Automated Testing in 2025: Trends and Practices

Automated Testing in 2025: Trends and Practices

Automated testing continues to play a key role in modern software development and quality assurance, especially in the context of ever faster evolving technologies and in the application of CI/CD processes that require continuous integration. These workflows typically include automating the build (e.g. using tools like Jenkins or GitHub Actions), running UNIT tests, and quickly performing and evaluating integration and UI tests. A well-planned and appropriately designed process ensures early detection of bugs and minimises the cost of repair and the risk of errors being introduced into the live system.

The needs remain basically unchanged compared to previous years, so the only question is: with what innovative tools and AI solutions can and will we meet these needs in the most efficient way possible in 2025?

Automated testing trends in 2025

This year’s hottest trends in automated testing include AI-driven testing, even simpler and faster scriptless (or in reality low-code) automation, service- and cloud-based testing with a wide range of tools, and the introduction of shift-left testing strategies. AI-driven testing is expected to give ground to tools that are capable of automatically generating test scripts, mimicking user behaviour and activity, and producing and using predictive analytics to ensure test optimisation. In addition, solutions that enable automation of testing without any scripting (or requiring minimal coding) will become increasingly widespread, which will result in particularly high flexibility and significantly improved quality in agile, fast iteration environments and projects. These advancements will make software quality assurance not only more cost-effective but also faster, more scalable, and more accurate.

In addition, solutions that enable automation of testing without any scripting (or requiring minimal coding) will become increasingly widespread, which will result in particularly high flexibility and significantly improved quality in agile, fast iteration environments and projects. These advancements will make software quality assurance not only more cost-effective but also faster, more scalable, and more accurate. . 

Key technological trends 

The trends are therefore primarily driven by the extent and pace at which the role of artificial intelligence (AI) will grow in software testing, particularly in the areas of self-testing, predictive analysis, and dynamic test script generation. 

Impact of automation on the industry

As time progresses, the impact of innovations in software quality assurance will become increasingly unpredictable for the industry and businesses. The extent to which companies will be able to benefit from these changes will depend on their ability to assess and rethink their operations, to incorporate customised, innovative methodologies and tools. Since automated testing enables companies to introduce new products more quickly and efficiently, to minimise human error and improve product quality, it will be well-thought-out, timely implemented, and effectively applied quality assurance methodologies that will decide the race and declare a winner.

Analysis of automation tools

The current market offers an increasingly wide range of tools for those interested. For this reason, it is especially important to involve experts in the design of large enterprise software quality assurance who can understand the needs and propose cost-effective, customised solutions using the most suitable applications and methods available on the market.  

Regarding the fundamental technologies used for testing, Selenium and Appium are still among the most popular, as they offer great flexibility for users to solve emerging tasks. While Selenium is ideal for testing web applications, Appium is one of the best choices for automating the testing of mobile applications. It allows automated testing to be implemented on both Android and iOS platforms. 

Alongside the above mentioned solutions, an increasing number of more advanced alternatives are emerging in the market. Our experience is that many companies have been thinking about upgrading or replacing these technologies (or even older ones) for some time, but are reluctant to do so because they already have a large number of scripts at their disposal, which would be time-consuming and costly to implement in a new solution.

Selenium and its alternatives

Selenium has been one of the most well-known tools for automated testing for many years, but in 2025, alternatives such as Playwright and Cypress are gaining more ground. Thanks to their more modern architectures, these tools offer faster and more stable test run as they communicate directly with the browser engine, rather than through an external WebDriver layer like Selenium. Additionally, they provide better support for performing integration operations, as they have built-in queuing mechanisms and a more easily developed API to facilitate the writing and maintenance of automated tests.

Scriptless Testing Solutions

In addition to the above mentioned technologies, scriptless or low-code testing tools such as Ranorex, Testim, and Katalon Studio remain highly popular. These tools allow professionals with less technical background to automate simpler (but often more extensive) testing processes. 

AI-driven testing

In our opinion, 2025 and the next few years could revolutionise the delivery of AI-assisted or AI-performed testing, as AI advances have opened up potential horizons that were previously unimaginable or unattainable. 

Artificial intelligence will soon be able to self-test, predictively detect errors, and autonomously interpret business processes and generate dynamic scripts built on them.

Predictive analysis with the help of AI

AI-powered predictive testing is a software testing approach that uses artificial intelligence (AI) and machine learning (ML) to predict potential bugs and problem areas before they actually occur in the system. Based on previous test data, existing code analyses, or user behaviour patterns, this method can identify the most critical areas to be tested, optimise and make the testing process more efficient.

AI-driven automation

AI-driven automation ensures the implementation of intelligent testing strategies, allowing it to remain consistently aligned with the dynamically changing requirements of testing. In this context, it can on the one hand enable the creation, ordering and optimised execution of new automation scripts (based on requirements and specification documents), and on the other hand support the transfer and recoding of existing frameworks and code bases for newer technologies.

Business process assessment with AI utilization

Recently, a growing number of developments are focusing on autonomous mapping and assessment of processes by AI. And not without reason, as there is a huge opportunity for large companies to explore and model their applied processes in a fast, deep and standardised way, and to automate the processes they become aware of. The operation is based on AI analysing log files, transactions, and user interactions within the system and automatically identifying existing processes and their optimisation opportunities. Additionally, with the help of machine learning and predictive analytics, it forecasts bottlenecks, anomalies, or efficiency issues, supporting decision-makers in testing, process automation, and the development of quality assurance strategies.

Summary

The continuous development of automated testing and the introduction of new technologies enable companies to adapt more quickly and efficiently to market needs. If you want to learn more about the possibilities of automated testing, contact us or request a demo right now!