Classifying non-functional requirements using RNN variants for quality software development

Abstract

Non-Functional Requirements (NFR), a set of quality attributes, required for software architectural design. Which are usually scattered in SRS and must be extracted for quality software development to meet user expectations. Researchers show that functional and non-functional requirements are mixed together within the same SRS, which requires a mammoth effort for distinguishing them. Automatic NFR classification would be a feasible way to characterize those requirements, where several techniques have been recommended e.g. IR, linguistic knowledge, etc. However, conventional supervised machine learning methods suffered for word representation problem and usually required hand-crafted features, which will be overcome by proposed research using RNN variants to categories NFR. The NFR are interrelated and one task happens after another, which is the ideal situation for RNN. In this approach, requirements are processed to eliminate unnecessary contents, which are used to extract features using word2vec to fed as input of RNN variants LSTM and GRU. Performance has been evaluated using PROMISE dataset considering several statistical analyses. Among those models, precision, recall, and f1-score of LSTM validation are 0.973, 0.967 and 0.966 respectively, which is higher over CNN and GRU models. LSTM also correctly classified minimum 60% and maximum 80% unseen requirements. In addition, classification accuracy of LSTM is 6.1% better than GRU, which concluded that RNN variants can lead to better classification results, and LSTM is more suitable for NFR classification from textual requirements.