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Duration 14 hours
Course Outline
Introduction to AI in Software Testing
- Overview of AI capabilities within testing and QA domains.
- Advantages and potential risks of AI-driven quality engineering.
Utilizing 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 for Exploratory and Edge Case Testing
- Using AI to identify untested branches or conditions.
- Simulating rare or abnormal usage scenarios.
- Implementing risk-based test generation strategies.
Automated UI and Regression Testing
- Employing AI tools such as Testim or mabl for UI test creation.
- Conducting AI-based regression impact analysis following code changes.
Failure Analysis and Test Optimization
- Clustering test failures using LLM or ML models.
- Minimizing flaky test runs and 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.
- Validating test quality during the pull request phase.
- Implementing automation rollbacks and smart test gating within pipelines.
Future Trends and Responsible AI Use in QA
- Assessing the accuracy and safety of AI-generated tests.
Summary and Next Steps
Requirements
- Practical experience in software testing, test planning, or QA automation.
- Working knowledge of testing frameworks like 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
