Analyze GitLab usage

History

GitLab provides different types of analytics insights for instances, groups, and projects.

Analytics features

Use these features to gain insights into your overall software development lifecycle.

Feature	Description	Project-level	Group-level	Instance-level
Value Streams Dashboard	Insights into DevSecOps trends, patterns, and opportunities for digital transformation improvements.	Yes	Yes	No
Value Stream Management Analytics	Insights into time-to-value through customizable stages.	Yes	Yes	No
DevOps adoption by group and by instance	Organization’s maturity in DevOps adoption, with feature adoption over time and feature distribution by group.	No	Yes	Yes
Usage trends	Overview of instance data and changes in data volume over time.	No	No	Yes
Insights	Customizable reports to explore issues, merged merge requests, and triage hygiene.	Yes	Yes	No
Product analytics	Understanding how users behave and interact with your product.	No	Yes	No
Analytics dashboards	Built-in and customizable dashboards to visualize collected data.	Yes	Yes	No

Use these features to gain insights into the productivity of your team on issues and merge requests.

Feature	Description	Project-level	Group-level	Instance-level
Issue analytics	Visualization of issues created each month.	Yes	Yes	No
Merge request analytics	Overview of merge requests, with mean time to merge, throughput, and activity details.	Yes	No	No
Productivity analytics	Merge request lifecycle, filterable down to author level.	No	Yes	No
Code review analytics	Open merge requests with information about merge request activity.	Yes	No	No

Use these features to gain insights into developer productivity and code coverage.

Feature	Description	Project-level	Group-level	Instance-level
Contribution analytics	Overview of contribution events made by group members, with bar chart of push events, merge requests, and issues.	No	Yes	No
Contributor analytics	Overview of commits made by project members, with line chart of number of commits.	Yes	No	No
Repository analytics	Programming languages used in the repository and code coverage statistics.	Yes	Yes	No

Use these features to gain insights into CI/CD performance.

Feature	Description	Project-level	Group-level	Instance-level
CI/CD analytics	Pipeline duration and successes or failures.	Yes	Yes	No
DORA metrics	DORA metrics over time.	Yes	Yes	No

Use these features to gain insights into security vulnerabilities and metrics.

Feature	Description	Project-level	Group-level	Instance-level
Security Dashboards	Collection of metrics, ratings, and charts for vulnerabilities detected by security scanners.	Yes	Yes	No

The following glossary provides definitions for common development metrics used in analytics features, and explains how they are measured in GitLab.

Metric	Definition	Measurement in GitLab
Mean Time to Change (MTTC)	The average duration between idea and delivery.	From when an issue is created until its related merge request is deployed to production.
Mean Time to Detect (MTTD)	The average duration that a bug goes undetected in production.	From when a bug is deployed to production until an issue is created to report it.
Mean Time to Merge (MTTM)	The average lifespan of a merge request.	From when a merge request is created until it is merged. Excludes merge requests that are closed or unmerged. For more information, see merge request analytics.
Mean Time to Recover / Repair / Resolution / Resolve / Restore (MTTR)	The average duration that a bug is not fixed in production.	From when a bug is deployed to production until the bug fix is deployed.
Velocity	The total issue burden completed in a specific period of time. The burden is usually measured in points or weight, often per sprint.	Total points or weight of issues closed in a specific period of time. For example, “30 points per sprint”.

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