Systems | Development | Analytics | API | Testing

In engineering, we talk a lot about technical debt — the shortcuts and compromises made in code that pile up over time. But there’s another kind of debt that’s just as dangerous and far more invisible: QA debt. QA debt is what happens when testing isn’t given the same attention as features, architecture, or performance. It’s the accumulation of missed edge cases, outdated test suites, incomplete automation, or skipped regression checks.

Every time a website crashes under load, or a feature responds so slowly that users abandon it, you’ve run into a missing or poorly defined non-functional requirement. Studies show that unclear requirements contribute to nearly 47% of project failures. Most of those failures come from missing or weak non-functional requirements.

Every second your software slows down, you lose customers. Studies show that a delay of just 100 milliseconds can decrease conversion rates by 7%. That’s how fragile performance can be — and why software performance engineering has become a core discipline for every business serious about growth and reliability.

In my work helping teams adopt canaries, I’ve seen several recurring mistakes. Here’s a “pro tip” list to avoid them: Pro Tip: Don’t skip staging testing. Canary isn’t your only safety net—use staging to catch basic issues first. Is Your Infrastructure Ready for Global Traffic Spikes? Unexpected load surges can disrupt your services.

You’ve probably been there: your AI coding assistant just generated what looks like a perfect solution to your problem. Decent code quality, reasonable structure, and even some comments. You run it, and… it works. So you ship it. Three weeks later, your production logs are full of 500 errors from edge cases the AI never considered, or worse, you discover the code has been making unvalidated database calls that could have been prevented with basic input sanitization.

An iPaaS, or integration platform as a service is a cloud-based integration platform that acts as a central hub to seamlessly connect and manage different applications, data sources, GenAI models, knowledge bases, and AI agents wherever they are deployed—cloud-based, on-premises, or hybrid.

Did you know that nearly 35% of software bugs detected in late stages could have been caught at the build stage—if a proper smoke test were in place? What’s worse: those bugs often cost 10× more to fix later. If you’re a business owner or DevOps lead pushing for faster releases, you can’t afford to skip basic checks.

AI coding tools like ChatGPT, GitHub Copilot, and Claude have completely changed how we write software. From humble beginnings where non-AI-enabled code assistants made intelligent code suggestions, like Intellisense, the latest agentic tools can generate entire functions, suggest optimal algorithms, and even scaffold complete applications in minutes. However, as any developer who’s worked with AI-generated code knows, the output isn’t always perfect.