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most citedDoes Model Size Matter? A Comparison of Small and Large Language Models for Requirements Classification

2 citations · 4 across the 11 of their papers we have counts for

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cs.SE2025★ 2 cited

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…

cs.SE2025

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…

cs.SE2025

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…

cs.SE2025

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…

cs.SE2025

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…

cs.SE2025

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…