Files with the highest combination of change frequency and waste ratio. These are candidates for refactoring or closer review.
Cumulative contribution over time. Watch developers race as positions shift month by month.
Commit activity distribution by hour and day of week across all contributors in this repository.
Performance has many faces. Navigara breaks down the effort to visualize what parts of codebase has been changed and where energy flowed. Our Architect AI can break the performance even further into particular components and patterns.
Breakdown of file changes over time. Play the timeline to see how change types evolved across periods.
Monthly overview of bugs introduced and fixed, based on symbol-level commit analysis. Fixes show whether the original author fixed their own bug (self-fix) or someone else did (cross-fix).
Bug attribution uses symbol-level matching from commit history. For each fix commit, we look at the changed symbols (functions, classes, methods) and trace backwards to find who last modified that symbol in a non-fix commit. This person is the probable bug introducer. The algorithm only works when commits have symbol-level data from the Navigara analysis engine — the coverage rate shows what percentage of fix commits had this data available.
The current metrics model has a semantic inversion: when developer A creates a feature with a bug, they receive grow (positive). When developer B fixes that bug, they receive waste (negative). The bug creator is rewarded while the fixer is penalized. Bug attribution addresses this by explicitly tracking who introduced bugs and who fixed them, providing a more accurate picture of code quality contributions.
Currently computed client-side from commit data. Ideal server-side endpoint:
POST /v1/repositories/{repositoryId}/bug-attributions
Content-Type: application/json
Request:
{
"startTime": "2025-01-01T00:00:00Z",
"endTime": "2025-12-31T23:59:59Z"
}
Response:
{
"totalBugsAttributed": 42,
"selfFixRate": 35,
"coverageRate": 78,
"attributions": [
{
"filePath": "src/lib/auth.ts",
"symbol": "validateToken",
"introducer": { "name": "Alice", "email": "alice@co.com", "commitSha": "abc123" },
"fixer": { "name": "Bob", "email": "bob@co.com", "commitSha": "def456" },
"fixedAt": "2025-06-15T10:30:00Z",
"isSelfFix": false
}
]
}Reclassifies engineering effort based on bug attribution. Commits that introduced bugs are retrospectively counted as poor investments.
Investment Quality reclassifies engineering effort based on bug attribution data. Commits identified as buggy origins (those that introduced bugs later fixed by someone) have their grow and maintenance time moved into the Wasted Time category. Their waste (fix commits) remains counted as productive. All other commits retain their standard classification: grow is productive, maintenance is maintenance, and waste (fixes) is productive.
The standard model classifies commits as Growth, Maintenance, or Fixes. Investment Quality adds a quality lens: a commit that introduced a bug is retrospectively counted as a poor investment — the engineering time spent on it was wasted because it ultimately required additional fix work. Fix commits (Fixes in the standard model) are reframed as productive, because fixing bugs is valuable work.
Currently computed client-side from commit and bug attribution data. Ideal server-side endpoint:
POST /v1/organizations/{orgId}/investment-quality
Content-Type: application/json
Request:
{
"startTime": "2025-01-01T00:00:00Z",
"endTime": "2025-12-31T23:59:59Z",
"bucketSize": "BUCKET_SIZE_MONTH",
"groupBy": ["repository_id" | "deliverer_email"]
}
Response:
{
"productivePct": 74,
"maintenancePct": 18,
"wastedPct": 8,
"buckets": [
{
"bucketStart": "2025-01-01T00:00:00Z",
"productive": 4.2,
"maintenance": 1.8,
"wasted": 0.6
}
]
}Latest analyzed commits in this repository.
