Future software testing methods will be more risk-based, and test automation will change significantly. AI is able to create test cases using real user data and learn from different user processes. In recent years, the field of software testing has seen a considerable transformation as new trends have entered the IT industry's services. The advent of new technology has enabled the most recent developments in software design, development, testing, and delivery.


The primary goal of firms everywhere is to lower costs. Therefore, the majority of IT leaders are in favour of using the most up-to-date IT practices within their organisations. Digital transformation is another competitive advantage for industries and businesses that excel at cloud computing and business analytics.

A lot of focus is being placed on reliability and quality issues, which lowers software application errors and improves security and application performance.


Today's businesses include testing methods like Agile earlier in the software development process. T-CoEs must also be formed in order to connect the testing mechanism with business growth and the production of goods that are "Ready for Business."


Some enterprises also work with independent testing companies for their software testing needs. By doing it this way, they save money on testing and don't even require internal resources. There are several more important trends in the field of software testing. Therefore, in order to help them, it is essential for all software industries to embrace the most modern testing approaches.

The Top Trends in Software Testing for 2022:

1.DevOps and Agile:

In response to the need for speed and rapidly changing requirements, organisations have adopted DevOps and Agile.


By combining development and operations duties, DevOps methods, practices, processes, and tools contribute to reducing the time it takes from development to operations by half. DevOps has become widely accepted by enterprises looking for methods to shorten the software lifecycle from development through delivery and operation.


Teams can generate high-quality software more quickly because of the teams' adoption of Agile and DevOps, also referred to as "Quality of Speed." This adoption has come a long way.


2.Test Automation :

If software teams wish to successfully use DevOps methods, they cannot disregard test automation because it is an essential part of the DevOps process.


They need to seek opportunities to replace manual testing with automated testing. Since test automation is regarded to be a significant DevOps bottleneck, at the very least, the majority of regression testing should be automated.


Given the popularity of DevOps and the fact that less than 20% of testing is now automated, there is plenty of room to expand the use of test automation in companies. More advanced methods and technologies need to be created in order to improve test automation in projects.


Selenium, Katalon, and TestComplete are examples of well-known automation tools that are still in use today and are continually evolving.


3.Automated API and service testing: 

Decoupling the client and server is a current trend in both web and mobile application designs.


Several programs and parts use APIs and services. As a result of these changes, teams now have to test APIs and services separately from the apps that use them.


When they are used across client apps and components, testing APIs and services is more effective and efficient than testing the client. The demand for API and service test automation is anticipated to keep increasing in line with the current trend, possibly surpassing that for end-user UI enhancements.


The right process, instrument, and solution must be used for API automation tests now more than ever.


4.Testing using artificial intelligence:

Though artificial intelligence and machine learning (AI/ML) techniques have been utilised for a while in the software research community to address challenges in software testing, recent advancements in AI/ML and the wealth of data that is now accessible offer new opportunities to employ AI/ML in testing.


However, the application of AI/ML to testing is still in its infancy. Businesses will discover ways to enhance their testing practices for AI and ML.


AI/ML algorithms are being developed in order to deliver better test cases, test scripts, test data, and reports. Predictive models may be useful for deciding where, what, and when to conduct tests. The teams' attempts to uncover faults, understand test coverage, identify high-risk areas, etc. are aided by intelligent analytics and visualisation.

5.Automated Mobile Testing:

The practice of creating mobile apps keeps growing as mobile devices get more advanced.


To support DevOps effectively, toolchains for DevOps must include mobile test automation. However, due in part to a lack of methodology and resources, only a very small portion of mobile testing is now automated.


Mobile application testing automation is becoming more popular. This trend is being driven by shorter time to market and the use of more advanced tools and methods for automating mobile tests.


The integration of test automation tools like Katalon and cloud-based mobile device labs like Kobiton may promote mobile automation.


6.Test Conditions and Information:

The rapid development of the Internet of Things (IoT) has resulted in an increase in the number of software systems operating in a variety of circumstances (see top IoT gadgets here). To ensure the appropriate level of test coverage, the testing teams must get through this obstacle. The absence of test environments and data is, in fact, a significant impediment when applying to testing in agile projects.


Cloud-based and containerized test environments will become more widely available and used. The generation of test data using AI/ML and the extension of data initiatives are two answers to the paucity of test data.


7.Integration of Resources and Tasks:

It can be difficult to use any testing tool that is not integrated with the other tools for application lifecycle management. Software teams must integrate the technologies used for all development phases and activities in order to efficiently apply AI/ML approaches. Multi-source data cannot be gathered before then.


Data from the requirements, design, and implementation phases must be combined with data from the testing phase when utilising AI/ML, for example, to decide where to focus testing.


Along with the trends of greater transformation toward DevOps, test automation, and AI/ML, we will see testing solutions that enable integration with the other tools and activities in ALM.



Since technology and digital transformation are causing previously unheard-of exponential changes in our world, one should be on the lookout for these new trends in software testing in 2022.


Organisations and individuals must stay current with industry developments. To stay competitive, test professionals, businesses, and teams would benefit from adhering to these trends.