21 Biggest Challenges in Automation Testing

Automation testing is one of the most rapidly growing segments of the software industry. With an anticipated CAGR of 16.4% from 2022 to 2027, and with close to 100% of companies using automation testing in some capacity, according to one study, automation can hardly be beat in terms of technology that is on everyone’s lips.

Test automation has countless benefits. It helps companies dramatically increase test coverage, detect and resolve bugs faster, speed up the release schedule, and save costs by creating reusable tests that produce reliable results. Still, automation is not necessarily a flaw-free process. Here are the most common challenges in automation testing and how to effectively overcome them.

15 Challenges in Automation Testing to Know About

All across the software industry, automated testing is considered to be the universal solution to all quality-related challenges. And while that is exactly the case most of the time, there are also some challenges the team can face in the process of automation testing setup, monitoring the progress, and obtaining the results. These are the 15 biggest automation challenges and how to overcome them.

1. Managing Expectations From Automated Testing

During the initial phase of implementing automation, and also before it, it’s crucial to keep the expectations of the project stakeholders realistic. Very often, especially when most of the stakeholders don’t have any hands-on experience with automation, the expectations across the team become too high.

Stakeholders may expect automation to instantly start delivering results with little to no prior investment, or tend to believe that automation needs to be set up once and then it can run on its own for as long as needed. When, unsurprisingly, things don’t live up to the expectations of the stakeholders, it can result in disappointment for all parties involved and a lack of enthusiasm for any future efforts to use automation in testing.

How to overcome

One of the most effective solutions to overcome the risk of unmet expectations is dedicating time to choosing realistic automation objectives and setting targets that can be met within a specified timeframe. This is not always easy to do when no one on the team has experience in creating a proper test automation strategy, proactively managing the risks, or managing scope creep.

This is where an automation testing partner can come in handy. They are great not only at identifying test automation objectives and setting achievable milestones, but can also provide training and ongoing support to make sure everyone’s expectations are met.

2. Getting Team Members On Board with Test Automation

Without a doubt, the idea of implementing automation may not be well received by everyone at an organization that has only dealt with manual testing until then. Manual testing teams are often worried that the whole test automation endeavor will gradually leave less work for them, diminishing their value for the company.

Still, while it’s not uncommon for manual testers to be somewhat skeptical about the benefits of automation, it can be even harder to get the rest of the team invested in automation testing. This is especially true for small and medium companies that don’t have an unlimited budget to build a test automation project to meet all of their quality-related needs. The stakeholders may understandably be concerned about the vast investment linked to automation without being fully convinced of the value it will bring.

How to overcome

The first step in making sure that one of the most common automation testing challenges is successfully mitigated is dedicating time to educating the team about the benefits of automated testing and the value it can bring both in the short-term and long-term perspective. In addition to that, it’s important to be honest about the risks and challenges of the endeavor, so that the team is fully informed. Finally, it’s a good idea to address common misconceptions about the launch of automation, which will help the entire team get on the same page in terms of plans and expectations.

3. Test Automation Requires a Significant Resource Investment

Without a doubt, introducing automation in an organization is a resource-intensive decision. Not only does this process need a significant investment of money, but it also requires a lot of time — and not just the time spent on designing the test strategy, choosing the most appropriate test automation framework, and setting up the rest of the process, but also the time invested in overseeing its completion, interpreting the results, and maintaining the project to prevent it from getting obsolete.

Given all that, it’s also safe to assume that one of the challenges in test automation for many organizations is the lack of personnel familiar with the required techniques and types of testing. This means the company will either need to undergo an expensive structural transformation by establishing an automation testing department, or go for the more financially sensible option of outsourcing automated testing to an external partner.

How to overcome

The number one solution for mitigating the major challenge of automated testing is to consider outsourcing some or all of the organization’s automation needs. This will allow the team to run multiple test scripts in parallel without increasing the size of the in-house AQA department. Additionally, it’s a good idea to create an automated testing strategy that prioritizes the efforts in order of criticality and anticipated ROI for the organization instead of just trying to do everything at once.

4. Selecting the Right Test Automation Tool and Automation Testing Stack

A properly selected, appropriate testing tool for the specific task is one of the most vital components that impact the entire testing process. This is even more true for automation testing, which usually relies on more than one tool to ensure the desired efficiency and test coverage. Coincidentally, tool selection is also one of the things teams involved in setting up automation processes struggle with the most.

Choosing the right tool can be challenging for a number of reasons. First of all, there are so many options to choose from that with multiple tools available for the same task, things can get confusing. Second, in many organizations, stakeholders insist on exclusively using open-source tools, when, in reality, it’s not always possible to ensure proper test coverage and execution speed with nothing but free tools. Third, the stakeholders may want the team to use the tools they’ve heard about or that are currently popular in the industry even though those tools don’t match the project’s needs and the team’s skill set.

