一句话：提出容器镜像可复现性问题的分类法及自动分类工具 Vessel Diff，并评估开源仓库中的失败模式。
容器镜像的逐位可复现性对确保部署系统按预期工作且未被篡改至关重要，但受外部因素影响几乎无法实现，且诊断复现失败的原因与严重程度既慢又费力。本文提出容器镜像可复现性问题的分类法，并实现工具 Vessel Diff，用于自动分类镜像构建中复现失败的类型与严重程度。作者分析了一组构建容器镜像的开源仓库，据此配置工具；结果显示约 87% 的复现失败可被自动分类，但其中绝大多数由琐碎问题导致，而非会显著改变容器应用执行且更难检测的非琐碎问题。这凸显了检测、分类和修复复现问题仍需更多研究与工具支持。
Build reproducibility of container images is essential to ensure that deployed systems will work as expected and have not been tampered with. However, bit-by-bit reproducibility of container images is almost never achievable due to external factors, and it is also very slow and labor intensive to determine the causes and severity of reproducibility failures. In this paper, we present a taxonomy of reproducibility issues for container images, as well as a tool, Vessel Diff, to help automatically categorize the type and severity of reproducibility failures in container images. We analyzed a set of open source repositories where container images are built to find common patterns and configure our tool to properly categorize failures. Our analysis shows that approximately 87% of their reproducibility failures were automatically classified by the tool according to our taxonomy. However, the vast majority of these failures were caused by trivial issues and not non-trivial issues, which could cause noticeable changes in execution of container applications and are more difficult to detect. These results highlight the need for additional research and tooling to detect, classify, and fix reproducibility issues, especially those that can lead to major failures.
DOI 原文 ·
@inproceedings{paperbot9365,
title = {Vessel: A Taxonomy of Reproducibility Issues for Container Images},
author = {Kevin Pitstick and Alex Derr and Lihan Zhan and Sebastián Echeverría},
booktitle = {ASE},
year = {2025},
doi = {10.1109/ase63991.2025.00348}
}