Giving Back: Contributions Congruent to Library Dependency Changes in a Software Ecosystem
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Abstract (arXiv)
Popular adoption of third-party libraries for contemporary software development has led to the creation of large inter-dependency networks, where sustainability issues of a single library can have widespread network effects. Maintainers of these libraries are often overworked, relying on the contributions of volunteers to sustain these libraries. In this work, we measure contributions that are aligned with dependency changes, to understand where they come from (i.e., non-maintainer, client maintainer, library maintainer, and library and client maintainer), analyze whether they contribute to library dormancy (i.e., a lack of activity), and investigate the similarities between these contributions and developers' typical contributions. Hence, we leverage socio-technical techniques to measure the dependency-contribution congruence (DC congruence), i.e., the degree to which contributions align with dependencies. We conduct a large-scale empirical study to measure the DC congruence for the NPM ecosystem using 1.7 million issues, 970 thousand pull requests (PR), and over 5.3 million commits belonging to 107,242 NPM packages. At the ecosystem level, we pinpoint in time peaks of congruence with dependency changes (i.e., 16% DC congruence score). Surprisingly, these contributions came from the ecosystem itself (i.e., non-maintainers of either client and library). At the project level, we find that DC congruence shares a statistically significant relationship with the likelihood of a package becoming dormant. Finally, by comparing source code of contributions, we find that congruent contributions are statistically different to typical contributions. Our work has implications to encourage and sustain contributions, especially to support library maintainers that require dependency changes.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 May 2022.
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Citing papers per year: 2024: 1; 2025: 1; 2026: 1.
3 papers.
- 2026Revealing Domain-Spatiality Patterns for Configuration Tuning: Domain Knowledge Meets Fitness Landscapescited by 4
- 2024Contexts Matter: An Empirical Study on Contextual Influence in Fairness Testing for Deep Learning Systemscited by 0
- 2025Evaluating Useful Surrogate Models for Configuration Tuning Beyond Accuracy: A Fitness Landscape Analysis Perspectivecited by 0
- 2026Revealing Domain-Spatiality Patterns for Configuration Tuning: Domain Knowledge Meets Fitness Landscapes20 Mar 2026
- 2025Evaluating Useful Surrogate Models for Configuration Tuning Beyond Accuracy: A Fitness Landscape Analysis Perspective26 Sep 2025
- 2024Contexts Matter: An Empirical Study on Contextual Influence in Fairness Testing for Deep Learning Systems12 Aug 2024