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.
Repository
FluidFramework
Library for building distributed, real-time collaborative web applications
Average Developer Performance (ETV)
Year-by-year Trend:+443%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 |
|---|---|---|---|
| e3bdb32 | Move non-tree dependent tree-agent functions to new package (#26862) | brrichards | grow |
| 953af52 | Enable Claude auto mode for AI codespace profiles (#26905) | Noah Encke | grow |
| ebf5edc | Bump Client to 2.93 (#26893) | Craig Macomber (Microsoft) | maint |
| a4ed4aa | Process changesets for 2.92.0 client release (#26907) | Craig Macomber (Microsoft) | maint |
| 2cfa6e1 | Update build-tools in gitrest and historian (#26906) | Craig Macomber (Microsoft) | – |
| be2f7f2 | Update perf benchmarks pipeline to use new version of script (#26897) | jzaffiro | maint |
| 70a8741 | Add telemetry to DistributedTokenBucketThrottler (#26879) | Brandon | grow |
| ca4faf7 | Revert "Remove isomorphic-fetch from odsp-doclib-utils" (#26899) | Alex Villarreal | waste |
| 444951f | Update test tenant acquisition for real service e2e test pipeline (#26892) | jzaffiro | maint |
| 6d35af5 | Improve codespace first-run onboarding experience (#26896) | Tyler Butler | maint |
Move non-tree dependent tree-agent functions to new package (#26862)
Enable Claude auto mode for AI codespace profiles (#26905)
Bump Client to 2.93 (#26893)
Process changesets for 2.92.0 client release (#26907)
Update build-tools in gitrest and historian (#26906)
Update perf benchmarks pipeline to use new version of script (#26897)
Add telemetry to DistributedTokenBucketThrottler (#26879)
Revert "Remove isomorphic-fetch from odsp-doclib-utils" (#26899)
Update test tenant acquisition for real service e2e test pipeline (#26892)
Improve codespace first-run onboarding experience (#26896)
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.
Contributors ranked by total performance (ETV) from analyzed commits.
| # | |||||
|---|---|---|---|---|---|
| 1 | Craig Macomber (Microsoft)457 commits | 89.8 | 15.2 | 68.5 | 6.1 |
| 2 | Joshua Smithrud313 commits | 77.4 | 8.4 | 67.3 | 1.7 |
| 3 | Tyler Butler227 commits | 33.8 | 5.8 | 26.4 | 1.6 |
| 4 | Tony Murphy113 commits | 26.8 | 5.9 | 18.4 | 2.5 |
| 5 | Jason Hartman157 commits | 25.2 | 4.8 | 17.7 | 2.7 |
| 6 | Navin Agarwal69 commits | 24.3 | 7.2 | 16 | 1.1 |
| 7 | Noah Encke58 commits | 17.3 | 5.9 | 10.9 | 0.5 |
| 8 | Matt Rakow78 commits | 14.2 | 2.4 | 11.3 | 0.5 |
| 9 | Mark Fields65 commits | 13.8 | 2.7 | 7.6 | 3.5 |
| 10 | yann-achard-MS58 commits | 13.8 | 1.4 | 9.9 | 2.5 |
| 11 | Abram Sanderson71 commits | 12.4 | 3.8 | 6.9 | 1.7 |
| 12 | Daniel Madrid87 commits | 11 | 2.4 | 8.1 | 0.5 |
| 13 | Scott Norton67 commits | 10.9 | 5.5 | 4.8 | 0.6 |
| 14 | brrichards49 commits | 7.6 | 1.5 | 6.1 | 0 |
| 15 | WillieHabi36 commits | 7.3 | 2 | 4.8 | 0.4 |
| 16 | Tong Chen29 commits | 6.8 | 0.5 | 6.2 | 0 |
| 17 | Jenn18 commits | 6.3 | 2.2 | 3.9 | 0.3 |
| 18 | dhr-verma44 commits | 6.3 | 3.4 | 1.6 | 1.3 |
| 19 | Tommy Brosman21 commits | 6.2 | 2.8 | 3.3 | 0.1 |
| 20 | Jatin Garg40 commits | 5.7 | 0.3 | 5.5 | 0 |
| 21 | Alex Villarreal100 commits | 5.2 | 0.7 | 3.3 | 1.2 |
| 22 | daesunp24 commits | 4.7 | 2.5 | 2 | 0.2 |
| 23 | Ji Kim23 commits | 4.6 | 2.8 | 1.4 | 0.4 |
| 24 | Rishhi Balakrishnan45 commits | 3.9 | 0.2 | 3.1 | 0.7 |
| 25 | Zach Newton23 commits | 3.8 | 2 | 1.1 | 0.7 |
| 26 | Sonali Deshpande28 commits | 3.8 | 0.8 | 1.8 | 1.2 |
| 27 | Wayne Ferrao23 commits | 2.2 | 0.5 | 1.5 | 0.2 |
| 28 | MarioJGMsoft32 commits | 1.9 | 0.4 | 1.4 | 0.1 |
| 29 | Kian Thompson14 commits | 1.6 | 0.5 | 0.5 | 0.6 |
| 30 | jzaffiro32 commits | 1.6 | 0.1 | 1.1 | 0.3 |
| 31 | Shubhangi13 commits | 1.4 | 0.4 | 0.6 | 0.5 |