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 โ 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 โ 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 |
|---|---|---|---|
| 8544314 | fix: restrict importlib provider loading to trusted namespaces (#7463) | Victor Dibia | waste |
| b047730 | fix: Improve AutoGen Studio: deprecate FunctionTool, harden MCP WebSocket endpoint (#7362) | Victor Dibia | maint |
| 13e144e | fix: order by clause (#7051) | 4shen0ne | waste |
| 6dc0a23 | update instructions for Quickstart (#7068) | Jirka Borovec | maint |
| 84f21a0 | Update README with maintenance and resource information (#7067) | Eric Zhu | maint |
| c027912 | Support MCP Elicitation, Sampling, and Roots via new McpSessionHost (#6833) | Tyler Payne | maint |
| a2bf539 | Update website for 0.7.5 (#7060) | Eric Zhu | maint |
| 83afbf5 | Update version to 0.7.5 (#7058) | Eric Zhu | โ |
| e045643 | Add missing reasoning_effort parameter support for OpenAI GPT-5 models (#7054) | Copilot | grow |
| 29931b3 | Fix(mcp): drain pending command futures on McpSessionActor failure (#7045) | Sungjun.Kim | maint |
fix: restrict importlib provider loading to trusted namespaces (#7463)
fix: Improve AutoGen Studio: deprecate FunctionTool, harden MCP WebSocket endpoint (#7362)
fix: order by clause (#7051)
update instructions for Quickstart (#7068)
Update README with maintenance and resource information (#7067)
Support MCP Elicitation, Sampling, and Roots via new McpSessionHost (#6833)
Update website for 0.7.5 (#7060)
Update version to 0.7.5 (#7058)
Add missing reasoning_effort parameter support for OpenAI GPT-5 models (#7054)
Fix(mcp): drain pending command futures on McpSessionActor failure (#7045)
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
autogen
A programming framework for agentic AI
Average Developer Performance (ETV)
Year-by-year Trend:+70%Contributors ranked by total performance (ETV) from analyzed commits.
| # | |||||
|---|---|---|---|---|---|
| 1 | Victor Dibia62 commits | 16.1 | 9.7 | 5.4 | 1 |
| 2 | Eric Zhu208 commits | 13.9 | 6.6 | 6 | 1.2 |
| 3 | J jaalber40 commits | 10.5 | 6.6 | 3.1 | 0.9 |
| 4 | R rysweet19 commits | 6.9 | 2.1 | 4.6 | 0.2 |
| 5 | J jack87 commits | 6.7 | 3.5 | 2.9 | 0.4 |
| 6 | T tylerpayne2 commits | 4.8 | 0.8 | 4 | 0 |
| 7 | J jayprakashthakursnr9 commits | 4.6 | 1.1 | 3.3 | 0.2 |
| 8 | L leosantospinheiro15 commits | 3.7 | 1.5 | 2.1 | 0.2 |
| 9 | 1 198982749+copilot16 commits | 3.3 | 1.9 | 1.2 | 0.3 |
| 10 | G griffinbassman35 commits | 3.1 | 0.9 | 2.2 | 0.1 |
| 11 | A abhinav3 commits | 2.9 | 1.1 | 1.8 | 0 |
| 12 | C chiyoung.song27 commits | 2.7 | 0.9 | 1.1 | 0.7 |
| 13 | afourney29 commits | 2.3 | 1.5 | 0.3 | 0.5 |
