Features

Feature Request: Add Pick Up Time in Review Collaboration Summary Introduce a unified Pick Up Time metric that captures the time from PR submission to the first reviewer interaction (comment, approval, or rejection). This will replace the current split metrics (“First Comment” and “Time to Approval”) as the primary measure of the initial touchpoint. Problem / Motivation Today, responsiveness in review collaboration is measured in two ways: First Comment Time → time until a reviewer leaves a comment. Time to Approval → time until a reviewer approves the PR. However, this split doesn’t fully reflect the first touchpoint between submitter and reviewer, since the reviewer’s first action could be either a comment, an approval, or a rejection. Teams lack a holistic view of when a PR is actually “picked up” for the first time. Proposed Solution Introduce a Pick Up Time metric defined as the time from PR submission to the first reviewer action of any type (comment, approval, or rejection). Display Pick Up Time alongside existing review metrics in dashboards. Maintain the ability to drill down into “First Comment Time” and “Time to Approval” separately for more detailed analysis. Expected Benefits Provides a single, clear indicator of review responsiveness. Reduces ambiguity about what counts as the “first response.” Helps identify bottlenecks earlier in the review workflow. Complements existing metrics by giving both a holistic view (Pick Up Time) and detailed views (comment vs approval).

We’re introducing a new Flow Efficiency breakdown by issue type (e.g., Task, Bug, Story, Improvement) to help teams understand where their time and effort are being invested — and whether technical debt is consuming too much capacity. Key Capabilities: Efficiency by Issue Type: Analyze flow efficiency separately for Bugs, Tasks, Stories, and other custom issue types. Technical Debt Awareness: Identify trends such as excessive time spent on bugs or rework, highlighting technical debt accumulation over time. Effort Investment Insights: Visualize how engineering time is split across planned work vs. unplanned fixes or support tasks. Use Cases: Detect if reactive work (bugs, support) is slowing down feature delivery Justify refactoring or tech debt reduction efforts Improve planning balance between innovation and maintenance User Value: Empowers engineering leaders to make data-driven decisions about resource allocation, tech debt prioritization, and workflow stability.