Accurate software cost estimation in Global Software Development(GSD)remains challenging due to reliance on historical data and expert judgments.Traditional models,such as the Constructive Cost Model(COCOMO II),rely h...Accurate software cost estimation in Global Software Development(GSD)remains challenging due to reliance on historical data and expert judgments.Traditional models,such as the Constructive Cost Model(COCOMO II),rely heavily on historical and accurate data.In addition,expert judgment is required to set many input parameters,which can introduce subjectivity and variability in the estimation process.Consequently,there is a need to improve the current GSD models to mitigate reliance on historical data,subjectivity in expert judgment,inadequate consideration of GSD-based cost drivers and limited integration of modern technologies with cost overruns.This study introduces a novel hybrid model that synergizes the COCOMO II with Artificial Neural Networks(ANN)to address these challenges.The proposed hybrid model integrates additional GSD-based cost drivers identified through a systematic literature review and further vetted by industry experts.This article compares the effectiveness of the proposedmodelwith state-of-the-artmachine learning-basedmodels for software cost estimation.Evaluating the NASA 93 dataset by adopting twenty-six GSD-based cost drivers reveals that our hybrid model achieves superior accuracy,outperforming existing state-of-the-artmodels.The findings indicate the potential of combining COCOMO II,ANN,and additional GSD-based cost drivers to transform cost estimation in GSD.展开更多
The use of interactive audience software,such as audience response systems(ARS),in medical education has become increasingly popular in recent years.This technology allows instructors to engage students in real time,e...The use of interactive audience software,such as audience response systems(ARS),in medical education has become increasingly popular in recent years.This technology allows instructors to engage students in real time,encouraging active participation and promoting effective learning.The benefits of interactive audience software in medical education include increased student engagement,promotion of active learning,and enhanced learning outcomes.However,there are also several challenges to its implementation,including technical difficulties,careful planning and preparation,over-reliance on technology,and ethical concerns related to privacy and data security.The cost of implementing interactive audience software may also be a barrier for some institutions.This paper specifically reviews six interactive software platforms,including Socrative,Quizizz,Pear Deck,Slido,Wooclap and ClassPoint.These platforms allow for real-time assessment of student understanding,feedback,and participation.They also enable instructors to adjust their teaching strategies based on student responses and feedback.Overall,interactive audience software has shown great potential to enhance learning and engagement in medical education.It is important for instructors to carefully consider the benefits and challenges of its implementation.While the cost of implementing interactive audience software may be a barrier for some institutions,there are free and low-cost options available.展开更多
When data privacy is imposed as a necessity,Federated learning(FL)emerges as a relevant artificial intelligence field for developing machine learning(ML)models in a distributed and decentralized environment.FL allows ...When data privacy is imposed as a necessity,Federated learning(FL)emerges as a relevant artificial intelligence field for developing machine learning(ML)models in a distributed and decentralized environment.FL allows ML models to be trained on local devices without any need for centralized data transfer,thereby reducing both the exposure of sensitive data and the possibility of data interception by malicious third parties.This paradigm has gained momentum in the last few years,spurred by the plethora of real-world applications that have leveraged its ability to improve the efficiency of distributed learning and to accommodate numerous participants with their data sources.By virtue of FL,models can be learned from all such distributed data sources while preserving data privacy.The aim of this paper is to provide a practical tutorial on FL,including a short methodology and a systematic analysis of existing software frameworks.Furthermore,our tutorial provides exemplary cases of study from three complementary perspectives:i)Foundations of FL,describing the main components of FL,from key elements to FL categories;ii)Implementation guidelines and exemplary cases of study,by systematically examining the functionalities provided by existing software frameworks for FL deployment,devising a methodology to design a FL scenario,and providing exemplary cases of study with source code for different ML approaches;and iii)Trends,shortly reviewing a non-exhaustive list of research directions that are under active investigation in the current FL landscape.The ultimate purpose of this work is to establish itself as a referential work for researchers,developers,and data scientists willing to explore the capabilities of FL in practical applications.展开更多
The advent of Industry 4.0 has compelled businesses to adopt digital approaches that combine software toenhance production efficiency. In this rapidly evolving market, software development is an ongoing process thatmu...The advent of Industry 4.0 has compelled businesses to adopt digital approaches that combine software toenhance production efficiency. In this rapidly evolving market, software development is an ongoing process thatmust be tailored to meet the dynamic needs of enterprises. However, internal research and development can beprohibitively expensive, driving many enterprises to outsource software development and upgrades to externalservice providers. This paper presents a software upgrade outsourcing model for enterprises and service providersthat accounts for the impact of market fluctuations on software adaptability. To mitigate the risk of adverseselection due to asymmetric information about the service provider’s cost and asymmetric information aboutthe enterprise’s revenues, we propose pay-per-time and revenue-sharing contracts in two distinct informationasymmetry scenarios. These two contracts specify the time and transfer payments for software upgrades. Througha comparative analysis of the optimal solutions under the two contracts and centralized decision-making withfull-information, we examine the characteristics of the solutions under two information asymmetry scenarios andanalyze the incentive effects of the two contracts on the various stakeholders. Overall, our study offers valuableinsights for firms seeking to optimize their outsourcing strategies and maximize their returns on investment insoftware upgrades.展开更多
The purpose of software defect prediction is to identify defect-prone code modules to assist software quality assurance teams with the appropriate allocation of resources and labor.In previous software defect predicti...The purpose of software defect prediction is to identify defect-prone code modules to assist software quality assurance teams with the appropriate allocation of resources and labor.In previous software defect prediction studies,transfer learning was effective in solving the problem of inconsistent project data distribution.However,target projects often lack sufficient data,which affects the performance of the transfer learning model.In addition,the presence of uncorrelated features between projects can decrease the prediction accuracy of the transfer learning model.To address these problems,this article propose a software defect prediction method based on stable learning(SDP-SL)that combines code visualization techniques and residual networks.This method first transforms code files into code images using code visualization techniques and then constructs a defect prediction model based on these code images.During the model training process,target project data are not required as prior knowledge.Following the principles of stable learning,this paper dynamically adjusted the weights of source project samples to eliminate dependencies between features,thereby capturing the“invariance mechanism”within the data.This approach explores the genuine relationship between code defect features and labels,thereby enhancing defect prediction performance.To evaluate the performance of SDP-SL,this article conducted comparative experiments on 10 open-source projects in the PROMISE dataset.The experimental results demonstrated that in terms of the F-measure,the proposed SDP-SL method outperformed other within-project defect prediction methods by 2.11%-44.03%.In cross-project defect prediction,the SDP-SL method provided an improvement of 5.89%-25.46% in prediction performance compared to other cross-project defect prediction methods.Therefore,SDP-SL can effectively enhance within-and cross-project defect predictions.展开更多
