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IF 1.3 4区计算机科学

IET Software Pub Date : 2026-02-25

引用次数: 0

A Novel Approach for Prioritizing Non-Functional Requirements Considering Stakeholders’ Organizational Influence 一种考虑涉众组织影响的非功能需求优先排序新方法

IF 1.3 4区计算机科学

IET Software Pub Date : 2026-02-15 DOI: 10.1049/sfw2/1906213

Ayoob Ramezani, Homayun Motameni, Meisam Yadollahzadeh Tabari, Behnam Barzegar

{"title":"A Novel Approach for Prioritizing Non-Functional Requirements Considering Stakeholders’ Organizational Influence","authors":"Ayoob Ramezani, Homayun Motameni, Meisam Yadollahzadeh Tabari, Behnam Barzegar","doi":"10.1049/sfw2/1906213","DOIUrl":"10.1049/sfw2/1906213","url":null,"abstract":"Requirement prioritization (RP) is considered one of the main activities of requirement engineering (RE). The stakeholders’ feedback and viewpoints in a project form the basis of RP. Two types of stakeholders can be included in a software project, one of which is the system architect and the other is the system users. After evaluating the organization and determining the non-functional requirements (NFRs), the system architects identify the technical relationships among these NFRs, while system users contribute usage‑driven viewpoints that reflect their operational experiences. Stakeholders possess varying levels of organizational power, which is determined by consolidating a defined set of parameters—namely influence, project involvement, and contribution—through an expert-driven assessment. While this study focuses on these core parameters, the framework allows the incorporation of additional organizational indicators in future applications if required. This composite metric was then quantified using the $100 method to assign a final power value to each stakeholder group. An NFR’s prioritization depends on both types of stakeholders. This study aimed to prioritize NFRs by collecting and extracting data from these two categories of stakeholders, leading to a heuristic smooth average approach (SAA). This approach increased the prioritization speed compared to the genetic algorithm (GA) and analytic hierarchy process (AHP) by 19% and 41%, respectively. The ease of use in prioritization also improved by 22.5% and 65%, respectively, compared to the other two approaches.","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"2026 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/sfw2/1906213","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146256561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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IF 1.3 4区计算机科学

IET Software Pub Date : 2026-02-15

引用次数: 0

Loan Default Prediction Method Based on Sample Optimisation and Bagging Integration With CatBoost-GRU 基于样本优化和Bagging集成的CatBoost-GRU贷款违约预测方法

IF 1.3 4区计算机科学

IET Software Pub Date : 2026-02-02 DOI: 10.1049/sfw2/9309999

Zhichao Xie, Xuan Huang

{"title":"Loan Default Prediction Method Based on Sample Optimisation and Bagging Integration With CatBoost-GRU","authors":"Zhichao Xie, Xuan Huang","doi":"10.1049/sfw2/9309999","DOIUrl":"10.1049/sfw2/9309999","url":null,"abstract":"Currently, with the development of the financial market and the increase of personal credit products, the phenomenon of personal loan default has also attracted widespread attention. How to make effective predictions of loan default has become a research hotspot and difficulty in this field. This article addresses the problems in existing loan default prediction methods. Based on the domain knowledge of consumer loan defaults and machine learning theory, it is proposed to use a hybrid sampling method based on Mahalanobis distance SMOTEENN to balance the dataset. The sampling method is further extended from the original credit card fraud research to the study of loan default prediction. The balanced dataset is used to integrate categorical boosting (CatBoost)-GRU model with Bagging for consumer loan default prediction. First, the loan dataset of Lending Club platform is selected for default prediction experiments. Then, to further validate the effectiveness of the method, the method is applied to the loan default dataset of Kiva for further validation. Finally, through the comparison of the experimental results, it is proved that the method based on Mahalanobis Distance SMOTEENN hybrid sampling and Bagging integrating CatBoost-GRU can be very effective in loan default prediction. The method has strong application potential and practical effects in loan default prediction research, which not only improves the accuracy and efficiency of default prediction but also provides a wide range of insights and methodological references for the solution of similar problems.","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"2026 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/sfw2/9309999","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146136020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IF 1.3 4区计算机科学

IET Software Pub Date : 2026-02-02

引用次数: 0

IF 1.3 4区计算机科学

IET Software Pub Date : 2026-02-02

引用次数: 0

A Structured Lifecycle Model for Quantum Software Engineering: Bridging Technical Challenges and Future Directions 量子软件工程的结构化生命周期模型：跨越技术挑战和未来方向

IF 1.3 4区计算机科学

IET Software Pub Date : 2026-02-02 DOI: 10.1049/sfw2/7827044

Hessa Alfraihi, Islam Zada, Mahmoud Ahmed, Sara Shahzad, Siwar Rekik, Abdullah Alshahrani, Nguyen Phi

