Authors: Alexandra González, Xavier Franch, David Lo, Silverio Martínez-Fernández
Abstract: Background: Open-Source Pre-Trained Models (PTMs) and datasets provide
extensive resources for various Machine Learning (ML) tasks, yet these
resources lack a classification tailored to Software Engineering (SE) needs.
Aims: We apply an SE-oriented classification to PTMs and datasets on a popular
open-source ML repository, Hugging Face (HF), and analyze the evolution of PTMs
over time. Method: We conducted a repository mining study. We started with a
systematically gathered database of PTMs and datasets from the HF API. Our
selection was refined by analyzing model and dataset cards and metadata, such
as tags, and confirming SE relevance using Gemini 1.5 Pro. All analyses are
replicable, with a publicly accessible replication package. Results: The most
common SE task among PTMs and datasets is code generation, with a primary focus
on software development and limited attention to software management. Popular
PTMs and datasets mainly target software development. Among ML tasks, text
generation is the most common in SE PTMs and datasets. There has been a marked
increase in PTMs for SE since 2023 Q2. Conclusions: This study underscores the
need for broader task coverage to enhance the integration of ML within SE
practices.
Source: http://arxiv.org/abs/2411.09683v1
