Flaky Tests: Why They Fail Randomly and How to Fix Them

It's 6 p.m. on release day. The pipeline turns red on a checkout test. A developer reruns it, and it passes. Nobody knows why it failed, so the team ships anyway and hopes for the best.

That moment repeats in almost every QA team. The test isn't catching a real bug. It's just unreliable. And the more often it happens, the less anyone trusts the results.

This article explains what flaky tests are, why they happen, what they cost, and how teams fix them for good.

What Are Flaky Tests?

A flaky test is an automated test that passes and fails on the same code, without any change to the application. Run it five times and you may get three passes and two failures.

That's different from a normal failing test. A failing test points to a real problem in the code. A flaky test points to a problem in the test, the data, or the environment.

Flaky Test vs Failing Test

• Failing test: fails every time until the bug is fixed.
• Flaky test: fails some of the time, and often passes on a rerun.
• Why the difference matters: teams learn to ignore flaky failures, and that habit hides real ones.

Why Do Automated Tests Fail Randomly?

Most flaky tests trace back to a handful of causes.

Timing and Waiting Problems

A test clicks a button before the page has finished loading. Or it checks a result before an API call returns. On a fast machine it passes. On a busy CI server it fails.

Shared or Dirty Test Data

Two tests use the same user account. One changes the account's settings, and the other fails because it expected the original values. Test order suddenly starts to matter.

Unstable Test Environments

Staging servers restart, run slowly, or share resources with other teams. A test that needs a response in two seconds gets it in four.

Third-Party Dependencies

Payment gateways, email services, and external APIs don't always respond the same way. When a test depends on them, it inherits their moods.

Changing Page Elements

Dynamic IDs, animations, and pop-ups move things around. A locator that worked yesterday can miss the element today.

Flaky Test Examples

Here are a few patterns most QA teams will recognize:

• A login test that passes locally and fails in CI because the CI machine loads the page more slowly.
• A report test that fails only after midnight because it depends on the current date.
• A cart test that fails when another test has already used the same product stock.
• A file upload test that fails when the previous run left a file behind.

What Flaky Tests Really Cost

The visible cost is wasted reruns. The bigger cost is trust.

Once a team believes "that test is just flaky," people stop investigating red builds. A real defect can slip through because it looks like the usual noise. That's how flaky tests turn into production bugs.

There's also a time cost. Engineers spend hours deciding whether a failure is real, and releases wait while they do it. Where that time actually goes is broken down in Where Does Testing Cycle Time Actually Go? A Time Breakdown for QA Teams.

How to Fix Flaky Tests

There's no single fix, but these habits remove most of the problem.

• Replace fixed waits with smart waits. Wait for a specific condition, like an element being visible, instead of pausing for a set number of seconds.
• Make each test independent. Every test should create its own data and clean up after itself.
• Isolate the environment. Use dedicated or containerized test environments so other teams' work can't interfere.
• Mock unreliable external services. Test your logic against a stable stand-in, and keep a small set of real integration checks.
• Quarantine, then fix. Move a flaky test out of the main pipeline so it stops blocking releases, but track it and fix it within a set time. Quarantine without follow-up just hides the problem.
• Track flakiness as a metric. Record how often each test flips between pass and fail. What gets measured gets fixed.

Flaky Tests in CI/CD Pipelines

Pipelines make flakiness more visible because tests run constantly, often in parallel, on shared machines. A test that fails one run in twenty will show up several times a week.

Teams that run continuous testing need reliable signals, since automated gates decide whether code moves forward. The pipeline side of this is covered in Continuous Testing in CI/CD with AI Release Pipelines. If the team runs tests through GitHub, AI Testing in GitHub Actions: A Complete Setup Guide shows how to set that up.

How AI Helps Reduce Flaky Tests

AI helps in a few specific ways, and it's worth being clear about the limits.

• It repairs broken locators. When a button's ID changes, AI can find the right element by other signals instead of failing. This is the idea behind self-healing test automation.
• It spots patterns. AI can flag tests that flip results most often, and link them to a likely cause like timing or shared data.
• It reduces script maintenance. Fewer hand-written scripts means fewer places for fragile code to hide.

The trade-off: AI can't fix a shared, unstable environment or a badly designed test flow. Those still need engineering decisions. AI reduces the noise. It doesn't replace good test design.

How TestMax Helps Teams Reduce Flakiness

TestMax is an AI test automation tool that builds tests from requirements instead of scripting them by hand. That matters for flakiness, because a lot of unreliable tests come from hastily written scripts tied to fragile page details.

With TestMax, teams get generated test cases tied to what the software is supposed to do, self-healing for changing elements, and results that trace back to requirements. When a test fails, it's easier to tell whether the application or the test is at fault. For teams tired of rerunning red builds, that clarity is where the time comes back.

FAQ

What is a flaky test?

A flaky test is an automated test that gives different results on the same code, sometimes passing and sometimes failing. The cause is usually timing, shared data, or an unstable environment, not a real bug in the application.

Why do automated tests fail randomly?

Random failures usually come from timing issues, tests sharing data, slow or unstable environments, third-party services, or page elements that change. Fixing the test design and isolating the environment removes most of them.

How do you fix flaky tests?

Use condition-based waits instead of fixed pauses, make each test independent, isolate test environments, mock unreliable services, and track flakiness over time. Quarantine flaky tests briefly, but always fix them.

Can AI fix flaky tests?

AI can fix a part of the problem, such as broken locators, and can highlight tests that flip results often. It can't repair unstable environments or poor test design, so human review is still needed.

Conclusion

A flaky test isn't just an annoyance. It teaches a team to ignore red builds, and that's when real bugs get through.

The fix is a mix of better test design, cleaner environments, and tooling that reduces fragile scripts. Teams that treat flakiness as a tracked problem, not a fact of life, get faster releases and results they can actually trust.

• flaky test vs failing test
• flaky tests in CI/CD pipelines
• quarantine flaky tests
• flaky tests