Top 8 Trends of Test Automation in 2023

The rise of AI and machine learning in test automation

One of the most significant trends in test automation is the increasing use of AI and machine learning technologies. These technologies provide new ways to automate testing tasks, such as data-driven testing, test case generation, and execution.

Utilizing AI (Artificial Intelligence) and ML (Machine Learning) based testing methodologies can be a game-changer for Quality Assurance (QA) teams who manage large and distributed IT systems. The AI and ML testing algorithms deliver improved automation, freeing the QA teams from repeating and refining tests manually.

The increasing popularity of open-source test automation tools

Another trend that is becoming increasingly popular is open-source test automation tools. Open-source test automation tools have become increasingly popular over the past few years due to their affordability and flexibility. These tools are often free and easy to use, making them ideal for small businesses or start-ups.

Additionally, they are often highly customizable, allowing developers to tailor the tests to their specific needs. The open-source nature of these tools also makes it easier for developers to share and collaborate with one another, further improving the test automation process.

The growth of cloud-based test automation

Cloud-based test automation is another area that is seeing significant growth. Cloud-based test automation solutions can quickly test across multiple platforms, browsers, and devices, allowing for a comprehensive view of the application’s performance. It enables parallel execution, meaning that tests can be run in multiple different environments simultaneously, resulting in faster test cycles.

Additionally, cloud-based test automation solutions provide scalability, allowing for rapid increases and decrease in the number of tests being run. It also enables continuous delivery, meaning that tests can be run continuously, ensuring that applications are always up to date. Finally, cloud-based test automation solutions reduce infrastructure costs since they require fewer resources to set up and maintain than traditional test automation solutions.

The rise of mobile test automation

Mobile test automation is also rising with the increasing use of mobile devices. This trend is driven by the need to ensure that mobile applications function correctly on various devices and operating systems. The rise of mobile test automation has revolutionized how software development teams ensure the quality of their applications. Automation testing has opened the door to a more efficient and reliable means of ensuring that apps function as expected across different devices and operating systems.

By automating the testing process, teams can quickly and easily test for issues such as compatibility and performance. In addition, automation testing allows for more extensive and detailed testing and can help to reduce the cost of manually testing applications. As mobile test automation tools continue to improve, more companies will be able to benefit from their use and ensure the quality of their apps.

The rise of DevTestOps

The rise of DevTestOps in test automation results from the increased speed of software development and the need to deploy applications quickly. DevTestOps is an approach that combines the development, testing, and operations processes to streamline the software development life cycle. It combines DevOps and TestOps principles, which are based on the idea of continuous integration and delivery.

The main purpose of DevTestOps is to improve the collaboration between different teams involved in the software development process and to automate processes like testing, deployment, and monitoring. This approach allows for faster feedback loops, faster releases, and improved product quality. DevTestOps has made it easier for developers to identify and address issues before they become major problems quickly. It also allows for faster deployment of applications and improved collaboration between development, testing, and operations teams.

Model-based Shift-left testing

Model-based Shift-left testing is a novel approach to software testing that is gaining traction in the industry. It involves shifting the testing process to an earlier stage in the software development cycle, such as requirements gathering and system design, allowing for more thorough and comprehensive testing. This method allows for identifying issues earlier, reducing the risk of releasing a product with errors or bugs.

In addition, it allows for developing more robust and reliable software better suited to meet customer needs. Model-based Shift-left testing is becoming increasingly popular due to its ability to reduce time to market and increase customer satisfaction. It is also seen as a more cost-effective solution since it allows for the identification of issues earlier on in the development process, reducing the need for expensive corrective measures.

The growth of IoT testing

The growth of IoT testing has been exponential over the past few years. With the emergence of connected devices, the demand for reliable and secure testing has increased dramatically. As more devices are connected, the need to ensure the data transmitted is safe and secure has become a priority.

IoT testing has evolved to include multiple layers of security and testing protocols to ensure the data is not compromised. This includes testing for vulnerabilities, authentication, data integrity, performance, and scalability. IoT testing has become essential to ensure the quality of products and services built on the internet of things.

The popularity of analytics in test automation

The popularity of analytics in test automation is increasing as it allows for more informed decisions about developing and implementing automated test plans. Analytics enables test automation teams to track the progress of their tests and identify any areas where there may be issues, as well as areas where optimization can be made. Analytics also enables teams to understand their tests performance better, making it easier to troubleshoot any issues that may arise.

This is especially beneficial for larger projects, where the sheer number of tests can make it challenging to analyze results manually. Analytics also helps teams to quickly identify new risks and develop better strategies for avoiding them. By taking advantage of analytics, test automation teams can improve the quality and efficiency of their tests, resulting in better software products.