标题： 回到基础：通过LLM辅助检索重新思考问题-提交链接 显示英文标题

标题： Back to the Basics: Rethinking Issue-Commit Linking with LLM-Assisted Retrieval

摘要： 问题-提交链接，即将问题与修复它们的提交连接起来，对于软件维护至关重要。现有的方法在自动恢复这些链接方面显示出潜力。对这些技术的评估旨在测试它们从看似合理但错误的链接中识别真实链接的能力。然而，这些评估忽略了这样一个事实：在现实中，当存储库拥有更多提交时，更多的看似相关但不相关的提交可能会干扰工具区分正确的修复提交。为了解决这个问题，我们提出了现实分布设置（RDS），并用它来构建一个包含20个开源项目的更现实的评估数据集。通过在这个数据集上评估工具，我们观察到最先进的基于深度学习的方法性能下降了一半以上，而传统的信息检索方法VSM则表现更好。受这些观察的启发，我们提出了EasyLink，它利用向量数据库作为一种现代的信息检索技术。为了解决问题和提交之间语义差距这一长期存在的问题，EasyLink利用大型语言模型对从数据库中检索到的提交进行重新排序。在我们的评估下，EasyLink实现了平均Precision@1为75.91%，比最先进的方法提高了四倍以上。此外，本文为推进问题-提交链接恢复的研究提供了实用的指南。 显示英文摘要

摘要： Issue-commit linking, which connects issues with commits that fix them, is crucial for software maintenance. Existing approaches have shown promise in automatically recovering these links. Evaluations of these techniques assess their ability to identify genuine links from plausible but false links. However, these evaluations overlook the fact that, in reality, when a repository has more commits, the presence of more plausible yet unrelated commits may interfere with the tool in differentiating the correct fix commits. To address this, we propose the Realistic Distribution Setting (RDS) and use it to construct a more realistic evaluation dataset that includes 20 open-source projects. By evaluating tools on this dataset, we observe that the performance of the state-of-the-art deep learning-based approach drops by more than half, while the traditional Information Retrieval method, VSM, outperforms it. Inspired by these observations, we propose EasyLink, which utilizes a vector database as a modern Information Retrieval technique. To address the long-standing problem of the semantic gap between issues and commits, EasyLink leverages a large language model to rerank the commits retrieved from the database. Under our evaluation, EasyLink achieves an average Precision@1 of 75.91%, improving over the state-of-the-art by over four times. Additionally, this paper provides practical guidelines for advancing research in issue-commit link recovery.