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Two Software Testing Trends to Disrupt in 2019 and Beyond

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Two Software Testing Trends to Disrupt in 2019 and Beyond

Disruption abounds in DevOps communities and environments, and two particular trends show no sign of slowing down.

· DevOps Zone ·
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Software Testing Trends 2019

Software testing has grown from a mere activity or process to an industry; independent software testing companies like us here at ClicQA exist by just testing software, so we consider software testing as another industry like healthcare or insurance.

In the last couple of years, software testing is among those industries that are driven by ever-evolving technological advancements and transformations. These advancements and transformations have been influencing the way software is tested. To stay ahead of the curve, it becomes vital for software testers, managers, CIO/CTOs, and other business or technical people to keep track of the latest trends.

As 2019 has started, it is the time to check all those software testing trends that disrupt in 2019 and beyond. After doing thorough research on all such trends, we wanted to highlight two trends which will drive software testing the most in the future.

Test Automation Leads to Faster and Frequent Software Releases

Agile evolved when organizations strived to be responsive with continuously changing requirements; DevOps evolved when organizations strived to be responsive with the need of faster time to market that the digital world brought.

Adoption of Agile increased the need of running regression tests by several times in a release. Whenever there are changes, manual execution of regression tests several times throughout the release burden organizations with effort, cost and time. Therefore, test automation automates the execution of repetitive regression tests to save effort, cost and time.

Adoption of DevOps brought the need for continuous software delivery, which becomes possible by bridging Continuous Integration and Continuous Delivery pipelines. Instrumenting test automation with CI tools is the key to achieve Continuous Testing to enable continuous delivery.

In  2019 and years to come, organizations strive to explore ways to deliver quality software at a faster pace. Among many such ways, blending Agile and DevOps with test automation has become the popular way.

Considering several surveys and research reports, the effectual utilization of test automation across organizations is very low, so this has to be improved widely. And, there is a need to implement test automation at the very early stage of SDLC, and all repetitive testing activities should be automated aggressively.”

AI to Disrupt the Software Testing Landscape

Digitalization is in the next stage of evolution where the digital world would be driven by leveraging AI for self-driving cars, fraud detection, chatbots and many more. While AI and machine learning is disrupting the other industries, it is still in its younger stages for software testing. Several opportunities offered by AI and machine learning can be availed to transform the way software and applications are tested.

In 2019 and further years to come, we must see how AI and machine learning in software testing would evolve. However, here we bring some opportunities where we predict disruption.

AI in Functional Testing

The essence of functional testing yields its best results when testers create ample functional tests around positive and negative test scenarios. Test data plays a vital role in leveraging both positive and negative test cases to identify defects that occur with a specific input condition. But the plethora of test data itself is a challenge.

With the help of AI and ML, organizations can employ deep reinforcement learning techniques to generate test data required for functional testing.

For automation testing purposes, AI and ML can be leveraged for automated test environment and test data setup whenever automated tests must be executed. This becomes very handy for DevOps environments, where multiple software releases in a week are planned. The other areas where AI and ML can be used in software testing include:

  • Identifying redundant tests across application testing cycles to optimize test suites
  • Whenever there is a change or enhancement for a specific feature or functionality, relevant tests identification, execution, and reporting can be automated.
  • Predicting the areas of an application, which can have defects when a change is introduced, and recommendations or remediations to avoid them.

A lot of revolutionary changes can be incorporated into software testing when data from requirements, test cases, test environment, test suites and defects is leveraged for AI and ML.

AI in Performance Testing

Performance testing is a proactive activity, which organizations perform to estimate their application or website’s performance behavior before big business days. If the performance behavior deviates from the expectations, then bottlenecks are identified and mitigated. Despite this, a lot of businesses employ performance testing, there are several reports about their downtime or crash during peak business days. As said earlier, performance testing is a proactive activity, but businesses are being reactive with performance testing.

The data from web traffic analytics tools such as Google Analytics, and APM tools such as New Relic, Dynatrace, and many more can be leveraged with AI to predict performance patterns of applications/websites and infrastructure. At the same time, analysis and a thorough understanding of proactive measures such as scaling up the infrastructure resources or disaster recovery can be done. And these proactive measures can be automated to avoid downtime or crash.

Bringing load testing tools, web traffic analytics tools and APM tools under one umbrella, and streamlining the use of data from these tools will lay down a road to employ AI for performance testing.

“Whether it is Automation Testing or Performance Testing, the ultimate goal of utilizing AI in software testing is to create software testing practice that can adapt to all software testing needs itself and mitigating the risks before the release.”

Topics:
artifical intelligence ,devops ,test automation ,performance testing ,software testing

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