IET Software最新文献

Retraction: [Yan Meng, Yunming Wu, Track and field competition data collection using a vision sensor system and machine learning, IET Software 2023 (https://doi.org/10.1049/sfw2.12086)].

The above article from IET Software, published online on 11 January 2023 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the Editor-in-Chief, Hana Chockler, the Institution of Engineering and Technology (the IET) and John Wiley and Sons Ltd.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.

Retraction: [Yanfeng Ma, Investigating the interactive audio-visual course mode for college English using virtual reality and artificial intelligence, IET Software 2023 (https://doi.org/10.1049/sfw2.12088)].

The above article from IET Software, published online on 9 January 2023 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the Editor-in-Chief, Hana Chockler, the Institution of Engineering and Technology (the IET) and John Wiley and Sons Ltd.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.

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article from IET Software, published online on 6 January 2023 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the Editor-in-Chief, Hana Chockler, the Institution of Engineering and Technology (the IET) and John Wiley and Sons Ltd.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.

The state space explosion restricts the error detection of concurrent software. The abstraction can provide a solution to avoid state space explosion, but it is easy to ignore important details, resulting in inaccurate detection results. This paper proposes a methodology of fine-coarse-grained automatic modelling for Java source programs. By the principle that the execution details of property-unchecked, non-interactive, and unrelated statements do not affect the model checking results, we model coarse-grained model fragments for such statements, while fine-grained model fragments for property-checked, interactive, and related statements. Our method reduces the model and state space and ensures the error detection of the source program based on model checking. Moreover, we prove the equivalence of the fine-grained model, the coarse-grained model, and the program. Finally, this paper gives an experiment to verify the effectiveness of the proposed method.

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.

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.

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.

Retraction: [Prabhdeep Singh, Rajbir Kaur, A software-based framework for the development of smart healthcare systems using fog computing, IET Software 2022 (https://doi.org/10.1049/sfw2.12081)].

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.