Software-defined networking(SDN)is an innovative paradigm that separates the control and data planes,introducing centralized network control.SDN is increasingly being adopted by Carrier Grade networks,offering enhance...Software-defined networking(SDN)is an innovative paradigm that separates the control and data planes,introducing centralized network control.SDN is increasingly being adopted by Carrier Grade networks,offering enhanced networkmanagement capabilities than those of traditional networks.However,because SDN is designed to ensure high-level service availability,it faces additional challenges.One of themost critical challenges is ensuring efficient detection and recovery from link failures in the data plane.Such failures can significantly impact network performance and lead to service outages,making resiliency a key concern for the effective adoption of SDN.Since the recovery process is intrinsically dependent on timely failure detection,this research surveys and analyzes the current literature on both failure detection and recovery approaches in SDN.The survey provides a critical comparison of existing failure detection techniques,highlighting their advantages and disadvantages.Additionally,it examines the current failure recovery methods,categorized as either restoration-based or protection-based,and offers a comprehensive comparison of their strengths and limitations.Lastly,future research challenges and directions are discussed to address the shortcomings of existing failure recovery methods.展开更多
Software-related security aspects are a growing and legitimate concern,especially with 5G data available just at our palms.To conduct research in this field,periodic comparative analysis is needed with the new techniq...Software-related security aspects are a growing and legitimate concern,especially with 5G data available just at our palms.To conduct research in this field,periodic comparative analysis is needed with the new techniques coming up rapidly.The purpose of this study is to review the recent developments in the field of security integration in the software development lifecycle(SDLC)by analyzing the articles published in the last two decades and to propose a way forward.This review follows Kitchenham’s review protocol.The review has been divided into three main stages including planning,execution,and analysis.From the selected 100 articles,it becomes evident that need of a collaborative approach is necessary for addressing critical software security risks(CSSRs)through effective risk management/estimation techniques.Quantifying risks using a numeric scale enables a comprehensive understanding of their severity,facilitating focused resource allocation and mitigation efforts.Through a comprehensive understanding of potential vulnerabilities and proactive mitigation efforts facilitated by protection poker,organizations can prioritize resources effectively to ensure the successful outcome of projects and initiatives in today’s dynamic threat landscape.The review reveals that threat analysis and security testing are needed to develop automated tools for the future.Accurate estimation of effort required to prioritize potential security risks is a big challenge in software security.The accuracy of effort estimation can be further improved by exploring new techniques,particularly those involving deep learning.It is also imperative to validate these effort estimation methods to ensure all potential security threats are addressed.Another challenge is selecting the right model for each specific security threat.To achieve a comprehensive evaluation,researchers should use well-known benchmark checklists.展开更多
Link failure is a critical issue in large networks and must be effectively addressed.In software-defined networks(SDN),link failure recovery schemes can be categorized into proactive and reactive approaches.Reactive s...Link failure is a critical issue in large networks and must be effectively addressed.In software-defined networks(SDN),link failure recovery schemes can be categorized into proactive and reactive approaches.Reactive schemes have longer recovery times while proactive schemes provide faster recovery but overwhelm the memory of switches by flow entries.As SDN adoption grows,ensuring efficient recovery from link failures in the data plane becomes crucial.In particular,data center networks(DCNs)demand rapid recovery times and efficient resource utilization to meet carrier-grade requirements.This paper proposes an efficient Decentralized Failure Recovery(DFR)model for SDNs,meeting recovery time requirements and optimizing switch memory resource consumption.The DFR model enables switches to autonomously reroute traffic upon link failures without involving the controller,achieving fast recovery times while minimizing memory usage.DFR employs the Fast Failover Group in the OpenFlow standard for local recovery without requiring controller communication and utilizes the k-shortest path algorithm to proactively install backup paths,allowing immediate local recovery without controller intervention and enhancing overall network stability and scalability.DFR employs flow entry aggregation techniques to reduce switch memory usage.Instead of matching flow entries to the destination host’s MAC address,DFR matches packets to the destination switch’s MAC address.This reduces the switches’Ternary Content-Addressable Memory(TCAM)consumption.Additionally,DFR modifies Address Resolution Protocol(ARP)replies to provide source hosts with the destination switch’s MAC address,facilitating flow entry aggregation without affecting normal network operations.The performance of DFR is evaluated through the network emulator Mininet 2.3.1 and Ryu 3.1 as SDN controller.For different number of active flows,number of hosts per edge switch,and different network sizes,the proposed model outperformed various failure recovery models:restoration-based,protection by flow entries,protection by group entries and protection by Vlan-tagging model in terms of recovery time,switch memory consumption and controller overhead which represented the number of flow entry updates to recover from the failure.Experimental results demonstrate that DFR achieves recovery times under 20 milliseconds,satisfying carrier-grade requirements for rapid failure recovery.Additionally,DFR reduces switch memory usage by up to 95%compared to traditional protection methods and minimizes controller load by eliminating the need for controller intervention during failure recovery.Theresults underscore the efficiency and scalability of the DFR model,making it a practical solution for enhancing network resilience in SDN environments.展开更多
Remote sensing and web-based platforms have emerged as vital tools in the effective monitoring of mangrove ecosystems, which are crucial for coastal protection, biodiversity, and carbon sequestration. Utilizing satell...Remote sensing and web-based platforms have emerged as vital tools in the effective monitoring of mangrove ecosystems, which are crucial for coastal protection, biodiversity, and carbon sequestration. Utilizing satellite imagery and aerial data, remote sensing allows researchers to assess the health and extent of mangrove forests over large areas and time periods, providing insights into changes due to environmental stressors like climate change, urbanization, and deforestation. Coupled with web-based platforms, this technology facilitates real-time data sharing and collaborative research efforts among scientists, policymakers, and conservationists. Thus, there is a need to grow this research interest among experts working in this kind of ecosystem. The aim of this paper is to provide a comprehensive literature review on the effective role of remote sensing and web-based platform in monitoring mangrove ecosystem. The research paper utilized the thematic approach to extract specific information to use in the discussion which helped realize the efficiency of digital monitoring for the environment. Web-based platforms and remote sensing represent a powerful tool for environmental monitoring, particularly in the context of forest ecosystems. They facilitate the accessibility of vital data, promote collaboration among stakeholders, support evidence-based policymaking, and engage communities in conservation efforts. As experts confront the urgent challenges posed by climate change and environmental degradation, leveraging technology through web-based platforms is essential for fostering a sustainable future for the forests of the world.展开更多
