DockInsight: A Knowledge-Augmented Dependency Extraction Approach for Dockerfile
Zhiling Zhu, Tieming Chen, Yunjin Zhong, Qijie Song · 2025
DevOps enhances software production through IT automation, continuous integration, and deployment, with Docker as a key tool that packages applications and their environments into standardized images for consistent and efficient deployment. Dockerfiles, which are text-based configuration files, define the composition and runtime actions of these images. Mismanagement of dependencies between Dockerfile instructions can cause build failures, highlighting the need for accurate dependency parsing. Current methods often miss implicit dependencies due to the complex syntax and logic of Dockerfile instructions. To address this, we propose DockInsight, a novel tool that uses a rule-based approach and semantic analysis to determine Dockerfile dependencies accurately. DockInsight features a unified feature structure representation, DVector, and a dependency type table to facilitate precise dependency determination. Evaluations demonstrate that DockInsight achieves 99.44% accuracy, significantly outperforming keyword matching and large language model methods by 64.84% and 55.74%, respectively. Additionally, DockInsight maintains stable processing times across various Dockerfile lengths, proving its efficiency and scalability. Our ablation study further highlights the importance of semantic information supplementation, particularly for RUN instructions, in enhancing accuracy. DockInsight’s robust performance makes it a valuable tool for developers and DevOps engineers, contributing to more reliable and maintainable Dockerfiles.