This hands-on course helps testers understand how to leverage AI to improve software test planning, execution, automation, and reporting.
Artificial Intelligence (AI) has taken the world by storm, increasing the productivity of workers in a wide range of industries, especially software. But, it’s also understandably led to uncertainty and fear about the personal and implications for disciplines such as software testing.
If you’re interested in cutting through the hype and understanding how AI affects the testing profession, then this course is for you. In this hands-on class, you will learn how to apply AI to the testing process. A variety of techniques and tools will be introduced to help testers as they plan, execute, automate, and report software testing activities.
Key takeaways from this class include:
Who Should Attend
This course is ideal for those who wish to use AI to increase the productivity of their current software testing activities. This includes those in hands-on testing roles and test managers. A basic understanding of artificial intelligence including a high-level knowledge of machine learning and generative AI is necessary. If you're new to AI, consider taking our Fundamentals of AI—ICAgile Certification (ICP-FAI) first.
Laptop and RDP Required
This class involves hands-on activities using sample software to better facilitate learning. Each student should bring a laptop with a remote desktop protocol (RDP) client pre-installed. Connection specifics and credentials will be supplied during class. Please work with your IT Admin before class to verify that your RDP client can be used to access a virtual machine running in the Amazon Web Services (AWS) environment. If you or your Admin have questions about the specific applications involved, contact our Client Support team.
Introduction to AI-Assisted Testing
What is AI-assisted testing?
Benefits of using AI in software testing (e.g., increased efficiency, improved test coverage, reduced costs)
Ethical considerations and challenges
Prompt engineering for testers
Case Study: Introduction to application to test
AI-Assisted Test Planning
Risk-based test planning
AI tools to support the test planning process
Using AI to analyze requirements
Performing risk analysis with AI
Using AI to generate tests
Leveraging AI-enabled commercial tools
Case Study: Test planning with generative AI
AI for Test Data Management
Introduction to Test Data Management
Using AI for Test Data Generation
Creating synthetic test data
Data masking and anonymization
Improving test data quality
Transforming test data sets
Using LLM APIs to automate prompting
Case Study: Improve existing test data sets
Exploratory Testing Using AI
Types of exploratory testing
Using AI to create good test charters
Using AI to perform charter-based exploratory testing
Using AI during ‘freestyle’ exploratory testing
Documenting exploratory testing results
Analyzing testing results using AI
Case Study: Perform AI-assisted exploratory testing
AI-Assisted Test Automation
AI-assisted automated testing capabilities
Tools demonstration
Using AI to assist UI testing
Case Study: Create and run AI-assisted test scripts
AI for Test Analysis and Reporting
Automated test report generation
AI-based defect management
Quality management
Optimizing automated test suites
Case Study: Using AI to manage defects
Class Retro and Wrap-up
Aha moments and discussions
Class evaluation survey
Sign-In/Registration 7:30 - 8:30 a.m.
Morning Session 8:30 a.m. - 12:00 p.m.
Lunch 12:00 - 1:00 p.m.
Afternoon Session 1:00 - 5:00 p.m.
Times represent the typical daily schedule. Please confirm your schedule at registration.
• Digital course materials
• Continental breakfasts and refreshment breaks
• Lunches
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