Towards Supporting Open Source Library Maintainers with Community-Based Analytics

2025-10-17

AI Summary

Open-source library maintainers lack actionable feedback on how downstream projects actually use their APIs, hindering test optimization, impact assessment of changes, and evolutionary guidance. Method: We propose a “community-level usage insight–driven maintenance decision” paradigm, introducing two quantitative metrics to measure test suite coverage of real-world API usage scenarios—thereby closing the maintenance feedback loop. Our approach integrates large-scale static API call analysis, stratified sampling of dependent projects, and empirical coverage evaluation, validated through a survey with open-source developers. Results: Experiments across 10 widely used Java libraries and their 500 downstream projects reveal that only 16% of exposed APIs are actively invoked, and among those, just 74% are covered by existing tests. Our metrics significantly strengthen data-driven maintenance decisions, delivering a practical, community-aware analytical framework for API evolution and test suite improvement.

Technology Category

• Machine Learning: Evaluation and Analysis
• Search and Optimization: Sampling/Simulation-based Search
• Reasoning under Uncertainty: Applications

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

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.

Problem

• Analyzing how OSS libraries are used across dependent ecosystems
• Identifying gaps between API methods offered and actually used
• Evaluating test suite coverage based on community API usage

Innovation

• Community-based analytics for OSS library usage
• Empirical study of 10 Java libraries ecosystems
• Two metrics for test suite evaluation