Test like production

Testing should mirror real-world conditions as closely as possible, minimizing the use of mocks and stubs. This approach ensures that code behaves correctly in actual production environments. Automated testing on a scheduled basis provides early detection of issues and maintains code quality.

TDD for AI Development
Test-Driven Development (TDD) serves as a powerful framework for guiding AI code generation by clearly defining expected behaviors before implementation. This methodology is particularly valuable for AI-assisted development as it provides clear success criteria and enables iterative refinement based on test results.

AI-Powered Test Resolution
When tests fail, AI systems can analyze error messages and automatically adjust their output to meet the specified requirements.

Why mocking backfires
- Mocks create a false sense of security
- Real integrations catch actual problems (API changes, timeout issues, rate limits)
- Setting up mocks often takes more time than just using the real service
- Mocks can hide subtle integration issues until they hit production

Recommendation

Implement comprehensive testing strategies that prioritize real-world scenarios over simulated environments. Set up automated testing pipelines that run on a regular schedule and integrate with deployment workflows. Write tests before implementing features, using detailed test cases to guide AI code generation. Include both happy path and edge cases in test suites. Ensure test output is machine-readable to facilitate automated analysis and correction by AI systems. Maintain test environments that closely mirror production configurations, including dependencies and data structures. When tests fail, capture detailed error information that can be used to guide AI-based code refinement.