10 Test Automation Problems Even Modern QA Teams Face

Test automation is a core part of modern software development. Whether it's a fast-moving startup or an enterprise with complex existing systems, teams rely heavily on automated testing to keep up with rapid release cycles. But despite how advanced today’s tools and frameworks have become, many QA teams, mine included, over the past couple of years have been facing recurring automation challenges that reduce trust in test automation results.

This blog explores 10 persistent automation problems that even experienced QA teams struggle with. I’ll break down the causes behind these challenges, why they still exist today, and what teams can do to overcome them.

1. Flaky tests that destroy trust

Flaky tests remain one of the most persistent and frustrating challenges in test automation. Even with stable frameworks and good quality test cases, flakiness still appears due to a combination of UI race conditions, inconsistent API responses, network instability, and unpredictable environment behavior. In mobile automation specifically, the problem becomes even more pronounced because device performance varies, animations behave differently across OS versions, and emulators, compared to physical device behavior, can be significantly different. 

The impact

Flakiness reduces reliability and trust in automated testing, creates unnecessary bottlenecks in the CI pipeline, and forces both QA and developers to spend hours analyzing failures that aren’t related to real product issues. The worst part is that flaky tests create noise, and when everything fails for unclear reasons, real defects become harder to spot. There can be situations where legitimate API failures were initially dismissed simply because the team had grown used to seeing random test failures every day.

Reducing flakiness

Teams can reduce flakiness by stabilizing environments, implementing better synchronization practices, isolating test environments, and enforcing strict standards for test case design. Preventing flakiness requires ongoing effort and discipline.

2. Slow test suits that hold everything up

Slow test execution continues to be a major challenge for test development teams. When tests take hours to run, QAs may not discover issues until long after they are introduced. This delay also affects CI/CD pipelines, as every build or deployment has to wait for the tests to complete. In fast-paced environments, time-consuming tests can reduce team agility, making it harder to quickly respond to code changes.

Common causes of slow test execution

Several factors contribute to slow test execution. Large test suites take more time to run, and when all tests are executed sequentially, it can stretch the total runtime into hours. Many test suites lack parallelization, meaning tests are not run concurrently even when they could be. Outdated tests add extra time without providing meaningful insights, and tests that rely heavily on external systems like databases or APIs often introduce additional delays.

Increasing speed

Slow tests reduce development speed and weaken team agility, especially in CI/CD environments. Below are some tips on how to get up to speed:

• Parallel test execution. Running tests simultaneously across multiple threads or machines speeds up overall runtime.
• Splitting test suites. Breaking large suites into smaller, focused groups lets teams run only relevant tests when needed.
• Prioritizing critical tests. Running high-value tests first ensures the most important feedback is received quickly.
• Optimizing test design. Making tests efficient, lightweight, and less dependent on external systems reduces delays.
• Removing outdated tests. Regularly cleaning up unnecessary or overlapping tests prevents wasting time.

By optimizing test design and running tests in parallel, teams can shorten feedback cycles. Regularly reviewing and refining test suites keeps execution fast and reliable.

3. Unmaintainable test code

In many development teams, test code is unintentionally treated as less important than application code. The focus naturally gravitates towards feature delivery, and as long as tests seem to work, they rarely receive the same level of review and refactoring. Because of this, automation frameworks often grow messy over time.

Symptoms of poorly structured automation frameworks

• Repeated locator definitions. Making even small UI changes expensive to update.
• Long, complex test methods. By validating too many behaviors at once, small changes can break multiple parts of a test, complicating debugging and leading to duplicated logic. This makes the test suite fragile and tedious to work with.
• Hard-coded test data. Makes scenarios brittle and environment-specific.
• No reusable components. Whether for UI flows or REST API validations, the test suite becomes a patchwork of hacks, quick fixes, and abandoned experiments.

Impact of automation code quality

The impact of poor test code is significant. Development slows down because QA engineers spend more time fixing old tests than writing new ones. New QA engineers struggle to onboard, and maintaining the suite becomes a full-time job in itself. On the other hand, a well-structured automation suite becomes a long-term asset and allows teams to deliver reliable test coverage with confidence.

