Containerization has revolutionized software deployment, with Docker leading the way due to its ease of use and consistent runtime environment. As Docker usage grows, optimizing Dockerfile performance, particularly by reducing rebuild time, has become essential for maintaining efficient CI/CD pipelines. However, existing optimization approaches primarily address single builds without considering the recurring rebuild costs associated with modifications and evolution, limiting long-term efficiency gains. To bridge this gap, we present Doctor, a method for improving Dockerfile build efficiency through instruction re-ordering that addresses key challenges: identifying instruction dependencies, predicting future modifications, ensuring behavioral equivalence, and managing the optimization computational complexity. We developed a comprehensive dependency taxonomy based on Dockerfile syntax and a historical modification analysis to prioritize frequently modified instructions. Using a weighted topological sorting algorithm, Doctor optimizes instruction order to minimize future rebuild time while maintaining functionality. Experiments on 2,000 GitHub repositories show that Doctor improves 92.75% of Dockerfiles, reducing rebuild time by an average of 26.5%, with 12.82% of files achieving over a 50% reduction. Notably, 86.2% of cases preserve functional similarity. These findings highlight best practices for Dockerfile management, enabling developers to enhance Docker efficiency through informed optimization strategies.

翻译：容器化技术已彻底改变软件部署方式，其中Docker因其易用性和一致的运行时环境而处于领先地位。随着Docker使用量的增长，优化Dockerfile性能（特别是减少重建时间）已成为维持高效CI/CD流水线的关键。然而，现有优化方法主要针对单次构建，未考虑与修改和演进相关的重复重建成本，限制了长期效率提升。为弥补这一不足，我们提出Doctor方法，通过指令重排序提升Dockerfile构建效率，该方法解决了以下核心挑战：识别指令依赖关系、预测未来修改、确保行为等价性以及管理优化计算复杂度。我们基于Dockerfile语法建立了完整的依赖分类体系，并通过历史修改分析确定频繁修改指令的优先级。Doctor采用加权拓扑排序算法优化指令顺序，在保持功能不变的前提下最小化未来重建时间。基于2000个GitHub仓库的实验表明，Doctor优化了92.75%的Dockerfile，平均减少26.5%的重建时间，其中12.82%的文件实现超过50%的优化效果。值得注意的是，86.2%的案例保持了功能相似性。这些发现为Dockerfile管理提供了最佳实践指导，使开发者能够通过科学的优化策略提升Docker效率。