Recurrent Neural Networks to automate Quality assessment of Software Requirements

TL;DR

This paper presents a neural network-based method that uses natural language processing to automatically evaluate the quality of software requirements, aiming to improve consistency and reduce ambiguities in requirements specifications.

Contribution

It introduces a novel approach combining NLP and recurrent neural networks for automatic quality assessment of requirements based on IEEE standards.

Findings

Achieved an average accuracy of 75% in quality assessment

Utilized a dataset of 1000 requirements for training neural models

Explored application to additional quality properties

Abstract

Many problems related to the quality of requirements arise during elicitation and specification activities since they are written in natural language. The flexibility and inherent nature of language make requirements prone to inconsistencies, redundancies, and ambiguities, and consequently, this influences negatively the later phases of the software life cycle. To address this problem, this paper proposes an innovative approach that combines natural language processing techniques and recurrent neural networks to automatically assess the quality of software requirements. Initially, the analysis of singular, complete, correct, and appropriate quality properties defined in the IEEE 29148: 2018 standard is addressed. The proposed neural models are trained with a data set composed of 1000 software requirements. The proposal provides an average accuracy of 75%. These promising results were a motivation to explore its application in the evaluation of other quality properties