4. Fragile UI locators that break after every release

Fragile UI locators are a major source of test instability, especially in modern apps. Frameworks like React, Vue, and Angular use dynamic, component-based rendering, so tests relying on CSS selectors or deep XPath break easily after minor UI changes. Mobile apps face similar issues, with regenerated view hierarchies and inconsistent accessibility identifiers. Tests using index-based selectors, text labels, or complex paths often fail when layouts or localization change.

Factors behind unstable test automation

Let’s look at why test automation can become unstable:

• Lack of stable element identifiers. Forces reliance on fragile selectors like class names, indexes, or visible text, which often change during UI updates or localization, leading to unstable tests.
• Developers not considering automation needs. UI components are built for visuals and functionality, often reusing IDs or generating dynamic elements that reduce testing possibilities.
• Dynamic DOM generation. Modern frameworks render elements during state or navigation changes, breaking structure-based locators in web and mobile tests.
• Overuse of complex locator strategies. Reliance on fragile XPath or deeply nested selectors increases maintenance and reduces test reliability.

Early QA and dev collaboration is key. Using data-test-id attributes for web and accessibility identifiers for mobile creates stable locators, making testability a shared responsibility. Purpose-built locators reduce maintenance and let teams focus on validating behavior instead of fixing broken tests.

5. Poor test data management

Unreliable test data is a major cause of automation failures. Even with correct logic, tests can fail when parallel suites collide, overwrite records, or compete for shared resources. API tests usually break when multiple suites use the same accounts, hitting rate limits or usage caps. These failures aren’t due to code issues but poor test data strategy, showing how shared or poorly managed data can undermine otherwise reliable automation.

Best practices for reliable test data

To create more reliable test data, consider the following best practices:

• Generating mocked data. Produce realistic, controlled data that doesn’t depend on shared or fragile sources.
• Setting up test data. Make sure each test starts with the right data, so tests run reliably without manual preparation.
• Isolation per test or per suite. Prevent tests from interfering with each other by keeping their data separate. 
• Automated cleanup after test execution. Remove leftover or corrupted data that could affect future runs. 
• Monitoring and alerting for test data issues. Detect rate limits, missing data, or unexpected changes early.

Improving test data management means treating data as a key part of the testing process, not an afterthought. Using strategies like creating reliable test data, isolating it per test, and doing cleanups can greatly increase test reliability. With structured data practices, test stability improves, and many previously mentioned failures are resolved.

6. Lack of visibility into automation ROI

Many automation teams struggle to show the true return on investment (ROI) of their work. While leadership sees the costs - tools, engineering time, and infrastructure - the benefits are often less visible. Automation is frequently viewed as a way to speed up tasks rather than a key safeguard for product reliability, making its value harder to justify and easier to question.

Metrics that matter

To build confidence and justify continued support, automation teams must adopt a disciplined approach to measurement. Tracking data that reflects meaningful impact is essential:

• Defects caught before release. Tracking defects caught before release shows the direct quality impact of automation. Finding issues early reduces hotfixes, escalations, and customer-reported problems.
• Reduction in regression time. Automation reduces regression cycle time compared to manual testing. The time saved boosts productivity and allows teams to focus on higher-value work.
• Stability of test suites. Test suite stability reflects consistent, trustworthy results with minimal flakiness. Monitoring it shows whether automation can reliably support rapid development and continuous integration (CI).
• Pipeline pass/fail trends. Long term results show build stability and can reveal brittle tests, environment issues, or deeper code quality problems.
• Coverage progression over time. Tracking test coverage growth (unit, API, UI, or end-to-end) indicates how well automation efforts expand the safety net for the application.

These metrics turn automation into clear evidence of value. By showing how automation reduces risk, speeds up releases, and improves efficiency, it builds leadership confidence and positions automation as a driver of long term stability.

7. Over-reliance on UI testing

Many teams fall into the trap of relying too heavily on UI testing because these tests are close to real user interactions and provide a sense of comfort to stakeholders. Watching a test click through screens feels intuitive, so organizations often assume that more UI coverage automatically means better quality. However, this approach leads to significant drawbacks. UI tests tend to run slowly because they depend on the full application stack, and even small interface changes can cause them to fail unexpectedly. As a result, teams spend considerable time fixing tests rather than improving product quality. Maintenance becomes increasingly demanding as the test suite grows, and it reduces overall development velocity.

