Reliable tests make a big difference when determining what’s an issue versus just noise in your QA team’s automation efforts. Flaky tests undermine QA efforts and erode trust in the test results, complicating your continuous integration and delivery (CI/CD) efforts. Let’s explore the issues caused by flaky tests, best practices for dealing with them, and how tools like Ranorex Studio help teams recognize and address flaky tests to ensure consistent software quality. 

What are flaky tests?

Flaky tests refer to automated test cases that generate pass or fail results without requiring developers to make codebase changes or perform any functionality testing. Flaky tests often occur in unstable test environments or because of unreliable external dependencies. The unpredictability of flaky tests means they can’t be fully trusted as an indicator of software quality.

Here’s how flaky tests impact automation efforts:

• QA time wasted: Testers must constantly rerun tests, review false negatives, and spend a lot of effort debugging issues unrelated to the application under review. All the false alarms and retries eat into productivity and distract from high-value tasks. 
• Release cycle slowed: DevOps environments depend on automated pipelines for rapid code delivery. Ongoing CI/CD runs delay test execution and block pull requests, reducing pipeline efficiency. You also end up with bottlenecks in the development process, leading to delayed release cycles. The cumulative effect reduces software delivery speed. 
• Lower confidence in automation: When a test fails, testers and developers must troubleshoot to determine whether it is a real problem or flakiness. If test failures are constantly proven unreliable, teams must ignore them completely. This undermines the value of automation testing as a quality signal and makes CI/CD pipelines less effective when it comes to regression testing.
• Impacts team morale: Flaky tests cause frustration among QA teams. They can make engineers less confident in testing and their ability to deliver high-quality source code. Getting multiple inconsistent test results disrupts the workflow and adds to stress levels when dealing with tight deadlines. Perhaps the most significant concern is team members becoming numb to alerts by dismissing them as unreliable.

Real-world impacts of flaky tests

Let’s say a tester writes UI tests designed to validate the ability for users to add items to a shopping cart for an e-commerce website. The test is designed to select a product, increment the quantity box to two, then click the “Add to Cart” button, resulting in the items appearing in that user’s shopping cart. Here’s what can happen with an automated flaky test.

• The developer runs the code and can consistently add three or four items to the shopping cart, so the test passes. 
• The same test sometimes fails within the CI/CD pipeline because the test environment takes longer to process the website’s requests. The test framework times out before the page can finish loading the request, causing a failed test. 

While the application functions correctly, variables within the test environment produce a false negative. The QA team and developers aren’t getting accurate results reflecting the health of the software. 

Common causes of flaky tests in automation

Flaky tests are a good indicator of weakness somewhere in the test environment, unstable dependencies, or poorly designed test cases. Each of these can undermine test automation. Addressing the core problem helps QA and developers:

• Reduce wasted effort investigating false test failures
• Shorten release cycles by reducing re-run tests
• Build confidence in automated testing and CI/CD workflows
• Make each test run capable of providing accurate and actionable feedback on code quality

Fixing flaky tests is a sound investment in the long-term success of your organization’s software delivery process. Below are some of the most common issues that lead to flaky tests. 

1. Synchronization issues

Improper synchronization often throws off UI tests. For example, a button, modal, or dropdown might not be ready when an automation script attempts to interact. Failures result when you have hard-coded waits or fixed sleep intervals that stop working because of variations in page rendering time. This typically happens due to a high server load, network conditions, or browser performance. Testers end up with non-deterministic results when tests pass once but fail on the next run without code changes. 

For example, an automated test may attempt to select an item from the header menu after the webpage loads. The form field may load quickly during one test run, resulting in a pass. However, the item may not be fully interactive when the automated test attempts a second run, causing a failure. 

The best way to address this issue is to eliminate static waits and replace them with explicit waits that check for element readiness. Use the synchronization features in platforms like Ranorex Studio to deal with concurrency and prevent race conditions. Adding smart retries when rerunning specific failing tests rather than the entire test suites is also a good idea. 

2. Environment or network instability

CI pipelines often incorporate tests run on various browsers, virtual machines (VMs), and servers. Dependencies within different test environments may contain external dependencies with problems like an unstable AI or third-party integration. Network latency or server timeout can lead to valid tests failing. For example, an end-to-end checkpoint test may use a third-party service that is unavailable, resulting in a failure even though the application functions correctly. 

One way to address issues like this is by:

• Using mocks or simulators in place of unreliable external dependencies
• Standardizing test environments to reduce inconsistencies
• Adding monitoring to detect instability in networks or servers
• Running smoke tests to verify environment readiness before complete test execution

3. Unstable or reused data

Running multiple tests from the same account, database entry, or record leads to conflicts. Testers end up with inconsistent outcomes because of expired data or a state change. For example, a test designed to update a customer profile may cause issues for a separate test looking for original values. The shared data conflict can result in the second test failing. 

The best way to handle these issues is by:

• Regenerating fresh test data at the start of each test run
• Creating disposable or isolated accounts versus reusing shared or expired ones
• Running a cleanup workflow that resets data after test execution
• Using test management tools in platforms like Ranorex Studio to provision on-demand data sets

4. Fragile test scripts and locators

Tests become more fragile if they contain hard-coded waits, outdated selectors, or overly specific locators. These become problems as the application evolves. A small UI change, like updating the DOM structure, can lead to test failures. For example, if a test script looks for a button at a specific XPath in a layout and there’s a change, the test fails even though the button functions correctly. 

