Test Automation: 7 Lessons Learned So Far In 2022 And Best Practices

Quality Automation
by Zymr

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Test Automation

In the face of the economic downturn and rising talent gap, the role of AI, Hyperautomation, and DevOps emerged as a promising prediction for QA teams toward the end of 2021 – especially for test automation use cases where the scale and speed of testing have a direct impact on software quality improvements. How have these trends evolved and what key lessons are learned in response to the predictable adoption trends of these technologies in test automation?

As we are a leading QA automation services company, we have compiled a list of valuable lessons that can help you understand the QA landscape better. Let’s explore 7 key lessons learned so far with respect to predictions for testing automation technologies, trends, and practices in 2022,

1. Hyperautomation in Testing is Replacing Distributed RPA

An effective test automation strategy requires end-to-end automation at the process level. Unlike the traditional approach of adopting multiple automation tools and operating siloed processes to realize Robotic Process Automation (RPA) goals, hyperautomation orchestrates a holistic and end-to-end automation strategy involving multiple tools, platforms, and technologies. In the realm of QA and software testing, hyperautomation enables enhanced test coverage on a larger scale, embeds decision policies and intelligence into automation scripting, and augments human capabilities with low-code/no-code testing functionality.

According to recent research, 80% of organizations are considering hyperautomation adoption over the next two years. Traditional RPA adoption currently stands at 22.5% while 56% of the organizations have already implemented four or more hyperautomation initiatives, including use cases in Software Development Lifecycle (SDLC) and test automation.

2. DevTestOps as a Paradigm for End-to-End Testing

The continuous testing approach of DevOps has gained a more systematic presence in the testing workflows of the modern SDLC organization. The promising new SDLC framework and methodology of DevTestOps aims to focus on effective CI/CD implementation with a thorough end-to-end testing strategy. The end goals of DevTestOps are similar to those of DevOps: faster delivery and software release cycles, high product quality, and collaboration between Devs and Ops teams. The DevTestOps manifesto adds a significant role of QA into the picture, encouraging a culture of collaboration between Devs, Ops as well as QA teams early during the SDLC pipeline process, through to software delivery and release. DevTestOps aims to achieve these goals effectively by shifting left continuous integration testing and embedding intelligence into the test automation practices and workflows.

3. Low Code/No Code is Reaching Maturity

Low Code/No Code technologies are fast reaching maturity as 70% of enterprises are expected to adopt the technology by the year 2025, source. Low Code/No Code platforms allow users to dictate automation process design and workflows through a graphical user interface or exhaustive scripting templates. In the context of hyperautomation and DevTestOps, these tools allow developers to spend less time on developing, maintaining, and updating automation test scripts. Instead, QA teams and executives can use an intuitive software interface to set up and operate continuous testing procedures without having to code repeatable test cases for a growing set of testing scenarios and metrics.

4. Automation Dilemma: Waste Process vs Continuous Automation

Automation can successfully augment human activities, but how much automation do you really need? In pursuit of a textbook DevOps SDLC mechanism, many organizations tend to overdo automation. Test automation is convenient and cost-effective with the vast availability of automation tools and the provision of scalable computing resources as a cloud service. The key challenge in 2022 is to understand an optimal scope of automation in your testing procedures. Continuous parallel testing can easily consume a vast provision of expensive cloud resources, which transforms automation from a profit-center to a cost-center. More importantly, knowing what not to automate (continuously) is necessary to eliminate bottlenecks: automating the waste process only adds to the delays and bottlenecks in your SDLC pipeline.

5. Cloud-Native Testing is Emerging as a Standard for Software Delivery

Cloud-native applications driven by open source solutions dynamically orchestrated application components packaged in decoupled containers and the microservices architecture pattern are bringing new challenges to the continuous testing practice. Traditionally, QA would follow a waterfall approach to testing. In the context of cloud-native application development, testing is inherently dynamic and requires strong considerations for the component aspect. In a bid to shift left testing procedures, QA is forced to rethink the testing approach that will guarantee end-to-end continuous testing workflows of each component, individually. As a result, QA-related decisions collectively impact testing professionals, devs, Ops, and tech executives.

6. API First Principles

One of these decisions is the increased adoption of an API-first testing strategy. API testing has gone beyond its traditional limitation of functional testing capabilities and is expanding to cover non-functional test cases. Following the principles of the API Economy, the API-First strategy requires defining and designing APIs and the underlying schema, before developing the integrations that depend on it. The general idea is to provide simplified access to data and functionality across the integrated set of application components that require testing at scale, in parallel, and continuously.

7. User-Centric Testing, AI, and the Challenges

DevOps follows the approach of a rapid and iterative software release process, incorporating end-user feedback every time to realize significant improvements in every release iteration. Continuous testing procedures are in place to ensure that every feature update and release better aligns the end product with user expectations. One of the key challenges in this context is the sheer volume and scale of information that must be processed to guide testing procedures in the right direction. Since software performance inherently impacts business performance, QA teams are encouraged to test for software performance metrics with the greatest impact on end-user satisfaction and ultimately, business goals.

Conclusion: What to do next?

Here are some of the key actionable steps that your QA team can take based on the current QA and Test Automation trends in 2022:

Consider QA not as an isolated procedure, but as an integrated component of the DevTestOps framework. Devs must contribute to test automation in line with the strategic business goals of QA, as well as establish an effective low-code/no-code testing mechanism for the wider QA team and IT executives.

Test Automation must aim to shift left the testing procedure. One of the key takeaways from recent predictions and trends centers around the ability to identify bottlenecks in the SDLC pipeline and waste processes that otherwise go under the radar. Establish a user-centric testing workflow. How can you identify and incorporate the most impactful software performance metrics into your test automation strategy?

In essence, testing automation is now an increasingly data-driven practice: making the right decisions on testing workflows and metrics selection requires a strong focus on an end-to-end hyperautomation strategy driven by AI/ML technologies.

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