Towards Supporting Open Source Library Maintainers with Community-Based Analytics
- URL: http://arxiv.org/abs/2510.15794v1
- Date: Fri, 17 Oct 2025 16:15:59 GMT
- Title: Towards Supporting Open Source Library Maintainers with Community-Based Analytics
- Authors: Rachna Raj, Diego Elias Costa,
- Abstract summary: We propose the use of community-based analytics to analyze how an OSS library is used across its dependent ecosystem.<n>Our results reveal that while library developers offer a wide range of API methods, only 16% are actively used by their dependent ecosystem.<n>We propose two metrics to help developers evaluate their test suite according to the APIs used by their community.
- Score: 1.4078020083560923
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Open-source software (OSS) is a pillar of modern software development. Its success depends on the dedication of maintainers who work constantly to keep their libraries stable, adapt to changing needs, and support a growing community. Yet, they receive little to no continuous feedback on how the projects that rely on their libraries actually use their APIs. We believe that gaining these insights can help maintainers make better decisions, such as refining testing strategies, understanding the impact of changes, and guiding the evolution of their libraries more effectively. We propose the use of community-based analytics to analyze how an OSS library is used across its dependent ecosystem. We conduct an empirical study of 10 popular Java libraries and each with their respective dependent ecosystem of 50 projects. Our results reveal that while library developers offer a wide range of API methods, only 16% on average are actively used by their dependent ecosystem. Moreover, only 74% of the used API methods are partially or fully covered by their library test suite. We propose two metrics to help developers evaluate their test suite according to the APIs used by their community, and we conduct a survey on open-source practitioners to assess the practical value of these insights in guiding maintenance decisions.
Related papers
- Why Authors and Maintainers Link (or Don't Link) Their PyPI Libraries to Code Repositories and Donation Platforms [83.16077040470975]
Metadata of libraries on the Python Package Index (PyPI) plays a critical role in supporting the transparency, trust, and sustainability of open-source libraries.<n>This paper presents a large-scale empirical study combining two targeted surveys sent to 50,000 PyPI authors and maintainers.<n>We analyze more than 1,400 responses using large language model (LLM)-based topic modeling to uncover key motivations and barriers related to linking repositories and donation platforms.
arXiv Detail & Related papers (2026-01-21T16:13:57Z) - Applications and Challenges of Fairness APIs in Machine Learning Software [3.3383488302533997]
bias detection and mitigation open-source software libraries (aka API libraries) are being developed and used.<n>In this paper, we conduct a qualitative study to understand in what scenarios these open-source fairness APIs are used in the wild.<n>We analyzed 204 GitHub repositories which used 13 APIs that are developed to address bias in ML software.
arXiv Detail & Related papers (2025-08-22T13:33:37Z) - Understanding API Usage and Testing: An Empirical Study of C Libraries [0.2532202013576546]
This study is the first to compare API usage and API testing at scale for the C/C++ ecosystem.<n>For our empirical study, we have developed LibProbe, a framework that can be used to analyse a large corpus of clients for a given library.
arXiv Detail & Related papers (2025-06-13T09:07:16Z) - Commit0: Library Generation from Scratch [77.38414688148006]
Commit0 is a benchmark that challenges AI agents to write libraries from scratch.<n>Agents are provided with a specification document outlining the library's API as well as a suite of interactive unit tests.<n> Commit0 also offers an interactive environment where models receive static analysis and execution feedback on the code they generate.
arXiv Detail & Related papers (2024-12-02T18:11:30Z) - Contributing Back to the Ecosystem: A User Survey of NPM Developers [10.154686574810501]
Survey of 49 developers from the NPM ecosystem.
We find that developers are more likely to maintain their own packages rather than contribute to the ecosystem.
Our results open up new avenues into tool support and research into how to sustain these ecosystems.
arXiv Detail & Related papers (2024-07-01T00:15:55Z) - EduNLP: Towards a Unified and Modularized Library for Educational Resources [78.8523961816045]
We present a unified, modularized, and extensive library, EduNLP, focusing on educational resource understanding.
In the library, we decouple the whole workflow to four key modules with consistent interfaces including data configuration, processing, model implementation, and model evaluation.
For the current version, we primarily provide 10 typical models from four categories, and 5 common downstream-evaluation tasks in the education domain on 8 subjects for users' usage.
arXiv Detail & Related papers (2024-06-03T12:45:40Z) - Lightweight Syntactic API Usage Analysis with UCov [0.0]
We present a novel conceptual framework designed to assist library maintainers in understanding the interactions allowed by their APIs.
These customizable models enable library maintainers to improve their design ahead of release, reducing friction during evolution.
We implement these models for Java libraries in a new tool UCov and demonstrate its capabilities on three libraries exhibiting diverse styles of interaction.
arXiv Detail & Related papers (2024-02-19T10:33:41Z) - SequeL: A Continual Learning Library in PyTorch and JAX [50.33956216274694]
SequeL is a library for Continual Learning that supports both PyTorch and JAX frameworks.
It provides a unified interface for a wide range of Continual Learning algorithms, including regularization-based approaches, replay-based approaches, and hybrid approaches.
We release SequeL as an open-source library, enabling researchers and developers to easily experiment and extend the library for their own purposes.
arXiv Detail & Related papers (2023-04-21T10:00:22Z) - Recommendation Systems in Libraries: an Application with Heterogeneous
Data Sources [66.81627042740679]
The Reading&Machine project exploits the support of digitalization to increase the attractiveness of libraries and improve the users' experience.
The project implements an application that helps the users in their decision-making process, providing recommendation system (RecSys)-generated lists of books the users might be interested in.
arXiv Detail & Related papers (2023-03-21T11:13:01Z) - Code Librarian: A Software Package Recommendation System [65.05559087332347]
We present a recommendation engine called Librarian for open source libraries.
A candidate library package is recommended for a given context if: 1) it has been frequently used with the imported libraries in the program; 2) it has similar functionality to the imported libraries in the program; 3) it has similar functionality to the developer's implementation, and 4) it can be used efficiently in the context of the provided code.
arXiv Detail & Related papers (2022-10-11T12:30:05Z) - Do Not Take It for Granted: Comparing Open-Source Libraries for Software
Development Effort Estimation [9.224578642189023]
This paper aims at raising awareness of the differences incurred when using different Machine Learning (ML) libraries for software development effort estimation (SEE)
We investigate 4 deterministic machine learners as provided by 3 of the most popular ML open-source libraries written in different languages (namely, Scikit-Learn, Caret and Weka)
The results of our study reveal that the predictions provided by the 3 libraries differ in 95% of the cases on average across a total of 105 cases studied.
arXiv Detail & Related papers (2022-07-04T20:06:40Z) - SacreROUGE: An Open-Source Library for Using and Developing
Summarization Evaluation Metrics [74.28810048824519]
SacreROUGE is an open-source library for using and developing summarization evaluation metrics.
The library provides Python wrappers around the official implementations of existing evaluation metrics.
It provides functionality to evaluate how well any metric implemented in the library correlates to human-annotated judgments.
arXiv Detail & Related papers (2020-07-10T13:26:37Z)
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.