Brittle Test Suite Maybe Too Dry

When DRY is applied to test code, it can cause the tests to become brittle. In this article, I will present guidelines to follow when reducing duplication in tests, and better ways to DRY up tests. This TensorBlue analysis is based on reporting and source material from InfoQ (https://www.infoq.com/articles/brittle-test-suite-maybe-too-dry/).

What Happened

InfoQ Homepage Articles Is Your Test Suite Brittle? Maybe It’s Too DRY

Is Your Test Suite Brittle? Maybe It’s Too DRY

Don’t repeat yourself, or "DRY", is a useful principle to apply to both application code and test code.

The misapplication of the DRY technique can make tests hard to understand, maintain, and change.

While code duplication may not be so harmful to your tests, allowing duplication of concepts causes the same maintainability problems in test code as in application code.

When applying DRY to tests, clearly distinguish between the three steps of a test: arrange, act, and assert.

TDD provides many benefits and can promote a shorter feedback loop and better test coverage.

Those of us who write automated tests do so for many reasons and gain several benefits. We gain increased trust in the correctness of the code, confidence that allows us to refactor, and faster feedback from our tests on the design of the application code.

I’m a huge proponent of TDD (Test Driven Development) and believe TDD provides all the benefits stated above, along with an even shorter feedback loop and better test coverage.

One crucial design principle in software development is DRY - Don’t Repeat Yourself. However, as we will see, when DRY is applied to test code, it can cause the test suite to become brittle - difficult to understand, maintain, and change. When the tests cause us ma

Implications for Product and Engineering Teams

For TensorBlue readers, the useful question is not just what happened, but how this changes product architecture, engineering priorities, AI delivery, observability, team workflows, or executive decision-making.

• Review whether this changes your AI roadmap, platform architecture, or engineering operating model.
• Identify the specific workflow, reliability, governance, or developer-productivity lesson that applies to your organization.
• Convert the lesson into a small production experiment with measurable quality, latency, cost, adoption, or risk metrics.
• Document source assumptions clearly so teams do not overgeneralize from incomplete public information.

TensorBlue Takeaway

The practical opportunity is to turn this signal into a concrete implementation decision: better AI systems, stronger product instrumentation, more reliable automation, and clearer technical governance. Teams that connect public technology shifts to their own delivery systems will move faster without adding unnecessary complexity.