Top 13 Test Automation Trends to Look Out for in 2024 and Beyond

Alona O. by Alona O. on 10/22/2023

Top 13 Test Automation Trends to Look Out for in 2024 and Beyond

There are two misconceptions when it comes to testing. One of them is that developers alone can test products to reduce costs. And second is that nothing much has changed since automated testing came to play. 

The truth is? The testing industry is evolving by leaps and bounds, and if you don’t invest in testing, the cost of glitches can be extremely high. You may have heard about Bangladesh Bank that was hacked and $81 million was stolen. However, they could have easily prevented this if not for the glitch in the system that interrupted the printing process and made it impossible to detect suspicious transactions in time. 

This is just one example of how cutting testing costs can have devastating consequences, but the idea is clear: testing isn’t the area that should be curtailed. Vice versa. It’s important to stay up to date with what’s happening in the world of testing and invest in the latest trends.  

In this article, we’ve compiled some of the most prominent automation testing trends for 2024 that have taken off this year and are set to continue. Be sure to explore the benefits of each and see how they can fit your testing strategies. 

Top 13 Test Automation Trends

13 Testing Trends for 2024

Some trends are just the flash in the pan. In theory, they are full of promise but come real-case tasks, they often reveal their limitations and impracticality. We’ve hand-picked test automation trends for 2024 that seem both realistic to implement and have the potential to truly make a difference in how we assure quality. 

QAOps Is Gaining Popularity

QAOps is a booming trend in 2024. As the name suggests, this methodology combines a Quality Assurance (QA) approach and IT operations to speed up testing. In other words, it integrates quality assurance and testing into the DevOps process, introducing thorough testing at every stage of the software development cycle, not just at the end. 

To elaborate, QAOps framework allows developers and testers to work together. Through this close-knit collaboration, companies can pinpoint the different scenarios that real users might encounter when they start interacting with the app, thereby improving the testing process. 

Some companies have given this approach their own twist. They organize so-called “testing parties”, inviting all interested team members to take part in testing. By doing so, they not only ensure there’s no bias in testing, but they also get the opportunity to test their products from different perspectives, allowing them to ultimately achieve better outcomes. 

It’s like building a house where developers build the foundation, walls, and the roof, and testers ensure from the beginning that all of these components are made strong. Later, when the house is ready for exploitation, no major rework will have to be done.


  • Improved collaboration between testers, developers, and IT operations teams.
  • Boosted productivity of the team through ongoing knowledge sharing.
  • Faster release of new products and features due to the quick detection of bugs.
  • Enhanced customer experience achieved by delivering software products of a high-quality standard.


Extensive Adoption of Robotic Process Automation 

Another trend that is growing in popularity in test automation is the use of Robotic Process Automation (RPA). RPA, also known as software robotics, has the ability to mirror testers’ interactions with software applications. By recording actions performed by testers and learning testing sequences, it can imitate the same process, saving you lots of time performing repetitive tasks. 

RPA has become widely popular due to the ease and speed of its implementation. Compared to traditional automation, which requires specialized hardware and software to automate repetitive tasks, RPA uses bots. Often run by humans, these bots can be easily programmed using off-the-shelf software and trained in a matter of hours or a couple of days, making RPA a more cost-effective testing solution for most companies. 

RPA testing techniques are being used across various industries and will become even more widespread in the near future. According to Statista, the RPA market will grow to $13.39 billion by 2030, which is a huge jump from $3.17 billion in 2022. 

Moreover, RPA will become much more intelligent thanks to the advances in artificial intelligence and machine learning soon. This means that RPA will not only copy human actions but also understand information, learn from experience, and proactively address issues. Ultimately, this will allow it to execute more complex decision-making processes without much guidance on the tester’s part. 


  • Reduced labor costs due to the automation of repetitive test scenarios.
  • Less of a risk for human error, as RPA accurately replicates testers’ interactions. 
  • Significant time and resource savings due to RPA bots’ ability to execute tests across multiple systems and applications. 
  • Scalability that allows organizations to perform small, large, or even enterprise-level tests on demand.


Active Use of AI and ML Testing

AI and ML tools are making a splash in the testing industry. With their ability to automate virtually every aspect of testing, from test case creation and test execution to test maintenance, they come indispensable for QA teams. 

The practical applications of AI and ML in automated testing are numerous. From identifying features for testing, creating test cases without manual test scripts, and running thousands of tests virtually in minutes, it can do all that and much more without human support. 