The advent of large language models(LLMs)has made knowledge acquisition and content creation increasingly easier and cheaper,which in turn redefines learning and urges transformation in software engineering education....The advent of large language models(LLMs)has made knowledge acquisition and content creation increasingly easier and cheaper,which in turn redefines learning and urges transformation in software engineering education.To do so,there is a need to understand the impact of LLMs on software engineering education.In this paper,we conducted a preliminary case study on three software requirements engineering classes where students are allowed to use LLMs to assist in their projects.Based on the students’experience,performance,and feedback from a survey conducted at the end of the courses,we characterized the challenges and benefits of applying LLMs in software engineering education.This research contributes to the ongoing discourse on the integration of LLMs in education,emphasizing both their prominent potential and the need for balanced,mindful usage.展开更多
This paper presents our endeavors in developing the large-scale, ultra-high-resolution E3SM Land Model (uELM), specifically designed for exascale computers furnished with accelerators such as Nvidia GPUs. The uELM is ...This paper presents our endeavors in developing the large-scale, ultra-high-resolution E3SM Land Model (uELM), specifically designed for exascale computers furnished with accelerators such as Nvidia GPUs. The uELM is a sophisticated code that substantially relies on High-Performance Computing (HPC) environments, necessitating particular machine and software configurations. To facilitate community-based uELM developments employing GPUs, we have created a portable, standalone software environment preconfigured with uELM input datasets, simulation cases, and source code. This environment, utilizing Docker, encompasses all essential code, libraries, and system software for uELM development on GPUs. It also features a functional unit test framework and an offline model testbed for comprehensive numerical experiments. From a technical perspective, the paper discusses GPU-ready container generations, uELM code management, and input data distribution across computational platforms. Lastly, the paper demonstrates the use of environment for functional unit testing, end-to-end simulation on CPUs and GPUs, and collaborative code development.展开更多
This article examines the architecture of software-defined networks (SDN) and its implications for the modern management of communications infrastructures. By decoupling the control plane from the data plane, SDN offe...This article examines the architecture of software-defined networks (SDN) and its implications for the modern management of communications infrastructures. By decoupling the control plane from the data plane, SDN offers increased flexibility and programmability, enabling rapid adaptation to changing user requirements. However, this new approach poses significant challenges in terms of security, fault tolerance, and interoperability. This paper highlights these challenges and explores current strategies to ensure the resilience and reliability of SDN networks in the face of threats and failures. In addition, we analyze the future outlook for SDN and the importance of integrating robust security solutions into these infrastructures.展开更多
Large-scale deep-seated landslides pose a significant threat to human life and infrastructure.Therefore,closely monitoring these landslides is crucial for assessing and mitigating their associated risks.In this paper,...Large-scale deep-seated landslides pose a significant threat to human life and infrastructure.Therefore,closely monitoring these landslides is crucial for assessing and mitigating their associated risks.In this paper,the authors introduce the So Lo Mon framework,a comprehensive monitoring system developed for three large-scale landslides in the Autonomous Province of Bolzano,Italy.A web-based platform integrates various monitoring data(GNSS,topographic data,in-place inclinometer),providing a user-friendly interface for visualizing and analyzing the collected data.This facilitates the identification of trends and patterns in landslide behaviour,enabling the triggering of warnings and the implementation of appropriate mitigation measures.The So Lo Mon platform has proven to be an invaluable tool for managing the risks associated with large-scale landslides through non-structural measures and driving countermeasure works design.It serves as a centralized data repository,offering visualization and analysis tools.This information empowers decisionmakers to make informed choices regarding risk mitigation,ultimately ensuring the safety of communities and infrastructures.展开更多
To address the severe challenges posed by the international situation and meet the needs of the national major development strategies,the traditional software engineering talent cultivation model lacks interdisciplina...To address the severe challenges posed by the international situation and meet the needs of the national major development strategies,the traditional software engineering talent cultivation model lacks interdisciplinary education focused on specific fields,making it difficult to cultivate engineering leaders with multidisciplinary backgrounds who are capable of solving complex real-world problems.To solve this problem,based on the decade-long interdisciplinary talent cultivation achievements of the College of Software Engineering at Sichuan University,this article proposes the“Software Engineering+”innovative talent cultivation paradigm.It provides an analysis through professional construction of interdisciplinary talents,the design of talent cultivation frameworks,the formulation of cultivation plans,the establishment of interdisciplinary curriculum systems,the reform of teaching modes,and the improvement of institutional systems.Scientific solutions are proposed,and five project models implemented and operated by the College of Software Engineering at Sichuan University are listed as practical examples,offering significant reference value.展开更多
With the rapid development of software engineering,traditional teaching methods are confronted with the challenges of short knowledge update cycles and the rapid emergence of new technologies.By analyzing the current ...With the rapid development of software engineering,traditional teaching methods are confronted with the challenges of short knowledge update cycles and the rapid emergence of new technologies.By analyzing the current situation of the mismatch between educational practices and industrial change,this study proposes an innovative teaching model—“Micro-practices”.This model integrates new knowledge and new technologies into the teaching process quickly and flexibly through practical teaching projects with“short class time,small capacity,and cloud environment”to meet the different educational needs of students,teachers,and enterprises.The aim is to train innovative software engineering talents who can meet the challenges of the future.展开更多
