KPI Tree
LinearIssue Tracking

Issue Reopening Rate

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.

Linear metric

Issue Tracking

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.

Full guide: definition, formula, and benchmarks

How to calculate Issue Reopening Rate

Reopening Rate = (Reopened Issues / Total Completed Issues) × 100

Why Issue Reopening Rate matters for Linear users

Reopened issues represent rework, which is one of the most expensive forms of waste in software delivery. Each reopening extends the true cycle time, disrupts planning, and indicates that the definition of done or quality checks are insufficient.

For Linear teams, a high reopening rate signals that work is being rushed through completion checkpoints. It helps teams identify whether the problem lies in unclear acceptance criteria, insufficient testing, or premature status transitions.

Driver

Conversion rate

23%
Granger-causal · lag 3d · q < 0.05

Outcome · 58% contribution

Revenue

15%

Understand and act on Issue Reopening Rate with KPI Tree

KPI Tree tracks issue state transitions from your Linear warehouse to calculate reopening rates. Place this in your quality tree alongside bug escape rate and technical debt metrics.

Assign RACI ownership to team leads for definition-of-done standards. Set alerts when reopening rates exceed your team's acceptable threshold.

Get started with your Linear data

Query using MCP
MCP

Pull metrics from Linear directly through the Model Context Protocol.

Data Warehouse
SnowflakeBigQueryDatabricksRedshift

Connect your existing warehouse where Linear data already lands.

Professional Services
FivetranSnowflakedbt

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.

Explore Issue Reopening Rate across integrations

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Map what drives your metrics, measure progress at any grain, prove what works statistically, and deliver personalised action plans to every team member.

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