REFSQ 2023
Mon 17 - Thu 20 April 2023 Barcelona, Spain

Context and motivation: Conventional Machine Learning (ML) algorithms and Natural Language Processing (NLP) techniques have effectively supported the automatic software requirements classification. The continuous evolution of Deep Learning (DL) has allowed the creation of complex architectures including pre-trained models. The emergence of pre-trained models for Natural Language Processing (NLP) provides promising results in several types of text classification. Question/problem: Most ML/DL approaches on requirements classification show a lack of analysis for requirements written in the Spanish language. Moreover, there has not been much research on pre-trained language models, like fastText and BETO (Bert for the Spanish language), neither in the validation of the generalization of the models. Principal ideas/results: We aim to investigate the classification performance and generalization of fastText and BETO classifiers in comparison with other ML/DL algorithms. The findings show that Shallow ML algorithms outperformed fastText and BETO when training and testing in the same dataset, but BETO outperformed other classifiers on prediction performance in a dataset with different origins. Contribution: Our evaluation provides a quantitative analysis of the classification performance of fastTest and BETO in comparison with ML/DL algorithms, the external validity of trained models on another Spanish dataset, and the translation of the PROMISE NFR dataset in Spanish.

Requirements classification using fastText and BETO in Spanish documents (REFSQ (1).pdf)285KiB

Tue 18 Apr

Displayed time zone: Brussels, Copenhagen, Madrid, Paris change

14:00 - 15:30
Session R4 - Requirements and App Review ClassificationResearch Papers at Llívia
Chair(s): Maya Daneva University of Twente
Technical design
Requirements classification using fastText and BETO in Spanish documents
Research Papers
P: Maria Isabel Limaylla Lunarejo Universidade da Coruña, A: Nelly Condori-Fernández Universidad de Santiago de Compostela, A: Miguel Rodríguez Luaces Universidade da Coruña, D: Michelle Binder University of Cologne, D: Annika Vogt University of Cologne
File Attached
Technical design
Automatically Classifying Kano Model Factors in App Reviews
Research Papers
P: Michelle Binder University of Cologne, P: Annika Vogt University of Cologne, A: Adrian Bajraktari University of Cologne, A: Andreas Vogelsang University of Cologne, D: Maria Isabel Limaylla Lunarejo Universidade da Coruña