Course Outline
Introduction to AI in Software Testing
- Overview of AI capabilities in testing and QA
- Types of AI tools used 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
- Experience in software testing, test planning, or QA automation
- Familiarity with testing frameworks such as JUnit, PyTest, or Selenium
- Basic understanding of CI/CD pipelines and DevOps environments
Audience
- QA engineers
- Software Development Engineers in Test (SDETs)
- Software testers working in agile or DevOps settings
Testimonials (3)
The session was highly interactive and applicable to the business.
Jorge Boscan - Chevron Global Technology Services Company
Course - Advanced GitHub Copilot & AI for Projects and Infrastructure
Machine Translated
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.
