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5 trends in test automation in 2024 and forecast for 2025-2030
In 2024, it is essential for testers to understand the latest trends in automation testing to improve their testing tactics and leverage new technologies. The most influential trends are considered to be AI-based automation and “containerization.” AI-driven automation uses machine learning to improve test coverage and accuracy, enabling more agile testing workflows. “Containerized automation” ensures consistency and scalability across different phases of development, promoting efficiency and rapid iteration.
1. AI driven test automation
Traditional test automation relies heavily on predefined test scripts and rules, which may have limited ability to handle complex scenarios or adapt to software changes. AI-driven automation, on the other hand, uses machine learning and other AI technologies to analyze data, identify patterns, and make intelligent decisions. One of the key benefits of AI-driven test automation is its ability to improve test coverage and accuracy. By analyzing large amounts of data from previous tests, AI algorithms can identify areas of software that are most prone to bugs or that require more thorough testing. This allows testers to distribute their efforts correctly and focus on the most critical areas. AI-driven automation can also dynamically generate and optimize test cases based on evolving requirements and usage patterns. In this way, testers can ensure that testing efforts remain in line with the changing needs of the software. AI algorithms can automatically generate test scripts, execute tests on different platforms and in different environments, and analyze test results to identify anomalies or unexpected behaviors. Overall, it is somewhat certain that AI will drastically change test automation and software testing. Read our article artificial intelligence tools : the best AI testing tools for test automation.
2. Containerized test automation
What does “containerized test automation” mean and how can it be used to improve and streamline test automation? “Containerization” is a method of “packaging” an application along with its dependencies and configurations into a “container”. This approach isolates the application from its environment and ensures that it functions uniformly despite differences in infrastructure. Unlike traditional virtualization, which involves virtual machines, each with its own operating system, containers share the host system’s operating system kernel, making them lighter and more efficient. This approach allows testers to package their test setups into separate units that can be easily deployed and scaled across different environments, such as development environments, test servers or cloud platforms. By containerizing test automation, you can achieve greater efficiency and flexibility in testing processes, test case iteration, parallel test execution, and test infrastructure scaling. In addition, because containers encapsulate all the dependencies and configurations needed for testing, there is less risk of environment inconsistency or compatibility issues between different test setups. This ensures reliable test execution across different phases of development and deployment. Overall, automating testing in containers increases efficiency and makes testing processes more reliable and reproducible. Tools like Docker and Kubernetes have become synonymous with “containerization”. Docker simplifies the process of building, deploying, and running applications using containers, while Kubernetes is an orchestration system for managing complex container ecosystems. Understanding these tools is key because they provide the framework in which testing takes place.
3. Test automation of Software Security
Software security testing automation involves the use of automated testing techniques and tools to identify and mitigate security vulnerabilities in software applications. The goal of this approach is to proactively identify potential security flaws, such as code vulnerabilities, configuration errors, or authentication issues, before they can be exploited by hackers and cyber criminals. Automated security testing tools can scan code databases, APIs, and infrastructure components for common security vulnerabilities such as SQL injection, cross-site scripting (XSS), and insecure authentication mechanisms and provide the necessary insights to remediate. Finally, software security testing automation helps integrate security testing into continuous integration/continuous development (CI/CD) pipelines. By automating security testing as part of the build and deployment process, it is possible to perform security checks at every stage of the software development lifecycle, from the development and test environments to the staging and production environments.
4. Automated Visual Testing
Visually verifying an app’s user interface across multiple devices and browsers has traditionally required a huge manual effort. Automated visual testing attempts to solve this problem through intelligent image analysis. Some of the ways in which it helps software testing automation are:
- Visual testing across browsers
Common browser compatibility issues include text clipping, alignment inconsistencies, and rendering errors. Automated visual testing performs UI tests across many browsers and versions, capturing screenshots for comparison. Image analysis tools identify differences.
- Responsive testing on all views
Apps need validation on all forms of computers, tablets and mobile devices. Automated visual testing tools resize viewports and capture screenshots for peer analysis. Element shifts, text overlaps, and similar inconsistencies are marked.
- Validation of user interface elements
In addition to screenshots, automated visual analysis verifies the presence, properties and location of individual UI elements. For example, an icon or button that is expected on a particular screen is checked. Automated scripts validate that the attributes of the elements match the specifications.
