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Duration 14 hours
Course Outline
Intro to AI in Software Testing
- Snapshot of AI capabilities within testing and QA
- Categories of AI tools applied in contemporary test workflows
- Advantages and potential risks of AI-driven quality engineering
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 branches or conditions via AI assistance
- Simulating rare or abnormal usage scenarios
- Risk-based strategies for test generation
Automated UI and Regression Testing
- Employing AI tools such as Testim or mabl for UI test development
- Ensuring UI test stability using self-healing selectors
- Conducting AI-based regression impact analysis following code modifications
Failure Analysis and Test Optimization
- Clustering test failures using LLM or ML models
- Minimizing flaky test executions 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
- Verifying test quality during pull request stages
- Implementing automation 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 trails for AI-enhanced test processes
- Emerging trends in AI-QA platforms and intelligent observability
Wrap-up and Future Directions
Requirements
- Practical experience in software testing, test planning, or QA automation
- Proficiency with testing frameworks like 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 within agile or DevOps frameworks
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
