StoryScale docs

Estimation method.

StoryScale turns point assignment into a sequence of relative complexity decisions.

Readiness First

Before estimation, StoryScale reviews imported issues with local rules. It flags thin descriptions, missing acceptance criteria, unlinked dependencies, broad component scope, technical risk, split candidates, and already-estimated work.

Comparative Ranking

During the session, the team compares two items and answers which one is more complex, or whether they are similar. StoryScale uses those decisions to build a live ranking from lower to higher complexity.

Story Points are hidden during comparison.
The board updates as decisions are made.
The final ranking can be adjusted by drag and drop.

Historical Anchors

When the project has enough completed and estimated historical work, StoryScale selects representative stories as hidden anchors. Current stories are compared against that calibrated scale, then Story Points are assigned from the nearest previous anchor.

Historical anchors are never published, never shown as sprint backlog items, and never included in the Jira write-back payload.

Guided Calibration

If there are fewer than two distinct historical Story Point buckets, StoryScale switches to guided calibration. The team completes the ranking first, then chooses threshold stories for 2, 3, 5, 8, 13, and 21 point buckets when needed.