What is the best way to track developer velocity in GitHub without relying on toxic metrics like lines of code written?
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Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
What is the best way to track developer velocity in GitHub without relying on toxic metrics like lines of code written?
1 real search · 1 real-search pass
DORA metrics software delivery performance four keys change lead time deployment frequency change failure rate failed deployment recovery time SPACE framework developer productivity dimensions GitHub insights pull request metricsGoogle ↗
OBSERVEDReason
Badge and edge weight = share of passes that produced the query. Hover a query for its intent and routing format.
Query classes
Google's semantic query class for every sub-query: what kind of answer each search is after, simulated vs real.
ChatGPT real searches (1)
Classes come from the query-classifier (a local Laya model). 1 of 1 calls are low-confidence (under 50%) — hover a query in the table to see its confidence.
Searches and sources
ChatGPT1 real searches · gpt-6
DORA metrics software delivery performance four keys change lead time deployment frequency change failure rate failed deployment recovery time SPACE framework developer productivity dimensions GitHub insights pull request metrics Google ↗
- DORA · dora.dev
- DORA | A history of DORA’s software delivery metrics · dora.dev
- DORA Four Keys project · github.com
Queries
1 queries · click a column to sort
| Query / type | Search | Intent | Query class | Routing format | Cluster | Freq ↓ | Weight |
|---|---|---|---|---|---|---|---|
DORA metrics software delivery performance four keys change lead time deployment frequency change failure rate failed deployment recovery time SPACE framework developer productivity dimensions GitHub insights pull request metrics OBSERVED | Google ↗ AI Mode ↗ | — | Reason | — | — | 1x of 1 |