How do other engineering leaders calculate the productivity gains of automated sprint planning tools when presenting quarterly updates to the board?
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Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
How do other engineering leaders calculate the productivity gains of automated sprint planning tools when presenting quarterly updates to the board?
3 real searches · 1 real-search pass
engineering productivity measurement SPACE framework developer productivity metrics outcomes not activity DORA officialGoogle ↗
OBSERVEDReason
agile sprint planning effectiveness metrics predictability cycle time estimation official guidanceGoogle ↗
OBSERVEDReason
engineering productivity board reporting automation ROI capacity savings measurement software engineeringGoogle ↗
OBSERVEDConsequence
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 (3)
Classes come from the query-classifier (a local Laya model). 3 of 3 calls are low-confidence (under 50%) — hover a query in the table to see its confidence.
Searches and sources
ChatGPT3 real searches · gpt-6
engineering productivity measurement SPACE framework developer productivity metrics outcomes not activity DORA official Google ↗
agile sprint planning effectiveness metrics predictability cycle time estimation official guidance Google ↗
engineering productivity board reporting automation ROI capacity savings measurement software engineering Google ↗
- The SPACE of Developer Productivity: There's more to it than you think.: Queue: Vol 19, No 1 · doi.org
- DORA | Choosing measurement frameworks to fit your organizational goals · dora.dev
- The Story Point Controversy - Estimating User Stories · resources.scrumalliance.org
- Scrum Metrics 101 · wac-cdn.atlassian.com
- Original research paper · doi.org
- Framework overview · commandline.microsoft.com
- How to measure developer productivity · mckinsey.com
Queries
3 queries · click a column to sort
| Query / type | Search | Intent | Query class | Routing format | Cluster | Freq ↓ | Weight |
|---|---|---|---|---|---|---|---|
engineering productivity measurement SPACE framework developer productivity metrics outcomes not activity DORA official OBSERVED | Google ↗ AI Mode ↗ | — | Reason | — | — | 1x of 1 | |
agile sprint planning effectiveness metrics predictability cycle time estimation official guidance OBSERVED | Google ↗ AI Mode ↗ | — | Reason | — | — | 1x of 1 | |
engineering productivity board reporting automation ROI capacity savings measurement software engineering OBSERVED | Google ↗ AI Mode ↗ | — | Consequence | — | — | 1x of 1 |