An explainable method for security requirements classification through transformer models

Classifying software requirements is a challenging task, as they are typically specified in natural language. In addition, identifying security-related requirements, especially in the early stages, helps integrate these concepts early in the development process and reduces the likelihood of unauthorized access, data breaches, and other security threats. In this paper, we propose a method for classifying security requirements. We fine-tuned several transformer models by exploiting all model weights during the training process and using the Low-Rank Adaptation approach, which provides more efficient and effective ways of adapting pre-trained transformers to specific tasks. In order to optimize their performance, we employed an hyperparameter tuning phase. Furthermore, we compared and analyzed the performance of the two best models on both approaches and provided explainability through three common methods: attention weight visualization, integrated gradients, and SHAP which help us to better understand the decision-making process of our models.