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