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Monitor automated tests at the individual-test level, not just by whether a CI job is green or red. Keep test results, durations, failure output, and relevant artifacts; review trends and flaky tests; and route actionable alerts to the people who can investigate. Run fast, useful checks on changes, then place slower suites at later pipeline stages or on a schedule that fits their risk and cost.
Choose when each test suite runs
Make the cadence match the value and cost of feedback. A useful baseline is to run a fast set of relevant tests on every change, add integration or regression checks in later stages, and schedule a full suite or longer-running load and performance tests when they do not belong on every commit.
HM Revenue & Customs’ engineering standard says automated tests should run regularly and ideally on every change. It also cautions that an oversized test pack can slow feedback until people stop running it or ignore its results. The right mix depends on risk, runtime, dependencies, and release process.
- On changes: Run checks that provide quick, dependable feedback about the changed code and its important dependencies.
- Later in the pipeline: Add broader integration and regression coverage when earlier checks pass.
- On a schedule or before release: Run suites whose duration or resource requirements make them unsuitable for every change, while ensuring their results still reach an owner.
Keep results and evidence for each test
A single aggregate status cannot tell you which test regressed, whether execution time is growing, or whether a failure came from code, test logic, or the environment. Store machine-readable test reports and retain run history. Make failed-test output available from the CI run.
Where tooling supports it, associate each result with the commit, branch, environment, test identifier, error, and stack trace. Keep useful logs and, for visual or UI failures, screenshots or video. Microsoft’s testing guidance recommends retaining details such as the failing test or environment, reproduction steps, and supporting logs or images; CircleCI documents storing test results and accessing failed-test output.
Track a small set of useful signals
- Outcomes: Pass or fail by run and by individual test.
- Duration: Execution time by test and pipeline stage, so slowdowns are visible.
- Failure patterns: Trends, repeated clusters, and tests that fail intermittently or have low success rates.
- Coverage of important risks: Use coverage to find important paths that lack tests, not as a score to maximize.
- Follow-up status: For failures tracked as defects, monitor severity, owner, and age.
Microsoft recommends tracking results, execution time, failure trends, and historical comparisons. CircleCI test insights surface flaky, low-success-rate, and slow tests. A high pass rate does not prove that a suite exercises relevant behavior, and coverage does not prove that assertions check the right outcomes. Use these measures to identify investigation priorities, not to optimize one percentage in isolation.
Make failure notifications actionable
Route failures to the team or owner responsible for the affected code. A useful alert identifies the test, commit or build, environment, error and stack trace, relevant artifacts, and a link to the run. Microsoft advises notifying the people who can investigate quickly. Postman supports notifications for monitor failures and errors.
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Find and manage flaky tests
A flaky test passes and fails intermittently on the same code. It weakens confidence in the test signal: people may start dismissing failures, including genuine regressions. Common causes include uncontrolled system state, shared or uncleared test data, order-dependent tests, interference from parallel execution, timing-sensitive assertions, thread-safety issues, and unstable infrastructure. The pytest documentation describes these as common sources of flaky behavior.
- Confirm the pattern. Compare the individual test’s history, first-attempt results, environment, and logs rather than relying only on the final job status.
- Check likely causes. Look for leaked state, shared data, ordering assumptions, parallel interference, timing-sensitive expectations, thread-safety problems, and infrastructure instability.
- Fix the underlying cause where possible. A retry can help expose intermittency, but it does not make an unreliable test reliable.
- If you quarantine a test temporarily, keep it visible. Assign an owner and a review or expiry point. The pytest guidance warns that permanent xfail or quarantine can let build-breaking changes through; HMRC’s standard says teams should investigate and mitigate flakiness.
Retries can limit disruption from intermittent failures, but they can also suppress an initial failure if a later attempt passes. CircleCI documents that behavior for auto-reruns and says consistent failures still exhaust retries and fail the job. Preserve retry counts and first-attempt outcomes in your reporting when the platform permits it.
Use existing CI features before adding a monitoring service
Start with your test framework and CI platform if they already retain individual results, timing, history, artifacts, and notifications. Add a dedicated service when you need capabilities your current workflow does not adequately provide—for example, cross-project aggregation, longer-term history, flaky-test triage, or scheduled API uptime checks. This is a practical starting point, not a claim that one setup fits every team.
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When comparing options, check compatibility with your framework and report format, test-level history and flaky detection, artifact retention, runtime and parallel-run visibility, alert routing and ownership, setup effort, and plan limits. For API checks, also consider schedule frequency, execution region, private-network access, and authentication requirements.
CI-native test results
CircleCI documents test-result storage, failed-test output, run timing, and insights into flaky, low-success, and slow tests. Some advanced test features have platform or authentication conditions, so check the relevant documentation for your setup.
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Scheduled API and workflow checks
Postman Monitors are an option for API health checks and critical workflows. Its documentation describes scheduled or CLI-triggered runs, notifications, run history, and region and runner choices. The page also notes limitations for OAuth 2.0 monitors, beta status for GraphQL and gRPC request support, and plan limits for minute schedules and multiple regions. Verify current product terms and limits before choosing a plan.
Operationalizing triage
GitLab’s handbook describes internal automation that analyzes failure data, identifies high-impact flaky files, creates issues, and routes them to owners. Treat that as an example of an operational process, not a set of thresholds or response targets every team should copy.
Use screenshots as supporting evidence for visual failures
For UI tests, a screenshot or video can make a failure easier to investigate when logs alone do not show what the browser rendered. Keep the test result and failure context as the source of truth; an external capture of a page is supporting evidence, not a replacement for collecting artifacts from the actual failing test run.
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Troubleshoot monitoring gaps
- The job is green, but a test failed on an earlier attempt: Check whether a retry passed and hid the first failure from the final status. Retain and review first-attempt outcomes and retry counts.
- Failures are hard to reproduce: Check that results include the commit, environment, test identifier, stack trace, and relevant logs or UI artifacts. Compare runs for state leakage, timing, parallelism, and infrastructure changes.
- Alerts are noisy or ignored: Route them to a responsible owner and include a direct run link and enough failure context to investigate. Preserve individual results even when notifications are grouped.
- Feedback arrives too late: Review which suites run on each change and which can move to a later pipeline stage or schedule. Avoid making the per-change test pack so large that teams stop relying on it.
- Coverage looks healthy but defects escape: Use coverage to locate untested risk areas, then assess whether assertions verify important behavior. Do not treat a coverage percentage as proof of test quality.
Frequently Asked Questions
Should a flaky test be deleted?
Not automatically. First determine whether the test covers important behavior and investigate its failure mechanism; quarantine it only as a visible, owned temporary measure when needed.
Is code coverage a good automated-test health score?
It is useful diagnostic evidence about exercised code, but it does not establish that tests assert the right behavior or cover the most important risks.
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