Is BERT the New Silver Bullet? - An Empirical Investigation of Requirements Dependency Classification

Bidirectional Encoder Representations from Transformers (BERT) is a successful transformer-based Machine Learning technique for Natural Language Processing (NLP) based tasks developed by Google. It has taken various domains by storm, and Software Engineering is one among them. But does this mean that BERT is the new Silver Bullet? It is certainly not. We demonstrate it through an empirical investigation of the Requirements Dependency Classification (RDC). In general, based on various criteria used for evaluation, decisions on classification method preference may vary. For RDC, we go beyond traditional metrics such as the F1 score and consider Return-on-Investment (ROI) to evaluate two techniques for such decision making. We study RDC-BERT (fine-tuned BERT using data specific to requirements dependency classification) and compare with Random Forest, our baseline. For RDC and data from FOSS system Redmine, we demonstrate how decisions on method preference vary based on (i) accuracy, (ii) ROI, and (iii) sensitivity analysis. Results show that for all the three scenarios, method preference decisions depend on learning and evaluation parameters. Although these results are with respected to the chosen data sets, we argue that the proposed methodology is a prospective approach to study similar questions for data analytics, in general.