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Duration 14 hours
Course Outline
Introduction to AI in Software Testing
- An overview of AI capabilities within testing and QA contexts
- A review of AI tools utilized in modern test workflows
- Examining the benefits and potential risks associated with AI-driven quality engineering
Utilising LLMs for Test Case Generation
- Prompt engineering techniques for creating 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
- Leveraging AI to identify untested branches or conditions
- Simulating rare or abnormal usage scenarios
- Implementing risk-based test generation strategies
Automated UI and Regression Testing
- Creating UI tests using AI tools such as Testim or mabl
- Maintaining stable UI tests via self-healing selectors
- Conducting AI-based regression impact analysis following code changes
Failure Analysis and Test Optimisation
- Clustering test failures using LLM or ML models
- Minimising flaky test runs and reducing alert fatigue
- Prioritising test execution based on historical insights
CI/CD Pipeline Integration
- Integrating AI test generation into Jenkins, GitHub Actions, or GitLab CI
- Validating test quality during pull request reviews
- 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
- Familiarity with testing frameworks such as JUnit, PyTest, or Selenium
- A 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 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
