How Flakey Tests Impact Software Quality Efficiency

Have you ever experienced automated software tests that fail repeatedly without any apparent reason? That phenomenon is known as flakey tests. These inconsistent tests can lead to unnecessary rework, delays, and, more importantly, wasted costs for QA and development teams.

According to a 2024 study by the Survey of Software Test Automation, around 30% of automated tests fail due to flakey conditions, resulting in hours spent investigating and fixing non-existent issues. This time could be better invested in meaningful activities, like optimizing tests or developing new features.

But the impact of flakey tests goes beyond wasted time. They create an environment of uncertainty. Each false positive undermines confidence in test results, making the QA process less reliable and increasing pressure on development teams to chase down issues that might not even exist.

In this article, we’ll explore the main causes of flakey tests, how to identify them, and, most importantly, how to reduce their impact, cutting costs and boosting team productivity.

What causes flakiness in automated tests?

Flakiness in automated tests can happen from several factors, typically related to unstable testing environments or poorly structured test design. One common issue is fragile or overly specific selectors. When a test relies on a UI element whose structure or location changes frequently, it becomes more prone to failure. For instance, a selector based on a dynamic ID or numerical value can easily break after a front-end update, resulting in false negatives.

Another critical factor is manual synchronization dependencies. Many tests rely on commands like time.sleep() or fixed waits to ensure that elements are present or processes are complete. However, these methods are no good, as response times can vary across environments, especially in systems using cloud infrastructure or continuous integration (CI). The result is tests that sporadically fail without any code changes.

Overloaded servers, under-resourced infrastructure, or inconsistent configurations between development and production environments can also lead to unpredictable results, causing further instability in the testing process.

Lastly, tests that depend on external states, such as non-isolated databases or unstable APIs, are particularly vulnerable to flakiness. Data changes or external service downtime can trigger intermittent failures, complicating the debugging process and undermining test reliability.

Why are flakey tests a big problem?

1.Constant Rework:

Each flaky test generates additional work: investigating, verifying that the error isn’t real, rerunning the test, and revalidating the result. While it may seem minor initially, it quickly adds up.

• Average Cost: Teams lose an average of 2 hours per week per flaky test, easily accumulating to hundreds of wasted hours annually.

2. Decreased Confidence in Automation:

When the tech team no longer trusts the automation suite, they may resort to manual testing as a safety net. This leads to double the cost — paying for automation and then again for manual testing.

• Average Cost: Companies can spend up to 30% more on QA due to the loss of confidence in automated tests.

3. Slower CI/CD Pipeline:

Flakey tests can slow down the continuous integration pipeline, delaying critical deployments and affecting the company’s ability to respond quickly to market demands.

• Average Cost: Every delayed deployment can represent real financial losses, particularly in competitive digital products.
  

How to solve this problem?

1.Quickly Identify and Remove Flakey Tests:

Use tools that automatically detect flaky tests. The sooner a flaky test is stabilized or removed, the less it costs in rework and wasted time.

2. Invest in Smart Testing Tools:

Consider platforms like TestBooster.ai, which run tests in natural language and understand software context, significantly reducing the risk of flakiness associated with fixed selectors and layout changes.

3. Establish Best Practices for Automation:

• Avoid fixed sleep commands and implement dynamic synchronizations.
• Isolate test scenarios to ensure independence.
• Continuously validate and review unstable tests to minimize recurrence.
  

The end of flakey tests: the smart approach with TestBooster.ai

Flakey tests are more than just technical issues — they’re real cost drivers. Addressing them quickly not only saves valuable time but also restores confidence in continuous delivery processes. 

TestBooster.ai offers a strategic approach to mitigating flakiness through advanced AI and machine learning techniques. The platform identifies patterns of failure, distinguishing false positives from real errors while dynamically adjusting wait times, eliminating the need for static commands like time.sleep().

TestBooster.ai executes tests in controlled, isolated environments, reducing the impact of external dependencies. Detailed reports provide actionable insights, helping QA teams prioritize fixes and streamline testing workflows, ultimately saving time, resources, and money.

Ready to turn the tide on flaky tests? Discover how TestBooster.ai can transform your testing process and eliminate unnecessary costs.