Reusing deep learning models: Challenges and directions in software engineering

JC Davis, P Jajal, W Jiang… - 2023 IEEE John …, 2023 - ieeexplore.ieee.org
Deep neural networks (DNNs) achieve state-of-the-art performance in many areas, including
computer vision, system configuration, and question-answering. However, DNNs are …

What do we know about Hugging Face? A systematic literature review and quantitative validation of qualitative claims

J Jones, W Jiang, N Synovic, G Thiruvathukal… - Proceedings of the 18th …, 2024 - dl.acm.org
Background: Software Package Registries (SPRs) are an integral part of the software supply
chain. These collaborative platforms unite contributors, users, and code for streamlined …

Models are codes: Towards measuring malicious code poisoning attacks on pre-trained model hubs

J Zhao, S Wang, Y Zhao, X Hou, K Wang… - 2024 39th IEEE/ACM …, 2024 - ieeexplore.ieee.org
The proliferation of pre-trained models (PTMs) and datasets has led to the emergence of
centralized model hubs like Hugging Face, which facilitate collaborative development and …

Challenges and practices of deep learning model reengineering: A case study on computer vision

W Jiang, V Banna, N Vivek, A Goel, N Synovic… - Empirical Software …, 2024 - Springer
Context Many engineering organizations are reimplementing and extending deep neural
networks from the research community. We describe this process as deep learning model …

Analysis of failures and risks in deep learning model converters: A case study in the onnx ecosystem

P Jajal, W Jiang, A Tewari, E Kocinare, J Woo… - arXiv preprint arXiv …, 2023 - arxiv.org
Software engineers develop, fine-tune, and deploy deep learning (DL) models using a
variety of development frameworks and runtime environments. DL model converters move …

Ecosystem of Large Language Models for Code

Z Yang, J Shi, P Devanbu, D Lo - arXiv preprint arXiv:2405.16746, 2024 - arxiv.org
The availability of vast amounts of publicly accessible data of source code and the advances
in modern language models, coupled with increasing computational resources, have led to …

Interoperability in deep learning: a user survey and failure analysis of ONNX model converters

P Jajal, W Jiang, A Tewari, E Kocinare, J Woo… - Proceedings of the 33rd …, 2024 - dl.acm.org
Software engineers develop, fine-tune, and deploy deep learning (DL) models using a
variety of development frameworks and runtime environments. DL model converters move …

Towards Semantic Versioning of Open Pre-trained Language Model Releases on Hugging Face

A Ajibode, AA Bangash, FR Cogo, B Adams… - arXiv preprint arXiv …, 2024 - arxiv.org
The proliferation of open Pre-trained Language Models (PTLMs) on model registry platforms
like Hugging Face (HF) presents both opportunities and challenges for companies building …

[PDF][PDF] Naming Practices of Pre-Trained Models in Hugging Face

W Jiang, C Cheung, M Kim, H Kim… - arXiv preprint arXiv …, 2024 - wenxin-jiang.github.io
Authors' addresses: Wenxin Jiang, Purdue University, West Lafayette, IN, USA, jiang784@
purdue. edu; Chingwo Cheung, Purdue University, West Lafayette, IN, USA, cheung59 …

A Large-Scale Study of Model Integration in ML-Enabled Software Systems

Y Sens, H Knopp, S Peldszus, T Berger - arXiv preprint arXiv:2408.06226, 2024 - arxiv.org
The rise of machine learning (ML) and its embedding in systems has drastically changed the
engineering of software-intensive systems. Traditionally, software engineering focuses on …