Master Generative AI for Software Testing: Manual to Auto

Overview

Why Generative AI in Software Testing?

Syllabus

• Introduction to Generative AI in Software Testing
Overview of Generative AI
Benefits of AI in Software Testing
Key terminology and concepts
• AI Tools and Platforms for Testing
Survey of popular AI tools used in testing
Comparative analysis of tools
Setting up your AI environment
• Understanding Generative Models
Basics of generative models: GANs, VAEs, etc.
Application of generative models in testing scenarios
Case studies of generative models in software development
• Manual Testing vs. AI-Driven Testing
Differences and complementarities
Transitioning from manual to AI-driven processes
Challenges and solutions in the transition
• Designing AI-Based Test Strategies
Mapping testing requirements to AI capabilities
Developing test cases with AI
Automated test generation using AI models
• Implementing Generative AI in Testing Workflow
Integration of AI tools in existing workflows
Continuous AI-driven test execution and monitoring
Real-world examples of AI-augmented testing workflows
• Evaluating AI-Generated Test Outcomes
Metrics and KPIs for evaluating AI-generated tests
Analyzing AI-generated test results
Debugging test failures and inaccuracies
• Ethical and Regulatory Considerations
Ethical use of AI in software testing
Understanding regulatory implications
Best practices for responsible AI testing
• Future Trends in AI for Software Testing
Emerging technologies in AI testing
The future of AI-enhanced testing processes
Skills and knowledge for staying current
• Capstone Project
Design and implement a generative AI testing solution
Present findings and outcomes
Peer review and feedback session

Taught by

Gianni Bruno

Subjects

Computer Science