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Software testing trends for 2021

Jasmine Chokshi
December 9, 2020
Software Testing Trends 2021

If there’s anything that 2020 has taught us, it is that the best laid plans of mice and men often go awry, to misquote Robert Burns. Yet, 2020 was the year where the software and ITES industries outdid themselves, showing repeatedly the power of technology, connectivity and collaboration.

At a time when the world almost came to a standstill in the wake of the pandemic, it was software and related services that kept everything running like a clockwork. When the power of cloud, potential of remote teams, and scalability of software as a service apps took the centerstage.

And if the importance of high-functioning, top quality software applications required further validation, here it was. Software was at the frontlines of all essential services. From AI and ML-enabled technologies to fight the virus, manage healthcare delivery, deliver vaccines at R&D labs of major pharma companies, video conferencing and remote working tools that kept the workforce going, to banking and payment systems, to eCommerce apps shipping orders in unprecedented numbers. Great quality software is indispensable for the world to grapple any crisis.

But building great quality software relies on the ability to anticipate the needs of a fast-transforming world and delivering on customer expectations. The need is to integrate quality assurance more tightly and build products that get it right the first time. Software testing and quality assurance are paramount for the success of any product now.

It is important both for software applications and remote workforce that the quality of software is maintained seamlessly. The mark of great products is that they either anticipate curveballs well or respond quickly to changing market trends and expectations.

Let’s look at some of the reigning trends in software testing for 2021 to stay ahead of the curve:

Top Software Testing Trends in 2021

  1. Intelligent Test Automation

AI and ML continue to dominate the software testing scene as the process of quality assurance transforms into a self-adapting and self-learning activity.

With this, the tools and underlying technologies continue to evolve as the outcomes improve each year. The future of test automation is intelligent – led by AI and ML technologies to solve some of the common challenges in automation.

High volume of test backlog, lack of visibility and direction, inadequate information on testing priority and lack of real-time, actionable insights.

Intelligent testing sharpens every area of business and extends automation’s capabilities to cover more ground faster. Leveraging vast amounts of existing data in the test automation suites, AI and ML-led self-healing suites can help you focus on high-risk error categories.

One application of intelligent testing is self-healing test cases. Many tools including QMetry Automation Studio have already deployed this feature where objects automatically update themselves reducing the maintenance cost and efforts. 2021 will see further investment and opportunity created by AI and ML led test automation resulting in the following outcomes:

  • Optimized test suites

  • Real-time feedback

  • Predictive and prescriptive analytics

  • Defect Analysis

  • Cognitive QA authoring

  1. Continuous Testing

At QMetry we have walked the talk when it comes to Continuous Testing to fulfill our customers’ need for speed. Continuous Testing is neither a novel concept nor a buzzword.

So, what’s next for Continuous Testing?

We believe that early adopters of Continuous Testing will now move towards maturing their Continuous Testing process. The following criteria will shape the future and maturity model for CT:

  1. Hybrid Model: COVID-19 has forced all the enterprises to go agile and hybrid in order to keep their operations afloat. In 2021 we will continue to see Decentralized Teams working with multiple Centralized SaaS based Tools to deliver the best results. It spells the end of one vendor strategy. Modern Agile Teams practice a Hybrid Development Methodology which is nothing but imbibing best practices from Waterfall, Agile & DevOps practice.

  1. Data Analytics: The other dominant trend in CT is the use of predictive and prescriptive analytics leveraging feedback from the CI/CD pipeline and the vast amounts of test data. This will optimize their process further and help them consolidate information from multiple tools and test cases to refine the QA lifecycle incrementally and continuously.

  1. Touchless Continuous Testing: Automated Continuous Testing is the holy grail of CT with minimum or zero human intervention. Test Orchestration will play a vital role in achieving this level of maturity, with continuous analysis of feedback data using intelligent QA tools.

  1. QAOps

Extending the idea of Continuous Testing further, QAOps merges the principles of DevOps with quality assurance. The idea behind QAOps is to speed up software delivery processes without compromising on the quality of the application.

The framework improves collaboration between QA engineers and developers and operations. In a nutshell, the model stresses on integrating the software process with CI/CD pipeline so that testing doesn’t remain a silo function.

Organizations can achieve QAOps by using automated testing, parallel testing, enhancing scalability and involving developers and IT Ops in the QA function.

  1. Scriptless test automation

Scriptless test automation will be one of the key enablers for intelligent test automation. It uses a unique approach in QA that allows key stakeholders to play an active role in the testing process. As the name suggests, it reduces the amount of coding or scripting involved in creating tests or frameworks. The aim is to help business user testing and reduce maintenance efforts and costs.

Scriptless test automation tools will help teams translate human-friendly test steps into the actual code resulting in an accessible way to define tests without coding or scripting knowledge. And using already existing test automation features like object spy repository, test recorder and reusable keywords and self-healing suites, you can significantly improve the performance of your test execution.

We believe that a number of test automation tools will come up with Scriptless Automation in 2021 making it one of the leading trends. QMetry Automation Studio has already anticipated this demand and offers scriptless test automation as part of its new release.

  1. Exploratory testing

As great as automation is, it is not the panacea for all practical-world usability challenges. Organizations using a purely automated solution run test on narrow use cases – based on the sub problems that are expected or solved.

On the other hand, exploratory testing digs much deeper. It helps testers to discover and explore during a black-box type testing session, think for themselves, arrive at solutions and notice crucial details that would otherwise be ignored.

In that sense, Exploratory testing is truly agile and helps uncovers some of the edge cases that are missed in the normal scope of testing. One key benefit of ET is that it opens up testing to all key stakeholders, not just trained QAs. It complements both conventional and automated testing to find some of the million-dollar defects that hide behind the defined workflow.

In 2020. we saw several tools add an Exploratory Testing feature to their offerings. QMetry has been leading this trend for a while now. Our tools have an in-built exploratory testing function that helps testers to explore, and record their observations more intuitively by adding voice memos, capturing screenshots, annotating as they explore an app.

Moving towards DevOps 2.0

With a renewed focus on cloud adoption and micro-services architectures, new tools will continue to emerge for planning, requirements management, version control, build automation, testing, deployment etc.

Value stream management will be key to improve development speed, agility and quality outcomes. And tools and processes that empower visibility, integration, traceability along with automation and analytics will empower teams for DevOps 2.0 success.

The pandemic has also accelerated digital transformation initiatives and driven home the importance of business continuity plans. Legacy organizations that haven’t yet considered the cloud will need to rethink their strategy and adopt measures to mitigate risks and match their pace with modern, agile teams.

Ultimately, Continuous Testing will be the bridge that helps organizations to achieve DevOps 2.0.

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