Skip to main content
9th World Conference on Information Systems and Technologies

Full Program »

A Deep Learning-Based Framework For The Classifi-Cation of Non-Functional Requirements

State-of-the-art solutions to the classification of Non-functional re-quirements (NFRs) are mostly based on supervised machine learning models, which require to invest a reasonable amount of time in feature engineering. Deep learning, on the other hand, does not need to define features explicitly. The objective of this research is to design and de-velop an automatic system to classify Non-functional requirements in multiple classes based on deep learning techniques. We used skip-gram as feature embedding and four neural networks; ANN (Artificial Neural Network), CNN (Convolutional Neural Network), LSTM (Long Short-term Memory) and GRU (Gated Recurrent Unit) to design the framework. However, these models require large, annotated corpus and overfitting is another challenging issue. To address this, we pro-posed a novel framework for text augmentation called CUSTOM data augmentation. This technique uses the sort and concatenates ap-proach to merge two sentences belonging to the same class to produce more samples yet preserving the domain vocabulary. Further to avoid overfitting we used this approach in combination to a skip-gram model pre-trained on Eng-CoNLL corpus and later fine-tuned on our corpus. We have compared our results with state-of the art (Easy data augmentation) EDA approach. Our findings indicate that deep learning model when trained with fine-tuned word embedding and CUSTOM augmentation approach improved upon the results from earlier experiments. Interestingly CNN turned out to be an outstanding learner with the jump in accura-cy from 60% to 96% when compares with the first approach.

Maliha Sabir
London South Bank University
United Kingdom

Ebad Banissi
London South Bank University
United Kingdom

Mike Child
London South Bank University
United Kingdom

 


Powered by OpenConf®
Copyright ©2002-2020 Zakon Group LLC