IET Software最新文献

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"2025 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148089751","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":"Blockchain-Based Model to Predict Agile Software Estimation Using Machine Learning Techniques","authors":"Mohammad Ayub Latif,&nbsp;Muhammad Khalid Khan,&nbsp;Maaz Bin Ahmad,&nbsp;Toqeer Mahmood,&nbsp;Muhammad Tariq Mahmood,&nbsp;Young-Bok Joo","doi":"10.1049/sfw2/9238663","DOIUrl":"10.1049/sfw2/9238663","url":null,"abstract":"<p>The importance of software estimation is utmost, as it is one of the most crucial activities for software project management. Although numerous software estimation techniques exist, the accuracy achieved by these techniques is questionable. This work studies the existing software estimation techniques for Agile software development (ASD), identifies the gap, and proposes a decentralized framework for estimation of ASD using machine-learning (ML) algorithms, which utilize the blockchain technology. The estimation model uses nearest neighbors with four ML techniques for ASD. Using an available ASD dataset, after the augmentation on the dataset, the proposed model emits results for the completion time prediction of software. Use of another popular dataset for ASD predicts the software effort using the same proposed model. The crux of the proposed model is that it simulates blockchain technology to predict the completion time and the effort of a software using ML algorithms. This type of estimation model, using ML, making use of blockchain technology, does not exist in the literature, and this is the core novelty of this proposed model. The final prediction of the software effort integrates another technique for improving the calculated estimation, the standard deviation technique proposed by the authors previously. This model helped lessening the overall mean magnitude of relative error (MMRE) of the original model from 6.82% to 1.73% for the augmented dataset of 126 projects. All four ML techniques used for the proposed model give a better <i>p</i>-value than the original model using statistical testing through the Wilcoxon test. The average of the MMRE for effort estimation of all four techniques is below 25% on a dataset of 136 projects. The application of the standard deviation technique further helps in lessening the MMRE of the proposed model at 70%, 80%, and 90% confidence levels. The work will give insight to researchers and experts and open the doors for new research in this area.</p>","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"2025 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/sfw2/9238663","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145366661","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":"2025-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148080221","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":"MMF: A Lightweight Approach of Multimodel Fusion for Malware Detection","authors":"Bo Yang,&nbsp;Mengbo Li,&nbsp;Li Li,&nbsp;Huai Liu","doi":"10.1049/sfw2/1046015","DOIUrl":"10.1049/sfw2/1046015","url":null,"abstract":"<p>Nowadays, the Android system is widely used in mobile devices. The existence of malware in the Android system has posed serious security risks. Therefore, detecting malware has become a main research focus for Android devices. The existing malware detection methods include those based on static analysis, dynamic analysis, and hybrid analysis. The dynamic analysis and hybrid analysis methods require the simulation of malware’s execution in a certain environment, which often incurs high costs. With the aid of contemporary deep learning technology, static method can provide comparably good results without running software. To address these challenges, we propose a novel and efficient multimodel fusion (MMF) malware detection method. MMF innovatively integrates various static features, including application programming interface (API) call characteristics, request permission (RP) features, and bytecode image features. This fusion approach allows MMF to achieve high detection performance without the need for dynamic execution of the software. Compared to existing methods, MMF exhibits a higher accuracy rate of 99.4% and demonstrates superiority over baseline techniques in various metrics. Our comprehensive analysis and experiments confirm MMF’s effectiveness and efficiency in detecting malware, making a significant contribution to the field of Android malware detection.</p>","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"2025 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/sfw2/1046015","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145316864","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":"2025-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148076707","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":"Automated NLP-Based Classification of Nonfunctional Requirements in Blockchain and Cross-Domain Software Systems Using BERT and Machine Learning","authors":"Touseef Tahir,&nbsp;Bilal Hassan,&nbsp;Hamid Jahankhani,&nbsp;Nimra Zia,&nbsp;Muhammad Sharjeel","doi":"10.1049/sfw2/9996509","DOIUrl":"10.1049/sfw2/9996509","url":null,"abstract":"<p>Automated nonfunctional requirements (NFRs) classification enhances consistency and traceability by systematically labeling requirements, saving effort, supporting early architectural and testing decisions, improving stakeholder communication, and enabling quality across diverse software domains. While prior work has applied natural language processing (NLP) and machine learning (ML) to NFR classification, existing datasets are often limited in size, domain diversity, and contextual richness. This study presents a novel dataset comprising over 2400 NFRs spanning 269 software projects across 26 software application domains, including nine blockchain projects. The raw requirements are standardized using Rupp’s boilerplate to reduce vagueness and ambiguity, and the classification of NFRs types follows ISO/IEC 25,010 definitions. We employ a range of traditional ML, deep learning (DL), and a transformer-based model (i.e., BERT-base) for automated classification of NFRs, evaluating performance across cross-domain and blockchain-specific NFRs. Results highlight that domain-aware adaptation significantly enhances classification accuracy, with traditional ML and DL models showing strong performance on blockchain requirements. This work contributes a publicly available, context-rich dataset and provides empirical insights into the effectiveness of NLP-based NFR classification in both general and blockchain-specific settings.