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

Shoaib Hassan (), Qianmu Li, Muhammad Zubair, Rakan A. Alsowail () and Muaz Ahmad Qureshi
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Shoaib Hassan: School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
Qianmu Li: School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
Muhammad Zubair: Faculty of Information Technology and Computer Science, University of Central Punjab, Lahore 54000, Pakistan
Rakan A. Alsowail: Computer Skills, Self-Development Skills Development, Deanship of Common First Year, King Saud University, Riyadh 11362, Saudi Arabia
Muaz Ahmad Qureshi: Department of Computer Science, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan

Sustainability, 2024, vol. 16, issue 14, 1-24

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.

Keywords: sustainability; BERT; machine learning; PROMISE_exp dataset; nonfunctional requirements; green IT factors (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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