As we progress in the future, the capabilities of AI and ML are expected to only evolve. In the near term, it will be possible to digitize testing processes by creating AI-powered avatars of famous testers, such as Bret Pettichord, Cem Karner, Tariq, you name it. By imparting their knowledge and expertise to bots, organizations will be able to create strong virtual teams of testers that can execute all types of testing and adequately measure the quality of a project based on input derivable. 

Other examples of using AI and ML in testing include:

  • Natural language processing. NLP algorithms can extract and analyze requirements from natural language documents, helping with test case creation and ensuring that tests align with the project’s goals. 
  • Visual testing with computer vision. AI-driven computer vision systems can automatically compare UI elements and screens to identify visual defects or inconsistencies in applications. 
  • Automated bug triaging. ML models can help prioritize and categorize incoming bug reports.
  • Behavior-driven testing with AI. AI can understand user behavior patterns and generate test scenarios that mimic real-world user interactions, enabling QA teams to cover more test cases. 
  • Security testing. AI can simulate cyberattacks and identify vulnerabilities in software.

Aside from that, AI excels at predicting outcomes. By analyzing historical data and patterns, it can identify with a high probability what bugs and issues are likely to occur and help prevent them before they become major issues.

“Remember, AI tools are just that — tools. They are there to augment our abilities, not to replace testers. So, whether you’re looking to leverage AI automation to improve workflows or optimize testing approaches, the resolution of these issues requires human insight and ingenuity.”

Taras Oleksyn, QA Lead


  • Faster release cycles;
  • Quick test case generation;
  • Automatic test maintenance;
  • Predicting the likelihood of bugs and testing outcomes based on historical data;
  • An extensive test coverage across various devices ensures that the software is thoroughly tested and no bugs slip through the cracks.  
Top 13 Test Automation Trends

Blockchain Testing Is Becoming a Hot Trend

While you most definitely have heard of blockchain, you might not have stumbled upon the possibilities blockchain testing brings to the table. Blockchain testing can be used for a variety of applications. From securing transactions to ensuring the integrity of supply chains and even verifying the authenticity of digital assets, it renders itself useful in a wide range of industries and scenarios that need accurate data validation.

If you’re looking to ensure peak Decentralized Applications (DAPPs) performance, definitely consider blockchain testing. Blockchain provides seamless scalability to DAPPs, ensuring they can efficiently handle increased volumes of data and transactions. Moreover, conducting comprehensive tests helps identify potential vulnerabilities, inefficiencies, and bottlenecks that may hinder the smooth operation of DAPPs. 

However, it’s worth noting that blockchain testing is conceptually different from other types. It’s made of components such as smart contracts, nodes, blocks, consensus mechanisms, transactions, and wallets, which need to be thoroughly tested and require a relevant stack. 


  • No risk of unauthorized access, fraud, and data breaches thanks to robust security protocols.
  • Quick removal of roadblocks that could become a costly problem in the production environment. 
  • Compliance with industry regulations and data protection laws, which can be particularly important for industries like finance and healthcare. 
  • Confidence that a blockchain is thoroughly tested and safe to use.

More Organizations Will Leverage Shift-Left Testing 

Many organizations are embracing Shift-Left Testing. This practice isn’t new, but it has evolved over time and shifted even further towards the left in the pipeline in 2024. Instead of waiting for code to be developed, now teams write unit tests before the coding phase begins. 

Involving testers early in the development cycle offers some undeniable advantages. And one of the most important of them is cost reduction. By checking the validity of the code early on, teams can identify and fix bugs while the cost to fix them is still rather low, rather than letting them escalate to later stages, where it can reach $7,600 and more.  

The Shift-Left approach also encourages the use of intelligent analytics. The Shift-Left approach also encourages the use of intelligent analytics. Testers can gauge customer satisfaction by monitoring their interactions with the software. If they find that the software needs some changes, they can introduce them during the initial stages of software development when they won’t cost much. 

It’s worth noting that despite the benefits shift-left testing offers, it’s not always appropriate to involve testers early in the development process. The decision to involve QA testing teams should be made based on the state of a project. 

“If a project is in its initial stages and the functionality is constantly changing, it wouldn’t make sense to start automation. The project has to reach a certain level of stability before it’s appropriate to automate.”

Taras Oleksyn, QA Lead


  • Reduced development costs through early detection and correction of bugs and errors.
  • The possibility to automate test cases earlier and streamline the entire testing process.
  • Faster time to market thanks to the optimized QA process.


