How to Measure Change Volume

Learn about Change Volume, including how to measure it, and leverage it in dashboards and visualizations with Metabase.

What is Change Volume?

Change volume describes the number of code changes that occur after a given release or specified period of time. This metric differs from deployment frequency in that it focuses on the amount of code changed instead of the amount of code deployed. You can use this DevOps metric to contribute to a greater calculation of perceived end-user value as well as look at how teams are approaching change. A high volume of deployments could be an indication that your team is working efficiently, or it could be an indication that a team is making too many unnecessary changes.

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Graphs of Change Volume

How to calculate Change Volume

Change volume is a simple visualization that shows the number of deployments made in a specified timeframe. It’s important to remember that, as we mentioned previously, the volume alone shouldn’t be the end all be all for your final analysis. You have to check on the purpose of the code being deployed. If you notice there’s a lot of minor unneeded changes, you can help guide your team into the right direction. Too many small changes can lead to wasted time.

Other KPIs to measure related to Change Volume

  • Application Change Time — how long application changes take to move from request to running in production.
  • Application Monitoring — coverage and health of the monitoring that watches your applications in production.
  • Code Simplicity — complexity trends in the codebase, a proxy for how maintainable the system stays.
  • Commit to Deploy Time — the lag between a commit landing and that change running in production.
  • Cost Performance Indicator — earned value divided by actual cost — whether delivery is tracking the budget.
  • F1 Score — the harmonic mean of precision and recall for a classification model.
  • Flow Efficiency — active work time as a share of total elapsed time — how much of the cycle is waiting.
  • Issue Resolution Time — how long issues take from report to resolution, best tracked with medians and percentiles.
  • Machine Learning Performance — accuracy, drift, and latency of machine learning models in production.

Why build a dashboard for Change Volume?

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How to use Metabase to measure Change Volume

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