I Audited 500 Commits. The AI Signal Was Hiding in the Diff
English summary
The author audited 500 code commits and found that AI-generated code can be identified without relying on watermarks. The detection approach uses the commit graph, a diff parser, and a willingness to handle irregular edge cases. The methodology suggests that AI authorship leaves discernible patterns in the structure of code changes and commit history. The article frames this as a practical pipeline for flagging AI-written contributions in version control.
Chinese summary
作者审计了500次代码提交，发现无需水印即可识别AI生成的代码。检测方法利用提交图、差异解析器，并需处理不规则的边缘情况。该方法表明，AI作者的痕迹会体现在代码变更和提交历史的结构模式中。文章将此描述为一套实用的代码审查流水线，用于标记版本控制中的AI编写贡献。
Key points
AI-generated code can be detected without embedded watermarks.
无需内置水印即可检测AI生成的代码。
Commit graph and diff outputs contain reliable authorship signals.
提交图和差异输出中包含可靠的身份线索。
Implementation requires a diff parser and robust handling of edge cases.
实现需要差异解析器和对边缘情况的稳健处理。
The method was validated by auditing 500 real commits.
该方法通过审计500次真实提交进行了验证。