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Start with the behaviors that matter most and the failures that would hurt most; then cover each with the narrowest reliable test. A useful first suite is small: quick tests for isolated logic, focused integration tests at important boundaries, and a few end-to-end tests for critical user journeys. Run fast tests locally and early in CI, and add broader coverage as the project’s risks become clearer.
1. Choose what to test before choosing a test framework
Write down a short list of externally observable behaviors and important boundaries. Prioritize by user impact, likelihood of failure, and the cost of getting a result wrong. Depending on the product, candidates might include input validation, authorization decisions, calculation rules, persistence behavior, or a core sign-up or purchase journey.
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For each candidate, describe the expected behavior in a way someone can verify. For example: “A user without the required role cannot view this record” is more actionable than “test permissions.” Keep the initial list small enough to implement and run consistently; a coverage percentage is not a substitute for deciding which failures matter.
2. Match the test level to the question
Teams use terms such as unit, integration, feature, and end-to-end differently. Agree on local definitions and choose the lowest level that can answer each question reliably. GitLab’s guidance recommends that approach, while Android’s testing guidance describes layers in terms of scope, fidelity, execution environment, and lifecycle. GitLab: Testing guide · Android Developers: Test fundamentals
| Level | What it checks | Typical trade-off |
|---|---|---|
| Unit | A small piece of logic in isolation, such as a validation or calculation rule. | Usually fast and focused; it may not reveal failures caused by real component interactions. |
| Integration or feature | Collaboration between components or services, such as application logic interacting with persistence. | More realistic boundary coverage, with more setup and potentially slower feedback. |
| End-to-end | A complete user journey through a deployed or near-production system. | Broad confidence in a critical flow, but greater infrastructure, execution, diagnosis, and maintenance costs. |
If an isolated test can reveal a defect, it is usually preferable to a full-journey test: the result is more focused and typically quicker to investigate. Use a broader level when the behavior depends on interactions that a narrow test cannot reliably validate.
3. Make the smallest runnable test project
Use a framework and runner that fit the project’s language, build system, and team workflow. First make a single behavior-focused test easy to run both locally and in the project’s normal automation. A repeatable command matters more than a large starter collection.
A C# example, not a universal setup
Microsoft Learn’s C# tutorial demonstrates creating a solution, a class library, and an MSTest project; adding the production project as a dependency; writing a test; and running it with dotnet test. It starts with a failing test, changes the implementation to make it pass, and then demonstrates data-driven cases. The tutorial showed MSTest 4.1.0 in its project template when accessed; tooling versions can change. Follow the tutorial only if this stack matches your project. Microsoft Learn: Unit testing C# with MSTest
4. Expand from one test to representative cases
Once the first test runs reliably, add cases that represent ordinary use and meaningful boundaries. Prefer tests that express an expected outcome over tests that merely mirror internal implementation details.
- Cover a normal valid input and the expected result.
- Check relevant boundary values, empty or malformed input, and other cases where the outcome should change.
- At component boundaries, verify that the parts exchange data or handle failures as intended.
- For critical user journeys, cover the important path and a small number of high-risk alternatives rather than every possible route.
Before adding a test, check whether existing coverage already verifies the same behavior. Duplicate coverage can increase maintenance without adding meaningful confidence.
5. Put tests at useful points in the workflow
Make the fast, dependable tests available to developers during normal work and run them early enough in continuous integration to inform a change. Schedule slower or environment-dependent tests at a point suited to their duration and risk—for example, pre-merge, post-merge, staging, or pre-release. The right triggers depend on the project’s deployment structure and infrastructure; a platform-specific schedule from another organization is not a universal template.
Rank #4
Track execution time and unreliable-test rates as the suite grows. The UK Home Office engineering guidance recommends early testing, practical automation, and a small end-to-end layer focused on critical or high-risk flows. UK Home Office: Automated testing
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Give the suite and its layers clear owners. When a test fails intermittently, investigate whether the cause is the test, the system, or its environment; fix the reliability problem or remove the test if it cannot provide dependable feedback. GitLab’s published testing strategy puts the principle bluntly: “Test Stability If a test can’t reliably block a merge, deployment, or release, it shouldn’t exist. Fix it or delete it.” GitLab: Testing levels
Best Value
Review runtime, failure patterns, and overlapping coverage as changes accumulate. Keep end-to-end tests for flows where validating the full system is worth their higher maintenance cost. The aim is not the biggest suite; it is a suite whose results people can act on.
Why there is no universal test-pyramid quota
Google’s 2015 testing-blog post offered 70% unit, 20% integration, and 10% end-to-end as a “good first guess,” while noting that the mix differs by team. Treat that as a historical heuristic, not a current requirement or allocation target. Google Testing Blog: Just say no to more end-to-end tests
GitLab’s documentation reported an estimated distribution for its own Community and Enterprise Edition codebase on February 3, 2025: 218,459 unit tests (75.66%), 57,127 integration tests (19.79%), 12,444 white-box system/feature tests (4.31%), and 704 black-box system/end-to-end tests (0.24%). Those counts describe GitLab’s repository, not a target for a new project. GitLab: Testing levels
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Architecture, risk, dependencies, and infrastructure all affect the useful balance. Use ratios as context only; choose test levels according to the questions your team needs answered and how reliably each level can answer them.
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