How Sleuth measures Change Failure Rate
Before you can measure the DORA metric for Change Failure Rate, you need to define what failure means. In this video, Sleuth's CTO Don Brown explains how Sleuth defines and measures Change Failure Rate, and how it ties failure back to deployments.
0:00 Change Failure Rate overview
0:20 Two types of failure: simple or anomaly detection
0:47 Simple failure type 1: Rollback
1:12 Simple failure type 2: Incident
1:29 Incident detection in Sleuth
2:35 Anomaly detection
2:56 Tracking errors in Sleuth for anomaly detection
3:28 Other metrics Sleuth can measure for anomaly detection
3:53 Customize anomaly detection with webhooks
4:06 Pointing Sleuth at builds to detect anomalies
4:33 Anomaly detection can be customized in project settings
4:58 Sensitivity to detect Change Failure Rate
5:17 Set sensitivity level
5:47 Determining failure - Last known deployment
Check out these videos on how Sleuth measures other DORA metrics:
- How Sleuth measures Deployment Frequency: https://www.youtube.com/watch...
- How Sleuth measures Change Lead Time: https://www.youtube.com/watch
- How Sleuth measures Mean Time to Recovery (MTTR): https://www.youtube.com/watch
Give Sleuth a try and see why it's a deploy-based Accelerate / DORA metrics tracker both managers and developers love.
Live Demo: https://app.sleuth.io/sleuth/sleuth/metrics/lead_time
Free Trial: https://app.sleuth.io/account/signup/