Requirements Classification Using FastText and BETO in Spanish Documents

Context and motivation: Machine Learning (ML) algorithms and Natural Language Processing (NLP) techniques have effectively supported the automatic software requirements classification. The emergence of pre-trained language models, like BERT, provides promising results in several downstream NLP tasks, such as 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.