Why take this course
AI-assisted testing allows testers to reduce repetitive tasks, accelerate test creation, and detect issues earlier in the development cycle. With AI, you can achieve up to 80% faster test creation, 40% better edge case coverage, and 90% less time spent on reporting bugs. This course equips QA engineers, testers, and team leads with the knowledge and skills to focus on analysis and quality rather than manual execution.
What you’ll learn
- Principles of AI-driven testing and differences from traditional testing
- Automating UI, E2E, and unit tests with AI
- Working with leading AI tools: Testim, Applitools, Mabl, Functionize, Tricentis Tosca, Test.ai, Diffblue Cover
- Creating and running AI-assisted testing projects
- Integrating AI-driven tests with Python, Pytest, Selenium, and CI/CD pipelines
- Applying best practices for security, fairness, and ethical AI testing
Certificate & Exam
Upon completion of the training, you will receive a personalized certificate confirming your practical skills in applying AI to software testing. There is no final exam – active participation in the sessions is sufficient.
Who is this course for
- Manual and automation testers
- QA engineers and developers
- IT professionals interested in AI-powered testing of web and mobile applications
Training schedule
Day 1: Introduction to AI in Testing
Goal: Understand the fundamentals, benefits, and challenges of AI-driven testing.
- Hours 1–2: AI-driven testing basics
- Differences from traditional testing
- Key benefits and challenges (non-determinism, bias, fairness)
- Case study discussion: AI reducing manual tests in web apps
- Hours 3–4: Tools and frameworks
- Overview: Testim, Applitools, Mabl, Functionize, Tricentis Tosca, Test.ai, Diffblue Cover
- Integration with Python (Selenium, Pytest + AI plugins)
- Exercise: Install and configure Applitools in VS Code
- Hours 5–6: Practical project
- Create a simple UI test with AI (e.g., Testim or Mabl)
- Debugging visual bias in AI-assisted testing
- Group discussion: How AI changes the QA role
Day 2: Advanced Practices and Implementation
Goal: Apply AI in practice, integrate with CI/CD, and adopt best practices.
- Hours 1–2: Best practices in AI-driven testing
- Strategies for bias/fairness, handling non-deterministic outputs, testing ML edge cases
- CI/CD integration with GitHub Actions and AI tools
- Security & ethics: compliance (e.g., GDPR in AI testing)
- Hours 3–4: Advanced tools and automation
- Using ML libraries (Scikit-learn, PyTorch) for predictive testing
- Generating unit/E2E tests with Functionize or Diffblue
- Exercise: Implement an AI-assisted test in Python (e.g., with Test.ai)
- Hours 5–6: Final project & wrap-up
- Build a full AI-powered testing scenario (web or mobile app) with Python + Mabl
- Simulate CI/CD pipeline with GitHub Actions
- Group discussion: The future of AI in testing and QA leadership
- Certification and summary
Prerequisites
This course is designed for participants with a basic understanding of manual or automated testing. Basic knowledge of Python is recommended.
Hardware/Software requirements:
- Computer with internet access
- Python 3.9+ and VS Code with AI plugins (e.g., GitHub Copilot)
- Accounts on testing platforms (Testim, Applitools – free trial versions available)
- Preinstalled libraries: Selenium, Pytest, Scikit-learn, PyTorch
