Flaky Tests: How to Identify and Fix Them for Stable CI/CD

The Silent Killer of Engineering Velocity

It is 3:00 AM. Your CI/CD pipeline fails on a critical hotfix. You check the logs, and the error is a cryptic TimeoutError: element not found. You haven't touched that part of the codebase in months. You hit 'Rerun Job.' Ten minutes later, it passes. No code changes, no environment tweaks—just a 'lucky' second run.

Welcome to the world of flaky tests.

In 2026, as software systems become increasingly distributed and asynchronous, flaky tests have evolved from a minor nuisance into a major economic drain. Studies suggest that engineers at mid-to-large scale enterprises spend up to 15-20% of their total development time dealing with non-deterministic test failures. That is nearly one full day per week lost to chasing ghosts.

At Increments Inc., we’ve spent over 14 years building complex platforms for global leaders like Freeletics and Abwaab. We’ve seen firsthand how flakiness erodes developer trust, slows down release cycles, and ultimately costs companies thousands of dollars in wasted CI/CD compute credits. If you're struggling with a brittle pipeline, our team offers a free AI-powered SRS document and a $5,000 technical audit to help you modernize your infrastructure and eliminate technical debt.

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What Exactly is a Flaky Test?

A flaky test is a software test that yields both passing and failing results without any changes to the underlying code or the test itself. It is the definition of non-determinism in software engineering.

The Trust Erosion Cycle

When tests fail inconsistently, a dangerous cultural shift occurs within engineering teams:

1. The False Alarm Phase: Developers investigate every failure.
2. The Skepticism Phase: Developers start suspecting the test, not the code.
3. The Ignoring Phase: Developers hit 'Rerun' habitually without looking at logs.
4. The Critical Failure: A real bug is masked by a flaky test, and it ships to production.

To prevent this, we must treat flaky tests not as 'unlucky breaks' but as high-priority bugs in the testing infrastructure.

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Identifying Flaky Tests: The Detection Phase

You cannot fix what you cannot measure. The first step in stabilizing your pipeline is implementing a robust detection mechanism.

1. The 'Rerun' Heuristic

One of the simplest ways to identify flakiness is through automatic retries. If a test fails once but passes on the second or third attempt within the same CI environment, it is flagged as flaky. Modern frameworks like Playwright, Cypress, and Pytest have built-in support for this.

2. Statistical Analysis of Test History

By aggregating test results over time in a database (like BigQuery or a dedicated tool like Testmo), you can calculate the Flakiness Score for every test case.

Test Name	Total Runs	Failures	Reruns Passed	Flakiness Score
Auth_Login_Flow	1,000	5	5	0.5% (Stable)
Checkout_Payment_Process	1,000	120	115	11.5% (Critical)
User_Profile_Update	1,000	2	0	0.2% (Real Bug)

3. Visualizing the Flaky Loop

[Developer Push] -> [CI Triggered] -> [Test Suite Run]
      ^                                     |
      |                                     v
      |                          {Test Fails: Error 408}
      |                                     |
      |                          [Auto-Retry Triggered]
      |                                     |
      |                          {Test Passes: Green}
      |                                     |
      +------- [FLAKY ALERT LOGGED] <-------+

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Common Root Causes and How to Fix Them

Through our work at Increments Inc., we have categorized the causes of flakiness into four primary 'Sins of Testing.'

1. Asynchrony and Race Conditions

This is the most frequent cause of flakiness in web and mobile applications. Tests often assume an element is ready before the JavaScript execution or API call has finished.

The Anti-Pattern (Hard Sleeps):

// BAD: Using a fixed timeout
await page.click('#submit-button');
await page.waitForTimeout(5000); // 5 seconds might not be enough on a slow CI agent
expect(await page.textContent('.success-msg')).toBe('Done');

The Solution (Deterministic Waiting):

// GOOD: Waiting for a specific state or assertion
await page.click('#submit-button');
const successMsg = page.locator('.success-msg');
await expect(successMsg).toBeVisible({ timeout: 10000 });
expect(await successMsg.textContent()).toBe('Done');

2. Shared State and Side Effects

Tests should be isolated. If Test A modifies a database record that Test B relies on, the order of execution becomes a source of flakiness—especially in parallelized CI environments.

