ModuleGuard:Understanding and Detecting Module Conflicts in Python Ecosystem

• URL: http://arxiv.org/abs/2401.02090v1
• Date: Thu, 4 Jan 2024 06:26:07 GMT
• Title: ModuleGuard:Understanding and Detecting Module Conflicts in Python Ecosystem
• Authors: Ruofan Zhu, Xingyu Wang, Chengwei Liu, Zhengzi Xu, Wenbo Shen, Rui Chang and Yang Liu
• Abstract summary: 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.
• Score: 13.242135844684505
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Raiders of the Lost Dependency: Fixing Dependency Conflicts in Python using LLMs [10.559292676550319]
  Python developers must manually identify and resolve environment dependencies and version constraints of third-party modules and Python interpreters. Traditional approaches face limitations due to the variety of dependency error types, large sets of possible module versions, and conflicts among. This study explores the potential of using large language models (LLMs) to automatically fix dependency issues in Python programs.
  arXiv  Detail & Related papers  (2025-01-27T16:45:34Z)
• PyPulse: A Python Library for Biosignal Imputation [58.35269251730328]
  We introduce PyPulse, a Python package for imputation of biosignals in both clinical and wearable sensor settings. PyPulse's framework provides a modular and extendable framework with high ease-of-use for a broad userbase, including non-machine-learning bioresearchers. We released PyPulse under the MIT License on Github and PyPI.
  arXiv  Detail & Related papers  (2024-12-09T11:00:55Z)
• SBOM Generation Tools in the Python Ecosystem: an In-Detail Analysis [2.828503885204035]
  We analyze four popular SBOM generation tools using the CycloneDX standard. We highlight issues related to dependency versions, metadata files, remote dependencies, and optional dependencies. We identify a systematic issue with the lack of standards for metadata in the PyPI ecosystem.
  arXiv  Detail & Related papers  (2024-09-02T12:48:10Z)
• Python Fuzzing for Trustworthy Machine Learning Frameworks [0.0]
  We propose a dynamic analysis pipeline for Python projects using Sydr-Fuzz. Our pipeline includes fuzzing, corpus minimization, crash triaging, and coverage collection. To identify the most vulnerable parts of machine learning frameworks, we analyze their potential attack surfaces and develop fuzz targets for PyTorch, and related projects such as h5py.
  arXiv  Detail & Related papers  (2024-03-19T13:41:11Z)
• pyvene: A Library for Understanding and Improving PyTorch Models via Interventions [79.72930339711478]
  $textbfpyvene$ is an open-source library that supports customizable interventions on a range of different PyTorch modules. We show how $textbfpyvene$ provides a unified framework for performing interventions on neural models and sharing the intervened upon models with others.
  arXiv  Detail & Related papers  (2024-03-12T16:46:54Z)
• MoTCoder: Elevating Large Language Models with Modular of Thought for Challenging Programming Tasks [50.61968901704187]
  We introduce a pioneering framework for MoT instruction tuning, designed to promote the decomposition of tasks into logical sub-tasks and sub-modules. Our investigations reveal that, through the cultivation and utilization of sub-modules, MoTCoder significantly improves both the modularity and correctness of the generated solutions.
  arXiv  Detail & Related papers  (2023-12-26T08:49:57Z)
• GENOME: GenerativE Neuro-symbOlic visual reasoning by growing and reusing ModulEs [64.49176353858792]
  We propose generative neuro-symbolic visual reasoning by growing and reusing modules. The proposed model performs competitively on standard tasks like visual question answering and referring expression comprehension. It is able to adapt to new visual reasoning tasks by observing a few training examples and reusing modules.
  arXiv  Detail & Related papers  (2023-11-08T18:59:05Z)
• Less is More? An Empirical Study on Configuration Issues in Python PyPI Ecosystem [38.44692482370243]
  Python is widely used in the open-source community, largely owing to the extensive support from diverse third-party libraries. Third-party libraries can potentially lead to conflicts in dependencies, prompting researchers to develop dependency conflict detectors. endeavors have been made to automatically infer dependencies.
  arXiv  Detail & Related papers  (2023-10-19T09:07:51Z)
• On the Feasibility of Cross-Language Detection of Malicious Packages in npm and PyPI [6.935278888313423]
  Malicious users started to spread malware by publishing open-source packages containing malicious code. Recent works apply machine learning techniques to detect malicious packages in the npm ecosystem. We present a novel approach that involves a set of language-independent features and the training of models capable of detecting malicious packages in npm and PyPI.
  arXiv  Detail & Related papers  (2023-10-14T12:32:51Z)
• ModuleFormer: Modularity Emerges from Mixture-of-Experts [60.6148988099284]
  This paper proposes a new neural network architecture, ModuleFormer, to improve the efficiency and flexibility of large language models. Unlike the previous SMoE-based modular language model, ModuleFormer can induce modularity from uncurated data.
  arXiv  Detail & Related papers  (2023-06-07T17:59:57Z)
• Modular Deep Learning [120.36599591042908]
  Transfer learning has recently become the dominant paradigm of machine learning. It remains unclear how to develop models that specialise towards multiple tasks without incurring negative interference. Modular deep learning has emerged as a promising solution to these challenges.
  arXiv  Detail & Related papers  (2023-02-22T18:11:25Z)

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.