Dora DevOps Metrics
DORA (Google’s The DevOps Research and Assessment team) metrics are used by SDLC teams to measure the DevOps performance and can achieve significantly better business outcomes along with DevOps maturity improvements. There are 4 metrics and they are,
To measure Velocity,
Deployment Frequency
Lead Time for Changes
To measure Stability,
Change Failure Rate: The percentage of failure deployments in production
Mean Time to Recovery: Time taken to recover the production env from failure
How to measure DORA Metrics:
Deployment Frequency: Frequency of successful software releases to production
Calculates how often the code changes are deployed to production. This metrics is used to determine the consistent the software delivery and evaluate whether the teams are meeting their continuous delivery goals
Captured using the ‘total number of deployments per day’ as a reference. More the deployments per day will improve the deployment frequency.
To improve deployment frequency, do smaller code changes and frequent deployments/deliveries.
Lead Time for Changes: Time taken for a commit to get into production
Measures the time taken to pass the newly committed code to reach production. This metrics indicates how agile and responsive a team handles the change request until production release.
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Measured using the time ‘difference between the exact time taken for a commit and production deployment’.
To reduce lead time for changes, follow feature-based development and enable automated code review process, automated deployment with best fit CI/CD flow by reducing the manual interventions.
Change Failure Rate: The percentage of failure deployments in production
Indicates the percentage of changes that were made to a code that then resulted in incidents, rollbacks, or any type of production failures. This metrics provides great insights on time spent to fix production issues vs total deployments.
Calculated by counting ‘the number of deployment failures divided by the total number of deployments’
To achieve lower change failure rate, reduce the production deployment failures with proper validation in lower environments and time delay to fix those failures.
Mean Time to Recovery: Time taken to recover the production env from failure
Measures the time taken to restore services back from a production disruptions like unplanned outages or incidents. This metric helps to investigate the stability of the software and environment, as well as the agility of the team in the face of a production failure. Also helps the team to build more robust systems.
Calculated by tracking the average time between an incident/outage detected and the moment the service is fixed, deployed and restored.
To have lower mean time to recovery, practice continuous monitoring and prioritize recovery with emergency incident response plan when a failure happens.
Additional metrics which can be captured in projects for successful delivery are Cycle time, Deployment time, Deployment size, Error rates, CI Duration, Mean time to Detection etc.
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