3 ms·
This is great for a simple monte carlo simulation! Choose a finished tasks time randomly, once per remaining task in the queue, and add this up to be a single
by codemac 5y ago
This is great for a simple monte carlo simulation!
Choose a finished tasks time randomly, once per remaining task in the queue, and add this up to be a single estimate. Do this 1000 times or so and get an estimated distribution of completion times for the current queue.
This type of thing is covered extensively in Evidence Based Scheduling[0], and is one of the reasons I still think FogBugz ' power is misunderstood.
[0]: https://www.joelonsoftware.com/2007/10/26/evidence-based-scheduling/ https://www.joelonsoftware.com/2007/10/26/evidence-based-sch...
- lelandfe 5y agoNice, I just recently left a company where this approach would have been tremendously useful and fairly easy to build - we had all the required data already, but were just looking at averages of past performance to budgets and using that as a multiplier on the schedule rather than going through a distribution. That being said, I also despise tracking time! …Suppose you could move that technique to story points (or whatever unit of measurement) though you would lose a ton of precision.