Operations | Monitoring | ITSM | DevOps | Cloud

Test coverage is one of those metrics everyone agrees matters until it’s time to actually write the tests. Between shipping features, fixing bugs, and handling production issues, writing comprehensive tests for edge cases and error paths often falls to the bottom of the backlog. The result is coverage gaps that accumulate technical debt and leave your codebase vulnerable to regressions. As AI-powered development tools reshape how we write code, the volume and velocity of changes is accelerating.

Bugs hide in plain sight. A date validator that rejects February 29th on leap years. An edge case that slips through code review. A flaky test that passes locally but fails in CI. These issues erode trust in your codebase and waste hours of debugging time. In the era of AI-assisted development, code is being written faster than ever. But speed creates risk.

For anyone shipping software in regulated industries, the word “control” gets thrown around all over. Compliance frameworks demand controls, auditors verify controls are used, engineering teams implement controls, and there are even Control Owners. But what exactly is a control? And more importantly, how do we design controls that actually serve their intended purpose while enabling rather than hindering delivery velocity?

Discover how Qovery leverages its own platform to accelerate AI development. Learn how an AI specialist deployed a complex stack; including LLMs, QDrant, and KEDA - in just one day without needing deep DevOps or Kubernetes expertise. See how the "dogfooding" approach fuels innovation for our DevOps Copilot.

Is OpenShift becoming too expensive or complex for your team? Discover the top 3 OpenShift pain points; from the "pricing inversion" to vendor lock-in and see why agile platform teams are migrating to modular, developer-first alternatives like Qovery.