In summary, Vasco says that counting stories always leads to better predictions than summing estimates. This is not true without modifications. Off the top of my head, here are some of Vasco's assumptions:

- There is a significant overhead spent on estimating and maintaining estimates, and the overhead grows exponentially with the number of items (finding one specific item from a list of ten is MUCH faster than finding one from a list of 1000)
- The estimation activity does not include working on the acceptance criteria, APIs, architecture etc.
- There are lots of stories (1000s per release)
- The stories are pretty small (on the order of hours)
- The team's estimates are worse than random — meaning that the team doesn't really know how to work with stories

Assumption #5 is in itself sufficient to void the "story counts are better than story points" controversy. Further, assumptions #1 and #2 may be mutually exclusive. And further, in his blog post Vasco uses data from a team where assumptions #3 and #4 are true, which indicates data bias.

Through Vasco there's some interesting data available now, and I'll try to make use of it and contribute to the information and knowledge we have. People seem to have so many opinions, but it's time to slam some data down on the table!

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