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OpenIFEM:A High Performance Modular Open-Source Software of the Immersed Finite Element Method for Fluid-Structure Interactions 认领 引用 被引量：3
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作者 Jie Cheng Feimi Yu Lucy T.Zhang 《Computer Modeling in Engineering & Sciences》 SCIE EI 2019年第4期91-124,共34页
We present a high performance modularly-built open-source software-OpenIFEM.OpenIFEM is a C++implementation of the modified immersed finite element method(mIFEM)to solve fluid-structure interaction(FSI)problems.This s... We present a high performance modularly-built open-source software-OpenIFEM.OpenIFEM is a C++implementation of the modified immersed finite element method(mIFEM)to solve fluid-structure interaction(FSI)problems.This software is modularly built to perform multiple tasks including fluid dynamics(incompressible and slightly compressible fluid models),linear and nonlinear solid mechanics,and fully coupled fluid-structure interactions.Most of open-source software packages are restricted to certain discretization methods;some are under-tested,under-documented,and lack modularity as well as extensibility.OpenIFEM is designed and built to include a set of generic classes for users to adapt so that any fluid and solid solvers can be coupled through the FSI algorithm.In addition,the package utilizes well-developed and tested libraries.It also comes with standard test cases that serve as software and algorithm validation.The software can be built on cross-platform,i.e.,Linux,Windows,and Mac OS,using CMake.Efficient parallelization is also implemented for high-performance computing for large-sized problems.OpenIFEM is documented using Doxygen and publicly available to download on GitHub.It is expected to benefit the future development of FSI algorithms and be applied to a variety of FSI applications. 展开更多
关键词 Immersed finite element method open-source parallelization fluid-structure interaction adaptive mesh refinement
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Open-Source Software Defined Networking Controllers:State-of-the-Art,Challenges and Solutions for Future Network Providers 认领 引用 被引量：1
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作者 Johari Abdul Rahim Rosdiadee Nordin Oluwatosin Ahmed Amodu 《Computers, Materials & Continua》 SCIE EI 2024年第7期747-800,共54页
Software Defined Networking(SDN)is programmable by separation of forwarding control through the centralization of the controller.The controller plays the role of the‘brain’that dictates the intelligent part of SDN t... Software Defined Networking(SDN)is programmable by separation of forwarding control through the centralization of the controller.The controller plays the role of the‘brain’that dictates the intelligent part of SDN technology.Various versions of SDN controllers exist as a response to the diverse demands and functions expected of them.There are several SDN controllers available in the open market besides a large number of commercial controllers;some are developed tomeet carrier-grade service levels and one of the recent trends in open-source SDN controllers is the Open Network Operating System(ONOS).This paper presents a comparative study between open source SDN controllers,which are known as Network Controller Platform(NOX),Python-based Network Controller(POX),component-based SDN framework(Ryu),Java-based OpenFlow controller(Floodlight),OpenDayLight(ODL)and ONOS.The discussion is further extended into ONOS architecture,as well as,the evolution of ONOS controllers.This article will review use cases based on ONOS controllers in several application deployments.Moreover,the opportunities and challenges of open source SDN controllers will be discussed,exploring carriergrade ONOS for future real-world deployments,ONOS unique features and identifying the suitable choice of SDN controller for service providers.In addition,we attempt to provide answers to several critical questions relating to the implications of the open-source nature of SDN controllers regarding vendor lock-in,interoperability,and standards compliance,Similarly,real-world use cases of organizations using open-source SDN are highlighted and how the open-source community contributes to the development of SDN controllers.Furthermore,challenges faced by open-source projects,and considerations when choosing an open-source SDN controller are underscored.Then the role of Artificial Intelligence(AI)and Machine Learning(ML)in the evolution of open-source SDN controllers in light of recent research is indicated.In addition,the challenges and limitations associated with deploying open-source SDN controllers in production networks,how can they be mitigated,and finally how opensource SDN controllers handle network security and ensure that network configurations and policies are robust and resilient are presented.Potential opportunities and challenges for future Open SDN deployment are outlined to conclude the article. 展开更多
关键词 ONOS open source software SDN software defined networking
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A Blockchain-Based Copyright Management Scheme for Open-Source Software and Hardware Designs 认领 引用
