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 |
|---|---|---|---|
| ee387f4 | Add a mechanism for signalling between old and new processes when doing graceful upgrades | Kevin Guthrie | grow |
| e7de90a | Fix body bytes count on v1 session | Edward Wang | waste |
| c4beff8 | expose pipe_subrequest outcome | Matthew Gumport | grow |
| 1d93711 | Replace tokio::sync::Mutex with parking_lot::Mutex for ListenFds | Kevin Guthrie | waste |
| 22ffdb8 | Add comments around pend behavior for abort_on_close | Edward Wang | maint |
| 542129f | Fix flaky tests: test_tls_psk, test_conn_timeout, test_1xx_caching, listener port collisions | Kevin Guthrie | maint |
| ea9d9ec | Expose Unexpected Data Counter from Connection Pool | Davis To | grow |
| b633683 | Fix listen fds not inherited during bootstrap_as_a_service graceful upgrade | Kevin Guthrie | waste |
| c1ca1e1 | Shutdown underlying h2 connection on stream read timeout | Kevin Guthrie | grow |
| 93630eb | Fix waiting on h2 upstream if downstream ended | Kevin Guthrie | waste |
Add a mechanism for signalling between old and new processes when doing graceful upgrades
Fix body bytes count on v1 session
expose pipe_subrequest outcome
Replace tokio::sync::Mutex with parking_lot::Mutex for ListenFds
Add comments around pend behavior for abort_on_close
Fix flaky tests: test_tls_psk, test_conn_timeout, test_1xx_caching, listener port collisions
Expose Unexpected Data Counter from Connection Pool
Fix listen fds not inherited during bootstrap_as_a_service graceful upgrade
Shutdown underlying h2 connection on stream read timeout
Fix waiting on h2 upstream if downstream ended
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
pingora
A library for building fast, reliable and evolvable network services.
Average Developer Performance (ETV)
Year-by-year Trend:+342%Contributors ranked by total performance (ETV) from analyzed commits.
| # | |||||
|---|---|---|---|---|---|
| 1 | Edward Wang106 commits | 38 | 11.5 | 5.3 | 21.1 |
| 2 | Andrew Hauck17 commits | 3.4 | 0 | 2.3 | 1.1 |
| 3 | Matthew Gumport28 commits | 2.9 | 1.3 | 1.2 | 0.4 |
| 4 | Fei Deng15 commits | 2.9 | 0.5 | 0.5 | 1.9 |