</p>","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"2025 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/sfw2/9996509","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145316635","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":"Design of Minimal Spanning Tree and Analytic Hierarchical Process (SAHP) Based Hybrid Technique for Software Requirements Prioritization","authors":"Muhammad Yaseen,&nbsp;Esraa Ali,&nbsp;Nadeem Sarwar,&nbsp;Leila Jamel,&nbsp;Irfanud Din,&nbsp;Farrukh Yuldashev,&nbsp;Foongli Law","doi":"10.1049/sfw2/8819735","DOIUrl":"10.1049/sfw2/8819735","url":null,"abstract":"<p>Prioritizing software requirements in a sustainable manner can significantly contribute to the success of a software project, adding substantial value throughout its development lifecycle. Analytic hierarchical process (AHP) is considered to yield more accurate prioritized results, but due to high pairwise comparisons, it is not considered to be scalable for prioritization of high number of requirements. To address scalability issue, a hybrid approach of minimal spanning trees (MSTs) and AHP, called as spanning tree and AHP (SAHP), is designed for prioritizing large set of functional requirements (FRs) with fewer comparisons, and thus scalability issue is solved. In this research, on-demand open object (ODOO) enterprise resource planning (ERP) system FRs are prioritized, and the results are compared with AHP. The results of the case study proved that SAHP is more scalable that can prioritize any type of requirement with only <i>n</i>–1 pairs of requirements. Total FRs considered for case from ODOO were 100, where 18 spanning trees were constructed from it. With only 90 pairwise comparisons, these FRs were prioritized with more consistency compared to AHP. Total pairwise comparisons with AHP reach 4950, which is 55 times more compared with SAHP. Consistency of results is measured from average consistency index (CI) value, which was below 0.1. The consistency ratio (CR) value below 0.1 shows results are consistent and acceptable.</p>","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"2025 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/sfw2/8819735","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145272241","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":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148072285","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":"Word-Level Nonequivalence and Translation Strategies in English–Chinese Translation Based on Image Processing Technology","authors":"Haihua Tu,&nbsp;Lingbo Han","doi":"10.1049/sfw2/5511556","DOIUrl":"10.1049/sfw2/5511556","url":null,"abstract":"<p>The process of translation is the process of accurately understanding the original work. It uses other languages to express the meaning of the original work and reproduce the original text in other languages. However, translation equivalence is a relative term, and there is no complete equivalence. In translation practice, translators often face different inequalities. The inequality of lexical levels means that no words matching the original text can be found in the specified language. These equivalence relationships are different to some extent, which brings great difficulties to translation. This paper first made a relevant interpretation of the common phenomenon of word-level inequality in English–Chinese translation, and analyzed the differences of source language concepts in translation. It made a relevant study on the lexical inequality in English–Chinese translation, and described the cultural inequality. After that, this paper studied and planned the equivalence requirements and solutions in English–Chinese translation. It was proposed to strengthen the learning and understanding of Chinese and Western cultures, and to translate based on the cultural characteristics of different regions. It was also proposed that transliteration should be used to ensure the accuracy of English–Chinese translation and reduce the nonequivalence between word levels. Subsequently, this paper introduced image processing technology into translation and used image processing technology to strengthen translation strategies. It also focused on analyzing the main types of image processing technology and used image processing technology to fully understand the translation process. It was necessary to use image processing technology to correctly express the translation. Finally, image processing technology was used to strengthen translation strategies and research. According to experiments and surveys, the use of image processing technology to create new English–Chinese translation strategies could effectively improve the satisfaction of 18% of translators.</p>","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"2025 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/sfw2/5511556","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145224163","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":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148100173","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}