One- and Two-Phase Software Requirement Classification Using Ensemble Deep Learning

Abstract

Recently, deep learning (DL) has been utilized successfully in different fields, achieving remarkable results. Thus, there is a noticeable focus on DL approaches to automate software engineering (SE) tasks such as maintenance, requirement extraction, and classification. An advanced utilization of DL is the ensemble approach, which aims to reduce error rates and learning time and improve performance. In this research, three ensemble approaches were applied: accuracy as a weight ensemble, mean ensemble, and accuracy per class as a weight ensemble with a combination of four different DL models—long short-term memory (LSTM), bidirectional long short-term memory (BiLSTM), a gated recurrent unit (GRU), and a convolutional neural network (CNN)—in order to classify the software requirement (SR) specification, the binary classification of SRs into functional requirement (FRs) or non-functional requirements (NFRs), and the multi-label classification of both FRs and NFRs into further experimental classes. The models were trained and tested on the PROMISE dataset. A one-phase classification system was developed to classify SRs directly into one of the 17 multi-classes of FRs and NFRs. In addition, a two-phase classification system was developed to classify SRs first into FRs or NFRs and to pass the output to the second phase of multi-class classification to 17 classes. The experimental results demonstrated that the proposed classification systems can lead to a competitive classification performance compared to the state-of-the-art methods. The two-phase classification system proved its robustness against the one-phase classification system, as it obtained a 95.7% accuracy in the binary classification phase and a 93.4% accuracy in the second phase of NFR and FR multi-class classification.

Automatic Requirements Classification Based on Graph Attention Network

“…In addition, they invited five security experts to classify the requirements of the security field, but the effect of manual classification was lower than the automatic requirements classification model. Rahimi et al [14] used a combination of three holistic approaches and four deep learning models (LSTM, BiLSTM, GRU, and CNN) to build a NFR classifier and developed a two-stage classification system. Hey et al [25] proposed NoRBERT based on the pre-trained model BERT, which achieved excellent results.…”

“…Dekhtyar and Fang [12] proposed a CNN classifier joined with Word2Vec [13] embeddings that significantly boosted precision over that of machine-learning methods without sacrificing recall rate. Rahimi et al [14] developed a two-stage automatic classification system using long shortterm memory (LSTM) that is more robust than a single-stage classification system.…”

Classification of Software Systems attributes based on quality factors using linguistic knowledge and machine learning: A review.

“…Disadvantages:When the number of requirements for some labels is less in the group unbalanced data, automatic classification performance suffers. Nouf Rahimi et al [75] published a study aimed at categorizing software requirements (SRs), binary classification of SRs into FRs or NFRs, and multi-label categorization of both FRs and NFRs into various experimental categories. With a combination of four different deep learning models: The strategy employed three group methods: accuracy as a weight ensemble, mean ensemble, and accuracy per class as a weight ensemble, as well as long short term memory (LSTM), bidirectional long short term memory (BiLSTM), a gated recurrent unit (GRU), and a convolutional neural network (CNN).…”

A Pragmatic Ensemble Strategy for Missing Values Imputation in Health Records

“…Zaho et al [42] devised an ensemble technique by integrating patch learning with dynamic selection ensemble classification, wherein the miscategorised data have been used to educate patch models in order to increase the variety of base classifiers. Rahimi et al [43] used ensemble deep learning approaches to construct a classification model that improved the accuracy and reliability of classifying software requirement specifications.…”