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Duration 14 hours
Course Outline
Introduction to AI in Software Testing
- Survey of AI capabilities within testing and QA domains
- Categories of AI tools applied in contemporary test workflows
- Advantages and potential risks of AI-driven quality engineering
Utilizing LLMs for Test Case Creation
- 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
- Detecting untested code paths or conditions with AI assistance
- Modeling rare or exceptional usage scenarios
- Formulating risk-based test generation approaches
Automated UI and Regression Testing
- Employing AI platforms like Testim or mabl for UI test authoring
- Ensuring UI test stability via self-healing selectors
- Conducting AI-based regression impact analysis following code modifications
Failure Analysis and Test Optimization
- Grouping test failures using LLM or ML algorithms
- Mitigating flaky test executions and reducing alert noise
- Prioritizing test execution based on historical data insights
CI/CD Pipeline Integration
- Integrating AI test generation into Jenkins, GitHub Actions, or GitLab CI
- Assessing test quality during the pull request process
- Implementing automated rollbacks and intelligent test gating within pipelines
Future Trends and Responsible AI Use in QA
- Assessing the precision and security of AI-generated tests
- Establishing governance and audit logs for AI-enhanced testing processes
- Tracking trends in AI-QA platforms and intelligent observability
Summary and Next Steps
Requirements
- Practical experience in software testing, test strategy planning, or QA automation.
- Proficiency with testing frameworks such as JUnit, PyTest, or Selenium.
- Foundational knowledge of CI/CD pipelines and DevOps ecosystems.
Target Audience
- QA Engineers
- Software Development Engineers in Test (SDETs)
- Testers operating within 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
