Operations | Monitoring | ITSM | DevOps | Cloud

While there are several solutions that try to help you improve your testing practices and tooling, we believe that high-quality software is not just limited to how well it’s tested. That’s why we’re expanding beyond code coverage and building the foundation for the first of its kind pre-release platform with Bundle Analysis, Test Analytics, and AI-Powered Code Review.

We’ve been exploring new approaches to make Sentry issues more actionable. Read on to learn how we’re using machine learning to improve issue creation (i.e. grouping) and alerts, with as little configuration as possible, so you can focus on fixing the most important issues.

Sentry knows a lot about the inner workings of an application’s codebase. So we got to thinking, how can we use this rich dataset to make debugging with Sentry even faster? Many generative AI (GenAI) tools (e.g. GitHub Copilot) improve developer productivity in their dev environment, though few have the contextual data Sentry has to help fix errors in production.

Today, we’re making it even easier to find and solve user-impacting issues with three new Sentry Performance features.

Four years ago, we stepped on some big toes with our developer-first performance monitoring. Since then thousands of software teams have adopted our modern APM solution. But while Performance checks off a lot of boxes, some dev teams juggle separate tools for metrics, leading to a fractured experience. And honestly, what good is a metric without all the context you get from Sentry? Not very – it makes tying problems back to underlying errors or performance issues unnecessarily difficult.