IET Software最新文献_第10页

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

Retracted: Self-attention Bi-RNN for developer emotion recognition based on EEG 基于EEG的开发者情绪识别的自注意双RNN

IF 1.6 4区计算机科学

IET Software Pub Date : 2022-12-23 DOI: 10.1049/sfw2.12080

Yingdong Wang, Yuhui Zheng, Lu Cao, Zhiling Zhang, Qunsehng Ruan, Qingfeng Wu

{"title":"Retracted: Self-attention Bi-RNN for developer emotion recognition based on EEG","authors":"Yingdong Wang, Yuhui Zheng, Lu Cao, Zhiling Zhang, Qunsehng Ruan, Qingfeng Wu","doi":"10.1049/sfw2.12080","DOIUrl":"https://doi.org/10.1049/sfw2.12080","url":null,"abstract":"Retraction: [Yingdong Wang, Yuhui Zheng, Lu Cao, Zhiling Zhang, Qunsehng Ruan, Qingfeng Wu, Self-attention Bi-RNN for developer emotion recognition based on EEG, IET Software 2022 (https://doi.org/10.1049/sfw2.12080)].The above article from IET Software, published online on 23 December 2022 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":"620-631"},"PeriodicalIF":1.6,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/sfw2.12080","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50141552","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

DOOSRA—Distributed Object-Oriented Software Restructuring Approach using DIM-K-means and MAD-based ENRNN classifier DOOSRA——基于DIM-K和MAD的ENRNN分类器的分布式面向对象软件重构方法

IF 1.6 4区计算机科学

IET Software Pub Date : 2022-12-21 DOI: 10.1049/sfw2.12076

G. Sudhakar, S. Nithiyanandam

{"title":"DOOSRA—Distributed Object-Oriented Software Restructuring Approach using DIM-K-means and MAD-based ENRNN classifier","authors":"G. Sudhakar, S. Nithiyanandam","doi":"10.1049/sfw2.12076","DOIUrl":"https://doi.org/10.1049/sfw2.12076","url":null,"abstract":"There exists a need to generate well-designed software systems because of the extensive adoption of object-oriented programming in software growth. Thus, the total software maintenance cost is decreased and the component's reusability is augmented. However, the software system's internal structure worsens owing to extended maintenance activities. For enhancing the system's overall internal structure without varying its external behaviour, restructuring is an extensively utilised solution in this circumstance. Thus, utilising the Deterministic Initialisation Method based K-Means (DIM-K-Means) and Median Absolute Deviation-based Elastic Net Regulariser Neural Network (MAD-ENRNN) classifier, a framework called Distributed Object-Oriented (DOO) software restructuring model is created by the study. Five steps are undertaken by the developed framework. Centred on source code along with change history, the interactions amongst the classes are initially pre-processed where the dependencies of disparate classes are detected and formulated into a graphical structure. After that, from the graph, the extraction of significant features is done. Utilising a multi variant objective-based Aquila optimiser, the most pertinent features are selected as of the extracted features. Next, for minimising the complexity, the selected features are created into clusters. Then, the formed clusters are offered to the classifier named MAD-ENRNN. The DOO software is effectively restructured by MAD-ENRNN. The proposed methodology's performance is contrasted with the prevailing systems in an experimental evaluation. The outcomes displayed that the proposed framework is capable of restructuring the DOO software with improved accuracy of 9.94% when analogised to the top-notch methods.","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"17 1","pages":"23-36"},"PeriodicalIF":1.6,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/sfw2.12076","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50139738","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

Realising benefits in public IT projects: A multiple case study 实现公共IT项目的效益：一个多案例研究

IF 1.6 4区计算机科学

IET Software Pub Date : 2022-12-13 DOI: 10.1049/sfw2.12079

Knut Kjetil Holgeid, Magne Jørgensen, Gro Holst Volden, Helene Berg

{"title":"Realising benefits in public IT projects: A multiple case study","authors":"Knut Kjetil Holgeid, Magne Jørgensen, Gro Holst Volden, Helene Berg","doi":"10.1049/sfw2.12079","DOIUrl":"https://doi.org/10.1049/sfw2.12079","url":null,"abstract":"Information Technology (IT) investments in the public sector are large, and it is essential that they lead to benefits for the organisations themselves and wider society. While there is evidence suggesting a positive connection between the existence of benefits management practices and the realisation of benefits, less is known about how to implement such practices effectively. The aim of this paper is to provide insights into when benefits are most likely to be realised, and how benefits management practices and roles should be implemented, in order to have a positive effect on the success of projects in terms of realising benefits. We collected data relating to 10 public IT projects in Norway. For each project, information on benefits management was collected from project documents by interviewing the project owners and benefits owners and via follow-up surveys. The benefits with the highest degree of realisation were those internal to the organisation, while those with the lowest degree were societal benefits. Projects assessed as having more specific, measurable, accountable, and realistically planned benefits were more successful in terms of realising benefits. Benefit owners were most effective when they were able to attract attention to the benefits to be realised, had a strong mandate, and had the domain expertise.","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"17 1","pages":"37-54"},"PeriodicalIF":1.6,"publicationDate":"2022-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/sfw2.12079","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50150281","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