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If you’ve ever watched a pull request sit for hours because of a flaky test, you know how quickly test suites can turn from safety net into bottleneck. As teams grow, test suites tend to grow even faster because every new feature, bug fix, and regression adds more tests, while old or redundant tests are rarely cleaned up, so over time you end up running far more tests than are strictly needed for reliable feedback.

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.

A slow CI/CD pipeline costs more than just time. Developers context-switch while waiting for builds, feedback loops stretch longer, and compute costs add up with every inefficient run. Most teams know their pipelines could be faster, but optimizing configurations requires deep knowledge of caching strategies, parallelism, and resource allocation. The challenge compounds with AI-assisted development. As AI coding assistants help teams ship code faster, pipelines run more frequently.

d function there, and before long you’re navigating a codebase full of inconsistent patterns, repeated logic, and code that’s harder to maintain than it should be. Refactoring is essential, but finding the time to clean up code while shipping features is a constant challenge. The rise of AI-assisted development has accelerated this tension. AI coding assistants help teams ship features faster, but they don’t always produce consistent code.

Last month we shared that a new navigation system is coming to Bitbucket, and we know many of you have been eager to see what it looks like. Today, we’re happy to share that the new navigation is available for to all Bitbucket users. This article covers what’s changing in Bitbucket, when it’s happening, and how you can share feedback with us.

Keeping your website fast as new features are introduced is a challenge. Performance regression is common issue that continues to plague websites, especially those of SaaS companies. In performance regression, newly shipped features introduce bloat, leading to slow page loads and reduced user conversion rates. This is exactly what setting performance budgets helps prevent.

If you have written any kind of end-to-end tests or UI tests you probably know that the greatest headache to deal with is test flakiness due to browser actions not behaving in the way that you expect them to behave. This flakiness can be a major bottleneck especially in CI/CD pipelines due to constant failures.

Static delivery pipelines are becoming a bottleneck. The best CI/CD tools for Kubernetes are those that move beyond simple code builds to provide total environment orchestration and developer self-service.