23 citations · 52 across the 14 of their papers we have counts for
15 papers
The top coefficient of the Links-Gould invariant is Murasugi-sum multiplicative
Cristina Anghel, Daniel López Neumann, Roland van der Veen
We prove that the Laurent polynomial in that is the top coefficient of the Links-Gould invariant of the boundary of a Seifert surface is multiplicative unde…
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…
Understanding Mobile App Recommendation Dynamics in General-Purpose LLMs: An Empirical Study
Quim Motger, Xavier Franch, Vincenzo Gervasi +1
Large Language Models (LLMs) are increasingly used to recommend mobile applications through natural language prompts, offering a flexible alternative to keyword-based app store sea…
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…
What About Emotions? Guiding Fine-Grained Emotion Extraction from Mobile App Reviews
Quim Motger, Marc Oriol, Max Tiessler +2
Opinion mining plays a vital role in analysing user feedback and extracting insights from textual data. While most research focuses on sentiment polarity (e.g., positive, negative,…
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…
Impact of ML Optimization Tactics on Greener Pre-Trained ML Models
Alexandra González Álvarez, Joel Castaño, Xavier Franch +1
Background: Given the fast-paced nature of today's technology, which has surpassed human performance in tasks like image classification, visual reasoning, and English understanding…
Leveraging Encoder-only Large Language Models for Mobile App Review Feature Extraction
Quim Motger, Alessio Miaschi, Felice Dell'Orletta +2
Mobile app review analysis presents unique challenges due to the low quality, subjective bias, and noisy content of user-generated documents. Extracting features from these reviews…
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…