The Quality Engineering Imperative in a Cloud Transformation

More companies than ever are migrating to the cloud, motivated by the opportunity for business growth and innovation. In the rush to adapt to a cloud-centric world, they are realizing that even with all its benefits, the cloud has more potential failure points than traditional business and production environments. An effective cloud system depends on a well-designed quality engineering (QE) program, one capable of predicting and preventing pitfalls.

The benefits of an effective cloud system and QE program are clear. A recent global research report from Wipro FullStride Cloud Services highlights that driving cloud transformation beyond migration alone can help organizations achieve a 12 percent improvement in their bottom line and a tenfold increase in ROI on cloud investments. By implementing testing across the software development lifecycle, organizations can achieve better output and significantly improve product quality, speed to market, and the customer experience. This business potential underscores the urgent need for companies to transform their QE programs to align with digital and cloud technologies and protect their cloud investments. 

QE Is Not One-Size-Fits-All 

As they seek to adapt quickly, many organizations look for a single QE silver bullet. Incorporating quality practices early in the development lifecycle is critical, but that alone is not enough to solve today’s problems. Given the complexity of modern cloud systems, enterprises must invest in strategic QE initiatives for production environments along with predictive and preventative maintenance. 

A robust QE strategy leverages anomaly prediction in addition to anomaly detection. Knowing what could go wrong and where it could go wrong is critical to preventing a catastrophic outage. And although many organizations believe the key to success is an automation-first approach to QE, the reality is thornier. An effective approach to automation requires investment in intelligent platforms that can “automate the automation,” removing the human test engineer from the picture completely. 

Transforming QE with a 4M approach 

As business needs and underlying platforms continue to evolve, organizations must embark on QE transformation journeys to fully address quality concerns. A 4M approach — Method, Model, Machinery, Mindset — can empower organizations to launch a successful cloud migration and transformation. 

Method: Observability for production and nonproduction environments

With the shift to the cloud, the number of latent failure points and modes of failure across apps, products, and infrastructure increases significantly, and it is vital that app owners proactively monitor for and mitigate failures at any point in time. Conventional monitoring techniques, however, provide a limited view of bottlenecks. Organizations must instead proactively engineer the quality of design deployment and operations by moving from monitoring to observability. 

Enabling observation of the entire IT landscape introduces an element of controllability. It becomes possible to identify the external factors that influence what has gone wrong and to simultaneously pinpoint the faulty internal systems or components that are the root cause. Observability allows teams to ensure that site reliability engineering (SRE) principles are built with quality at the source.

Model: Pod-based delivery 

A pod-based delivery is a federated delivery of QE that aligns with products (front-end, client-side systems) and platforms (back-end core systems), resulting in self-sustaining pods with minimal dependence on shared services for delivery. This type of delivery allows for meaningful, shared services for specialized testing enablers — namely in the management of test environments and test data — rather than for testing delivery. 

Pod-based delivery should also be complemented with gig economy enablers: as-a-service-based delivery for handling surges in workload with dependence on internal crowds (employees), public crowds (global crowd-sourcing platforms), or a combination of both (hybrid crowds). To succeed, this model must be supported by a QE transformation team with strong expertise in infrastructure, apps, and products to effectively create a QE strategy aligned with digital and cloud migration. 

Machinery: AI-based platforms for advanced test automation 

AI/ML-powered QE has allowed for the formation of virtual personas to execute day-to-day testing tasks with minimal human engineer intervention, an approach that aligns with continuous testing and DevOps ecosystems. Importantly, AI/ML (especially generative AI) has permeated far beyond the test design and maintenance phase. It now covers the entire end-to-end testing lifecycle, and provides significant technology coverage, spanning beyond web and API toward mobile-native and commercial off-the-shelf (COTS) products deployed in a software-as-a-service (SaaS) model. 

In terms of test types, generative AI-powered QE enables coverage across activities such as defect triaging and logging, unit-test scripting, test-failure analysis, defect prediction, test-data generation, and test-environment healing. The highest degree of “automating the automation” has been achieved in SaaS-based COTS products, wherein new tools can now scan the entire product instance from end to end, identifying customizations, communicating between app modules, generating test cases, and handling execution and maintenance. 

Mindset: Hiring QAOps engineers with a shift-right mindset

A well-designed QE program cannot be built with only functionality and performance in mind. It is also vital to prioritize system availability, stability, robustness, and reliability. To drive a broader shift in mindset toward QE objectives, companies must start by hiring and developing QAOps engineers with shift-right mindsets. 

QAOps engineers enable organizations to create more effective QE programs and achieve better results. Their expertise means a finger on the pulse of production instance feedback, prioritizing elements such as resilience and production testing to build a QE program that is more reliable and robust.

The Path Forward for QE Optimization

The distributed, cloud-centric world offers unprecedented business opportunities, but its complexities come with new hurdles that modern organizations must overcome. As companies continue to embrace the cloud, they can reduce the risk of failure by making QE a top priority within the broader digital transformation of their business. 

For that to happen, organizations must look at testing not just as a way to deliver bug-free applications, but as part of an infrastructure that supports the rapid delivery of high-quality products and services. Such an approach ensures that QE initiatives can enable enterprises to respond more quickly to fast-changing market dynamics, deliver a superior experience, and build brand loyalty. Moving away from the traditional dimensions of speed, skill, and structure toward becoming an integrated QE and testing organization will be the path forward for modern companies looking to drive measurable growth and innovation. 

For more insights, see the Wipro report, Quality Engineering in a Cloud-Centric World.

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