This paper presents a case study of the collaborative integration between the School of Information and Software Engineering at the University of Electronic Science and Technology of China(UESTC)and SI-TECH,highlighti...This paper presents a case study of the collaborative integration between the School of Information and Software Engineering at the University of Electronic Science and Technology of China(UESTC)and SI-TECH,highlighting the complementary advantages of both the University and the enterprise.By jointly establishing research institutes and engaging in diversified collaborative initiatives,the University and the enterprise have embarked on a pathway of School-enterprise Integration.Through a virtuous cycle of cooperation and continuous advancement,they have explored a comprehensive talent cultivation model in“5G”software engineering innovation practices based on this integration.Furthermore,this endeavor aims to facilitate the transformation of technological achievements and provides valuable insights for fostering innovative talents in the field of electronic information through enhanced integration between the University and the enterprise.展开更多
Building a collaborative education mechanism,improving students’engineering practice and innovation abilities,and cultivating software engineering innovation talents that meet industry needs are of great significance...Building a collaborative education mechanism,improving students’engineering practice and innovation abilities,and cultivating software engineering innovation talents that meet industry needs are of great significance for fully implementing the“Excellent Engineer Education and Training Program”of the Ministry of Education and achieving the goal of building a strong engineering education country.The School of Information and Software Engineering of the University of Electronic Science and Technology of China(UESTC)has been thoroughly studying and implementing XI Jinping’s thought on socialism with Chinese characteristics and the spirit of the 20th CPC National Congress.The school has steadfastly promoted the Project of Nurturing the Soul of the New Era.The school has taken moral education as its core,deeply explored the resources of“all staff,throughout the process,in all aspects”,and constructed and implemented the collaborative education mechanism.These efforts have laid a solid foundation for cultivating excellent talents in software engineering in the new era.展开更多
This paper proposes a multivariate data fusion based quality evaluation model for software talent cultivation.The model constructs a comprehensive ability and quality evaluation index system for college students from ...This paper proposes a multivariate data fusion based quality evaluation model for software talent cultivation.The model constructs a comprehensive ability and quality evaluation index system for college students from a perspective of engineering course,especially of software engineering.As for evaluation method,relying on the behavioral data of students during their school years,we aim to construct the evaluation model as objective as possible,effectively weakening the negative impact of personal subjective assumptions on the evaluation results.展开更多
In response to the current issues in the construction of software engineering(SE)degree granting program,such as insufficient resource integration,low level of internationalization,and inadequate quality control,we pr...In response to the current issues in the construction of software engineering(SE)degree granting program,such as insufficient resource integration,low level of internationalization,and inadequate quality control,we propose the Software Engineering Degree Granting Program Construction Practice Project at Harbin Institute of Technology(HIT).This project aims to explore new models for software talent cultivation,establish a superior SE degree granting program,and ultimately cultivate outstanding internationalized composite SE professionals to support the high-quality development of the national software industry.To this end,we design a distinctive overall construction idea and plan for the SE degree granting program,which are characterized by“3I3S:three highlights for specialized cultivation and strictness in three aspects to ensure quality control”.After years of practice and validation of the project at the School of Software at HIT,this project has proven effective in optimizing talent cultivation models,enhancing students’practical abilities,promoting international exchange and cooperation,advancing industry-education integration,and meeting industrial needs.展开更多
Under the background of training practical compound talents in software engineering,this paper analyzes the problems existing in the current teaching of software engineering courses represented by software project man...Under the background of training practical compound talents in software engineering,this paper analyzes the problems existing in the current teaching of software engineering courses represented by software project management,puts forward the team task mechanism of software engineering courses with AI empowerment and cooperation and competition,develops a unified project management platform to support the implementation of team tasks,and proves the effectiveness of the scheme through the results obtained.展开更多
Spectrum-based fault localization (SBFL) generates a ranked list of suspicious elements by using the program execution spectrum, but the excessive number of elements ranked in parallel results in low localization accu...Spectrum-based fault localization (SBFL) generates a ranked list of suspicious elements by using the program execution spectrum, but the excessive number of elements ranked in parallel results in low localization accuracy. Most researchers consider intra-class dependencies to improve localization accuracy. However, some studies show that inter-class method call type faults account for more than 20%, which means such methods still have certain limitations. To solve the above problems, this paper proposes a two-phase software fault localization based on relational graph convolutional neural networks (Two-RGCNFL). Firstly, in Phase 1, the method call dependence graph (MCDG) of the program is constructed, the intra-class and inter-class dependencies in MCDG are extracted by using the relational graph convolutional neural network, and the classifier is used to identify the faulty methods. Then, the GraphSMOTE algorithm is improved to alleviate the impact of class imbalance on classification accuracy. Aiming at the problem of parallel ranking of element suspicious values in traditional SBFL technology, in Phase 2, Doc2Vec is used to learn static features, while spectrum information serves as dynamic features. A RankNet model based on siamese multi-layer perceptron is constructed to score and rank statements in the faulty method. This work conducts experiments on 5 real projects of Defects4J benchmark. Experimental results show that, compared with the traditional SBFL technique and two baseline methods, our approach improves the Top-1 accuracy by 262.86%, 29.59% and 53.01%, respectively, which verifies the effectiveness of Two-RGCNFL. Furthermore, this work verifies the importance of inter-class dependencies through ablation experiments.展开更多
