Master Generative Ai For Software Testing: Manual To Auto

Master Generative AI for Testing: Python, Playwright, and Behave BDD Frameworks for Manual and Automation Testers

What you'll learn

Use Generative AI tools like ChatGPT and OpenAI API to create, analyze, and refine test plans, cases, and BDD scenarios dynamically.

Master Python essentials: variables, functions, file handling, and libraries like Requests for effective automation testing.

Build AI-enhanced frameworks using Behave BDD for API and Playwright for UI testing, focusing on dynamic and efficient test creation.

Optimize automation tests with AI: refine BDD steps, generate test reports, automate cleanup, and integrate advanced features.

Configure Jenkins CI pipelines to execute automated UI and API tests, generate reports, and streamline test workflows seamlessly.

Ensure test data privacy: anonymize sensitive data, use environment variables, and follow ethical AI best practices with ChatGPT.

Gain hands-on experience with real-world projects: automate Contact Us, Login APIs, and Goal Tracker API using practical exercises.

Learn advanced testing techniques like custom test runners, targeted execution with tags, and detailed reporting using Allure.

Requirements

No Prior Python Knowledge Required: The course covers Python basics step-by-step, making it beginner-friendly.

Basic Understanding of Software Testing: Familiarity with concepts like test cases and manual testing will help you follow along more effectively.

OpenAI API Key (Recommended for Hands-On Practice): To implement AI-powered features practically/programmatically (e.g., generating test cases), you’ll need an OpenAI API key. OpenAI offers free credits for new accounts, but existing users may need to add minimal funds (~$5). Watching specific lectures without practical implementation is also an option.

Some Programming Knowledge (Optional): While not required, basic familiarity with programming concepts can help you progress faster.

Curiosity and Enthusiasm: A willingness to explore generative AI and automation testing will help you get the most out of this course.

Ask Questions in the Q&A Section: If something isn’t clear or doesn’t work, guidance and support are always available.

Description

Why Generative AI in Software Testing?Generative AI is transforming the software testing landscape by enabling dynamic test case generation, optimizing test execution, and improving coverage. Tools like OpenAI’s API and GPT4All empower testers to:Reduce manual effort.Uncover edge cases faster.Enhance both manual and automated testing workflows.Why Python, Behave BDD, and AI-Driven Tools?Python: A versatile, beginner-friendly programming language widely used for automation.Behave BDD: A Python-based Behavior-Driven Development tool that uses the same Gherkin syntax as Cucumber BDD, simplifying test case creation and ensuring scenarios are clear for all stakeholders.AI Tools: Leverage OpenAI’s API (ChatGPT) and offline tools like GPT4All to dynamically create, optimize, and refine test scenarios, reducing manual effort and improving test coverage.Together, these tools allow testers to:Automate UI and API tests with AI, Python, Behave BDD, and Playwright.Dynamically generate and refine test cases using AI tools like ChatGPT and OpenAI APIs.Integrate into Jenkins CI pipelines for scalability and continuous test execution.Why This Course?This course is practical, easy-to-follow, and designed for manual testers and automation testers looking to upgrade their skills. Whether you’re new to automation or experienced in testing, you’ll gain hands-on experience with AI-powered testing.(Note: To fully implement AI features programmatically, an OpenAI API key is recommended. OpenAI provides free credits for new accounts, but existing users may need to add minimal funds (~$5). Watching specific lectures without practical implementation is also an option.)The course includes Before and After code examples, attached to the relevant lecture resources, to help you understand concepts step-by-step and implement them seamlessly.What Will You Learn?Generative AI for Test Case CreationUse ChatGPT (free or paid version) to generate test plans and test cases outside of code.Use OpenAI APIs to dynamically generate test cases and suggest step definition code within the framework (API key and minimal credits required for hands-on implementation).Explore tools like GPT4All for offline AI-powered testing.Quickly create optimized, AI-powered test scenarios.Mastering Python Fundamentals for TestingPython basics: Variables, data structures, functions, and file handling.Work with JSON data and external libraries like Requests for API testing.Building AI-Enhanced Automation FrameworksBehavior-Driven Development (BDD): Simplify test requirements using Gherkin and Behave.Automate UI Testing with Playwright and Behave (BDD).Automate API Testing using Python and Behave (BDD).Advanced Features for AutomationUse Generative AI to refine BDD scenarios and step definitions.Leverage OpenAI API to analyze step definition files and recommend optimized code solutions.Implement tags, custom runners, and generate detailed test reports with Allure.CI/CD Integration: Learn how to run tests continuously using Jenkins CI pipelines.Data Privacy and Security in AI TestingProtect sensitive data when using AI tools like OpenAI APIs.Follow best practices for anonymizing data and managing credentials securely.Additional FeaturesReal-World Projects: Automate tests for a Contact Us Page, Login Page, and a Goal Tracker API.Practical Exercises: Step-by-step recordings with before-and-after downloadable code examples.AI-Powered Optimizations: Generate, analyze, and refine test scripts dynamically.Reporting: Generate detailed reports and attach screenshots for better test visibility.Ready to Master AI-Driven Software Testing?By the end of this course, you’ll have the skills to:Integrate Generative AI into manual and automated testing workflows.Build scalable and dynamic automation frameworks using Python, Behave BDD, Playwright, and Jenkins CI.Leverage AI tools to optimize and streamline testing processes effectively.Let’s revolutionize software testing with Generative AI together!

