Course Outline

Foundations of AI-Driven Test Engineering for Government

  • Addressing modern testing challenges and the role of artificial intelligence (AI)
  • Principles and terminology of generative testing
  • Machine learning models utilized in automated test creation

Transforming Requirements and Code into AI-Generated Tests for Government

  • Extracting intent from requirements and user stories for government systems
  • Utilizing language models to generate structured test cases for government applications
  • Ensuring determinism and reproducibility in AI-generated tests for government use

Automated Unit Test Generation for Government

  • Producing unit tests from the context of source code for government projects
  • Generating input permutations and edge cases for government applications
  • Integrating generated tests with common unit testing frameworks used in government environments

AI-Assisted Integration and End-to-End Test Creation for Government

  • Mapping system behavior to test flows for government systems
  • Creating integration paths using AI-driven analysis for government applications
  • Balancing human oversight with automated generation in a government context

Coverage Prediction and Risk Modeling for Government

  • Using machine learning models to identify under-tested code regions in government systems
  • Predicting high-risk areas based on historical failures in government applications
  • Prioritizing tests using coverage and risk predictions for government use

Applying AI-Based Test Intelligence in CI/CD for Government

  • Embedding AI analysis steps into continuous integration and deployment (CI/CD) pipelines for government projects
  • Triggering dynamic test selection based on risk scores in government environments
  • Maintaining a feedback loop for continuously improved predictions in government applications

Validation, Governance, and Quality Assurance for Government

  • Evaluating the reliability of AI-generated tests for government systems
  • Managing bias and avoiding false positives in government testing
  • Establishing guardrails for production use in government contexts

Scaling AI-Powered Test Generation Across Teams for Government

  • Adoption strategies for quality assurance (QA) and DevOps organizations within the government
  • Standardizing workflows and documentation for government teams
  • Driving continuous improvement with metrics and insights in government settings

Summary and Next Steps for Government

 14 Hours

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