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作者 Yuhang Jin Li Ling 《Tsinghua Science and Technology》 SCIE EI CAS CSCD 2025年第3期1157-1173,共17页
Plagiarism in software code and hardware design threatens the open-source movement and the software and hardware industries.It is essential to differentiate between the unethical act of plagiarism and the legitimate u... Plagiarism in software code and hardware design threatens the open-source movement and the software and hardware industries.It is essential to differentiate between the unethical act of plagiarism and the legitimate use of open-source resources.Existing copyright protection measures,such as license design,inadequately address copyright ownership and protection issues.Furthermore,they fail to detect plagiarism methods for open-source hardware projects,such as circuit location modification.To address these challenges,this paper proposes a blockchain-based copyright management scheme,which introduces a general originality detection model based on community detection,extracting adjustable granularity digests from code and design files.These digests are stored on a peer-to-peer blockchain,enabling nodes to verify the originality via smart contracts.Additionally,the scheme improves the storage structure,protecting the rights of authors and contributors.Experimental results demonstrate the effectiveness and runtime efficiency of the proposed model in extracting digests for blockchain storage while maintaining verification accuracy.The scheme offers enhanced generality,practical performance,and suitability for distributed development and maintenance,with considerable implications for evidence gathering,fostering innovation and integrity. 展开更多
关键词 blockchain copyright management open-source software and hardware originality detection
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OPFEM:architectural design and implementation of a CAE software for finite element modeling and simulation 认领 引用
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作者 Wei Huang Qun Huang +3 位作者 Jie Yang Xiaowei Bai Tianyun He Heng Hu 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第2期264-333,共70页
To address challenges in architectural extensibility and cross-module collaboration of CAE software,this study proposes OPFEM(open-source Python-based finite element modeling)—an open-source framework featuring a uni... To address challenges in architectural extensibility and cross-module collaboration of CAE software,this study proposes OPFEM(open-source Python-based finite element modeling)—an open-source framework featuring a unified four-layer architecture.The geometric modeling framework achieves plug-in support for geometric kernels through an interface abstraction layer and adapter patterns,decoupling kernel-specific implementations while enabling state machine-driven interaction design and parametric sketching.The pre-processing modules establish multi-level associations among materials,sections,and geometric entities using Composite and Factory patterns,while implementing Observer pattern to ensure geometric-mesh consistency and employing finite-state machines to optimize boundary workflows.The computational modules implement a modular finite element library that decouples topology from element attributes,along with a surface boundary element technique for load conversion and task-scheduling management,validated through a cantilever beam large-deformation case.The post-processing module facilitates standardized data storage and dynamic field visualization through architecture-level standardized interface definitions and hierarchical component design.Collectively,OPFEM achieves full-process integration from parametric modeling to nonlinear solving and visualization,enhancing configuration efficiency and providing an extensible,pattern-driven solution for complex CAE challenges. 展开更多
关键词 Finite element method CAE software architecture Object-oriented programming Modular design Opensource software
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Software and layout optimization of HIRFL-CSR external-target experiment 认领 引用
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作者 Jian-Wang Hong Chen-Lu Hu +29 位作者 Yu-Hong Yu Nu Xu Zhi-Yu Sun Hao Qiu Zhi-Gang Xiao Ming Shao Li-Min Duan Xiong-Hong He Zhi-Hui Xu Yi Wang Dong Han Zi-Xuan Chen Feng-Yi Zhao He-Run Yang Xiang-Lun Wei Rong-Jiang Hu Feng Liu Hua Pei Ya-Ping Wang Ye Tian Zhi Qin Dong-Dong Hu Guo-Dong Shen Li-Jun Mao Wei Wu Wei You Yu-Quan Chen Peng Yang De-Qing Fang Ya-Peng Zhang 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2026年第5期289-297,共9页
