The Financial Risk of Flaky Tests in a CI/CD Pipeline

Flaky tests feel small, but they easily accumulate into big problems. One minute your pipeline is green, the next it fails for no good reason. You rerun it, it passes, then it fails again.

Now multiply that by hundreds of tests, and an entire team waiting on results that might not be reliable.

It slows down development. It burns time. It breaks trust in automation.

💡Research confirms that flaky tests are common as 59 % of developers encounter them at least monthly. They waste time debugging these failures. And over time, the cost of flaky tests starts to show up on your balance sheet.

In this article, we’ll explore:

• How to calculate the real cost of a flaky test
• Where unstable tests cause the most damage in CI/CD pipelines
• What pipeline delays and context switching really cost your team
• How flaky test impact stretches beyond engineering and into the business
• What fixes actually reduce flakiness and which ones waste time

If you’ve ever wondered why your builds stall or how much that one flaky test is really costing you, this is for you.

Let’s break it down.

The cost formula of a flaky test

The most direct way to measure the cost of flaky tests is through time. Every time a test fails without cause, someone has to stop, inspect it, and rerun it. That’s time pulled away from feature work.

You can calculate the financial impact with a simple formula:

A five-year industrial case study found that dealing with flaky tests consumed at least 2.5 % of total productive developer time: 1.1 % investigating failures, 1.3 % repairing them, and 0.1 % maintaining detection tools.

Here’s what that looks like in action:

• Failures per week: 6
• Time wasted per failure: 30 minutes
• Hourly rate: $80
• Developers affected: 5

That adds up to a total of $12,000 per quarter. And that’s just one team. When this pattern repeats across multiple squads, the cost grows quickly.

We’ve broken it down further below.

When you multiply each false failure across several teams and builds, you start to understand the impact of unstable tests. It’s not just time. It’s developer focus. It’s delivery speed. It’s money.

And for engineering leaders asking how to calculate flaky test cost, this formula gives you a place to start.

💡 Explore our guide to calculating test automation ROI

Pipeline delays and context switching

💡Every time a flaky test fails, the pipeline stops. Studies show such failures misleadingly fail builds and require manual intervention, which directly reduces CI efficiency.

This triggers a full pause for the team. Someone has to review the logs, while everyone else waits for answers. As a result, work slows down.

This is what teams call a "stop-the-line" event. It stalls progress and stretches delivery timelines.

In a CI/CD environment, one of the key metrics is Mean Time to Green. It measures how long it takes for a broken build to become stable again. Flaky tests inflate this number and reduce delivery efficiency.

Developers often switch tasks while waiting. That switch has a cost, since mental overhead increases and focus drops.

💡Empirical HCI research confirms that interruptions like these increase stress, frustration, time pressure, and effort, all of which reduce effective output.  And once the build is green again, it takes extra time to return to the original task.

Now add those delays across multiple test suites and teams, and this CI/CD pipeline delays become visible.

The impact of unstable tests is not just in reruns. It’s in the quiet time lost to waiting and switching. It’s in the speed of your engineering process.

💡Delivery performance is also tightly linked to organizational outcomes, so extended recovery time from flaky failures can slow overall business performance.

If you want faster releases, you need reliable tests. That’s where the gain begins.

⚙️ See how Katalon improves CI/CD stability and delivery speed

The business impact of flaky tests

💡Flaky tests affect more than the build. They affect the business. DORA’s multi-year studies show that stronger CI/CD reliability correlates with better organizational performance, reinforcing that test instability directly hinders business results. When your release gets pushed, your revenue does too.

Time-to-market also matters. A single delay can push a feature launch into the next quarter, which affects forecasts and momentum.

📝 Every delay also affects the team. Developers want to ship with confidence. When pipelines feel unreliable, motivation dips. Over time, the quality of the work shifts. And your hiring and retention plans feel the impact.

When teams spend too much time rerunning tests, they lose faith in the system. Instead of trusting automation, they start reviewing changes manually. That slows everything and also shrinks the return on your testing investments.

💡Independent research links better CI/CD and delivery performance with improved organizational performance, so unstable pipelines manifest as real business drag.

At scale, the productivity loss from flaky tests becomes visible. Engineers get less done. Releases slow. Roadmaps shift. What looks like a small issue in QA becomes a drag on your engineering throughput.

📝 Flaky tests also reduce business agility. Teams can't respond to change quickly. Decisions stall while tests run again. Fast feedback loops become longer. Competitive advantage fades.

