A flaky test is an automated test that sometimes passes and sometimes fails without a relevant change in the code. At first glance, it may look like a technical nuisance: a failed build, a rerun, a delay in CI/CD. But at scale, flaky tests become a business risk. They slow down releases, waste engineering time, reduce trust in automated testing, hide real defects, and weaken confidence in business-critical software. For organizations in banking, telecommunications, aerospace, and other regulated or high-dependability environments, test flakiness is not just a QA problem. It is an operational risk that must be managed deliberately.
A flaky test produces inconsistent results under the same or equivalent conditions. Google defines a flaky test result as one where the same code produces both passing and failing outcomes. In other words, the failure is not reliably linked to a real product defect or a specific code change. Google also identified common causes such as concurrency issues, nondeterministic behavior, undefined behavior, third-party code instability, and infrastructure problems.
Flaky tests can appear at any level of automated testing, but they are especially common in complex integration, end-to-end, UI, mobile, and performance-related environments. These tests often depend on timing, network conditions, test data, asynchronous processes, external services, browser behavior, devices, or shared infrastructure. The more moving parts a test depends on, the greater the chance that the result becomes unstable.
Slack categorizes flaky tests into two broad types: independent flaky tests and flaky tests caused by systemic issues. Independent flaky tests can fail even when run in isolation. Systemic flaky tests fail because of shared state, CI environment differences, or interactions with the broader test set. Slack notes that systemic flakiness is harder to detect and debug because the behavior changes as the test environment or test suite changes.
This distinction matters. A flaky test is not always “a bad test.” Sometimes it reveals a real architectural weakness: race conditions, hidden dependencies, weak isolation, poor test data management, unstable infrastructure, or insufficient observability. Treating flaky tests only as noise can cause teams to miss signals that point to deeper reliability problems.
The direct cost of a flaky test is easy to underestimate. One failed build may look minor. One rerun may not seem expensive. One investigation may take only a few minutes. But in large engineering organizations, this cost compounds quickly.
Google reported that across its test corpus, around 1.5% of all test runs produced flaky results, and almost 16% of tests had some degree of flakiness. Google also observed that around 84% of pass-to-fail transitions in post-submit testing involved flaky tests, which created repetitive work to determine whether a failure was real or flaky.
Slack’s experience shows the same issue from another angle. In its mobile codebases, more than 120 developers created over 550 pull requests per week, while the test suite included more than 16,000 Android automated tests and more than 11,000 iOS automated tests. Slack found that each flaky test failure took about 28 minutes to manually triage. That triage time justified a dedicated automation effort for flaky test detection and suppression.
Atlassian also quantified the impact. In Jira Frontend, test flakiness had historically contributed to as much as 21% of master build failures. In Jira backend repositories, approximately 15% of failures were attributed to flaky tests, leading to reruns and more than 150,000 hours of wasted developer time each year.
The financial cost is not only developer time. Flaky tests consume CI resources, delay pull requests, block releases, interrupt planned work, increase cloud or device-farm execution costs, and create frustration among engineering teams. Atlassian reported that its Flakinator system recovered more than 22,000 builds and identified 7,000 unique flaky tests, improving build reliability and reducing CI resource consumption.
A flaky test damages the most important asset in test automation: trust.
Automated testing only creates business value when teams believe the results. If a red build usually means “rerun it,” the signal has already weakened. If developers assume failures are noise, real defects can slip through. If management sees repeated delays without clear cause, confidence in the delivery process declines.
Google warns that legitimate failures are often ignored when they appear in flaky tests because people become accustomed to false positives. This can delay the discovery of real problems and increase the number of changes that may have contributed to a failure. In pre-submit testing, ignoring legitimate failures can lead to broken code being submitted.
Slack makes a similar point: bugs can hide under the disguise of a flaky test, and a test loses its purpose when developers ignore failures because of its flaky nature. Slack also notes that flaky tests reduce CI stability, increase time to merge, reduce developer trust, and harm developer experience.
This is why flaky tests are a business risk. They weaken release confidence. They create uncertainty around delivery dates. They make quality metrics less reliable. They slow down time to market. In regulated or business-critical environments, they also weaken the evidence needed to support release approval, audit readiness, and operational resilience.
A bank cannot afford uncertainty around transaction processing tests. A telecommunications provider cannot treat provisioning or billing failures as random noise. An aerospace company cannot rely on unstable validation evidence for critical workflows. In these contexts, unreliable tests create unreliable decisions.
Flaky tests hurt most where software delivery depends on fast, trusted feedback.
If a build fails unpredictably, developers lose time investigating unrelated issues. Pull requests wait longer. Release trains slow down. Teams start bypassing or ignoring test failures. This creates a dangerous loop: the less trusted the pipeline becomes, the less effectively it protects production.