Heavy-ion collisions(HICs)is a unique experimental tool for investigating the properties of nuclear matter under extreme conditions in the laboratory.At HIRFL-CSR energies,HICs can create nuclear matter with 2-3 times... Heavy-ion collisions(HICs)is a unique experimental tool for investigating the properties of nuclear matter under extreme conditions in the laboratory.At HIRFL-CSR energies,HICs can create nuclear matter with 2-3 times the saturation density(ρ0).The HIRFL-CSR external-target experiment(CEE)is a large-acceptance spectrometer designed to explore frontier topics in high-energy nuclear physics,such as the QCD phase structure and nuclear matter equation of states.In this letter,we introduce simulation and analysis software for the CEE experiment(CeeROOT).Based on the CEE conceptual design and CeeROOT software,the configurations of its subdetectors were optimized by considering foreseeable physical constraints.The final detector layout of the CEE spectrometer and its acceptances were validated through simulations of U+U collisions at 500 MeV/u and pp collisions at 2.8 GeV,which demonstrated that the CEE experiment will serve as a detector with wide acceptance and multi-particle identification capabilities for studying high-energy nuclear physics topics at HIRFL-CSR energies with pp,pA,and A A collisions. 展开更多
关键词 CEE experiment Simulation software Optimization HIRFL-CSR
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QIMIG:A Quantum-Inspired Evolutionary Framework for Software Library Migration 认领 引用
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作者 Yun Liu Jinghua Zhao +3 位作者 Liang Ma Zijie Huang Lizhi Cai Jianxin Ge 《Computers, Materials & Continua》 SCIE EI 2026年第9期795-812,共18页
Automated library migration reduces refactoring costs but challenges traditional evolutionary algorithms,which often suffer from premature convergence and poor recall in sparse,complex API mapping spaces.To address th... Automated library migration reduces refactoring costs but challenges traditional evolutionary algorithms,which often suffer from premature convergence and poor recall in sparse,complex API mapping spaces.To address this,we propose QIMIG,a multi-objective optimization framework integrating quantum-inspired encoding with quality-aware and greedy heuristic filtering.QIMIG utilizes a probabilistic Q-bit representation to maintain population diversity and avoid local optima.Simultaneously,its heuristic components leverage historical usage context to filter semantic noise and guide the search toward valid mappings.Evaluated on 9 real-world migration rules derived from 57,447 open-source projects,QIMIG statistically significantly outperforms state-of-the-art baselines such as UNSGA-III.The framework achieves a global mean F1-score of 0.92,exceeding the best-performing baseline by an absolute margin of 0.05,and demonstrates strong stability in resolving complex mapping structures. 展开更多
关键词 Library migration API mapping search-based software engineering quantum-inspired evolutionary algorithm multi-objective optimization
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Methods,Algorithms,Codes,and Software for Theoretical and Computational Chemistry 认领 引用
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作者 Bin Jiang 《Chinese Journal of Chemical Physics》 SCIE EI CAS CSCD 2026年第3期301-302,共2页
Theoretical and computational chemistry has profoundly impacted a wide range of disciplines,from chemistry and physics to biology and materials science.In recent years,remarkable advances in electronic structure theor... Theoretical and computational chemistry has profoundly impacted a wide range of disciplines,from chemistry and physics to biology and materials science.In recent years,remarkable advances in electronic structure theory,molecular dynamics,and machine learning methods——coupled with increasingly powerful algorithms and software—have equipped chemists with an unprecedented arsenal of tools to tackle complex chemical problems. 展开更多
关键词 theoretical computational chemistry electronic structure theorymolecular dynamicsand algorithms codes software methods materials sciencein theoretical chemistry
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Software-hardware co-design accelerates materials simulations 认领 引用
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作者 Xiaozhe Wang Wei Zhang En Ma 《Journal of Semiconductors》 EI CAS CSCD 2026年第7期28-31,共4页
Accurate atomistic and electronic-structure calculations based on density functional theory(DFT)and DFT-based ab initio molecular dynamics(AIMD)calculations underpin much of modern computational chemistry and material... Accurate atomistic and electronic-structure calculations based on density functional theory(DFT)and DFT-based ab initio molecular dynamics(AIMD)calculations underpin much of modern computational chemistry and materials science[1].DFT calculations offer quantitative insights into chemical bonding,charge transport,phase stability,reaction pathways,etc[2]. 展开更多
关键词 chemical bondingcharge software hardware co design density functional theory dft dft based ab initio molecular dynamics ab initio molecular dynamics aimd calculations materials simulations materials science density functional theory
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Predicting Software Security Bugs Using Machine Learning and Quality Metrics: An Empirical Study 认领 引用
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作者 Mohamed Diouf Elisée Toe +2 位作者 Manel Grichi Haïfa Nakouri Fehmi Jaafar 《Computers, Materials & Continua》 SCIE EI 2026年第6期436-477,共42页
