Deep-transfer learning inspired natural language processing system for software requirements classification

In the software engineering domain, the distinction between functional (FRs) and non-functional requirements (NFRs) is paramount, as it directly influences the design and development of software systems. However, several challenges, such as dealing with limited training data, domain-specific datasets, and high computational costs, have driven the need for innovative solutions, particularly those related to classifying functional and non-functional software requirements. The limited availability of labeled data for training deep learning models and their high computational costs have hindered progress. This study proposes a novel hierarchical transfer learning (HTL) approach to address the challenges of limited training data and high computational costs associated with deep learning models. The HTL model leverages transfer learning techniques, incorporating pre-trained models such as global vectors for word representation (GloVe) for text vectorization and a bidirectional long short-term memory (BiLSTM) architecture. By harnessing knowledge from large text corpora and capturing both high-level semantic relationships and detailed syntactic patterns, the HTL model demonstrates enhanced classification performance. We have evaluated the model's performance using precision, recall, F1-score, and the area under the receiver operating characteristic curve. For FRs classification, we have observed a 26% improvement in precision, a 9% improvement in recall, and an 18% in F1-score for small datasets. Similarly, for NFRs, classification achieves a 20% improvement in precision, a 38.8% improvement in recall, and a 31.8% improvement in F1-score. For large datasets, we have observed a 25% improvement in precision, a 7% improvement in recall, and a 15% improvement in F1-score for FRs classification. For NFRs classification, it achieves a 24% improvement in precision, a 39.8% improvement in recall, and a 41.8% improvement in F1-score. Our study presents a pioneering HTL approach for FRs and NFRs classification, demonstrating superior performance compared to traditional methods. Furthermore, we identify areas for future research, including improving model interpretability, handling data biases, and fine-tuning hyperparameters, which will further enhance the capabilities and applicability of the HTL model.