LF Research is the Linux Foundation’s research arm. It was set up in 2021 and publishes studies on how open source is built and used, drawing on surveys and project data.
Its latest report, Navigating Open Source Software Development in the Generative AI Era, surveyed 234 contributors and maintainers on how generative AI is affecting the way open source gets made.
The survey estimates that about 25% of incoming pull requests are low-quality AI slop, with the estimate climbing to 31% on larger projects.
Red Hat sponsored the report, with additional contributions from OpenSSF, LF AI & Data, and Futurewei.
The findings
Large projects, the ones with more than 50 contributors, are affected the most.
Among their maintainers, 69% report more incoming contributions, against 59% across all maintainers, and 64% say reviews now take longer. Burnout is their top concern at 63%, narrowly ahead of contribution quality at 60%.
You will also find that policies have not caught up to the AI-assisted coding and vulnerability reporting trend.
Of the 119 respondents who answered the question, 35% said their project has a formal policy permitting AI use. Another 32% have none and no plans for one, 23% plan to write one, and 10% ban AI contributions outright.
You might remember that some projects, like LLVM, allow AI-generated code with conditions. Separately, 26% of projects have no way of verifying AI-assisted contributions at all.
The report calls on contributors to give effort to written policies, disclosure of AI use, and testing and documentation.
Interestingly, AI does seem to save developers’ time. Respondents estimate that code authoring takes 52% less time on average, while on more complex tasks, 21% said the work took longer with AI.
Most maintainers (71%) say AI makes it easier for newcomers to start contributing. Just over half (53%) say it has not improved community trust, while only 13% say it has.
Some caveats do apply.
Respondents were recruited through Linux Foundation newsletters and social channels, plus partner channels that include Red Hat. As for the 25% slop figure, it is an estimate from 111 respondents, not a measured count.
That sounds fair, but disclosure is mostly left to individual choice right now, and a quarter of projects have no way to check anything. It only works if the people sending slop make an effort to do it.
And as we have already seen time and again, the ones sending slop rarely bother.
I’m curious to know what the quality rate was like before AI. Anecdotally, my marginally popular repo got a few PRs, but many of them were pretty much garbage as well. No tests, broken tests, half baked code. Human written didnt mean good
Slop has existed before AI but it has lowered the barrier to generating it. Having no AI policies isn’t really working because the slop posters don’t declare it and as a reviewer you don’t want to be second guessing a potential contributor.
We are probably going to move to a policy that allows narrow use of AI tools but emphasises human understanding and ownership as mandatory components of contribution.
I’m lucky, so far no AI slop PRs yet, so I’m in a different boat. I’m happy to keep my little thing going and hope that it slowly dies away with no further attention.
That really the best you can hope for, although I dunno how you can force ownership. Its not easy to hunt down the original developer if there is a bug in their code.