Cataloguing Hugging Face Models to Software Engineering Activities: Automation and Findings

TL;DR

This paper creates a comprehensive taxonomy and catalog of 2,205 Hugging Face pre-trained models tailored for Software Engineering tasks, revealing usage patterns, gaps, and opportunities for automation in model selection.

Contribution

It introduces a novel SE-specific classification of PTMs, a curated catalog, and insights into their application trends and evaluation limitations.

Findings

Most models target code generation and coding tasks.

Text generation is the dominant ML task among SE PTMs.

Limited evaluation reports, with only 9.6% providing benchmark results.

Abstract

Context: Open-source Pre-Trained Models (PTMs) provide extensive resources for various Machine Learning (ML) tasks, yet these resources lack a classification tailored to Software Engineering (SE) needs to support the reliable identification and reuse of models for SE. Objective: To address this gap, we derive a taxonomy encompassing 147 SE tasks and apply an SE-oriented classification to PTMs in a popular open-source ML repository, Hugging Face (HF). Method: Our repository mining study followed a five-phase pipeline: (i) identification SE tasks from the literature; (ii) collection of PTM data from the HF API, including model card descriptions and metadata, and the abstracts of the associated arXiv papers; (iii) text processing to ensure consistency; (iv) a two-phase validation of SE relevance, involving humans and LLM assistance, supported by five pilot studies with human annotators and a generalization test; (v) and data analysis. This process yielded a curated catalogue of 2,205 SE PTMs. Results: We find that most SE PTMs target code generation and coding, emphasizing implementation over early or late development stages. In terms of ML tasks, text generation dominates within SE PTMs. Notably, the number of SE PTMs has increased markedly since 2023 Q2, while evaluation remains limited: only 9.6% report benchmark results, mostly scoring below 50%. Conclusions: Our catalogue reveals documentation and transparency gaps, highlights imbalances across SDLC phases, and provides a foundation for automated SE scenarios, such as the sampling and selection of suitable PTMs.