New age of software testing :-

Credits : Dqindia

Where digital transformation continues to ripple across all the industries; from banking to retail, businesses turn up to mass adoption of digital technologies for innovation and survival in the long run. The business ecosystem is giving a thrust to the C-suite gems to fuel revenue growth with dynamic business models, which entice the customers with innovative services, and top-class experiences. This may sound a plausible idea at prima facie, but the challenge is real when it comes to draw a pliant business model and devising a customer-centric operational plan to make it work. Technologically advanced tools, and software can set a go-get plan in motion, for which, CIOs and business leaders must learn, how digital innovation should be harnessed if improving value chain is in their heads and hearts. However, digital innovation and transformation is different for every business, yet are based on the commonly shared principle of customer-centricity.
True, companies do not usually turn up to customer-centric projects, and expensive digital transformation, unless they lag behind the competition, nearing to their downfall. Therefore, chasing efficiency and agility, business leaders are in the pursuit of digital products and services that combine their strategy with the technology to achieve success. These applications or software enable the businesses to connect with their customers or seamlessly, upgrade the performance of their employees, and digitize their internal business operations.

As most of the companies are now aggressively shifting toward digital transformation, the compatibility of the applications or software with different networks and channels in a specific condition remains a big concern. All thanks to Digital Quality Assurance, which makes the business, take a step forward towards digital transformation; the integrated testing of multiple embedded software and devices is now possible and easy to execute.

Switching to digital quality assurance

ShepHyken, an American customer service expert, said: “A brand is defined by the customer’s experience. The experience is delivered by the employees.” It is the founding principle of Digital Quality Assurance, which aims to deliver customers an impeccable customer experience. In the traditional landscape, Quality Assurance (QA) stood for keeping a check on time, costs, and quality throughout the software development life cycle.

Always considered sluggish, and redundant due to the waterfall model, the traditional QA replaced by the Digital Quality Assurance—a breakthrough in digital testing which saves time, repetitive efforts, customer experience for the companies. These are the end-goals of almost every business’ digital transformation strategy, which digital assurance fulfils. In digital assurance, the speed of the product development is optimized from the initial stages, and the quality of the product is tracked throughout the development stages.  Costs and development efforts are significantly reduced because of a concentrated focus on building a viable product in the first attempt only using the customer-data and various automation technologies.

Pump it up with data and analytics

To develop an application that drives customer engagement, having precise customer and behavioral data before or at the time of development in hand is the key. Earlier, QA testers used to rely on reported defects, surveyed shortcomings, reviews, and feedbacks of the customers. In this new age of testing, the teams can directly access the customer’s data through Big Data- and AI-powered systems and test the software for a better-quality insight.
With Agile/DevOps, digital QA testing teams can reduce the errors and shorten up the development cycle by drawing inferences from data sourced through Facebook, Twitter, web portals, digital assets, and web analytics, etc.

Achieve higher customer satisfaction levels

A customer starts looking for an alternative, as soon as his application is bogged down by the technical glitches. In the wake of agile development, business leaders strive to align the development of the product with the needs and requirements of the customers for winning customer loyalty and trust. For this, Digital Quality Assurance yields early visibility of the product across production stages as well as accelerate the process through a context-driven testing approach.

Improving the understanding of the context, in which customers engage with the application, it empowers the QA teams to impart the customer expectations to the developers, reducing the risks of errors from the ideation stage. The timely customer feedback and experiences from previous development cycles serve as the inputs for testers and developers to collaboratively work to deliver a quality application.

Get improved organizational efficiency

Destined to facilitate continual collaboration, and communication between development and operations teams, DevOps is widely adopted by businesses to achieve organizational efficiency as well as effectiveness. But, utilization of the old strategy for testing is defeating the purpose of DevOps in most organizations.

In today’s world, with need to deliver at rapid pace, where development teams have adopted rapid development techniques, it’s the need of the hour for quality assurance teams to move away from traditionally pre-defined smoke/sanity/functional/regression test suites to the ones created dynamically using machine learning to identify high probability point of failures.

Automated machines and human QA testers

QA testing has always remained imperative to the development of reliable and quality software. In this new age of Digital Assurance role of functional manual testers will be redefined because of automated testing tools backed up by AI and ML.

Looking at the pace of innovation in the field, the days are not far enough when the machines, trained by humans, will write and implement test codes. We are already seeing the advent of self-healing automation scripts, automated test data generators, test scenario, documentation and analysis, to performance load model generations using ML and AI.

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