IET Software最新文献_第10页

Retracted: Track and field competition data collection using a vision sensor system and machine learning 收回：使用视觉传感器系统和机器学习收集田径比赛数据

IF 1.6 4区计算机科学

IET Software Pub Date : 2023-01-11 DOI: 10.1049/sfw2.12086

Yan Meng, Yunming Wu

引用次数: 0

Retracted: Investigating the interactive audio-visual course mode for college English using virtual reality and artificial intelligence 收回：利用虚拟现实和人工智能探索大学英语互动视听课程模式

IF 1.6 4区计算机科学

IET Software Pub Date : 2023-01-09 DOI: 10.1049/sfw2.12088

Yanfeng Ma

引用次数: 0

Retracted: Scientific programming using optimized machine learning techniques for software fault prediction to improve software quality 收回：使用优化的机器学习技术进行软件故障预测以提高软件质量的科学编程

IF 1.6 4区计算机科学

IET Software Pub Date : 2023-01-06 DOI: 10.1049/sfw2.12091

Muhammad Shafiq, Fatemah H. Alghamedy, Nasir Jamal, Tahir Kamal, Yousef Ibrahim Daradkeh, Mohammad Shabaz

{"title":"Retracted: Scientific programming using optimized machine learning techniques for software fault prediction to improve software quality","authors":"Muhammad Shafiq, Fatemah H. Alghamedy, Nasir Jamal, Tahir Kamal, Yousef Ibrahim Daradkeh, Mohammad Shabaz","doi":"10.1049/sfw2.12091","DOIUrl":"https://doi.org/10.1049/sfw2.12091","url":null,"abstract":"Retraction: [Muhammad Shafiq, Fatemah H. Alghamedy, Nasir Jamal, Tahir Kamal, Yousef Ibrahim Daradkeh, Mohammad Shabaz, Scientific programming using optimized machine learning techniques for software fault prediction to improve software quality, IET Software 2023 (https://doi.org/10.1049/sfw2.12091)].The above article from IET Software, published online on 6 January 2023 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the Editor-in-Chief, Hana Chockler, the Institution of Engineering and Technology (the IET) and John Wiley and Sons Ltd. This article was published as part of a Guest Edited special issue. Following an investigation, the IET and the journal have determined that the article was not reviewed in line with the journal’s peer review standards and there is evidence that the peer review process of the special issue underwent systematic manipulation. Accordingly, we cannot vouch for the integrity or reliability of the content. As such we have taken the decision to retract the article. The authors have been informed of the decision to retract.","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"17 4","pages":"694-704"},"PeriodicalIF":1.6,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/sfw2.12091","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50133403","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}

引用次数: 0

Scientific programming using optimized machine learning techniques for software fault prediction to improve software quality 利用优化的机器学习技术进行软件故障预测的科学编程，以提高软件质量

IF 1.6 4区计算机科学

IET Software Pub Date : 2023-01-06 DOI: 10.1049/sfw2.12091

Muhammad Shafiq, Fatemah H. Alghamedy, Nasir Jamal, Tahir Kamal, Y. Daradkeh, Mohammad Shabaz

引用次数: 0

Concurrent software fine-coarse-grained automatic modelling by Coloured Petri Nets for model checking 基于有色Petri网的并行软件粗粒度自动建模

IF 1.6 4区计算机科学

IET Software Pub Date : 2022-12-30 DOI: 10.1049/sfw2.12084

Wenjie Zhong, Jian-tao Zhou, Tao Sun

引用次数: 0

A model for developing dependable systems using a component-based software development approach (MDDS-CBSD) 使用基于组件的软件开发方法开发可靠系统的模型（MDDS-CBSD）

IF 1.6 4区计算机科学

IET Software Pub Date : 2022-12-30 DOI: 10.1049/sfw2.12085

Hasan Kahtan, Mansoor Abdulhak, Ahmad Salah Al-Ahmad, Yehia Ibrahim Alzoubi

{"title":"A model for developing dependable systems using a component-based software development approach (MDDS-CBSD)","authors":"Hasan Kahtan, Mansoor Abdulhak, Ahmad Salah Al-Ahmad, Yehia Ibrahim Alzoubi","doi":"10.1049/sfw2.12085","DOIUrl":"https://doi.org/10.1049/sfw2.12085","url":null,"abstract":"Component-based software development (CBSD) is an emerging technology that integrates existing software components to swiftly develop and deploy big and complex software systems with little engineering effort, money, and time. CBSD, on the other hand, has difficulties with security trust, particularly dependability. When a system provides the desired outcomes while causing no harm to the environment, it is said to be dependable. Dependability encompasses several attributes, including availability, confidentiality, integrity, reliability, safety, and maintainability. Developing dependable component software is achieved by embedding dependability attributes in CBSD. Thus, the CBSD model must address the dependability attributes. Hence, the objectives of this work are: (1) to propose a model for developing a dependable system using component-based software development approach (hereafter the model is referred to as MDDS-CBSD), which aims to mitigate software component vulnerabilities, and (2) to assess the proposed model. The best-practice method was used to frame the CBSD architecture phases and processes, as well as embed the six dependability attributes. The MDDS-CBSD architecture was evaluated using expert opinion. The MDDS-CBSD was also used to develop an information and communications technology (ICT) portal using an empirical study method. Vulnerability Assessment Tools were used to assess the developed ICT portal's dependability. The MDDS-CBSD may be used to create web application systems and to protect them from attacks. Model developers may use CBSD to describe and assess dependability attributes at any point during the model development process. The reliability of this model can also let companies utilise CBSD with confidence.","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"17 1","pages":"76-92"},"PeriodicalIF":1.6,"publicationDate":"2022-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/sfw2.12085","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50155694","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}

