mlsanityData to 7 Oct 2026

LLM Agents for Automated Dependency Upgrades

arXiv 2510.03480 ↗First posted 3 Oct 2025cs.SEcited by 1

On the map: LLM agents and tool use → Software engineering coding agents

Abstract (arXiv)

As a codebase expands over time, its library dependencies can become outdated and require updates to maintain innovation and security. However, updating a library can introduce breaking changes in the code, necessitating significant developer time for maintenance. To address this, we introduce a framework of LLM agents to be used in combination with migration documentation to automatically recommend and apply code updates and ensure compatibility with new versions. Our solution can automatically localize updated library usages in live Java codebases and implement recommended fixes in a user-friendly manner. The system architecture consists of multiple key components: a Summary Agent, Control Agent, and Code Agent. To validate our approach, we apply the framework on an industrial use case by which we create three synthetic code repositories with major Upgrade changes and benchmark our approach against state-of-the-art methods. Results show that our approach not only performs upgrades using fewer tokens across all cases but also achieves a precision of 71.4%, highlighting its efficiency and effectiveness compared to state-of-the-art methods.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 15 of its 28 references that are in the map. First posted on arXiv 3 Oct 2025.

Builds on

15 of 28 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024OpenHands: An Open Platform for AI Software Developers as Generalist Agents23 Jul
  2. 2024SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering6 May

Other references · 13

  1. 2021Evaluating Large Language Models Trained on Codecited by 8,053
  2. 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,193
  3. 2018SequenceR: Sequence-to-Sequence Learning for End-to-End Program Repaircited by 144
  4. 2021CURE: Code-Aware Neural Machine Translation for Automatic Program Repaircited by 85
  5. 2020Code Prediction by Feeding Trees to Transformerscited by 70
  6. 2021Studying the Usage of Text-To-Text Transfer Transformer to Support Code-Related Taskscited by 70
  7. 2017Do Developers Update Their Library Dependencies? An Empirical Study on the Impact of Security Advisories on Library Migrationcited by 23
  8. 2020Empirical Study of Transformers for Source Codecited by 13
  9. 2022Automatically Recommend Code Updates: Are We There Yet?cited by 7
  10. 2025Meta-RAG on Large Codebases Using Code Summarizationcited by 4
  11. 2024An Empirical Study on Package-Level Deprecation in Python Ecosystemcited by 2
  12. 2025Popularity and Innovation in Maven Centralcited by 1
1 more
  1. 2025PCREQ: Automated Inference of Compatible Requirements for Python Third-party Library Upgradescited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

Similar, not cited

Close in wording, no citation either way

  1. 2024An Empirical Study on LLM-based Agents for Automated Bug Fixing
  2. 2025RefAgent: A Multi-agent LLM-based Framework for Automatic Software Refactoring
  3. 2026LegacyTranslate: LLM-based Multi-Agent Method for Legacy Code Translation
  4. 2024Agent-Driven Automatic Software Improvement
  5. 2025Autonomous Legacy Web Application Upgrades Using a Multi-Agent System
  6. 2025VAPU: System for Autonomous Legacy Code Modernization
  7. 2026Evaluating LLM Agents on Automated Software Analysis Tasks
  8. 2025A Survey on Code Generation with LLM-based Agents