61 citations · 123 across the 15 of their papers we have counts for
5 papers · 1 filter
FeClustRE: Hierarchical Clustering and Semantic Tagging of App Features from User Reviews
Max Tiessler, Quim Motger
[Context and motivation.] Extracting features from mobile app reviews is increasingly important for multiple requirements engineering (RE) tasks. However, existing methods struggle…
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,…