SABDM: A self‐attention based bidirectional‐RNN deep model for requirements classification

Abstract

The success of software depends upon functional and non‐functional requirements as both requirements are equally important in software development. However, the requirements engineering community still lacks in comprehensive understanding of functional and non‐functional requirements. In addition, the requirements in software documents are expressed in natural language and also intertwined with each other. Requirements classification is a crucial task that correctly extracts functional and non‐functional requirements and organizes them in specified categories. Automated classification of software requirements leads to reduced ambiguity, misunderstanding, and development cost. Most of the recent studies have used machine learning and deep learning techniques for automatic classification of requirements. However, there is one drawback of such techniques, that is, poor generalization. To address these problems, this research work proposes self‐attention based bidirectional LSTM deep model. This automated approach has used recurrent neural network, which handle long sequential natural language requirements statements and classify them into five classes such as capability, maintainability, performance, security, and usability. The proposed approach train and evaluate over pre‐labeled dataset comprised of 34 industrial requirements specifications and PROMISE dataset. Over this dataset, the proposed approach yields 95% of precision, 96% of recall, 96% of F‐measure, and 96% of accuracy. The proposed approach can be applied to wide variety of datasets with different domain. Furthermore, this paper applies pre‐processing techniques to improve the performance of the requirements classification model. The results of the proposed model are compared with existing baseline state‐of‐art techniques, and it is shown that the proposed model outperforms the baseline models in requirements classification.

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