Abstract Virtual reality (VR) technology is often used in video or image processing to create a virtual environment close to reality. Computer vision (CV) can use computer technology to simulate human thinking for target action recognition, tracking and calculation. The relevant public information in the VR application field was sorted out, and VR applications in different fields were obtained.This paper aims to investigate a technique for motion recognition of dance videos by combining VR and CV. This paper proposes the use of CV for dance movement analysis and action recognition, the use of 3D VR for video image processing, the use of VR glasses for users to watch images and videos, and the simulation of teaching scenarios for dance learning. And the program is actually tested to understand its actual effect. The test results show that the system power consumption of the scheme designed in this paper is about 50% of that of the traditional scheme.The running time is about 1/2 of the traditional scheme, that is, the calculation speed is about 2 times that of the traditional scheme. Moreover, it can perform action recognition for most dances, and the recognition accuracy is about 20% higher than the traditional one, reaching a recognition accuracy rate of about 95%. This article is protected by copyright. All rights reserved.

In the fast‐changing landscape of global mobility, the need for secure, efficient, and interoperable visa, passport, and immigration verification systems has never been higher. Traditional systems are inefficient, have security vulnerabilities, and exhibit poor interoperability. This study introduces a novel approach for the blockchain solution in passport verification inefficiencies‐BLOCKVISA. BLOCKVISA, in its nature, uses decentralized and immutable blockchain technology to make the system more secure, automate the verification process, and ensure data sharing frictionlessly across jurisdictions. Core components of the system include smart contracts developed in Solidity, a user interface (UI) created with Next.js, and integration with MetaMask and Web3.js for safe interactions with the blockchain. Rigorous testing was done using Mocha, and more intensive benchmarking was done using Hyperledger Caliper against Ganache, Hyperledger Besu, as well as all the test networks, that is, Rinkeby, Ropsten, Goerli, Kovan, among others. Experiments showed that with BLOCKVISA, high throughput and low latency in controlled settings can be achieved, with almost perfect success rates being recorded. It also gave insights into how it would perform even better when deployed on a public network. The article undertakes a comparative analysis of performance metrics, brings out robust security features of the system, and discusses its scalability and feasibility for real‐world implementation. By integrating advanced blockchain technology into the visa, passport, and immigration verification process, BLOCKVISA sets a new standard for global mobility solutions, promising enhanced efficiency, security, and interoperability.

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 p ‐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.

In today’s rapidly changing software development landscape, ensuring code quality is essential to reliability, maintainability, and security among other aspects. Identifying code quality issues can be tackled; however, implementing code quality improvements can be a complex and time‐consuming task. To address this problem, we present a novel methodology designed to assist developers by suggesting alternative code snippets that not only match the functionality of the original code but also improve its quality based on predefined metrics. Our system is based on a language‐agnostic approach that allows the analysis of code snippets written in different programming languages. It employs advanced techniques to assess functional similarity and evaluates syntactic similarity, suggesting alternatives that minimize the need for extensive modification. The evaluation of our system on multiple axes demonstrates the effectiveness of our approach in providing usable code alternatives that are both functionally equivalent and syntactically similar to the original snippets, while significantly improving quality metrics. We argue that our methodology and tool can be valuable for the software engineering community, bridging the gap between the identification of code quality problems and the implementation of practical solutions that improve software quality.

Abstract Corona Virus disease 2019 (COVID‐19) has shattered people's daily lives and is spreading rapidly across the globe. Existing non‐pharmaceutical intervention solutions often require timely and precise selection of small areas of people for containment or even isolation. Although such containment has been successful in stopping or mitigating the spread of COVID‐19 in some countries, it has been criticized as inefficient or ineffective, because of the time‐delayed and sophisticated nature of the statistics on determining cases. To address these concerns, we propose a GSA‐ELM model based on a gravitational search algorithm to forecast the global number of active cases of COVID‐19. The model employs the gravitational search algorithm, which utilises the gravitational law between two particles to guide the motion of each particle to optimise the search for the global optimal solution, and utilises an extreme learning machine to address the effects of nonlinearity in the number of active cases. Extensive experiments are conducted on the statistical COVID‐19 dataset from Johns Hopkins University, the MAPE of the authors’ model is 7.79%, which corroborates the superiority of the model to state‐of‐the‐art methods.

