IET Software最新文献

{"title":"Knowledge Extraction From Big Data Using Feature Classification and Ensemble Learning With Granular Computing","authors":"Mohammad Nematpour,&nbsp;Farnaz Mahan,&nbsp;Witold Pedrycz,&nbsp;Habib Izadkhah","doi":"10.1049/sfw2/8673542","DOIUrl":"https://doi.org/10.1049/sfw2/8673542","url":null,"abstract":"<p>Managing large-scale data, particularly those with high-dimensional features, poses a significant challenge in data analysis. With the rapid growth in data generation and the increasing importance of data analysis for researchers, extracting meaningful patterns from big data has become a critical concern. Data analysts utilize data mining techniques to discover knowledge, extract patterns, and generate rules. However, the high number of features often leads to substantial computational costs, requiring advanced hardware resources. Moreover, the ability to derive interpretable classification rules in high-dimensional big data systems is essential. This paper proposes a method for knowledge extraction from big data using rule-based granular computing (GrC). Initially, the architecture extracts preliminary fuzzy rules using GrC techniques and fuzzy clustering. Subsequently, it employs ensemble learning to generate optimized Takagi–Sugeno–Kang (TSK) rules. The results indicate that effective feature selection through fuzzy clustering significantly contributes to the generation of optimal rules. Furthermore, experimental outcomes demonstrate that the proposed model achieves notable performance improvements compared to conventional algorithms such as Random Forest and standard gradient Boosting.</p>","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":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/sfw2/8673542","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148079625","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":"MAD-HAR: Privacy-Preserving On-Device Human Action Recognition via Multiagent LLM Debate","authors":"Xuecheng Zhou,&nbsp;Zepeng Gu,&nbsp;Chenyu Zuo,&nbsp;Shan Jiang,&nbsp;Mingjin Zhang,&nbsp;Jianguo Chen","doi":"10.1049/sfw2/8714926","DOIUrl":"10.1049/sfw2/8714926","url":null,"abstract":"<p>Human action recognition (HAR) is a fundamental component of ubiquitous computing, yet its wide-range applications are hindered by privacy concerns. Specifically, high-accuracy models typically require cloud-based processing that compromises sensitive visual data, while privacy-preserving on-device models suffer from limited reasoning capacities and frequent hallucinations. To resolve this conflict, we introduce multiagent debate for HAR (MAD-HAR), a novel framework designed for strictly local environments. MAD-HAR leverages a lightweight vision–language model (VLM) with a granular prompt to convert visual inputs into semantic captions, anonymizing data before inference. To mitigate reasoning failures, a heterogeneous ensemble of <span></span><math></math> diverse small and medium language model agents (ranging from 8B to 14B parameters) engages in a structured multiround debate. Rather than outputting simple labels, agents are prompted to generate structured rationales to explicitly justify their logic, utilizing collaborative critique to override hallucinations. We evaluate our approach on public benchmarks. Preliminary experiments guided the selection of the optimal VLM backbone, while extensive main and ablation studies suggest that scaling to a seven-agent pool with rationale-driven debate synthesizes higher-order reasoning. Experimental results show that MAD-HAR significantly improves macro-F1, while maximizing consensus and yielding consistent net error rectification.</p>","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"2026 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2026-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/sfw2/8714926","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147708248","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":"2026-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148070568","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":"2026-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148062971","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":"Refactoring Object-Oriented Software With ChatGPT: An Empirical Study","authors":"Hanady M. Abdulsalam,&nbsp;Ranya Alawadhi,&nbsp;Bader A. Ali,&nbsp;Aisha A. Alkandari,&nbsp;Jehad Al Dallal","doi":"10.1049/sfw2/3348017","DOIUrl":"10.1049/sfw2/3348017","url":null,"abstract":"<p>Generative AI (GenAI) is currently being utilized in many tasks to improve their quality. Because GenAI tools are highly qualified in text-based applications, they have the potential to automate tasks across the software engineering lifecycle. In this study, we empirically investigate ChatGPT’s ability to perform code refactoring tasks. Considering five widely employed refactoring scenarios, we propose testing scenarios upon which we derive 200 test cases for five Java open-source applications. These test cases are applied using ChatGPT and NetBeans, and the results are compared and evaluated. To enable ChatGPT to perform refactoring, we follow prompt engineering approaches to design effective prompts. The results show that ChatGPT has generated refactored pieces of code, which have been successfully compiled for 88% of the test cases. However, it has correctly performed the intended refactoring for only 29% of the cases, compared to NetBeans, which has achieved a 67% correctness rate. These findings indicate that although ChatGPT has some potential to contribute to code refactoring tasks, it is not yet ready to be used as a fully automated refactoring tool for large-scale real-world applications. Its outputs still require human oversight to improve the refactored code’s correctness.