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Add Chart for DORA "Time to restore service" metric

Release notes

Enhancing visibility for Software delivery performance with a new trend chart for DORA Time to restore service metric.

With the addition of the four DORA metrics tiles to the VSA overview dashboard, we are now adding overtime visualization to the "Time to restore service" metric. With this new chart, users will have a better understanding of software stability and reliability trends over time.

The new Time to restore service chart shows information about how long it takes an organization to recover from a failure in production. GitLab measures this as the median time required to close the incidents in the given time period.

It is the third DORA chart that is available out of the box in GitLab. We plan to keep improving the visibility into DORA metrics and to add charts for the fourth metric - Change failure rate.

Problem to solve

When viewing the mean time to restore service metric in VSA, I want to drill in and see more detailed information about my deployments so that I can identify trends and understand deployment behaviors of my teams & projects.

Once we implement #355304 (closed) the user will need an overtime visualization for Time to restore service Metric.

Proposal

  1. Add a new chart under CI/CD Analytics page.
  2. Order of the charts tabs (left to right): Release statistics - Deployment frequency - Lead time - Time to restore service - Change Failure Rate - Shared Runners Usage
  3. Chart type: same as https://docs.gitlab.com/ee/user/analytics/ci_cd_analytics.html#view-lead-time-for-changes-chart.
  4. Tile for "Time to restore service" should also be included in the chart.
  5. Chart and tile should be filtered based on Date pickers with 3 options: Last week, Last month, Last 90 days.
  6. Trendline with median time is needed.
  7. Consider performance vs data to ensure smooth page usability.
  8. GitLab Premium will see project level.
  9. GitLab Ultimate will see also see group level. Screen_Shot_2022-05-04_at_15.07.27

Intended users

Feature Usage Metrics

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Edited by Haim Snir