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Saved February 14, 2026
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Mark Seemann argues that test code deserves the same attention as production code. He highlights common mistakes, like code duplication and commented-out sections, and stresses that maintaining high standards in test code improves overall code quality and maintainability. Some exceptions exist, especially regarding security, but the overall principle remains that test code should be well-structured and clear.
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Mark Seemann emphasizes the importance of treating test code with the same care as production code. He points out that many developers neglect test code, allowing it to become messy and poorly structured. Common issues include violating the DRY (Don't Repeat Yourself) principle, leading to excessive duplication and "Zombie Code," which consists of commented-out or unused code. These shortcomings arise from a misconception that test code is less important simply because it isn't deployed to production. Seemann argues that poor-quality test code ultimately makes future work more challenging, just as it does in production.
Seemann also addresses specific practices for writing effective test code. He references "xUnit Test Patterns," which outlines best practices for structuring and organizing tests. While some argue for DAMP (Descriptive And Meaningful Phrases) over DRY, he believes maintaining clarity and avoiding duplication is essential. He shares personal insights, having previously thought duplication was acceptable in tests but later realizing it leads to complications like "Shotgun Surgery," where multiple tests fail due to a single change.
There are exceptions to the standards applied to production code. For example, hard-coding passwords in tests can be acceptable since test code is assumed to remain undeployed. Certain input validation steps may be skipped because developers control the test inputs. Seemann acknowledges that while security practices can be relaxed, overall coding standards should still apply to test code to ensure maintainability. He concludes with a nod to the balance between simplicity and abstraction in test code, suggesting that while abstractions can be helpful, they should be used judiciously.
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