Version Release Success Rate
Version Release Success Rate measures the percentage of Jira version releases that are delivered on time, within scope, and without critical post-release defects. It provides a holistic view of release quality and predictability.
Jira metric
Release Success Rate = (Successful Releases / Total Releases) × 100
Version Release Success Rate measures the percentage of Jira version releases that are delivered on time, within scope, and without critical post-release defects. It provides a holistic view of release quality and predictability.
Full guide: definition, formula, and benchmarksHow to calculate Version Release Success Rate
Release Success Rate = (Successful Releases / Total Releases) × 100
Why Version Release Success Rate matters for Jira users
Releases are the moment of truth for engineering teams. A failed release, whether late, incomplete, or defect-ridden, erodes customer trust and creates costly emergency fixes. Tracking success rate reveals whether the release process is reliable and improving.
For Jira teams managing versioned releases, this metric connects delivery process quality to customer outcomes. High success rates indicate mature testing, deployment, and rollback processes. Low rates point to specific areas needing investment, such as staging environments, automated testing, or release checklists.
Driver
Conversion rate
Outcome · 58% contribution
Revenue
Understand and act on Version Release Success Rate with KPI Tree
KPI Tree evaluates release outcomes from your Jira version and post-release defect data in your warehouse. Place this at the top of your release quality tree, with defect density and epic progress as inputs.
Assign RACI ownership to release managers. Set alerts when release success rates decline or when post-release defect rates spike.
Get started with your Jira data
Pull metrics from Jira directly through the Model Context Protocol.
Connect your existing warehouse where Jira 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 Jira metrics Ready to add to your trees.
Defect Density
Issue TrackingDefect Density = Total Defects / Units of Work Delivered
Defect Density measures the number of defects found per unit of delivered work, such as per story point, per feature, or per release in Jira. It provides a normalised quality indicator that accounts for the volume of work delivered.
View metric
Release Burnup Analysis
Issue TrackingRelease Burnup Analysis tracks the cumulative amount of work completed for a Jira release version over time, plotted against the total release scope. Unlike burndown charts, burnup charts also visualise scope changes, making them ideal for releases where requirements evolve.
View metric
Component Quality Trends
Issue TrackingComponent Quality Trends analyses the volume and severity of defects and issues filed against specific Jira components over time. It identifies components with improving or deteriorating quality trajectories, guiding targeted investment in testing and refactoring.
View metric
Epic Progress Tracking
Issue TrackingEpic Progress = (Completed Story Points / Total Epic Story Points) × 100
Epic Progress Tracking monitors the advancement of Jira epics by measuring the proportion of child issues completed, story points delivered, and time elapsed against planned timelines. It provides feature-level visibility into delivery progress.
View metricExplore Version Release Success Rate across integrations
All Jira metrics
Empower your team to understand and act on Jira data
Map what drives your metrics, measure progress at any grain, prove what works statistically, and deliver personalised action plans to every team member.