Software security bugs present significant security risks to modern systems,leading to unauthorized access,data breaches,and severe operational and financial consequences.Early prediction of such vulnerabilities is th... Software security bugs present significant security risks to modern systems,leading to unauthorized access,data breaches,and severe operational and financial consequences.Early prediction of such vulnerabilities is therefore essential for strengthening software reliability and reducing remediation costs.This study investigates the extent to which static software quality metrics can identify vulnerable code and evaluates the effectiveness of machine learning models for large-scale security-bug prediction.We analyze a dataset of 338,442 source files,including 33,294 buggy files,collected from seven major open-source ecosystems.These ecosystems include GitHub Security Advisories(GHSA),Python Package Index(PyPI),OSS-Fuzz(Google’s open-source fuzzing service),Node Package Manager(npm),Packagist(the PHP package repository),Apache Maven,and NuGet(the.NET package manager).Using the Open Source Vulnerabilities(OSV)platform,we identify 7685 confirmed security bugs and extract 25 static software quality metrics per file with the Understand analysis tool.We apply five complementary feature-importance techniques and evaluate eleven machine-learning classifiers under a time-series cross-validation protocol.Our analysis reveals three key findings.First,six core metrics consistently show strong associations with the presence of security bugs across all feature-selection methods.Second,buggy files exhibit substantially higher metric values,with medians approximately three times those of non-buggy files,a pattern we term the“3×rule”;Mann-Whitney U tests confirm that these differences are statistically significant.Third,the machine-learning models achieve strong predictive performance,with XGBoost providing the best results(recall=0.82,precision=0.95,Receiver Operating Characteristic-Area Under the Curve(ROC-AUC)=0.91).Based on these findings,we propose data-driven warning and critical thresholds for the most influential metrics to support proactive security assessment.Overall,this work provides large-scale empirical evidence that software quality metrics,combined with machine learning,offer actionable signals for detecting security bugs and for integrating automated vulnerability prediction into software development workflows. 展开更多
关键词 Security bugs vulnerability prediction software metrics machine learning code complexity software quality
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Data-Driven Test Case Prioritization(DD-TCP):A Machine Learning Framework for Intelligent Software Quality Assurance 认领 引用
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作者 Hafiz Arslan Ramzan Kamrul Islam +3 位作者 Md Ahbab Hussain Raiyan Muntasir Monim Sabit Md Asad Sadia Ramzan 《Computers, Materials & Continua》 SCIE EI 2026年第7期1369-1389,共21页
Regression testing of large-scale,data-intensive software systems demands efficient test-case prioritization strategies to detect faults early while minimizing computational cost.Conventional prioritization methods,su... Regression testing of large-scale,data-intensive software systems demands efficient test-case prioritization strategies to detect faults early while minimizing computational cost.Conventional prioritization methods,such as coverage-based and risk-based approaches,lack adaptability to evolving project dynamics and fail to leverage the rich test-execution data accumulated over continuous integration cycles.This study presents a Data-Driven Test-Case Prioritization(DD-TCP)Framework that incorporates statistical and machine-learning techniques to model the relationship between test-case features and historical fault detection outcomes.The framework extracts multidimensional attributes including code-change frequency,dependency metrics,execution duration,and past failure density,which are normalized and embedded into a predictive ranking model based on gradient-boosted decision trees.Test cases are then dynamically reordered using a probabilistic gain function that maximizes early fault detection probability.Comprehensive simulations on representative open-source project datasets and synthetically generated large-scale test suites reveal that the proposed Data-Driven Test-Case Prioritization(DD-TCP)framework consistently achieves superior performance,yielding a 32.4%improvement in Average Percentage of Faults Detected(APFD)and a 27.1%reduction in execution overhead relative to baseline methods.The results demonstrate the feasibility of data-centric intelligence for scalable regression testing and provide an analytical foundation for integrating machine learning into next-generation Software Quality Assurance pipelines. 展开更多
关键词 Data-driven test-case prioritization regression testing software quality assurance machine learning continuous integration fault detection efficiency intelligent software systems
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Structural Design Optimization of the Turbine Baffle Based on the Slime Mould Algorithm of Zhizhou Software 认领 引用
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作者 PEI Ben LIU Yiyuan +4 位作者 CHEN Yalong TENG Da HOU Naixian MI Dong YAN Cheng 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI CSCD 2026年第3期412-426,共15页
