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Duration 14 hours
Course Outline
Introduction to AI in Software Testing
- An overview of AI's capabilities in testing and QA
- Categories of AI tools applied in modern test workflows
- The advantages and potential risks of AI-driven quality engineering
Leveraging LLMs for Test Case Generation
- Applying prompt engineering to generate 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
- Utilizing AI to identify untested branches or conditions
- 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
- Conducting AI-based regression impact analysis following code modifications
Failure Analysis and Test Optimization
- Clustering test failures using LLMs or ML models
- Minimizing flaky test runs and reducing alert fatigue
- Prioritizing test execution by analyzing historical insights
CI/CD Pipeline Integration
- Embedding AI test generation in Jenkins, GitHub Actions, or GitLab CI
- Verifying test quality during pull request reviews
- 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
- 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.
- Fundamental 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
