Itemization framework of requirements using machine reading comprehension - Peng, Siyu; Xu, Luo; Jiang, Wei (SPIE, 2022)
In practice, it is very important to determine the requirements items of the proposed software system from the requirements document. However, due to the problems of ambiguity, redundancy, ambiguity, and difficulty in traceability of changes in the requirements documents described in natural language, the estimation results are subject to a certain degree, and the process is labor-intensive and cost-intensive. In this paper, we propose a new method to automatically extract requirement entries from requirement text by leveraging a set of natural language processing techniques and machine learning models. Our method is inspired by imitating the process of expert extraction of itemized requirements, which usually consists of three main processes: locating requirement locations and boundaries, building models, and extracting fine-grained requirement semantics. We performed evaluations in the field of military arguments, and the results showed that our model was nearly 80 percent accurate. Our approach can provide sound advice to help industry practitioners extract requirements items faster and easier.