As a QA professional, you certainly recognise the popularity and importance of automated testing. With the ability to test code faster than humanly possible, it has become a cornerstone of modern software development. But where is it heading? What will be the main trends in automated testing in the following years? In this article, we explore cutting-edge technologies and emerging trends that will shape the future of automated testing.
What is automated testing?
Automated testing is a technique that involves using specialised tools to run pre-scripted tests on software. The purpose of automated testing is no different from manual testing: ensure that the software functions well, meets quality standards, and does not have potentially damaging bugs or defects. The main difference is the scale at which you perform the tests.
You can use automated testing for many testing types, like functional, performance, and regression testing. Usually, test engineers or QA professionals create the tests, and automated tools run these tests to compare expected results and actual results. When properly applied, automated testing saves time, increases testing accuracy, and helps deliver higher-quality software products.
Also, test automation reduces the risk of human error. As automated processes are less prone to mistakes caused by fatigue, distraction, or other factors, they lead to better testing results and fewer defects in the final product. This is why test automation is the reality of software testing, and further optimisations will shape the future of software testing.
Future trends of automation testing
Although it is impossible to predict the future 100%, below are some key automated testing trends that are expected to become more popular in the following years.
Artificial Intelligence and machine learning in test automation
AI and machine learning have the potential to revolutionise automated testing as they enable more adaptive and intelligent testing tools. In the future, we can expect to see automated testing tools that analyse results in real time and use that data to improve the efficiency of future testing efforts.
You can use AI and machine learning algorithms to identify patterns in testing results. Data on common failure areas and unexpected behaviour can be used to optimise testing strategies, prioritise test cases, and get suggestions for tests you have not considered. Also, AI and machine learning can help you build predictive models to anticipate potential issues before they arise. This means addressing problems before they impact end-users and reducing the likelihood of unnoticed bugs or defects.
Robotic Process Automation (RPA) in automated testing
Robotic Process Automation (RPA) is a technology that automates repetitive processes when they do not require complex decision-making.
RPA speeds up the test automation routine, like setting up test environments, launching test cases, and generating test reports. This is how you further increase the time and efficiency savings from automated testing.
Shift-Left testing and Continuous Testing
Shift-Left Testing and Continuous Testing are closely related trends that are gaining momentum in the software development and testing world.
In the Shift-Left Testing, rather than waiting until later stages of development to test, you test early and often to identify and address issues as early as possible. Continuous Testing, on the other hand, involves testing throughout the development process, from the beginning to post-deployment. This means automating testing processes as much as possible, using tools and technologies in real time, and integrating testing into software development.
Together, these approaches allow for faster remediation and reduced risk of issues being discovered late in development or after deployment.
Test automation in the age of IoT and connected devices
Test automation is a major part of IoT due to the complexity of integrated systems as well as increasingly obscure software and hardware dependencies. . To ensure their reliability and security, you need more than traditional testing approaches.
Automated testing tools and frameworks help you identify and address issues faster and more efficiently, allowing you to keep pace with the rapid development and deployment of IoT cycles and connected devices. They also scale quickly and efficiently to help you test thousands of interconnected devices across multiple systems simultaneously. The large scale also calls for automated testing, as human error becomes more and more likely when testing for the same issues too often.
The impact of cloud computing on test automation
Cloud computing is also expected to transform the testing process, with a significant impact on test automation. With cloud computing, you can access testing resources and infrastructure through the cloud and reduce the need for on-premises hardware and software. This way, you can scale resources up or down as needed and pay only for what you use.
Cloud computing makes testing consistent worldwide. Team members only need an internet connection to conduct testing on a global company’s scale and guidelines, which improves collaboration and increases efficiency. You can integrate advanced testing tools and platforms with automated frameworks and conduct more comprehensive and complex testing, including load, performance, and security testing. Cloud computing also allows you to leverage advanced analytics and machine learning to draw insights into testing results and leverage them to make the software more reliable.
Codeless test automation and low-code platforms
Codeless test automation and low-code platforms are the next emerging trends in test automation on our list. They allow testing teams to create tests and applications more efficiently without the need for extensive coding skills. Codeless automation and low-code platforms provide a more user-friendly experience, making testing and development accessible to a wider range of individuals. This can also lead to more innovation, as more non-technical individuals can participate in the process and contribute their ideas and insights.
Containerisation and test automation testing
Containerisation will also be a major trend in the QA testing future that involves packaging applications and their dependencies into containers for easy deployment and management. This approach is getting increasingly popular in test automation and allows testing teams to quickly and easily set up testing environments and execute tests. With containerisation, you can make isolated testing environments consistent across different systems and platforms and improve the reliability and accuracy of testing. This approach helps to scale testing resources up or down as needed, further reducing the need for expensive hardware and infrastructure.
The growing importance of security testing automation
As you probably know, traditional manual security testing is more than just time-consuming. Any potential human error is more costly than anywhere else, and it is difficult to keep up with the pace of modern threats.
With automated security testing tools, on the other hand, you can perform a lot of security tests quickly and precisely, identify vulnerabilities and potential threats before they can be exploited, and have developers take care of them. These tools are also more efficient, thorough and comprehensive than traditional manual testing methods, ensuring that systems are secure and resilient against cyber attacks.
Final thoughts
In conclusion, the future of test automation will be shaped by rapidly evolving trends and technologies. AI and machine learning, containerisation, security testing automation, low-code platforms, and shift-left and continuous testing are among the key trends.
Enhanced test automation brings a number of benefits, including greater efficiency, scalability, and accuracy in the testing process. To keep up with this rapidly growing industry, you must track the trends, evaluate new technologies, and adopt them as needed. This is the only way to keep up with the market while improving the reliability of your software and delivering better end-products to meet users’ ever-changing needs.