Module 1: Introduction to Generative AI in Software Testing
1.1 Overview of Generative AI
1.2 Applications of Generative AI in Software Testing and Quality Assurance
1.3 Challenges and Opportunities in Implementing Generative AI in Testing
2.1 Principles of Automated Test Generation
2.2 Using Generative AI for Test Case Generation
2.3 Test Coverage Improvement through Automated Test Generation
3.1 Utilizing API Access for Chat GPT and Google Bard
3.2 Creating Custom Consumers for Bard and GPT
3.3 Integrating Generative AI with API Testing
4.1 Streamlining Requirements Gathering with Generative AI
4.2 Role of Generative AI in Software Development Life Cycle
4.3 Case Studies and Best Practices for Automated User Story Creation
5.1 Significance of Test Data in Software Testing
5.2 Generating Comprehensive Test Data with Generative AI
5.3 Ensuring Data Privacy and Security in Test Data Generation
6.1 Investigating Complex Code Using Generative AI
6.2 Identifying Potential Issues and Dependencies
6.3 Improving Code Quality through Code Explanation
7.1 Integrating Generative AI into Existing Workflows
7.2 Improving Software Quality and Accelerating Delivery
7.3 Case Studies on Productivity Boost with Generative AI
8.1 Creating a Performance Testing Framework with CI/CD on Cloud using AI
8.2 Building an API Testing Framework with Java and REST-Assured with AI
8.3 Developing a Code Quality Validation Framework for Java
9.1 Understanding Differences between ChatGPT and Google Bard
9.2 Evaluating Features and Use Cases for Each Platform
9.3 Choosing the Right Generative AI Solution for Specific Testing Needs
10.1 Overview of Google Cloud AI Solution - Vertex AI
10.2 Model Training Techniques and Best Practices
10.3 Implementing Google Cloud AI Solution in Software Testing
11.1 Main Features of BARD AI and CHAT GPT
11.2 Setting Up CI/CD Pipelines for Generative AI
11.3 Creating Performance and API Testing Frameworks with Cloud-based Generative AI
12.1 Training Your Own Instance of GPT
12.2 Implementing Gen AI Examples: Story Enhancement, Self-Healing Code, Recursive Testing, Self-Service Bot, Recommendation Engine
12.3 Exploring Different Models and ML Types for Software Testing
13.1 Basics of Machine Learning for Quality Engineering
13.2 Enhancing Test Automation with Generative AI
14.1 Connecting to ChatGPT API
14.2 Enhancing Efficiency with Google Bard in Test Tools
14.3 Practical Tips for Using Generative AI in Quality Engineering
15.1 Review of Key Learnings
15.2 Future Trends and Developments in Generative AI and Software Testing
15.3 Final Q&A and Feedback Session