As a critical component of the turbine rotor,the baffle plays an essential role in ensuring safe and reliable operation of the aero-engine.This study addresses the issue of excessive local stress in the baffle of a hi... As a critical component of the turbine rotor,the baffle plays an essential role in ensuring safe and reliable operation of the aero-engine.This study addresses the issue of excessive local stress in the baffle of a high-pressure turbine disc.The structural design optimization is performed using the self-developed Zhizhou software integrated with the slime mould algorithm(SMA).Leveraging advanced algorithms and comprehensive simulation interfaces,the Zhizhou software effectively exploits the potential of structural design,leading to significant improvements in structural performance.The SMA algorithm employs a positive feedback mechanism and adaptive strategies through the incorporation of fitness weights and oscillation factors.These parameters simulate the oscillatory contraction behavior of slime moulds,allowing the algorithm to dynamically adjust search direction and speed,thereby achieving an effective balance between local exploration and global optimization.During the optimization process,a sector submodel is established,and the contact model is simplified using a force load equivalence approach to improve computational efficiency.Subsequently,a parametric model of the baffle is developed based on geometric characteristics,stress responses,and boundary constraints,with the variation ranges of key parameters being determined.A mathematical model is then formulated with the objective of minimizing the maximum equivalent stress,under the constraint of the axial support reaction force at the contact surface.Finally,an integrated design optimization workflow is constructed using Zhizhou in combination with Unigraphics(UG)and Workbenchs,as well as incorporating the SMA algorithm to optimize the baffle structure.After optimization,the maximum equivalent stress is decreased from 1382.4 to 1235.4 MPa,a reduction of 10.6%.Meanwhile,the axial support reaction force is increased from 4158.9 to 4330.6 N,a variation of 4.0%,which satisfies the requirement for being within 12%.These results validate the effectiveness of the SMA algorithm in the structural design optimization of the baffle and demonstrate the practical value of the Zhizhou software in engineering applications. 展开更多
关键词 high-pressure turbine baffle Zhizhou software slime mould algorithm(SMA) structural optimization
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GranuSAS:Software of rapid particle size distribution analysis from small angle scattering data 认领 引用
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作者 Qiaoyu Guo Fei Xie +3 位作者 Xuefei Feng Zhe Sun Changda Wang Xuechen Jiao 《Chinese Physics B》 SCIE EI CAS CSCD 2026年第2期216-225,共10页
Small angle x-ray scattering(SAXS)is an advanced technique for characterizing the particle size distribution(PSD)of nanoparticles.However,the ill-posed nature of inverse problems in SAXS data analysis often reduces th... Small angle x-ray scattering(SAXS)is an advanced technique for characterizing the particle size distribution(PSD)of nanoparticles.However,the ill-posed nature of inverse problems in SAXS data analysis often reduces the accuracy of conventional methods.This article proposes a user-friendly software for PSD analysis,GranuSAS,which employs an algorithm that integrates truncated singular value decomposition(TSVD)with the Chahine method.This approach employs TSVD for data preprocessing,generating a set of initial solutions with noise suppression.A high-quality initial solution is subsequently selected via the L-curve method.This selected candidate solution is then iteratively refined by the Chahine algorithm,enforcing constraints such as non-negativity and improving physical interpretability.Most importantly,GranuSAS employs a parallel architecture that simultaneously yields inversion results from multiple shape models and,by evaluating the accuracy of each model's reconstructed scattering curve,offers a suggestion for model selection in material systems.To systematically validate the accuracy and efficiency of the software,verification was performed using both simulated and experimental datasets.The results demonstrate that the proposed software delivers both satisfactory accuracy and reliable computational efficiency.It provides an easy-to-use and reliable tool for researchers in materials science,helping them fully exploit the potential of SAXS in nanoparticle characterization. 展开更多
关键词 small angle x-ray scattering data analysis software particle size distribution inverse problem
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A Hybrid Approach to Software Testing Efficiency:Stacked Ensembles and Deep Q-Learning for Test Case Prioritization and Ranking 认领 引用
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作者 Anis Zarrad Thomas Armstrong Jaber Jemai 《Computers, Materials & Continua》 SCIE EI 2026年第3期1726-1746,共21页
