DORA metrics have become a widely accepted way to measure software delivery performance in modern engineering teams. They help teams understand not just how fast they ship code, but how reliably they do it. When tracked correctly inside CI/CD pipelines, DORA metrics provide actionable insights that directly improve deployment quality, stability, and developer productivity.
This article explains how to track DORA metrics in CI/CD pipelines in a practical, engineering-focused way, without turning measurement into overhead.
What are DORA metrics?
DORA metrics originate from the DevOps Research and Assessment (DORA) team and focus on four key indicators of software delivery performance:
Deployment frequency
Lead time for changes
Change failure rate
Mean time to recovery (MTTR)
Together, these metrics provide a balanced view of speed and stability. Tracking DORA metrics inside CI/CD pipelines ensures the data reflects real delivery behavior rather than manual reporting.
Why track DORA metrics in CI/CD pipelines?
CI/CD pipelines are the most reliable source of truth for delivery events. They capture code commits, build executions, test results, deployments, rollbacks, and failures automatically. Tracking DORA metrics directly from pipelines helps teams:
Eliminate manual data collection
Measure real system behavior
Detect delivery risks early
Align engineering improvements with business outcomes
When DORA metrics are derived from CI/CD systems, they remain objective, consistent, and scalable.
Tracking deployment frequency
Deployment frequency measures how often code is successfully deployed to production or a production-like environment.
To track deployment frequency in CI/CD pipelines:
Identify the pipeline stage that represents a production deployment
Count successful executions of that stage over time
Group deployments by service, environment, or team
Deployment frequency should focus on meaningful deployments. Counting every redeploy or rollback can distort the metric, so pipelines should clearly distinguish between standard releases and operational fixes.
Measuring lead time for changes
Lead time for changes measures how long it takes for a code change to go from commit to production.
In CI/CD pipelines, lead time can be tracked by:
Recording commit timestamps from version control
Capturing deployment timestamps from pipeline runs
Calculating the difference between the two
To improve accuracy, teams should ensure that pipelines consistently tag deployments with commit identifiers. This allows lead time to be measured per change rather than per batch.
Tracking change failure rate
Change failure rate measures the percentage of deployments that result in failures requiring remediation, such as rollbacks, hotfixes, or incident responses.
CI/CD pipelines can track this by:
Monitoring failed production deployments
Detecting automated rollbacks
Linking incidents or alerts to recent deployments
Change failure rate should focus on customer-impacting failures, not test failures in pre-production stages. Clear pipeline signals help teams distinguish between expected failures and real regressions.
Measuring mean time to recovery (MTTR)
MTTR measures how quickly a team can restore service after a failure.
To track MTTR using CI/CD pipelines:
Capture the time when a failure is detected (alerts, failed health checks, incident creation)
Capture the time when service is restored (successful rollback or fix deployment)
Calculate the duration between these events
Integrating CI/CD pipelines with monitoring and incident management tools improves MTTR accuracy and reduces manual correlation.
Integrating DORA metrics with observability
DORA metrics become more powerful when combined with observability data. CI/CD pipelines can emit metadata that links deployments to logs, traces, and metrics.
This integration allows teams to:
Correlate performance degradation with specific deployments
Validate deployment health automatically
Detect failures earlier using real-time signals
Observability-driven pipelines help ensure that DORA metrics reflect real system health, not just pipeline outcomes.
Avoid common pitfalls when tracking DORA metrics
Teams often struggle with DORA metrics due to inconsistent definitions or poor data quality. Common mistakes include:
Counting non-production deployments
Ignoring partial outages or silent failures
Measuring lead time without commit-level tracking
Treating DORA metrics as performance targets instead of indicators
DORA metrics should be used for learning and improvement, not for individual performance evaluation.
Using DORA metrics to drive improvement
Once tracked consistently, DORA metrics should guide engineering decisions. Examples include:
High lead time indicating slow reviews or flaky tests
High change failure rate pointing to gaps in testing or validation
Poor MTTR highlighting weak rollback or observability practices
CI/CD pipelines provide the feedback loop needed to continuously refine delivery practices.
Conclusion
Tracking DORA metrics in CI/CD pipelines gives engineering teams a clear, data-driven view of software delivery performance. By collecting deployment frequency, lead time, change failure rate, and MTTR directly from pipelines, teams gain reliable insights without manual effort.
When used correctly, DORA metrics help teams balance speed and stability, reduce deployment risk, and continuously improve how software is delivered. Integrated deeply into CI/CD pipelines, they become a powerful tool for building resilient and high-performing engineering organizations.
