Abstract
Requirements classification is a significant task for requirements engineering, which is time-consuming and challenging. The traditional requirements classification models usually rely on manual pre-processing and have poor generalization capability. Moreover, these traditional models ignore the sentence structure and syntactic information in requirements. To address these problems, we propose an automatic requirements classification based BERT and graph attention network (GAT), called DBGAT. We construct dependency parse trees and then utilize the GAT for mining the implicit structure feature and syntactic feature of requirements. In addition, we introduce BERT to improve the generalization ability of the model. Experimental results of the PROMISE datasets demonstrate that our proposed DBGAT significantly outperforms existing state-of-the-art methods. Moreover, we investigate the impact of graph construction methods on non-functional requirements classification. DBGAT achieved the best classification results on both seen (F1-scores of up to 91%) and unseen projects (F1-scores of up to 88%), further demonstrating the strong generalization ability.
Highlights
T HE comprehensive and accurate descriptions of functional and non-functional requirements is essential for requirements engineering [1]
To address the two limitations mentioned above, we propose an automatic requirements classification method called DBGAT, which is based on a graph attention network and introduces the pre-training model BERT [18] to initialize node embedding to obtain richer feature information
A graph attention network (GAT) [24] can capture sentence structure and grammatical information based on the dependency graph and add it to the node attributes for storage
Summary
T HE comprehensive and accurate descriptions of functional and non-functional requirements is essential for requirements engineering [1]. Traditional methods [5] use machine-learning techniques [6] to extract features such as POS tags, BoW, and TF-IDF, but the classification accuracy is low On this basis, some studies combining pre-processing of data with machine learning have achieved high classification accuracy [7, 8]. Most of the existing methods based on requirements classification techniques ignore structural features and syntactic information. To address the two limitations mentioned above, we propose an automatic requirements classification method called DBGAT, which is based on a graph attention network and introduces the pre-training model BERT [18] to initialize node embedding to obtain richer feature information. A graph attention network (GAT) [24] can capture sentence structure and grammatical information based on the dependency graph and add it to the node attributes for storage. 2) Experimental performance results show that the proposed DBGAT achieves the best classification results and strong generalization ability in the NFR subclass classification task
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