一句话：首次系统研究 Dockerfile 构建不稳定性，提出成因分类法与结合 LLM 的混合修复框架 FLAKIDOCK。
该论文研究 Dockerfile 的不稳定性（flakiness）——由外部依赖与环境演化导致、在 Dockerfile 未修改时也会出现的不可预测构建失败，它会削弱部署可靠性并增加调试开销。作者对 8,132 个 Docker 化项目进行了为期九个月的分析，发现约 10% 的项目存在不稳定性，并据此提出涵盖依赖错误、服务器连接问题等原因的分类法。针对既有工具依赖预定义规则、泛化能力不足的问题，论文提出 FLAKIDOCK：结合静态与动态分析、相似性检索，并以大语言模型驱动的迭代反馈回路进行修复。评估显示其修复准确率达 73.55%，显著优于现有工具与基线。
Dockerfile flakiness-unpredictable temporal build failures caused by external dependencies and evolving environments-undermines deployment reliability and increases debugging overhead. Unlike traditional Dockerfile issues, flakiness occurs without modifications to the Dockerfile itself, complicating its resolution. In this work, we present the first comprehensive study of Dockerfile flakiness, featuring a nine-month analysis of 8,132 Dockerized projects, revealing that around 10% exhibit flaky behavior. We propose a taxonomy categorizing common flakiness causes, including dependency errors and server connectivity issues. Existing tools fail to effectively address these challenges due to their reliance on pre-defined rules and limited generalizability. To overcome these limitations, we introduce FLAKIDOCK, a novel repair framework combining static and dynamic analysis, similarity retrieval, and an iterative feedback loop powered by Large Language Models (LLMs). Our evaluation demonstrates that FLAKIDOCK achieves a repair accuracy of 73.55%, significantly surpassing state-of-the-art tools and baselines.
DOI 原文 ·
@inproceedings{paperbot8694,
title = {Dockerfile Flakiness: Characterization and Repair},
author = {Taha Shabani and Noor Nashid and Parsa Alian and Ali Mesbah},
booktitle = {ICSE},
year = {2025},
doi = {10.1109/icse55347.2025.00238}
}