DockerFill: Automatically Completing Dockerfile Code With Syntax-Aware Multi-Task Learning

Yiwen Wu

doi:10.1109/TSE.2025.3632074

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DockerFill: Automatically Completing Dockerfile Code With Syntax-Aware Multi-Task Learning

2026-01-01

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DOI：10.1109/TSE.2025.3632074

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Abstract

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As a kind of infrastructure-as-code, Dockerfile specifies the structure and functionality of a built Docker image and thus plays an important role in the containerized software development process. Nowadays developers need to spend extra time and effort configuring their Dockerfiles in addition to their regular coding work, which requires knowledge and skills orthogonal to those entailed in other software-related experiences. Poorly written Dockerfile code often introduces errors and maintenance costs. However, little automated support is available for assisting developers in configuring Dockerfiles. In this study, we first conduct an online survey to investigate Docker developers' perceptions of Dockerfile writing, highlighting the needs and potential benefits of Dockerfile auto-completion techniques. Then, we introduce DockerFill, a pre-trained model based approach that provides completion suggestions for Dockerfile-specific code. DockerFill leverages multi-layer Transformer architecture with syntax-aware multi-task learning, which includes contextual file information and three pre-training tasks, i.e., masked language modeling, syntax type identification, and masked identifier prediction. To evaluate DockerFill's effectiveness, we collect a dataset of 6,350 high-quality real-world Dockerfiles. Our empirical results show that DockerFill provides up to 52.38% accuracy for token-level completion and 19.69% exact match for line-level completion, outperforming the baselines by 7.32%-37.67% and 1.97%-19.69%, respectively. Also, DockerFill obtains significantly higher human evaluation scores compared to the baselines.

Keywords:

Codes

Surveys

Transformers

Writing

Training

Multitasking

Syntactics

Manuals

Containers

Accuracy

Dockerfile