5. High-level metrics to track DevOps effectiveness - DORA metrics
In our view, the metrics above give the most complete view of the overall effectiveness of the SDLC (from design through to delivery to live) and its ability to generate value by efficiently delivering your roadmap priorities.
However, we should also consider the DORA metrics, which are more focused on the final stages of the SDLC (integration and deployment) – as they are an increasingly popular starting point for teams to improve the overall Agile DevOps capability.
There are four DORA metrics which have become increasingly popular. These are:
- Lead Time for Changes
- Deployment Frequency (described above)
- Change Failure Rate
- Mean Time to Recovery
Lead Time for Changes
Lead Time for Changes is defined in ‘Accelerate: The Science of Lean Software and DevOps’ by Kim, Humble and Forsgren (the book which popularised the DORA metrics) as: ‘the time taken to go from code committed to code successfully running in production’.
Code Cycle Time
Code Cycle Time is a broader metric in providing insight into the different stages a Pull Request goes through, including the time it takes to deploy. The stages are defined as:
- Time to Review – From open to the first comment or review
- Time to Approve – From the previous stage to approved
- Time to Merge (Commit)/Close – From the previous stage to merge/close
- Time to Deploy – From the previous stage to deployed to production.
Change Failure Rate
Change Failure Rate measures the changes (deployments) that fail when they reach production (are released to end-users). It is expressed as a percentage of the total number of deployments over the same period.
Mean Time to Recovery
Time to Recovery (also known as Time to Restore) measures how long it takes to restore service when a service incident or defect impacts customers.
