The Power of AI in Quality Engineering

As the speed of transformation increases and customers exceedingly demand better experiences, banks and financial institutions are compelled to take a hard look at how they engineer services, products, and assure them for seamless operations. To integrate sustainable quality in products and services, organizations are re-engineering quality assurance to make it one with product development.

Ensuring robust QE practices is vital if businesses are to leverage the latest tech at the pace they want to, and differentiate their products and services in a landscape of fierce competition from FinTech and large tech players.

Developments in AI give us hope this could become a reality soon. A recent study shows that 78% banking executives believe AI having a significant impact on the industry in the next few years. Let’s explore how much of a blessing AI is in QE.

What Do We Mean by AI in Testing?

Since speed to market and quality assurance stand right beside each other in priority, banks are now looking to add extreme automation to how they measure, analyze, and improve the quality of offerings. This is where artificial intelligence has a role to play.

With the ability to infuse understanding into computer code, AI can help testers accelerate testing and be sure of the results. For instance, test cases and test logs can be studied by an AI algorithm to investigate functions that can be highly malicious.

As AI agents learn through the process, they will be able to evolve and find new testing functions with less human intervention.

How AI Enhances Quality Engineering Across the Software Development Lifecycle in a Bank

An AI program can be made to run twenty-four hours a day, seven days a week at a stretch. Testers can execute tests as and when required. Furthermore, these tests can take place in real-time, with higher accuracy and precision, and in the background.

AI programs can help QE professionals look at the entire picture and find gaps in the quality that need immediate attention. This way, QA functions can be prioritized to meet the needs that require urgent remediation.

Customer experience enhances when codes can be tested and deployed faster, leading to quicker developments in products and tech competency. Consider the shift from quality control to quality assurance at the Bank of England. With a central role of QA automation, the bank is now pioneering testing transformation for other institutions to follow.

Cognitive Algorithms and Their Role in QE for Banks

Cognitive computing is a product of AI. The major facets of cognitive systems include machine learning, natural language processing, human interaction, deep learning, and self-healing.

As banking applications grow in complexity and become more interconnected, lenders need to trust automated testing to ensure the integrity and quality of these applications.

Cognitive algorithms can be integrated within the different stages of the financial software lifecycle to yield use case such as:

• Self-learning and self-healing can disrupt how software is developed, maintained, and assured within banks.
• As AI systems are fed better and more data, they can predict the cost of quality across the software lifecycle by taking into account historical project data and QA costs.
• Artificial intelligence can also help predict which modules or components in the software code can be expected to have more defects. AI can suggest better testing strategies, automate test design, and improve test coverage.
• AI can help chalk out defect patterns for the next project release by considering data on previous defects such as root cause, severity, discovery phase, etc.
• AI systems can leverage production data to analyze the procedures and functions and determine which ones are extensively used. Based on this insight, for new requirements, they can predict what changes might be needed in specific modules, reducing the regression test design and execution time and effort.
• AI can help businesses leverage automation to undertake testing rapidly and ensure faster installation of the latest tech in banks with minimal testing effort.

In order to successfully transform customer experiences, banking businesses need to accelerate time to market for both new products and new releases for existing products. As the market gets even more overcrowded and competitive, companies need to stand out through continuous development and delivery with assured quality, something that cannot be achieved with the traditional QA methodologies.

In a nutshell, AI can boost accuracy, maximize test coverage, and ensure faster turnaround on software products- something we can all use for rapid digital transformation.

At Maveric, an entire suite of services to help companies embed shift-left strategies in QE. The extreme automation practices help you install digital transformation quicker and with a greater sense of satisfaction and reliability.

We help organizations implement early-stage testing with a strong focus on product quality which aids in producing defect-free code. We also support non-functional testing at an earlier stage in the SDLC and aid extreme and effective automation through service virtualization.

Not having to wait for design components to get in shape before testing has a huge impact on the robustness of your product. Learn more about how Maveric can be an able partner to your organization.

N.N.Subramanian (Subbu), Co-Founder and Director – Quality Engineering business, Maveric Systems.

• By N.N.Subramanian (Subbu), Co-Founder and Director – Quality Engineering business, Maveric Systems.