Linux 基金会的一项调查显示，生成式 AI 正在为开源开发者节省时间，但也让维护者面临更多需要筛选的“废料”。受访者估计，四分之一的传入拉取请求——即提议的代码变更——属于低质量的“AI 垃圾”。过去一年里，开源项目一直在应对 AI 生成的贡献、有用的漏洞发现、误报以及重复报告。Linus Torvalds 对这些工具的态度有所软化，但仍对低质量提交持批评态度。该基金会认为，项目需要更清晰的政策和更好的工具来应对这一涌入潮。
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.
近四分之三（71%）的维护者认为生成式 AI 降低了为开源做贡献的门槛，但略超半数（53%）不同意该技术提升了社区信任。受访者估计，AI 平均将代码编写时间缩减了 52%。但在高度复杂的任务中，21% 的受访者表示耗时反而变长。
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.
在治理方面，仅 35% 的项目制定了允许使用生成式 AI 的正式政策，而 32% 的项目既无政策也无制定计划，导致社区大部分领域的期望不明确。Linux 基金会研究部高级副总裁 Hilary Carter 表示：“开源项目需要为不断增长的 AI 生成贡献者群体进行设计，而不仅仅是被动应对。这意味着要为生成式 AI 设定明确期望，将测试和披露的责任放在贡献者身上，并给维护者提供更好的工具来管理传入的拉取请求。”
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."
在大型项目中，被归类为 AI 垃圾的传入拉取请求的估计占比上升至 31%。工作量不仅限于提议的代码变更。curl 项目本周宣布，将把低危和中危严重程度的缺陷移至公开视图，以保护一个几乎被 AI 辅助报告压垮的安全团队。
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.
项目维护者Daniel Stenberg表示：“我们作为一个项目正在挣扎，因为所有核心维护者都感到紧张、超负荷，并已被推向我们所能承受的极限。” 关于维护者对更好工具的需求，Carter表示：“如果我们把这些基础工作做好，AI就能在不增加维护者负担和成本的情况下扩大参与度。” ®
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." ®