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Duration 14 hours
Course Outline
Introduction to AI in Software Testing
- Overview of AI capabilities within testing and QA domains
- Classification of AI tools employed in contemporary test workflows
- Advantages and potential risks associated with AI-driven quality engineering
Leveraging LLMs for Test Case Generation
- Applying prompt engineering to create unit and functional tests
- Developing parameterized and data-driven test templates
- Transforming user stories and requirements into executable test scripts
AI Applications in Exploratory and Edge Case Testing
- Detecting untested branches or conditions with the aid of AI
- Simulating rare or abnormal usage scenarios
- Implementing risk-based strategies for test generation
Automated UI and Regression Testing
- Employing AI tools like Testim or mabl for UI test creation
- Ensuring stable UI tests 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
Integration with CI/CD Pipelines
- Embedding AI test generation within Jenkins, GitHub Actions, or GitLab CI
- Assessing test quality during pull request stages
- Implementing automation rollbacks and intelligent test gating within pipelines
Emerging Trends and Responsible AI Use in QA
- Assessing the accuracy and safety of AI-generated tests
- Establishing governance and audit trails for AI-enhanced testing processes
- Exploring trends in AI-QA platforms and intelligent observability
Recap and Future Directions
Requirements
- Background in software testing, test planning, or QA automation
- Knowledge of testing frameworks such as JUnit, PyTest, or Selenium
- Fundamental grasp 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
