Training: AI for Testers

Level

Beginner

Duration

24h / 3 days

Date

Individually arranged

Price

Individually arranged

The AI for Testers training is a comprehensive course combining knowledge of artificial intelligence with practical software testing. Over three intensive days, participants will learn how to leverage AI for test data analysis, test case generation, QA process automation, and software quality optimization. The program covers both theoretical foundations and hands-on workshops, where participants will practice prompt creation, AI integration with popular testing tools, and risk management when using generative AI in testing processes.

What will you learn?

Fundamentals of AI and ML in the context of QA
Creating and optimizing prompts for generating test cases
Automating test result analysis with AI
Generating test cases for different types of tests
Integrating AI with popular testing tools (Selenium, Playwright, Cypress)
Verifying test accuracy and coverage with AI support
Identifying limitations of AI models and managing risks of their application

Who is this training for?

QA professionals or software testers (manual or automated)
Specialists with basic knowledge of testing tools
Testers interested in using AI to automate, analyze, and optimize QA processes
Professionals wanting to explore practical AI tools that support testers

Training Program

Day 1 – Introduction to AI in Testing
- Fundamentals of AI and ML in the QA context
- AI applications in testing: test data analysis, automation, test case generation
- LLMs and SLMs – language model evolution, how they work, and how they can support testers
- Overview of tools and environments: OpenAI API, open-source tools, integrations with popular testing frameworks
- AI and software quality – how AI transforms the testing lifecycle and the tester’s role
- Challenges of adopting AI in QA: reliability of results, security, compliance
Day 2 – AI in a Tester’s Daily Practice
- Introduction to Prompt Engineering for testers: how to write prompts for generating test cases and QA scenarios
- Generating test cases for different types of tests (unit, integration, exploratory)
- Automating test result analysis with AI
- Supporting the creation of test scripts and test data
- Verifying test accuracy and coverage with AI support
- AI limitations in testing – hallucinations, analysis errors, risk assessment
Day 3 – Tools and Case Studies
- AI integration with test automation tools (e.g., Selenium, Playwright, Cypress)
- Generating and analyzing test reports with AI support
- QA Copilot – using GenAI-powered tools to assist in testing
- Practical use cases:
  - Regression testing with AI
  - Identifying test coverage gaps
  - Automatic suggestions for exploratory testing
  - Preparing test data with AI – opportunities and potential risks
- The future of AI in QA: predictive bug detection and risk analysis
- Closing discussion: how to effectively implement AI in QA teams

Contact us

we will organize training for you tailored to your needs

Przemysław Wołosz

Key Account Manager

+48 730 830 801

przemyslaw.wolosz@infoShareAcademy.com

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