ReqGo: A Semi-Automated Requirements Management Tool

Abstract

This study deals with issues of changes in requirements management by dealing with requirements ambiguity and prioritization. A hypothesis about the possibility of integrating machine learning techniques and requirements management processes has been proven. It highlights the efforts in automating requirements ambiguity identification, requirements classification, and prioritization considering multi-criteria in decision-making through the utilization of Natural Language Processing (NLP) techniques and Universal Sentence Encoder. Naïve Bayes (NB) classifier has been applied with its remarkable performance on binarily classifying requirements. Although existing methods proved to improve one or two of the process significantly, it rarely integrates the whole requirements management activity. The proposed tool helps the development team to manage the requirements systematically. The prioritization algorithm is proved to work as expected by considering multiple constraints before calculating the priority value. Meanwhile, it identifies the ambiguity that exists in the requirement automatically. The ambiguity classifier successfully identifies 87.5% of requirements accurately. Possible future work could be done in improving the prioritization module by allowing automated estimation of priority value upon requirements change. Future work may extend the automation coverage by providing test case generation.