GitHub Metric
Engineering
Cycle Time = Deployment Timestamp − First Feature Commit Timestamp
Feature Development Cycle Time measures the elapsed time from the first commit on a feature branch to successful deployment to production. It encompasses coding, review, testing, and release phases. Shorter cycle times enable faster user feedback and more responsive product development.
GitHubEngineeringFeature Development Cycle Time
Feature Development Cycle Time measures the elapsed time from the first commit on a feature branch to successful deployment to production. It encompasses coding, review, testing, and release phases. Shorter cycle times enable faster user feedback and more responsive product development.
How to calculate feature development cycle time
Why feature development cycle time matters for GitHub users
Long cycle times mean users wait longer for value, feedback loops stretch, and work-in-progress accumulates. Understanding where time is spent - coding versus waiting for review versus waiting for deployment - reveals targeted improvement opportunities.
For GitHub teams, cycle time analysis across feature branches highlights systemic bottlenecks: perhaps PRs wait days for review, or staging environments are a shared bottleneck. Each identified bottleneck is an opportunity to ship faster.
Understand and act on feature development cycle time with KPI Tree
Trace feature branches from first commit through PR merge and deployment in your warehouse. Model cycle time in KPI Tree and decompose it into coding, review, and deployment phases within your metric tree.
Assign RACI ownership to delivery leads and set threshold alerts when cycle time exceeds your team target, triggering process review.
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Related GitHub metrics
Lead Time for Changes
EngineeringMetric Definition
Lead Time = Production Deployment Timestamp − Commit Timestamp
Lead Time for Changes measures the elapsed time from when a code change is committed to when it is successfully running in production. It is one of the four DORA metrics and a key indicator of delivery pipeline efficiency. Elite performers achieve lead times measured in hours rather than days or weeks.
Code Review Velocity
EngineeringMetric Definition
Code Review Velocity = Median(First Review Timestamp − PR Ready Timestamp)
Code Review Velocity measures the elapsed time from when a pull request is opened or marked ready for review to when the first substantive review is submitted. It is a key driver of lead time for changes. Long review waits are one of the most common causes of developer context-switching.
Branch Lifecycle Analysis
EngineeringMetric Definition
Branch Lifecycle Analysis measures the duration from branch creation to merge or deletion across a repository. It surfaces stale or abandoned branches that inflate cognitive overhead and merge-conflict risk. Tracking this metric helps teams enforce hygiene policies and maintain a clean codebase.
Sprint Velocity Tracking
EngineeringMetric Definition
Sprint Velocity = Sum of Story Points (or Issue Count) Completed in Sprint
Sprint Velocity Tracking measures the amount of work - typically in story points or issue count - completed during each sprint or iteration, as tracked through GitHub Projects. It provides a baseline for capacity planning and helps teams set realistic commitments for upcoming sprints.
All GitHub metrics
Branch Lifecycle AnalysisBug Fix RateCode Churn RateCode Coverage TrendCode Quality Trend AnalysisCode Review Quality ScoreCode Review VelocityCommit FrequencyDeployment FrequencyDeveloper Contribution PatternsDeveloper Productivity ScoreDevOps Pipeline EfficiencyDiscussion Engagement RateIssue Resolution TimeLead Time for ChangesOpen Source Contribution AnalysisPull Request Approval RatePull Request Bottleneck AnalysisRelease VelocityRepository Health ScoreSecurity Alert Resolution TimeSecurity Vulnerability TrendsSprint Velocity TrackingTeam Collaboration IndexTechnical Debt AccumulationEmpower your team to understand and act on GitHub data
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