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DORA Metrics - Configuration & Definitions

Configure how LinearB calculates and reports DORA metrics across your organization. These articles guide you through defining incidents, releases, and recovery signals so your DORA metrics accurately reflect real delivery and reliability performance.

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6 articles by 1 author

Change Failure Rate (CFR): Definition and Calculation

Learn how LinearB defines and calculates Change Failure Rate, including how incidents are mapped to deployments and what data is required for accurate CFR reporting.

How LinearB Detects Releases for DORA Metrics

Understand how LinearB identifies and models releases across repositories and services. This article explains how release data is used to calculate Deployment Frequency and other DORA metrics.

Incident API – Configure Incident Detection for DORA Metrics

Configure the LinearB Incident API to ingest incidents from your incident management tools. This article explains setup, supported workflows, and best practices for ensuring incidents are accurately reflected in DORA metrics such as Change Failure Rate and MTTR.

Mean Time to Restore (MTTR): Definition and Calculation

Understand how LinearB measures Mean Time to Restore, how recovery windows are calculated, and which incident signals are used to reflect real recovery time.

Reducing Change Failure Rate Through Smaller Deployments

Learn practical strategies for reducing Change Failure Rate by improving deployment practices, breaking work into smaller changes, and aligning coding workflows with DORA measurement principles.

⭐️ Start here: Understanding and Configuring DORA Metrics

Learn how LinearB defines, calculates, and reports DORA metrics across your organization. Start with how releases and incidents are detected, then review how Change Failure Rate (CFR) and Mean Time to Restore (MTTR) are calculated and tuned—so your DORA metrics accurately reflect real delivery and recovery performance.

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