Systems | Development | Analytics | API | Testing

In Part One, we discussed how QA debt builds silently over time - causing slower releases, late-night firefights, and unpredictable test cycles. The next step is understanding how much debt you have and where it hides. This post goes deeper into measuring QA debt - what to track, how to collect data, and how to use those insights to create a sustainable plan for improvement.

Generative AI can produce code faster than humans, and developers feel more productive with it integrated into their IDEs. That productivity is only real if CI/CD tests are solid and automated. When not appropriately tested, you may encounter a production issue that you haven't seen before. According to the State of Software Delivery 2025 report, 67% of developers spend more time debugging and resolving security vulnerabilities in code generated by AI. That cancels out the efficient gains that they get from faster AI code generation.

Postman has long been a go-to tool for API developers and QA engineers in providing a streamlined environment for testing and documenting APIs. Its user-friendly interface and robust features made it an industry standard. However, Postman isn't suitable for everyone. Its complexity has increased over the years, leading to a UI that can be overwhelming, and a steep learning curve - especially for beginners.

In API testing, speed, accuracy, and confidence in test results are everything. Regardless of whether you're validating functionality, testing performance under load, or ensuring compliance with your security posture and standards, the ultimate goal is the same: catching problems before they reach production. But what if your tests are lying to you? Lurking beneath even the most sophisticated test suites is a subtle, pervasive threat: data coupling. Data coupling is a silent API killer that doesn't throw immediate errors or break builds in obvious ways.

Quantum computing hasn't gone mainstream yet, but signs increasingly suggest that it may do so soon. Some researchers and industry experts estimate that we could see significant breakthroughs as early as the next decade. This anticipated shift is often referred to as Y2Q, like Y2K that we had at the beginning of this millennium. While the timeline remains uncertain, the growing pace of research and investment in the field makes it important to start considering the practical implications now, especially in areas like cybersecurity and data protection.

It's 2PM on a Thursday. Your engineering team is knee-deep in bugs from a recent release. But what's the Slack channel buzzing about? Not flaky tests. Not integration coverage. Not mocking services. It's whether to order brown sugar boba or taro with oat milk. Let's be honest: for many companies, it's easier to justify $8 on boba than $800 on testing tools. And we're not here to judge-we're here to understand why.

Especially, if you have a globally distributed team, CDEs give you a smoother developer experience just by its online nature. Instead of wrestling with conflicting dependencies, trudging with inconsistent local setups, or waiting for your code to compile, you have a powerful, instantly accessible development environment in the cloud. CDEs remove typical limitations like hardware and scalability. You can quickly get started with minimal setup and configuration, but confidently move forward due to the flexibility and customization features CDEs provide.

Developing APIs can often be a complex web of dependencies, external dependencies, and murky network traffic. In order to build better, developers need a certain amount of stability to test a query or feature against, and when this stability is lacking, development can get more complicated and difficult. Enter API mocking. API mocking is an approach to generating a mock service that provides dependable data for a variety of testing purposes. This data can then be used as a test case for actual API calls, allowing for more complete and accurate development.