All of this points to one truth. Reliable testing fuels velocity. Stable pipelines support growth. And investing in test quality is a direct investment in business success.

📈 Learn how Katalon helps reduce QA costs and business delays

Case study: Counting the Real Cost of Flakiness

A large commercial software project with roughly 30 developers and one million lines of code was analyzed over five years to understand the impact of flaky tests.

Researchers found that flaky tests consumed at least 2.5 % of total productive developer time, divided as follows:

• 1.1 % spent investigating suspected flaky failures
• 1.3 % devoted to repairing those tests
• 0.1 % invested in building and maintaining monitoring tools

While automated test reruns were relatively inexpensive, the major cost came from diagnosing and repairing flakes, which repeatedly interrupted normal development work and delayed releases.

💡These findings come from a peer-reviewed industrial study by Leinen et al. (2023). While this is one project, the measurable loss illustrates a baseline; costs often scale higher in larger orgs.

This real-world evidence demonstrates that even in a well-managed CI/CD environment, flaky tests can quietly drain several percent of every developer’s time. This loss scales directly into higher engineering payroll and delayed time-to-market.

🚀 Read how System Automation cut test time by 120 hours monthly

Fixing Flakiness: Effort vs Automation

Every team wants reliable tests. Some try to fix flakiness by tuning their environment, others invest in better test design. These are solid strategies, but they take effort.

✅ Stabilizing the environment is often the first step. Teams upgrade dependencies. They align configurations. They reduce test timing issues. This improves consistency.

Design also plays a role. When tests use clear assertions and wait conditions, they become more predictable. Reviewing flaky patterns helps identify fragile steps and replace them with stronger logic.

Some teams create a triage routine. They monitor test results daily. They tag unstable cases. They build a feedback loop between QA and engineering. This tightens the feedback cycle.

💡Because rerunning failing tests is costly and slows development, and flakiness undermines downstream techniques like fault localization and mutation testing, structural fixes yield compounding efficiency gains.

Here’s the tradeoff. These actions take time. They add maintenance work. They require dedicated resources to keep up with changes.

✅ In many teams, the test maintenance costs start to rise. Engineers pause feature work to fix tests. QA builds custom tooling to track flakiness. Work shifts from building to stabilizing.

That’s why teams begin exploring automation-driven solutions. It reduces manual effort. It scales better. And it gives your team time back.

📚 Explore how to handle flaky tests effectively

How automation and AI reduce flaky test risk

Automation and emerging AI techniques are reshaping how teams detect and repair flaky tests. Research shows that automated flaky-test detection can dramatically cut the need for repeated reruns.

💡For example, industrial studies describe advanced tools are able to identify flaky tests automatically and isolate root causes without manual triage.

Commercial test platforms now embed these principles. Many include self-healing locators or equivalent smart selectors that adapt when an element’s position or attribute changes. Instead of failing, the test adjusts and continues.

This reduces false failures and shortens the time to return to a green build. The benefits are concrete:

• Fewer stop-the-line events – less wasted developer time and fewer pipeline delays.
• Higher test reliability – engineers trust automated feedback and expand coverage with confidence.
• Faster delivery cycles – less context switching and fewer manual interventions.

While the exact AI implementation varies by vendor, the underlying lesson is consistent with current research: automating flaky-test detection and repair minimizes reruns, stabilizes pipelines, and frees developers for higher-value work.

🤖 See how Katalon’s self-healing locators stabilize your tests

Conclusion: Treat flaky tests as business risks

Flaky tests waste time. They slow release, stretch budgets, and they reduce the return on your test automation investments.

✅ Every false failure adds to the total. Over time, the cost of flaky tests becomes a financial signal. It shows up in lost hours and missed delivery windows. It affects both team output and business outcomes.

DevOps leaders can start tracking this cost alongside other CI/CD KPIs. That includes test stability, build turnaround, and mean time to recovery. These metrics help teams prioritize with confidence.

If you manage a delivery pipeline, it helps to quantify the risk. Use your own numbers. Measure failure frequency, time lost, and developer rates. Then explore solutions that reduce flakiness and improve efficiency.

This is not just a QA problem. It is a delivery problem. A productivity issue. A business risk. And once you see the cost clearly, you can move faster toward fixing it.

🔍 Request a demo to see how Katalon prevents flaky test failures