Overview

Section 1: Introduction to Generative AI in Software Testing

Lecture 1 Course Overview and Objectives

Lecture 2 Introduction to Generative AI in Software Testing

Lecture 3 Practical Applications of AI Tools in Software Testing

Lecture 4 Real-World Testing Project: Contact Us Page Introduction

Lecture 5 Hands-On: Generating Test Plans and Cases Using AI Prompts

Lecture 6 Real-World Challenge: Generate Test Cases for the Login Page

Lecture 7 Real-World Solution: Review & Refine Login Page Test Cases

Section 2: No-Code AI Tools for Manual Testers

Lecture 8 Using AI Prompts for Manual Testing Tasks

Lecture 9 Uploading Requirement Documents for Test Case Generation

Lecture 10 Introduction to GPT4ALL: Offline AI Model for Test Case Generation

Lecture 11 Setting Up GPT4ALL Locally

Lecture 12 Practical Exercises with No-Code AI Tools (Online and Offline)

Section 3: Introduction to Automation and Coding Concepts for Testers

Lecture 13 Why Python is Ideal for Testing and AI

Lecture 14 Overview of Coding Concepts Relevant to Testing

Lecture 15 Low-Code Exercise: AI-Enhanced Test Data Management

Section 4: Setting Up Your Python Environment for AI-Powered Testing

Lecture 16 Installing Python

Lecture 17 Setting Up a Python IDE (VSCode & Extensions)

Lecture 18 Verifying the Python Environment

Section 5: Python Fundamentals for Testers

Lecture 19 Python Basics – Variables, Data Types, and Basic Operations – (Part 1/2)

Lecture 20 Python Basics – Variables, Data Types, and Basic Operations – (Part 2/2)

Lecture 21 Control Flow – Conditionals and Loops

Lecture 22 Functions and Reusability

Lecture 23 Lists, Dictionaries, and Data Structures in Testing

Lecture 24 File Handling for Test Data Management

Lecture 25 Exception Handling for Robust Testing

Lecture 26 Working with JSON Data for API Testing

Lecture 27 Installing and Using External Libraries with pip – (Part 1/2)

Lecture 28 Installing and Using External Libraries with pip – (Part 2/2)

Section 6: Foundations of Generative AI in Software Testing

Lecture 29 Large Language Models (LLMs) and Their Role in Testing

Lecture 30 Introduction to Prompt Engineering for Automated Test Case Generation

Lecture 31 Using OpenAI API for Test Case Generation

Lecture 32 Setting Up OpenAI API for Test Automation

Lecture 33 Creating a Python VM and Installing OpenAI Package

Lecture 34 Practical Exercise: Generating Test Cases with OpenAI API

Lecture 35 Using GPT-Neo: An Open-Source Model for AI-Powered Testing

Lecture 36 Choosing the Right Model for AI-Powered Testing: OpenAI, Open-Source and GPT4All

