The great debate between Quality Assurance (QA) and Quality Engineering (QE) has always been an intriguing subject. In comparison to QA, QE drives the fundamentals of engineering a product or service right from its conceptualisation stage. With digital transformation picking its pace on a regular basis, the need for QE led business innovation is significantly rising. It propels continuous delivery through a constant feedback mechanism across the entire software development life cycle (SDLC), engineered with speed at a supreme scale.
So, a successful quality engineering (QE) business philosophy centers on continuous quality built on the twin premise – engineered4speed and powering QE at scale.
It is pertinent to ask what are the constituents of Continuous Quality?
Quality journeys embedded with early age testing, predictive analytics, optimization tactics, rapid continuous feedback are a few constituents of Continuous quality. These constituents are sharply focused on elevating the levels of customer experience. Test designs are crafted in alignment to product/service journeys with 95% automation promise. Continuous testing is initiated across the SDLC for empanelling agile and devops led culture.
The operating themes in today’s business growth narrative are quality, speed, scale and innovation. This article endeavours to examine Continuous quality (its historical trends) and the role of AI in reinventing the continuous quality pipeline with speed and scale.
At the outset, the elephant in the room needs to be addressed.
When it comes to digital transformation, most enterprises focus on customer experience, operational efficiency, agility and profitability through process and technology modernization. Quality Engineering is an afterthought. If businesses are to realize the benefits of continuous quality and the AI enabled automation philosophy, this mind-set needs to change.
Before we dwell further into this topic, let us first understand the heart-of-the-engine: AI enabled Platform.
What does an AI enabled Platform do?
AI enabled platforms combine intelligent decision-making algorithms with data, which enables to arrive an apt business solution.
These platforms offer pre-built algorithms and simplistic workflows such as sentiment analysis, image recognition, data scraping, natural language processing (NLP), voice recognition, recommendation systems, and predictive / prescriptive / cognitive analytics, in addition to other machine learning (ML) capabilities.
Let us change gears, and explore the history of testing, particularly the way its trends have influenced the industry?
The evolution of Automation in QA/QE
Automation in QA has existed for years.
- In the first generation of automation, the focus was largely UI-based and centered on regression. The goal was to build a framework that could accelerate automation using commercial tools. Automation evolved to include keyword-driven, data-driven and later, business process-driven frameworks that brought significant savings to clients
- The next wave of automation included the functional side of business in the form of API / middleware automation, test data automation and more. This truly brought the value of automation into testing activities, particularly test executions.
- Third wave of automation evolved further with an increased focus on continuous testing. Test Driven Design (TDD) and Behaviour Driven Design (BDD) forced integrated automation solutions to join the mainstream and were not limited to testers alone.
- Today, automation in the test execution phase is further evolving with wide adoption of open source and no-code / low-code automation solutions with in-built optimization, agile led continuous testing, plug n play third party system integrations and pointed solutions around digital / mobile testing. It is in this scenario that, AI-enabled cognitive automation solutions combine the best of automation approaches thereby enabling superior results.
The focus of an AI-enabled continuous quality platform is three dimensional as show below in the figure:
Figure- Three dimensional AI enabled continuous quality platform
Finally, if we were to summarise the world class features of an AI-enabled continuous quality platform, we invariably have to ask these 4 broad questions:
- Is the system delivering analytics powered Insights / integrated dashboard for informed decisions through Test logs, defect logs, incidents?
- Does the platform seize defects early through predictive failure hotspot identification through continuous feedback and monitoring?
- Does the system perform comprehensive levels of test automation and optimization through no-code / low-code intelligent automation?
- And finally, does the platform reduce overall costs, improve agility and achieves higher customer satisfaction by incorporating these following key inputs?
- Voice of Process
- Voice of Customer
- Voice of Machines (logs, incidents, CRM data etc)
Maveric’s AI enabled QE platform answers most of the aforesaid questions. The platform helps in rapidly accelerating your continuous quality pipeline through a combination of domain-driven and cognitive solutions for the banking business. With a substantial record of supporting more than 65 banking transformations and the vast banking domain heritage, Maveric Systems has been innovating the QE roadmap for customer success.
This was originally published on Business Live Middle East website and is being reproduced here.