{"title":"A Structured Lifecycle Model for Quantum Software Engineering: Bridging Technical Challenges and Future Directions","authors":"Hessa Alfraihi, Islam Zada, Mahmoud Ahmed, Sara Shahzad, Siwar Rekik, Abdullah Alshahrani, Nguyen Phi","doi":"10.1049/sfw2/7827044","DOIUrl":"10.1049/sfw2/7827044","url":null,"abstract":"Quantum computing is one of the research areas progressing rapidly toward practical deployment, yet the engineering of scalable and reliable quantum software remains underdeveloped. Current quantum software engineering (QSE) practices are largely tools-driven and ad hoc that providing limited support for managing probabilistic execution, hybrid quantum–classical workflows, noise sensitivity, and hardware constraints. This study proposed a structured QSE lifecycle that integrates quantum-specific characteristics with disciplined software engineering practices and principles. The proposed lifecycle organizes development into six phases, encompassing quantum requirements engineering, formal modeling, architecture and circuit design, hybrid integration, noise-aware testing, and deployment with monitoring. Each phase is supported by explicit artifacts and quantitative criteria to enable systematic progression and iterative refinement. The QSE is validated through expert assessment and simulation-based experimentation using representative variational quantum algorithms under the realistic noise conditions. The results show improved fidelity convergence, reduced resource overhead, enhanced development stability (DS), and more reliable validation compared with unstructured workflows, demonstrating the value of lifecycle-driven engineering for quantum software systems.","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"2026 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/sfw2/7827044","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146139281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Lung Disease Detection Using Hybrid CNN-XGBoost Classifier With New Loss Function 基于新损失函数的CNN-XGBoost混合分类器的肺部疾病检测

IF 1.3 4区计算机科学

IET Software Pub Date : 2026-02-01 DOI: 10.1049/sfw2/6391804

Bahman Jafari Tabaghsar, Yahya Forghani, Reza Sheibani, Reza Tavoli

{"title":"Lung Disease Detection Using Hybrid CNN-XGBoost Classifier With New Loss Function","authors":"Bahman Jafari Tabaghsar, Yahya Forghani, Reza Sheibani, Reza Tavoli","doi":"10.1049/sfw2/6391804","DOIUrl":"10.1049/sfw2/6391804","url":null,"abstract":"Lung disease is one of the fatal and common illnesses, affecting many people each year through various forms such as asthma, pneumonia, and lung obstruction. Numerous diagnostic methods have been proposed, most of which rely on machine learning algorithms or simple convolutional neural networks (CNNs) that often lack sufficient accuracy. Given the diversity of lung diseases and X-ray images, accurate diagnosis and classification remain complex and challenging tasks. To address this, a hybrid algorithm combining CNN and Extreme Gradient Boosting (XGBoost) is introduced, where the CNN first processes the training data, and its output is passed to the XGBoost classifier to produce the final prediction. Additionally, a novel loss function has been designed to overcome the limitations of the conventional softmax loss—such as sensitivity, class imbalance constraints, and mismatches between intraclass and interclass distances. The CNN-XGBoost hybrid is employed to mitigate the weaknesses each model exhibits when used independently. This proposed method was evaluated on the NIH CXR dataset, which includes 15 classes (14 disease classes and one healthy class), achieving a test accuracy of 97.92%, significantly outperforming previous approaches.","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"2026 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/sfw2/6391804","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146154595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IF 1.3 4区计算机科学

IET Software Pub Date : 2026-02-01

引用次数: 0

ESORecon-Net: A Novel Framework for Enhanced Brain MRI Image Reconstruction Using Echo State Networks and Osprey Optimization ESORecon-Net：基于回声状态网络和鱼鹰优化的增强脑MRI图像重建新框架

IF 1.3 4区计算机科学

IET Software Pub Date : 2026-01-19 DOI: 10.1049/sfw2/2067926

N. Sashi Prabha, N. Rama Rao

{"title":"ESORecon-Net: A Novel Framework for Enhanced Brain MRI Image Reconstruction Using Echo State Networks and Osprey Optimization","authors":"N. Sashi Prabha, N. Rama Rao","doi":"10.1049/sfw2/2067926","DOIUrl":"10.1049/sfw2/2067926","url":null,"abstract":"This study uses advanced approaches on the enlarged BRATS dataset to increase brain magnetic resonance imaging (MRI) image reconstruction accuracy and reliability. This study addresses MRI image processing issues such as noise, artifacts, and high-quality reconstruction. These traits are essential for brain tumor detection and analysis. This effort aims to establish a comprehensive image processing pipeline that standardizes MRI images, reduces noise, and improves clarity for better image reconstruction. ESORecon-Net, which combines the echo state network (ESN) and osprey optimization algorithm (OSPREY), manages raw k-space data cleverly and improves reconstruction. The model’s dual-phase optimization ensures accuracy and efficiency in reconstructing high-quality MRI images. The proposed ESORecon-Net achieved a peak signal-to-noise ratio (PSNR) of 49.12 dB and a structural similarity index measure (SSIM) of 0.993, surpassing existing methods such as the fully sampled k-space-trained network (FS-kNet) and motion-informed deep learning network (MIDNet). These results confirm ESORecon-Net’s effectiveness in enhancing brain MRI image reconstruction, improving both image quality and computational performance.","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"2026 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/sfw2/2067926","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146057876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0