The advancement of Internet of Things(IoT)technology is driving industries toward intelligent digital transformation,highlighting the crucial role of software engineering.Despite this,the integration of software engin...The advancement of Internet of Things(IoT)technology is driving industries toward intelligent digital transformation,highlighting the crucial role of software engineering.Despite this,the integration of software engineering into IoT engineering education remains underexplored.To address this gap,the School of Software at North University of China,in collaboration with QST Innovation Technology Group Co.,Ltd.(QST),has developed an innovative educational mechanism.This initiative focuses on the software engineering IoT track and optimizes the teaching process through the outcome-based education(OBE)concept.It incorporates military-industrial characteristics,introduces advanced information and technology curricula,and enhances laboratory infrastructure.The goal is to cultivate innovative talents with unique capabilities,thereby fostering the comprehensive development and application of IoT technology.展开更多
BACKGROUND Knowledge-based systems(KBS)are software applications based on a knowledge database and an inference engine.Various experimental KBS for computerassisted medical diagnosis and treatment were started to be u...BACKGROUND Knowledge-based systems(KBS)are software applications based on a knowledge database and an inference engine.Various experimental KBS for computerassisted medical diagnosis and treatment were started to be used since 70s(VisualDx,GIDEON,DXPlain,CADUCEUS,Internist-I,Mycin etc.).AIM To present in detail the“Electronic Pediatrician(EPed)”,a medical non-machine learning artificial intelligence(nml-AI)KBS in its prototype version created by the corresponding author(with database written in Romanian)that offers a physiopathology-based differential and positive diagnosis and treatment of ill children.METHODS EPed specifically focuses on the physiopathological reasoning of pediatric clinical cases.EPed has currently reached its prototype version 2.0,being able to diagnose 302 physiopathological macro-links(briefly named“clusters”)and 269 pediatric diseases:Some examples of diagnosis and a previous testing of EPed on a group of 34 patients are also presented in this paper.RESULTS The prototype EPed can currently diagnose 269 pediatric infectious and noninfectious diseases(based on 302 clusters),including the most frequent respiratory/digestive/renal/central nervous system infections,but also many other noninfectious pediatric diseases like autoimmune,oncological,genetical diseases and even intoxications,plus some important surgical pathologies.CONCLUSION EPed is the first and only physiopathology-based nml-AI KBS focused on general pediatrics and is the first and only pediatric Romanian KBS addressed to medical professionals.Furthermore,EPed is the first and only nml-AI KBS that offers not only both a physiopathology-based differential and positive disease diagnosis,but also identifies possible physiopathological“clusters”that may explain the signs and symptoms of any child-patient and may help treating that patient physiopathologically(until a final diagnosis is found),thus encouraging and developing the physiopathological reasoning of any clinician.展开更多
With the rapid advancement of information technology,the quality assurance and evaluation of software engineering education have become pivotal concerns for higher education institutions.In this paper,we focus on a co...With the rapid advancement of information technology,the quality assurance and evaluation of software engineering education have become pivotal concerns for higher education institutions.In this paper,we focus on a comparative study of software engineering education in China and Europe,aiming to explore the theoretical frameworks and practical pathways employed in both regions.Initially,we introduce and contrast the engineering education accreditation systems of China and Europe,including the Chinese engineering education accreditation framework and the European EUR-ACE(European Accreditation of Engineering Programmes)standards,highlighting their core principles and evaluation methodologies.Subsequently,we provide case studies of several universities in China and Europe,such as Sun Yat-sen University,Tsinghua University,Technical University of Munich,and Imperial College London.Finally,we offer recommendations to foster mutual learning and collaboration between Chinese and European institutions,aiming to enhance the overall quality of software engineering education globally.This work provides valuable insights for educational administrators,faculty members,and policymakers,contributing to the ongoing improvement and innovative development of software engineering education in China and Europe.展开更多
Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detecti...Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detection efficiency. Additionally, this technology provides developers with a means to quickly identify errors, thereby improving software robustness and overall quality. However, current research in software defect prediction often faces challenges, such as relying on a single data source or failing to adequately account for the characteristics of multiple coexisting data sources. This approach may overlook the differences and potential value of various data sources, affecting the accuracy and generalization performance of prediction results. To address this issue, this study proposes a multivariate heterogeneous hybrid deep learning algorithm for defect prediction (DP-MHHDL). Initially, Abstract Syntax Tree (AST), Code Dependency Network (CDN), and code static quality metrics are extracted from source code files and used as inputs to ensure data diversity. Subsequently, for the three types of heterogeneous data, the study employs a graph convolutional network optimization model based on adjacency and spatial topologies, a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) hybrid neural network model, and a TabNet model to extract data features. These features are then concatenated and processed through a fully connected neural network for defect prediction. Finally, the proposed framework is evaluated using ten promise defect repository projects, and performance is assessed with three metrics: F1, Area under the curve (AUC), and Matthews correlation coefficient (MCC). The experimental results demonstrate that the proposed algorithm outperforms existing methods, offering a novel solution for software defect prediction.展开更多
文摘Software-defined networking(SDN)is an innovative paradigm that separates the control and data planes,introducing centralized network control.SDN is increasingly being adopted by Carrier Grade networks,offering enhanced networkmanagement capabilities than those of traditional networks.However,because SDN is designed to ensure high-level service availability,it faces additional challenges.One of themost critical challenges is ensuring efficient detection and recovery from link failures in the data plane.Such failures can significantly impact network performance and lead to service outages,making resiliency a key concern for the effective adoption of SDN.Since the recovery process is intrinsically dependent on timely failure detection,this research surveys and analyzes the current literature on both failure detection and recovery approaches in SDN.The survey provides a critical comparison of existing failure detection techniques,highlighting their advantages and disadvantages.Additionally,it examines the current failure recovery methods,categorized as either restoration-based or protection-based,and offers a comprehensive comparison of their strengths and limitations.Lastly,future research challenges and directions are discussed to address the shortcomings of existing failure recovery methods.
