Strict Predictability of Feature Set

Learn how strict predictability in terms of features is supported by Agile.

If you have a fixed cost and schedule and need to predict exactly what features can be delivered for that fixed cost and schedule, the approach is similar to what was just described. Here’s how the strict-feature-set approach plays out across the key Agile practices involved.

Creation of the product backlog

The backlog must be fully populated just as it was in the approach used to achieve strict predictability of cost and schedule.

If the team defines and refines stories that add up to more story points than the team has time for, some of that definition and refinement work will be wasted. The more the team can perform its backlog population work from high priority to low priority, the less waste there will be.

Computation of velocity, used to predict functionality

Velocity is used similarly to how it is used in the strict predictability of cost and schedule scenario. However, instead of using velocity to predict an end date, velocity is used to predict the amount of functionality that can be delivered (that is, a number of story points). The variability transfers onto the feature set rather than into the schedule.

Using the same example as above—with a one year schedule—you can apply the confidence interval to predict the story points that can be completed within a fixed number of sprints, rather than predicting the number of sprints required to deliver a fixed number of story points.

Based on a 90% confidence interval after the first 4 sprints, the team should deliver a total of 1,158–1,442 story points after a total of 26 sprints, and chances are good that the team will be able to meet its goal of 1,200 total points.

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