AI-Driven Test Data Creation

Importance of diverse and realistic test data
Challenges in manual test data generation

Generating Test Data with Generative AI

Creating structured test data (JSON, XML, CSV)
Using OpenAPI schema & SQL schemas for data generation
Generating synthetic test data for edge cases
Hands-on: Using AI to generate test data for API and UI testing

Transforming and Validating Test Data

Converting test data formats
Ensuring data consistency and integrity
Hands-on: Using AI to transform test data (SQL to JSON, XML to CSV)

Session 4: Advanced AI in Test Automation

Automating UI & API Tests with AI

Using AI for Playwright test automation
AI-assisted test script generation
Hands-on: Creating Playwright tests with AI-generated test cases

AI-Powered Workflow Automation

Using AI agents for intelligent test execution
Workflow automation using LLMs with n8n
Hands-on: Setting up an AI-driven test automation workflow

Model Context Protocol & AI Test Assistants

How AI agents learn from testing patterns
Implementing Model Context Protocol for intelligent test execution
Hands-on: Building an AI-powered test execution assistant

Final Project: AI-Augmented Testing

Participants will work on a real-world testing scenario using AI tools
Apply AI-generated test cases, test data, and automation techniques
Present results and findings

Prerequisites

Basic understanding of software testing
Experience with test automation (Selenium, Playwright, or API testing tools)
Familiarity with Python or JavaScript is beneficial

Tools & Technologies

OpenAI GPT (API access)
Playwright for test automation
n8n for automation workflows
OpenAPI schema for structured test generation

References

📖 Software Testing with Generative AI by Mark Winteringham (Manning Publications, 2025)

Next1. Overview of Generative AI