RE-BERT
Adailton Ferreira De Araújo, Ricardo Marcondes Marcacini
Abstract
Traditionally, developers restricted themselves to collecting opinions from a small group of users by using techniques such as interviews, questionnaires, and meetings. With the popularization of social media and mobile applications, these professionals have to deal with crowd users' opinions, who want to voice the software's evolution. In this context, one of the main related tasks is the automatic identification of software requirements from app reviews. Recent studies show that existing methods fail at this task, since review texts usually contain informal language, contain grammatical and spelling errors, as well as the difficulty in filtering out irrelevant information that has no practical value for developers. In this paper, we present the RE-BERT (Requirements Engineering using Bidirectional Encoder Representations from Transformers). Our method innovates by using pre-trained neural language models to generate semantic textual representations with contextual word embeddings. Our RE-BERT performs fine-tuning of the BERT model with a focus on the local context of the software requirement tokens. A statistical analysis of the experimental results involving eight different apps showed that our RE-BERT outperforms existing state-of-the-art methods.