| Hash | Message | Author | Effort |
|---|---|---|---|
| 8c8c99c | Add subscript & superscript characters for Quick Accent #41922 (#45540) | Salehnaz | grow |
| feae285 | CmdPal: Revert focus restoration on Extensions settings page (#46642) | Jiří Polášek | – |
| c34fb7f | CmdPal: Harden ListViewModel fetch synchronization (#46429) | Jiří Polášek | waste |
| 7d171a4 | Repository: Add .claude local settings to .gitignore (#46630) | Jiří Polášek | maint |
| 2d037c4 | CmdPal: Fix bad merge (#46639) | Jiří Polášek | waste |
| 0a69c93 | PowerToys Extension: Include deps in Microsoft.CmdPal.Ext.PowerToys slnf (#46136) | Jiří Polášek | maint |
| a022a9f | CmdPal: Make Dock stay on top of all other windows (#46163) | Jiří Polášek | grow |
| 0b7d780 | CmdPal: improve full-screen detection (#45891) | Jiří Polášek | grow |
| 7685cd1 | CmdPal: Fix binary file corruption in Create Extension (#46490) | Jiří Polášek | maint |
| 72bdfb0 | CmdPal: Fix exception when converting calc result to different bases (#46176) | Jiří Polášek | maint |
Add subscript & superscript characters for Quick Accent #41922 (#45540)
CmdPal: Revert focus restoration on Extensions settings page (#46642)
CmdPal: Harden ListViewModel fetch synchronization (#46429)
Repository: Add .claude local settings to .gitignore (#46630)
CmdPal: Fix bad merge (#46639)
PowerToys Extension: Include deps in Microsoft.CmdPal.Ext.PowerToys slnf (#46136)
CmdPal: Make Dock stay on top of all other windows (#46163)
CmdPal: improve full-screen detection (#45891)
CmdPal: Fix binary file corruption in Create Extension (#46490)
CmdPal: Fix exception when converting calc result to different bases (#46176)
Average context complexity and engagement score of file changes over time. Higher complexity means more intricate changes; higher impact means broader effect on the codebase.
Repository
PowerToys
Microsoft PowerToys is a collection of utilities that supercharge productivity and customization on Windows
Average Developer Performance (ETV)
Year-by-year Trend:+714%Contributors ranked by total performance (ETV) from analyzed commits.
| # | |||||
|---|---|---|---|---|---|
| 1 | Jiří Polášek238 commits | 53.4 | 24.5 | 11.1 | 17.8 |
| 2 | Yu Leng74 commits | 20.1 | 7.7 | 11 | 1.3 |
| 3 | Mike Griese106 commits | 17.8 | 9.3 | 5.9 | 2.6 |
| 4 | Michael Jolley41 commits | 14.9 | 4.8 | 8.6 | 1.5 |
| 5 | leileizhang94 commits | 14.4 | 6.2 | 5.3 | 2.9 |
| 6 | Kai Tao109 commits | 13.8 | 4.6 | 5.2 | 4.1 |
| 7 | Niels Laute82 commits | 9.9 | 3.4 | 5.9 | 0.6 |
| 8 | Shawn Yuan41 commits | 6.3 | 2.7 | 2.4 | 1.1 |
| 9 | Dave Rayment32 commits | 6.2 | 1.5 | 1.3 | 3.5 |
| 10 | Heiko20 commits | 5.6 | 4.5 | 0.8 | 0.3 |
| 11 | Gordon Lam54 commits | 5.4 | 2.5 | 2.1 | 0.9 |
| 12 | Mike Hall11 commits | 4.8 | 3.9 | 0.9 | 0 |
| 13 | Jaylyn Barbee27 commits | 3.2 | 1.9 | 0.9 | 0.4 |
| 14 | Davide Giacometti27 commits | 3 | 1.2 | 0.9 | 0.9 |
| 15 | Jessica Dene Earley-Cha14 commits | 1.5 | 0.7 | 0.3 | 0.6 |
| 16 | Jaime Bernardo29 commits | 1.3 | 0.4 | 0.2 | 0.7 |
| 17 | PesBandi19 commits | 1.2 | 0.8 | 0.1 | 0.2 |
| 18 | Clint Rutkas23 commits | 0.6 | 0 | 0.6 | 0 |
| 19 | Dustin L. Howett23 commits | 0.5 | 0.1 | 0.4 | 0 |
| 20 | Kayla Cinnamon12 commits | 0.2 | 0.1 | 0.1 | 0 |