一句话：首个基于 17 万余次 Docker 构建的实证研究，刻画构建时长并构建高精度预测模型。
Docker 构建是容器化工作流的关键环节，构建过慢会消耗大量人力与计算资源，但其现状与应对手段尚不明确。本文首次对来自 5,833 个开源项目的 171,439 次 Docker 构建开展实证研究：先通过探索性分析刻画真实项目中的构建时长分布，再借助问卷调查获取开发者对慢构建的感知。基于探索性研究结果，作者从构建相关上下文与配置中提取 27 个手工特征，并采用 8 种回归算法建立 Docker 构建时长预测模型。实验表明，随机森林模型表现最优，Spearman 相关系数达 0.781，相比随机基线在 RMSE、MAE 和 MAPE 上分别提升 82.9%、90.6% 和 94.4%。该研究为理解并改进 Docker 构建过程提供了实证依据与实践启示。
Docker building is a critical component of containerized workflow, which automates the process by which sources are packaged and transformed into container images. If not run properly, Docker builds can bring long durations (i.e., slow builds), which increases the cost in human and computing resources, and thus inevitably affect the software development. However, the current status and remedy for the duration cost in Docker builds remain unclear and need an in-depth study. To fill this gap, this paper provides the first empirical investigation on 171,439 Docker builds from 5,833 open source software (OSS) projects. Starting with an exploratory study, the Docker build durations can be characterized in real-world projects, and the developers’ perceptions of slow builds are obtained via a comprehensive survey. Driven by the results of our exploratory study, we propose a prediction modeling of Docker build duration, leveraging 27 handcrafted features from build-related context and configuration and 8 regression algorithms for the prediction task. Our results demonstrate that Random Forest model provides the superior performance with a Spearman’s correlation of 0.781, outperforming the baseline random model by 82.9% in RMSE, 90.6% in MAE, and 94.4% in MAPE, respectively. The implications of this study will facilitate research and assist practitioners in improving the Docker build process.
@inproceedings{paperbot7326,
title = {Understanding and Predicting Docker Build Duration: An Empirical Study of Containerized Workflow of OSS Projects},
author = {Yiwen Wu and Yang Zhang and Kele Xu and Tao Wang and Huaimin Wang},
booktitle = {ASE},
year = {2022},
doi = {10.1145/3551349.3556940}
}