IET Software最新文献

{"title":"Adaptive Multi-Lens Phase Modulation for Scale-Aware Privacy-Preserving Human Pose Recognition","authors":"Weilong Peng,&nbsp;Quanwei Deng,&nbsp;Mingjie Li,&nbsp;Yangtao Wang,&nbsp;Yan Wang,&nbsp;Lisheng Fan,&nbsp;Zhaoyang Yu,&nbsp;Meie Fang","doi":"10.1049/sfw2/7879383","DOIUrl":"10.1049/sfw2/7879383","url":null,"abstract":"<p>Recently, optical privacy protection has emerged as a promising approach for safeguarding visual privacy at the physical acquisition stage. However, existing methods often face a trade-off between privacy strength and human pose recognition accuracy, particularly in long-range and multi-scale scenarios. To address this challenge, we propose a novel adaptive optical privacy-preserving framework that integrates a learnable optical modulation system with a human pose recognition network. The core of our method lies in a sparse-weighted multi-lens model, where a lightweight multilayer perceptron (MLP) predicts a sparse set of coefficients to linearly combine predefined lens phase profiles based on facial region geometry. This enables dynamic control over the point spread function (PSF), adapting the degree of image degradation to subject scale in real time. Additionally, we introduce a privacy-aware loss function that selectively reduces facial localization accuracy while preserving body pose information. Extensive experiments on MSCOCO and FLIC datasets demonstrate that the proposed method achieves a favorable balance between privacy protection and pose estimation, outperforming previous optical- and software-based baselines.</p>","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"2025 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/sfw2/7879383","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145686378","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":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"2025 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148066412","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"2025 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148062540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of Co-Occurrences of Code Smells and Design Patterns on Internal Code Quality Attributes","authors":"Sania Imran,&nbsp;Irum Inayat,&nbsp;Maya Daneva","doi":"10.1049/sfw2/5579438","DOIUrl":"10.1049/sfw2/5579438","url":null,"abstract":"<p>The structural features of a code section that may indicate a more serious issue with the design of a system or code are known as code smells. Design patterns, on the other hand, are meant to describe the best reusable solution for creating object-oriented software systems. Even though design patterns and code smells are very different, they may co-occur. In fact, there may be a significant connection among the two, which requires further research. This study aims to (i) identify design patterns and code smells in web gaming code, (ii) investigate the co-occurrence of the two, and (iii) analyze the effects of these co-occurrences on internal quality aspects of code. An experiment is carried out on JavaScript (JS) web games utilizing machine learning classifiers to investigate the influence of co-occurrence on potential code smells and design patterns to evaluate games from a quality perspective. Moreover, statistical testing is performed to identify the impact of co-occurrences of code smells and design patterns on internal quality attributes. After examining the data, we determined that random forest is the most effective classifier, achieving an accuracy of 99.126% and 98.99% for both experimental situations, respectively. Moreover, on applying the Wilcoxon signed rank test, we found that co-occurrence has no impact on the coupling and complexity of web games codes, whereas there is a significant impact of co-occurrence on cohesion, size, and inheritance. Our results may guide developers in writing efficient games code to add to this swiftly growing market.</p>","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"2025 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/sfw2/5579438","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145686301","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-Audited Federated Learning: Securing Data and Model Updates With On-Chain Provenance","authors":"Seid Mehammed,&nbsp;Girma Bewuketu,&nbsp;Demeke Getaneh,&nbsp;Md Nasre Alam,&nbsp;Shakir Khan,&nbsp;Fatimah Alhayan","doi":"10.1049/sfw2/6670439","DOIUrl":"10.1049/sfw2/6670439","url":null,"abstract":"<p>We present a permissioned blockchain–audited federated learning (FL) framework that strengthens data provenance and model-update integrity. Our contribution is primarily engineering and architectural: a modular two-channel design (provenance vs. update-audit), lightweight on-chain validation with off-chain analytics, and a practical mapping to the 1 + 5 architectural views. In a TensorFlow Federated + Hyperledger Fabric prototype with 10 clients, we observe ≈18% faster anomaly detection under attack and a + 0.4 pp accuracy delta versus a baseline FL setup, with ~6% communication and ~8% energy overhead. We also provide a proof-of-concept zero-knowledge succinct noninteractive argument of knowledge (zk-SNARK) flow to validate per-client summary properties off-chain while anchoring results on-chain. These contributions collectively advance the practical deployment of secure, auditable FL systems.