mlsanityData to 14 Oct 2026

Do Developers Update Their Library Dependencies? An Empirical Study on the Impact of Security Advisories on Library Migration

arXiv 1709.04621 ↗First posted 14 Sep 2017cs.SEcited by 23

Not on the map. Why?

Abstract (arXiv)

Third-party library reuse has become common practice in contemporary software development, as it includes several benefits for developers. Library dependencies are constantly evolving, with newly added features and patches that fix bugs in older versions. To take full advantage of third-party reuse, developers should always keep up to date with the latest versions of their library dependencies. In this paper, we investigate the extent of which developers update their library dependencies. Specifically, we conducted an empirical study on library migration that covers over 4,600 GitHub software projects and 2,700 library dependencies. Results show that although many of these systems rely heavily on dependencies, 81.5% of the studied systems still keep their outdated dependencies. In the case of updating a vulnerable dependency, the study reveals that affected developers are not likely to respond to a security advisory. Surveying these developers, we find that 69% of the interviewees claim that they were unaware of their vulnerable dependencies. Furthermore, developers are not likely to prioritize library updates, citing it as extra effort and added responsibility. This study concludes that even though third-party reuse is commonplace, the practice of updating a dependency is not as common for many developers.

Key facts. Cited by 23 papers in the map (data through 14 Oct 2026). First posted on arXiv 14 Sep 2017.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 4; 2024: 1; 2025: 5; 2026: 13.

23 papers.

Sort:

By area:

  1. 2025Migrating Code At Scale With LLMs At Googlecited by 14
  2. 2023Multi-Granularity Detector for Vulnerability Fixescited by 4
  3. 2025MatchFixAgent: Language-Agnostic Autonomous Repository-Level Code Translation Validation and Repaircited by 4
  4. 2023MELT: Mining Effective Lightweight Transformations from Pull Requestscited by 2
  5. 2024Risks of ignoring uncertainty propagation in AI-augmented security pipelinescited by 1
  6. 2025LLM Agents for Automated Dependency Upgradescited by 1
  7. 2026SPELL: Synthesis of Programmatic Edits using LLMscited by 1
  8. 2026Correct Code, Vulnerable Dependencies: A Large Scale Measurement Study of LLM-Specified Library Versionscited by 1
  9. 2026RepoRescue: An Empirical Study of LLM Agents on Whole-Repository Compatibility Rescuecited by 1
  10. 2023Code Ownership in Open-Source AI Software Securitycited by 0
  11. 2023CodeLL: A Lifelong Learning Dataset to Support the Co-Evolution of Data and Language Models of Codecited by 0
  12. 2025LibRec: Benchmarking Retrieval-Augmented LLMs for Library Migration Recommendationscited by 0
11 more
  1. 2025Which Is Better For Reducing Outdated and Vulnerable Dependencies: Pinning or Floating?cited by 0
  2. 2026The Invisible Hand of AI Libraries Shaping Open Source Projects and Communitiescited 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. 2026The Impact of Configuring Agentic AI Coding Tools on Build-vs-Buy Decisions: A Study Protocolcited by 0
  6. 2026Agentic Generation of AST Transformation Rules for Fixing Breaking Updatescited by 0
  7. 2026LibEvoBench: Probing Temporal Knowledge Stratification in Code Generation Modelscited by 0
  8. 2026An Empirical Study of Model Context Protocol Applicationscited by 0
  9. 2026What Aggregate Scores Miss: Measuring Item-Level Regressions in Commercial LLM API Migrationscited by 0
  10. 2026Update from Hell: Can Coding Agents Survive Hidden Breakage in Dependency Upgrades?cited by 0
  11. 2026Escaping Python Dependency Hell: A Hybrid Replay-and-Repair Pipeline for Python Dependency Resolutioncited by 0