Docker Does Not Guarantee Reproducibility
- URL: http://arxiv.org/abs/2601.12811v1
- Date: Mon, 19 Jan 2026 08:16:43 GMT
- Title: Docker Does Not Guarantee Reproducibility
- Abstract summary: Containerization technologies like Docker address the problem by encapsulating software environments into shareable snapshots known as images.<n>While Docker is frequently cited in the literature as a tool that enables in theory, the extent of its guarantees and limitations in practice remains under-explored.<n>First, we conduct a systematic review to examine how Docker is framed in scientific discourse on literature.<n>Then, we perform a large-scale empirical study of 5298 Dockerfiles collected from GitHub.
- Score: 8.363502352389188
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The reproducibility of software environments is a critical concern in modern software engineering, with ramifications ranging from the effectiveness of collaboration workflows to software supply chain security and scientific reproducibility. Containerization technologies like Docker address this problem by encapsulating software environments into shareable filesystem snapshots known as images. While Docker is frequently cited in the literature as a tool that enables reproducibility in theory, the extent of its guarantees and limitations in practice remains under-explored. In this work, we address this gap through two complementary approaches. First, we conduct a systematic literature review to examine how Docker is framed in scientific discourse on reproducibility and to identify documented best practices for writing Dockerfiles enabling reproducible image building. Then, we perform a large-scale empirical study of 5298 Docker builds collected from GitHub workflows. By rebuilding these images and comparing the results with their historical counterparts, we assess the real reproducibility of Docker images and evaluate the effectiveness of the best practices identified in the literature.
Related papers
- Beyond the Tip of the Iceberg: Understanding SATD in Dockerfiles through the Lens of Co-evolution [10.059038321113546]
We study self-admitted technical debt (SATD) in Dockerfiles alongside the related source code.<n>We find that approximately 27% of admission events and 40% of repayment events are coupled to non-Dockerfile artifacts.
arXiv Detail & Related papers (2026-05-20T14:26:56Z) - SynthID-Image: Image watermarking at internet scale [55.5714762895087]
We introduce SynthID-Image, a deep learning-based system for invisibly watermarking AI-generated imagery.<n>This paper documents the technical desiderata, threat models, and practical challenges of deploying such a system at internet scale.
arXiv Detail & Related papers (2025-10-10T11:03:31Z) - gh0stEdit: Exploiting Layer-Based Access Vulnerability Within Docker Container Images [0.0]
gh0stEdit allows an attacker to maliciously edit Docker images.<n>This attack can also be carried out against signed images without invalidating the image signature.<n>We highlight the issues in the current approach to Docker image security and trust, and expose an attack method which could potentially be exploited in the wild without being detected.
arXiv Detail & Related papers (2025-06-09T20:38:17Z) - Toward Automated Test Generation for Dockerfiles Based on Analysis of Docker Image Layers [1.1879716317856948]
The process for building a Docker image is defined in a text file called a Dockerfile.<n>A Dockerfile can be considered as a kind of source code that contains instructions on how to build a Docker image.<n>We propose an automated test generation method for Dockerfiles based on processing results rather than processing steps.
arXiv Detail & Related papers (2025-04-25T08:02:46Z) - Refactoring for Dockerfile Quality: A Dive into Developer Practices and Automation Potential [0.0]
This paper explores the utility and practicality of automating Dockerfile using 600files from 358 open-source projects.<n>Our approach leads to an average reduction of 32% in image size and a 6% decrease in build duration, with improvements in understandability and maintainability observed in 77% and 91% of cases.
arXiv Detail & Related papers (2025-01-23T23:10:47Z) - Codev-Bench: How Do LLMs Understand Developer-Centric Code Completion? [60.84912551069379]
We present the Code-Development Benchmark (Codev-Bench), a fine-grained, real-world, repository-level, and developer-centric evaluation framework.
Codev-Agent is an agent-based system that automates repository crawling, constructs execution environments, extracts dynamic calling chains from existing unit tests, and generates new test samples to avoid data leakage.
arXiv Detail & Related papers (2024-10-02T09:11:10Z) - Dockerfile Flakiness: Characterization and Repair [6.518508607788089]
We present the first comprehensive study of Dockerfile flakiness, featuring a nine-month analysis of 8,132 Dockerized projects.<n>We propose a taxonomy categorizing common flakiness causes, including dependency errors and server connectivity issues.<n>We introduce FLAKIDOCK, a novel repair framework combining static and dynamic analysis, similarity retrieval, and an iterative feedback loop powered by Large Language Models.
arXiv Detail & Related papers (2024-08-09T23:17:56Z) - Exploring Incompatible Knowledge Transfer in Few-shot Image Generation [107.81232567861117]
Few-shot image generation learns to generate diverse and high-fidelity images from a target domain using a few reference samples.
Existing F SIG methods select, preserve and transfer prior knowledge from a source generator to learn the target generator.
We propose knowledge truncation, which is a complementary operation to knowledge preservation and is implemented by a lightweight pruning-based method.
arXiv Detail & Related papers (2023-04-15T14:57:15Z) - Benchopt: Reproducible, efficient and collaborative optimization
benchmarks [67.29240500171532]
Benchopt is a framework to automate, reproduce and publish optimization benchmarks in machine learning.
Benchopt simplifies benchmarking for the community by providing an off-the-shelf tool for running, sharing and extending experiments.
arXiv Detail & Related papers (2022-06-27T16:19:24Z) - Studying the Practices of Deploying Machine Learning Projects on Docker [9.979005459305117]
Docker is a containerization service that allows for convenient deployment of websites, databases, applications' APIs, and machine learning (ML) models with a few lines of code.
We conducted an exploratory study to understand how Docker is being used to deploy ML-based projects.
arXiv Detail & Related papers (2022-06-01T18:13:30Z) - From ImageNet to Image Classification: Contextualizing Progress on
Benchmarks [99.19183528305598]
We study how specific design choices in the ImageNet creation process impact the fidelity of the resulting dataset.
Our analysis pinpoints how a noisy data collection pipeline can lead to a systematic misalignment between the resulting benchmark and the real-world task it serves as a proxy for.
arXiv Detail & Related papers (2020-05-22T17:39:16Z) - A Transformer-based Approach for Source Code Summarization [86.08359401867577]
We learn code representation for summarization by modeling the pairwise relationship between code tokens.
We show that despite the approach is simple, it outperforms the state-of-the-art techniques by a significant margin.
arXiv Detail & Related papers (2020-05-01T23:29:36Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.