</p>","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"2025 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/sfw2/6670439","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145626002","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":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"2025 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148091624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research on the Application of Artificial Intelligence Technology in Color Matching in Product Appearance and Function Design","authors":"Chi Zhang,&nbsp;Wanli Gu,&nbsp;Yan Gu,&nbsp;Changyuan Geng,&nbsp;Duk-hwan Kim","doi":"10.1049/sfw2/4103554","DOIUrl":"10.1049/sfw2/4103554","url":null,"abstract":"<p>In the context of intensified market competition, consumer demand for aesthetically pleasing and functionally designed products has grown significantly. Color matching in product appearance plays a critical role in influencing consumer choice. However, designers often face challenges related to low color recognition accuracy, which hampers efficiency and design quality. This study explores the application of artificial intelligence (AI) technology to assist in the color matching process of product appearance and functional design. Experimental evaluation across various product types demonstrates that AI integration improves color accuracy by 2.09% and enhances the stability of image representation by 3.47%. Additionally, it reduces design analysis time, increases designer productivity, and boosts satisfaction scores by 5.4%. The findings confirm that AI technology effectively supports designers in achieving more accurate, efficient, and satisfactory color matching outcomes.</p>","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"2025 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/sfw2/4103554","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145581007","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":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"2025 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148086604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigation and Research on Several Key Issues of Software Defect Prediction","authors":"Ya Zhang,&nbsp;Ningzhong Liu","doi":"10.1049/sfw2/6615496","DOIUrl":"10.1049/sfw2/6615496","url":null,"abstract":"<p>With the increasing size and complexity of software code, hidden defects can pose serious problems to systems, making zero-defect software an urgent need for current industrial software applications. Software defect prediction (SDP) serves to identify defective modules or classes, with prediction models trained using historical defect data from various projects. This enables defect prediction in test projects, aiding in the rational allocation of test resources and the enhancement of software quality. The efficacy of SDP closely hinges on the quality of the defect dataset, the selected metric index, the trained model, and the algorithm design. This article reviews recent literature on SDP, summarizing existing research from three key perspectives: the dataset and metric elements employed in SDP, dataset optimization processing techniques, and defect prediction model techniques. It primarily focuses on introducing commonly used datasets and two types of defect metrics for SDP. Regarding dataset optimization processing technology, it discusses methods for handling abnormal data, high-dimensional data, class imbalance data, and data disparity issues. Furthermore, it analyzes the construction of prediction models across four dimensions: supervised learning, semi-supervised learning, unsupervised learning, and deep learning (DL). Key observations include: (i) Researchers utilize datasets of varying quality, performance evaluation metrics, and SDP models. The efficacy of software product metrics and development process metrics varies across different application scenarios, necessitating flexible metric selection based on actual requirements. (ii) Commonly used datasets like Promise and NASA exhibit varying data quality. Appropriate data preprocessing methods and dataset creation are crucial before training SDP models. (iii) In scenarios with limited labeled data, cross-project transfer learning, semi-supervised, or unsupervised learning methods tend to better utilize a broader range of training data. Given that each step in the SDP process corresponds to different unresolved issues, each requiring varying levels of response measures, we suggest that researchers comprehensively consider research objectives such as dataset quality, SDP model, performance evaluation indicators, and the need for model interpretability when conducting SDP-related research. It’s important to note that no universal dataset or model can perform optimally across different application scenarios.</p>","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"2025 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/sfw2/6615496","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145521834","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":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"2025 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148074087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}