How to overcome

The first thing to do when selecting automated testing tools is to consider the test automation approach and project goals. For medium and long-term projects, it’s also beneficial to conduct Proof of Concept and make sure the tool is a good fit for the project and the team’s skills. To keep the project budget reasonable, the team should also consider Lifetime Ownership Costs: how much a paid tool will cost for the whole duration of the project — not just the upfront investment, but also the cost of training the staff, obtaining ongoing support, and investing in internal testing infrastructure.

Open-source and paid tools can complement each other by combining the flexibility and community-driven innovation of open-source solutions alongside the advanced features, integrations, and support offered by paid tools. Together, they allow teams to achieve cost-effective testing while addressing complex or enterprise-specific needs, such as scalability, analytics, and cross-platform capabilities.

If you don’t have any hands-on experience with automation tools, you can always consider the most popular tools first: for example, Playwright for end-to-end testing, Mabl for low-code automation, and Appium for mobile app testing.

5. Choosing a Testing Framework That Best Fits the Project

Assembling the ideal tool stack for a project can be challenging, but teams often face an even bigger challenge when working on the most efficient automation testing framework. Since automation frameworks affect every aspect and stage of the project, this is one of the most important choices to make throughout the project.

You want a framework that is scalable and suits the complexity level of the project. The framework should also be relatively easy to maintain and match the tools that are already being used by the organization. Finally, the framework needs to have an adequate learning curve to prevent the team from spending too much time just to understand how it works, while also providing community support and sufficient training. Trying to take all of that into account is what makes it hard to make the right choice and get everyone on board with it.

How to overcome

The key strategy for overcoming this challenge is to make the selection process a collaborative effort. Every project stakeholder should participate and outline their requirements and expectations. An integral step of making the decision is to consider the functionality offered by the framework and whether it meets the needs of the project. Compatibility with the project’s infrastructure and tools is also important, as is the availability of training materials to get everyone up to speed quickly.

6. Setting Up the Proper Testing Environment and Testing Infrastructure

One of the most common automation testing challenges is how difficult it often is to set up the test environment. A typical automation project includes many complex issues with multi-level dependencies like databases, APIs, and third-party services. Taking all of that into account and combining all the dependencies for the best results is time-consuming and prone to error, which does not bode well for a fault-free testing environment.

Setting up the infrastructure, including cloud-based solutions, is also a challenging part of the process, as it can require too much of the team’s time and resources to experiment with different solutions, while a wrong choice of infrastructure can lead to a test automation failure already in the early stages of the project.

How to overcome

To reduce the brittleness of the infrastructure, the team can employ environment monitoring and self-healing environment solutions that save time and effort in maintaining the environment by detecting and resolving issues automatically.

7. Slow Test Execution Delays Results

Even with the most expertly designed test strategy and the most realistic objectives, a team can only do as much as the resources and equipment allow them to do. Without a significant investment into the infrastructure and the expansion of the team, slow test execution is pretty much inevitable. This prevents the timely detection of defects, disturbing the feedback cycle and delaying the transfer of them to the development team. This, in turn, slows down the quality assurance process and leads to a longer software release schedule, which is something most companies cannot afford in today’s highly competitive world.

How to overcome

The most effective solution for overcoming the challenge of slow progress is to outsource some or all of the aspects of automation to an external provider. That way, you will be able to execute test scripts in parallel without investing in additional machines and office space for new personnel, as your outsourcing vendor will take care of it on their side. Cloud-based testing infrastructure can also help do more in the same amount of time and obtain more valuable data in one test execution. Finally, it’s important to prioritize critical tests and optimize test scripts for performance to maximize the output.

8. Browser and Platform Compatibility

A lot of challenges in applying automated testing come down to the variability of platforms and device settings. With hundreds of devices, dozens of operating systems and browsers, and an endless number of network configurations, the sheer volume of possible platform combinations often becomes a challenge of its own. It can be hard to account for all possible combinations in a large number of automation pipelines.

As a result, the team can get incomplete information on how the application behaves on certain platforms, leading to defects being discovered later in the development cycle or even by the end users, which is something teams should strive to avoid.

How to overcome

A highly effective strategy for increasing platform coverage for different forms of testing is using abstraction layers. They will encapsulate the differences in various operating systems and browsers, allowing the team to quickly and easily incorporate those differences in their test environments and configurations. 

