AI 虽然缩短了编码时间，但也让开源项目的收件箱塞满了垃圾代码

维护者估计收到的拉取请求中有四分之一是垃圾内容，在较大型项目中这一比例上升至 31%

根据 Linux 基金会的一项调查，生成式 AI 在为开源开发者节省时间的同时，也让维护者不得不筛选更多的垃圾内容。

受访者估计，收到的拉取请求（PR，即提议的代码变更）中，有四分之一属于低质量的「AI 垃圾（AI slop）」。

过去一年里，开源项目一直在努力应对 AI 生成的贡献内容、有价值的漏洞发现、误报以及重复报告。Linus Torvalds 虽然对这些工具的态度有所缓和，但对低质量的提交仍持批评态度。

该基金会认为，各个项目需要更明确的政策和更好的工具来应对这种涌入。

近四分之三（71%）的维护者认为生成式 AI 降低了向开源贡献代码的门槛，但略超半数（53%）的人不同意该技术改善了社区信任。受访者估计，AI 平均将代码编写时间缩短了 52%。但在处理高度复杂的任务时，有 21% 的人表示耗时反而更长了。

此外还有治理问题。仅有 35% 的项目制定了允许使用生成式 AI 的正式政策，而 32% 的项目既没有相关政策，也没有制定政策的计划，导致社区中大部分成员的预期并不明确。

Linux 基金会研究高级副总裁 Hilary Carter 表示：“开源项目需要针对不断增长的 AI 生成贡献者群体进行针对性设计，而不是单纯被动应对。这意味着要对生成式 AI 设定明确的预期，把测试和披露的责任交由贡献者承担，并为维护者提供更好的工具来管理收到的拉取请求。”

在大型项目中，被归类为 AI 垃圾的拉取请求估计比例上升到了 31%。

这种工作负担还不仅限于提议的代码变更。curl 项目本周宣布，将把中低严重级别的安全漏洞转入公开视角，以保护几乎被 AI 辅助生成的漏洞报告压垮的安全团队。

项目维护者 Daniel Stenberg 表示：“我们整个项目都在苦苦挣扎，所有核心维护者都感到精疲力竭、不堪重负，已被推向了我们所能承受的极限。”

谈到维护者对更好工具的需求时，Carter 表示：“如果我们把这些基础环节做扎实，AI 就能在不让维护者负担过重或增加成本的情况下，扩大开发者参与度。”

ai and ml

AI cuts coding time, but it's also filling open source's inbox with crap

Maintainers reckon a quarter of incoming pull requests are slop, rising to 31% on larger projects

Generative AI is saving open source developers time while giving maintainers more rubbish to sift through, according to a Linux Foundation survey.

Respondents estimated that a quarter of incoming pull requests – proposed code changes – were low-quality "AI slop."

Open source projects have spent the past year grappling with AI-generated contributions, useful vulnerability discoveries, false positives, and duplicate reports. Linus Torvalds has warmed to the tools, while remaining critical of low-quality submissions.

The foundation argues that projects need clearer policies and better tools to handle the influx.

Almost three-quarters (71 percent) of maintainers reckon GenAI has lowered the barriers to contributing to open source, but just over half (53 percent) disagree that the technology has improved community trust. Respondents estimated that AI cut code-authoring time by 52 percent on average. But for highly complex tasks, 21 percent reported taking longer.

Then there is governance. Only 35 percent of projects have a formal policy permitting GenAI use, while 32 percent have no policy and no plans for one, leaving expectations unclear across much of the community.

Hilary Carter, SVP of Research at the Linux Foundation, said: "Open source projects need to design for a growing AI-generated contributor base, not simply react to it. That means setting clear expectations for GenAI, putting responsibility for testing and disclosure on contributors and giving maintainers better tools to manage incoming pull requests."

Among large projects, the estimated share of incoming pull requests classified as AI slop rises to 31 percent.

The workload extends beyond proposed code changes. The curl project announced this week it will move low and medium severity severity flaws to public view to protect a security team almost overwhelmed by AI-assisted reports.

Project maintainer Daniel Stenberg said: "We are struggling as a project as all key maintainers feel strained, overloaded and brought to the brink of what we can muster."

On the need for better tools for maintainers, Carter said: "If we get those fundamentals right, AI can expand participation without overburdening and increasing costs for maintainers." ®