Software project outcomes heavily depend on natural language requirements,often causing diverse interpretations and issues like ambiguities and incomplete or faulty requirements.Researchers are exploring machine learn...Software project outcomes heavily depend on natural language requirements,often causing diverse interpretations and issues like ambiguities and incomplete or faulty requirements.Researchers are exploring machine learning to predict software bugs,but a more precise and general approach is needed.Accurate bug prediction is crucial for software evolution and user training,prompting an investigation into deep and ensemble learning methods.However,these studies are not generalized and efficient when extended to other datasets.Therefore,this paper proposed a hybrid approach combining multiple techniques to explore their effectiveness on bug identification problems.The methods involved feature selection,which is used to reduce the dimensionality and redundancy of features and select only the relevant ones;transfer learning is used to train and test the model on different datasets to analyze how much of the learning is passed to other datasets,and ensemble method is utilized to explore the increase in performance upon combining multiple classifiers in a model.Four National Aeronautics and Space Administration(NASA)and four Promise datasets are used in the study,showing an increase in the model’s performance by providing better Area Under the Receiver Operating Characteristic Curve(AUC-ROC)values when different classifiers were combined.It reveals that using an amalgam of techniques such as those used in this study,feature selection,transfer learning,and ensemble methods prove helpful in optimizing the software bug prediction models and providing high-performing,useful end mode.展开更多
The healthcare sector holds valuable and sensitive data.The amount of this data and the need to handle,exchange,and protect it,has been increasing at a fast pace.Due to their nature,software-defined networks(SDNs)are ...The healthcare sector holds valuable and sensitive data.The amount of this data and the need to handle,exchange,and protect it,has been increasing at a fast pace.Due to their nature,software-defined networks(SDNs)are widely used in healthcare systems,as they ensure effective resource utilization,safety,great network management,and monitoring.In this sector,due to the value of thedata,SDNs faceamajor challengeposed byawide range of attacks,such as distributed denial of service(DDoS)and probe attacks.These attacks reduce network performance,causing the degradation of different key performance indicators(KPIs)or,in the worst cases,a network failure which can threaten human lives.This can be significant,especially with the current expansion of portable healthcare that supports mobile and wireless devices for what is called mobile health,or m-health.In this study,we examine the effectiveness of using SDNs for defense against DDoS,as well as their effects on different network KPIs under various scenarios.We propose a threshold-based DDoS classifier(TBDC)technique to classify DDoS attacks in healthcare SDNs,aiming to block traffic considered a hazard in the form of a DDoS attack.We then evaluate the accuracy and performance of the proposed TBDC approach.Our technique shows outstanding performance,increasing the mean throughput by 190.3%,reducing the mean delay by 95%,and reducing packet loss by 99.7%relative to normal,with DDoS attack traffic.展开更多
The development of defect prediction plays a significant role in improving software quality. Such predictions are used to identify defective modules before the testing and to minimize the time and cost. The software w...The development of defect prediction plays a significant role in improving software quality. Such predictions are used to identify defective modules before the testing and to minimize the time and cost. The software with defects negatively impacts operational costs and finally affects customer satisfaction. Numerous approaches exist to predict software defects. However, the timely and accurate software bugs are the major challenging issues. To improve the timely and accurate software defect prediction, a novel technique called Nonparametric Statistical feature scaled QuAdratic regressive convolution Deep nEural Network (SQADEN) is introduced. The proposed SQADEN technique mainly includes two major processes namely metric or feature selection and classification. First, the SQADEN uses the nonparametric statistical Torgerson–Gower scaling technique for identifying the relevant software metrics by measuring the similarity using the dice coefficient. The feature selection process is used to minimize the time complexity of software fault prediction. With the selected metrics, software fault perdition with the help of the Quadratic Censored regressive convolution deep neural network-based classification. The deep learning classifier analyzes the training and testing samples using the contingency correlation coefficient. The softstep activation function is used to provide the final fault prediction results. To minimize the error, the Nelder–Mead method is applied to solve non-linear least-squares problems. Finally, accurate classification results with a minimum error are obtained at the output layer. Experimental evaluation is carried out with different quantitative metrics such as accuracy, precision, recall, F-measure, and time complexity. The analyzed results demonstrate the superior performance of our proposed SQADEN technique with maximum accuracy, sensitivity and specificity by 3%, 3%, 2% and 3% and minimum time and space by 13% and 15% when compared with the two state-of-the-art methods.展开更多
Measuring software quality requires software engineers to understand the system’s quality attributes and their measurements.The quality attribute is a qualitative property;however,the quantitative feature is needed f...Measuring software quality requires software engineers to understand the system’s quality attributes and their measurements.The quality attribute is a qualitative property;however,the quantitative feature is needed for software measurement,which is not considered during the development of most software systems.Many research studies have investigated different approaches for measuring software quality,but with no practical approaches to quantify and measure quality attributes.This paper proposes a software quality measurement model,based on a software interconnection model,to measure the quality of software components and the overall quality of the software system.Unlike most of the existing approaches,the proposed approach can be applied at the early stages of software development,to different architectural design models,and at different levels of system decomposition.This article introduces a software measurement model that uses a heuristic normalization of the software’s internal quality attributes,i.e.,coupling and cohesion,for software quality measurement.In this model,the quality of a software component is measured based on its internal strength and the coupling it exhibits with other component(s).The proposed model has been experimented with nine software engineering teams that have agreed to participate in the experiment during the development of their different software systems.The experiments have shown that coupling reduces the internal strength of the coupled components by the amount of coupling they exhibit,which degrades their quality and the overall quality of the software system.The introduced model can help in understanding the quality of software design.In addition,it identifies the locations in software design that exhibit unnecessary couplings that degrade the quality of the software systems,which can be eliminated.展开更多
The developed auxiliary software serves to simplify, standardize and facilitate the software loading of the structural organization of a complex technological system, as well as its further manipulation within the pro...The developed auxiliary software serves to simplify, standardize and facilitate the software loading of the structural organization of a complex technological system, as well as its further manipulation within the process of solving the considered technological system. Its help can be especially useful in the case of a complex structural organization of a technological system with a large number of different functional elements grouped into several technological subsystems. This paper presents the results of its application for a special complex technological system related to the reference steam block for the combined production of heat and electricity.展开更多
