IET Software最新文献

{"title":"A Real Time Cardiomyopathy Detection Tool Using Ml Ensemble Models","authors":"Salvador de Haro,&nbsp;Esteban Becerra,&nbsp;Pilar González-Férez,&nbsp;José M. García,&nbsp;Gregorio Bernabé","doi":"10.1049/sfw2/4518420","DOIUrl":"https://doi.org/10.1049/sfw2/4518420","url":null,"abstract":"<div>\u0000 <p>Left Ventricular noncompaction (LVNC) is a recently classified form of cardiomyopathy. Although various methods have been proposed for accurately quantifying trabeculae in the left ventricle (LV), consensus on the optimal approach remains elusive. Previous research introduced DL-LVTQ, a deep learning solution for trabecular quantification based on a UNet 2D convolutional neural network (CNN) architecture and a graphical user interface (GUI) to streamline its use in clinical workflows. Building on this foundation, this work presents LVNC detector, an enhanced application designed to support cardiologists in the automated diagnosis of LVNC. The application integrates two segmentation models: DL-LVTQ and ViTUNet, the latter inspired by modern hybrid architectures combining convolutional neural networks (CNNs) and transformer-based designs. These models, implemented within an ensemble framework, leverage advancements in deep learning to improve the accuracy and robustness of magnetic resonance imaging (MRI) segmentation. Key innovations include multithreading to optimize model loading times and ensemble methods to enhance segmentation consistency across MRI slices. Additionally, the platform-independent design ensures compatibility with Windows and Linux, eliminating complex setup requirements. The LVNC detector delivers an efficient and user-friendly solution for LVNC diagnosis. It enables real-time performance and allows cardiologists to select and compare segmentation models for improved diagnostic outcomes. This work demonstrates how state-of-the-art machine learning techniques can seamlessly integrate into clinical practice to reduce human error and expedite diagnostic processes.</p>\u0000 </div>","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"2025 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/sfw2/4518420","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144725499","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}

{"title":"Implementation of Neural Style Transformation Technique for Artistic Image Processing Using VGG19","authors":"Xin Cheng,&nbsp;Feng Wang,&nbsp;Ali Akbar Siddique,&nbsp;Zain Anwar Ali","doi":"10.1049/sfw2/4145192","DOIUrl":"https://doi.org/10.1049/sfw2/4145192","url":null,"abstract":"<div>\u0000 <p>Image transformation is performed for basic image generation and color correction. In many applications, images are used for visual analysis or mainly for creating content. Similarly, stylized transformation is a process of transforming images into art-based content. To perform this artistic rendition through the process of image-stylized transformation, this article used the VGG19 classifier. The procedure begins by preprocessing both the content image and style image for reference, which includes resizing them to a maximum dimension while keeping their initial aspect ratio and transforming them into an array. The utility function reprocesses the image by clipping and normalizing pixel values. Content loss is calculated by comparing the feature maps of the derived content with the processed or stylized image generated by the model. Gradients of the loss concerning the generated image are computed and used to iteratively update the generated image. The process involves sequential display and processing of intermediate images until the process reaches 1000 iterations. In the end, the process produced a stylized image that depicts the artwork as its original counterpart.</p>\u0000 </div>","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"2025 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/sfw2/4145192","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144635252","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}

{"title":"Predicting Software Perfection Through Advanced Models to Uncover and Prevent Defects","authors":"Tariq Shahzad,&nbsp;Sunawar Khan,&nbsp;Tehseen Mazhar,&nbsp;Wasim Ahmad,&nbsp;Khmaies Ouahada,&nbsp;Habib Hamam","doi":"10.1049/sfw2/8832164","DOIUrl":"https://doi.org/10.1049/sfw2/8832164","url":null,"abstract":"<div>\u0000 <p>Software defect prediction is a critical task in software engineering, enabling organizations to proactively identify and address potential issues in software systems, thereby improving quality and reducing costs. In this study, we evaluated and compared various machine learning models, including logistic regression (LR), random forest (RF), support vector machines (SVMs), convolutional neural networks (CNNs), and eXtreme Gradient Boosting (XGBoost), for software defect prediction using a combination of diverse datasets. The models were trained and tested on preprocessed and feature-selected data, followed by optimization through hyperparameter tuning. Performance evaluation metrics were employed to analyze the results comprehensively, including classification reports, confusion matrices, receiver operating characteristic–area under the curve (ROC-AUC) curves, precision–recall curves, and cumulative gain charts. The results revealed that XGBoost consistently outperformed other models, achieving the highest accuracy, precision, recall, and AUC scores across all metrics. This indicates its robustness and suitability for predicting software defects in real-world applications.</p>\u0000 </div>","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"2025 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/sfw2/8832164","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144125962","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}