</p>","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"2026 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2026-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/sfw2/3348017","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147707954","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":"Two-Stream Modeling for Document-Level Event Argument Extraction Using Contextual Clue and AMR Structures","authors":"Yiqing Song,&nbsp;Xinna Shang,&nbsp;Guiren Dai,&nbsp;Wenfa Li,&nbsp;Zhi Yu","doi":"10.1049/sfw2/9533798","DOIUrl":"10.1049/sfw2/9533798","url":null,"abstract":"<p>Document-level (Doc-level) event argument extraction (EAE) needs to deal with longer text inputs and complex semantic relationships than sentence-level, making it a challenging information extraction task. Extracting event arguments from an entire document primarily faces two critical issues: (i) how to handle the long-distance dependency between trigger and role arguments and (ii) how to extract key event contextual information. We propose a two-stream modeling framework using contextual clues and abstract meaning representation (AMR) parsing (TSCA). TSCA employs two-stream encoding to semantically model the document from event-critical context and event-semantic structure two perspectives. This approach leverages both contextual clues and semantic structure information to better mitigate the two issues. We incorporate AMR to assist in the semantic understanding of complex event structures and effectively capture long-distance dependencies. Additionally, we introduce a span indicator based on triggers to adaptively merge the two-stream information, enhancing the capture of semantic relevance between triggers and candidate arguments. We validated the effectiveness of our method on the public datasets RAMs and Wikievents, where TSCA achieved the best scores in various subtasks, surpassing state-of-the-art models by 3.02 F1 and 1.01 F1, respectively.</p>","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"2026 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2026-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/sfw2/9533798","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147637089","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":"2026-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148063851","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":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148061449","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":"Proximity-Based Filtering: An Improved Adaptive Random Testing Approach Based on Orthogonal Bisections and Imaginary Offsets","authors":"Hilary Ackah-Arthur,&nbsp;Jinfu Chen,&nbsp;Dave Towey,&nbsp;Jiaxiang Xi,&nbsp;Michael Omari,&nbsp;Haibo Chen","doi":"10.1049/sfw2/4814836","DOIUrl":"10.1049/sfw2/4814836","url":null,"abstract":"<p>Random testing (RT) is an essential software testing strategy that selects test cases based on a random distribution. Adaptive RT (ART) encompasses a group of test-case selection/generation methods that, based on the insight that test cases that are widely spread are more prone to find nonpoint failure-causing regions, attempts to improve on the failure-finding ability of traditional RT. Two common approaches to implementing ART are distance-based ART and partition-based ART. A well-studied distance-based ART method is fixed size candidate set ART (FSCS-ART), which has been found to have superior effectiveness to RT in terms of failure-finding ability, but can incur high computational overheads. ART by bisection (ART-B) is a partition-based ART method that improves on the time efficiency of FSCS-ART. A limitation of ART-B, however, is its potential for selecting very close test cases, which reduces its failure-finding effectiveness. Adoption of an orthogonal recursive bisection (ORB) strategy in a partition-based ART method, ART by ORB (ART-ORB) has shown improvements in both failure-finding effectiveness and computational overheads; however, it also has the potential for selecting very close test cases. The present paper proposes ART-ORB with imaginary offsets (ART-ORBIO), an innovative ART method that combines the advantages of FSCS-ART, ART-B, and ART-ORB. ART-ORBIO applies a repeating input domain geometric bisection, integrated with a novel concept of imaginary offsets, to enhance the spread of test cases, and minimize the overheads of computation. Our empirical results show that ART-ORBIO provides better failure-finding effectiveness than RT and comparable effectiveness to FSCS-ART. ART-ORBIO also has significantly improved efficiency, although not comparable to ART-ORB and is more useful particularly in input domains with larger dimensions.</p>","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"2026 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/sfw2/4814836","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147637038","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":"AI–Augmented Real-Time Collaborative Blended Modeling Framework for Automotive Embedded Systems","authors":"Misbah Mehboob Awan,&nbsp;Muhammad Waseem Anwar,&nbsp;Wasi Haider Butt,&nbsp;Farooque Azam","doi":"10.1049/sfw2/6696040","DOIUrl":"10.1049/sfw2/6696040","url":null,"abstract":"<p>Model-driven engineering (MDE) has emerged as a foundational paradigm for the development of embedded systems in domains such as automotive, where stringent correctness and structural consistency must be maintained throughout the engineering process. However, traditional modeling practices often suffer from fragmented toolchains, siloed notations, and limited support for real-time collaboration across distributed stakeholders. This paper presents an artificial intelligence (AI)–augmented and role-aware collaborative framework that enables blended modeling through seamless synchronization of textual and tree-based/graphical views, underpinned by a robust service-oriented architecture. Rooted in automotive system engineering standards, specifically electronics architecture and software technology–architecture description language (EAST–ADL), and instantiated via an industrial-grade domain-specific modeling language (DSML) use case, the proposed framework facilitates stakeholder-specific modeling via both textual and tree-based/graphical editors. At the core of this infrastructure lies a real-time coordination engine that governs session orchestration, conflict resolution, and model merging, ensuring semantic consistency across heterogeneous representations. A key innovation is the integration of an explainable AI (XAI) based log analysis engine, which leverages large language models (LLMs) to generate human-readable summaries of user activity, model evolution, and design rationale. This not only enhances transparency and auditability but also introduces explainability as a first-class concern in collaborative MDE workflows. Empirical validation confirms the framework’s effectiveness in enabling synchronized, traceable, and fault-tolerant co-modeling for automotive embedded systems. By fusing collaborative computing, XAI, and domain-specific rigor, this research advances the state of the art in intelligent, scalable, and human-centered system modeling environments.</p>","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"2026 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2026-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/sfw2/6696040","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147637199","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}