With the rapid development of information technology,the demand for talents in the field of software engineering is growing.In order to cultivate high-quality software engineering talents who meet the market demand,un...With the rapid development of information technology,the demand for talents in the field of software engineering is growing.In order to cultivate high-quality software engineering talents who meet the market demand,universities have continuously carried out the construction of software engineering majors.Accreditation Board for Engineering and Technology(ABET)certification,as an internationally recognized higher education quality assurance system,provides important reference and guidance for the construction of software engineering majors.Guided by student learning outcomes and core competencies,combined with the characteristics of software engineering talent cultivation,the innovation of talent cultivation mode takes industry-education integration and school-enterprise cooperation as the main development paths and explores comprehensive reform of the major in terms of professional positioning and goals,curriculum system,teaching conditions,and teachers.This comprehensive reform model has effectively promoted the development of major construction and improved the quality of talent cultivation.展开更多
Over the past decade, open-source software use has grown. Today, many companies including Google, Microsoft, Meta, RedHat, MongoDB, and Apache are major participants of open-source contributions. With the increased us...Over the past decade, open-source software use has grown. Today, many companies including Google, Microsoft, Meta, RedHat, MongoDB, and Apache are major participants of open-source contributions. With the increased use of open-source software or integration of open-source software into custom-developed software, the quality of this software component increases in importance. This study examined a sample of open-source applications from GitHub. Static software analytics were conducted, and each application was classified for its risk level. In the analyzed applications, it was found that 90% of the applications were classified as low risk or moderate low risk indicating a high level of quality for open-source applications.展开更多
In the early stage of software development,a software requirements specification(SRS)is essential,and whether the requirements are clear and explicit is the key.However,due to various reasons,there may be a large numb...In the early stage of software development,a software requirements specification(SRS)is essential,and whether the requirements are clear and explicit is the key.However,due to various reasons,there may be a large number of misunderstandings.To generate high-quality software requirements specifications,numerous researchers have developed a variety of ways to improve the quality of SRS.In this paper,we propose a questions extraction method based on SRS elements decomposition,which evaluates the quality of SRS in the form of numerical indicators.The proposed method not only evaluates the quality of SRSs but also helps in the detection of defects,especially the description problem and omission defects in SRSs.To verify the effectiveness of the proposed method,we conducted a controlled experiment to compare the ability of checklist-based review(CBR)and the proposed method in the SRS review.The CBR is a classicmethod of reviewing SRS defects.After a lot of practice and improvement for a long time,CBR has excellent review ability in improving the quality of software requirements specifications.The experimental results with 40 graduate studentsmajoring in software engineering confirmed the effectiveness and advantages of the proposed method.However,the shortcomings and deficiencies of the proposed method are also observed through the experiment.Furthermore,the proposed method has been tried out by engineers with practical work experience in software development industry and received good feedback.展开更多
In project management,effective cost estimation is one of the most cru-cial activities to efficiently manage resources by predicting the required cost to fulfill a given task.However,finding the best estimation results i...In project management,effective cost estimation is one of the most cru-cial activities to efficiently manage resources by predicting the required cost to fulfill a given task.However,finding the best estimation results in software devel-opment is challenging.Thus,accurate estimation of software development efforts is always a concern for many companies.In this paper,we proposed a novel soft-ware development effort estimation model based both on constructive cost model II(COCOMO II)and the artificial neural network(ANN).An artificial neural net-work enhances the COCOMO model,and the value of the baseline effort constant A is calibrated to use it in the proposed model equation.Three state-of-the-art publicly available datasets are used for experiments.The backpropagation feed-forward procedure used a training set by iteratively processing and training a neural network.The proposed model is tested on the test set.The estimated effort is compared with the actual effort value.Experimental results show that the effort estimated by the proposed model is very close to the real effort,thus enhanced the reliability and improving the software effort estimation accuracy.展开更多
Forecasting on success or failure of software has become an interesting and,in fact,an essential task in the software development industry.In order to explore the latest data on successes and failures,this research fo...Forecasting on success or failure of software has become an interesting and,in fact,an essential task in the software development industry.In order to explore the latest data on successes and failures,this research focused on certain questions such as is early phase of the software development life cycle better than later phases in predicting software success and avoiding high rework?What human factors contribute to success or failure of a software?What software practices are used by the industry practitioners to achieve high quality of software in their day-to-day work?In order to conduct this empirical analysis a total of 104 practitioners were recruited to determine how human factors,misinterpretation,and miscommunication of requirements and decision-making processes play their roles in software success forecasting.We discussed a potential relationship between forecasting of software success or failure and the development processes.We noticed that experienced participants had more confidence in their practices and responded to the questionnaire in this empirical study,and they were more likely to rate software success forecasting linking to the development processes.Our analysis also shows that cognitive bias is the central human factor that negatively affects forecasting of software success rate.The results of this empirical study also validated that requirements’misinterpretation and miscommunication were themain causes behind software systems’failure.It has been seen that reliable,relevant,and trustworthy sources of information help in decision-making to predict software systems’success in the software industry.This empirical study highlights a need for other software practitioners to avoid such bias while working on software projects.Future investigation can be performed to identify the other human factors that may impact software systems’success.展开更多
Modal and damage identification based on ambient excitation can greatly improve the efficiency of high-speed railway bridge vibration detection.This paper first describes the basic principles of stochastic subspace id...Modal and damage identification based on ambient excitation can greatly improve the efficiency of high-speed railway bridge vibration detection.This paper first describes the basic principles of stochastic subspace identification,peak-picking,and frequency domain decomposition method in modal analysis based on ambient excitation,and the effectiveness of these three methods is verified through finite element calculation and numerical simulation,Then the damage element is added to the finite element model to simulate the crack,and the curvature mode difference and the curvature mode area difference square ratio are calculated by using the stochastic subspace identification results to verify their ability of damage identification and location.Finally,the above modal and damage identification techniques are integrated to develop a bridge modal and damage identification software platform.The final results show that all three modal identification methods can accurately identify the vibration frequency and mode shape,both damage identification methods can accurately identify and locate the damage,and the developed software platform is simple and efficient.展开更多
