DevOps metrics list

Code change volume metrics

The code change volume metrics indicate the code change frequency of developers in a source control, such as AWS CodeCommit. These metrics give DevOps leaders better visibility into the coding activities of their development teams. They can answer questions, such as who makes the most number of code changes and which repositories are the most active over time. The underlying data for these metrics are AWS CodeCommit events. To view an example dashboard for these metrics, refer to Code change volume dashboards.

Mean time to recover metrics

MTTR metrics present outage minutes and the average time it takes to restore an application from a failed state. These metrics help DevOps leaders correlate change activity to system stability, track problematic applications and identify opportunities to improve the stability of applications. Currently MTTR metrics track two types of applications: REST API and AWS CodePipeline. The underlying data for these metrics are Amazon CloudWatch alarm events for Synthetics canary and CodePipeline respectively. To view an example dashboard for these metrics, refer to Mean time to recover dashboards.

Change failure rate metrics

The change failure rate metrics indicate how often deployment failures occur for an application. These metrics help DevOps leaders track the code quality of their development teams and drive improvements to reduce change failure rate over time. The underlying data for this metric are AWS CodeDeploy events. To view an example dashboard for these metrics, refer to Change failure rate dashboards.

Deployment metrics

Deployment metrics present data about application deployment, such as deployment state (failure or success) and frequency. These metrics help DevOps leaders track the frequency and quality of their continuous software release to end-users. The underlying data for this metric are AWS CodeDeploy events. To view an example dashboard for these metrics, refer to Deployment dashboards.

Build metrics

Build metrics present data about CodeBuild activities, such as build duration, build state (failure or success) and frequency, along with resource utilization metrics for CPU, memory, and storage utilization. These metrics help DevOps leaders track the frequency and quality of their code build process. These metrics can indicate which build projects or phases take the longest time to run, which build projects are the most active over time, and which build projects fail the most. The underlying data for these metrics are AWS CodeBuild metrics. To view an example dashboard for these metrics, refer to Build dashboards.

Pipeline metrics

The Pipeline metrics present data about CodePipeline, such as pipeline execution state (failure, success, or other), execution duration and frequency along with state at stage and action level. These metrics give DevOps leaders better visibility into the pipeline activities of their development teams. These metrics can indicate which pipelines fail the most, which pipelines take the longest time to run, and which pipelines are the most active over time. The underlying data for these metrics are AWS CodePipeline events. To view an example dashboard for these metrics, refer to Pipeline dashboards.

GitHub activity metrics

The GitHub activity metrics present code change volumes in GitHub, such as the number and frequency of pushes and commits by authors and repositories. These metrics give DevOps leaders better visibility into the coding activities of their development teams. They can answer questions, such as who makes the greatest number of pushes or commits and which repositories are the most active over time. The underlying data for these metrics are push events from GitHub. To view an example dashboard for these metrics, refer to GitHub activity dashboards.