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A failed test is a reason to investigate, not proof that the product is defective. A useful anomaly report preserves what happened, helps establish whether the failure is new or intermittent, points investigators toward likely causes, and records who will do what next. Use the workflow below to turn a red test result into an evidence-backed investigation and a verified fix.
What an anomaly report should do
In software testing, an anomaly report records an unexpected result so a team can investigate and decide what action is warranted. The cause may be a product defect, a problem in the test or its data, an execution environment issue, or nondeterministic behavior. Do not label the product code as the cause until the evidence supports it.
A strong report lets someone who was not present at the failure understand the result and retrace the investigation. It should preserve the original evidence, show relevant execution history and context, and make the next action and its owner visible.
What to include in a failed test report
- Test identity and outcome: the test name or ID, what it was expected to do, what happened, and whether it failed, timed out, or produced another unexpected result.
- Execution context: build or release, branch, commit or change when available, date and time, and environment details such as operating system, browser, device, or relevant service configuration.
- Reproduction information: the steps performed, inputs and test data, prerequisites, and any comments that clarify how the result was produced.
- Failure evidence: the complete error message and stack trace where available, plus relevant logs, screenshots, recordings, or other attachments. Preserve the original evidence rather than replacing it with a later, cleaner reproduction.
- History and links: related test executions, existing bugs or work items, and associated code changes or requirements where applicable.
- Ownership and next action: who will investigate, what is currently suspected (clearly marked as a hypothesis), and what will happen next.
In Azure DevOps, test-run details can include run summaries, linked work items, step outcomes, automated-run stack traces, analysis information, and attachments. The exact information available depends on the run and how the team configured it. A report in any system should preserve equivalent evidence where possible.
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How to investigate a failed or intermittent test
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Preserve the result and its context
Save the failure details and attachments before rerunning or editing the test. Record the build, branch, environment, time, inputs, and steps. Link the result to an existing bug or work item if one is relevant, so the evidence and later decisions remain connected.
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Check whether it is new, recurring, or intermittent
Review multiple executions over a useful period instead of inferring a pattern from one pass or failure. Look for the first failure, frequency, changes in outcomes, and whether failures cluster around a particular build, time, environment, or code change. A single passing rerun does not establish that an intermittent problem is fixed.
Azure DevOps Test Analytics provides views such as top failing tests and drill-down into execution instances. Its reporting range and interface are product-specific; check the current UI rather than relying on a default range as a universal setting. For persistent failures, compare the failure history with associated changes to identify where the behavior began.
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Separate the main cause categories
Investigate at least four possibilities before assigning a cause:
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- Product code: a regression or defect in the system under test.
- Test code or data: an incorrect assertion, invalid assumption, stale or shared test data, or a test that depends on another test’s state.
- Environment or infrastructure: configuration differences, resource constraints, unavailable services, or other operating conditions.
- Nondeterministic behavior: timing, ordering, concurrency, or variable behavior in the test, application, or its dependencies.
A consistent failure can help narrow the investigation, but consistency alone does not prove which category is responsible. Use the stack trace, execution details, history, and changes together.
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Test for state, order, timing, and dependency effects
When a test passes sometimes and fails other times, inspect its setup and teardown, state assumptions, ordering dependencies, test data, resource use, runner, system under test, and external dependencies. Run the test independently when doing so can reveal dependence on another test or leftover state. Check whether the test is waiting for a meaningful application condition or merely hoping a fixed delay is long enough. Arbitrary sleeps can make tests both slower and less reliable; synchronize on the state the test actually needs.
For timing-related investigations, logging when relevant resources or application states are accessed can help distinguish a race or wait problem from a product defect. Avoid changing assertions or adding delays simply to make the failure disappear: first identify which assumption the evidence challenges.
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Choose a remedy and assign it
Fix the underlying cause where possible. Depending on the evidence, that may mean correcting product code, making tests independent, initializing and cleaning up state explicitly, validating test data and assumptions, synchronizing on application state, removing uncontrolled environmental assumptions, or addressing inadequate runner resources. If the evidence supports a product defect, create or update a defect with severity, ownership, and links to the affected results.
