GitClear offers numerous different stats, as well as several goals (notifications), that pertain to code quality.
link"Code Quality" Tab
Within the "Issues & Quality" tab of the main navigation, you can find a tab with stats focused on how the quality of code is changing for the chosen team and/or committer.
Code Quality stats can be found within the Issues main tab
linkChurn Line Percent
How often is code authored, pushed to the main branch, and then subsequently removed or revised within the following two weeks? Churned Line Percent answers the question on a per-repo basis.
What percentage of recent work was removed or reverted within a couple weeks of being committed?
This is another metric worth keeping an eye toward as AI adoption grows.
linkDuplicate Block Line Counts
Duplicated code works against consistent delivery velocity, by confronting developers with the decision about whether to consolidate multiple similar implementations vs. building their own, new implementation of an existing method.
Duplicate code block lines authored for the selected team
Duplicate block detection can be viewed on a per-repo basis, to understand which projects have had the most difficulty promoting reuse of existing methods.
linkQuality Cornerstones ("Tests" & "Documentation")
How much of the team's change energy has been directed toward "Documentation" and "Tests"?
How much of the team's work has been on "Tests" and "Documentation"?
Teams with an Elite plan can also view their "Documentation" and "Test" work relative to "Median" and "Elite" industry benchmarks.
linkCopy/Paste vs. Move
Another code quality stat inspired by GitClear's AI Code Quality research: teams can see at a glance how their percentage of duplicated code vs. refactored code has changed over past months or years.
Modern AI-based teams often register more copy/paste than "moved"
linkLine Operation Counts
Not an issue stat per-se, but this graph tells an important part of the story when teams begin using AI, and find that their rate of defects has increased. In these cases, we always recommend that teams check how their percentage of "Moved" and "Deleted" code compares to the amount of "Added" code that has occurred during the examined interval.
How much work went into copy/pasted code vs. moved code? When the former exceeds the latter, it is a classic sign of AI use without adequate review
For more context, see GitClear's widely-cited research on the concerning trends in code operations during the AI era. When developers are actively refactoring code, the volume of "Moved" lines should exceed "Copy/Pasted" lines.