It’s also important to constantly stay on top of new device and software releases, as well as the operating system and browser versions, to be able to update the test settings and serve the customers better. Cloud-based testing platforms can be an excellent choice for testing across a variety of platforms without physical devices at hand, although physical devices obviously give a more comprehensive idea of how the application under test behaves in real-life conditions.

Tools like BrowserStack and Sauce Labs are widely used for cross-platform testing thanks to their to a variety of devices, browsers, and operating systems. Containerization tools like Docker, paired with frameworks such as TestContainers or Selenium Grid with Docker, enable consistent, isolated environments for running automated tests across different setups. This combination ensures both scalability and reliability for cross-platform automation.

9. Rapid Changes in User Interfaces

For teams who design and maintain automation frameworks on a daily basis, testing UI components often proves to be one of the challenging tasks. There are two key reasons why this aspect of testing typically proves to be harder than it looks. First, UI testing largely depends on the complexity and variety of ways humans interact with the interface, which is a challenge on its own when it comes to automation. Second, UI elements are particularly prone to frequent changes and dynamic updates, meaning that automated UI tests that worked well a week ago may no longer be fully usable today.

How to overcome

Since UI changes are done to improve user experience and increase the product’s appeal to the customers, the only way to overcome this challenge is to adapt to the changing interface components. One of the most effective techniques to use is visual testing tools that can quickly indicate regressions caused by UI changes. Dynamic locators or XPath expressions can also be an efficient tool for adapting to the updated user interface. Moreover, the team should have robust error-handling mechanisms in place to resolve UI-related issues quickly.

AI-powered visual regression tools like Virtuoso and Applitools use machine learning to detect even the smallest visual changes between application versions, making them ideal for automating UI testing. These tools help teams identify layout issues, rendering problems, and visual inconsistencies across multiple browsers and devices with high accuracy, reducing false positives. By integrating AI, they enhance the efficiency and scalability of visual testing, allowing for faster feedback during CI/CD cycles​.

10. Large Number of Third-Party Integrations to Consider

Modern software rarely exists and functions in complete isolation. Developers use a variety of third-party solutions to quickly enhance the functionality, performance, and user experience of a software product. This primarily includes an endless variety of APIs, as well as services like payment gateways or AI-powered bots. The variety and complexity of third-party systems create additional automation testing challenges, as the team now has to test not only the third-party solution on its own, but also the way it impacts the entire system and other integrations.

Moreover, the growing adoption of microservices architecture presents unique challenges for API testing automation, particularly due to the distributed nature of microservices and their complex interdependencies.

How to overcome

One of the popular ways to overcome the issue of multiple dependencies on third-party providers is to employ mocking or stubbing techniques to simulate responses from external dependencies during testing. Other solutions require the testing team to venture beyond their own organization to ensure comprehensive quality assurance. For example, the team may need to collaborate with the development team of the third-party service to better understand all the integration capabilities and potential challenges. Contract testing can also be a useful technique for mitigating the volatility of third-party integrations.

As testing APIs in microservice architecture, it requires ensuring not only the individual functionality of each service but also seamless communication and integration between them, which can be difficult to automate effectively. Tools like Postman and SoapUI are increasingly being adapted for microservices, but testing can be complicated by the need for service virtualization and contract testing to simulate the behavior of unavailable or incomplete services​.

11. Extensive Maintenance Is Required for an Effective Test Automation System

An efficient test suite ensures comprehensive test coverage across different functional areas and incorporates edge cases that add significant value to the quality assurance process. However, when allowed to grow without close supervision, when it seems like another test script or a dozen will only benefit the project, an automated testing suite can start expanding uncontrollably, leading to a large number of cases that only take time to run without having any effect on the software quality.

Moreover, the team that got started with test automation may no longer be there for the whole duration of the project, which makes maintaining the test suite even more challenging.

How to overcome

There is no way to go around the need for regular maintenance — otherwise, the organization risks ending up with thousands of test scripts that only take time away from the team without generating any real results. There needs to be a maintenance schedule, meaning that every month, every three months, or at any other agreed-upon time, the team allocates resources to review the test suite and get rid of tests that are no longer needed.

12. Some Aspects of QA Are Simply Better Suited for Manual Testing

There are certain areas of testing and quality assurance that are a perfect match for the capabilities and scope provided by automated testing. For example, automated regression testing is relatively easy to set up and can save a lot of valuable time for the QA team. Other common types of testing to automate include unit testing, integration testing, load and performance testing, and security testing.

However, some testing types are either challenging to automate or are simply a better fit for manual testing. These primarily include UX and usability testing, exploratory and ad hoc testing, and testing at the early stages of development when the code base is either very small or doesn’t exist at all yet.

