Where Can I Find Repository-Level Metrics?

Repository-Level Metrics

Paid versions of LinearB allow you to build reports specific to any repo linked to LinearB. In order to view repo-level metrics go to the Metrics tab in LinearB. Select any pre-built report and change the Filter by: option from "People" to "Repository". LinearB will remember the team or repo selection made for this dashboard, when you return the same repo or team will be displayed. You can also build custom dashboards which will always display your selected repo.

Building a repo-specific dashboard

In order to build a dashboard focusing solely on one repo, follow these quick steps!

1. Click on the plus icon in the top left of your Metrics page.
2. Name your dashboard.
3. Click on Repository filter, and select the desired repo.
4. Click on the "+ Add metrics" block below, and select your desired metrics.
5. Click the Save button in the top left.

What To Do With Repo-Level Reports

There are many benefits to having repo-level metrics available.

Compare Repo Performance

Build a series of dashboards with the same metrics. Apply these dashboards to different repos and you can quickly move from repo to repo to analyze their overall performance.

Identify Repos That Need Help

Build a quality check dashboard for your repo. This can help identify bottlenecks in code, or a rise in inefficiency of your repo. Below are some metrics we would recommend.

• PR Size: In general, a rise in PR size indicates work on this repo is becoming cumbersome, and work on this repo should be broken into smaller, more actionable branches.
• Rework: Code that has to be reworked shortly after merging can be a sign the code is buggy or poorly written.
• Refractor: A rise in refractor could indicate this code is no longer serving its purpose, and may need a revisit. Highly refactored code might also indicate this repo is no longer functioning well with other repos or services.
• Review Depth: Is this code readable? Longer reviews might indicate your team is struggling to understand this code.
• Coding Time: Longer coding time could indicate this code is hard to read, or difficult to edit and update.