ModuleGuard: 

Introduction

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 on GitHub, classify the characteristics and causes of these MC issues, summarize three different conflict patterns, and analyze their potential threats. Then we conduct 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 discover that module conflicts still impact numerous open-source software packages in PyPI. Our work reveals Python’s shortcomings in handling naming conflicts and provides a tool and guidelines for developers to detect conflicts. 

Main Contribution

• New study. We conduct a systematic study on module conflicts (MC) in the Python ecosystem. We conduct an issue study from Github and StackOverflow and summarize three MC patterns--- module-to-TPL, module-to-Lib, and module-in-Dep conflicts and their two potential threats. 

• New technique. We propose InstSimulator, which leverages the semantics and installation simulation to achieve accurate and efficient module extraction. 

• Based on this, we implement a tool ModuleGuard to detect MCs for the Python ecosystem. We construct benchmarks for evaluating the capabilities of module information extraction and dependency graph resolution.

• Ecosystem-scale analysis. Utilizing ModuleGuard, we conduct a large-scale study and analyze 4.2 million packages on PyPI (434,823 latest version packages as of April 2023). We get a lot of interesting findings, shed some light on the nature of module conflicts, and provide some guidance for developers.

• Issue reporting. We examine 93,487 tags of 3,711 popular GitHub projects, of which 108 are now or ever affected by MC. We reported issues and a lot of issues are confirmed and fixed. This proves that our work can help developers understand previously unrealized errors and help them fix potential threats.

Implementation of ModuleGuard

Here are some examples of details not mentioned in the paper.

• Module path extraction: InstSimulation

• Dependency resolution: EnvResolution

• Evaluation

Resources

• Github project.