Section 7: Building an AI-Enhanced Automation Framework with BDD, Playwright, and Python

Lecture 37 Introduction to Behavior-Driven Development (BDD)

Lecture 38 Overview of the Framework

Lecture 39 Setting Up the Automation Framework

Lecture 40 Setting Up OpenAI Integration

Lecture 41 Leveraging OpenAI for Dynamic Test Generation

Lecture 42 Analyzing and Refining Generated BDD Scenarios

Lecture 43 Implementing Setup and Teardown with Behave - (Part 1/2)

Lecture 44 Implementing Setup and Teardown with Behave - (Part 2/2)

Lecture 45 Automating Step Definition Templates with Behave

Lecture 46 Setting Up VS Code for Behave (BDD)

Lecture 47 Optimizing Step Definitions Dynamically

Lecture 48 Writing Your First Step Definitions

Lecture 49 Building a Custom Context – Simplify Your Code with DRY

Lecture 50 Optimizing Steps with AI - Part (1/2)

Lecture 51 Optimizing Steps with AI - Part (2/2)

Lecture 52 Finalising our First Scenario - Part (1/3)

Lecture 53 Finalising our First Scenario - Part (2/3)

Lecture 54 Finalising our First Scenario - Part (3/3)

Lecture 55 Finalising all Scenarios & Steps - Part (1/2)

Lecture 56 Finalising all Scenarios & Steps - Part (2/2)

Lecture 57 Introduction to Scenario Outlines

Lecture 58 Applying Scenario Outlines - Part (1/2)

Lecture 59 Applying Scenario Outlines - Part (2/2)

Lecture 60 Introduction to the Background Keyword

Lecture 61 Background Keyword – In Action

Lecture 62 Introduction to Tags

Lecture 63 Tags – In Action

Lecture 64 Creating a Custom Runner

Lecture 65 Generating Reports

Lecture 66 Attaching Images to Reports

Lecture 67 Automating Data Clean-up and Framework Optimization

Section 8: AI-Driven API Testing with Behave and Python

Lecture 68 What exactly is an API?

Lecture 69 Generating API Test Scenario with AI

Lecture 70 Implementing API Test Scenarios in Behave

Lecture 71 Testing API Responses

Lecture 72 Optimizing and refactoring for UI & API testing - Part (1/2)

Lecture 73 Optimizing and refactoring for UI & API testing - Part (2/2)

Section 9: Jenkins CI Integration for Automation Framework

Lecture 74 Introduction to Jenkins (CI)

Lecture 75 Setting Up Java JDK for Jenkins

Lecture 76 Downloading and Setting Up Jenkins

Lecture 77 Configuring Jenkins for Automation

Lecture 78 Optimizing Jenkins Jobs for Test Automation

Lecture 79 Running Tests with Custom Tags in Jenkins

Lecture 80 Generating Allure Reports in Jenkins

Section 10: Ethics and Security in AI Testing

Lecture 81 Ethical AI Usage in Software Testing

Lecture 82 Data Privacy and Security with OpenAI API

Manual Testers looking to streamline test case creation and enhance their skills using AI tools like ChatGPT and OpenAI API.,Automation Testers wanting to build AI-enhanced frameworks for UI (Playwright) and API (Behave + Requests) testing with Python.,QA Professionals aiming to integrate generative AI into their workflows for faster test case generation and smarter test execution.,Beginners transitioning into automation testing with Python, Behave BDD, and Jenkins, with step-by-step, real-world examples.,SDETs and Automation Engineers seeking to optimize and scale test automation using AI-powered tools, Jenkins CI, and dynamic reporting.,Tech Enthusiasts eager to learn how to leverage AI tools like ChatGPT and GPT4All to create, analyze, and automate test scenarios.,QA Managers and Leads interested in adopting AI-driven testing practices to improve productivity, test coverage, and efficiency.