The Demand for Cloud-Based Cross Browser Testing Is Increasing

Among the growing trends in test automation adopted by organizations this year, cloud-based cross-browser testing stands out. As the variety of devices increases yearly, it’s become essential for companies to thoroughly test their solutions across all of them. 

However, in practice, achieving such extensive coverage is often out of reach for many small companies because it is expensive to build such an extensive testing infrastructure. As a result, an increasing number of companies are turning to third-party providers that offer access to cloud technologies and thousands of virtual environments for testing. 

Sure enough, with the emergence of cloud testing platforms, the market has also witnessed a rise in cloud-based testing tools. These tools provide support for all popular browsers and devices, enabling QA teams to create and run cross-compatible tests.   

The global cloud application market’s value is expected to rise from $171 billion in 2020 to $365 billion by 2025. 


  • No need for costly in-house testing infrastructure, which means companies pay only for the resources they use.
  • An extensive testing coverage, including a wide range of devices, browsers, operating systems, and screen sizes.
  • The ability to scale testing resources up or down based on the project’s needs.
  • Support for parallel testing across most cloud-based platforms, which significantly reduces testing timelines and speeds time to market.  


Exploratory Testing Will Become Inevitable 

4-Top 13 Test Automation Trends

Exploratory testing has emerged as a practice that veers away from rigid test cases and scripts. Instead, it gives testers the freedom to explore and test software intuitively. This element of randomness not only allows QA teams to uncover unique use cases that haven’t been described by scripted testing but also find issues in areas where they wouldn’t typically look.


  • Eliminating the need to document test cases or features speeds up testing. 
  • The ability to catch issues and bugs that other testing methods and techniques might miss.
  • Quick start due to the lack of extensive test case preparation. This is especially important in situations where there’s limited documentation or time for test case creation. 


Microservices Testing Rapidly Evolves

The popularity of microservices architecture has given rise to microservices testing. This testing approach is aimed at testing the software as a suite of small, individual functional pieces rather than the entire architecture and monitoring closely the ongoing performance.

Seeing how microservice-based applications rapidly appear on the market, microservices testing will continue to evolve, and the skill of testing microservices will be in high demand. 


  • The ability to test individual components and changes in one microservice without affecting others.
  • Faster development cycles due to the option to release and iterate right on microservices.
  • Faster project delivery thanks to the ability to work on multiple microservices at the same time. 
  • Optimized usage of resources, which leads to cost savings.


 In-Sprint Automation Is Expected to Grow

According to Marketsplash, 71% of organizations are adopting agile methodologies, and more companies are considering embracing agile in the coming years. This trend has driven the growth of in-spirit automation. 

In-sprint automation refers to the integration of test automation efforts within each sprint or iteration of the agile development process. By following this approach, companies can significantly speed up their release cycles, consolidating all the fundamental functions of testing in short increments. 


  • In-sprint automation, which allows for faster and more frequent releases. 
  • Automated tests run continuously during development, enabling early detection of defects and issues. 
  • Real-time feedback on the quality of the software, which allows for more accurate project planning, better resource allocation, and improved forecasting of project timelines.
  • Automated tests created during one sprint can be reused in subsequent sprints for regression testing. 
  • Quick validation of new requirements and ability to tailor the testing approach accordingly. 


Integration of Crowdsourced Testing

Crowdsourced testing is a progressive approach to software QA that engages a diverse community of testers, often from around the world, to test products under real-world conditions on real devices. 

The great thing about crowdsourcing is that it allows companies to avoid resource constraints. They don’t have to worry about whether the tester has the right test automation tools or skills. Instead, tasks are distributed according to the resources that the tester already has, which greatly speeds up the time to market. 

Crowdsourced testing is often used to accelerate automation, in particular when the company is on the brink of a product release and/or is looking to extend its reach to global markets. In the future, it will be used more extensively as companies realize the positive impact of involving end users in the testing process.


  • The ability to tap a diverse testing experience.
  • An extended test coverage comprising an array of devices, operating systems, browsers, and resolutions.
  • An easy way to scale up or down testing capacity depending on the project’s needs.
  • Involving end users in the process contributes to the delivery of a well-received product. 


 Scriptless Test Automation 

5-Top 13 Test Automation Trends-1

This testing practice implies using automation testing tools to evaluate software quality without traditional scripts or code. 

The concept is simple. The tools record the actions that testers take while navigating through the software and then, based on the results, generate the most likely use cases for different scenarios. Scriptless test automation platforms are designed to perform all types of testing, including UI/UX testing, functional testing, etc., making them suitable for many different projects. 

