Change Failure Rate
CFR(Change Failure Rate) refers to the proportion or percentage of deployments that result in failure or errors, indicating the rate at which changes negatively impact the stability or functionality of the system.
Since the definition of Failure is specific to teams, there are multiple ways this metric can be configured. Here are some guidelines on what can indicate a failure :
A deployment that needs a rollback or a hotfix For such cases, any Pull Request having a title/tag/label that represents a rollback/hotfix that is merged to production can be considered a failure
A high-priority production incident For such cases, any ticket in your Issue Tracker having a title/tag/label that represents a high-priority production incident can be considered a failure
A deployment that failed during the production workflow For such cases, Typo can integrate with your CI/CD tool and consider any failed deployment as a failure
To calculate the final percentage, the total number of failures is divided by the total number of deployments (this can be picked either from the Deployment PRs or from the CI/CD tool deployments).
Click here to learn more about CFR(Change Failure Rate) configuration
š” Here is an important tip on how you can use CFR on the Typo dashboard
Benchmarking CFR will help you evaluate performance and establish a baseline for acceptable change failure rates, enabling you to set targets for reducing failures and enhancing the overall quality.
You can click on the ā>ā arrow to see all the deployments.
How does measuring CFR help in improving the Engineering teams' efficiency?
Measuring change failure rate provides critical insights into the stability and reliability of an engineering team's development and deployment processes. This metric can help identify potential risks, assess the effectiveness of quality control measures, and guide improvements to enhance team efficiency. Here are some specific insights that can be gained from measuring change failure rate and how they can be used to improve engineering team efficiency:
Release Quality: A high change failure rate suggests that there may be issues with the development and testing processes that need to be addressed.
Testing Effectiveness: A high change failure rate may indicate gaps or weaknesses in the testing process. Measuring this metric helps in evaluating the effectiveness of automated and manual testing efforts.
Root Cause Analysis: Change failure rate can serve as a starting point for conducting root cause analysis of failures in the production environment.
Continuous Improvement: Tracking the change failure rate over time allows the team to monitor the impact of process changes and improvements.
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