文摘Software-related security aspects are a growing and legitimate concern,especially with 5G data available just at our palms.To conduct research in this field,periodic comparative analysis is needed with the new techniques coming up rapidly.The purpose of this study is to review the recent developments in the field of security integration in the software development lifecycle(SDLC)by analyzing the articles published in the last two decades and to propose a way forward.This review follows Kitchenham’s review protocol.The review has been divided into three main stages including planning,execution,and analysis.From the selected 100 articles,it becomes evident that need of a collaborative approach is necessary for addressing critical software security risks(CSSRs)through effective risk management/estimation techniques.Quantifying risks using a numeric scale enables a comprehensive understanding of their severity,facilitating focused resource allocation and mitigation efforts.Through a comprehensive understanding of potential vulnerabilities and proactive mitigation efforts facilitated by protection poker,organizations can prioritize resources effectively to ensure the successful outcome of projects and initiatives in today’s dynamic threat landscape.The review reveals that threat analysis and security testing are needed to develop automated tools for the future.Accurate estimation of effort required to prioritize potential security risks is a big challenge in software security.The accuracy of effort estimation can be further improved by exploring new techniques,particularly those involving deep learning.It is also imperative to validate these effort estimation methods to ensure all potential security threats are addressed.Another challenge is selecting the right model for each specific security threat.To achieve a comprehensive evaluation,researchers should use well-known benchmark checklists.
文摘Link failure is a critical issue in large networks and must be effectively addressed.In software-defined networks(SDN),link failure recovery schemes can be categorized into proactive and reactive approaches.Reactive schemes have longer recovery times while proactive schemes provide faster recovery but overwhelm the memory of switches by flow entries.As SDN adoption grows,ensuring efficient recovery from link failures in the data plane becomes crucial.In particular,data center networks(DCNs)demand rapid recovery times and efficient resource utilization to meet carrier-grade requirements.This paper proposes an efficient Decentralized Failure Recovery(DFR)model for SDNs,meeting recovery time requirements and optimizing switch memory resource consumption.The DFR model enables switches to autonomously reroute traffic upon link failures without involving the controller,achieving fast recovery times while minimizing memory usage.DFR employs the Fast Failover Group in the OpenFlow standard for local recovery without requiring controller communication and utilizes the k-shortest path algorithm to proactively install backup paths,allowing immediate local recovery without controller intervention and enhancing overall network stability and scalability.DFR employs flow entry aggregation techniques to reduce switch memory usage.Instead of matching flow entries to the destination host’s MAC address,DFR matches packets to the destination switch’s MAC address.This reduces the switches’Ternary Content-Addressable Memory(TCAM)consumption.Additionally,DFR modifies Address Resolution Protocol(ARP)replies to provide source hosts with the destination switch’s MAC address,facilitating flow entry aggregation without affecting normal network operations.The performance of DFR is evaluated through the network emulator Mininet 2.3.1 and Ryu 3.1 as SDN controller.For different number of active flows,number of hosts per edge switch,and different network sizes,the proposed model outperformed various failure recovery models:restoration-based,protection by flow entries,protection by group entries and protection by Vlan-tagging model in terms of recovery time,switch memory consumption and controller overhead which represented the number of flow entry updates to recover from the failure.Experimental results demonstrate that DFR achieves recovery times under 20 milliseconds,satisfying carrier-grade requirements for rapid failure recovery.Additionally,DFR reduces switch memory usage by up to 95%compared to traditional protection methods and minimizes controller load by eliminating the need for controller intervention during failure recovery.Theresults underscore the efficiency and scalability of the DFR model,making it a practical solution for enhancing network resilience in SDN environments.
