Integrating Artificial Intelligence into Software Engineering (SE) requires having a curated collection of models suited to SE tasks. With millions of models hosted on Hugging Face (HF) and new ones continuously being created, it is infeasible to identify SE models without a dedicated catalogue. To address this gap, we present SEMODS: an SE-focused dataset of 3,427 models extracted from HF, combining automated collection with rigorous validation through manual annotation and large language model assistance. Our dataset links models to SE tasks and activities from the software development lifecycle, offering a standardized representation of their evaluation results, and supporting multiple applications such as data analysis, model discovery, benchmarking, and model adaptation.
@article{arxiv.2601.00635,
title = {SEMODS: A Validated Dataset of Open-Source Software Engineering Models},
author = {Alexandra González and Xavier Franch and Silverio Martínez-Fernández},
journal= {arXiv preprint arXiv:2601.00635},
year = {2026}
}
Comments
Accepted at the 3rd ACM international conference on AI Foundation Models and Software Engineering (FORGE 2026)