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[-] CanadaPlus@lemmy.sdf.org 2 points 5 days ago* (last edited 5 days ago)

So what's the actual error margin for estimating feature implementation time? It's going to be nearly the whole thing, right?

[-] psud@aussie.zone 4 points 5 days ago* (last edited 5 days ago)

The estimate is not a promise, it's a guess. I prefer to estimate in sprints because that's about the resolution we can have confidence in, but management want hours so my process is to estimate the number of hours in a sprint (73.5 for us) plus one sprint

200% overruns are common, especially when requirements change significantly

[-] Ephera@lemmy.ml 3 points 5 days ago

Wildly depends on the complexity of the feature. If it only takes 4 hours to implement, you might have good enough of an idea what needs to be done that you can estimate it with 1-hour-precision. That is, if you're only doing things that you've done in a similar form before.

If the feature takes two weeks to implement, there's so many steps involved in accomplishing that, that there's a high chance for one of the steps to explode in complexity. Then you might be working on it for six weeks.

But yeah, I also double any estimate that I'm feeling, because reality shows that that ends up being more accurate, since I likely won't have all complexity in mind, so in some sense my baseline assumed error is already 100%.

[-] CanadaPlus@lemmy.sdf.org 1 points 4 days ago* (last edited 4 days ago)

Hmm, so kinda O(n^1.5^) scaling? (Of the ratio between definitely required time and possibly required time, anyway, since a -110% error wouldn't make sense)

[-] Ephera@lemmy.ml 2 points 4 days ago

Really not sure an estimate for algorithmic complexity is the right way to specify this. ๐Ÿ˜…
But if your supposed input unit is days, then I guess, yeah, that kind of works out.

this post was submitted on 28 Mar 2025
606 points (99.0% liked)

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