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An Empirical Comparison of Dependency Network Evolution in Seven Software Packaging Ecosystems

arXiv 1710.04936 ↗First posted 13 Oct 2017cs.SEcited by 20

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Abstract (arXiv)

Nearly every popular programming language comes with one or more package managers. The software packages distributed by such package managers form large software ecosystems. These packaging ecosystems contain a large number of package releases that are updated regularly and that have many dependencies to other package releases. While packaging ecosystems are extremely useful for their respective communities of developers, they face challenges related to their scale, complexity, and rate of evolution. Typical problems are backward incompatible package updates, and the risk of (transitively) depending on packages that have become obsolete or inactive. This manuscript uses the libraries.io dataset to carry out a quantitative empirical analysis of the similarities and differences between the evolution of package dependency networks for seven packaging ecosystems of varying sizes and ages: Cargo for Rust, CPAN for Perl, CRAN for R, npm for JavaScript, NuGet for the .NET platform, Packagist for PHP, and RubyGems for Ruby. We propose novel metrics to capture the growth, changeability, resuability and fragility of these dependency networks, and use these metrics to analyse and compare their evolution. We observe that the dependency networks tend to grow over time, both in size and in number of package updates, while a minority of packages are responsible for most of the package updates. The majority of packages depend on other packages, but only a small proportion of packages accounts for most of the reverse dependencies. We observe a high proportion of fragile packages due to a high and increasing number of transitive dependencies. These findings are instrumental for assessing the quality of a package dependency network, and improving it through dependency management tools and imposed policies.

Key facts. Cited by 20 papers in the map (data through 14 Oct 2026). First posted on arXiv 13 Oct 2017.

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Citing papers per year: 2022: 3; 2023: 1; 2024: 1; 2025: 5; 2026: 10.

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  1. 2023An Empirical Study of Pre-Trained Model Reuse in the Hugging Face Deep Learning Model Registrycited by 47
  2. 2024What do we know about Hugging Face? A systematic literature review and quantitative validation of qualitative claimscited by 9
  3. 2022Towards a Change Taxonomy for Machine Learning Systemscited by 6
  4. 2025AI-Generated Code Is Not Reproducible (Yet): An Empirical Study of Dependency Gaps in LLM-Based Coding Agentscited by 5
  5. 2026Skills Are Not Islands: Measuring Dependency and Risk in Agent Skill Supply Chainscited by 3
  6. 2022An Empirical Study of Library Usage and Dependency in Deep Learning Frameworkscited by 2
  7. 2025How Robust are LLM-Generated Library Imports? An Empirical Study using Stack Overflowcited by 2
  8. 2025Empirical Evaluation of AI-Assisted Software Package Selection: A Knowledge Graph Approachcited by 2
  9. 2026ADK Arena: Evaluating Agent Development Kits via LLM-as-a-Developercited by 2
  10. 2026From Registry to Repository: How AI Agent Skills Are Written, Adapted, and Maintainedcited by 2
  11. 2025Investigating Security Implications of Automatically Generated Code on the Software Supply Chaincited by 1
  12. 2025LLMs as Packagers of HPC Softwarecited by 1
8 more
  1. 2026RepoRescue: An Empirical Study of LLM Agents on Whole-Repository Compatibility Rescuecited by 1
  2. 2022Call Graph Evolution Analytics over a Version Series of an Evolving Software Systemcited by 0
  3. 2026DepRadar: Agentic Coordination for Context Aware Defect Impact Analysis in Deep Learning Librariescited by 0
  4. 2026CCCE: A Continuous Code Calibration Engine for Autonomous Enterprise Codebase Maintenance via Knowledge Graph Traversal and Adaptive Decision Gatingcited by 0
  5. 2026Agentic Generation of AST Transformation Rules for Fixing Breaking Updatescited by 0
  6. 2026Govern the Repository, Not the Agent: Measuring Ecosystem-Level Risk in AI-Native Softwarecited by 0
  7. 2026On the Maintenance and Co-evolution of Agent Plugins: An Empirical Study of Claude Code Plugin Marketplacescited by 0
  8. 2026After the Party: Growth, Governance, and Security Scanning in the OpenClaw Agent Skill Ecosystemcited by 0