# DockerizeMe: Automatic Inference of Environment Dependencies for Python Code Snippets

arXiv: https://arxiv.org/abs/1905.11127 · HTML: https://mlsanity.com/paper/1905.11127/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 May 2019.

## Built on by (most cited first, 13 of 13)

- [Repo2Run: Automated Building Executable Environment for Code Repository at Scale](https://mlsanity.com/paper/2502.13681/) (2025)
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- [CXXCrafter: An LLM-Based Agent for Automated C/C++ Open Source Software Building](https://mlsanity.com/paper/2505.21069/) (2025)
- [EvoConfig: Self-Evolving Multi-Agent Systems for Efficient Autonomous Environment Configuration](https://mlsanity.com/paper/2601.16489/) (2026)
- [The Last Dependency Crusade: Solving Python Dependency Conflicts with LLMs](https://mlsanity.com/paper/2501.16191/) (2025)
- [Environment-in-the-Loop: Rethinking Code Migration with LLM-based Agents](https://mlsanity.com/paper/2602.09944/) (2026)
- [RepoLaunch: Automating Build and Management of Code Repositories across Languages and Platforms](https://mlsanity.com/paper/2603.05026/) (2026)
- [MEMRES: A Memory-Augmented Resolver with Confidence Cascade for Agentic Python Dependency Resolution](https://mlsanity.com/paper/2604.16941/) (2026)
- [Text Classification for Task-based Source Code Related Questions](https://mlsanity.com/paper/2111.00580/) (2021)
- [Evaluating LLM Agents on Automated Software Analysis Tasks](https://mlsanity.com/paper/2604.11270/) (2026)
- [BootstrapAgent: Distilling Repository Setup into Reusable Agent Knowledge](https://mlsanity.com/paper/2605.15815/) (2026)
- [Escaping Python Dependency Hell: A Hybrid Replay-and-Repair Pipeline for Python Dependency Resolution](https://mlsanity.com/paper/2609.26952/) (2026)
- [Graph-Guided Repository Environment Construction](https://mlsanity.com/paper/2609.33429/) (2026)

## Abstract (arXiv)

Platforms like Stack Overflow and GitHub's gist system promote the sharing of ideas and programming techniques via the distribution of code snippets designed to illustrate particular tasks. Python, a popular and fast-growing programming language, sees heavy use on both sites, with nearly one million questions asked on Stack Overflow and 400 thousand public gists on GitHub. Unfortunately, around 75% of the Python example code shared through these sites cannot be directly executed. When run in a clean environment, over 50% of public Python gists fail due to an import error for a missing library. We present DockerizeMe, a technique for inferring the dependencies needed to execute a Python code snippet without import error. DockerizeMe starts with offline knowledge acquisition of the resources and dependencies for popular Python packages from the Python Package Index (PyPI). It then builds Docker specifications using a graph-based inference procedure. Our inference procedure resolves import errors in 892 out of nearly 3,000 gists from the Gistable dataset for which Gistable's baseline approach could not find and install all dependencies.