12
Lessons
48h
Duration
English
Language
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OBJECTIVE:
- By the end of this course, participants will have gained a deep understanding of how generative AI can revolutionize software testing and test automation practices, enabling them to harness the power of AI to enhance the quality, efficiency, and reliability of their software products.
Course features:
- Practical hands on
- Lab sessions
- Training by experienced faculty
PRE-REQUISITES:
- Familiarity with basic scripting languages, preferably Python, is beneficial.
- Understanding Python syntax and basic programming concepts will help you grasp the AI-driven automation techniques.
Learning Path
- 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