Search arXivSearch

arXiv · 2601.20240

Understanding npm Developers' Practices, Challenges, and Recommendations for Secure Package Development

Abstract

Background: The Node Package Manager (npm) ecosystem plays a vital role in modern software development by providing a vast repository of packages and tools that developers can use to implement their software systems. However, recent vulnerabilities in third-party packages have led to serious security breaches, compromising the integrity of applications that depend on them. Objective: This study investigates how npm package developers perceive and handle security in their work. We examined developers' understanding of security risks, the practices and tools they use, the barriers to stronger security measures, and their suggestions for improving the npm ecosystem's security. Method: We conducted an online survey with 75 npm package developers and undertook a mixed-methods approach to analyzing their responses. Results: While developers prioritize security, they perceive their packages as only moderately secure, with concerns about supply chain attacks, dependency vulnerabilities, and malicious code. Only 40% are satisfied with the current npm security tools due to issues such as alert fatigue. Automated methods such as two-factor authentication and npm audit are favored over code reviews. Many drop dependencies due to abandonment or vulnerabilities, and typically respond to vulnerabilities in their packages by quickly releasing patches. Key barriers include time constraints and high false-positive rates. To improve npm security, developers seek better detection tools, clearer documentation, stronger account protections, and more education initiatives. Conclusion: Our findings will benefit npm package contributors and maintainers by highlighting prevalent security challenges and promoting discussions on best practices to strengthen security and trustworthiness within the npm landscape.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Anthony Peruma, Truman Choy, Gerald Lee, Italo De Oliveira Santos. 2026-01-28. Understanding npm Developers' Practices, Challenges, and Recommendations for Secure Package Development. https://doi.org/10.1145/3794860.3794908

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Combining Type Inference and Automated Unit Test Generation for Python

Automated unit test generation is an established research field that has so far focused on statically-typed programming languages. The lack of type information in dynamically-typed programming languages, such as Python, inhibits test generators, which heavily rely on information about parameter and return types of functions to select suitable arguments when constructing test cases. Since automated test generators inherently rely on frequent execution of candidate tests, we make use of these frequent executions to address this problem by introducing type tracing, which extracts type-related information during execution and gradually refines the available type information. We implement type tracing as an extension of the Pynguin test-generation framework for Python, allowing it (i) to infer parameter types by observing how parameters are used during runtime, (ii) to record the types of values that function calls return, and (iii) to use this type information to increase code coverage. The approach leads to up to 87.8 % more branch coverage, improved mutation scores, and to type information of similar quality to that produced by other state-of-the-art type-inference tools.

cs.SE

Guidelines for Empirical Studies in Software Engineering involving Large Language Models

Large Language Models (LLMs) are widely used in software engineering (SE) research and practice, yet their non-determinism, opaque training data, and rapidly evolving models threaten the reproducibility and replicability of empirical studies. We address this challenge through a collaborative effort of 22 researchers, presenting a taxonomy of seven study types that organizes how LLMs are used in SE research, together with eight guidelines for designing and reporting such studies. Each guideline distinguishes requirements (must) from recommendations (should) and is contextualized by the study types it applies to. Our guidelines recommend that researchers: (1) declare LLM usage and role; (2) report model versions, configurations, and customizations; (3) document the system and prompt design beyond the model; (4) report session traces, i.e., interaction logs and runtime traces; (5) use suitable baselines, benchmarks, and metrics; (6) include an open LLM as a baseline; (7) validate LLM outputs against human judgment; and (8) articulate limitations and mitigations. We complement the guidelines with an applicability matrix mapping guidelines to study types and a reporting checklist for authors and reviewers. We maintain the study types and guidelines online as a living resource for the community to use and shape (llm-guidelines$.$org).

cs.SE

MigrateLib: a tool for end-to-end Python library migration

Library migration is the process of replacing a library with a similar one in a software project. Manual library migration is time consuming and error prone, as it requires developers to understand the Application Programming Interfaces (API) of both libraries, map equivalent APIs, and perform the necessary code transformations. Due to the difficulty of the library migration process, most of the existing automated techniques and tooling stop at the API mapping stage or support a limited set of libraries and code transformations. In this paper, we develop an end-to-end solution that can automatically migrate code between any arbitrary pair of Python libraries that provide similar functionality. Due to the promising capabilities of Large Language Models (LLMs) in code generation and transformation, we use LLMs as the primary engine for migration. Before building the tool, we first study the capabilities of LLMs for library migration on a benchmark of 321 real-world library migrations. We find that LLMs can effectively perform library migration, but some post-processing steps can further improve the performance. Based on this, we develop MigrateLib, a command line application that combines the power of LLMs, static analysis, and dynamic analysis to provide accurate library migration. We evaluate MigrateLib on 717 real-world Python applications that are not from our benchmark. We find that MigrateLib can migrate 32% of the migrations with complete correctness. Of the remaining migrations, only 14% of the migration-related changes are left for developers to fix for more than half of the projects.

cs.SE