With the development of the digital city,data and data analysis have become more and more important.The database is the foundation of data analysis.In this paper,the software system of the urban land planning database...With the development of the digital city,data and data analysis have become more and more important.The database is the foundation of data analysis.In this paper,the software system of the urban land planning database of Shanghai in China is developed based on MySQL.The conceptual model of the urban land planning database is proposed,and the entities,attributes and connections of this model are discussed.Then the E-R conceptual model is transformed into a logical structure,which is supported by the relational databasemanagement system(DBMS).Based on the conceptual and logical structures,by using Spring Boot as the back-end framework and using MySQL and Java API as the development tools,a platformwith datamanagement,information sharing,map assistance and other functions is established.The functionalmodules in this platformare designed.The results of J Meter test show that the DBMS can add,store and retrieve information data stably,and it has the advantages of fast response and low error rate.The software system of the urban land planning database developed in this paper can improve the efficiency of storing and managing land data,eliminating redundant data and sharing data.展开更多
Recently,researchers have shown increasing interest in combining more than one programming model into systems running on high performance computing systems(HPCs)to achieve exascale by applying parallelism at multiple ...Recently,researchers have shown increasing interest in combining more than one programming model into systems running on high performance computing systems(HPCs)to achieve exascale by applying parallelism at multiple levels.Combining different programming paradigms,such as Message Passing Interface(MPI),Open Multiple Processing(OpenMP),and Open Accelerators(OpenACC),can increase computation speed and improve performance.During the integration of multiple models,the probability of runtime errors increases,making their detection difficult,especially in the absence of testing techniques that can detect these errors.Numerous studies have been conducted to identify these errors,but no technique exists for detecting errors in three-level programming models.Despite the increasing research that integrates the three programming models,MPI,OpenMP,and OpenACC,a testing technology to detect runtime errors,such as deadlocks and race conditions,which can arise from this integration has not been developed.Therefore,this paper begins with a definition and explanation of runtime errors that result fromintegrating the three programming models that compilers cannot detect.For the first time,this paper presents a classification of operational errors that can result from the integration of the three models.This paper also proposes a parallel hybrid testing technique for detecting runtime errors in systems built in the C++programming language that uses the triple programming models MPI,OpenMP,and OpenACC.This hybrid technology combines static technology and dynamic technology,given that some errors can be detected using static techniques,whereas others can be detected using dynamic technology.The hybrid technique can detect more errors because it combines two distinct technologies.The proposed static technology detects a wide range of error types in less time,whereas a portion of the potential errors that may or may not occur depending on the 4502 CMC,2023,vol.74,no.2 operating environment are left to the dynamic technology,which completes the validation.展开更多
In recent years,satellite networks have been proposed as an essential part of next-generation mobile communication systems.Software defined networking techniques are introduced in satellite networks to handle the grow...In recent years,satellite networks have been proposed as an essential part of next-generation mobile communication systems.Software defined networking techniques are introduced in satellite networks to handle the growing challenges induced by time-varying topology,intermittent inter-satellite link and dramatically increased satellite constellation size.This survey covers the latest progress of software defined satellite networks,including key techniques,existing solutions,challenges,opportunities,and simulation tools.To the best of our knowledge,this paper is the most comprehensive survey that covers the latest progress of software defined satellite networks.An open GitHub repository is further created where the latest papers on this topic will be tracked and updated periodically.Compared with these existing surveys,this survey contributes from three aspects:(1)an up-to-date SDN-oriented review for the latest progress of key techniques and solutions in software defined satellite networks;(2)an inspiring summary of existing challenges,new research opportunities and publicly available simulation tools for follow-up studies;(3)an effort of building a public repository to track new results.展开更多
Currently, open-source software is gradually being integrated into industrial software, while industry protocolsin industrial software are also gradually transferred to open-source community development. Industrial pr...Currently, open-source software is gradually being integrated into industrial software, while industry protocolsin industrial software are also gradually transferred to open-source community development. Industrial protocolstandardization organizations are confronted with fragmented and numerous code PR (Pull Request) and informalproposals, and differentworkflowswill lead to increased operating costs. The open-source community maintenanceteam needs software that is more intelligent to guide the identification and classification of these issues. To solvethe above problems, this paper proposes a PR review prediction model based on multi-dimensional features. Weextract 43 features of PR and divide them into five dimensions: contributor, reviewer, software project, PR, andsocial network of developers. The model integrates the above five-dimensional features, and a prediction model isbuilt based on a Random Forest Classifier to predict the review results of PR. On the other hand, to improve thequality of rejected PRs, we focus on problems raised in the review process and review comments of similar PRs.Wepropose a PR revision recommendation model based on the PR review knowledge graph. Entity information andrelationships between entities are extracted from text and code information of PRs, historical review comments,and related issues. PR revisions will be recommended to code contributors by graph-based similarity calculation.The experimental results illustrate that the above twomodels are effective and robust in PR review result predictionand PR revision recommendation.展开更多
文摘Accurate software cost estimation in Global Software Development(GSD)remains challenging due to reliance on historical data and expert judgments.Traditional models,such as the Constructive Cost Model(COCOMO II),rely heavily on historical and accurate data.In addition,expert judgment is required to set many input parameters,which can introduce subjectivity and variability in the estimation process.Consequently,there is a need to improve the current GSD models to mitigate reliance on historical data,subjectivity in expert judgment,inadequate consideration of GSD-based cost drivers and limited integration of modern technologies with cost overruns.This study introduces a novel hybrid model that synergizes the COCOMO II with Artificial Neural Networks(ANN)to address these challenges.The proposed hybrid model integrates additional GSD-based cost drivers identified through a systematic literature review and further vetted by industry experts.This article compares the effectiveness of the proposedmodelwith state-of-the-artmachine learning-basedmodels for software cost estimation.Evaluating the NASA 93 dataset by adopting twenty-six GSD-based cost drivers reveals that our hybrid model achieves superior accuracy,outperforming existing state-of-the-artmodels.The findings indicate the potential of combining COCOMO II,ANN,and additional GSD-based cost drivers to transform cost estimation in GSD.
