Recommended 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.
Course Outline:
Lesson 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
Lesson 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)
Lesson 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
Lesson 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
Lesson 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
Lesson 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
Lesson 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
Lesson 8: Continuous Testing with AI
- 8.1 Continuous Testing Overview
- 8.2 AI for Regression Testing
- 8.3 Use-Case: Risk-Based Continuous Testing
Lesson 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
