Course Outline

Introduction to Artificial Intelligence in Software Testing for Government

  • Overview of AI capabilities in testing and quality assurance for government applications
  • Types of AI tools utilized in modern test workflows for government systems
  • Benefits and risks associated with AI-driven quality engineering for government operations

Large Language Models for Test Case Generation for Government

  • Prompt engineering techniques for generating unit and functional tests in government software projects
  • Creating parameterized and data-driven test templates for government applications
  • Converting user stories and requirements into test scripts for government systems

AI in Exploratory and Edge Case Testing for Government

  • Identifying untested branches or conditions using AI in government software development
  • Simulating rare or abnormal usage scenarios to enhance security and reliability of government systems
  • Implementing risk-based test generation strategies for government applications

Automated User Interface and Regression Testing for Government

  • Utilizing AI tools like Testim or mabl for creating UI tests in government software
  • Maintaining stable UI tests through self-healing selectors in government applications
  • Conducting AI-based regression impact analysis after code changes in government systems

Failure Analysis and Test Optimization for Government

  • Clustering test failures using large language models or machine learning models to improve government software reliability
  • Reducing flaky test runs and alert fatigue in government testing environments
  • Prioritizing test execution based on historical insights for efficient government operations

Continuous Integration/Continuous Deployment Pipeline Integration for Government

  • Embedding AI test generation in Jenkins, GitHub Actions, or GitLab CI for government projects
  • Validating test quality during pull requests to ensure compliance with government standards
  • Implementing automation rollbacks and smart test gating in pipelines for government applications

Future Trends and Responsible Use of AI in Quality Assurance for Government

  • Evaluating the accuracy and safety of AI-generated tests for government software
  • Establishing governance and audit trails for AI-enhanced test processes in government systems
  • Trends in AI-QA platforms and intelligent observability for government applications

Summary and Next Steps for Government

Requirements

  • Experience in software testing, test planning, or quality assurance automation for government projects
  • Familiarity with testing frameworks such as JUnit, PyTest, or Selenium
  • Basic understanding of CI/CD pipelines and DevOps environments

Audience

  • Quality Assurance Engineers
  • Software Development Engineers in Test (SDETs)
  • Software Testers working in agile or DevOps environments for government initiatives
 14 Hours

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