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Duration 14 hours
Course Outline
Introduction to AI in Software Testing
- Overview of AI capabilities within testing and QA
- Categories of AI tools utilized in contemporary test workflows
- Advantages and potential risks of AI-driven quality engineering
LLMs for Test Case Generation
- Prompt engineering techniques for generating unit and functional tests
- Developing 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 AI assistance
- Simulating rare or abnormal usage scenarios
- Risk-based strategies for test generation
Automated UI and Regression Testing
- Utilizing AI tools such as Testim or mabl for UI test creation
- Ensuring stable UI tests via self-healing selectors
- AI-powered regression impact analysis following code changes
Failure Analysis and Test Optimization
- Clustering test failures using LLM or ML models
- Minimizing flaky test runs and reducing alert fatigue
- Prioritizing test execution based on historical data insights
CI/CD Pipeline Integration
- Integrating AI test generation into Jenkins, GitHub Actions, or GitLab CI
- Verifying test quality during pull request processes
- Implementing automation rollbacks and intelligent test gating within pipelines
Future Trends and Responsible Use of AI 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
- Background in software testing, test planning, or QA automation
- Proficiency with testing frameworks like 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 (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