| 14 | T tejas.dharani107 commits | 1.7 | 0.2 | 1 | 0.5 |
| 15 | peterychang25 commits | 1.7 | 0.8 | 0.5 | 0.4 |
| 16 | A abdo.fourfriends2 commits | 1.6 | 0.5 | 1.1 | 0 |
| 17 | E eitan.yarmush7 commits | 1.2 | 0.6 | 0.6 | 0 |
| 18 | A abhijeet0404036 commits | 1.1 | 0.9 | 0.2 | 0 |
| 19 | 6 61495946+federicovilla553 commits | 1.1 | 0.4 | 0.6 | 0.1 |
| 20 | V varad.srivastava261 commits | 0.9 | 0.1 | 0.8 | 0 |
| 21 | 1 165097110+justin-cechmanek3 commits | 0.9 | 0.3 | 0.6 | 0 |
| 22 | G gagb8 commits | 0.8 | 0.7 | 0.1 | 0 |
| 23 | M masquerlin2 commits | 0.8 | 0.6 | 0.2 | 0 |
| 24 | B benconstable2 commits | 0.8 | 0 | 0.4 | 0.3 |
| 25 | S srjoglekar2463 commits | 0.7 | 0.3 | 0.3 | 0 |
| 26 | 4 41174521+alpha-xone1 commits | 0.6 | 0.2 | 0.3 | 0 |
| 27 | 1 125090336+davidyu002 commits | 0.5 | 0.4 | 0.1 | 0 |
| 28 | A aribornstein1 commits | 0.5 | 0.3 | 0.2 | 0 |
| 29 | G gyiko.richard1 commits | 0.4 | 0.2 | 0.2 | 0 |
| 30 | L leondeandrade2 commits | 0.4 | 0.2 | 0.2 | 0 |
| 31 | 3 37175212+zenwayne3 commits | 0.4 | 0.3 | 0.1 | 0 |
| 32 | 6 66320465+amith-ajith1 commits | 0.4 | 0.3 | 0.1 | 0 |
| 33 | B bigmiao.zhang5 commits | 0.3 | 0 | 0.3 | 0 |
| 34 | P peterj5 commits | 0.3 | 0 | 0.1 | 0.2 |
| 35 | K kimsungjun6 commits | 0.3 | 0.2 | 0.1 | 0 |
| 36 | E enhaoz1 commits | 0.2 | 0.2 | 0 | 0 |
| 37 | M mr.junaidshaukat1 commits | 0.2 | 0 | 0.1 | 0.1 |
| 38 | C chengtan20133 commits | 0.2 | 0 | 0 | 0.2 |
| 39 | 5 59463198+torypan1 commits | 0.2 | 0.1 | 0.1 | 0 |
| 40 | S stuart4 commits | 0.2 | 0.1 | 0.1 | 0 |
| 41 | C chrisblaa1 commits | 0.2 | 0.1 | 0.1 | 0 |
| 42 | 4 40957033+cedricmendelin1 commits | 0.1 | 0.1 | 0.1 | 0 |
| 43 | Z zhanluxianshen10 commits | 0.1 | 0 | 0.1 | 0 |
| 44 | R riloynd2 commits | 0.1 | 0.1 | 0 | 0 |
| 45 | 3 37926341+srikarmannepalli1 commits | 0.1 | 0 | 0.1 | 0 |
| 46 | J johan2 commits | 0.1 | 0 | 0.1 | 0 |
| 47 | 9 95917343+nour-bouzid2 commits | 0.1 | 0.1 | 0.1 | 0 |
| 48 | 1 119150866+nissa-seru2 commits | 0.1 | 0 | 0 | 0.1 |
| 49 | H hmozannar4 commits | 0.1 | 0.1 | 0 | 0 |
| 50 | B bjoern.holtvogt1 commits | 0.1 | 0.1 | 0 | 0 |
| 51 | 4 41017744+rylativity1 commits | 0.1 | 0 | 0.1 | 0 |
| 52 | G gerardo291011 commits | 0.1 | 0.1 | 0 | 0 |
| 53 | H husseinkoprly1 commits | 0.1 | 0.1 | 0 | 0 |
| 54 | 3 38664481+seunggil11 commits | 0.1 | 0 | 0 | 0.1 |
| 55 | P pdabrowski1 commits | 0.1 | 0 | 0.1 | 0 |
| 56 | S smalltalkman1 commits | 0.1 | 0 | 0 | 0.1 |
| 57 | L linznin2 commits | 0.1 | 0 | 0 | 0.1 |
| 58 | S savy912 commits | 0.1 | 0 | 0 | 0 |
| 59 | M mwistuba11 commits | 0.1 | 0 | 0 | 0 |
| 60 | K k.kutrowski1 commits | 0.1 | 0 | 0.1 | 0 |
| 61 | A alexey.pelykh1 commits | 0.1 | 0 | 0.1 | 0 |