RepGen：通过上下文感知代码生成与验证复现深度学习缺陷
原标题：RepGen: Reproducing Deep Learning Bugs through Context-Aware Code Generation and Verification
AI 摘要
研究者提出开源工具 RepGen，通过上下文感知的代码生成与迭代验证来复现深度学习缺陷。该工具探索代码库构建上下文、制定复现计划并生成候选脚本，再借助静态检查和 LLM 反馈进行精炼，并可自动准备环境、执行脚本并比对失败行为。在 106 个真实 DL 缺陷中成功复现 85 个（80.19%），27 名开发者的对照实验显示复现成功率提升 23.35%、耗时减少 56.8%。
正文节选
RepGen: Reproducing Deep Learning Bugs through Context-Aware Code Generation and Verification Abstract Reproducing deep learning (DL) bugs is challenging because failures can involve source code, models, data, dependencies, and execution environments. Incomplete bug reports further complicate reproduction by omitting essential details. We present RepGen, an open-source tool for reproducing DL bugs through context-aware code generation and iterative verification. Given a bug report and repository