{"title":"DAA-UNet: A Dense Connectivity and Atrous Spatial Pyramid Pooling Attention UNet Model for Retinal Optical Coherence Tomography Fluid Segmentation","authors":"Tianhan Hu,&nbsp;Jiao Ding,&nbsp;Yuting Liu,&nbsp;Yantao Zhang,&nbsp;Li Yang","doi":"10.1049/sfw2/6006074","DOIUrl":"https://doi.org/10.1049/sfw2/6006074","url":null,"abstract":"<div>\u0000 <p>Retinal optical coherence tomography (OCT) fluid segmentation is a vital tool for diagnosing and treating various ophthalmic diseases. Based on clinical manifestations, retinal fluid accumulation is classified into three categories: intraretinal fluid (IRF), subretinal fluid (SRF), and pigment epithelial detachment (PED). PED is primarily associated with diabetic macular edema (DME). In contrast, IRF and SRF play critical roles in diagnosing age-related macular degeneration (AMD) and retinal vein occlusion (RVO). To address challenges posed by variations in OCT imaging devices, as well as the varying sizes, irregular shapes, and blurred boundaries of fluid accumulation areas, this study proposes DAA-UNet, an enhanced UNet architecture. The proposed model incorporates dense connectivity, Atrous Spatial Pyramid Pooling (ASPP), and attention gate (AG) in the paths of UNet. Dense connectivity expands the model’s depth, whereas ASPP facilitates the extraction of multiscale image features. The AG emphasize critical spatial location information, improving the model’s ability to distinguish different fluid accumulation types. Experimental results on the MICCAI 2017 RETOUCH challenge dataset showed that DAA-UNet demonstrates superior performance, with a mean Dice Similarity Coefficient (<i>mDSC</i>) of 90.2%, 91.6%, and 90.5% on cirrus, spectralis, and topcon devices, respectively. These results outperform existing models, including UNet, SFU, Attention-UNet, Deeplabv3+, nnUNet RASPP, and MsTGANet.</p>\u0000 </div>","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"2025 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/sfw2/6006074","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144100924","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}

{"title":"A Data-Driven Methodology for Quality Aware Code Fixing","authors":"Thomas Karanikiotis,&nbsp;Andreas L. Symeonidis","doi":"10.1049/sfw2/4147669","DOIUrl":"https://doi.org/10.1049/sfw2/4147669","url":null,"abstract":"<div>\u0000 <p>In today’s rapidly changing software development landscape, ensuring code quality is essential to reliability, maintainability, and security among other aspects. Identifying code quality issues can be tackled; however, implementing code quality improvements can be a complex and time-consuming task. To address this problem, we present a novel methodology designed to assist developers by suggesting alternative code snippets that not only match the functionality of the original code but also improve its quality based on predefined metrics. Our system is based on a language-agnostic approach that allows the analysis of code snippets written in different programming languages. It employs advanced techniques to assess functional similarity and evaluates syntactic similarity, suggesting alternatives that minimize the need for extensive modification. The evaluation of our system on multiple axes demonstrates the effectiveness of our approach in providing usable code alternatives that are both functionally equivalent and syntactically similar to the original snippets, while significantly improving quality metrics. We argue that our methodology and tool can be valuable for the software engineering community, bridging the gap between the identification of code quality problems and the implementation of practical solutions that improve software quality.</p>\u0000 </div>","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"2025 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/sfw2/4147669","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143909443","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}

