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Choose tests by the risk they detect
Start with the lowest test level that can reliably detect the behavior at issue. A unit test is usually the right place to check a small piece of logic. Integration tests cover interactions between components; system tests exercise larger assembled behavior; end-to-end tests cover complete user-facing journeys. Broader tests are valuable when the risk crosses boundaries, but they tend to cost more to run and maintain.
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Most of a suite should generally be at the unit level, with fewer tests at higher levels. That is a directional principle, not a mandatory ratio: the right balance depends on architecture, failure risks, and how reliably each level catches defects in your codebase. GitLab’s testing-level guidance illustrates the distinction with its own inventory, estimated February 3, 2025: 218,459 unit tests (75.66%), 57,127 integration tests (19.79%), 12,444 system or feature tests (4.31%), and 704 end-to-end tests (0.24%) across GitLab Community and Enterprise Edition suites. Those counts describe GitLab, not an industry benchmark or target for other teams.
Place checks where their feedback is useful
Organize pipeline jobs so developers get quick, relevant feedback before waiting on wider coverage. A typical progression might look like this; treat it as a starting design, not a universal standard.
- Merge request: run formatting, static checks, and fast tests that directly cover changed code. Make dependable, high-signal checks blocking when a failure should prevent the change from merging.
- Broader validation: add integration and system tests for component interactions and assembled behavior. Choose whether these block merge or run in a later tier according to the risk they cover and the cost of delaying feedback.
- Deployment boundary: run focused smoke checks to establish that critical paths work in the deployed environment.
- Later or scheduled pipeline: run wider end-to-end coverage, longer suites, or checks whose value does not justify slowing every merge request.
GitLab’s documented testing strategy uses merge-request pipelines for unit tests, broadens integration and system coverage in later tiers, and reserves full end-to-end checks for selected higher tiers or scheduled pipelines; its deployment example includes smoke checks. This is one organization’s model. Map equivalent stages to your repository’s change risks and feedback needs rather than copying its exact tiers. GitLab Testing Strategy describes the approach.
Decide what should block a merge
A check belongs on the blocking path when it is both important to the change’s risk and dependable enough that engineers can act on its result. For each proposed job, consider the same decision questions:
- How quickly will the result reach the author?
- What kind of failure risk does the test detect, and is that risk relevant to this change or release?
- How often does the job produce false alarms or fail because of unstable test or runner conditions?
- What runtime and infrastructure capacity does it consume?
- Who investigates failures, and what is the response when a check fails?
- Should it block merge, deployment, or release—or report without blocking?
There is no universal acceptable CI duration, flake rate, retry count, or coverage percentage established by the cited guidance. Set thresholds from your release risk and baseline suite behavior, then review them when the codebase or infrastructure changes. Do not treat coverage percentage alone as evidence of test quality: it measures execution coverage, not whether assertions detect the failures your team cares about.
Speed up a slow pipeline without hiding work
Find the actual bottleneck
Measure job and suite elapsed times before changing the pipeline. Identify which suites dominate the wait and whether their work can be divided evenly. Optimizing a small job will not materially improve the end-to-end wait if another job remains the critical path.
Run independent jobs concurrently
CI systems can run independent jobs in parallel; dependencies still need to run in the required order. GitLab describes stages and jobs, with jobs in a stage able to run concurrently, while GitHub Actions jobs may run sequentially or in parallel. The exact configuration syntax depends on the CI platform. See GitLab CI/CD pipelines and GitHub Actions workflow concepts.
Split large suites into shards
If the runner and test framework support it, divide a large test job into smaller parallel jobs. GitLab supports parallel jobs with the parallel keyword and documents an RSpec example in Control how jobs run. Before adopting sharding, check that test distribution is effective, all shards contribute results to a complete report, and failures remain attributable to the tests that produced them. Compare the reduction in elapsed time with added runner use; parallelism shifts work across capacity rather than eliminating it.
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Keep the feedback loop trustworthy
After each change, compare pipeline elapsed time, runner consumption, test-result completeness, and whether failures are still easy to diagnose. Avoid dropping a slow check solely to improve a dashboard number: first decide what risk it covers and whether that risk is covered elsewhere or can be tested at a cheaper level.
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GitLab defines a flaky test as one that is unreliable, fails occasionally, and eventually passes if retried enough. Causes can include a brittle test, unstable infrastructure, or an unstable application. GitLab’s handbook warns: “Flaky tests undermine test results, leading to engineers disregarding test failures as flaky.” See Flaky tests.
Best Value
A retry that passes is diagnostic evidence, not proof that the change is safe. For an intermittent failure:
- Preserve the original failure details, including logs and the conditions under which it occurred.
- Reproduce it where practical; compare the failing and passing environments and look for timing, ordering, data, or infrastructure differences.
- Determine whether the source is the test, the infrastructure, or the application, and assign a person or team to the follow-up.
- If it must be quarantined to keep unrelated work moving, track that state and its owner. Keep the test out of trusted blocking results only as a managed temporary measure.
- Fix it promptly and monitor it until it demonstrates stable behavior before returning it to the blocking suite.
GitLab’s pipeline-triage guidance describes quarantining flaky tests until proven stable, fixing them as soon as possible, and monitoring them until fixed. Quarantine without a tracked return path becomes silent loss of coverage. See Pipeline triage.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Make suite health an explicit responsibility
Test stages need ongoing maintenance, not just an initial YAML design. Review which tests provide distinct risk coverage, which are redundant, where runtime is growing, and whether owners respond to unstable results. Make changes to blocking rules, stage placement, and quarantine status explicit decisions so that faster feedback does not quietly weaken confidence in a release.
GitLab’s strategy emphasizes fast feedback, progressive testing, resource efficiency, ownership, and stability. Its wording for the feedback principle is: “Fast Feedback Prioritize speed by running the most relevant tests first – fail fast, fix fast.” The useful principle is to put relevant checks early, while retaining wider tests where they protect against risks that narrow checks cannot catch.
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Common pipeline problems and fixes
- Merge requests wait on broad suites: identify which jobs are on the critical path; move checks that do not need to block every change to a later tier, while preserving appropriate coverage for deployment or scheduled runs.
- More parallel jobs do not shorten the wait: check whether the work is actually divisible and evenly distributed, and whether a dependent job remains the bottleneck. Compare elapsed-time gains against additional runner use.
- A retry turns red into green: retain and investigate the first failure. A passing retry does not make the test reliable; determine the cause and track an owner.
- Quarantined tests stay out of the suite: give quarantine an owner and tracked repair and requalification path, then monitor the test until it is stable.
- A high coverage percentage still misses regressions: review whether tests assert the risky behavior and whether coverage is at an appropriate test level; do not use coverage alone as a quality verdict.
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