However, similar to other tools, scriptless test automation platforms have limitations when it comes to customization. While this limitation may not be an issue for most projects with straightforward requirements, it can pose constraints for highly complex applications. In such cases, go for traditional script-based automation. 


  • Speeding up the product delivery process.
  • Higher ROI due to reduced automation costs.
  • Flexibility in reusing automation scripts in various scenarios. 


Continuous Testing Will Simplify Build Releases 

Continuous testing (CT) helps businesses to evaluate risks associated with software launch, ensuring informed decisions about whether to proceed or make adjustments. CT is performed after each product change and can be integrated into the CI/CD pipeline. 

As businesses increasingly recognize the benefits of CT, its demand and adoption are expected to continue growing in the software testing industry. According to Report and Data, 21% of QA testers have already incorporated CT into their processes to accelerate code releases, while the rest are keen on doing so in the near future. 


  • Early detection of defects and issues in the software development life cycle, which helps reduce the cost and effort required to fix them.
  • Adaptability to various development methodologies, including Agile, DevOps, and Waterfall. 
  • Fast delivery of high quality software.
  • Ability to access testing reports at any point. 


Mobile Automation Comes at the Frontier

The recent surge in mobile production has brought about the importance of mobile test automation. 

In 2024 and the upcoming years, as the number of devices continues to grow, mobile app testing will become even more widespread. Companies will exponentially invest  in robust mobile automation tools to stay competitive and will be looking for testers with relevant experience. 


  • Reliable app functioning in all parts of the world.
  • Faster deployment times due to streamlined testing activities.
  • Meticulous functioning of the app, including its UI and UX.
  • 100% test coverage.


The Driving Force Behind Test Automation Trends

As you can see, there are a lot of things going on in the market, and while not all trends are long-lasting, some may be too costly to miss. Therefore, it’s important to stay abreast of the latest trends in order not to miss out on opportunities that can amp up your testing practices. 

Now, the question is: what is driving the change in test automation? And is there a way to predict the trends that are looming on the horizon? Well, you can definitely anticipate certain trends if you keep up with the shifts in the technology world. 

Here are some of the things that have driven transformation in recent years and are going to have a big impact on the testing landscape in 2024 and beyond. 

  • Technological advancements. The ongoing evolution of technology, such as AI and machine learning, is a major driver. These technologies enable smarter and more efficient testing practices.
  • Changing user expectations. As users demand more seamless and user-friendly software experiences, testing practices always evolve to ensure software meets these expectations. 
  • Agile and DevOps adoption. The widespread adoption of Agile and DevOps methodologies has not gone unnoticed in the testing industries. With the rising number of teams embracing agile and combining dev and operational processes, CT and integration have become the new norm. 
  • Security concerns. As the amount of data exchanged over the Internet grows rapidly, security has become paramount. Testing practices address these concerns by focusing more on identifying vulnerabilities, ensuring robust security measures, and safeguarding sensitive data.
  • Market dynamics. Of course, competition and market demands play a crucial role in driving trends as well. Products need to be high quality and delivered quickly, which pushes organizations to adopt and adapt to the latest testing practices. 
  • Evolving software architecture. Changes in software architecture, such as microservices and cloud-based solutions, require new testing approaches to ensure compatibility and reliability. 
  • Remote work. The rise of remote work has a direct impact on collaboration, testing environments, and tools used in testing. 

These driving forces, along with emerging technologies and methodologies, shape the future of test automation. By keeping a close eye on these factors, organizations can anticipate upcoming trends and make informed decisions to strengthen their testing practices. 


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As we’ve covered so many trends, you may be wondering if all of them are going to still be relevant beyond 2024. The answer is — not necessarily, although they are all very popular these days. Looking ahead, we can make an assumption that scriptless and codeless automation testing won’t last too long due to their restrictions. At the same time, trends like AL, ML, RPA, and blockchain will gain more strength as websites and applications become more sophisticated. What’s also definite is that software test automation will not go anywhere anytime soon. It will become a big thing, and the sooner you adapt and start using the technology — the better. 

Written by
Alona O., Сopywriter at TestFort

A copywriter with 13 years of experience in marketing and tech-related fields. Loves researching about topics and investing them in depth. Has a passion for learning new things and expanding her horizons. Her greatest joy is bringing value to readers by imparting her knowledge and insights through well-researched and compelling content.

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