文摘Remote sensing and web-based platforms have emerged as vital tools in the effective monitoring of mangrove ecosystems, which are crucial for coastal protection, biodiversity, and carbon sequestration. Utilizing satellite imagery and aerial data, remote sensing allows researchers to assess the health and extent of mangrove forests over large areas and time periods, providing insights into changes due to environmental stressors like climate change, urbanization, and deforestation. Coupled with web-based platforms, this technology facilitates real-time data sharing and collaborative research efforts among scientists, policymakers, and conservationists. Thus, there is a need to grow this research interest among experts working in this kind of ecosystem. The aim of this paper is to provide a comprehensive literature review on the effective role of remote sensing and web-based platform in monitoring mangrove ecosystem. The research paper utilized the thematic approach to extract specific information to use in the discussion which helped realize the efficiency of digital monitoring for the environment. Web-based platforms and remote sensing represent a powerful tool for environmental monitoring, particularly in the context of forest ecosystems. They facilitate the accessibility of vital data, promote collaboration among stakeholders, support evidence-based policymaking, and engage communities in conservation efforts. As experts confront the urgent challenges posed by climate change and environmental degradation, leveraging technology through web-based platforms is essential for fostering a sustainable future for the forests of the world.
基金supported in part by the Teaching Reform Project of Chongqing University of Posts and Telecommunications,China under Grant No.XJG23234Chongqing Municipal Higher Education Teaching Reform Research Project under Grant No.203399the Doctoral Direct Train Project of Chongqing Science and Technology Bureau under Grant No.CSTB2022BSXM-JSX0007。
文摘The advent of large language models(LLMs)has made knowledge acquisition and content creation increasingly easier and cheaper,which in turn redefines learning and urges transformation in software engineering education.To do so,there is a need to understand the impact of LLMs on software engineering education.In this paper,we conducted a preliminary case study on three software requirements engineering classes where students are allowed to use LLMs to assist in their projects.Based on the students’experience,performance,and feedback from a survey conducted at the end of the courses,we characterized the challenges and benefits of applying LLMs in software engineering education.This research contributes to the ongoing discourse on the integration of LLMs in education,emphasizing both their prominent potential and the need for balanced,mindful usage.
文摘This paper presents our endeavors in developing the large-scale, ultra-high-resolution E3SM Land Model (uELM), specifically designed for exascale computers furnished with accelerators such as Nvidia GPUs. The uELM is a sophisticated code that substantially relies on High-Performance Computing (HPC) environments, necessitating particular machine and software configurations. To facilitate community-based uELM developments employing GPUs, we have created a portable, standalone software environment preconfigured with uELM input datasets, simulation cases, and source code. This environment, utilizing Docker, encompasses all essential code, libraries, and system software for uELM development on GPUs. It also features a functional unit test framework and an offline model testbed for comprehensive numerical experiments. From a technical perspective, the paper discusses GPU-ready container generations, uELM code management, and input data distribution across computational platforms. Lastly, the paper demonstrates the use of environment for functional unit testing, end-to-end simulation on CPUs and GPUs, and collaborative code development.
文摘This article examines the architecture of software-defined networks (SDN) and its implications for the modern management of communications infrastructures. By decoupling the control plane from the data plane, SDN offers increased flexibility and programmability, enabling rapid adaptation to changing user requirements. However, this new approach poses significant challenges in terms of security, fault tolerance, and interoperability. This paper highlights these challenges and explores current strategies to ensure the resilience and reliability of SDN networks in the face of threats and failures. In addition, we analyze the future outlook for SDN and the importance of integrating robust security solutions into these infrastructures.
基金funded by the So Lo Mon project“Monitoraggio a Lungo Termine di Grandi Frane basato su Sistemi Integrati di Sensori e Reti”(Longterm monitoring of large-scale landslides based on integrated systems of sensors and networks),Program EFRE-FESR 2014–2020,Project EFRE-FESR4008 South Tyrol–Person in charge:V.Mair。
文摘Large-scale deep-seated landslides pose a significant threat to human life and infrastructure.Therefore,closely monitoring these landslides is crucial for assessing and mitigating their associated risks.In this paper,the authors introduce the So Lo Mon framework,a comprehensive monitoring system developed for three large-scale landslides in the Autonomous Province of Bolzano,Italy.A web-based platform integrates various monitoring data(GNSS,topographic data,in-place inclinometer),providing a user-friendly interface for visualizing and analyzing the collected data.This facilitates the identification of trends and patterns in landslide behaviour,enabling the triggering of warnings and the implementation of appropriate mitigation measures.The So Lo Mon platform has proven to be an invaluable tool for managing the risks associated with large-scale landslides through non-structural measures and driving countermeasure works design.It serves as a centralized data repository,offering visualization and analysis tools.This information empowers decisionmakers to make informed choices regarding risk mitigation,ultimately ensuring the safety of communities and infrastructures.
基金supported by the 2023 Sichuan Province Higher Education Talent Cultivation and Teaching Reform Major Project“Exploration and Practice of Interdisciplinary and Integrated Industrial Software Talent Cultivation Model”(JG2023-14)the Sichuan University Higher Education Teaching Reform Project(10th Phase)Research and Exploration of Practical Teaching Mode under the New Major Background of“Cross Disciplinary and Integration”(SCU10128)。
文摘To address the severe challenges posed by the international situation and meet the needs of the national major development strategies,the traditional software engineering talent cultivation model lacks interdisciplinary education focused on specific fields,making it difficult to cultivate engineering leaders with multidisciplinary backgrounds who are capable of solving complex real-world problems.To solve this problem,based on the decade-long interdisciplinary talent cultivation achievements of the College of Software Engineering at Sichuan University,this article proposes the“Software Engineering+”innovative talent cultivation paradigm.It provides an analysis through professional construction of interdisciplinary talents,the design of talent cultivation frameworks,the formulation of cultivation plans,the establishment of interdisciplinary curriculum systems,the reform of teaching modes,and the improvement of institutional systems.Scientific solutions are proposed,and five project models implemented and operated by the College of Software Engineering at Sichuan University are listed as practical examples,offering significant reference value.
