AI+ Quality Assurance™

Module 1: Introduction to Quality Assurance (QA) and AI

• 1.1 Overview of QA
• 1.2 Introduction to AI in QA
• 1.3 QA Metrics and KPIs
• 1.4 Use of Data in QA

Module 2: Fundamentals of AI, ML, and Deep Learning

• 2.1 AI Fundamentals
• 2.2 Machine Learning Basics
• 2.3 Deep Learning Overview
• 2.4 Introduction to Large Language Models (LLMs)

Module 3: Test Automation with AI

• 3.1 Test Automation Basics
• 3.2 AI-Driven Test Case Generation
• 3.3 Tools for AI Test Automation
• 3.4 Integration into CI/CD Pipelines

Module 4: AI for Defect Prediction and Prevention

• 4.1 Defect Prediction Techniques
• 4.2 Preventive QA Practices
• 4.3 AI for Risk-Based Testing
• 4.4 Case Study: Defect Reduction with AI

Module 5: NLP for QA

• 5.1 Basics of NLP
• 5.2 NLP in QA
• 5.3 LLMs for QA
• 5.4 Case Study: Using NLP for Bug Triaging

Module 6: AI for Performance Testing

• 6.1 Performance Testing Basics
• 6.2 AI in Performance Testing
• 6.3 Visualization of Performance Metrics
• 6.4 Case Study: AI in Performance Testing of a Cloud App

Module 7: AI in Exploratory and Security Testing

• 7.1 Exploratory Testing with AI
• 7.2 AI in Security Testing
• 7.3 Case Study: Enhancing Security Testing with AI

Module 8: Continuous Testing with AI

• 8.1 Continuous Testing Overview
• 8.2 AI for Regression Testing
• 8.3 Use-Case: Risk-Based Continuous Testing

Module 9: Advanced QA Techniques with AI

• 9.1 AI for Predictive Analytics in QA
• 9.2 AI for Edge Cases
• 9.3 Future Trends in AI + QA

Module 10: Capstone Project