Name: AI Driven Testing [EN]
Price: 464 EUR
Availability: InStock

The number of participants 5-15 people
Duration 2 days

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

Have questions about this training?

Katarzyna Borowiak Sales and Delivery Operations Specialist

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464 EUR

418 EUR

+ VAT

Lowest price in the last 30 days before the reduction: 464 EUR

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