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.

Over the past few years, the application and usage of Machine Learning (ML) techniques have increased exponentially due to continuously increasing the size of data and computing capacity. Despite the popularity of ML techniques, only a few research studies have focused on the application of ML especially supervised learning techniques in Requirement Engineering (RE) activities to solve the problems that occur in RE activities. The authors focus on the systematic mapping of past work to investigate those studies that focused on the application of supervised learning techniques in RE activities between the period of 2002–2023. The authors aim to investigate the research trends, main RE activities, ML algorithms, and data sources that were studied during this period. Forty‐five research studies were selected based on our exclusion and inclusion criteria. The results show that the scientific community used 57 algorithms. Among those algorithms, researchers mostly used the five following ML algorithms in RE activities: Decision Tree, Support Vector Machine, Naïve Bayes, K‐nearest neighbour Classifier, and Random Forest. The results show that researchers used these algorithms in eight major RE activities. Those activities are requirements analysis, failure prediction, effort estimation, quality, traceability, business rules identification, content classification, and detection of problems in requirements written in natural language. Our selected research studies used 32 private and 41 public data sources. The most popular data sources that were detected in selected studies are the Metric Data Programme from NASA, Predictor Models in Software Engineering, and iTrust Electronic Health Care System.

As the trend to use the latest machine learning models to automate requirements engineering processes continues, security requirements classification is tuning into the most researched field in the software engineering community. Previous literature studies have proposed numerous models for the classification of security requirements. However, adopting those models is constrained due to the lack of essential datasets permitting the repetition and generalization of studies employing more advanced machine learning algorithms. Moreover, most of the researchers focus only on the classification of requirements with security keywords. They did not consider other nonfunctional requirements (NFR) directly or indirectly related to security. This has been identified as a significant research gap in security requirements engineering. The major objective of this study is to propose a security requirements classification model that categorizes security and other relevant security requirements. We use PROMISE_exp and DOSSPRE, the two most commonly used datasets in the software engineering community. The proposed methodology consists of two steps. In the first step, we analyze all the nonfunctional requirements and their relation with security requirements. We found 10 NFRs that have a strong relationship with security requirements. In the second step, we categorize those NFRs in the security requirements category. Our proposed methodology is a hybrid model based on the Convolutional Neural Network (CNN) and Extreme Gradient Boosting (XGBoost) models. Moreover, we evaluate the model by updating the requirement type column with a binary classification column in the dataset to classify the requirements into security and non-security categories. The performance is evaluated using four metrics: recall, precision, accuracy, and F1 Score with 20 and 28 epochs number and batch size of 32 for PROMISE_exp and DOSSPRE datasets and achieved 87.3% and 85.3% accuracy, respectively. The proposed study shows an enhancement in metrics values compared to the previous literature studies. This is a proof of concept for systematizing the evaluation of security recognition in software systems during the early phases of software development.

The software industry was greatly affected by the pandemic. The emotions and human behavior of software developers and users greatly affected the development of software systems. Accurate effort estimation is a key role in the success of any software project. Among many software effort estimation models, the use-case point (UCP) model is one of software development’s maximum-used effort estimation models. So many research studies have addressed the effort estimation of software projects through the UCP model, but the effort estimation of specific quality and emotional requirements for the software system receives minimal focus. To fill this gap, we have proposed an extension to the existing standard UCP model. In the extended UCP model, we have introduced two new quality and emotional components. We applied the extended UCP estimation model to estimate the effort to build all types of software system requirements and analyze the influence of quality and emotional requirements on the effort estimation value. A real-time emotion-based healthcare case study was used by the authors to create and compare effort estimation models for estimating requirement-type efforts through UCP size. We have taken two more case studies named Internet banking system, and E-commerce application for further validation of our study. We found the mean magnitude of relative error (MMRE =0.086), mean magnitude of error relative (MMER=0.092), and mean absolute percentage error (MAPE =8.6%). Our proposed methodology gives an 8.6% error, showing our model’s accuracy. Expert opinions have also been taken to assess our study. Most industry experts validated our study by suggesting three additional human factors (social, cultural, and economic) that may impact the software project’s effort estimation.

Blockchain technology presents a capable solution for secure and standardized data sharing with its consensus mechanisms and smart contracts. The major objective of this study is to propose a novel blockchain architecture for secure healthcare data sharing by detecting all factors necessary for healthcare data sharing. After analyzing numerous blockchain architectures for healthcare systems, this study found three essential categories of factors called healthcare, security, and blockchain, which are vital for successful data sharing in healthcare environments. The prototypes and pseudocodes of the proposed architecture were validated by using expert opinions from 15 software experts. The study's major contribution lies in the development of this robust architecture, which not only addresses the core issues of data privacy, security, and interoperability but also lays a strong foundation for future advancements in the secure exchange of medical records using blockchain technology.

Artificial intelligence (AI) is disrupting healthcare in the form of disease diagnostics, clinical decision support, and remote patient monitoring. Nevertheless, such developments raise many ethical issues regarding patient safety, data sensitivity, and fair access. This chapter focuses on AI in healthcare as a process that is guided by the principles of clinical validation, transparency, and constant monitoring to avoid diagnostic mistakes and algorithmic bias. It discusses privacy-protective solutions like blockchain governance, federated learning, and differential privacy to ensure sensitive health information is not leaked. Moreover, it states that inclusive datasets and models of fairness are essential to providing equitable access, especially in low- and middle-income countries. Based on the case studies of NHS AI Lab (UK), Mayo Clinic (USA) and AIoT-based diabetes monitoring (Singapore), the chapter suggests an ethical AI implementation system that considers both innovation and trust to provide safe, confidential, and equity healthcare to all populations.