Test names should be both descriptive and expressive, clearly indicating the intent of the test and helping to quickly understand its purpose and expected outcome. This clarity is beneficial when reading test results or documentation.
Somewhat recommended for production code: Descriptive yet concise naming in production code is beneficial. It should be clear, encapsulate functionality, maintain modularity, and follow naming conventions.
In test automation, it's crucial to make the setup actions and initial state both clear and relevant, explicitly showing the conditions under which the test runs and including only the data necessary for understanding the test's purpose. This approach avoids hidden states or configurations, such as a prepopulated database that isn't directly referenced in the test code, and ensures that no extraneous information obfuscates what is being tested.
Not recommended for production code: Production code typically uses implicit setups like constructors and needs complete object initialization. Excessive detailing in setups can hinder maintainability.
Test code benefits from reducing duplication through shared setup methods or data builders. This practice keeps tests cleaner and easier to maintain.
Recommended for production code: Avoiding duplication is a core principle in all coding, but the means of achieving it may differ. In production, more sophisticated patterns like dependency injection may be preferred.
A test should ideally perform a single action or operation, ensuring that there's only one reason for it to fail and making the cause of failure straightforward.
Not strictly applicable to production code: Production code often performs multiple actions in sequence as part of normal operation and is more concerned with handling the outcomes robustly.
Tests should have assertions that are targeted and clear, focusing on a single aspect of the test's outcome for easy debugging.
Not applicable to production code: Production code typically doesn't use assertions for flow control and instead employs exception handling and validation strategies.
This pattern helps keep tests organized by clearly separating the preparation, the action under test, and the verification steps.
Not applicable to production code: While good organization is crucial, production code doesn't follow this pattern but rather focuses on clear workflows and error handling.
Each test case should operate independently to avoid inter-test dependencies that could cause cascading failures.
Not directly applicable to production code: Production code often involves interdependent components working in a shared state, which requires careful management of state and transactions.
Tests should use named constants or data generators for values to make the purpose and origin of these values clear, reducing the risk of "magic numbers" or "magic strings."
Recommended for production code: Using well-named constants and avoiding magic values is a good practice to improve code readability and maintainability.
Tests should use minimal mocking to avoid brittle tests that fail due to unrelated changes in the codebase.
Not applicable to production code: Mocking is a testing practice and is not used in production, where actual implementations of interfaces and services are used.
In testing, leveraging helper methods, data builders, constants, and data generators can significantly simplify setup, enhancing both readability and maintenance. Helpers and builders facilitate the creation of complex objects, while constants and generators provide clear and unambiguous values, crucial for the transparency and predictability of tests.
Recommended for production code: These practices are equally beneficial in production code. Helper methods and builders aid in encapsulating complex constructions and configurations, and using named constants prevents the pitfalls of hardcoded values, leading to code that is both easier to understand and maintain.