An end-to-end deep learning system for requirements classification using recurrent neural networks

Context: Existing requirements classification approaches mainly use lexical and syntactical features to classify requirements using both traditional machine learning and deep learning approaches with promising results. However, the existing techniques depend on word and sentence structures and employ preprocessing and feature engineering techniques to classify requirements from textual natural language documents. Moreover, existing studies deal with requirements classification as binary or multiclass classification problems and not as multilabel classification, although a given requirement can belong to multiple classes at the same time. Objective: The objective of this study is to classify requirements into functional and different non-functional types with minimal preprocessing and to model the task as a multilabel classification problem. Method: In this paper, we use Bidirectional Gated Recurrent Neural Networks (BiGRU) to classify requirements using raw text. We investigated two different approaches: (i) using word sequences as tokens and (ii) using character sequences as tokens. Results: Experiments conducted on the publicly available PROMISE and EHR datasets show the effectiveness of the presented techniques. We achieve state-of-the-art results on most of the tasks using word sequences as tokens. Conclusion: Requirements can be effectively classified into functional and different non-functional categories using the presented recurrent neural networks-based deep learning system, which involves minimal text prepossessing and no feature engineering.