A non-functional requirements classification model based on cooperative attention mechanism fused with label embedding

Abstract

Intelligent classification of software requirements is a hot research issue in the field of requirements engineering. Complete and accurate identification of functional requirements (FRs) and non-functional requirements (NFRs) is the primary task of requirements engineering. However, in real software projects, NFRs are easily neglected and may become a potential risk of project failure. Text is the main source of information about software requirements. With the increasing scale of software projects, a large number of complex types of text materials are used for software requirements analysis. Manual identification of NFRs of software projects has the problems of easy omission, ambiguity, vagueness, and high time-consuming cost. Based on the above existing defects, a deep neural network model named CAFLE is designed in this paper to solve it. CAFLE is composed of two parts, Text-label cooperative attention encoder (TLCAE) and Label decoder (LD). TLCAE adopts a Bi-directional long short-term memory network (Bi-LSTM) and multi-head cooperative attention mechanism to generate an encoded representation of the mutual involvement of requirement classification labels and requirement text. LD is an LSTM decoder with an attention mechanism constructed for the multi-class classification task of requirement text. LD utilizes the representation generated by TLCAE for prediction. Experimental results on the PROMISE benchmark dataset show that CAFLE outperforms existing NFRs classification methods with an F1 score of 95%.