{"title":"A Commit Classification Framework Incorporated With Prompt Tuning and External Knowledge","authors":"Jiajun Tong,&nbsp;Xiaobin Rui","doi":"10.1049/sfw2/5566134","DOIUrl":"https://doi.org/10.1049/sfw2/5566134","url":null,"abstract":"<div>\u0000 <p>Commit classification is an important task in software maintenance, since it helps software developers classify code changes into different types according to their nature and purpose. This allows them to better understand how their development efforts are progressing, identify areas where they need improvement, and make informed decisions about when and how to release new versions of their software. However, existing methods are all discriminative models, usually with complex architectures that require additional output layers to produce class label probabilities, making them task-specific and unable to learn features across different tasks. Moreover, they require a large amount of labeled data for fine tuning, and it is difficult to learn effective classification boundaries in the case of limited labeled data. To solve the above problems, we propose a generative framework that incorporates prompt tuning for commit classification with external knowledge (IPCK), which simplifies the model structure and learns features across different tasks, only based on the commit message information as the input. First, we proposed a generative framework based on T5 (text-to-text transfer transformer). This encoder–decoder construction method unifies different commit classification tasks into a text-to-text problem, simplifying the model’s structure by not requiring an extra output layer. Second, instead of fine tuning, we design a prompt tuning solution that can be adopted in few-shot scenarios with only limited samples. Furthermore, we incorporate external knowledge via an external knowledge graph to map the probabilities of words into the final labels in the speech machine step to improve performance in few-shot scenarios. Extensive experiments on two open available datasets demonstrate that our framework can solve the commit classification problem simply but effectively for both single-label binary classification and single-label multiclass classification purposes with 90% and 83% accuracy. Further, in the few-shot scenarios, our method improves the adaptability of the model without requiring a large number of training samples for fine tuning.</p>\u0000 </div>","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"2025 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/sfw2/5566134","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143875664","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}

{"title":"Multisource Heterogeneous Data Fusion Methods Driven by Digital Twin on Basis of Prophet Algorithm","authors":"Min Li","doi":"10.1049/sfw2/5041019","DOIUrl":"https://doi.org/10.1049/sfw2/5041019","url":null,"abstract":"<div>\u0000 <p>With the development of intelligent manufacturing and the wider application of the Internet of Things (IoT), it is crucial to fuse heterogeneous sensor data from multiple sources. However, the current data fusion methods still have problems, such as low accuracy of fused data, insufficient data integrity, poor data fusion efficiency, and poor scalability of fusion methods. In response to these issues, this article explores a multisource heterogeneous data fusion method based on the Prophet algorithm digital twin drive to improve the fusion effect of sensor data and provide more support for subsequent decision-making. The article first used curve and sequence alignment to extract data features and then analyzed the trend of data changes using the Prophet algorithm. Afterward, this article constructed a digital twin model to provide analytical views and data services. In conclusion, this paper used tensor decomposition to merge text and image data from sensor data. Deep learning algorithms and Kalman filtering techniques were also examined to confirm the efficacy of data fusion under the Prophet algorithm. The experimental results showed that after fusing the data using the Prophet algorithm, the average accuracy can reach 92.63%, while the average resource utilization at this time was only 9.97%. The results showed that combining Prophet with digital twin technology can achieve higher accuracy, fusion efficiency, and better scalability. The research in this paper can provide new ideas and means for the fusion and analysis of heterogeneous data from multiple sources.</p>\u0000 </div>","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"2025 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/sfw2/5041019","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143861545","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}

