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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
Testimonials (1)
Lecturer's knowledge in advanced usage of copilot & Sufficient and efficient practical session