Many developers relying on open-source digital infrastructure expect continuous maintenance, but even the most critical packages can become unmaintained. Despite this, there is little understanding of the prevalence of abandonment of widely-used packages, of subsequent exposure, and of reactions to abandonment in practice, or the factors that influence them. We perform a large-scale quantitative analysis of all widely-used npm packages and find that abandonment is common among them, that abandonment exposes many projects which often do not respond, that responses correlate with other dependency management practices, and that removal is significantly faster when a package's end-of-life status is explicitly stated. We end with recommendations to both researchers and practitioners who are facing dependency abandonment or are sunsetting packages, such as opportunities for low-effort transparency mechanisms to help exposed projects make better, more informed decisions.

The proliferation of open source software (OSS) and different types of reuse has made it incredibly difficult to perform an essential legal and compliance task of accurate license identification within the software supply chain. This study presents a reusable and comprehensive dataset of OSS licenses, created using the World of Code (WoC) infrastructure. By scanning all files containing “license” in their file paths, and applying the approximate matching via winnowing algorithm to identify the most similar license from the SPDX and Open Source list, we found and identified 5.5 million distinct license blobs in OSS projects. The dataset includes a detailed project-to-license (P2L) map with commit timestamps, enabling dynamic analysis of license adoption and changes over time. To verify the accuracy of the dataset we use stratified sampling and manual review, achieving a final accuracy of 92.08%, with precision of 87.14%, recall of 95.45%, and an F1 score of 91.11%. This dataset is intended to support a range of research and practical tasks, including the detection of license noncompliance, the investigations of license changes, study of licensing trends, and the development of compliance tools. The dataset is open, providing a valuable resource for developers, researchers, and legal professionals in the OSS community.

In Open Source Software, resources of any project are open for reuse by introducing dependencies or copying the resource itself. In contrast to dependency-based reuse, the infrastructure to systematically support copy-based reuse appears to be entirely missing. Our aim is to enable future research and tool development to increase efficiency and reduce the risks of copy-based reuse. We seek a better understanding of such reuse by measuring its prevalence and identifying factors affecting the propensity to reuse. To identify reused artifacts and trace their origins, our method exploits World of Code infrastructure. We begin with a set of theory-derived factors related to the propensity to reuse, sample instances of different reuse types, and survey developers to better understand their intentions. Our results indicate that copy-based reuse is common, with many developers being aware of it when writing code. The propensity for a file to be reused varies greatly among languages and between source code and binary files, consistently decreasing over time. Files introduced by popular projects are more likely to be reused, but at least half of reused resources originate from “small” and “medium” projects. Developers had various reasons for reuse but were generally positive about using a package manager.

In Open Source Software, the source code and any other resources available in a project can be viewed or reused by anyone subject to often permissive licensing restrictions. In contrast to some studies of dependency-based reuse supported via package managers, no studies of OSS-wide copy-based reuse exist. This dataset seeks to encourage the studies of OSS-wide copy-based reuse by providing copying activity data that captures whole-file reuse in nearly all OSS. To accomplish that, we develop approaches to detect copy-based reuse by developing an efficient algorithm that exploits World of Code infrastructure: a curated and cross referenced collection of nearly all open source repositories. We expect this data will enable future research and tool development that support such reuse and minimize associated risks.

Motivation: A key premise of open source software is the ability to copy code to other open source projects (white-box reuse). Such copying accelerates development of new projects, but the code flaws in the original projects, such as vulnerabilities, may also spread even if fixed in the projects from where the code was appropriated. The extent of the spread of vulnerabilities through code reuse, the potential impact of such spread, or avenues for mitigating risk of these secondary vulnerabilities has not been studied in the context of a nearly complete collection of open source code.

The Open-Source Software community has become the center of attention for many researchers, who are investigating various aspects of collaboration in this extremely large ecosystem. Due to its size, it is difficult to grasp whether or not it has structure, and if so, what it may be. Our hackathon project aims to facilitate the understanding of the developer collaboration structure and relationships among projects based on the bi-graph of what projects developers contribute to by providing an interactive collaboration graph of this ecosystem, using the data obtained from World of Code [1] infrastructure. Our attempts to visualize the entirety of projects and developers were stymied by the inability of the layout and visualization tools to process the exceedingly large scale of the full graph. We used WoC to filter the nodes (developers and projects) and edges (developer contributions to a project) to reduce the scale of the graph that made it amenable to an interactive visualization and published the resulting visualizations. We plan to apply hierarchical approaches to be able to incorporate the entire data in the interactive visualizations and also to evaluate the utility of such visualizations for several tasks.