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If several reports share one root cause, connect or manage them as related reports rather than treating each as a separate underlying defect. Preserve each execution’s evidence; grouping reports should not erase the separate symptoms or conditions that may help confirm the scope.
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Verify the change and keep recurrence visible
Run the affected test after the change and inspect later executions for recurrence. Record what was changed and what evidence supports closing the issue. If the team marks a test as flaky, treat that as an analysis and tracking choice, not proof that a new failure is harmless. Microsoft documents that changing a flaky-test designation affects future executions rather than retroactively changing the current pipeline result; review the applicable workflow before relying on that designation.
How to capture browser evidence for a test report
When a failed test concerns a web page, attach a screenshot alongside the failure detail if it helps show the visible state at the point of failure. Capture the page at the relevant viewport and, where possible, preserve the failed state rather than only a later successful rerun. A screenshot can document what was visible; it does not replace logs, stack traces, execution context, or test history.
For a manual capture, reproduce the failure in the relevant browser and environment, navigate to the affected page, restore the test’s relevant state, and save a screenshot with the test result and timestamp. If a consent banner, popup, or chat widget is itself part of the failure, keep it in the evidence; removing it could hide the behavior under investigation.
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ScreenshotNeo is a website screenshot API and MCP server. One GET request can return a screenshot or PDF; the capture can also be useful as an attachment when a web test failure needs visual context. Its clean-shot steps accept cookie or consent banners and remove more than 60 known consent platforms, newsletter popups, and chat widgets before capture, and each step can be turned off. If one of those elements is the thing being tested, disable the relevant cleanup step so the evidence retains it.
For example, save a web-page screenshot as WebP with cURL:
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Whether your team uses a test-management system, issue tracker, or CI interface, check that the workflow supports the investigation rather than merely recording a pass or fail.
- Evidence depth: Can investigators access steps, stack traces, screenshots, logs, and attachments?
- Historical context: Can they inspect multiple runs, identify the first failure, and see intermittent patterns?
- Traceability: Can results be linked to requirements, bugs, branches, or code changes?
- Flake handling: Can known intermittent tests be tracked without hiding new failures or losing history?
- Follow-through: Can the team record analysis, severity, ownership, status, and the next action?
Azure DevOps documentation describes examples of analytics, test-run evidence, traceability, and flaky-test workflows. Those product-specific capabilities do not establish that one reporting tool is best for every team; choose based on the evidence and follow-through your team needs.
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Common investigation mistakes to avoid
- Calling every failure a product bug: the failure may originate in test logic, data, environment, infrastructure, or nondeterminism.
- Trusting one rerun: a pass after a failure may reflect intermittent behavior rather than a repair.
- Changing the test before understanding it: weakening an assertion or adding an arbitrary delay can conceal the symptom without fixing its cause.
- Dropping context from the report: a stack trace without its build, environment, or execution history may not be enough to reproduce or explain the issue.
- Marking a test flaky and moving on: a label can help track known instability, but it should not replace an investigation or make new failures disappear from review.
- Splitting one root cause into disconnected reports: link related reports while retaining each result’s evidence and conditions.
Frequently Asked Questions
How many times should I rerun a flaky test?
There is no universal rerun count that proves a test is stable. Choose a repeat strategy that reflects the suspected variability and the test’s execution cost, and assess the full history and conditions rather than treating a single passing rerun as proof.
Is a flaky test the same thing as a failed test?
No. A failed test is an outcome in one execution. A flaky test is characterized by differing pass and fail outcomes under the same code; the variation still needs investigation to find its source.
Should I close an issue after the test passes once?
A single pass is not sufficient evidence that an intermittent issue is resolved. Close it when the proposed change has been checked and subsequent relevant executions support that conclusion, with the verification recorded.
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