新手开发者为项目维护者产生更大的审查负担的同时进行 vibe 编码
Novice Developers Produce Larger Review Overhead for Project Maintainers while Vibe Coding
AI总结：
研究发现低经验 vibe 编码者生成更多代码但增加审查负担，建议结合培训与自适应审查流程
摘要：
AI 编程代理允许软件开发者快速生成代码，这为项目经理和开源维护者提出了一个实际问题：缺乏开发经验的 vibe 编码者能否替代专家开发者？为了探讨在 AI 辅助开发中开发者经验是否仍然重要，我们研究了 AIDev 数据集中的 GitHub 仓库中的 22,953 个 Pull Requests（PRs），这些 PRs 来自 1,719 个 vibe 编码者。我们将 vibe 编码者分为低经验（Exp_Low）和高经验（Exp_High）两类，并比较不同 PR 类别的贡献规模和 PR 批准率。我们发现，Exp_Low 提交的 PR 涉及更多的提交（2.15 倍）和更改的文件（1.47 倍）。此外，与 Exp_High 相比，Exp_Low 的 PR 接收的审查评论多 4.52 倍，批准率低 31%，且在解决前保持开放时间长 5.16 倍。我们的结果表明，低经验的 vibe 编码者倾向于生成更多代码，而将验证负担转移到了审查者身上。在实践中，项目经理可能无法安全地用低经验的 vibe 编码者替代经验丰富的开发者，而无需增加审查能力。因此，开发团队应结合针对新手的定向培训与自适应的 PR 审查周期。
英文摘要：
AI coding agents allow software developers to generate code quickly, which raises a practical question for project managers and open source maintainers: can vibe coders with less development experience substitute for expert developers? To explore whether developer experience still matters in AI-assisted development, we study $22,953$ Pull Requests (PRs) from $1,719$ vibe coders in the GitHub repositories of the AIDev dataset. We split vibe coders into lower experience vibe coders ($\mathit{Exp}_{Low}$) and higher experience vibe coders ($\mathit{Exp}_{High}$) and compare contribution magnitude and PR acceptance rates across PR categories. We find that $\mathit{Exp}_{Low}$ submits PRs with larger volume ($2.15\times$ more commits and $1.47\times$ more files changed) than $\mathit{Exp}_{High}$. Moreover, $\mathit{Exp}_{Low}$ PRs, when compared to $\mathit{Exp}_{High}$, receive $4.52\times$ more review comments, and have $31\%$ lower acceptance rates, and remain open $5.16\times$ longer before resolution. Our results indicate that low-experienced vibe coders focus on generating more code while shifting verification burden onto reviewers. For practice, project managers may not be able to safely replace experienced developers with low-experience vibe coders without increasing review capacity. Development teams should therefore combine targeted training for novices with adaptive PR review cycles.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