2. Regression testing
Regression suites are meant to provide confidence that existing functionality still works after change. If regression tests are flaky, the organization loses one of its main safeguards against defect leakage. A flaky regression suite may be technically large but strategically weak.
3. End-to-end testing
E2E tests often cover the workflows most visible to users and business stakeholders. They also tend to be more fragile because they depend on many system layers. Slack observed that test failure rates vary by test type, with unit tests generally less flaky than functional tests, and functional tests less flaky than E2E tests.
4. Performance and infrastructure-dependent testing
Business-critical systems often fail not only because of functional defects, but because of timing, load, concurrency, environment instability, or external dependencies. If performance or integration tests are flaky, teams may miss early warnings about scalability, resilience, or service degradation.
5. Regulated delivery
In banking, telecommunications, and aerospace, quality evidence matters. A flaky test result can create ambiguity: was the system defective, was the environment unstable, was the test wrong, or was the risk never properly understood? Ambiguity is expensive when release approval depends on traceable, repeatable, auditable evidence.
The first solution is detection. Teams need visibility into which tests are flaky, how often they fail, which systems they affect, who owns them, and whether the flakiness trend is improving or worsening. Without data, flaky tests become anecdotal. With data, they become manageable.
The second solution is classification. Not every failure should be treated the same. Slack emphasizes the importance of differentiating flaky tests from consistently failing tests, infrastructure failures, crashes, and backend/API failures. Its solution included detection, suppression, Jira ticket creation, ownership mapping, and team notifications.
The third solution is quarantine, but with caution. Google describes one mitigation strategy where tests with excessive flakiness are automatically quarantined and removed from the critical path while a bug is filed for developers to reduce flakiness.
However, Google also warns that quarantine can mask real race conditions or other defects in the code being tested.
The fourth solution is ownership. A flaky test without an owner becomes permanent noise. Atlassian’s Flakinator identifies flaky tests, connects them to ownership, creates tickets with deadlines, and sends notifications. Atlassian also highlights the importance of dashboards, trend analysis, root-cause analysis, custom thresholds, and smooth integration into existing CI/CD workflows.
The fifth solution is better test architecture. Reduce unnecessary end-to-end coverage. Push checks down to API, component, contract, or unit levels where possible. Isolate test data. Remove hidden dependencies. Avoid hard-coded waits. Stabilize environments. Use service virtualization where appropriate. Separate infrastructure issues from product defects. Make tests deterministic wherever possible.
The sixth solution is continuous measurement. Track flaky test rate, rerun rate, build stability, time to merge, triage time, escaped defects, test ownership, and time to resolution. Slack’s automation improved main branch stability from 19.82% to 96% and reduced test job failure from 56.76% to 3.85%. Slack also reported 553 hours of triage time saved at that stage of the project.
For business-critical software, flaky test management should be part of the broader quality engineering strategy. It should not be treated as occasional cleanup.
Start by identifying tests that cover business-critical paths: payments, billing, authentication, provisioning, reporting, order processing, integrations, safety-related workflows, and customer-facing services. These tests should have the highest reliability expectations because they support the most important release decisions.
Next, define acceptable flakiness thresholds by test type. A unit test should have almost no tolerance for flakiness. A complex E2E or mobile test may require different handling, but it still needs ownership, monitoring, and a remediation process.
Then connect test results to business risk. A flaky test in a rarely used internal function does not carry the same risk as a flaky test in a core payment flow. Prioritization should reflect business impact, not only technical frequency.
Finally, treat flaky test elimination as a product quality investment. The goal is not just cleaner CI dashboards. The goal is faster feedback, fewer blocked releases, better developer productivity, stronger audit evidence, and reduced production risk.
A flaky test is more than a technical annoyance. It is a warning signal that the testing process, test architecture, CI/CD pipeline, or system under test may not be reliable enough to support confident delivery.
When ignored, flaky tests waste engineering time, damage developer trust, delay releases, increase CI costs, and allow real defects to hide behind false alarms. When managed properly, they become an opportunity to improve test automation maturity, stabilize delivery, and strengthen business-critical quality assurance.
ProofIT has the appropriate references in automated testing and performance testing of complex, critical systems in the banking, telecommunications, and aero industries. If your organization wants to reduce flaky test risk, improve CI/CD reliability, and build trusted automated testing around business-critical paths, ProofIT can help you design, stabilize, and scale a testing strategy built for real-world resilience.
Assess your flaky exposure. Request a flaky audit or a consultation from our experts at business@proofit.tech or +44 73 6048 4722. Our expert team can help your complex and critical or urgent functional and performance testing process.
