Cover Image

When AI Models Become Dependencies: Studying the Evolution of Pre-Trained Model Reuse in Downstream Software Systems

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Banyongrakkul, Peerachai, Zahedi, Mansooreh, Treude, Christoph, Gao, Haoyu, Thongtanunam, Patanamon
Format:	Preprint
Published:	2026
Subjects:	Software Engineering
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

From Release to Adoption: Challenges in Reusing Pre-trained AI Models for Downstream Developers
by: Banyongrakkul, Peerachai, et al.
Published: (2025)

On Autopilot? An Empirical Study of Human-AI Teaming and Review Practices in Open Source
by: Gao, Haoyu, et al.
Published: (2026)

Adapting Installation Instructions in Rapidly Evolving Software Ecosystems
by: Gao, Haoyu, et al.
Published: (2023)

Documenting Ethical Considerations in Open Source AI Models
by: Gao, Haoyu, et al.
Published: (2024)

AI Failures in the Eyes of the Downstream Developer: A First Look at Concerns, Practices, and Challenges
by: Gao, Haoyu, et al.
Published: (2025)

Encoding Version History Context for Better Code Representation
by: Nguyen, Huy, et al.
Published: (2024)

CodeReviewQA: The Code Review Comprehension Assessment for Large Language Models
by: Lin, Hong Yi, et al.
Published: (2025)

Enhancing Neural Code Representation with Additional Context
by: Nguyen, Huy, et al.
Published: (2025)

An Empirical Study of Static Analysis Tools for Secure Code Review
by: Charoenwet, Wachiraphan, et al.
Published: (2024)

Toward Effective Secure Code Reviews: An Empirical Study of Security-Related Coding Weaknesses
by: Charoenwet, Wachiraphan, et al.
Published: (2023)

Does My README File Need To Be Updated? Exploring LLM-Based README Maintenance
by: Gao, Haoyu, et al.
Published: (2026)

Code Ownership: The Principles, Differences, and Their Associations with Software Quality
by: Thongtanunam, Patanamon, et al.
Published: (2024)

Fine-grained Approaches for Confidence Calibration of LLMs in Automated Code Revision
by: Lin, Hong Yi, et al.
Published: (2026)

Improving Automated Code Reviews: Learning from Experience
by: Lin, Hong Yi, et al.
Published: (2024)

Enhancing Code Review through Fuzzing and Likely Invariants
by: Charoenwet, Wachiraphan, et al.
Published: (2025)

Are You a Real Software Engineer? Best Practices in Online Recruitment for Software Engineering Studies
by: Alami, Adam, et al.
Published: (2024)

Open Source Software Development Tool Installation: Challenges and Strategies For Novice Developers
by: Salerno, Larissa, et al.
Published: (2024)

Should Code Models Learn Pedagogically? A Preliminary Evaluation of Curriculum Learning for Real-World Software Engineering Tasks
by: Khant, Kyi Shin, et al.
Published: (2025)

A Systematic Literature Review on Reasons and Approaches for Accurate Effort Estimations in Agile
by: Pasuksmit, Jirat, et al.
Published: (2024)

Too Noisy To Learn: Enhancing Data Quality for Code Review Comment Generation
by: Liu, Chunhua, et al.
Published: (2025)

Leveraging Reviewer Experience in Code Review Comment Generation
by: Lin, Hong Yi, et al.
Published: (2024)

Software Dependencies 2.0: An Empirical Study of Reuse and Integration of Pre-Trained Models in Open-Source Projects
by: Yasmin, Jerin, et al.
Published: (2025)

AI Slop and the Software Commons
by: Baltes, Sebastian, et al.
Published: (2026)

Exploring the Potential of Large Language Models in Fine-Grained Review Comment Classification
by: Nguyen, Linh, et al.
Published: (2025)

Qualitative Data Analysis in Software Engineering: Techniques and Teaching Insights
by: Treude, Christoph
Published: (2024)

GenAI Is No Silver Bullet for Qualitative Research in Software Engineering
by: Ernst, Neil A., et al.
Published: (2026)

Hallucinations in Code Change to Natural Language Generation: Prevalence and Evaluation of Detection Metrics
by: Liu, Chunhua, et al.
Published: (2025)

"An Endless Stream of AI Slop": The Growing Burden of AI-Assisted Software Development
by: Baltes, Sebastian, et al.
Published: (2026)

Adversarial Attacks on Code Models with Discriminative Graph Patterns
by: Nguyen, Thanh-Dat, et al.
Published: (2023)

A Systematic Survey on Debugging Techniques for Machine Learning Systems
by: Nguyen, Thanh-Dat, et al.
Published: (2025)

Automatically Recommend Code Updates: Are We There Yet?
by: Liu, Yue, et al.
Published: (2022)

The Shift from Writing to Pruning Software: A Bonsai-Inspired IDE for Reshaping AI Generated Code
by: Kula, Raula Gaikovina, et al.
Published: (2025)

Breaking Changes in Software Ecosystems: A Systematic Literature Review
by: Chen, Juntao, et al.
Published: (2026)

Rethinking Artifact Evaluation for Software Engineering in the Age of Generative AI
by: Treude, Christoph, et al.
Published: (2026)

Interacting with AI Reasoning Models: Harnessing "Thoughts" for AI-Driven Software Engineering
by: Treude, Christoph, et al.
Published: (2025)

Context Engineering for AI Agents in Open-Source Software
by: Mohsenimofidi, Seyedmoein, et al.
Published: (2025)

AI-Assisted Code Review as a Scaffold for Code Quality and Self-Regulated Learning: An Experience Report
by: Oliveira, Eduardo, et al.
Published: (2026)

Generative AI and Empirical Software Engineering: A Paradigm Shift
by: Treude, Christoph, et al.
Published: (2025)

Gender Influence on Student Teams' Online Communication in Software Engineering Education
by: Garcia, Rita, et al.
Published: (2025)

Accountable Agents in Software Engineering: An Analysis of Terms of Service and a Research Roadmap
by: Treude, Christoph
Published: (2026)