Software Reusability: Approaches and Challenges

Anasuodei, Moko; Ojekudo, None; Akpofure, Nathaniel

doi:10.51584/ijrias.2021.6510

Cited by 4 publications

(1 citation statement)

References 4 publications

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“…Decision-making Our results indicate that the decision-making process of PTMs (Figure 4) is more complex than traditional packages [23,85,86], both in terms of the assessment and evaluation. Traditional software package reuse is integrated throughout the software development process [87] to improve productivity [85]. Our result shows that DL engineers behave similarly with PTMs.…”

Section: B Comparison To Traditional Package Registriesmentioning

confidence: 74%

An Empirical Study of Pre-Trained Model Reuse in the Hugging Face Deep Learning Model Registry

Jiang¹,

Synovic²,

Hyatt³

et al. 2023

Preprint

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Deep Neural Networks (DNNs) are being adopted as components in software systems. Creating and specializing DNNs from scratch has grown increasingly difficult as stateof-the-art architectures grow more complex. Following the path of traditional software engineering, machine learning engineers have begun to reuse large-scale pre-trained models (PTMs) and fine-tune these models for downstream tasks. Prior works have studied reuse practices for traditional software packages to guide software engineers towards better package maintenance and dependency management. We lack a similar foundation of knowledge to guide behaviors in pre-trained model ecosystems.In this work, we present the first empirical investigation of PTM reuse. We interviewed 12 practitioners from the most popular PTM ecosystem, Hugging Face, to learn the practices and challenges of PTM reuse. From this data, we model the decision-making process for PTM reuse. Based on the identified practices, we describe useful attributes for model reuse, including provenance, reproducibility, and portability. Three challenges for PTM reuse are missing attributes, discrepancies between claimed and actual performance, and model risks. We substantiate these identified challenges with systematic measurements in the Hugging Face ecosystem. Our work informs future directions on optimizing deep learning ecosystems by automated measuring useful attributes and potential attacks, and envision future research on infrastructure and standardization for model registries.

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Section: B Comparison To Traditional Package Registriesmentioning

confidence: 74%

An Empirical Study of Pre-Trained Model Reuse in the Hugging Face Deep Learning Model Registry

Jiang¹,

Synovic²,

Hyatt³

et al. 2023

Preprint

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Software Model for Robot Programming and Example of Implementation for Navigation System

Chaychi

Zampunieris

Reis

2023

2023 9th International Conference on Automation, Robotics and Applications (ICARA)

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In this paper, we are going to consider a current challenge in a robotic software system. We consider a problem, which is the lack of separation of concerns in robotic systems, and propose a software model to address the problem and resolve the current challenges. The core purpose of this paper is to demonstrate the advantages of using separation of concerns principles to create a well-ordered model of independent components that address separated concerns individually. Considering the problem, we developed a software model with the help of a proactive engine to address the challenges. We use robotic operating systems to help us to implement the robot simulator.

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An Empirical Study of Pre-Trained Model Reuse in the Hugging Face Deep Learning Model Registry

Jiang

Synovic

Hyatt

et al. 2023

2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE)

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Software Reusability: Approaches and Challenges

Cited by 4 publications

References 4 publications

An Empirical Study of Pre-Trained Model Reuse in the Hugging Face Deep Learning Model Registry

An Empirical Study of Pre-Trained Model Reuse in the Hugging Face Deep Learning Model Registry

Software Model for Robot Programming and Example of Implementation for Navigation System

An Empirical Study of Pre-Trained Model Reuse in the Hugging Face Deep Learning Model Registry

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