Návrh Školení

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

Požadavky

  • 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 hodiny

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