文摘The use of interactive audience software,such as audience response systems(ARS),in medical education has become increasingly popular in recent years.This technology allows instructors to engage students in real time,encouraging active participation and promoting effective learning.The benefits of interactive audience software in medical education include increased student engagement,promotion of active learning,and enhanced learning outcomes.However,there are also several challenges to its implementation,including technical difficulties,careful planning and preparation,over-reliance on technology,and ethical concerns related to privacy and data security.The cost of implementing interactive audience software may also be a barrier for some institutions.This paper specifically reviews six interactive software platforms,including Socrative,Quizizz,Pear Deck,Slido,Wooclap and ClassPoint.These platforms allow for real-time assessment of student understanding,feedback,and participation.They also enable instructors to adjust their teaching strategies based on student responses and feedback.Overall,interactive audience software has shown great potential to enhance learning and engagement in medical education.It is important for instructors to carefully consider the benefits and challenges of its implementation.While the cost of implementing interactive audience software may be a barrier for some institutions,there are free and low-cost options available.
基金the R&D&I,Spain grants PID2020-119478GB-I00 and,PID2020-115832GB-I00 funded by MCIN/AEI/10.13039/501100011033.N.Rodríguez-Barroso was supported by the grant FPU18/04475 funded by MCIN/AEI/10.13039/501100011033 and by“ESF Investing in your future”Spain.J.Moyano was supported by a postdoctoral Juan de la Cierva Formación grant FJC2020-043823-I funded by MCIN/AEI/10.13039/501100011033 and by European Union NextGenerationEU/PRTR.J.Del Ser acknowledges funding support from the Spanish Centro para el Desarrollo Tecnológico Industrial(CDTI)through the AI4ES projectthe Department of Education of the Basque Government(consolidated research group MATHMODE,IT1456-22)。
文摘When data privacy is imposed as a necessity,Federated learning(FL)emerges as a relevant artificial intelligence field for developing machine learning(ML)models in a distributed and decentralized environment.FL allows ML models to be trained on local devices without any need for centralized data transfer,thereby reducing both the exposure of sensitive data and the possibility of data interception by malicious third parties.This paradigm has gained momentum in the last few years,spurred by the plethora of real-world applications that have leveraged its ability to improve the efficiency of distributed learning and to accommodate numerous participants with their data sources.By virtue of FL,models can be learned from all such distributed data sources while preserving data privacy.The aim of this paper is to provide a practical tutorial on FL,including a short methodology and a systematic analysis of existing software frameworks.Furthermore,our tutorial provides exemplary cases of study from three complementary perspectives:i)Foundations of FL,describing the main components of FL,from key elements to FL categories;ii)Implementation guidelines and exemplary cases of study,by systematically examining the functionalities provided by existing software frameworks for FL deployment,devising a methodology to design a FL scenario,and providing exemplary cases of study with source code for different ML approaches;and iii)Trends,shortly reviewing a non-exhaustive list of research directions that are under active investigation in the current FL landscape.The ultimate purpose of this work is to establish itself as a referential work for researchers,developers,and data scientists willing to explore the capabilities of FL in practical applications.
文摘The advent of Industry 4.0 has compelled businesses to adopt digital approaches that combine software toenhance production efficiency. In this rapidly evolving market, software development is an ongoing process thatmust be tailored to meet the dynamic needs of enterprises. However, internal research and development can beprohibitively expensive, driving many enterprises to outsource software development and upgrades to externalservice providers. This paper presents a software upgrade outsourcing model for enterprises and service providersthat accounts for the impact of market fluctuations on software adaptability. To mitigate the risk of adverseselection due to asymmetric information about the service provider’s cost and asymmetric information aboutthe enterprise’s revenues, we propose pay-per-time and revenue-sharing contracts in two distinct informationasymmetry scenarios. These two contracts specify the time and transfer payments for software upgrades. Througha comparative analysis of the optimal solutions under the two contracts and centralized decision-making withfull-information, we examine the characteristics of the solutions under two information asymmetry scenarios andanalyze the incentive effects of the two contracts on the various stakeholders. Overall, our study offers valuableinsights for firms seeking to optimize their outsourcing strategies and maximize their returns on investment insoftware upgrades.
