DORA Metrics are key for assessing the efficiency of software development teams. Developed by the DevOps Research and Assessment (DORA) team, these metrics provide valuable insights into software delivery performance and quality. Here, we’ll help you understand how to measure each of these metrics, what counts as low, medium, and high performance, and provide practical examples of calculations.
Deployment Frequency
Deployment Frequency measures how often a team releases code to production. This metric is crucial for understanding a team’s agility and ability to quickly roll out new features and fix bugs.
How to Calculate Deployment Frequency?
To calculate Deployment Frequency, you need to know the total number of deployments made during a specific period and then divide that number by the number of days in that period. Here’s how:
– Define the period (e.g., one week).
– Count the number of deployments during this period.
– Divide the number of deployments by the number of days in the period.
For example, if your team made 20 deployments in 10 days, the frequency would be 2 deployments per day.
Market Standards According to State of DevOps
- Low Performance: Less than once a month. This indicates a slow development process, possibly due to manual processes, lack of automation, or other significant bottlenecks.
Medium Performance: Between once a month and once a week. The team has a reasonable pace but could improve automation and pipeline efficiency.
High Performance: At least once a week. This reflects a highly efficient team with automated processes and a robust CI/CD pipeline.
Lead Time for Changes
Lead Time for Changes measures the time from when development starts (first commit) until the code is in production. It’s a measure of development and deployment pipeline efficiency.
How to Calculate Lead Time for Changes?
Calculating Lead Time for Changes involves tracking the time from the first code commit until the change is deployed in production. This metric provides a clear view of process efficiency and readiness to release new features.
Steps to Measure Lead Time for Changes:
– Identify the First Commit: Determine the timestamp of the first commit related to a specific change. Tools like Git can help easily identify this initial commit.
– Track the Pull Request: Monitor the Pull Request (PR) associated with the change. Record when the PR is opened and when it is approved and merged.
– Record Production Deployment: Note the timestamp when the change is finally deployed in production.
– Calculate Lead Time: The Lead Time for Changes is the time difference between the first commit and when the code is in production.
Consider a change that started with an initial commit on January 1st. The Pull Request was opened on January 2nd, approved and merged on January 5th, and deployed on the same day. So, the calculation would be:
– First commit: January 1st
– Production deployment: January 5th
– Lead Time for Changes: 5 – 1 = 4 days
Market Benchmark
Low Performance:** More than a month. This suggests significant bottlenecks in development from coding to deployment.
Medium Performance: Between one week and one month. The team is reasonably efficient but could benefit from improvements in code review and testing processes.
High Performance: Less than a week. Reflects an agile team with efficient processes allowing quick deliveries.
Change Failure Rate
Change Failure Rate measures the proportion of changes that result in failures like bugs, rollbacks, or incidents requiring intervention.
How to Calculate Change Failure Rate?
Calculating Change Failure Rate involves identifying and counting failed deployments and dividing them by total deployments. This gives a clear view of change quality and testing process efficiency.
Steps to Measure Change Failure Rate:
– Define Measurement Period: Choose the period over which you want to measure failure rate (daily, weekly, monthly).
– Collect Deployment Data: Use CI/CD tools to log all deployments during this period.
– Identify Failed Deployments: Analyze logs to find which deployments failed (e.g., required rollback or urgent fixes).
– Calculate Failure Rate: Divide failed deployments by total deployments and multiply by 100 for percentage.
Imagine your team made 20 deployments in a month with 3 failures. The calculation would be:
– Total deployments: 20
– Failed deployments: 3
– Change Failure Rate = (3 / 20) * 100 = 15%
Market Benchmark
Low Performance: More than 15%. Indicates significant issues with code quality and testing processes leading to frequent production problems.
Medium Performance: Between 5% and 15%. The team has an acceptable failure rate but needs improvements in code quality and testing effectiveness.
High Performance: Less than 5%. Reflects excellent QA practices with robust automated tests and effective code review processes.
Mean Time to Recovery (MTTR)
MTTR measures how long it takes for a team to restore service after a production failure. This metric is crucial for assessing response capability and resilience.
How to Calculate MTTR?
Calculating Mean Time to Recovery involves tracking incident resolution time from detection to resolution. This metric helps evaluate recovery process effectiveness.
Steps to Measure Mean Time to Recovery:
– Define Measurement Period: Choose when you want to measure MTTR (daily, weekly, monthly).
– Collect Incident Data: Use monitoring tools like PagerDuty or Datadog to log each production incident with detection/resolution timestamps.
– Record Recovery Times: For each incident, record total recovery time by subtracting detection timestamp from resolution timestamp.
– Calculate Average Recovery Times: Sum all recovery times over the period then divide by total incidents for MTTR.
Imagine three incidents last month with recovery times:
1. Incident 1: 30 minutes
2. Incident 2: 45 minutes
3. Incident 3: 60 minutes
The calculation would be:
– Total incidents: 3
– Total recovery time: 30 + 45 + 60 = 135 minutes
– MTTR = 135 / 3 = 45 minutes
Market Benchmark
Low Performance: More than one day. Indicates significant difficulties resolving incidents quickly due possibly inefficient recovery processes or lack of tools.
Medium Performance: Between one hour and one day. Reasonably effective but could improve recovery/monitoring processes.
High Performance: Less than one hour. Reflects highly agile/prepared teams handling incidents efficiently with effective recovery/monitoring tools.
Why Correctly Measuring DORA Metrics Matters
Correctly measuring DORA Metrics is crucial for understanding development team performance and identifying pipeline bottlenecks. These metrics provide clear insights into software delivery helping leadership make informed decisions on improvement efforts/resource investment. Without these measurements identifying problems/effective changes becomes difficult.
Additionally DORA Metrics allow teams compare performance against industry standards helping adjust strategies stay competitive. Monitoring/optimizing these metrics over time creates continuous improvement cycles essential maintaining agile teams/high-quality development.