{"title":"A Systematic Literature Review on Graphical User Interface Testing Through Software Patterns","authors":"Ambreen Kousar,&nbsp;Saif Ur Rehman Khan,&nbsp;Atif Mashkoor,&nbsp;Javed Iqbal","doi":"10.1049/sfw2/9140693","DOIUrl":"https://doi.org/10.1049/sfw2/9140693","url":null,"abstract":"<div>\u0000 <p><b>Context:</b> Graphical user interface (GUI) testing of mobile applications (apps) is significant from a user perspective to ensure that the apps are visually appealing and user-friendly. Pattern-based GUI testing (PBGT) is an innovative model-based testing (MBT) approach designed to enhance user satisfaction and reusability while minimizing the effort required to model and test UIs of mobile apps. In the literature, several primary studies have been conducted in the domain of PBGT.</p>\u0000 <p><b>Problem:</b> The current state-of-the-art lacks comprehensive secondary studies within the PBGT domain. To our knowledge, this area has insufficient focus on in-depth research. Consequently, numerous challenges and limitations persist in the existing literature.</p>\u0000 <p><b>Objective:</b> This study aims to fill the gaps mentioned above in the existing body of knowledge. We highlight popular research topics and analyze their relationships. We explore current state-of-the-art approaches and techniques, a taxonomy of tools and modeling languages, a list of reported UI test patterns (UITPs), and a taxonomy of writing UITPs. We also highlight practical challenges, limitations, and gaps in the targeted research area. Furthermore, the current study intends to highlight future research directions in this domain.</p>\u0000 <p><b>Method:</b> We conducted a systematic literature review (SLR) on PBGT in the context of Android and web apps. A hybrid methodology that combines the Kitchenham and PRISMA guidelines is adopted to achieve the targeted research objectives (ROs). We perform a keyword-based search on well-known databases and select 30 (out of 557) studies.</p>\u0000 <p><b>Results:</b> The current study identifies 11 tools used in PBGT and devises a taxonomy to categorize these tools. A taxonomy for writing UITPs has also been developed. In addition, we outline the limitations of the targeted research domain and future directions.</p>\u0000 <p><b>Conclusion:</b> This study benefits the community and readers by better understanding the targeted research area. A comprehensive knowledge of existing tools, techniques, and methodologies is helpful for practitioners. Moreover, the identified limitations, gaps, emerging trends, and future research directions will benefit researchers who intend to work further in future research.</p>\u0000 </div>","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"2025 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/sfw2/9140693","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143822296","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}

{"title":"Automated Hybrid Methodology for Software Architecture Style Selection Using Analytic Hierarchy Process and Fuzzy Analytic Hierarchy Process","authors":"Muna Alrazgan,&nbsp;Ahmed Ghoneim,&nbsp;Luluah Albesher,&nbsp;Razan Aldossari,&nbsp;Shahad Alotaibi,&nbsp;Lama Alsaykhan,&nbsp;Norah Alshahrani,&nbsp;Maha Alshammari","doi":"10.1049/sfw2/9943825","DOIUrl":"https://doi.org/10.1049/sfw2/9943825","url":null,"abstract":"<div>\u0000 <p>In software engineering, selecting the appropriate architectural style for software systems is risky and sensitive. The selection process is a multicriteria decision-making (MCDM) problem. Consequently, selecting a suitable architecture is a key challenge in software development. This study presents an automated hybrid methodology based on the analytic hierarchy process (AHP) and fuzzy analytic hierarchy process (FAHP) to evaluate and suggest multiple architectural styles based on quality attributes (QAs) alone rather than relying on expert opinions. A Tera-PROMISE dataset is presented to illustrate the proposed methodology and then compare the result of the methodology with expert judgments. Moreover, to support the proposed methodology, a case study is carried out to compare the proposed method to previous studies.</p>\u0000 </div>","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"2025 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/sfw2/9943825","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143770403","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}

{"title":"Blockchain Consensus Scheme Based on the Proof of Distributed Deep Learning Work","authors":"Hui Zhi,&nbsp;HongCheng Wu,&nbsp;Yu Huang,&nbsp;ChangLin Tian,&nbsp;SuZhen Wang","doi":"10.1049/sfw2/3378383","DOIUrl":"https://doi.org/10.1049/sfw2/3378383","url":null,"abstract":"<div>\u0000 <p>With the development of artificial intelligence and blockchain technology, the training of deep learning models needs large computing resources. Meanwhile, the Proof of Work (PoW) consensus mechanism in blockchain systems often leads to the wastage of computing resources. This article combines distributed deep learning (DDL) with blockchain technology and proposes a blockchain consensus scheme based on the proof of distributed deep learning work (BCDDL) to reduce the waste of computing resources in blockchain. BCDDL treats DDL training as a mining task and allocates different training data to different nodes based on their computing power to improve the utilization rate of computing resources. In order to balance the demand and supply of computing resources and incentivize nodes to participate in training tasks and consensus, a dynamic incentive mechanism based on task size and computing resources (DIM-TSCR) is proposed. In addition, in order to reduce the impact of malicious nodes on the accuracy of the global model, a model aggregation algorithm based on training data size and model accuracy (MAA-TM) is designed. Experiments demonstrate that BCDDL can significantly increase the utilization rate of computing resources and diminish the impact of malicious nodes on the accuracy of the global model.</p>\u0000 </div>","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"2025 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/sfw2/3378383","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143117532","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}