Linear Metric
Issue Tracking
Resolution Time = Issue Resolved Date − Issue Created Date
Issue Resolution Time measures the total elapsed time from when a Linear issue is created to when it is resolved. It encompasses both waiting time and active work time, providing a full lifecycle view of how long issues take to address.
LinearIssue TrackingIssue Resolution Time
Issue Resolution Time measures the total elapsed time from when a Linear issue is created to when it is resolved. It encompasses both waiting time and active work time, providing a full lifecycle view of how long issues take to address.
How to calculate issue resolution time
Why issue resolution time matters for Linear users
Resolution time is the customer-facing measure of responsiveness. Whether it is a feature request or a bug report, the total time from creation to resolution determines how quickly the organisation responds to needs. Long resolution times erode trust and satisfaction.
For Linear teams, analysing resolution time by issue type and priority reveals where the process is fast and where it lags. It helps teams set and meet service level expectations, ensuring that critical issues receive appropriately rapid attention.
Understand and act on issue resolution time with KPI Tree
KPI Tree calculates resolution time from Linear issue lifecycle data in your warehouse. Place this centrally in your responsiveness tree, with priority distribution and workflow analysis as supporting metrics.
Assign RACI ownership to team leads for resolution time targets. Set alerts when resolution times for high-priority issues exceed defined service level expectations.
Get started with your Linear data
MCPPull metrics from Linear directly through the Model Context Protocol.
Connect your existing warehouse where Linear data already lands.
Our professional services team can build you turn-key AI foundations in a matter of weeks. Data warehouse on Snowflake/BigQuery, ELT with Fivetran, all modelled in dbt with a semantic layer.
Related Linear metrics
Feature Delivery Cycle Time
Issue TrackingMetric Definition
Feature Delivery Cycle Time = Delivery Date − Development Start Date
Feature Delivery Cycle Time measures the total elapsed time from when work begins on a feature in Linear to when it is delivered. It captures the full pipeline duration including development, review, testing, and deployment stages.
Issue Reopening Rate
Issue TrackingMetric Definition
Reopening Rate = (Reopened Issues / Total Completed Issues) × 100
Issue Reopening Rate measures the percentage of Linear issues that are moved back to an active state after being marked as done. It serves as a quality indicator, reflecting whether work is genuinely complete when marked as such.
Issue Aging Analysis
Issue TrackingMetric Definition
Issue Aging Analysis examines the age profile of open issues in Linear, categorising them into brackets to reveal how much of the backlog is recent versus stale. It tracks aging trends over time and identifies patterns in which issue types age fastest.
Cycle Burndown Rate
Issue TrackingMetric Definition
Cycle Burndown Rate tracks the rate at which remaining work decreases during a Linear cycle. It compares the actual burndown trajectory against the ideal straight-line path, revealing whether the team is on pace to complete their cycle commitment.
Explore issue resolution time across integrations
All Linear metrics
Bug Escape RateCode Review Bottleneck AnalysisComment Collaboration RateCross-Team Dependency ImpactCycle Burndown RateCycle Commitment AccuracyDeveloper Workload BalanceEpic Completion ForecastingFeature Delivery Cycle TimeIntegration Impact AnalysisIssue Aging AnalysisIssue Priority Distribution AnalysisIssue Reopening RateLabel Work Classification AnalysisMilestone Delivery PredictabilityProject Health ScoreRoadmap Progress TrackingSeasonal Development PatternsTeam Capacity UtilisationTeam Velocity AnalysisTechnical Debt Accumulation RateUser Story Size ConsistencyWorkflow State Transition AnalysisEmpower your team to understand and act on Linear data
Map what drives your metrics, measure progress at any grain, prove what works statistically, and deliver personalised action plans to every team member.
