Agentic Coding, powered by autonomous agents such as GitHub Copilot and Cursor, enables developers to generate code, tests, and pull requests from natural language instructions alone. While this accelerates implementation, it produces larger volumes of code per pull request, shifting the burden from implementers to reviewers. In practice, a notable portion of AI-generated code is eventually deleted during review, yet reviewers must still examine such code before deciding to remove it. No prior work has explored methods to help reviewers efficiently identify code that will be removed.In this paper, we propose a prediction model that identifies functions likely to be deleted during PR review. Our results show that functions deleted for different reasons exhibit distinct characteristics, and our model achieves an AUC of 87.1%. These findings suggest that predictive approaches can help reviewers prioritize their efforts on essential code.

翻译：智能体编程（Agentic Coding）借助GitHub Copilot和Cursor等自主智能体，使开发者能够仅通过自然语言指令生成代码、测试和拉取请求。虽然这加速了实现过程，但每个拉取请求产生的代码量更大，将负担从实现者转移到了审查者。实践中，相当一部分AI生成的代码最终会在审查阶段被删除，但审查者仍须在决定删除前检查这些代码。目前尚无研究探索帮助审查者有效识别将被删除代码的方法。本文提出一种预测模型，用于识别在PR审查中可能被删除的函数。我们的结果表明，因不同原因被删除的函数具有明显特征，且该模型的AUC达到87.1%。这些发现表明预测性方法能够帮助审查者将精力优先集中在核心代码上。