How to overcome

This is possibly the one challenge that cannot be overcome completely, meaning the automation testing technology is not yet at a level of development that allows to fully automate usability, exploratory, and other types of testing that are traditionally carried out manually. This is why the best strategy here is to combine manual and automated testing to obtain the most reliable results.

Still, automated testing can come in handy to supplement, speed up, and strengthen manual testing processes in any organization. Specifically, automation can be used to streamline environment configuration, data preparation, and test data generation. In testing activities that largely depend on the way humans interact with the software, automation can be implemented to perform smoke testing or prerequisite checks to make sure all the basic functionality is there and the solution is ready to be tested.

Moreover, AI-driven tools like Applitools Eyes, Test.ai, and Usetracker can bridge the usability testing gap in automation by simulating real user behavior and interactions, helping identify issues related to navigation, accessibility, and user experience that traditional automation tools might miss. These tools leverage machine learning and computer vision to detect subtle UI/UX problems, such as misalignment, color contrast issues, or elements that interfere with user flow. Furthermore, AI can analyze large datasets from user sessions to predict user behavior patterns, enabling more realistic and effective test scenarios, and improving the overall quality of user-facing applications.

13. Limited Scalability of a Typical Test Automation Project

Ideally, an automated testing project should be designed with scalability in mind. When new features are added, the application undergoes any other changes, or testing demands increase to better suit customer and stakeholder needs and expectations, the project should be expected to scale quickly and smoothly.

However, when scalability is not included as one of the key requirements, scaling a project often becomes its weak point. The team will either spend an inadequate amount of time and resources trying to scale the project, or the project becomes too narrow and brittle due to the inability to keep up with the demands.

How to overcome

Designing test suites with scalability possibilities in mind is the most effective way to mitigate this challenge. Other solutions include implementing cloud-based testing infrastructure to scale the project up and down on demand without critical delays. Using multiple machines or nodes for parallel test testing helps achieve distributed test execution and increased throughput, which is an integral component of a scalable testing project.

14. Trouble Properly Interpreting the Results

With the vast amount of data an automated testing project produces, getting the team to effectively interpret the obtained results and turn them into usable improvements can be difficult. The process can become even more challenging when the results are getting increasingly complex and the team lacks the context to fully understand their impact on software quality and business objectives. This can happen, for example, when there isn’t an effective collaboration process in place and the testing and development departments hardly know what the other team is doing.

How to overcome

The first thing to do to improve the reporting and interpretation process is to define clear report guidelines. They should contain information on the project stakeholders, what kind of information they need to proceed with their respective tasks, how often the reports should be generated and shared, and how often it should happen. It’s also vital to supply contextual information together with the reports — this can include test objectives, test environment configurations, test execution history, and relevant code changes.

Significant parts of report generation and analysis can also be automated — this both speeds up the delivery of reports and the detailed analysis of the results. Finally, it can be a good idea to use visual tools to present the results in an easily understandable manner in the form of charts, graphs, dashboards, and trend reports.

15. Increasing Flakiness of Tests

Automation test flakiness, or inconsistency and instability of tests, are occurring more commonly than one may think. Flaky tests are a notorious issue of automated testing. Even Reddit’s developers and QAs are not just well aware of this problem, but are actively seeking ways to mitigate it. When ignored for an extended period of time, flaky tests lead to inconsistent results that only waste the team’s time instead of having a genuine impact on the quality of the application. For example, a flaky test will pass or fail without any visible pattern, even when the application under test has not been changed in any way.

How to overcome

The number one solution for overcoming automation testing challenges linked to flakiness is to invest effort in test maintenance, quickly identifying and fixing flaky tests before they start draining the project’s resources without producing any valuable outcomes. A stable test environment with minimized dependency of tests on external factors can also help maintain a flakiness-free test suite. Finally, the test code should be regularly refactored to ensure its readability and dependability.

Moreover, AI/ML tools can identify patterns in flaky tests by analyzing historical test data and detecting recurring issues such as timing errors, environmental inconsistencies, or unreliable test dependencies. Machine learning models can learn from previous failures and find dependencies between factors such as test sequence, environment setup, or code changes, predicting when and why tests might fail. Tools like Selenium Grid with AI, Test.ai, and Mabl use machine learning to identify trends in flaky tests, suggest potential root causes, and offer recommendations for improving stability and reliability. This helps teams proactively address issues and optimize test performance.

16. Maintaining Test Stability in Dynamic Environments

Dynamic web applications, especially those with modern JavaScript frameworks, often have rapidly changing elements, such as IDs that update on each load, unpredictable UI layouts, or animations. These changes can cause automated scripts to fail frequently, producing unreliable results and false positives. This instability complicates identifying genuine issues and undermines confidence in automation, creating one of the most common challenges in automation testing.

