Course Outline
Introduction to AI in Software Testing
- Overview of AI capabilities within testing and QA domains
- Types of AI tools employed in contemporary test workflows
- Advantages and potential risks of AI-driven quality engineering
Leveraging LLMs for Test Case Generation
- Prompt engineering for developing unit and functional tests
- Constructing parameterized and data-driven test templates
- Translating user stories and requirements into executable test scripts
AI in Exploratory and Edge Case Testing
- Identifying untested branches or conditions with the aid of AI
- Reproducing rare or abnormal usage scenarios
- Implementing risk-based test generation strategies
Automated UI and Regression Testing
- Employing AI tools such as Testim or mabl for UI test creation
- Ensuring stable UI tests via self-healing selectors
- Conducting AI-based regression impact analysis following code modifications
Failure Analysis and Test Optimization
- Grouping test failures using LLM or ML models
- Minimizing flaky test runs and reducing alert fatigue
- Prioritizing test execution based on historical data insights
Integration into CI/CD Pipelines
- Incorporating AI test generation into Jenkins, GitHub Actions, or GitLab CI
- Verifying test quality during pull requests
- Managing automation rollbacks and smart test gating within pipelines
Future Trends and Responsible AI Usage in QA
- Assessing the accuracy and safety of AI-generated tests
- Establishing governance and audit trails for AI-enhanced test processes
- Emerging trends in AI-QA platforms and intelligent observability
Summary and Next Steps
Requirements
- Practical experience in software testing, test planning, or QA automation.
- Proficiency with testing frameworks such as JUnit, PyTest, or Selenium.
- Foundational knowledge of CI/CD pipelines and DevOps ecosystems.
Target Audience
- QA engineers.
- Software Development Engineers in Test (SDETs).
- Software testers operating within agile or DevOps frameworks.
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from 2600 € + VAT*
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Testimonials (1)
That i gained a knowledge regarding streamlit library from python and for sure i'll try to use it to improve applications in my team which are made in R shiny
