Automated tests fail. That’s normal. But when the same test passes on one run and fails on the next—without any code changes—your team stops trusting the signal.

Engineers re-run pipelines until they pass. CI time climbs. Investigations drag. Eventually, people start double-checking with manual testing “just to be safe.” The suite still exists, but it no longer provides confidence in releases.

These failures are called flaky tests. A flaky test produces inconsistent results despite zero changes to the code or test. They’re typically caused by timing issues, race conditions, shared state, environment instability, test interdependencies, or resource contention in CI.

You can’t eliminate flakes entirely. But you get them under control—to the point where they account for less than 10% of failures—and treat them as structured signals instead of random noise. That shift requires a system that logs, classifies, de-dupes, and tracks flakes over time so your team can see patterns and fix root causes instead of chasing one-off failures.

When you do that, flakes cease to be agents of chaos and become agents of change. The suite regains credibility and decisions are based on signal, not guesswork.

What causes test flakes in automated testing?

Test flakes stem from five common sources:

1. Timing issues and race conditions. Tests fail intermittently because they don't wait long enough for asynchronous work to complete. A button isn’t clickable yet, an API call is still pending, or a DOM element hasn’t finished rendering.
2. Environment instability. Staging servers slow down, test databases get out of sync, or network latency spikes. The test logic may be correct, but the environment behaves inconsistently. 
3. Test interdependencies. Tests that rely on execution order or shared state. Test A passes when run alone but fails when Test B runs first and leaves the database in an unexpected state.
4. Unreliable third-party services. External APIs, payment processors, or authentication services occasionally time out or return unexpected responses.
5. Resource conflicts. Parallel test execution competing for the same resources—database connections, file handles, or browser instances—can cause unpredictable failures.

The category of failure determines how your team fixes it. But classification alone isn’t enough. You need a system that responds to failures automatically.

How a test system should handle flaky failures

An effective test system does three things when instability appears: filters out temporary failures, tracks recurring patterns, and assigns ownership for resolution.

Separate noise from regressions automatically

Automatic test retries reduce flakes by re-running failed tests without changing the code or environment. This separates genuine bugs from false failures without forcing your team to re-run the entire suite or manually guess which tests are unstable. 

If a test fails once and passes on retry, that's a flake. If it continues to fail after multiple successive retries, that's something worth investigating. Retries create a clear boundary between noise and signal.

Operational rules:

• Run retries on the same commit and environment to eliminate drift.
• Cap retries at 2–3 attempts to avoid masking real instability.
• Log the retry behavior alongside the test result for traceability.
• Bonus: For each round of retries, adjust the conditions—add waits, reduce parallelism, or limit test scope—to help surface timing issues, resource conflicts, or order-dependent flakes that don't show up in default runs

Track recurring failures with structured history

Automatic retries will reduce the number of failures to investigate on any given run of your test suite. But if you're not logging and analyzing which tests need retries, you won't be able to identify the tests with setup problems, race conditions, or environment-specific bugs. Without structured history, they'll keep passing on the second or third try and slipping through unnoticed.

That's why your system needs two things:

1. A historical log of test outcomes
2. Failure signatures that group recurring flakes by behavior

Log the essentials:

• Test name or ID
• Timestamp of the run
• Error message and stack trace
• Basic environment info (browser, device)
• Commit hash or run ID

Trends indicate where things are deteriorating. To go further, generate failure signatures—standardized identifiers created from error messages and execution logs that group similar failures across tests and environments.

For example, if 12 different tests all time out waiting for the same UI element, the system creates one failure signature instead of treating them as 12 separate issues. That grouping surfaces shared root causes instead of scattered failures.

Operational rules:

• Log test results with consistent metadata.
• Normalize and hash the error + stack trace to generate signatures.
• Track flake frequency per test and signature.
• Monitor trends: which signatures are rising, stable, or fixed?
• Let reviewers reclassify or merge signatures when needed.
• Bonus: Create a report that identifies tests that flake 30% of the time or more, and use it to investigate the root cause.

Assign ownership and route failures deliberately

A flaky test without ownership is just noise. If the test execution system can't identify where a flake originated or who is responsible for it, your team will just end up playing whack-a-mole. Your system needs to do more than detect and group flakes. It has to route them to the right person, with the right context, at the right time.

Every recurring flake should be linked to a known issue:

• Product bug: A real defect in the app, such as race conditions, mismatched feature flags, or unstable third-party dependencies. 
• Test bug: An error in the test logic, selectors, assertions, or setup. 
• Infrastructure problem: An environment issue such as slow staging, authentication timeouts, resource exhaustion, or CI instability. 
• Won’t fix: A known and accepted instability, documented with clear rationale.

Linking each recurring flaky failure to a tracked issue converts repeated failures into actionable work. 

Operational rules:

• Show unresolved flake signatures in a live dashboard.
• Link each flake to tickets, status pages, or code annotations.
• Assign test bugs to the test maintainer, not the product team.
• Route clean product bugs to the owning team automatically.
• Escalate infrastructure flakes instead of working around them.
• Periodically review bugs in "won't fix" and "cannot reproduce" resolution statuses—they have a habit of coming back.
• Bonus: Build automated handoffs and chains of responsibility—so when flakes spike, the system can automatically determine when to raise the alarm and who to notify.

Flaky coverage is fake coverage

Left to their own devices, flakes give you no information. But worse, they force your team to repeat the same investigations over and over, which slows down product delivery.

You can't eliminate flakes. But you can turn each one into a signal that makes your suite more resilient. A real flake-tolerant system automates retries, fingerprints errors, tracks patterns, links known defects, and drives action—without ever asking a developer to re-run a test "just to be sure." That's how you get trustworthy test results and stop wasting time chasing ghosts.

We've built this system. If you're ready for test infrastructure that treats flakes like data—not drama—talk to us.