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ModuleGuard:Understanding and Detecting Module Conflicts in Python Ecosystem

Ruofan Zhu, Xingyu Wang, Chengwei Liu, Zhengzi Xu, Wenbo Shen, Rui Chang, Yang Liu

TL;DR

This work systematically investigates module conflicts (MC) in Python and introduces ModuleGuard, a tool built from InstSimulator for installation-free module extraction and EnvResolution for environment-aware dependency resolution. Together, they enable scalable ecosystem-wide MC detection across 4.2 million PyPI packages and 3,711 GitHub projects. The study identifies three MC patterns—module-to-Lib, module-to-TPL, and module-in-Dep—and quantifies their prevalence and potential threats, revealing significant risks from both direct and transitive dependencies. Findings show MCs remain prevalent due to naming conflicts and insufficient dependency awareness, highlighting the need for better module isolation, explicit conflict warnings, and improved tooling for developers and package maintainers. The work provides benchmarks, methodological guidelines, and practical insights to mitigate MC risks in Python software development.

Abstract

Python has become one of the most popular programming languages for software development due to its simplicity, readability, and versatility. As the Python ecosystem grows, developers face increasing challenges in avoiding module conflicts, which occur when different packages have the same namespace modules. Unfortunately, existing work has neither investigated the module conflict comprehensively nor provided tools to detect the conflict. Therefore, this paper systematically investigates the module conflict problem and its impact on the Python ecosystem. We propose a novel technique called InstSimulator, which leverages semantics and installation simulation to achieve accurate and efficient module extraction. Based on this, we implement a tool called ModuleGuard to detect module conflicts for the Python ecosystem. For the study, we first collect 97 MC issues, classify the characteristics and causes of these MC issues, summarize three different conflict patterns, and analyze their potential threats. Then, we conducted a large-scale analysis of the whole PyPI ecosystem (4.2 million packages) and GitHub popular projects (3,711 projects) to detect each MC pattern and analyze their potential impact. We discovered that module conflicts still impact numerous TPLs and GitHub projects. This is primarily due to developers' lack of understanding of the modules within their direct dependencies, not to mention the modules of the transitive dependencies. Our work reveals Python's shortcomings in handling naming conflicts and provides a tool and guidelines for developers to detect conflicts.

ModuleGuard:Understanding and Detecting Module Conflicts in Python Ecosystem

TL;DR

This work systematically investigates module conflicts (MC) in Python and introduces ModuleGuard, a tool built from InstSimulator for installation-free module extraction and EnvResolution for environment-aware dependency resolution. Together, they enable scalable ecosystem-wide MC detection across 4.2 million PyPI packages and 3,711 GitHub projects. The study identifies three MC patterns—module-to-Lib, module-to-TPL, and module-in-Dep—and quantifies their prevalence and potential threats, revealing significant risks from both direct and transitive dependencies. Findings show MCs remain prevalent due to naming conflicts and insufficient dependency awareness, highlighting the need for better module isolation, explicit conflict warnings, and improved tooling for developers and package maintainers. The work provides benchmarks, methodological guidelines, and practical insights to mitigate MC risks in Python software development.

Abstract

Python has become one of the most popular programming languages for software development due to its simplicity, readability, and versatility. As the Python ecosystem grows, developers face increasing challenges in avoiding module conflicts, which occur when different packages have the same namespace modules. Unfortunately, existing work has neither investigated the module conflict comprehensively nor provided tools to detect the conflict. Therefore, this paper systematically investigates the module conflict problem and its impact on the Python ecosystem. We propose a novel technique called InstSimulator, which leverages semantics and installation simulation to achieve accurate and efficient module extraction. Based on this, we implement a tool called ModuleGuard to detect module conflicts for the Python ecosystem. For the study, we first collect 97 MC issues, classify the characteristics and causes of these MC issues, summarize three different conflict patterns, and analyze their potential threats. Then, we conducted a large-scale analysis of the whole PyPI ecosystem (4.2 million packages) and GitHub popular projects (3,711 projects) to detect each MC pattern and analyze their potential impact. We discovered that module conflicts still impact numerous TPLs and GitHub projects. This is primarily due to developers' lack of understanding of the modules within their direct dependencies, not to mention the modules of the transitive dependencies. Our work reveals Python's shortcomings in handling naming conflicts and provides a tool and guidelines for developers to detect conflicts.
Paper Structure (17 sections, 4 equations, 4 figures, 4 tables)

This paper contains 17 sections, 4 equations, 4 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Background
  3. ModuleGuard Design
  4. InstSimulator: Installation-free Module Extraction
  5. EnvResolution: Environment-aware Dependency Resolution
  6. ModuleGuard Evaluation
  7. Large-scale Study
  8. RQ1: Issue Study
  9. Data Collection.
  10. Module Conflict Types.
  11. Module Conflict Threats
  12. RQ2: PyPI Packages Study
  13. RQ3: GitHub Projects Study
  14. Limitations
  15. Discussion
  16. ...and 2 more sections

Figures (4)

  • Figure 1: Module conflict example. Example (a) illustrates the overwriting module when downloading the package. Example (b) illustrates importing confusion when running the code.
  • Figure 2: Overview of our work.
  • Figure 3: Module paths change after installation. Specific parameters in the configuration file control these behaviors.
  • Figure 4: Statistics of the number of packages released and the number of conflict packages in each year.