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Measuring the impact of AI on experienced open-source developer productivity

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688 points dheerajvs | 1 comments | 10 Jul 25 16:29 UTC | HN request time: 0.287s | source
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dash2 ◴[10 Jul 25 16:55 UTC] No.44523032[source]▶
The authors say "High developer familiarity with repositories" is a likely reason for the surprising negative result, so I wonder if this generalizes beyond that.
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kennywinker ◴[10 Jul 25 17:04 UTC] No.44523100[source]▶
Like if it generalizes to situations where the developer is not familiar with the repo? That doesn’t seem like generalizing, that seems like specifying. Am I wrong in saying that the majority of developer time is spent in repos that they’re familiar with? Every job and project I’ve worked has been on a fixed set of repos the entire time. If AI is only helpful for the first week or two on a project, that’s not very many cases it’s useful for.
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1. jbeninger ◴[10 Jul 25 22:09 UTC] No.44526219[source]▶
I'd say I write the majority of my code in areas I'm familiar with, but spend the majority of my _time_ on sections I'm not familiar with, and ai helps a lot more with the latter than the former. I've always felt my coding life is speeding through a hundred lines of easy code then getting stuck on the 101st. Then as I get more experienced that hundred becomes 150, then 200, but always speeding through the easy part until I have to learn something new.

So I never feel like I'm getting any faster. 90% of my time is still spent in frustration, even when I'm producing twice the code at higher quality