Quality Assurance (QA) has long been essential in software engineering, ensuring the development of products and applications with established standards and metrics. But QA has been reactive, focusing on defect detection through manual and automated testing. With the evolution of software development technology and methodologies, the limitations of traditional QA are evident. This perspective paper delves into the evolution of Quality Engineering (QE), which has transformed the approach to software quality. QE goes beyond QA and Test Automation to integrate quality practices throughout the Software Development Lifecycle (SDLC); it also addresses complexities in modern architectures such as microservices and cloud environments.  

The journey from QA to QE is marked by several key milestones. Initially, software testing was a separate phase, conducted after development was complete. With the advent of Agile and DevOps methodologies, the need for continuous testing and early defect detection became apparent. This shift fostered the evolution of testing practices, embedding quality checks throughout the development cycle with the emerging adoption of cloud-native modern architecture, paving the way for what is now called Quality Engineering. Unlike the reactive nature of traditional QA and QA Automation, QE represents a proactive and integrated approach throughout the development lifecycle.

Organizations can significantly enhance product or application quality, optimize development workflows, and mitigate risks by addressing QE concerns at every phase of the SDLC. They could leverage structured QE approaches as mentioned above, and focus on a holistic view of quality in modern architecture.

Read our Perspective Paper for more insights on the evolution of quality engineering in software development.

The global financial markets create and deal in multiple asset classes, including equities, bonds, forex, derivatives, and real estate investments. Each of them constitutes a multi-trillion-dollar market. These traditional markets encounter numerous challenges in terms of time and cost which impede accessibility, fund liquidity, and operational efficiencies. Consequently, the expected free flow of capital is hindered, leading to fragmented, and occasionally limited, inclusion of investors.

In response to these challenges, today's financial services industry seeks to explore innovative avenues, leveraging advancements such as Distributed Ledger Technology (DLT). Using DLTs, it is feasible to tokenize assets, thus enabling issuance, trading, servicing and settlement digitally, not just in whole units, but also in fractions.

Asset tokenization is the process of converting and portraying the unique properties of a real-world asset, including ownership and rights, on a Distributed Ledger Technology (DLT) platform. Digital and physical real-world assets, such as real estate, stocks, bonds, and commodities, are depicted by tokens with distinctive symbols and cryptographic features. These tokens exhibit specific behavior as part of an executable program on a blockchain.

Many domains, especially financial institutions, have started recognizing the benefits of tokenization and begun to explore this technology. Some of the benefits are fractional ownership, increased liquidity, efficient transfer of ownership, ownership representation and programmability.

With the recent surge in the adoption of tokenization, a diverse array of platforms has emerged, paving the way for broader success, but at the same time creating fragmented islands of ledgers and related assets. As capabilities mature and adoption grows, interconnectivity and interoperability across ledgers representing different institutions issuing/servicing different assets could improve, creating a better integrated market landscape. This would be critical to realizing the promise of asset tokenization using DLT.

Read our Perspective Paper for more insights on asset tokenization and its potential to overcome the challenges, the underlying technology, successful use cases, and issues associated with implementation.

Many large and small enterprises utilize business-managed applications (BMAs) in their value chain to supplement technology-managed applications (TMAs). BMAs are applications or software that end users create or procure off-the-shelf and implement on their own; these typically are low-code or no-code software applications. Such BMAs offer the ability to automate or augment team-specific processes or information to enable enterprise-critical decision-making.

Technology teams build and manage TMAs to do a lot of heavy lifting by enabling business unit workflows and transactions and automating manual processes. TMAs are often the source systems for analytics and intelligence engines that drive off data warehouses, marts, lakes, lake-houses, etc. BMAs dominate the last mile in how these data infrastructures support critical reporting and decision making. 

While BMAs deliver value and simplify complex processes, they bring with them a large set of challenges in security, opacity, controls collaboration, traceability and audit. Therefore, on an ongoing basis, business-critical BMAs that have become relatively mature in their capabilities must be industrialized with optimal time and investment. Low-code platforms provide the right blend of ease of development, flexibility and governance that enables the rapid conversion of BMAs to TMAs with predictable timelines and low-cost, high-quality output. 

Read our Perspective Paper for more insights on using low-code platforms to convert BMAs to TMAs that provide end users with rich interfaces, powerful configurations, easy integrations, and enhanced controls.