一句话：TRACE 交替使用神经归纳预测语义编辑与工具演绎预测语法编辑，提升项目级代码编辑的范围、准确率与效率。
在工业与开源软件开发中，开发者常需完成特征增强、重构与缺陷修复等项目级代码编辑任务，现有基于 LLM 的方案难以兼顾预测范围、准确率与效率：Cursor 在跨文件编辑上性能下降，CoEdPilot 在项目级编辑上效率受限。本文提出 TRACE（Tool-integrated RecommendAtion for Code Editing），其核心思想是代码编辑由语义或语法原因触发，因此将神经归纳（语义编辑预测）与基于工具的演绎（语法编辑预测）交替进行，工具可为任意 IDE 设施（如 rename 重构、use-def 检查），以较好地推导编辑位置并生成编辑。技术挑战在于何时在两者间切换，以及如何提升神经预测性能：前者通过训练神经模型判断何时调用 IDE 编辑工具，后者提出细粒度编辑表示以增强神经编辑模型。实验表明，相比 CoEdPilot、GrACE、CCT5 等最先进方法，TRACE 将编辑定位提升 43.76%、编辑生成提升 11.16%；交互式编辑模拟中接受率比 Cursor 高 6.15%；24 名参与者、三个编辑任务的用户研究显示，使用 TRACE 的实验组在跨文件全局编辑上表现领先，并揭示了用户处理错误预测的行为。
In industrial and open-source software engineering tasks, developers often perform project-wise code editing tasks, including feature enhancement, refactoring, and bug fixing, where the leading AI models are expected to support the productivity. Hence, researchers and practitioners have proposed and adopted many LLM-based solutions to facilitate their real-world development. However, they largely suffer from the balance among predicting scope, accuracy, and efficiency. For example, solutions like Cursor achieve high accuracy only in a local editing scope while its performance drops on cross-file edits. In contrast, solutions like CoEdPilot exhibit efficiency limitations when used to predict project-wise edits.In this work, we propose TRACE (Tool-integrated RecommendAtion for Code Editing), a novel subsequent code editing solution to push the boundary of scope, accuracy, and efficiency. Our rationale lies in that code edits are triggered for either semantic or syntactic reasons. Therefore, TRACE predicts subsequent edits by interleaving neural-based induction for semantic edit prediction and tool-based deduction for syntactic edit prediction. The tools can be any IDE facilities, such as refactoring tools (e.g., rename) or linting tools (e.g., use-def), providing decent performance of deducing edit-location and edit-generation. Technically, we address the challenge of (1) when to interleave between neural-based and tool-based prediction and (2) how to further improve the performance of neural-based prediction. As for the former, we learn a neural model to detect when to invoke IDE editing tools. As for the latter, we propose a novel and fine-grained editing representation to further boost the performance of neural editing models.Our extensive experiments show that, in comparison to the state-of-the-arts such as CoEdPilot, GrACE, and CCT5, TRACE significantly improves the performance of edit location (by 43.76%) and edit generation (by 11.16%). Our simulation experiment on an interactive editing setting shows that TRACE achieves an acceptance rate 6.15% higher than Cursor. Moreover, our user study consists of 24 participants on Cursor, CoEdPilot, and TRACE, on three code editing tasks. The results show that the experimental group with TRACE achieves leading performance on cross-file global edits. In addition, we observe concerning user behaviours on how participants deal with false predictions by the tools, shedding light on the design of future code-editing tools.
@inproceedings{paperbot9348,
title = {Learning Project-wise Subsequent Code Edits via Interleaving Neural-based Induction and Tool-based Deduction},
author = {Chenyan Liu and Yun Lin and Yuhuan Huang and Jiaxin Chang and Binhang Qi and Bo Jiang and Zhiyong Huang and Jin Song Dong},
booktitle = {ASE},
year = {2025},
doi = {10.1109/ase63991.2025.00117}
}