Top Software Testing Trends In 2021

1) Agile and DevOps

Organizations have taken up Agile as a response to quickly changing requirements and DevOps as a response to the demand for speed. DevOps involves practices, rules, processes, and tools that help to integrate development and operation activities to scale back the time from development to operations. DevOps has become a widely accepted solution for organizations which are observing ways to reduce the software life span from development to delivery and operation.

The adoption of both Agile and DevOps helps the teams to develop and deliver quality software faster, which successively is additionally referred to as “Quality of Speed”. This adoption has gained much interest over the past five years and continues to accentuate within the coming years too.

2) Test Automation

In order to implement DevOps practices effectively, software teams cannot ignore test automation because it is an important element of the DevOps process. They need to seek out the opportunities to exchange manual testing with automated testing. As test automation is taken into account to be a crucial bottleneck of DevOps, at a minimum, most regression testing should be automated. Given the recognition of DevOps and therefore the proven fact that test automation is underutilized, with but 20% of testing is automated, there's tons of room to extend the adoption of test automation in organizations.

More improved methods and tools should emerge to allow better usages of test automation in projects. Existing popular automation tools like Selenium, Katalon, and TestComplete still evolve with new features that make automation much easier and simpler too.

3) API and Services Test Automation

Decoupling the client and server may be a current trend in designing both Web and mobile applications. API and services are reused in addition to one application or component. These changes, in turn, need the teams to ascertain API and services independent from the appliance using them. When API and services are used across client applications and components, testing them is easier and efficient than testing the client.

The necessity for API and services test automation continues to increase, possibly outpacing that of the functionality employed by the end-users on user interfaces.Therefore, it's worth your effort in learning the simplest API Testing Tools for your testing projects.

4) AI for Testing

Even though applying the artificial intelligence and machine learning (AI/ML) approaches to affect the difficulties in software testing isn't new within the software research community, the recent progress in AI/ML with an outsized amount of data available pose new opportunities to use AI/ML in testing. However, the application of AI/ML in testing remains within the early stages. Organizations will find techniques to modify their testing practices in AI/ML. AI/ML algorithms are developed to get better test cases, test scripts, test data, and reports.

Predictive models would help to form decisions about where, what, and when to check . Smart analytics and visualization help the teams to catch faults, to recognize test coverage, areas of high risk, etc. We hope to discover more applications of AI/ML in addressing problems like quality forecast, test suite prioritization, fault categorization and function within the upcoming years.

5) Mobile Test Automation

The tendency of mobile app development keeps up to grow as mobile devices are progressively more capable. To fully support DevOps, mobile test automation must be a neighborhood of DevOps toolchains. However, this utilization of mobile test automation is extremely low, partly because of the shortage of methods and tools.

The trend of automated testing for mobile apps continues to extend. This trend is driven by the necessity to shorten time-to-market and more advanced methods and tools for mobile test automation. The integration between cloud-based mobile device labs like Kobiton and test automation tools like Katalon may help in bringing mobile automation to subsequent levels.

6) Test Environments and Data

The fast growth of the Internet of Things (IoT) (see top IoT devices here) means more software systems are operating in several different environments. This places a challenge for the testing teams to make sure the proper level of test coverage. Indeed, the shortage of test environments and data may be a top challenge when applying to check in agile projects. We will see growth in offering and using cloud-based and containerised test environments. The application of AI/ML to get test data and therefore the growth of knowledge projects are some solutions for the shortage of test data.

7) Integration of Tools and Activities

It is hard to use any testing tool that's not integrated with the opposite tools for application lifecycle management. Software teams have to integrate the tools used for all development phases and activities in order that multi-source data are often gathered to use AI/ML approaches effectively.

For Example, using AI/ML to detect where to focus testing on, needs not only data from the testing phase but also from the wants , design, and implementation phases.

Together with the craze of accelerating transformation toward DevOps, test automation, and AI/ML, we'll see testing tools that permit integration with the opposite tools and activities in ALM.

Conclusion

These are the appearing Software Testing swings that one should be careful in 2020 as we sleep in the planet of unparalleled exponential changes driven by technology and digital transformation. Organizations and individuals got to remain aware of the developments within the industry.

At Oodles ERP, we provide Custom Web application development services to address cross-industry business challenges. Our experienced QA engineers holistically test your software application to detect bugs and glitches for faster resolution. For more information, reach us out at [email protected]