Avoid issues related to fragile scripts and fixed locators by:

• Refactoring scripts to use selectors like IDs or data-test attributes that are more resilient
• Using page-object models to centralize locator management
• Adding dynamic waits versus hard-coded ones, capable of responding to the system state

How flaky tests impact automated QA and CI/CD pipelines

Flaky tests produce inconsistent results that undermine QA workflows. Failures triggered by flaky tests force teams to rerun test suites to confirm the validity of a failure. This results in a slowed development process and delays in release approvals. The longer this continues, the more QA engineers and developers lose faith in automatic testing and reporting accuracy. 

Test owners end up having to constantly debug false failures, update scripts, and track test environments to locate root causes. A test that fails consistently can be diagnosed and repaired more quickly, so it causes less damage. 

CI/CD pipeline speed

Flaky tests can pass or fail without team members implementing changes to the codebase. That unpredictability makes it harder to determine a true failure versus a false alarm. Consistently failing tests makes it easier to pinpoint if the core issue is the code, a configuration issue, or a problem with the test environment. 

Every failure a flaky test produces forces DevOps teams to rerun builds or pipelines. Continuous integration can trigger full test execution for hundreds of cases, consuming valuable resources. 

Trust erosion in automation reports

If a developer sees a failure, it’s hard to immediately determine if the problem is within the code, coming from the test framework used, or the flaky test itself. If an API integration fails because of network latency, the cause doesn’t appear in the results. Once a developer confirms the API is functioning correctly, they may begin ignoring future failures, which reduces the value of using CI/CD pipelines for quality checks. 

Increased maintenance

Diagnosing flaky tests requires more effort because they do not consistently produce failures. QA analysts must look through test reports and logs before re-running test cases. If a test fails because of reused test data that conflicts with another test, it leads to more wasted time. 

Delayed release approvals

Release pipelines require testers to explain each failure before they can deploy. The unreliability of flaky tests makes that more challenging. Teams must prove that the bug is not real and is caused by test flakiness, which delays development. If a build is ready to stage, one flaky test during an end-to-end check can lead to managers’ hesitation to sign off on a release until QA achieves a pass on a rerun test. 

Flaky tests vs. consistently failing tests summary

How to detect and fix flaky tests

Taking a structured approach to flaky test detection helps teams locate patterns of flaky tests, which helps them diagnose the root cause and apply a fix. 

1. Check for patterns in test results

Start by looking for inconsistent outcomes in CI/CD pipelines. If you’re using Ranorex Studio, check the test reports for test cases that produce both pass and fail results. Reviewing historical test runs shows you patterns. For example, a UI test may only fail when using a particular browser or when run on a specific server during peak load. It’s also a good idea for teams to track metrics like the number of retries, rerun frequency, and test suite failure percentages to identify recurring issues. 

2. Check logs, screenshots, and video captures

Gather evidence from places like pipeline logs, screenshots, and video captures of automated test execution. This helps teams narrow down the cause, like delayed UI rendering or network instability. Using multiple resources speeds up troubleshooting and eliminates much of the guesswork around locating a root cause for the flaky test. 

3. Stabilize tests

Teams can stabilize flaky tests by doing the following:

• Getting rid of hard-coded waits and replacing them with explicit or dynamic waits to deal with UI timing issues
• Using stable selectors like IDs to reduce locator failures
• Removing dependencies on unstable servers or APIs by using mocking services and isolating test data
• Cleaning up redundant steps in tests and following best practices for test automation

4. Quarantine flaky tests

Remove flaky tests until your team implements fixes. Keeping flaky tests out of the main CI/CD workflow keeps them off reports, where they could block release approvals. QA personnel can still run tests in parallel environments for monitoring, but they’re kept from slowing down the critical delivery path. 

5. Verify stability

Check the stability of flaky tests by running them repeatedly in the CI/CD pipeline. Teams should see tests that produce false negatives start generating more consistent results. Once confidence is achieved, move the test back to the main test suite. 

Flaky test detection summary

Best practices and tools to prevent flaky tests

Platforms like Ranorex Studio allow QA teams to address test creation and environments proactively. Using best practices and modern automation tools reduces test flakiness and produces more reliable test suites. QA professionals can provide accurate feedback to the CI/CD pipeline. 

1. Robust locators: Use stable locators like data-test attributes instead of hard-coded XPath or CSS selectors, which often break when an application’s interface changes. Implementing page-object models to centralize and manage locators makes it easier to refactor as the application changes.
2. Use stable environments: Set up isolated and consistent test environments. Use standardized browser and OS configurations for test runs. Create reproducible environments by using containerization or virtual machines. Isolate dependencies by mocking services and APIs.
3. Maintain clean data: Generate fresh data for every test execution. Make sure to reset or clean up tests after each run. Use test management tools to achieve consistency across pipelines.
4. Leverage tools like Ranorex Studio: Ranorex Studio’s advanced locator technology adapts to UI changes automatically, which reduces the chances of test failures tied to locator-related issues. The platform integrates with popular CI/CD tools like Jenkins to ensure stable test execution. Ranorex Studio also has built-in reporting and integrates with Test Rail to provide more visibility into flaky failures. 

Flaky tests can significantly impact QA teams’ productivity. Learning to recognize the root causes speeds up resolution and the operation of CI/CD pipelines. Ranorex Studio provides reliable object recognition tools and pipeline integration to help organizations create automated workflows that help teams achieve their testing goals. Try Ranorex free for 14 days today! 

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