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Course Outline

Introduction to AI in Software Testing

  • Overview of AI capabilities in testing and QA.
  • Types of AI tools utilized in modern test workflows.
  • Benefits and risks associated with AI-driven quality engineering.

LLMs for Test Case Generation

  • Prompt engineering techniques for generating unit and functional tests.
  • Developing parameterized and data-driven test templates.
  • Translating 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 following 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

Requirements

  • Prior experience in software testing, test planning, or QA automation.
  • Familiarity with testing frameworks such as JUnit, PyTest, or Selenium.
  • Foundational 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 settings.
 14 Hours

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