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Professional Usage Examples

This section provides technical demonstrations of TestGen-AI in real-world development scenarios.

Scenario 1: Initializing Tests for a Python Library

In this scenario, we use the generate command to create a test suite for a new library module.

Command: 

testgen generate ./src/auth --output ./tests/auth

Technical Result: The system analyzed the auth directory, extracted AST metadata for all authentication classes, and generated a corresponding suite using the Google-style docstring patterns identified in the source.

Scenario 2: Autonomous Verification in a Polyglot Repository

In this scenario, we execute the auto command to handle a mixed Python and TypeScript repository.

Command: 

testgen auto ./backend ./frontend --verbose

Technical Result: TestGen-AI orchestrated both pytest for the backend and jest for the frontend. It unified the disparate output formats into a single results matrix and generated a consolidated HTML report.

Scenario 3: Real-Time Test-Driven Development (TDD)

In this scenario, watch mode is enabled to provide instantaneous feedback during a refactoring session.

Command: 

testgen generate ./src --watch

Technical Result: As the developer modified local functions, TestGen-AI detected the WRITE events and regenerated the relevant unit tests within milliseconds, ensuring that the test suite evolved alongside the implementation.

Scenario 4: CI/CD Pipeline Integration

TestGen-AI can be integrated into GitHub Actions or GitLab CI to provide automated quality gates.

Example GitHub Action Step: 

- name: Execute TestGen-AI Verification
  run: |
    testgen auto ./src --output ./test-results
    if [ $? -ne 0 ]; then exit 1; fi