AI代码生成：廉价代码背后的昂贵判断
摘要
一项12周的案例研究表明，AI编码代理虽能快速生成大量代码，但软件工程的核心挑战转向如何确保代码的可检查、可纠正和可维护。研究者提出“治理转换”理论，解释工程师如何通过判断将失败转化为治理机制。
背景解释
随着AI编码工具普及，软件工程从稀缺实现转向丰富代码生产。该研究通过专家使用前沿AI代理构建文档可访问性修复系统的实践，揭示了高速代理开发中出现的结构性失败模式，并提出了治理转换模型，为AI辅助开发的可控性提供了新视角。
原文译文
以下为抓取到的原文内容译文，已统一为站内阅读格式。
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Computer Science > Software Engineering
arXiv:2607.01087v1 (cs)
\Submitted on 1 Jul 2026 (this version), _latest version 4 Jul 2026_ ( [v2)]
Title:Cheap Code, Costly Judgment: A Case Study on Governable Agentic Software Engineering
Authors:James C. Davis,Paschal C. Amusuo,Tanmay Singla,Berk Çakar,Kirsten A. Davis
View a PDF of the paper titled Cheap Code, Costly Judgment: A Case Study on Governable Agentic Software Engineering, by James C. Davis and 4 other authors
Abstract:Generative AI is shifting software engineering from a practice organized around scarce implementation effort toward one organized around abundant, low-cost code production. This shift changes the central engineering problem: not whether AI can generate useful code, but how engineers organize architectures, tools, evidence, and feedback loops so that AI-mediated development remains inspectable, correctable, and maintainable.
We study this problem through a first-person case study: a 12-week development effort in which a single expert software engineer used frontier AI coding agents to build a document accessibility remediation system. The empirical record comprises 88 contemporaneous field notes, 420 KLOC of production code, and 1.16 MLOC of tests, lints, supporting documentation, and agent tooling. From this record, we develop a candidate middle-range theory of governance conversion, expressed as a process model explaining how high-velocity agentic implementation becomes governable. The model explains how agentic implementation velocity surfaces recurring structural failure classes, and how engineering judgment sustains velocity by converting those failures into durable governance mechanisms. In contrast to existing governance models that derive controls from known obligations, governance conversion explains how controls are discovered from failures that become visible only during agentic work. We use our model to make testable predictions and to describe implications for software engineering research and practice.
| | | | --- | --- | | Comments: | 12 pages | | Subjects: | Software Engineering (cs.SE); Artificial Intelligence (cs.AI) | | Cite as: |arXiv:2607.01087[cs.SE] | | | (orarXiv:2607.01087v1[cs.SE] for this version) | | |https://doi.org/10.48550/arXiv.2607.01087<br>Focus to learn more<br>arXiv-issued DOI via DataCite |
Submission history
From: James Davis \[view email]
[v1] Wed, 1 Jul 2026 15:44:15 UTC (240 KB)
[[v2]](https://arxiv.org/abs/2607.01087v2) Sat, 4 Jul 2026 20:01:57 UTC (240 KB)
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