GMR-165 An Empirical Study of Prompt-based Non-functional Requirements Classification

In modern software development, Non-Functional Requirements (NFR) are essential to satisfy users’ needs. Distinguishing different categories of NFR is tedious, error-prone, and time consuming due to the complexity of software systems. In our project, we conducted a comprehensive study to evaluate the performance of prompt-based NFR classification by designing various handcraft templates and soft templates on the pre-trained language model (i.e., BERT). Our experimental results show that handcraft templates can achieve best effectiveness (e.g., 83.52% in terms of F1 score) but with unstable performance for different templates.