一句话：提出 Chrome 扩展 BackportCheck，用 XGBoost 加 LLM 预测 Gerrit 变更可否安全 backport 并给出理由。
在 OpenStack 等大型生态中，向稳定分支后向移植（backport）变更是关键但高风险的维护任务，许多仓库要求显式的 backport 投票才能合并或发布，然而判断哪些变更可安全 backport 仍主要靠人工，导致决策延迟与不一致。本文提出 BackportCheck，一个集成在 Gerrit 代码评审界面中的 Chrome 扩展决策支持工具。其核心是一个基于历史流程数据训练的梯度提升模型（XGBoost），用于预测 backport 决策，并辅以大型语言模型（LLM）生成简洁、可读的理由解释。作者在 3,422 个 OpenStack 变更上评估工具的实用性与可用性，准确率达 81.31%，端到端平均响应时间为 1.39 秒，优于基于 CodeBERT 的分类器和单独使用的 LLM。工具已在 GitHub 开源并提供演示视频。
Backporting changes to stable releases is a critical yet high-stakes maintenance task. In practice, large-scale ecosystems like Open-Stack rely on explicit governance rules for stable branches, where many repositories enforce that changes cannot be merged or released without an explicit backport-related vote (e.g., Backport-Candidate). However, identifying changes that are safe to back-port remains largely manual, leading to significant decision latency and inconsistency. To alleviate this challenge, we present BackportCheck, a decision-support tool implemented as a Chrome extension for the Gerrit code review interface. BackportCheck is based on a Gradient Boosting model (XGBoost), trained on historical process data, combined with a Large Language Model (LLM) that generates concise, human-readable justifications for predicted backport decisions. We evaluate BackportCheck for both usefulness and usability across 3,422 OpenStack changes, achieving 81.31% accuracy while maintaining a mean end-to-end response time of 1.39 seconds, outperforming CodeBERT-based classifiers and standalone LLMs. BackportCheck is available via GitHub1. A video demonstrating the tool usage is available on YouTube2.
@inproceedings{SghaierDCCHM26,
title = {BackportCheck: An Open-Source Tool to Support Backport Decisions in Large Software Ecosystems},
author = {Salma Sghaier and Mohamed Anas Daoud and Marouene Chaieb and Moataz Chouchen and Mohammad Hamdaqa and Mohamed Wiem Mkaouer},
booktitle = {FSE},
year = {2026},
doi = {10.1145/3803437.3806423}
}