- Design consistency testing
The user interface should maintain a consistent look and feel throughout the application. visual regression tools perform pixel-by-pixel comparisons to identify variations in fonts, colors, opacity, gradients, and other elements on different screens.
- Accessibility testing
Automated tools analyse colour contrast ratios and keyboard navigation to detect accessibility issues. This helps ensure compliance with laws and guidelines such as WCAG 2.1 for users with disabilities.
- Testing animations and transitions
Visual effects such as image carousels, pop-ups/modals with hover also need validation. Automated scripts go through the UI flow and take snapshots to check for errors in animations and transitions. Automated visual testing essentially replaces human eyes with computer vision and pattern matching techniques. This unlocks a new dimension of speed, consistency, and coverage in UI testing. We wrote about this topic in our article Visual regression testing tools and the top free tools.
5. Adapting to quantum computing
In 2024, with the advent of quantum computing, quality assurance may face new challenges in ensuring the resilience and security of applications to quantum computing. This emerging trend implies a gap in QA that requires specialized skills and knowledge. Quantum computing is already in industries such as banking. It aims to extend the capabilities of AI for high-frequency trading, fraud detection and risk management using this technology. The race to exploit potential of quantum computing and integrate it into mainstream technology infrastructure is just beginning.
3 major trends in test automation between 2025 and 2030
Predicting trends for the coming year may be a bit easy, as it’s not that far in the future. But predicting the situation five years from now is another thing altogether, especially when it comes to software, where things change very quickly. Still, let’s take a guess and see in a few years whether we were wrong or right.
1. Self healing test automation
Self-healing test automation refers to the ability of automated test systems to autonomously detect and resolve errors or inconsistencies that occur during test execution. In essence, these systems are designed to proactively identify problems and implement corrective actions without manual intervention. This reduces the need for human involvement in test maintenance and troubleshooting quite significantly. Self-healing test automation can change test scripts and settings when things change in the software or its environment. For example, if a test fails because of a small design change or network issue, the system can fix the test script or try the test in a different setup. Overall, test automation plays a significant role in improving testing processes and reducing manual work.
2. Autonomous Testing
Autonomous testing and test automation are two interrelated concepts aimed at improving software testing processes. Autonomous testing refers to the ability of test systems to work autonomously, make decisions and execute test cases without human intervention. This includes not only executing tests, but also generating test cases, setting up the environment and analysing the results. Test automation, on the other hand, uses software tools and scripts to automate repetitive and manual testing tasks such as regression testing, functional testing, and performance testing. These two activities work together to improve the efficiency and effectiveness of software testing. Autonomous testing and test automation reduce the time and effort involved in testing. Automated test scripts execute tests faster and more consistently, enabling faster feedback cycles and continuous testing. By eliminating human error and variability, they increase the reliability and repeatability of testing. Automated tests provide more reliable results and better coverage of different scenarios. In conclusion, autonomic testing and test automation increase the efficiency, reliability, and quality of testing.
3. Data analytics and use of Big Data in Test Automation
Simply put, big data are larger and more complex data sets, especially from new data sources. These datasets are so voluminous that traditional data processing software simply cannot handle them. However, these huge volumes of data can be used to solve business problems that we couldn’t solve before. Using data analytics and Big Data in test automation involves leveraging large volumes of test data and applying analytics techniques to optimize testing strategies and increase efficiency. Data analytics and big data enable predictive analytics to anticipate and prevent potential problems. Analysis of historical test data, such as defect rates and test coverage, helps identify areas that are error-prone or require additional testing. Overall, the use of data analytics and big data in test automation enables testers to make informed decisions and improve testing efficiency and effectiveness.
Conclusion
offersIn conclusion, the trends we have described represent real advances in test automation, each offering something unique to improve software testing in general. AI-driven test automation brings autonomy and adaptability by leveraging machine learning to improve test coverage, accuracy, and efficiency. Containerized test automation simplifies test environments, which helps improve consistency and reproducibility across different phases of development. It also enables rapid iteration and scalability. Meanwhile, software security test automation addresses the critical need for proactive security measures to identify and mitigate vulnerabilities early. Together, these trends point to a shift towards more efficient, reliable and secure software testing practices, setting the stage for further advances in this area. If you speak German and are an IT Tester Consultant Medior or automated tester, check out our employee benefits and respond to job offers.