引用次数: 2

Android malware detection via efficient application programming interface call sequences extraction and machine learning classifiers 通过高效的应用程序编程接口调用序列提取和机器学习分类器检测安卓恶意软件

IF 1.6 4区计算机科学

IET Software Pub Date : 2022-12-26 DOI: 10.1049/sfw2.12083

Tanjie Wang, Yueshen Xu, Xinkui Zhao, Zhiping Jiang, Rui Li

{"title":"Android malware detection via efficient application programming interface call sequences extraction and machine learning classifiers","authors":"Tanjie Wang, Yueshen Xu, Xinkui Zhao, Zhiping Jiang, Rui Li","doi":"10.1049/sfw2.12083","DOIUrl":"https://doi.org/10.1049/sfw2.12083","url":null,"abstract":"Malware detection is an important task for the ecosystem of mobile applications (APPs), especially for the Android ecosystem, and is vital to guarantee the user experience of Android APPs. There have been some exiting methods trying to solve the problem of malware detection, but the methods suffer from several defects, such as high time complexity and mediocre accuracy, which seriously decrease the practicability of existing methods. To solve these problems, in this study, we propose a novel Android malware detection framework, where we contribute an efficient Application Programming Interface (API) call sequences extraction algorithm and an investigation of different types of classifiers. In API call sequences extraction, we propose an algorithm for transforming the function call graph from a multigraph into a directed simple graph, which successfully avoids the unnecessary repetitive path searching. We also propose a pruning search, which further reduces the number of paths to be searched. Our algorithm greatly reduces the time complexity. We generate the transition matrix as classification features and investigate three types of machine learning classifiers to complete the malware detection task. The experiments are performed on real-world Android Packages (APKs), and the results demonstrate that our method significantly reduces the running time and produces high detection accuracy.","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"17 4","pages":"348-361"},"PeriodicalIF":1.6,"publicationDate":"2022-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/sfw2.12083","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50144704","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}

引用次数: 0

A systematic mapping study on machine learning methodologies for requirements management 用于需求管理的机器学习方法的系统映射研究

IF 1.6 4区计算机科学

IET Software Pub Date : 2022-12-24 DOI: 10.1049/sfw2.12082

Chi Xu, Yuanbang Li, Bangchao Wang, Shi Dong

{"title":"A systematic mapping study on machine learning methodologies for requirements management","authors":"Chi Xu, Yuanbang Li, Bangchao Wang, Shi Dong","doi":"10.1049/sfw2.12082","DOIUrl":"https://doi.org/10.1049/sfw2.12082","url":null,"abstract":"Requirements management (RM) plays an important role in requirements engineering. The development of machine learning (ML) is in full swing, and many ML software management techniques had been used to improve the performance of RM methods. However, as no research study is known that exists systematically to summarise the ML methods used in RM. To fill this gap, this paper adopts the systematic mapping study to survey the state-of-the-art ML methods for RM primary studies and were finally selected in this mapping, which was published on 36 conferences and journals. The 24 factors affecting the ML method of RM are determined, of which 9, 11 and 4 are the three parts of RM, namely requirements baseline maintenance, requirements traceability and requirements change management separately. The 18 objectives of the ML method for RM are summarised, of which 6, 7 and 5 are the three parts of RM. The eight ML methods used in RM and their time sequence are summarised. The 18 evaluation indexes for RM in the ML method are determined, and the performance of these methods on these parameters is analysed. The research direction of this paper is of great significance to the research of researchers in demand management.","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"17 4","pages":"405-423"},"PeriodicalIF":1.6,"publicationDate":"2022-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/sfw2.12082","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50142330","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}

引用次数: 0

Are Infinite-Failure NHPP-Based Software Reliability Models Useful? 基于无限故障nhpp的软件可靠性模型有用吗?

IF 1.6 4区计算机科学

IET Software Pub Date : 2022-12-23 DOI: 10.3390/software2010001

Siqiao Li, T. Dohi, H. Okamura

{"title":"Are Infinite-Failure NHPP-Based Software Reliability Models Useful?","authors":"Siqiao Li, T. Dohi, H. Okamura","doi":"10.3390/software2010001","DOIUrl":"https://doi.org/10.3390/software2010001","url":null,"abstract":"In the literature, infinite-failure software reliability models (SRMs), such as Musa-Okumoto SRM (1984), have been demonstrated to be effective in quantitatively characterizing software testing processes and assessing software reliability. This paper primarily focuses on the infinite-failure (type-II) non-homogeneous Poisson process (NHPP)-based SRMs and evaluates the performances of these SRMs comprehensively by comparing with the existing finite-failure (type-I) NHPP-based SRMs. In more specific terms, to describe the software fault-detection time distribution, we postulate 11 representative probability distribution functions that can be categorized into the generalized exponential distribution family and the extreme-value distribution family. Then, we compare the goodness-of-fit and predictive performances with the associated 11 type-I and type-II NHPP-based SRMs. In numerical experiments, we analyze software fault-count data, collected from 16 actual development projects, which are commonly known in the software industry as fault-count time-domain data and fault-count time-interval data (group data). The maximum likelihood method is utilized to estimate the model parameters in both NHPP-based SRMs. In a comparison of the type-I with the type-II, it is shown that the type-II NHPP-based SRMs could exhibit better predictive performance than the existing type-I NHPP-based SRMs, especially in the early stage of software testing.","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"1 1","pages":""},"PeriodicalIF":1.6,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88562197","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}

引用次数: 1

Retracted: A software-based framework for the development of smart healthcare systems using fog computing 收回：使用雾计算开发智能医疗系统的基于软件的框架

IF 1.6 4区计算机科学

IET Software Pub Date : 2022-12-23 DOI: 10.1049/sfw2.12081

Prabhdeep Singh, Rajbir Kaur

引用次数: 1