Jira Metric
Issue Tracking
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.
JiraIssue TrackingVersion 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.
How to calculate version release success rate
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.
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
MCPPull 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
Defect Density
Issue TrackingMetric Definition
Defect 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.
Release Burnup Analysis
Issue TrackingMetric Definition
Release 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.
Component Quality Trends
Issue TrackingMetric Definition
Component 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.
Epic Progress Tracking
Issue TrackingMetric Definition
Epic 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.
All Jira metrics
Backlog Health AnalysisBlocked Time PercentageCode Review Cycle TimeComponent Quality TrendsCross-Team Dependency AnalysisCycle TimeDefect DensityEpic Progress TrackingEscalation Pattern AnalysisFlow EfficiencyIssue Age DistributionIssue Resolution RateLead TimePriority Distribution AnalysisRelease Burnup AnalysisSprint Burndown AnalysisSprint Commitment AccuracySprint Goal Achievement RateSprint VelocityStory Point Estimation AccuracyTeam Capacity UtilisationTeam Collaboration IndexTechnical Debt RatioUser Productivity AnalysisWorklog AccuracyEmpower your team to understand and act on Jira data
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