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Course Outline
Foundations of AI-Driven Test Engineering for Government
- Modern testing challenges and the role of artificial intelligence (AI) in addressing them
- 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 to inform test development
- Utilizing language models to generate structured test cases
- Ensuring determinism and reproducibility in AI-generated tests for consistent results
Automated Unit Test Generation for Government
- Producing unit tests from the context of source code
- Generating input permutations and edge cases to enhance test coverage
- Integrating generated tests with common unit testing frameworks for seamless implementation
AI-Assisted Integration and End-to-End Test Creation for Government
- Mapping system behavior to test flows to ensure comprehensive coverage
- Creating integration paths using AI-driven analysis for efficient testing
- Balancing human oversight with automated generation to maintain accuracy and reliability
Coverage Prediction and Risk Modeling for Government
- Using machine learning (ML) models to identify under-tested code regions and improve coverage
- Predicting high-risk areas based on historical failure data to prioritize testing efforts
- Prioritizing tests using coverage and risk predictions to optimize resource allocation
Applying AI-Based Test Intelligence in CI/CD for Government
- Embedding AI analysis steps into continuous integration and continuous deployment (CI/CD) pipelines
- Triggering dynamic test selection based on risk scores to enhance efficiency
- Maintaining a feedback loop for continuously improved predictions and outcomes
Validation, Governance, and Quality Assurance for Government
- Evaluating the reliability of AI-generated tests to ensure accuracy and effectiveness
- Managing bias and avoiding false positives in test results
- Establishing guardrails for production use to maintain high standards of quality and security
Scaling AI-Powered Test Generation Across Teams for Government
- Adoption strategies for quality assurance (QA) and DevOps organizations to promote widespread implementation
- Standardizing workflows and documentation to ensure consistency and efficiency
- Driving continuous improvement with metrics and insights to enhance overall testing processes
Summary and Next Steps for Government
Requirements
- Knowledge of software testing methodologies for government applications
- Experience with automated testing frameworks to ensure robust and reliable systems
- Familiarity with programming concepts and CI/CD pipelines to support continuous integration and deployment processes
Audience for Government
- Quality Assurance (QA) engineers
- Software Development Engineers in Test (SDETs)
- DevOps teams with testing responsibilities
14 Hours
