IET Software最新文献

{"title":"Can Blockchain Decentralize Education? A Systematic Review of Challenges and Pathways","authors":"Abdul Razzaq,&nbsp;Muhammad Numair,&nbsp;Salman Ahmed,&nbsp;Waqas Junaid","doi":"10.1049/sfw2/5556408","DOIUrl":"https://doi.org/10.1049/sfw2/5556408","url":null,"abstract":"<div>\u0000 \u0000 <section>\u0000 \u0000 <h3> Context</h3>\u0000 \u0000 <p>The blockchain technology has become a secure, transparent, and decentralized infrastructure in data handling and its potential in the education sector has gained more and more attention especially in securing academic records, preventing credential fraud, and enhancing confidence in the verification procedures. Nonetheless, regardless of this interest, there is a lack of synthesis of the way in which blockchain is being implemented into real educational environment and the challenge that hinder its use.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Objectives</h3>\u0000 \u0000 <p>The objective of this study is to conduct a systematic review of applications of blockchain in education, determine the main areas of its implementation, evaluate the advantages and limitations mentioned in the literature, and offer recommendations to subsequent research and real-world implementation.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>The systematic literature review was carried out according to the methodology suggested by Okoli and Schabram. A total of 64 peer-reviewed studies published between 2017 and 2025 were analyzed.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The results showed that blockchain is mostly utilized in credential and certificate management, microcredential and credit transfer, competency and assessment tracking, and secure collaborative e-learning, with other applications in copyright protection and decentralized examination systems. Compared with conventional educational technologies, blockchain enables tamper-evident records, independently verifiable credentials, and reduced verification costs through cryptographic auditability and decentralized trust.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Contributions</h3>\u0000 \u0000 <p>This review provides a consolidated evidence base on blockchain-enabled education, highlighting realistic implementation opportunities, such as QR-hash diplomas and inter-institution credit exchanges, while emphasizing the need for privacy-preserving architectures and shared governance standards. The findings support researchers, practitioners, and policymakers to assess and promote safe, reliable, and effective blockchain-based learning ecosystems.</p>\u0000 </section>\u0000 </div>","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"2026 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2026-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/sfw2/5556408","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148237780","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":"VulFinder: A Multi-Agent-Driven Test Generation Framework for Guiding Vulnerability Reachability Analysis","authors":"Yu Zhao,&nbsp;Yiren Zhou,&nbsp;Jian Xie,&nbsp;Zhiqiu Huang,&nbsp;Lina Gong","doi":"10.1049/sfw2/6602283","DOIUrl":"https://doi.org/10.1049/sfw2/6602283","url":null,"abstract":"<p>Software engineering promotes the reuse of software components to accelerate the development process; however, reusing third-party components in AI4SE’s software supply chain (SSC) can introduce vulnerability risks. Once a new vulnerability in a third-party component emerges, developers need to determine whether the current project is affected by the vulnerability, that is, assess the vulnerability reachability issue in the SSC, which requires a significant amount of manpower and resources for assessment. To address this issue, we propose VulFinder, a multi-agent-driven framework for automated vulnerability reachability validation. VulFinder begins by using static code analysis tools to construct function call paths between downstream applications and dependency vulnerability APIs. Leveraging a multi-agent mechanism comprising a distillator, discriminator, generator, and validator, VulFinder iteratively generates exploit tests for methods along the call graph, effectively validating vulnerability reachability by executing these tests on downstream applications. We evaluate the performance of VulFinder across different programming language ecosystems, which were constructed from vulnerability datasets for Java and Python. Experiments show that VulFinder achieves 21% accuracy improvement over the state-of-the-art tool VESTA and a 7% accuracy improvement over the popular baseline tool TRANSFER on the Java dataset and also demonstrates robust generalizability on the Python dataset, significantly reducing false positives and false negatives and delivering an average efficiency improvement of over 1.5 times. We recommend that researchers and practitioners use VulFinder in practice, which integrates the code comprehension capabilities of large language models (LLMs) with the multi-agent framework to reduce false alarms and missed alarms in the reachability of vulnerabilities in SSC.</p>","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"2026 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2026-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/sfw2/6602283","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148237408","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":"GPT-Monkey: Enhancing Automated GUI Testing for Android Apps via LLM-Driven Interface Understanding and Function Segmentation","authors":"Zhanhui Yuan,&nbsp;Kai Chen,&nbsp;Zhi Yang,&nbsp;Dongxue Jiang,&nbsp;Jinglei Tan,&nbsp;Hongqi Zhang","doi":"10.1049/sfw2/9976714","DOIUrl":"https://doi.org/10.1049/sfw2/9976714","url":null,"abstract":"<p>Automated graphical user interface (GUI) testing is essential for ensuring mobile app quality. However, existing related methods lack deep GUI understanding and cannot segment specific functions for targeted testing, resulting in wasted test events and low efficiency. This study aims to leverage large language models (LLMs) to enhance Monkey-based GUI testing by enabling function-level segmentation and tailored parameter generation driven by user requirements. We propose GPT-Monkey, which integrates Monkey’s randomness with LLM’s understanding capability. It establishes global interface associations and cross-modal alignment for LLM-driven function segmentation, employs parameter-retrieval augmented generation (RAG) to guide tailored parameter generation, and adopts a dual-feedback mechanism to iteratively optimize testing. Experiments show that GPT-Monkey improves crash detection by 16.7% and efficiency by 34.5% over the optimal baseline, and achieves 95% function segmentation accuracy and uncovers 397 real-world crashes on 1000 Google Play apps. The results demonstrate that LLM-driven function segmentation and tailored parameter generation significantly enhance the precision and efficiency of automated GUI testing.