Abstract
State-of-the-art solutions to the classification of Non-functional requirements are mostly based on supervised machine learning models, requiring a reasonable amount of time in feature engineering. Deep learning, on the other hand, does not need to define features explicitly. This research aims to design and develop an automatic system to classify Non-functional requirements in multiple classes based on deep learning techniques. Specifically, we investigate the design and application of four neural network models; Artificial Neural Network, Convolutional Neural Network, Long Short-term Memory, and Gated Recurrent Unit to classify Non-functional requirements into five classes: reliability, usability efficiency, maintainability, and portability. However, these models require a large, annotated corpus and prone to overfitting. To address this, we proposed a novel framework for text augmentation. This technique uses a sort and concatenates approach to merge two sentences belonging to the same class to generate a two-time increase in data size yet preserving the domain vocabulary. We have compared our results with the state-of-the-art Easy data augmentation approach.
Our findings indicate that the NFRs classification model improved when trained with fine-tuned word embedding and CUSTOM augmentation approach. Interestingly Convolutional Neural Network turned out to be an outstanding learner with a jump in accuracy from 60% to 96% compared to the first approach. This simple text augmentation approach can add value for tasks where domain-specific terminologies play an essential role.
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Sabir, M., Banissi, E., Child, M. (2021). A Deep Learning-Based Framework for the Classification of Non-functional Requirements. In: Rocha, Á., Adeli, H., Dzemyda, G., Moreira, F., Ramalho Correia, A.M. (eds) Trends and Applications in Information Systems and Technologies . WorldCIST 2021. Advances in Intelligent Systems and Computing, vol 1366. Springer, Cham. https://doi.org/10.1007/978-3-030-72651-5_56
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