Course Prerequisites:
Programming Skills: Basic knowledge of Python and familiarity with software testing lifecycle and tools.
Basics of QA: Basic knowledge of Quality Assurance principles and practices.
Basics of AI: Foundational knowledge of machine learning concepts is beneficial but not mandatory.
Modules:
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 Test Automation Basics
4.4 Use of AI for Continuous Monitoring
Module 5: NLP for QA
5.1 Basics of NLP
5.2 NLP in QA
5.3 Large Language Models for QA
5.4 NLP for Bug Resolution and Analysis
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 AI for Predictive Load Balancing
Module 7: AI in Exploratory and Security Testing
7.1 Exploratory Testing with AI
7.2 AI in Security Testing
7.3 Advanced Techniques in Security Testing
7.4 AI for Threat Analytics
Module 8: Continuous Testing with AI
8.1 Continuous Testing Overview
8.2 AI for Regression Testing
8.3 Advanced Continuous Testing Techniques
8.4 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 with QA
9.4 Integration of Emerging Technologies
Module 10: Capstone Project
AI+ Quality Assurance Detailed Curriculum Date Issued: 18/05/2025