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The 3-Way Cognitive Quality Engineering Ecosystem for Improving design, development and operations

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By Rajesh Vasudevan, Vice President – Quality Engineering, Maveric Systems

Evolving distributed digital technologies have raised the expectation levels of quality assurance (QA). The QA role has significantly shifted towards early quality engineering (QE), aided with blazing speed at an unprecedented scale.

As banking industry and services keep pace with the digital revolution, the need for cognitive ways of maintaining Continuous Quality is of paramount essence. In order to create competitive advantage, and provide specific services as per customers’ expectations, banks have to maneuver their testing and QA towards Cognitive Quality Engineering.

Intelligent automation with the forces of Artificial Intelligence (AI) technologies such as SR, ML, DL, NLP etc., can be a significant game changer for accelerating business growth and service delivery. These technologies live and feed on the right volumes of data to reflect the right results. As you feed more, the accuracy levels become better and sharper.

According to the World Quality Report 2018 (WQR2018), for the first time ever the top objective of QA and testing strategy is “end-user satisfaction”. Traditional approaches of test automation will soon perish as cognitive-led automation, with its superior quality performance, takes over.

Infusing Cognitive QE
It is needless to say that low levels of automation adversely affect quality engineering and software testing efficiencies. And, banks are well aware of this. Historical insights drawn from customers’ transactions, domains, service feature categories, commonly seen scenarios, occurrences, usage and patterns, can formulate an intelligent algorithm to predict and resolve recurring pitfalls much faster.

Banks could infuse these intelligent algorithms at a continuous pace, for assuring their software releases and future initiatives. The shift-left (i.e requirement and design stages) and shift-right (i.e. development and post go-live stages) scenarios can be engineered using Cognitive QE techniques for better efficiency. This will help maintain continuous quality across the entire software lifecycle.

The 3-Way Cognitive QE Ecosystem for Improving design, development and operations
The 3-way Cognitive QE ecosystem is built to offer an insight-led, proactive, decision-making intelligence to resolve pitfalls in the smartest ways.

The first of these is the Customer: Start capturing your target customer requirements a bit deeper using their historical data transactions to understand their needs, patterns of usage and failure points. This could vary across every geography and types of product. Look at the customer segments and study the customer journey and relationships, transaction volumes/logs, learn the channels and touchpoints for connecting and transacting and pay close attention to customer experience levels. This in-depth comprehensive study would fetch tons of meaningful insights for designing a user friendly and sustainable system. Now that we have studied the customer, the next step is to place these meaningful insights in the right buckets of cognizance.

The knowledge base would be the key theme for setting the right accuracy standard. The data analysis drawn out of the customer study, requires its placement in the right domain groups. The real-time data sets and logs including functional defects and system failures, provides the scenarios for testing. These data sets are to be maintained periodically or else it can adversely impact the target outcome.

The third component is Modeling Algorithms. The data collected as part of knowledge base is fed into modelling algorithms to analyze pass/fail scenarios, relationships and usage patterns. It paves the way to build predictable models for testing the system based on the insights gathered and learning over a period of time.

The 3-Way Cognitive QE ecosystem could be run over a continuous loop, to test and validate software across the various stages of design, development and support. Functional and non-functional validations can be much more efficient, with self-based learning and healing tactics.

With a real-time monitoring dashboard in place, typical pitfalls can be arrested much earlier before they manifest. Also, continuous improvement measures could be added for enhancing your cognitive QE approach. Banks can expect benefits of increased operational efficiency and better productivity rates.

Pushing features will also help with faster time to market. In addition, better accuracy and customer satisfaction are bonuses not to be underestimated.

Cognitive QE techniques today can not only gather and analyze data but also use push technology to offer customers what they are looking for – based on their history. To make the customer’s experience smoother and error-free, cognitive quality engineering is the way banks need to go. It’s smart, intuitive, fast and like the human brain, learns and adapts as it gathers more information and insights.


If you have an interesting article / experience / case study to share, please get in touch with us at [email protected]

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