Overview

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This comprehensive Generative AI for Software Testing Training course builds essential skills in AI-assisted testing, requirement analysis, test planning, test case generation, test data creation, regression testing, and bug detection. Learn to use Generative AI and GitHub Copilot with JavaScript to create, prioritize, execute, and optimize testing workflows while improving coverage, efficiency, quality, and reliability. By the end of this course, you will be able to: - Master GenAI for Testing: Apply Generative AI to modern software testing workflows - Analyze Requirements: Refine requirements and create acceptance criteria using GenAI - Generate Test Cases: Create test cases, scenarios, and synthetic test data with GitHub Copilot - Optimize Test Execution: Improve regression testing, test prioritization, and dynamic testing - Detect Bugs: Use AI-assisted techniques for failure prediction, bug detection, and test cycle closure - Build Practical Skills: Apply GenAI testing through hands-on demos, GitHub Actions, and real-world case studies Ideal for beginners, students, software testers, QA professionals, developers, and aspiring AI testing professionals with basic software testing and JavaScript knowledge who want to build practical Generative AI, GitHub Copilot, and AI-assisted software testing skills.

Syllabus

Introduction to Generative AI in Software Testing
- Build a strong foundation in Generative AI for software testing with GitHub Copilot and JavaScript. Learn software testing fundamentals and understand how Generative AI enhances traditional testing practices. Compare traditional and AI-assisted testing approaches, explore how Generative AI works in software testing, and understand its key benefits. Gain practical experience creating and comparing test cases using GitHub Copilot.
Requirement Analysis and Test Planning with GenAI
- This module focuses on using Generative AI for software testing, requirement analysis, and test planning. Learn how GenAI supports testing phases, refines software requirements, and creates acceptance criteria from user stories. Explore practical GitHub Copilot demonstrations for requirement analysis and optimize test planning for a healthcare analytics platform using AI-assisted testing workflows.
GenAI-Driven Test Case and Scenario Generation
- This module focuses on GenAI-driven software testing, automated test case generation, and dynamic testing. Learn to generate test cases, scenarios, and test data using Generative AI and GitHub Copilot. Explore regression testing optimization, failure prediction, dynamic test case adjustment, exploratory testing, and integration with testing tools. Apply hands-on workflows using GitHub Copilot and GitHub Actions to improve testing efficiency, coverage, and reliability.
GenAI in Test Case Development and Execution
- This module focuses on using GenAI for test case development, synthetic data generation, test prioritization, and execution. Learn how Generative AI supports test case creation, documentation, automation, and environment setup. Explore synthetic and augmented datasets with GitHub Copilot, prioritize test scenarios, and optimize test execution through practical AI-assisted workflows to improve software testing efficiency, coverage, and reliability.
Test Cycle Closure and Bug Detection
- This module focuses on GenAI for test cycle closure, bug detection, and emerging software testing trends. Learn how Generative AI and GitHub Copilot streamline test cycle closure, identify software bugs, and improve testing efficiency. Explore emerging advancements in GenAI testing and analyze a Microsoft case study on using GitHub Copilot to enhance software testing. Apply key takeaways to strengthen AI-driven testing workflows.