I am trying to replicate a simple Sprint Velocity Chart, based on this example: Sprint velocity chart - Issues - Jira Demo - eazyBI
The problem is in the way our team works. Our Sprints focus on Dev + Test only, where an issue is considered “Done” from a Dev + Test perspective once it successfully passes QA testing in the Sprint.
In essence there are 2 instances of “Done” in the life of a JIRA issue:
- Dev + Test “Done”, once QA testing is complete, then . . .
- UAT Complete, where UAT is handled outside of the Sprint, usually close to release time . . . every month or so.
Has anyone a recommendation or options on how to proceed?
Your observations are correct - sometimes, data in eazyBI and Jira are different because of approach of how data are calculated. eazyBI imports issue information, not pre-calculated report data from Jira reports.
Let me explain how eazyBI calculates those measures.
Measure " Sprint Story points committed" sums up story points assigned to a sprint at the moment when the sprint was started (button [Start sprint] was pushed). This value includes Story points in all statuses, also resolved before the sprint was started.
Committed points can’t be changed by removing/adding issues later or changing story point values: committed points value is a snapshot of that exact time moment.
Measure " Sprint Story points completed" sums up story points from completed issues at sprint completion moment. Issue story points are treated as completed if the issue was in one of the status category Done statuses at sprint completion moment. The board configuration (a column mapping to statuses) is not taken into account for this measure.
Therefore, if your sprint board configuration is different (the last swim line in the board - Completed - contains issues in other statuses, not those from Done category), you may create your own calculated measure, where you use your statuses instead of category Done:
[Measures].[Sprint Story Points at closing] )
That should group story points at closing for just two statuses.
Martins / eazyBI
Thank you Martins! This is very helpful.