Top Software Testing Trends in 2021

#1) Agilie and DevOps

Agile was the solution to rapidly changing requirements, while DevOps was the solution to the need for speed for companies.

DevOps includes practices, rules, processes, and tools that help integrate development and operations activities to reduce the time from development to operation. DevOps has become a widely accepted solution for organizations that aim to shorten the software lifecycle from development to delivery, as well as operation.

Implementing Agile DevOps helps teams develop and deliver quality software faster, which is known as “Quality of Speed” in English. This methodology has aroused great interest over the past five years, and in the coming period this trend will only intensify.

# 2) Test automation

In order to effectively implement DevOps practices, software teams should not neglect test automation, as it is an essential element of the process.

Considering the popularity of DevOps and the fact that only 20% of companies are exploiting the potential of test automation, there is a definite growth in this area.

Currently popular automation tools such as Selenium, Katalon and TestComplete continue to evolve with new features that also make automation much easier and more efficient.

List of the best test automation devices for 2021 hereinyou will find.

# 3) API & Servizi

Client and server separation is the current trend in web and mobile application design.

Sometimes the API is reused in multiple applications or their components, which in turn results in teams having to test the API regardless of the application that uses it.

As this area is also becoming more and more important in the lives of companies, it is becoming more critical than ever to choose the right process, tool and solution for these tests as well.

# 4) Artificial intelligence in testing

Although the use of artificial intelligence and machine learning (AI/ML) in software testing is not new, the large amount of data available offers new opportunities in this area as well.

AI/ML algorithms can also be used to generate better test cases, test scripts, test data and reports, as well as help you make decisions about where, what and when to test. Intelligent data analysis and visualization help teams detect errors and test coverage of high-risk areas.

We hope that the application of AI/ML will become significant in the coming years in areas such as quality forecasting and prioritization of test cases and errors.

# 5) Mobile test automation

The current utilization of mobile test automation is very low, in part due to a lack of methods and tools, although the trend towards automated testing of mobile applications continues to increase. This is justified, among other things, by the competition around the speed of entry of the product to the market, as well as the ever-evolving methods and tools.

The integration of cloud-based platforms like Kobiton and test automation tools like Katalon could help take mobile automation to the next level.

#6) Testing Environments and Data

The rapid growth of IoT means that multiple software systems operate in many different environments, so ensuring adequate coverage of tests is often a challenge for test teams. In an agile project, in fact, the lack of test environments and data is the main challenge during a deployment.

The supply and use of cloud and containerized test environments is also expected to grow in the coming years. The application of AI/ML in the production of test data can be a solution to the lack of test data.

#7) Integration

Managing the application lifecycle can become difficult for any testing tool that is not integrated with other tools. Software teams need to integrate all the tools used in the development and operational phases, in order to ensure that data from a myriad of sources provides an adequate basis for the effective application of AI/ML.

We could cite as an example the use of AI/ML to determine the true focus of testing, since in this case, in addition to the data of the testing phase, the information generated during the analysis of the business requirements, as well as the data of the phases of design and implementation are required.

Due to the continuous spread of DevOps, test automation and AI/ML, we will increasingly encounter testing tools that allow integration with other application lifecycle management (ALM) tools.

Conclusion

These are the emerging software testing trends to watch out for in 2021 as we live in a world of unprecedented exponential change driven by technology and digital transformation.

Organizations and professionals need to be aware of developments in the industry, as these trends can give them the opportunity to stay ahead of trends, or even lead them.