# 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.

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### **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

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### **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**

* [OpenAI GPT](https://openai.com/) (ChatGPT, Codex, Copilot)
* [Google Gemini](https://gemini.google.com/)
* [Meta LLaMA](https://www.llamaindex.ai/)
* [Grok](https://x.ai/)
* [DeepSeek](https://www.deepseek.com/)
* [GTP4All](https://www.nomic.ai/gpt4all)
* [Claude](https://claude.ai/)
* [Github Copilot](https://github.com/features/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

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### **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)

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### **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)

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### **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

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### **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

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### **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

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#### **References**

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