Course Outline

Introduction to AI in Software Testing for Government

  • Overview of AI capabilities in testing and quality assurance (QA) for government
  • Types of AI tools used in modern test workflows for government
  • Benefits and risks of AI-driven quality engineering for government

LLMs for Test Case Generation

  • Prompt engineering techniques for generating unit and functional tests in a government context
  • 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

  • Identifying untested branches or conditions using AI in government software
  • Simulating rare or abnormal usage scenarios relevant to government operations
  • Implementing risk-based test generation strategies for government applications

Automated UI and Regression Testing

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

Failure Analysis and Test Optimization

  • Clustering test failures using LLM or ML models to improve government software reliability
  • Reducing flaky test runs and alert fatigue in government testing processes
  • Prioritizing test execution based on historical insights for enhanced government system performance

CI/CD Pipeline Integration

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

Future Trends and Responsible Use of AI in QA for Government

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

Summary and Next Steps

Requirements

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

Audience

  • Quality assurance engineers
  • Software Development Engineers in Test (SDETs)
  • Software testers working in agile or DevOps settings
 14 Hours

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