Abstract Requirements engineering is a fundamental process in software development phases. At the same time, it is a difficult phase and exposed many ethical violations. The main purpose is proposing an ethical framework for software requirements engineering that addresses the identified concerns. These concerns include problems associated with a knowledge gap, requirements identification, quality‐related concerns, unwillingness to give requirements, and practicing forbidden activities. These concerns are grouped into a category as the proposed framework components. Each of the categories encompasses more than one problem domain. The proposed framework suggests resolving mechanisms as collections of clauses for each of those concerns. An expert evaluation technique is used to validate the proposed framework. The experts are purposefully selected from software industries and institutions. Questionnaires and focus group discussions were used as data‐gathering tools for the validation of the proposed framework. The validity (face validity, content validity, and construct validity) and the reliability of the proposed framework were checked. The evaluation results show that the proposed framework has an acceptable range of validity and reliability. The proposed framework can be used as a guideline for software engineers to minimise the occurrence of those identified concerns during the requirements engineering process.

Abstract Mobile apps with tested Graphical User Interface (GUI) tend to have higher downloads in the apps store. In recent years, few efforts were made to analyse the research community and research status of the literature for GUI testing on mobile apps, which brings an obstacle to characterise and understand this field. In this study, the authors propose a systematic mapping study to gain insights into the field. First, the authors conduct an extensive search of relevant literature over seven popular digital libraries. From 4427 candidate studies, 114 primary studies published between January 2011 and September 2022 were selected. Next, the authors analyse these primary studies from the perspectives of bibliometric and qualitative analysis. For the bibliometric analysis, first, the authors analyse the popular research topics and their relationships. Second, the authors study the authors' community. For the qualitative analysis, the authors analyse the objectives, approaches and evaluation metrics employed in these primary studies. Their investigation reports several major findings: (1) there are relatively more studies on two topics, that is, test case generation and the automated test; (2) the most productive authors tend to collaborate and often have relatively broad research interests; (3) the functionality is the main objective of GUI testing; the model‐based approach is the most widely used.

Abstract Retraction: [Taixiang Tang, Huihua Zhang, Data exploration of college students' humanistic literacy in the internet age under the big data environment, IET Software 2023 (https://doi.org/10.1049/sfw2.12087)].The above article from IET Software, published online on 20 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.

Abstract Retraction: [Nitin Nandkumar Sakhare, Imambi S. Shaik, Suman Saha, Prediction of stock market movement via technical analysis of stock data stored on blockchain using novel History Bits based machine learning algorithm, IET Software 2023 ( https://doi.org/10.1049/sfw2.12092 )]. The above article from IET Software , published online on 12 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.

In the realm of software project management, predicting and mitigating risks are pivotal for successful project execution. Traditional risk assessment methods have limitations in handling complex and dynamic software projects. This study presents a novel approach that leverages artificial neural networks (ANNs) to enhance risk prediction accuracy. We utilize historical project data, encompassing project complexity, financial factors, performance metrics, schedule adherence, and user‐related variables, to train the ANN model. Our approach involves optimizing the ANN architecture, with various configurations tested to identify the most effective setup. We compare the performance of mean squared error (MSE) and mean absolute error (MAE) as error functions and find that MAE yields superior results. Furthermore, we demonstrate the effectiveness of our model through comprehensive risk assessment. We predict both the overall project risk and individual risk factors, providing project managers with a valuable tool for risk mitigation. Validation results confirm the robustness of our approach when applied to previously unseen data. The achieved accuracy of 97.12% (or 99.12% with uncertainty consideration) underscores the potential of ANNs in risk management. This research contributes to the software project management field by offering an innovative and highly accurate risk assessment model. It empowers project managers to make informed decisions and proactively address potential risks, ultimately enhancing project success.