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コース概要
Introduction to AI in Software Testing
- Overview of AI capabilities in testing and QA
- Types of AI tools used in modern test workflows
- Benefits and risks of AI-driven quality engineering
LLMs for Test Case Generation
- Prompt engineering for generating unit and functional tests
- Creating parameterized and data-driven test templates
- Converting user stories and requirements into test scripts
AI in Exploratory and Edge Case Testing
- Identifying untested branches or conditions using AI
- Simulating rare or abnormal usage scenarios
- Risk-based test generation strategies
Automated UI and Regression Testing
- Using AI tools like Testim or mabl for UI test creation
- Maintaining stable UI tests through self-healing selectors
- AI-based regression impact analysis after code changes
Failure Analysis and Test Optimization
- Clustering test failures using LLM or ML models
- Reducing flaky test runs and alert fatigue
- Prioritizing test execution based on historical insights
CI/CD Pipeline Integration
- Embedding AI test generation in Jenkins, GitHub Actions, or GitLab CI
- Validating test quality during pull requests
- Automation rollbacks and smart test gating in pipelines
Future Trends and Responsible Use of AI in QA
- Evaluating the accuracy and safety of AI-generated tests
- Governance and audit trails for AI-enhanced test processes
- Trends in AI-QA platforms and intelligent observability
Summary and Next Steps
要求
- Experience in software testing, test planning, or QA automation
- Familiarity with testing frameworks such as JUnit, PyTest, or Selenium
- Basic understanding of CI/CD pipelines and DevOps environments
Audience
- QA engineers
- Software Development Engineers in Test (SDETs)
- Software testers working in agile or DevOps settings
14 時間
お客様の声 (1)
Lecturer's knowledge in advanced usage of copilot & Sufficient and efficient practical session
