I’m thrilled to sit down with Anand Naidu, our resident development expert with extensive knowledge in both frontend and backend technologies. With a deep understanding of various coding languages, Anand offers invaluable insights into the pressing challenges of software quality, particularly in the context of Singapore and the broader APAC region. In this interview, we dive into the critical issues of technical debt, the complexities of modernizing legacy systems, the role of AI in transforming testing practices, and the importance of embedding quality into software development from the ground up.
Can you walk us through what technical debt means in software development and why it’s become such a significant issue for companies in Singapore and the APAC region?
Technical debt refers to the long-term consequences of taking shortcuts in software development, like skipping proper testing or rushing out code to meet deadlines. It’s like borrowing time now but paying a heavier price later with system fragility and maintenance headaches. In Singapore and across APAC, this is a huge problem because many companies are under pressure to innovate quickly. The financial impact is staggering—some Singaporean firms report losses of around half a million USD annually due to poor software quality stemming from this debt. It disrupts operations and erodes customer trust, even if it doesn’t show up directly in financial statements.
How do legacy systems pose risks for businesses in this region, especially when it comes to modernization efforts?
Legacy systems, often built on monolithic architectures or aging infrastructure, are a major hurdle in APAC. These systems are rigid, making updates slow and costly, and they struggle to integrate with modern, agile workflows. For businesses trying to modernize, this creates a risky gap—operations can stall, and compatibility issues with newer tech can lead to inefficiencies or outright failures. The inability to adapt quickly in a fast-moving market is a significant competitive disadvantage.
There’s often a rush to modernize, but it seems to come at a cost. Can you explain how this pressure for speed impacts software quality?
Absolutely. The drive to modernize or release software faster often pushes teams to cut corners—skipping thorough testing, applying quick fixes, or bypassing best practices. This speed-over-quality mindset creates fragile systems that are prone to bugs and breakdowns. In the short term, you might hit a deadline, but over time, you’re building a house of cards that can collapse under the weight of unaddressed issues, costing far more to fix than doing it right the first time.
What strategies can companies adopt to monitor and manage technical debt before it spirals into a crisis?
Managing technical debt starts with treating it as a measurable risk, not just a vague problem. One effective approach is test gap analysis, where you pinpoint untested or under-tested areas of code that pose high risks. Tools that analyze code changes and highlight coverage gaps are invaluable here. It’s also critical to track trends in test coverage rather than obsessing over 100% coverage—focus on the parts of the system that change often or are most critical. This way, you catch potential issues early and allocate resources smartly to prevent bigger problems down the line.
Test automation maintenance seems to be a sticking point for many organizations. What challenges do companies face in this area, especially with traditional approaches?
Traditional script-based automation struggles as systems grow and integrate. A small change in one part of an application can create a domino effect, breaking tests across connected systems and requiring extensive manual updates. As software becomes more complex, maintaining these test assets becomes a time sink. It’s tough to keep pace with constant updates, and often, teams end up with outdated or irrelevant tests that don’t reflect the current state of the application, undermining the whole point of automation.
How does AI-driven model-based test automation differ from older methods, and what benefits does it bring to the table?
Unlike script-based automation, which relies heavily on manually written tests that need constant tweaking, AI-driven model-based automation uses models of the application to generate and update tests automatically. This approach adapts to changes in the software without requiring endless manual intervention. It ensures end-to-end quality by keeping tests aligned with the system’s current state, saving time and reducing errors. In Singapore, it’s no surprise that over 80% of organizations see this as a productivity booster—AI helps teams focus on innovation rather than maintenance.
Can you elaborate on the concept of continuous quality engineering and why it’s becoming essential for businesses today?
Continuous quality engineering is about embedding reliability into software from the very start, rather than treating quality as an afterthought. It involves catching defects, inefficiencies, and design flaws early in the development cycle through automation and collaboration across teams. In today’s world, where slow performance is as bad as downtime, this approach is critical. It helps businesses deliver faster without sacrificing stability, reduces costly late-stage fixes, and ultimately builds trust with users by ensuring consistent, high-quality products.
Looking ahead, what is your forecast for the role of AI in tackling technical debt and improving software quality in the coming years?
I believe AI will become a game-changer in addressing technical debt and enhancing software quality, especially as tools become more sophisticated. We’ll see AI not just automating testing but also predicting potential debt areas before they emerge, integrating legacy systems more seamlessly, and democratizing quality assurance so even non-technical stakeholders can contribute. However, human oversight will remain crucial to avoid pitfalls like inaccurate outputs. Over the next few years, I expect AI to shift from a helpful tool to a core component of development strategies, driving efficiency and reliability across the board.
