FNReq-Net: A hybrid computational framework for functional and non-functional requirements classification

Requirements classification is a key component of software development life cycle. It enhances our understanding about project requirements, which in turn enables us to effectively identify and mitigate risks that could lead to project failure. Existing requirements classification predictors do not utilize feature selection methods competence in their predictive pipelines and lack in performance. To empower the process of automatic requirements classification, contributions of this paper are manifold. Firstly, it explores the potential of 7 filter-based feature selection techniques and 11 traditional machine learning classifiers. Secondly, for the first time it investigates combined potential of traditional feature selection and 9 diverse types of deep learning predictors. Thirdly, it presents a hybrid computational predictor namely FNReq-Net that reaps combine benefits of traditional feature selection and a novel deep learning predictor based on attention mechanism. Over two public benchmark datasets, large-scale experimental results reveal feature selection not only improves predictive performance of traditional machine learning predictors, but it also improves performance of deep learning predictors. The proposed FNReq-Net predictor outperforms state-of-the-art functional and non-functional requirements classification predictors by 4% and 1% in terms of F1-score over Promise and Promise-exp datasets, respectively. (c) 2023 The Author(s). Published by Elsevier B.V. on behalf of King Saud University.