Software Requirements Classification Using NLP Techniques

Abstract

The software requirement specification is a document that specifies what the software will do and how it will work. Delivery of good quality software depends on the software requirements as it is the building block of software development and software engineering depends on these requirements. Therefore, automation of requirements classification is a topic of discussion because it may take the tediousness of human labeling and reduce the need for domain knowledge. This thesis investigates how deep learning techniques can classify software requirements, specifically using different pre-trained word embeddings for feature extraction when training different versions of Recurrent Neural Networks. In the past, researchers have investigated methods used to classify the software requirements, but most use information retrieval and traditional machine learning, which require handcrafted features that can be error-prone and prove to be costly in the case of powerful enterprise software. This thesis used the PROMISE dataset to evaluate the model’s performance using precision, recall, and F1-score. The findings of the research indicate that the best classification model for software requirements is when Bidirectional LSTM combines with CNN and Fast Text word embedding as it achieves an encouraging Precision and F1-score of 0.83 and 0.75 respectively on multi-class software requirement classification task. In addition, it is concluded that the Fast Text word embedding model performs better as compared to Glove and Word2Vec.