基金funded by Universityindustry Collaborative Education Program(No.220605181024725)the Undergraduate Education and Teaching Reform Research Project of Northwestern Polytechnical University(No.22GZ13083)。
文摘With the rapid development of software engineering,traditional teaching methods are confronted with the challenges of short knowledge update cycles and the rapid emergence of new technologies.By analyzing the current situation of the mismatch between educational practices and industrial change,this study proposes an innovative teaching model—“Micro-practices”.This model integrates new knowledge and new technologies into the teaching process quickly and flexibly through practical teaching projects with“short class time,small capacity,and cloud environment”to meet the different educational needs of students,teachers,and enterprises.The aim is to train innovative software engineering talents who can meet the challenges of the future.
文摘This paper presents a case study of the collaborative integration between the School of Information and Software Engineering at the University of Electronic Science and Technology of China(UESTC)and SI-TECH,highlighting the complementary advantages of both the University and the enterprise.By jointly establishing research institutes and engaging in diversified collaborative initiatives,the University and the enterprise have embarked on a pathway of School-enterprise Integration.Through a virtuous cycle of cooperation and continuous advancement,they have explored a comprehensive talent cultivation model in“5G”software engineering innovation practices based on this integration.Furthermore,this endeavor aims to facilitate the transformation of technological achievements and provides valuable insights for fostering innovative talents in the field of electronic information through enhanced integration between the University and the enterprise.
文摘Building a collaborative education mechanism,improving students’engineering practice and innovation abilities,and cultivating software engineering innovation talents that meet industry needs are of great significance for fully implementing the“Excellent Engineer Education and Training Program”of the Ministry of Education and achieving the goal of building a strong engineering education country.The School of Information and Software Engineering of the University of Electronic Science and Technology of China(UESTC)has been thoroughly studying and implementing XI Jinping’s thought on socialism with Chinese characteristics and the spirit of the 20th CPC National Congress.The school has steadfastly promoted the Project of Nurturing the Soul of the New Era.The school has taken moral education as its core,deeply explored the resources of“all staff,throughout the process,in all aspects”,and constructed and implemented the collaborative education mechanism.These efforts have laid a solid foundation for cultivating excellent talents in software engineering in the new era.
基金supported in part by the Education Reform Key Projects of Heilongjiang Province(Grant No.SJGZ20220011,SJGZ20220012)the Excellent Project of Ministry of Education and China Higher Education Association on Digital Ideological and Political Education in Universities(Grant No.GXSZSZJPXM001)。
文摘This paper proposes a multivariate data fusion based quality evaluation model for software talent cultivation.The model constructs a comprehensive ability and quality evaluation index system for college students from a perspective of engineering course,especially of software engineering.As for evaluation method,relying on the behavioral data of students during their school years,we aim to construct the evaluation model as objective as possible,effectively weakening the negative impact of personal subjective assumptions on the evaluation results.
基金supported in part by the Education Reform Key Projects of Heilongjiang Province under Grant Nos.SJGZ20220011,SJGZ20220012,and SJGZY2024008。
文摘In response to the current issues in the construction of software engineering(SE)degree granting program,such as insufficient resource integration,low level of internationalization,and inadequate quality control,we propose the Software Engineering Degree Granting Program Construction Practice Project at Harbin Institute of Technology(HIT).This project aims to explore new models for software talent cultivation,establish a superior SE degree granting program,and ultimately cultivate outstanding internationalized composite SE professionals to support the high-quality development of the national software industry.To this end,we design a distinctive overall construction idea and plan for the SE degree granting program,which are characterized by“3I3S:three highlights for specialized cultivation and strictness in three aspects to ensure quality control”.After years of practice and validation of the project at the School of Software at HIT,this project has proven effective in optimizing talent cultivation models,enhancing students’practical abilities,promoting international exchange and cooperation,advancing industry-education integration,and meeting industrial needs.
文摘Under the background of training practical compound talents in software engineering,this paper analyzes the problems existing in the current teaching of software engineering courses represented by software project management,puts forward the team task mechanism of software engineering courses with AI empowerment and cooperation and competition,develops a unified project management platform to support the implementation of team tasks,and proves the effectiveness of the scheme through the results obtained.
基金funded by the Youth Fund of the National Natural Science Foundation of China(Grant No.42261070).
文摘Spectrum-based fault localization (SBFL) generates a ranked list of suspicious elements by using the program execution spectrum, but the excessive number of elements ranked in parallel results in low localization accuracy. Most researchers consider intra-class dependencies to improve localization accuracy. However, some studies show that inter-class method call type faults account for more than 20%, which means such methods still have certain limitations. To solve the above problems, this paper proposes a two-phase software fault localization based on relational graph convolutional neural networks (Two-RGCNFL). Firstly, in Phase 1, the method call dependence graph (MCDG) of the program is constructed, the intra-class and inter-class dependencies in MCDG are extracted by using the relational graph convolutional neural network, and the classifier is used to identify the faulty methods. Then, the GraphSMOTE algorithm is improved to alleviate the impact of class imbalance on classification accuracy. Aiming at the problem of parallel ranking of element suspicious values in traditional SBFL technology, in Phase 2, Doc2Vec is used to learn static features, while spectrum information serves as dynamic features. A RankNet model based on siamese multi-layer perceptron is constructed to score and rank statements in the faulty method. This work conducts experiments on 5 real projects of Defects4J benchmark. Experimental results show that, compared with the traditional SBFL technique and two baseline methods, our approach improves the Top-1 accuracy by 262.86%, 29.59% and 53.01%, respectively, which verifies the effectiveness of Two-RGCNFL. Furthermore, this work verifies the importance of inter-class dependencies through ablation experiments.
