Image de couverture de livre

Do LLMs Provide Links to Code Similar to what they Generate? A Study with Gemini and Bing CoPilot

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Bifolco, Daniele, Cassieri, Pietro, Scanniello, Giuseppe, Di Penta, Massimiliano, Zampetti, Fiorella
Format:	Preprint
Publié:	2025
Sujets:	Software Engineering
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

CodeGenLink: A Tool to Find the Likely Origin and License of Automatically Generated Code
par: Bifolco, Daniele, et autres
Publié: (2025)

Machine Learning in the Wild: Early Evidence of Non-Compliant ML-Automation in Open-Source Software
par: Arshid, Zohaib, et autres
Publié: (2026)

Augmenting Software Bills of Materials with Software Vulnerability Description: A Preliminary Study on GitHub
par: Fucci, Davide, et autres
Publié: (2025)

Guidelines to Prompt Large Language Models for Code Generation: An Empirical Characterization
par: Midolo, Alessandro, et autres
Publié: (2026)

A Taxonomy of Self-Admitted Technical Debt in Deep Learning Systems
par: Pepe, Federica, et autres
Publié: (2024)

Beyond Rules: LLM-Powered Linting for Quantum Programs
par: Cassieri, Pietro, et autres
Publié: (2026)

How are MLOps Frameworks Used in Open Source Projects? An Empirical Characterization
par: Zampetti, Fiorella, et autres
Publié: (2026)

Automated Refactoring of Non-Idiomatic Python Code: A Differentiated Replication with LLMs
par: Midolo, Alessandro, et autres
Publié: (2025)

Developers and Generative AI: A Study of Self-Admitted Usage in Open Source Projects
par: Tufano, Rosalia, et autres
Publié: (2026)

Optimizing Datasets for Code Summarization: Is Code-Comment Coherence Enough?
par: Vitale, Antonio, et autres
Publié: (2025)

From Human to Machine Refactoring: Assessing GPT-4's Impact on Python Class Quality and Readability
par: Midolo, Alessandro, et autres
Publié: (2026)

Evaluating the Impact of Post-Training Quantization on Large Language Models for Code Generation
par: Giagnorio, Alessandro, et autres
Publié: (2025)

Dealing with SonarQube Cloud: Initial Results from a Mining Software Repository Study
par: Nocera, Sabato, et autres
Publié: (2025)

Not All Tokens Matter: Data-Centric Optimization for Efficient Code Summarization
par: Afrin, Saima, et autres
Publié: (2026)

Further Evidence on a Controversial Topic about Human-Based Experiments: Professionals vs. Students
par: Romano, Simone, et autres
Publié: (2025)

How the Training Procedure Impacts the Performance of Deep Learning-based Vulnerability Patching
par: Mastropaolo, Antonio, et autres
Publié: (2024)

"Let it be Chaos in the Plumbing!" Usage and Efficacy of Chaos Engineering in DevOps Pipelines
par: Fossati, Stefano, et autres
Publié: (2025)

Automatic Categorization of GitHub Actions with Transformers and Few-shot Learning
par: Nguyen, Phuong T., et autres
Publié: (2024)

Unveiling ChatGPT's Usage in Open Source Projects: A Mining-based Study
par: Tufano, Rosalia, et autres
Publié: (2024)

MBSR at Work: Perspectives from an Instructor and Software Developers
par: Romano, Simone, et autres
Publié: (2025)

Do LLMs Favor Their Providers? Measuring Vertical Integration Bias in Code Generation
par: Catal, Melih, et autres
Publié: (2026)

BOMs Away! Inside the Minds of Stakeholders: A Comprehensive Study of Bills of Materials for Software Systems
par: Stalnaker, Trevor, et autres
Publié: (2023)

Do Code LLMs Do Static Analysis?
par: Su, Chia-Yi, et autres
Publié: (2025)

Future of Software Engineering Research: The SIGSOFT Perspective
par: Di Penta, Massimiliano, et autres
Publié: (2026)

Software Security Analysis in 2030 and Beyond: A Research Roadmap
par: Böhme, Marcel, et autres
Publié: (2024)

Engineering Pitfalls in AI Coding Tools: An Empirical Study of Bugs in Claude Code, Codex, and Gemini CLI
par: Zhang, Ruixin, et autres
Publié: (2026)

Developers' Perspectives on Software Licensing: Current Practices, Challenges, and Tools
par: Wintersgill, Nathan, et autres
Publié: (2025)

An Empirical Analysis of Machine Learning Model and Dataset Documentation, Supply Chain, and Licensing Challenges on Hugging Face
par: Stalnaker, Trevor, et autres
Publié: (2025)

Developer Perspectives on Licensing and Copyright Issues Arising from Generative AI for Software Development
par: Stalnaker, Trevor, et autres
Publié: (2024)

Do Code LLMs Understand Design Patterns?
par: Pan, Zhenyu, et autres
Publié: (2025)

SelfPiCo: Self-Guided Partial Code Execution with LLMs
par: Xue, Zhipeng, et autres
Publié: (2024)

Do Machines and Humans Focus on Similar Code? Exploring Explainability of Large Language Models in Code Summarization
par: Li, Jiliang, et autres
Publié: (2024)

CoEdPilot: Recommending Code Edits with Learned Prior Edit Relevance, Project-wise Awareness, and Interactive Nature
par: Liu, Chenyan, et autres
Publié: (2024)

Large Language Models for Code Analysis: Do LLMs Really Do Their Job?
par: Fang, Chongzhou, et autres
Publié: (2023)

Understanding the AI-powered Binary Code Similarity Detection
par: Fu, Lirong, et autres
Publié: (2024)

Will It Break in Production? Metric-Driven Prediction of Residual Defects in Python Systems
par: De Rosa, Giuseppe, et autres
Publié: (2026)

Detecting Malicious Source Code in PyPI Packages with LLMs: Does RAG Come in Handy?
par: Ibiyo, Motunrayo, et autres
Publié: (2025)

Migrating Code At Scale With LLMs At Google
par: Ziftci, Celal, et autres
Publié: (2025)

Optimizing Deep Learning Models to Address Class Imbalance in Code Comment Classification
par: Mock, Moritz, et autres
Publié: (2025)

From LLMs to Agents in Programming: The Impact of Providing an LLM with a Compiler
par: Kjellberg, Viktor, et autres
Publié: (2026)