</p>","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"2026 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2026-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/sfw2/9976714","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148174023","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":"2026 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148099932","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":"2026 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148099933","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":"Domain-Driven Data and AI Platforms: Governing Analytics and Machine Learning Pipelines at Scale","authors":"Akshay Sharma","doi":"10.1049/sfw2/4364820","DOIUrl":"https://doi.org/10.1049/sfw2/4364820","url":null,"abstract":"<p>Modern data and machine learning (ML) platforms have become critical infrastructure for enterprise decision-making, yet many organizations continue to struggle with unreliable pipelines, poor data quality, fragmented feature engineering, and models whose behavior is difficult to explain or govern. While substantial progress has been made in data platform and MLOps tooling, most systems remain organized around technical layers rather than business domains, leading to unclear ownership, semantic drift, and fragile governance processes. This paper presents a domain-driven architecture for data and AI platforms that treats data products, pipelines, features, and models as first-class domain artifacts. The approach applies domain-driven analysis to the entire analytics and ML lifecycle, enabling governance, quality enforcement, and lineage by construction rather than by after-the-fact controls. We introduce the core system model, describe the key architectural components, and present a reference architecture for large-scale enterprise environments. The approach is illustrated using an end-to-end enterprise analytics and AI platform scenario. The result is a platform that is not only scalable but also reliable, explainable, and governable.</p>","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"2026 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/sfw2/4364820","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148098342","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":"KAN–U-Net: An Enhanced KAN-Fused U-Net for Medical Image Segmentation","authors":"Cheng Zhu,&nbsp;Weiping Zhu,&nbsp;Jin Liu,&nbsp;Wei Li,&nbsp;Yongqiang Tang,&nbsp;Xiao Liu,&nbsp;Xin Wang","doi":"10.1049/sfw2/2709395","DOIUrl":"https://doi.org/10.1049/sfw2/2709395","url":null,"abstract":"<p>The U-Net model has demonstrated strong performance in the field of medical image segmentation. Moreover, several enhanced and improved versions of this model have emerged by incorporating transformer or MLP modules. However, these network models still face challenges in overcoming the limitations of linear modeling and the lack of interpretability. Based on the excellent performance of Kolmogorov–Arnold network (KAN) in terms of accuracy and interpretability, we propose a new architecture called KAN–U-Net. This architecture fuses the KAN network module into the U-Net model, allowing KAN–U-Net to inherit the original performance of U-Net while also fusing the nonlinear representation ability and interpretability of KAN networks. The experiments on multiple datasets demonstrate that the KAN–U-Net model outperforms in terms of precision and accuracy.</p>","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"2026 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/sfw2/2709395","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148098341","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":"2026 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2026-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148079220","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":"MACD: Online Video Anomaly Detection via Multi-Agent Counterfactual Dialectics","authors":"Xiaoye Ouyang,&nbsp;Shijie Liu,&nbsp;Liu Yuan,&nbsp;Yuan Wang,&nbsp;Xiaoling Zhu,&nbsp;Jipeng Qiang","doi":"10.1049/sfw2/6947479","DOIUrl":"https://doi.org/10.1049/sfw2/6947479","url":null,"abstract":"<p>Online video anomaly detection (VAD) necessitates real-time inference without access to future temporal context. Existing training-free methods, while promising, often suffer from correlation bias, misclassifying infrequent but benign actions (e.g., running for exercise) as anomalies due to a lack of causal reasoning. In this paper, we introduce multi-agent counterfactual dialectics (MACD), a novel framework that reframes VAD from statistical outlier detection to causal falsification. MACD operationalizes this via a structured adversarial debate among three specialized agents: a Prosecutor for high-recall risk screening, a Skeptic that performs instruction-conditioned causal interventions to generate benign counterfactual explanations, and an Arbitrator that adjudicates the final verdict based on the “evidence gap.” To ground this reasoning in visual reality, we construct a visual-semantic world model using dynamic scene graphs (DSG) and set-of-mark (SoM) prompting, reinforced by a visual re-verification loop (VRL) and a contextual memory bank to mitigate hallucinations. Our “screen-debate-decide” protocol ensures computational efficiency by reserving expensive dialectics only for ambiguous frames. Extensive experiments on UCF-Crime and XD-Violence demonstrate that MACD achieves state-of-the-art zero-shot performance.</p>","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"2026 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2026-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/sfw2/6947479","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148081405","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":"2026 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148078566","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}