基金supported by the NationalNatural Science Foundation of China(Grant No.61867004)the Youth Fund of the National Natural Science Foundation of China(Grant No.41801288).
文摘The purpose of software defect prediction is to identify defect-prone code modules to assist software quality assurance teams with the appropriate allocation of resources and labor.In previous software defect prediction studies,transfer learning was effective in solving the problem of inconsistent project data distribution.However,target projects often lack sufficient data,which affects the performance of the transfer learning model.In addition,the presence of uncorrelated features between projects can decrease the prediction accuracy of the transfer learning model.To address these problems,this article propose a software defect prediction method based on stable learning(SDP-SL)that combines code visualization techniques and residual networks.This method first transforms code files into code images using code visualization techniques and then constructs a defect prediction model based on these code images.During the model training process,target project data are not required as prior knowledge.Following the principles of stable learning,this paper dynamically adjusted the weights of source project samples to eliminate dependencies between features,thereby capturing the“invariance mechanism”within the data.This approach explores the genuine relationship between code defect features and labels,thereby enhancing defect prediction performance.To evaluate the performance of SDP-SL,this article conducted comparative experiments on 10 open-source projects in the PROMISE dataset.The experimental results demonstrated that in terms of the F-measure,the proposed SDP-SL method outperformed other within-project defect prediction methods by 2.11%-44.03%.In cross-project defect prediction,the SDP-SL method provided an improvement of 5.89%-25.46% in prediction performance compared to other cross-project defect prediction methods.Therefore,SDP-SL can effectively enhance within-and cross-project defect predictions.
基金This Research is funded by Researchers Supporting Project Number(RSPD2024R947),King Saud University,Riyadh,Saudi Arabia.
文摘Software project outcomes heavily depend on natural language requirements,often causing diverse interpretations and issues like ambiguities and incomplete or faulty requirements.Researchers are exploring machine learning to predict software bugs,but a more precise and general approach is needed.Accurate bug prediction is crucial for software evolution and user training,prompting an investigation into deep and ensemble learning methods.However,these studies are not generalized and efficient when extended to other datasets.Therefore,this paper proposed a hybrid approach combining multiple techniques to explore their effectiveness on bug identification problems.The methods involved feature selection,which is used to reduce the dimensionality and redundancy of features and select only the relevant ones;transfer learning is used to train and test the model on different datasets to analyze how much of the learning is passed to other datasets,and ensemble method is utilized to explore the increase in performance upon combining multiple classifiers in a model.Four National Aeronautics and Space Administration(NASA)and four Promise datasets are used in the study,showing an increase in the model’s performance by providing better Area Under the Receiver Operating Characteristic Curve(AUC-ROC)values when different classifiers were combined.It reveals that using an amalgam of techniques such as those used in this study,feature selection,transfer learning,and ensemble methods prove helpful in optimizing the software bug prediction models and providing high-performing,useful end mode.
基金extend their appreciation to Researcher Supporting Project Number(RSPD2023R582)King Saud University,Riyadh,Saudi Arabia.
文摘The healthcare sector holds valuable and sensitive data.The amount of this data and the need to handle,exchange,and protect it,has been increasing at a fast pace.Due to their nature,software-defined networks(SDNs)are widely used in healthcare systems,as they ensure effective resource utilization,safety,great network management,and monitoring.In this sector,due to the value of thedata,SDNs faceamajor challengeposed byawide range of attacks,such as distributed denial of service(DDoS)and probe attacks.These attacks reduce network performance,causing the degradation of different key performance indicators(KPIs)or,in the worst cases,a network failure which can threaten human lives.This can be significant,especially with the current expansion of portable healthcare that supports mobile and wireless devices for what is called mobile health,or m-health.In this study,we examine the effectiveness of using SDNs for defense against DDoS,as well as their effects on different network KPIs under various scenarios.We propose a threshold-based DDoS classifier(TBDC)technique to classify DDoS attacks in healthcare SDNs,aiming to block traffic considered a hazard in the form of a DDoS attack.We then evaluate the accuracy and performance of the proposed TBDC approach.Our technique shows outstanding performance,increasing the mean throughput by 190.3%,reducing the mean delay by 95%,and reducing packet loss by 99.7%relative to normal,with DDoS attack traffic.
文摘The development of defect prediction plays a significant role in improving software quality. Such predictions are used to identify defective modules before the testing and to minimize the time and cost. The software with defects negatively impacts operational costs and finally affects customer satisfaction. Numerous approaches exist to predict software defects. However, the timely and accurate software bugs are the major challenging issues. To improve the timely and accurate software defect prediction, a novel technique called Nonparametric Statistical feature scaled QuAdratic regressive convolution Deep nEural Network (SQADEN) is introduced. The proposed SQADEN technique mainly includes two major processes namely metric or feature selection and classification. First, the SQADEN uses the nonparametric statistical Torgerson–Gower scaling technique for identifying the relevant software metrics by measuring the similarity using the dice coefficient. The feature selection process is used to minimize the time complexity of software fault prediction. With the selected metrics, software fault perdition with the help of the Quadratic Censored regressive convolution deep neural network-based classification. The deep learning classifier analyzes the training and testing samples using the contingency correlation coefficient. The softstep activation function is used to provide the final fault prediction results. To minimize the error, the Nelder–Mead method is applied to solve non-linear least-squares problems. Finally, accurate classification results with a minimum error are obtained at the output layer. Experimental evaluation is carried out with different quantitative metrics such as accuracy, precision, recall, F-measure, and time complexity. The analyzed results demonstrate the superior performance of our proposed SQADEN technique with maximum accuracy, sensitivity and specificity by 3%, 3%, 2% and 3% and minimum time and space by 13% and 15% when compared with the two state-of-the-art methods.
