Course Overview

This intensive 4-Part course is designed to provide participants with in-depth knowledge and hands-on skills in automating web application testing using AI-driven tools and frameworks. As web applications grow in complexity, traditional testing approaches often fall short in ensuring comprehensive and efficient testing. This course aims to bridge that gap, leveraging AI-based testing tools to improve the accuracy, efficiency, and reliability of web application testing.
Participants will explore the core principles of test automation, the role of AI in testing, and how to implement AI-driven testing strategies. The course includes theoretical lectures, hands-on labs, and practical exercises using popular AI-powered testing tools.

Learning Objectives

By the end of the course, participants will be able to:
Understand the fundamentals of web application testing and automation.
Explore the role of AI in transforming traditional web application testing.
Set up and configure popular AI-driven testing tools and frameworks.
Design, implement, and execute automated tests for web applications using AI techniques.
Integrate AI-based testing in continuous integration and deployment (CI/CD) pipelines.
Evaluate and troubleshoot automated test cases using AI-powered insights.

Part 1: Introduction to Web Application Testing and AI in Testing

Topics:

Module 1: Overview of Web Application Testing
Types of web application testing (functional, performance, security, usability)
Challenges in traditional web application testing
Introduction to automation in web testing

Module 2: Introduction to AI in Testing
What is AI in testing, and why is it necessary?
Differences between traditional test automation and AI-powered automation
Overview of AI concepts (machine learning, NLP) relevant to testing

Module 3: Getting Started with Automation Tools
Overview of popular automation frameworks (e.g., Selenium, Appium)
Introduction to AI-driven testing tools (Testim, Applitools, Mabl, etc.)
Installation and setup of AI-powered testing tools

Activities:
Hands-on setup of automation frameworks and AI-driven testing tools
Practical exercise: Write and execute basic automated test scripts

Part 2: Building and Implementing Automated Tests with AI

Topics:

Module 4: AI-Driven Test Case Design

How AI enhances test case generation and coverage
Creating AI-driven test cases for different test scenarios
Understanding self-healing and dynamic locators

Module 5: Implementing AI-Powered Automated Tests

Writing test cases using AI-driven tools
Implementing visual testing, regression testing, and user flow testing with AI
Data-driven testing and parameterization with AI-based tools

Module 6: Running and Debugging Tests

Executing AI-powered automated tests in a real environment
Debugging AI-driven test cases and analyzing test results
Practical tips for managing AI-based automated testing in production

Activities:

Practical lab: Design and implement AI-based test cases using Testim or Applitools
Practical exercise: Execute tests and troubleshoot common issues

Part 3: Advanced AI Testing Techniques and Integration with CI/CD

Topics:

Module 7: Advanced Testing Techniques

Visual testing with AI for pixel-perfect UI validation
AI-driven performance testing and monitoring
Generating test scenarios based on user behavior analytics

Module 8: Integrating Automated Testing in CI/CD Pipelines

Setting up a CI/CD pipeline with automated AI tests
Running automated tests in Jenkins, GitHub Actions, or other CI/CD tools
Test reporting and analysis using AI insights

Module 9: Self-Healing and Adaptive Test Automation

How AI identifies and adjusts to changes in the application
Self-healing test automation workflows
Dynamic locators and AI-powered maintenance reduction

Activities:
Practical lab: Integrate AI-driven automated tests in a CI/CD pipeline
Practical exercise: Implement a self-healing automation setup

Part 4: Hands-On Project, Best Practices, and Future Trends

Topics:

Module 10: Hands-On Project: AI-Driven Automation Framework
Building a mini-framework using AI-driven tools
Applying learned techniques to test a sample web application

Module 11: Best Practices in AI-Driven Web Testing
Effective strategies for AI-driven testing implementation
Handling data privacy and ethical considerations in AI testing
Maintaining and scaling AI-powered test automation

Module 12: Future of AI in Web Application Testing
Emerging trends and technologies in AI-based testing
Evolution of autonomous testing and predictive analytics
Skills and tools to stay updated in AI-driven testing