The pursuit of continuous quality with the drive to embed ever higher quality standards in the software lifecycle has seen quality engineering (QE) evolve from primitive and discrete manual testing practices into a quality continuum.
Over the years, the need to instill measures that bring agility, innovation, cost control, and risk mitigation remains unchanged. However, enterprises are curbing spend on run-of-the-mill manual testing and dedicating higher wallet share to disruptive themes around cloud journeys, data transformation, connected systems, customer experience, site reliability engineering (SRE), and cybersecurity through an automation-first approach.
As business and technology strategies get more closely aligned, large-scale transformative programs are mushrooming within enterprises. As we move into uber-connected environments, technologies like cloud and 5G add to the complexity by generating humungous data from endpoint devices. The result is that large monolithic systems are being broken up into manageable microservices architectures. Further, machine learning algorithms are becoming a key tool to help companies infer insights from large datastores.
Unlike ever before, enterprises are now thinking beyond cost. They want faster technology adoption to modernize the business in order to better service their end customer. Conversations are shifting from traditional functionality to cybersecurity, site reliability, availability, resiliency, etc. This is precisely where next-gen technology leaders need active quality engineering support.
Amid this scenario of growing risks, uncertainty, and opportunity, here are some reasons for the rising prominence of quality engineering:
1. Cloud transformation journeys: Cloud is fast becoming all-pervasive. Hence, it is intuitive to weave quality engineering into application migration. Enterprises are migrating to both cloud-native and digital SaaS packages, which come with inherent complexities for large transformation programs. During the configuration phase, cloud infrastructure deployment is susceptible to manual errors like compute or storage errors that can trigger performance issues or downtime, thus resulting in revenue leakage. Hence, technology leaders need partners that offer full-stack testing services.
2. Pivot to data on cloud: In recent years, enterprises have turned to lift-and-shift without planning for data transformation prior to cloud migration. But managing multiple large datastores on the cloud costs substantially more than on-premises storage. Choosing from options such as relooking at data architecture, leveraging multi-cloud to reduce cost, or even migrating data to private cloud is not an easy play. To meet the standards of data quality and performance, technology leaders want established automated assurance solution providers to validate large-volume data migrations and transformation programs.
3. Proliferation of the Internet-of-Everything: Across industries, the connected ecosystem is evolving faster than anticipated. Some popular examples are smart wearables, software-as-a-medical device, smart utilities, software-defined and connected vehicles, connected factories, and smart buildings, to name a few. Such use cases must be tested end-to-end right from the data-generating devices, microservices, and integration technologies all the way to the backend systems in order to ensure that business needs meet specifications.
4. Adopt SRE to de-risk and achieve predictable growth: Accidental errors, erroneous procedures, network and security lapses, and natural disasters derail growth. Dynamic geo-political environments, increase in cyber threats, and the work-from-anywhere model necessitate well thought-out programs that allow high availability, resilience, and security. SRE encompasses monitoring, reliability, availability, scalability, performance, capacity, and security compliance to achieve all-round resilience.
5. Focus on industry solutions: With cloud becoming the common denominator, differentiation comes with industry solutions. Many SaaS vendors are incorporating domain-specific products into their offering portfolio. Package implementations are complicated but industry-specific solutions add to the SaaS products niche functionalities making cloud adoption easier and faster. For easy cloud adoption, these days, business owners constitute shadow IT teams that need help with user acceptance testing around these packages.
6. Enabling continuous delivery in Agile/DevOps: It is imperative to integrate testing into the software development lifecycle. Each scrum is estimated to reserve 20-25% of testing effort. A tester’s profile has rapidly changed from traditional manual testing to software development engineer in test (SDETs) with fair coding skills. Hence, to succeed, organizations are focusing on automation-first approaches with partners that can elevate the intelligent enterprise automation experience.
7. AI is a gamechanger: As a technology, AI does more than simply lower cost and increase efficiency; it is driving the shift towards human-centricity. AI is fast becoming the bedrock for improved resource utilization, lower test case load, and better management of growing application complexity. AI enables intelligent automation with cloud testing capabilities, machine learning, and natural language processing (NLP), all of which will redefine an enterprise’s QE strategy. Organizations need the right partners to help develop accurate AI-enabled testing models customized to the priorities of their business. Whether personal or commercial software, AI-based testing has arrived.
To sum up, quality engineering has evolved exponentially. It is now part of every new-age technology leader’s conversation, particularly in the context of the changing behavior of the consumer who is demanding ever better experiences.