• Understanding the basics of generative artificial intelligence (AI)
• Exploring the potential applications of generative AI in software testing and test automation
• Overview of key concepts and terminology

4 hours

• Recap of software testing principles and methodologies
• Introduction to test automation frameworks and tools
• Identifying challenges and limitations in traditional software testing approaches

4 hours

• Introduction to generative AI techniques such as generative adversarial networks (GANs), variational autoencoders (VAEs), and reinforcement learning (RL)
• Understanding how generative AI models learn and generate data

4 hours

• Leveraging generative AI for generating synthetic test data
• Techniques for ensuring data diversity, relevance, and coverage
• Integration with existing test data management systems

4 hours

• Automating test case generation using generative AI models
• Strategies for generating comprehensive and effective test cases
• Evaluating the quality and relevance of generated test cases

4 hours

• Introduction to fault injection and mutation testing techniques
• Using generative AI to simulate and inject faults into software systems
• Analyzing the impact of injected faults and mutations on system behavior

4 hours

• Construction of intelligent test oracles using generative AI models
• Techniques for verifying system behavior and expected outcomes
• Handling complex and dynamic software environments

4 hours

• Introduction to reinforcement learning (RL) and its applications in test automation
• Training RL agents to perform automated testing tasks
• Case studies and examples of RL-driven test automation solutions

4 hours

• Identifying common challenges and limitations in applying generative AI to software testing
• Addressing ethical considerations and biases in generative AI models
• Strategies for mitigating risks and ensuring reliability

4 hours

• Best practices for integrating generative AI into software testing workflows
• Real-world case studies and success stories of organizations adopting generative AI for test automation
• Lessons learned and recommendations for future implementations

4 hours

• Hands-on labs and practical exercises for applying generative AI techniques to
  software testing scenarios
• Guided demonstrations using popular tools and frameworks
• Opportunities for participants to experiment and explore generative AI in a controlled environment

4 hours

• Recap of key learnings and takeaways from the course
• Future directions and emerging trends in generative AI for software testing

4 hours