23 citations · 52 across the 14 of their papers we have counts for
10 papers · 1 filter
Predicting LLM Performance from Prompt Linguistic Features: An Empirical Study in Requirements Engineering
Quim Motger, Alessio Miaschi, Xavier Franch +2
Background. LLM outputs are highly sensitive to prompt formulation: small wording changes can substantially affect output quality. This matters in software engineering, where promp…
Multi-Agent Debate Strategies: Survey, Taxonomy, and Challenges
Quim Motger, Marc Oriol, Jordi Marco +1
Multi-Agent Debate (MAD) is a promising paradigm for improving the accuracy and robustness of Large Language Model (LLM)-based agentic systems. It enables multiple agents to exchan…
Characterizing Datasets for LLM-based Requirements Engineering: A Systematic Mapping Study
Quim Motger, Carlota Catot, Xavier Franch
Large Language Models (LLMs) depend on high-quality, domain-specific natural language datasets. This dependency is particularly pronounced in Requirements Engineering (RE), where c…
Multi-Agent Debate Strategies to Enhance Requirements Engineering with Large Language Models
Marc Oriol, Quim Motger, Jordi Marco +1
Context: Large Language Model (LLM) agents are becoming widely used for various Requirements Engineering (RE) tasks. Research on improving their accuracy mainly focuses on prompt e…
Dissecting the SWE-Bench Leaderboards: Profiling Submitters and Architectures of LLM- and Agent-Based Repair Systems
Matias Martinez, Xavier Franch
The rapid progress in Automated Program Repair (APR) has been driven by advances in AI, particularly large language models (LLMs) and agent-based systems. SWE-Bench is a recent ben…
Lessons Learned from Mining the Hugging Face Repository
Joel Castaño, Silverio Martínez-Fernández, Xavier Franch
The rapidly evolving fields of Machine Learning (ML) and Artificial Intelligence have witnessed the emergence of platforms like Hugging Face (HF) as central hubs for model developm…
T-FREX: A Transformer-based Feature Extraction Method from Mobile App Reviews
Quim Motger, Alessio Miaschi, Felice Dell'Orletta +2
Mobile app reviews are a large-scale data source for software-related knowledge generation activities, including software maintenance, evolution and feedback analysis. Effective ex…
Automated Requirements Relation Extraction
Quim Motger, Xavier Franch
In the context of requirements engineering, relation extraction involves identifying and documenting the associations between different requirements artefacts. When dealing with te…
Unveiling Competition Dynamics in Mobile App Markets through User Reviews
Quim Motger, Xavier Franch, Vincenzo Gervasi +1
User reviews published in mobile app repositories are essential for understanding user satisfaction and engagement within a specific market segment. Manual analysis of reviews is i…
Analyzing the Evolution and Maintenance of ML Models on Hugging Face
Joel Castaño, Silverio Martínez-Fernández, Xavier Franch +1
Hugging Face (HF) has established itself as a crucial platform for the development and sharing of machine learning (ML) models. This repository mining study, which delves into more…