Unveiling the Correlation between Nonfunctional Requirements and Sustainable Environmental Factors Using a Machine Learning Model

Abstract

Integrating environmental features into software requirements during the requirements engineering (RE) process is known as sustainable requirements engineering. Unlike previous studies, we found that there is a strong relationship between nonfunctional requirements and sustainable environmental factors. This study presents a novel methodology correlating nonfunctional requirements (NFRs) with precise, sustainable green IT factors. Our mapping methodology consists of two steps. In the first step, we link sustainability dimensions to the two groups of green IT aspects. In the second step, we connect NFRs to sustainability aspects. Our proposed methodology is based on the extended PROMISE_exp dataset in combination with the Bidirectional Encoder Representations from Transformers (BERT) language model. Moreover, we evaluate the model by inserting a new binary classification column into the dataset to classify the sustainability factors into socio-economic and eco-technical groups. The performance of the model is assessed using four performance metrics: accuracy, precision, recall, and F1 score. With 16 epochs and a batch size of 32, 90% accuracy was achieved. The proposed model indicates an improvement in performance metrics values yielding an increase of 3.4% in accuracy, 3% in precision, 3.4% in recall, and 16% in F1 score values compared to the competitive previous studies. This acts as a proof of concept for automating the evaluation of sustainability realization in software during the initial stages of development.