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Ecosystem of Large Language Models for Code

https://doi.org/10.1145/3731753
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-031-08129-3_6openalex-snapshot
Prevalence and Evolution of License Violations in npm and RubyGems Dependency Networks
inferredcites →https://doi.org/10.1007/s10515-024-00453-wopenalex-snapshot
Data cleaning and machine learning: a systematic literature review
inferredcites →https://doi.org/10.1007/s10664-018-9660-3openalex-snapshot
Categorizing the Content of GitHub README Files
inferredcites →https://doi.org/10.1007/s10664-020-09916-6openalex-snapshot
What makes a popular academic AI repository?
inferredcites →https://doi.org/10.1007/s10664-023-10377-wopenalex-snapshot
The software heritage license dataset (2022 edition)
inferredcites →https://doi.org/10.1007/s10664-024-10486-0openalex-snapshot
A comprehensive analysis of challenges and strategies for software release notes on GitHub
inferredcites →https://doi.org/10.1017/cbo9780511815478openalex-snapshot
Social Network Analysis
inferredcites →https://doi.org/10.1109/ase56229.2023.00149openalex-snapshot
Code Difference Guided Adversarial Example Generation for Deep Code Models
inferredcites →https://doi.org/10.1109/esem56168.2023.10304801openalex-snapshot
Exploring the Carbon Footprint of Hugging Face's ML Models: A Repository Mining Study
inferredcites →https://doi.org/10.1109/icpr56361.2022.9956690openalex-snapshot
Backdoors in Neural Models of Source Code
inferredcites →https://doi.org/10.1109/icse43902.2021.00125openalex-snapshot
Technical Leverage in a Software Ecosystem: Development Opportunities and Security Risks
inferredcites →https://doi.org/10.1109/icse48619.2023.00206openalex-snapshot
An Empirical Study of Pre-Trained Model Reuse in the Hugging Face Deep Learning Model Registry
inferredcites →https://doi.org/10.1109/isi58743.2023.10297271openalex-snapshot
Assessing the Vulnerabilities of the Open-Source Artificial Intelligence (AI) Landscape: A Large-Scale Analysis of the Hugging Face Platform
inferredcites →https://doi.org/10.1109/jva60410.2023.00015openalex-snapshot
Reusing Deep Learning Models: Challenges and Directions in Software Engineering
inferredcites →https://doi.org/10.1109/msr.2017.67openalex-snapshot
An Empirical Analysis of the Docker Container Ecosystem on GitHub
inferredcites →https://doi.org/10.1109/msr59073.2023.00021openalex-snapshot
PTMTorrent: A Dataset for Mining Open-source Pre-trained Model Packages
inferredcites →https://doi.org/10.1109/msr59073.2023.00024openalex-snapshot
What Do Users Ask in Open-Source AI Repositories? An Empirical Study of GitHub Issues
inferredcites →https://doi.org/10.1109/saner50967.2021.00048openalex-snapshot
Empirical Analysis of Security Vulnerabilities in Python Packages
inferredcites →https://doi.org/10.1109/saner56733.2023.00028openalex-snapshot
On the Effect of Transitivity and Granularity on Vulnerability Propagation in the Maven Ecosystem
inferredcites →https://doi.org/10.1109/saner56733.2023.00095openalex-snapshot
Technical Debt Diffuseness in the Apache Ecosystem: A Differentiated Replication
inferredcites →https://doi.org/10.1109/saner60148.2024.00059openalex-snapshot
Deep Learning Model Reuse in the HuggingFace Community: Challenges, Benefit and Trends
inferredcites →https://doi.org/10.1109/tse.2024.3361661openalex-snapshot
Stealthy Backdoor Attack for Code Models
inferredcites →https://doi.org/10.1109/tse.2024.3482719openalex-snapshot
Gotcha! This Model Uses My Code! Evaluating Membership Leakage Risks in Code Models
inferredcites →https://doi.org/10.1145/1718918.1718973openalex-snapshot
Information needs in bug reports
inferredcites →https://doi.org/10.1145/2372251.2372257openalex-snapshot
Systematic literature studies
inferredcites →https://doi.org/10.1145/2568225.2568233openalex-snapshot
Analyze this! 145 questions for data scientists in software engineering
inferredcites →https://doi.org/10.1145/2597073.2597120openalex-snapshot
Co-evolution of project documentation and popularity within github
inferredcites →https://doi.org/10.1145/2601248.2601268openalex-snapshot
Guidelines for snowballing in systematic literature studies and a replication in software engineering
inferredcites →https://doi.org/10.1145/3180155.3180209openalex-snapshot
Adding sparkle to social coding
inferredcites →https://doi.org/10.1145/3359591.3359735openalex-snapshot
The adverse effects of code duplication in machine learning models of code
inferredcites →https://doi.org/10.1145/3377811.3380426openalex-snapshot
Watchman
inferredcites →https://doi.org/10.1145/3379597.3387510openalex-snapshot
The Software Heritage Graph Dataset
inferredcites →https://doi.org/10.1145/3428230openalex-snapshot
Adversarial examples for models of code
inferredcites →https://doi.org/10.1145/3446905openalex-snapshot
On the Impact of Sample Duplication in Machine-Learning-Based Android Malware Detection