How to Fix:

• Database Transactions: Wrap each test in a transaction and roll it back after the test completes.
• Unique Data Seeding: Use libraries like Faker to create unique user IDs and emails for every single test run.
• State Reset: Ensure local storage, cookies, and caches are cleared between tests.

3. External Dependency Flakiness

If your integration tests hit a real Stripe API or a third-party weather service, you are at the mercy of their uptime and latency.

The Increments Inc. Approach:
For our enterprise clients, we implement Contract Testing or use tools like Prism or WireMock to mock external APIs. This ensures that the test environment remains hermetic (sealed off from the outside world).

4. Time and Date Logic

Tests that depend on new Date() often fail at midnight, on leap years, or across different time zones in CI servers (which often default to UTC).

The Fix:
Always mock the system clock. In Jest, use jest.useFakeTimers(). In Python, use freezegun.

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Architecture for Stability: The 'Quarantine' Strategy

When a test is identified as flaky, it shouldn't be allowed to block the pipeline. However, simply deleting it is dangerous. At Increments Inc., we recommend a Quarantine Workflow:

1. Identify: CI detects a flaky test based on retry success.
2. Quarantine: The test is automatically moved to a separate 'Quarantine' suite that runs but does not fail the build.
3. Alert: A Jira ticket or GitHub Issue is automatically created for the engineering team to fix the test.
4. Re-integrate: Once the fix is verified (e.g., 50 consecutive passes in CI), the test is moved back to the main suite.

This strategy maintains a 'Green Pipeline' culture, ensuring that when the build fails, it is always because of a real code regression.

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Why Increments Inc. Prioritizes Test Stability

Building a product is easy; maintaining a high-velocity engineering culture is hard. When we take on a project—whether it's a FinTech platform in Dubai or an E-commerce engine in the US—we start with a rigorous Quality Assurance Framework.

Our unique offer includes a free AI-powered SRS document (IEEE 830 standard). This document doesn't just list features; it defines the non-functional requirements for testability and system observability. Furthermore, our $5,000 technical audit analyzes your current codebase for architectural bottlenecks that lead to flakiness.

If you're tired of seeing red on your CI dashboard, start a project with Increments Inc. today and let our experts stabilize your release cycle.

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Comparison: Testing Strategies & Their Flakiness Risk

Test Level	Execution Speed	Flakiness Risk	Primary Cause of Flakiness
Unit Tests	Very Fast	Very Low	Global state, shared mocks
Integration Tests	Medium	Low/Medium	Database state, API race conditions
End-to-End (E2E)	Slow	High	Network, DOM rendering, 3rd party services
Visual Regression	Slow	Medium	Subpixel rendering differences, fonts

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Advanced Tooling for 2026

In 2026, we no longer rely solely on manual debugging. AI-driven tools are now capable of analyzing logs to suggest fixes for flaky tests.

• Playwright Trace Viewer: Allows you to record a full trace of the test execution, including screencasts, console logs, and network traffic, making it easy to spot race conditions.
• Buildkite Test Engine: Automatically identifies and flakes-out tests that show non-deterministic behavior.
• AI Log Analyzers: Tools that ingest CI logs and identify patterns (e.g., 'This test only fails when the AWS us-east-1 region has >20ms latency').

At Increments Inc., we integrate these tools into our standard DevOps stack to ensure our clients have the most resilient infrastructure possible.

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Key Takeaways for Technical Leaders

• Flakiness is a Bug: Stop treating flaky tests as 'noise.' Treat them as high-priority defects in your infrastructure.
• Prefer Assertions over Timeouts: Never use sleep() or waitForTimeout(). Always wait for a specific state change.
• Isolate Your Data: Use unique data for every test run to prevent cross-contamination.
• Implement Quarantining: Don't let a single flaky test block your entire team's productivity.
• Invest in Audits: If your flakiness is systemic, it’s likely an architectural issue. A technical audit from Increments Inc. can identify the root cause.

Ready to Build a Bulletproof Pipeline?

Don't let non-deterministic tests slow down your innovation. Partner with a team that has 14+ years of experience delivering high-quality, stable software products. Whether you need custom software development, AI integration, or a complete platform modernization, Increments Inc. is here to help.

Contact us via WhatsApp or start your project online to claim your free AI-powered SRS document and $5,000 technical audit today.