基金supported in part by the Universityindustry Collaborative Education Program of the Ministry of Education under Grant No.202102383004。
文摘The advancement of Internet of Things(IoT)technology is driving industries toward intelligent digital transformation,highlighting the crucial role of software engineering.Despite this,the integration of software engineering into IoT engineering education remains underexplored.To address this gap,the School of Software at North University of China,in collaboration with QST Innovation Technology Group Co.,Ltd.(QST),has developed an innovative educational mechanism.This initiative focuses on the software engineering IoT track and optimizes the teaching process through the outcome-based education(OBE)concept.It incorporates military-industrial characteristics,introduces advanced information and technology curricula,and enhances laboratory infrastructure.The goal is to cultivate innovative talents with unique capabilities,thereby fostering the comprehensive development and application of IoT technology.
文摘BACKGROUND Knowledge-based systems(KBS)are software applications based on a knowledge database and an inference engine.Various experimental KBS for computerassisted medical diagnosis and treatment were started to be used since 70s(VisualDx,GIDEON,DXPlain,CADUCEUS,Internist-I,Mycin etc.).AIM To present in detail the“Electronic Pediatrician(EPed)”,a medical non-machine learning artificial intelligence(nml-AI)KBS in its prototype version created by the corresponding author(with database written in Romanian)that offers a physiopathology-based differential and positive diagnosis and treatment of ill children.METHODS EPed specifically focuses on the physiopathological reasoning of pediatric clinical cases.EPed has currently reached its prototype version 2.0,being able to diagnose 302 physiopathological macro-links(briefly named“clusters”)and 269 pediatric diseases:Some examples of diagnosis and a previous testing of EPed on a group of 34 patients are also presented in this paper.RESULTS The prototype EPed can currently diagnose 269 pediatric infectious and noninfectious diseases(based on 302 clusters),including the most frequent respiratory/digestive/renal/central nervous system infections,but also many other noninfectious pediatric diseases like autoimmune,oncological,genetical diseases and even intoxications,plus some important surgical pathologies.CONCLUSION EPed is the first and only physiopathology-based nml-AI KBS focused on general pediatrics and is the first and only pediatric Romanian KBS addressed to medical professionals.Furthermore,EPed is the first and only nml-AI KBS that offers not only both a physiopathology-based differential and positive disease diagnosis,but also identifies possible physiopathological“clusters”that may explain the signs and symptoms of any child-patient and may help treating that patient physiopathologically(until a final diagnosis is found),thus encouraging and developing the physiopathological reasoning of any clinician.
基金supported by the Guangdong Higher Education Association’s“14th Five Year Plan”2024 Higher Education Research Project(24GYB03)the Natural Science Foundation of Guangdong Province(2024A1515010255)。
文摘With the rapid advancement of information technology,the quality assurance and evaluation of software engineering education have become pivotal concerns for higher education institutions.In this paper,we focus on a comparative study of software engineering education in China and Europe,aiming to explore the theoretical frameworks and practical pathways employed in both regions.Initially,we introduce and contrast the engineering education accreditation systems of China and Europe,including the Chinese engineering education accreditation framework and the European EUR-ACE(European Accreditation of Engineering Programmes)standards,highlighting their core principles and evaluation methodologies.Subsequently,we provide case studies of several universities in China and Europe,such as Sun Yat-sen University,Tsinghua University,Technical University of Munich,and Imperial College London.Finally,we offer recommendations to foster mutual learning and collaboration between Chinese and European institutions,aiming to enhance the overall quality of software engineering education globally.This work provides valuable insights for educational administrators,faculty members,and policymakers,contributing to the ongoing improvement and innovative development of software engineering education in China and Europe.
文摘Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detection efficiency. Additionally, this technology provides developers with a means to quickly identify errors, thereby improving software robustness and overall quality. However, current research in software defect prediction often faces challenges, such as relying on a single data source or failing to adequately account for the characteristics of multiple coexisting data sources. This approach may overlook the differences and potential value of various data sources, affecting the accuracy and generalization performance of prediction results. To address this issue, this study proposes a multivariate heterogeneous hybrid deep learning algorithm for defect prediction (DP-MHHDL). Initially, Abstract Syntax Tree (AST), Code Dependency Network (CDN), and code static quality metrics are extracted from source code files and used as inputs to ensure data diversity. Subsequently, for the three types of heterogeneous data, the study employs a graph convolutional network optimization model based on adjacency and spatial topologies, a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) hybrid neural network model, and a TabNet model to extract data features. These features are then concatenated and processed through a fully connected neural network for defect prediction. Finally, the proposed framework is evaluated using ten promise defect repository projects, and performance is assessed with three metrics: F1, Area under the curve (AUC), and Matthews correlation coefficient (MCC). The experimental results demonstrate that the proposed algorithm outperforms existing methods, offering a novel solution for software defect prediction.