inferredcites →https://doi.org/10.1145/3510003.3510142openalex-snapshot
Demystifying the vulnerability propagation and its evolution via dependency trees in the NPM ecosystem
inferredcites →https://doi.org/10.1145/3510003.3510146openalex-snapshot
Natural attack for pre-trained models of code
inferredcites →https://doi.org/10.1145/3540250.3549145openalex-snapshot
Are we building on the rock? on the importance of data preprocessing for code summarization
inferredcites →https://doi.org/10.1145/3540250.3549153openalex-snapshot
You see what I want you to see: poisoning vulnerabilities in neural code search
inferredcites →https://doi.org/10.1145/3551349.3556964openalex-snapshot
Compressing Pre-trained Models of Code into 3 MB
inferredcites →https://doi.org/10.1145/3560835.3564547openalex-snapshot
An Empirical Study of Artifacts and Security Risks in the Pre-trained Model Supply Chain
inferredcites →https://doi.org/10.1145/3611643.3616302openalex-snapshot
Towards Greener Yet Powerful Code Generation via Quantization: An Empirical Study
inferredcites →https://doi.org/10.1145/3639475.3640097openalex-snapshot
Greening Large Language Models of Code
inferredcites →https://doi.org/10.1145/3643664.3648204openalex-snapshot
Lessons Learned from Mining the Hugging Face Repository
inferredcites →https://doi.org/10.1145/3643916.3644412openalex-snapshot
How do Hugging Face Models Document Datasets, Bias, and Licenses? An Empirical Study
inferredcites →https://doi.org/10.1145/3661167.3661215openalex-snapshot
Automated categorization of pre-trained models in software engineering: A case study with a Hugging Face dataset
inferredcites →https://doi.org/10.1145/3674805.3686665openalex-snapshot
What do we know about Hugging Face? A systematic literature review and quantitative validation of qualitative claims
inferredcites →https://doi.org/10.1145/3695988openalex-snapshot
Large Language Models for Software Engineering: A Systematic Literature Review
inferredcites →https://doi.org/10.1145/3708525openalex-snapshot
Efficient and Green Large Language Models for Software Engineering: Literature Review, Vision, and the Road Ahead
inferredcites →https://doi.org/10.1145/3708531openalex-snapshot
Large Language Model Supply Chain: A Research Agenda
inferredcites →https://doi.org/10.1145/3714461openalex-snapshot
Exploring Parameter-Efficient Fine-Tuning Techniques for Code Generation with Large Language Models
inferredcites →https://doi.org/10.18653/v1/2023.acl-long.540openalex-snapshot
Backdooring Neural Code Search

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1007/978-3-032-23607-4_19openalex-snapshot
The Next Stage of Evolutionary Computation in the Era of Agentic Generative AI
inferred← citeshttps://doi.org/10.1007/s10664-025-10631-3openalex-snapshot
Towards semantic versioning of open pre-trained language model releases on hugging face
inferred← citeshttps://doi.org/10.1016/j.jss.2025.112574openalex-snapshot
Insights into resource utilization of code small language models serving with runtime engines and execution providers
inferred← citeshttps://doi.org/10.1109/fllm67465.2025.11391194openalex-snapshot
GPT-5 vs Other LLMs in Long Short-Context Performance
inferred← citeshttps://doi.org/10.1109/gcwot69191.2026.11499481openalex-snapshot
Benchmarking AI Models for Automated Code Generation and Testing Using HumanEval
inferred← citeshttps://doi.org/10.1145/3708525openalex-snapshot
Efficient and Green Large Language Models for Software Engineering: Literature Review, Vision, and the Road Ahead
inferred← citeshttps://doi.org/10.1145/3709360openalex-snapshot
From Triumph to Uncertainty: The Journey of Software Engineering in the AI Era
inferred← citeshttps://doi.org/10.1145/3714464openalex-snapshot
Research Directions in Software Supply Chain Security
inferred← citeshttps://doi.org/10.1145/3736407openalex-snapshot
CodeUltraFeedback: An LLM-as-a-Judge Dataset for Aligning Large Language Models to Coding Preferences
inferred← citeshttps://doi.org/10.1145/3744916.3773236openalex-snapshot
Training on Clean Data but Getting Backdoored Models! A Poisoning Attack on Code Encoders
inferred← citeshttps://doi.org/10.1145/3773034openalex-snapshot
Synthesizing Efficient and Permissive Programmatic Runtime Shields for Neural Policies
inferred← citeshttps://doi.org/10.1145/3803437.3805590openalex-snapshot
The Stylistic Blind Spot: Uncovering the Hidden Implicit Bias of Coding Style on LLM Code Evaluation
inferred← citeshttps://doi.org/10.1145/3832175openalex-snapshot
Hidden Licensing Risks in the PTMware Ecosystem
inferred← citeshttps://doi.org/10.1145/3832783.3837507openalex-snapshot
Looks Good, but Does It Run? A Large-Scale Empirical Study of the Executability of Example Code in Model Cards
inferred← citeshttps://doi.org/10.2139/ssrn.5373331openalex-snapshot
Learning From the Best: What Makes Popular Hugging Face Models? A Registered Report
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