2 citations · 4 across the 11 of their papers we have counts for
10 papers · 1 filter
A Tool for Automatically Cataloguing and Selecting Pre-Trained Models and Datasets for Software Engineering
Alexandra González, Oscar Cerezo, Xavier Franch +1
The rapid growth of machine learning assets has made it increasingly difficult for software engineers to identify models and datasets that match their specific needs. Browsing larg…
SEMODS: A Validated Dataset of Open-Source Software Engineering Models
Alexandra González, Xavier Franch, Silverio Martínez-Fernández
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…
Does Model Size Matter? A Comparison of Small and Large Language Models for Requirements Classification
Mohammad Amin Zadenoori, Vincenzo De Martino, Jacek Dabrowski +2
[Context and motivation] Large language models (LLMs) show notable results in natural language processing (NLP) tasks for requirements engineering (RE). However, their use is compr…
Green Prompt Engineering: Investigating the Energy Impact of Prompt Design in Software Engineering
Vincenzo De Martino, Mohammad Amin Zadenoori, Xavier Franch +1
Language Models are increasingly applied in software engineering, yet their inference raises growing environmental concerns. Prior work has examined hardware choices and prompt len…
A Methodological Framework for LLM-Based Mining of Software Repositories
Vincenzo De Martino, Joel Castaño, Fabio Palomba +2
Large Language Models (LLMs) are increasingly used in software engineering research, offering new opportunities for automating repository mining tasks. However, despite their growi…
Cataloguing Hugging Face Models to Software Engineering Activities: Automation and Findings
Alexandra González, Xavier Franch, David Lo +1
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 E…
Aggregating empirical evidence from data strategy studies: a case on model quantization
Santiago del Rey, Paulo Sérgio Medeiros dos Santos, Guilherme Horta Travassos +2
Background: As empirical software engineering evolves, more studies adopt data strategiesapproaches that investigate digital artifacts such as models, source code, or system log…
Addressing Quality Challenges in Deep Learning: The Role of MLOps and Domain Knowledge
Santiago del Rey, Adrià Medina, Xavier Franch +1
Deep learning (DL) systems present unique challenges in software engineering, especially concerning quality attributes like correctness and resource efficiency. While DL models exc…
A Framework for Using LLMs for Repository Mining Studies in Empirical Software Engineering
Vincenzo de Martino, Joel Castaño, Fabio Palomba +2
Context: The emergence of Large Language Models (LLMs) has significantly transformed Software Engineering (SE) by providing innovative methods for analyzing software repositories.…
Towards a Classification of Open-Source ML Models and Datasets for Software Engineering
Alexandra González, Xavier Franch, David Lo +1
Background: Open-Source Pre-Trained Models (PTMs) and datasets provide extensive resources for various Machine Learning (ML) tasks, yet these resources lack a classification tailor…