Course Outline
Introduction to AI in Software Testing
- Overview of AI capabilities in testing and QA
- Types of AI tools utilized in modern test workflows
- Benefits and risks of AI-driven quality engineering
LLMs for Test Case Generation
- Prompt engineering for generating unit and functional tests
- Creating parameterized and data-driven test templates
- Converting user stories and requirements into test scripts
AI in Exploratory and Edge Case Testing
- Identifying untested branches or conditions using AI
- Simulating rare or abnormal usage scenarios
- Risk-based test generation strategies
Automated UI and Regression Testing
- Using AI tools like Testim or mabl for UI test creation
- Maintaining stable UI tests through self-healing selectors
- AI-based regression impact analysis after code changes
Failure Analysis and Test Optimization
- Clustering test failures using LLM or ML models
- Reducing flaky test runs and alert fatigue
- Prioritizing test execution based on historical insights
CI/CD Pipeline Integration
- Embedding AI test generation in Jenkins, GitHub Actions, or GitLab CI
- Validating test quality during pull requests
- Automation rollbacks and smart test gating in pipelines
Future Trends and Responsible Use of AI in QA
- Evaluating the accuracy and safety of AI-generated tests
- Governance and audit trails for AI-enhanced test processes
- Trends in AI-QA platforms and intelligent observability
Summary and Next Steps
Requirements
- Background in software testing, test planning, or QA automation
- Familiarity with testing frameworks such as JUnit, PyTest, or Selenium
- Foundational knowledge of CI/CD pipelines and DevOps environments
Target Audience
- QA engineers
- Software Development Engineers in Test (SDETs)
- Software testers operating in agile or DevOps contexts
Testimonials (2)
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
Michal Maj - XL Catlin Services SE (AXA XL)
Course - GitHub Copilot for Developers
Trainer able to adjust the course level during training to fit our understanding level on the topic, so that we could gain more useful knowledge that could further help us harness the tools in our daily works.