How to overcome

Teams need to use robust locator strategies, such as relative XPath, CSS selectors, or ARIA attributes, to identify elements. It’s also a good idea to implement AI-driven tools capable of self-healing scripts, which adjust to UI changes. Moreover, regularly reviewing and refactoring test scripts and combining automation with exploratory testing helps address edge cases effectively.

17. Ensuring Scalability in Automation Frameworks

As projects grow, automation frameworks that were initially sufficient may struggle with expanding test cases, more complex workflows, or integration with advanced CI/CD pipelines. Without scalability, the test suite becomes a bottleneck, limiting team efficiency and software quality assurance.

How to overcome

To start with, design modular frameworks with reusable components to simplify test case addition and updates. You can also implement configuration management tools like Ansible for scalability and invest in containerized solutions like Docker to isolate environments. Enhancing your CI/CD pipelines with tools like Jenkins or CircleCI allows you to automate and streamline framework scaling with minimal overhead.

18. Managing Test Data

Automated tests require large amounts of data for scenarios like database validation, user workflows, and more. Creating and managing realistic test data can be complex and time-consuming, especially when it’s necessary to maintain data consistency across different environments.

How to Overcome

Implement data-driven testing to separate test logic from test data, making it easier to reuse test cases with different datasets. It’s also a good practice to automate the creation, cleanup, and reset of test data to avoid manual intervention. Consider using mock data or synthetic data for non-production environments, ensuring that it mimics real-world scenarios without exposing sensitive information.

19. Integration with CI/CD Pipelines

Integrating automated tests into Continuous Integration/Continuous Delivery pipelines can be challenging, especially when tests are time-consuming, prone to failures, or not aligned with the continuous integration flow. This can delay deployments and slow down the overall development process.

How to Overcome

To ensure smooth integration, break down your tests into smaller, independent units that can be run quickly and efficiently within the CI/CD pipeline. Avoid running long-running tests in early stages of CI, such as unit tests, and reserve more extensive tests, such as UI tests, for later stages. Use parallel test execution and cloud-based services like Selenium Grid or browser testing platforms to distribute tests across multiple environments and browsers to speed up execution. Finally, implement clear and actionable test result reporting, so that developers and testers can quickly identify and resolve issues in the pipeline.

20. Automating Tests for AI/ML-Based Applications

One of the most recent challenges faced in automation testing has to do with the hottest technologies of the 2020s — Artificial Intelligence and Machine Learning. Testing AI/ML-based systems presents unique challenges due to the unpredictable nature of machine learning models and their dependency on constantly evolving datasets. Traditional automation frameworks are not designed to handle probabilistic outputs or validate AI-driven behaviors, making these tests complex and less straightforward.

How to overcome

Start by designing test cases based on model accuracy, fairness, and performance metrics rather than fixed outputs. Use synthetic datasets to ensure consistency while training the model. Integrating AI testing frameworks like DeepChecks or Microsoft’s Responsible AI tools allows you to monitor and validate the model’s behavior over time.

21. Security Testing

Dynamic vulnerabilities, such as race conditions, runtime configuration errors, and memory leaks, depend on specific runtime conditions or sequences of events. Automated tools often rely on predefined patterns, making it difficult to identify vulnerabilities that are influenced by environmental factors or specific user interactions. Also, tools can struggle to understand the dynamic context, such as conditional execution paths or runtime data manipulation. 

How to overcome

Use tools to generate realistic traffic, replicate user interactions, and simulate edge cases. Also, apply tools like OWASP ZAP, Burp Suite, and others that offer advanced scanning and runtime analysis. Moreover, tools like Snyk, SonarQube, or GitHub Advanced Security can integrate directly into CI/CD workflows to perform static and dynamic analysis.

Final Thoughts: Are Test Automation Challenges Really Dealbreakers?

Automation testing has become more ubiquitous than ever, as evidenced even by many Reddit users on software testing and quality assurance subreddits talking about the shifting roles of QA engineers and how they are increasingly dealing with software development tasks in addition to testing. Part of that transformation is the substantial number of automation testing challenges that require significant experience both in testing and development to be effectively mitigated.

But is the presence of some common test automation challenges a big enough reason to withhold automation efforts completely? Not in the slightest, and more and more companies are going for test automation even though the occasional hurdle is almost inevitable. As long as everyone in the company is invested in the transformation to automation, and the team chooses a well-thought-out approach to automation, challenges in applying test automation are not something that should put you off the idea of implementing automated testing on your project.