文摘Measuring software quality requires software engineers to understand the system’s quality attributes and their measurements.The quality attribute is a qualitative property;however,the quantitative feature is needed for software measurement,which is not considered during the development of most software systems.Many research studies have investigated different approaches for measuring software quality,but with no practical approaches to quantify and measure quality attributes.This paper proposes a software quality measurement model,based on a software interconnection model,to measure the quality of software components and the overall quality of the software system.Unlike most of the existing approaches,the proposed approach can be applied at the early stages of software development,to different architectural design models,and at different levels of system decomposition.This article introduces a software measurement model that uses a heuristic normalization of the software’s internal quality attributes,i.e.,coupling and cohesion,for software quality measurement.In this model,the quality of a software component is measured based on its internal strength and the coupling it exhibits with other component(s).The proposed model has been experimented with nine software engineering teams that have agreed to participate in the experiment during the development of their different software systems.The experiments have shown that coupling reduces the internal strength of the coupled components by the amount of coupling they exhibit,which degrades their quality and the overall quality of the software system.The introduced model can help in understanding the quality of software design.In addition,it identifies the locations in software design that exhibit unnecessary couplings that degrade the quality of the software systems,which can be eliminated.
文摘The developed auxiliary software serves to simplify, standardize and facilitate the software loading of the structural organization of a complex technological system, as well as its further manipulation within the process of solving the considered technological system. Its help can be especially useful in the case of a complex structural organization of a technological system with a large number of different functional elements grouped into several technological subsystems. This paper presents the results of its application for a special complex technological system related to the reference steam block for the combined production of heat and electricity.
基金Digital Twin and Acoustic Perception Research Team(2021XJTD06)。
文摘With the rapid development of information technology,the demand for talents in the field of software engineering is growing.In order to cultivate high-quality software engineering talents who meet the market demand,universities have continuously carried out the construction of software engineering majors.Accreditation Board for Engineering and Technology(ABET)certification,as an internationally recognized higher education quality assurance system,provides important reference and guidance for the construction of software engineering majors.Guided by student learning outcomes and core competencies,combined with the characteristics of software engineering talent cultivation,the innovation of talent cultivation mode takes industry-education integration and school-enterprise cooperation as the main development paths and explores comprehensive reform of the major in terms of professional positioning and goals,curriculum system,teaching conditions,and teachers.This comprehensive reform model has effectively promoted the development of major construction and improved the quality of talent cultivation.
文摘Over the past decade, open-source software use has grown. Today, many companies including Google, Microsoft, Meta, RedHat, MongoDB, and Apache are major participants of open-source contributions. With the increased use of open-source software or integration of open-source software into custom-developed software, the quality of this software component increases in importance. This study examined a sample of open-source applications from GitHub. Static software analytics were conducted, and each application was classified for its risk level. In the analyzed applications, it was found that 90% of the applications were classified as low risk or moderate low risk indicating a high level of quality for open-source applications.
基金This work was partially supported by the Natural Science Foundation of Jiangsu Province under Grant No.BK20201462partially supported by the Scientific Research Support Project of Jiangsu Normal University under Grant No.21XSRX001.
文摘In the early stage of software development,a software requirements specification(SRS)is essential,and whether the requirements are clear and explicit is the key.However,due to various reasons,there may be a large number of misunderstandings.To generate high-quality software requirements specifications,numerous researchers have developed a variety of ways to improve the quality of SRS.In this paper,we propose a questions extraction method based on SRS elements decomposition,which evaluates the quality of SRS in the form of numerical indicators.The proposed method not only evaluates the quality of SRSs but also helps in the detection of defects,especially the description problem and omission defects in SRSs.To verify the effectiveness of the proposed method,we conducted a controlled experiment to compare the ability of checklist-based review(CBR)and the proposed method in the SRS review.The CBR is a classicmethod of reviewing SRS defects.After a lot of practice and improvement for a long time,CBR has excellent review ability in improving the quality of software requirements specifications.The experimental results with 40 graduate studentsmajoring in software engineering confirmed the effectiveness and advantages of the proposed method.However,the shortcomings and deficiencies of the proposed method are also observed through the experiment.Furthermore,the proposed method has been tried out by engineers with practical work experience in software development industry and received good feedback.
基金This work was supported by the Technology development Program of MSS[No.S3033853].
文摘In project management,effective cost estimation is one of the most cru-cial activities to efficiently manage resources by predicting the required cost to fulfill a given task.However,finding the best estimation results in software devel-opment is challenging.Thus,accurate estimation of software development efforts is always a concern for many companies.In this paper,we proposed a novel soft-ware development effort estimation model based both on constructive cost model II(COCOMO II)and the artificial neural network(ANN).An artificial neural net-work enhances the COCOMO model,and the value of the baseline effort constant A is calibrated to use it in the proposed model equation.Three state-of-the-art publicly available datasets are used for experiments.The backpropagation feed-forward procedure used a training set by iteratively processing and training a neural network.The proposed model is tested on the test set.The estimated effort is compared with the actual effort value.Experimental results show that the effort estimated by the proposed model is very close to the real effort,thus enhanced the reliability and improving the software effort estimation accuracy.
基金supported by the BK21 FOUR(Fostering Outstanding Universities for Research)funded by the Ministry of Education and National Research Foundation of Korea.
文摘Forecasting on success or failure of software has become an interesting and,in fact,an essential task in the software development industry.In order to explore the latest data on successes and failures,this research focused on certain questions such as is early phase of the software development life cycle better than later phases in predicting software success and avoiding high rework?What human factors contribute to success or failure of a software?What software practices are used by the industry practitioners to achieve high quality of software in their day-to-day work?In order to conduct this empirical analysis a total of 104 practitioners were recruited to determine how human factors,misinterpretation,and miscommunication of requirements and decision-making processes play their roles in software success forecasting.We discussed a potential relationship between forecasting of software success or failure and the development processes.We noticed that experienced participants had more confidence in their practices and responded to the questionnaire in this empirical study,and they were more likely to rate software success forecasting linking to the development processes.Our analysis also shows that cognitive bias is the central human factor that negatively affects forecasting of software success rate.The results of this empirical study also validated that requirements’misinterpretation and miscommunication were themain causes behind software systems’failure.It has been seen that reliable,relevant,and trustworthy sources of information help in decision-making to predict software systems’success in the software industry.This empirical study highlights a need for other software practitioners to avoid such bias while working on software projects.Future investigation can be performed to identify the other human factors that may impact software systems’success.
