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Duration 14 hours
Course Outline
Introduction to AI in Software Testing
- Overview of AI capabilities within testing and QA domains
- Categories of AI tools utilized in modern test workflows
- Advantages and potential risks associated with AI-driven quality engineering
Leveraging LLMs for Test Case Generation
- Prompt engineering techniques for creating 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 user 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 stability in UI tests via self-healing selectors
- Performing AI-based regression impact analysis following code changes
Failure Analysis and Test Optimization
- Clustering test failures using LLMs or ML models
- Mitigating flaky test runs and reducing alert fatigue
- Prioritizing test execution leveraging historical insights
CI/CD Pipeline Integration
- Embedding AI test generation within Jenkins, GitHub Actions, or GitLab CI
- Validating test quality during the pull request process
- Implementing automation rollbacks and smart test gating in 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
- Exploring 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 understanding 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