A more balanced test strategy

A more balanced approach involves shifting most validation away from the UI towards faster, more stable layers of the system. When most of the logic is verified at lower levels, teams no longer rely on the most fragile part of the stack to ensure correctness. Unit tests handle detailed logic efficiently, while API tests confirm that components communicate and behave as expected. With fewer responsibilities assigned to the UI layer, UI tests can focus solely on validating critical user journeys rather than serving as an all-purpose safety net.

By restructuring test coverage in this way, teams improve the reliability of their pipelines and reduce the time required to get feedback on changes. This not only shortens development cycles but also allows UI tests to serve their true purpose.

8. Constantly changing tools and frameworks

The never-ending upgrade cycle

Automation tools evolve quickly, forcing teams into constant upgrades. Frameworks like Cypress, Selenium, and Playwright frequently change APIs and compatibility, causing once-stable tests to break. In large suites, delayed upgrades compound technical debt, making future updates riskier and increasing maintenance costs.

Best practices

Consider the following best practices for a more effective testing cycle:

• Schedule quarterly dependency reviews. Quarterly reviews keep teams aware of breaking changes, deprecations, and security updates. This prevents large version gaps, makes upgrades easier, and reduces the risk of sudden incompatibilities.
• Use containers to lock base versions. Containers standardize toolchains by fixing browser, driver, and framework versions, ensuring consistent test runs across all environments and reducing flaky tests from version mismatches.
• Allocate time in each sprint for automation maintenance. Set aside sprint capacity for automation maintenance, including refactoring, dependency updates, and fixing flaky tests.

Teams that recognize automation as a long-term investment, rather than a one-time setup, are better equipped to handle constant changes and avoid disruptive upgrade cycles.

9. Misalignment between developers and testers

Misalignment between developers and testers is a major barrier to reliable automation. Even on agile teams, developers often focus on delivering features under tight deadlines, providing limited context to QA. Uncommunicated changes, such as modified UI flows, can break tests and lead to days of avoidable rework. 

Solving the alignment gap

Successful teams integrate QA deeply into the development lifecycle:

• QA participates in grooming and architectural discussions. Helps clarify intent, identify risks, refine requirements, and plan needed test coverage before development begins.
• Tests are designed alongside features. Writing tests during development keeps automation aligned with the product and promotes more modular, testable code.
• Developers review automation design ideas. Developer input on automation helps identify dependencies, constraints, and upcoming changes, fostering shared ownership and reducing unstable tests.
• QA provides early feedback on testability. Early review of designs and prototypes lets QA identify hard-to-test areas, leading to cleaner interfaces, better observability, and smoother automation later.

Teams that are better at working together create automation that is easier to maintain, more reliable, and closer to what users actually need.

10. Unrealistic expectations about test automation

Many leaders assume automation can cover every scenario. But experienced QA engineers understand that certain tests require human judgment, especially exploratory and usability. One of the most common misconceptions I’ve encountered throughout my career is the belief that test automation should eventually cover every scenario in a product. It’s a reassuring idea on paper: automate everything, eliminate manual work, speed up releases, and achieve perfect consistency. But anyone who has worked closely with mobile applications, especially those relying heavily on REST APIs, knows this expectation doesn’t reflect reality. Beyond that, there’s a fundamental limitation - automation cannot truly evaluate the user experience. It cannot judge whether an onboarding screen is confusing, whether a gesture feels natural, or whether the transitions between screens are smooth. These are things real users notice immediately, and they require human judgment.

The goal should be:

• Automate highly repeatable and deterministic flows
• Use manual testing for discovery and edge cases
• Focus automation efforts where ROI is the highest

Teams can work faster and produce higher-quality results when stakeholders aim for clever automation instead of trying to automate everything.

Conclusion

Even with modern tooling, AI-powered platforms, and advanced CI/CD pipelines, test automation remains a complex engineering discipline. Flaky tests, slow pipelines, unstable locators, poor test data practices, misalignment between teams, and unrealistic expectations still challenge even the best QA organizations.

Across my six years in QA, I’ve learned one core truth - automation success isn’t determined by the tools you choose but by the engineering culture behind them. Strong collaboration, thoughtful test architecture, and consistent maintenance practices matter far more than adopting the latest trends.

When product managers, decision-makers, and engineering leaders understand these automation challenges and invest the time and resources to address them, QA teams can deliver stable, scalable, and ROI-driven automation that accelerates product development instead of slowing it down.