一句话：提出运行时反馈驱动的迭代式日志生成框架 ReLog，以下游调试任务而非文本相似度评估日志质量。
现有自动日志生成方法依赖静态分析、单次生成，忽略运行时行为，并以与开发者日志的文本相似度作为评价标准；在 LLM 时代日志还需服务缺陷定位等下游任务，该假设已不适用。本文提出 ReLog：一个由运行时反馈引导的迭代式日志生成框架，让 LLM 反复生成、执行、评估并精化日志语句，从而更好地支持下游任务。作者基于 Defects4J 构建基准，在直接与间接（无源码）调试设置下评测缺陷定位与修复：直接设置 F1 达 0.520、修复 97 个缺陷，间接设置最佳 F1 为 0.408，均优于全部基线；跨 LLM 与消融实验验证了通用性及迭代精化与编译修复的重要性。
Logging statements are essential for software debugging and maintenance. However, existing approaches to automatic logging generation rely on static analysis and produce statements in a single pass without considering runtime behavior. They are also typically evaluated by similarity to developer-written logs, assuming these logs form an adequate gold standard. This assumption is increasingly limiting in the LLM era, where logs are consumed not only by developers but also by LLMs for downstream tasks. As a result, optimizing logs for human similarity does not necessarily reflect their practical utility. To address these limitations, we introduce ReLog, an iterative logging generation framework guided by runtime feedback. ReLog leverages LLMs to generate, execute, evaluate, and refine logging statements so that runtime logs better support downstream tasks. Instead of comparing against developer-written logs, we evaluate ReLog through downstream debugging tasks, including defect localization and repair. We construct a benchmark based on Defects4J under both direct and indirect debugging settings. Results show that ReLog consistently outperforms all baselines, achieving an F1 score of 0.520 and repairing 97 defects in the direct setting, and the best F1 score of 0.408 in the indirect setting where source code is unavailable. Additional experiments across multiple LLMs demonstrate the generality of the framework, while ablations confirm the importance of iterative refinement and compilation repair. Overall, our work reframes logging as a runtime-guided, task-oriented process and advocates evaluating logs by their downstream utility rather than textual similarity.
@inproceedings{paperbot12320,
title = {ReLog: Execution-Aware Logging with Runtime Feedback for LLM-Oriented Debugging},
author = {Xin Wang and Yang Feng and Xiaoqian Jiao and Yang Zhang and Zhenhao Li and Zishuo Ding},
booktitle = {ASE},
year = {2026},
doi = {10.1145/3832783.3837463}
}