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Duration 14 hours
Course Outline
Introduction to AI in Software Testing
- Understanding the scope of AI capabilities in testing and QA.
- Examining the advantages and potential risks of AI-driven quality engineering.
Leveraging LLMs for Test Case Generation
- Applying prompt engineering techniques 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 specific 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.
- Ensuring UI test stability through self-healing selectors.
- Conducting AI-based regression impact analysis following code changes.
Failure Analysis and Test Optimization
- Grouping test failures using LLM or ML models.
- Mitigating 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.
- Validating test quality during the pull request process.
- Implementing automation rollbacks and smart 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.
- 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 environments.
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