文摘Modal and damage identification based on ambient excitation can greatly improve the efficiency of high-speed railway bridge vibration detection.This paper first describes the basic principles of stochastic subspace identification,peak-picking,and frequency domain decomposition method in modal analysis based on ambient excitation,and the effectiveness of these three methods is verified through finite element calculation and numerical simulation,Then the damage element is added to the finite element model to simulate the crack,and the curvature mode difference and the curvature mode area difference square ratio are calculated by using the stochastic subspace identification results to verify their ability of damage identification and location.Finally,the above modal and damage identification techniques are integrated to develop a bridge modal and damage identification software platform.The final results show that all three modal identification methods can accurately identify the vibration frequency and mode shape,both damage identification methods can accurately identify and locate the damage,and the developed software platform is simple and efficient.
基金funded by Start-Up Funds for Scientific Research of Shenzhen University,Grant No.000002112313.
文摘With the development of the digital city,data and data analysis have become more and more important.The database is the foundation of data analysis.In this paper,the software system of the urban land planning database of Shanghai in China is developed based on MySQL.The conceptual model of the urban land planning database is proposed,and the entities,attributes and connections of this model are discussed.Then the E-R conceptual model is transformed into a logical structure,which is supported by the relational databasemanagement system(DBMS).Based on the conceptual and logical structures,by using Spring Boot as the back-end framework and using MySQL and Java API as the development tools,a platformwith datamanagement,information sharing,map assistance and other functions is established.The functionalmodules in this platformare designed.The results of J Meter test show that the DBMS can add,store and retrieve information data stably,and it has the advantages of fast response and low error rate.The software system of the urban land planning database developed in this paper can improve the efficiency of storing and managing land data,eliminating redundant data and sharing data.
基金[King Abdulaziz University][Deanship of Scientific Research]Grant Number[KEP-PHD-20-611-42].
文摘Recently,researchers have shown increasing interest in combining more than one programming model into systems running on high performance computing systems(HPCs)to achieve exascale by applying parallelism at multiple levels.Combining different programming paradigms,such as Message Passing Interface(MPI),Open Multiple Processing(OpenMP),and Open Accelerators(OpenACC),can increase computation speed and improve performance.During the integration of multiple models,the probability of runtime errors increases,making their detection difficult,especially in the absence of testing techniques that can detect these errors.Numerous studies have been conducted to identify these errors,but no technique exists for detecting errors in three-level programming models.Despite the increasing research that integrates the three programming models,MPI,OpenMP,and OpenACC,a testing technology to detect runtime errors,such as deadlocks and race conditions,which can arise from this integration has not been developed.Therefore,this paper begins with a definition and explanation of runtime errors that result fromintegrating the three programming models that compilers cannot detect.For the first time,this paper presents a classification of operational errors that can result from the integration of the three models.This paper also proposes a parallel hybrid testing technique for detecting runtime errors in systems built in the C++programming language that uses the triple programming models MPI,OpenMP,and OpenACC.This hybrid technology combines static technology and dynamic technology,given that some errors can be detected using static techniques,whereas others can be detected using dynamic technology.The hybrid technique can detect more errors because it combines two distinct technologies.The proposed static technology detects a wide range of error types in less time,whereas a portion of the potential errors that may or may not occur depending on the 4502 CMC,2023,vol.74,no.2 operating environment are left to the dynamic technology,which completes the validation.
基金This work is supported by the Fundamental Research Funds for the Central Universities.
文摘In recent years,satellite networks have been proposed as an essential part of next-generation mobile communication systems.Software defined networking techniques are introduced in satellite networks to handle the growing challenges induced by time-varying topology,intermittent inter-satellite link and dramatically increased satellite constellation size.This survey covers the latest progress of software defined satellite networks,including key techniques,existing solutions,challenges,opportunities,and simulation tools.To the best of our knowledge,this paper is the most comprehensive survey that covers the latest progress of software defined satellite networks.An open GitHub repository is further created where the latest papers on this topic will be tracked and updated periodically.Compared with these existing surveys,this survey contributes from three aspects:(1)an up-to-date SDN-oriented review for the latest progress of key techniques and solutions in software defined satellite networks;(2)an inspiring summary of existing challenges,new research opportunities and publicly available simulation tools for follow-up studies;(3)an effort of building a public repository to track new results.
基金support of National Social Science Fund(NSSF)under Grant(No.22BTQ033).
文摘Currently, open-source software is gradually being integrated into industrial software, while industry protocolsin industrial software are also gradually transferred to open-source community development. Industrial protocolstandardization organizations are confronted with fragmented and numerous code PR (Pull Request) and informalproposals, and differentworkflowswill lead to increased operating costs. The open-source community maintenanceteam needs software that is more intelligent to guide the identification and classification of these issues. To solvethe above problems, this paper proposes a PR review prediction model based on multi-dimensional features. Weextract 43 features of PR and divide them into five dimensions: contributor, reviewer, software project, PR, andsocial network of developers. The model integrates the above five-dimensional features, and a prediction model isbuilt based on a Random Forest Classifier to predict the review results of PR. On the other hand, to improve thequality of rejected PRs, we focus on problems raised in the review process and review comments of similar PRs.Wepropose a PR revision recommendation model based on the PR review knowledge graph. Entity information andrelationships between entities are extracted from text and code information of PRs, historical review comments,and related issues. PR revisions will be recommended to code contributors by graph-based similarity calculation.The experimental results illustrate that the above twomodels are effective and robust in PR review result predictionand PR revision recommendation.
