Code Generation Workflow

Step 2: Add unit tests for coverage

• Use pytest for unit testing.
• Write tests that verify the behavior of individual components and services.
• Use unittest.mock.patch or pytest-mock to mock external dependencies.
• Aim for at least 95% coverage across the repository.
• Ensure each PR maintains 95% coverage for the code it introduces or changes.
• Prefer small, deterministic tests that are easy to review and maintain.
• Place unit tests in tests/unit/ mirroring the DDD structure (e.g., tests/unit/domain/ for domain tests).

Unit test best practices

• Test each public function or method in isolation.
• Mock external calls, database access, and network interactions.
• Validate both success and failure paths.
• Keep fixture setup simple and reusable.
• Measure coverage with a tool such as pytest-cov.

Step 3: Add integration tests when the feature is finished

• Add integration tests after the core feature implementation is complete.
• Integration tests should exercise multiple modules together.
• Mock only external services and APIs; let internal systems run normally.
• Validate workflow behavior, data transformations, and component interactions.
• Keep integration tests separate from unit tests in tests/integration/ mirroring the DDD structure (e.g., tests/integration/application/ for workflow tests).

Step 4: Generate feature documentation

• Document the feature in README.md or in a dedicated documentation page.
• Describe the feature purpose, design decisions, and how it fits the domain.
• Document the testing strategy and coverage expectations.
• Add architecture diagrams when relevant, using Mermaid for clarity.
• Update wiki/architecture.md if the feature changes system boundaries or workflows.

Recommended process for pull requests

• Open the PR after the feature implementation and unit tests are in place.
• Run flake8 and mypy to ensure code quality and correctness before creating the PR.
• Include a summary of changed files and test coverage results.
• Run integration tests before final review.
• Refresh documentation for the feature and verify that the README summary is up to date.
• Use the generated documentation workflow to keep the repo consistent.

Example artifact flow

feature-branch/
  settings/
    environment.yml
  src/
    domain/
    application/
    infrastructure/
  tests/
    unit/
      domain/
      infrastructure/
    integration/
      application/
  scripts/
    generate_documentation_commit_push.py
  wiki/
    architecture.md
  README.md

Notes

• This workflow is aligned with MLOps lifecycle control and DDD architecture.
• Keep documentation and tests as part of the same feature cycle, not as an afterthought.
• Use automation where possible to enforce coverage and documentation generation.