Test case prioritization and ranking play a crucial role in software testing by improving fault detection efficiency and ensuring software reliability.While prioritization selects the most relevant test cases for opti... Test case prioritization and ranking play a crucial role in software testing by improving fault detection efficiency and ensuring software reliability.While prioritization selects the most relevant test cases for optimal coverage,ranking further refines their execution order to detect critical faults earlier.This study investigates machine learning techniques to enhance both prioritization and ranking,contributing to more effective and efficient testing processes.We first employ advanced feature engineering alongside ensemble models,including Gradient Boosted,Support Vector Machines,Random Forests,and Naive Bayes classifiers to optimize test case prioritization,achieving an accuracy score of 0.98847 and significantly improving the Average Percentage of Fault Detection(APFD).Subsequently,we introduce a deep Q-learning framework combined with a Genetic Algorithm(GA)to refine test case ranking within priority levels.This approach achieves a rank accuracy of 0.9172,demonstrating robust performance despite the increasing computational demands of specialized variation operators.Our findings highlight the effectiveness of stacked ensemble learning and reinforcement learning in optimizing test case prioritization and ranking.This integrated approach improves testing efficiency,reduces late-stage defects,and improves overall software stability.The study provides valuable information for AI-driven testing frameworks,paving the way for more intelligent and adaptive software quality assurance methodologies. 展开更多
关键词 Software testing test case prioritization test case ranking machine learning reinforcement learning deep Q-learning
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Exploring Reform Strategies for Software Engineering Talent Development Models in the AI Era 认领 引用
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作者 Linying Jiang Guibing Guo +1 位作者 Jianzhe Zhao Xiaochun Yang 《计算机教育》 2026年第3期95-100,共6页
The rapid development of artificial intelligence(AI)has placed significant pressure on universities to rethink how they train software engineering students.Tools like GitHub Copilot can now generate basic code in seco... The rapid development of artificial intelligence(AI)has placed significant pressure on universities to rethink how they train software engineering students.Tools like GitHub Copilot can now generate basic code in seconds.This raises important questions:What is the value of traditional programming education?What role should instructors play when AI becomes a powerful teaching assistant?How should the goals of software engineering programs change as companies increasingly use AI to handle coding tasks?This paper explores the key challenges AI brings to software engineering education and proposes practical strategies for updating talent development models to meet these changes. 展开更多
关键词 Artificial intelligence Software engineering education Talent development Reform strategies
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Enhancing Code Quality with LLM in Software Static Analysis 认领 引用
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作者 Niu Zhi Dong Luming 《ZTE Communications》 2026年第1期65-71,共7页
In the modern era of ubiquitous and highly interconnected information technology,cybersecurity threats stemming from software code vulnerabilities have become increasingly severe,posing significant risks to the confid... In the modern era of ubiquitous and highly interconnected information technology,cybersecurity threats stemming from software code vulnerabilities have become increasingly severe,posing significant risks to the confidentiality,integrity,and availability of modern information systems.To enhance software code quality,enterprises often integrate static code analysis tools into Continuous Integration(CI) pipelines.However,the high rates of false positives and false negatives remain a challenge.The advent of large language models(LLMs),such as ChatGPT,presents a new opportunity to address these challenges.In this paper,we propose AI-SCDF,a framework that utilizes the custombuilt Nebula-Coder AI model for detecting and fixing code security issues in real time during the developer ' s personal build process.We construct a static code checking rule knowledge base through summarizing and classifying Common Weakness Enumeration(CWE) code security problems identified by security and quality assurance teams.The rule knowledge base is combined with CodeFuse-processed code contexts to serve as input for an AI code security detection microservice,which assists in identifying code quality and security issues.If any abnormalities are detected,they are addressed by an AI code security patching microservice,which alerts the developer and requests confirmation before committing the code into the repository.Experimental results show that our approach effectively improves code quality.We also develop a VS Code plugin for code alert detection and fix based on LLMs,which facilitates test shift-left and lowers the risk of software development. 展开更多
关键词 software static analysis LLM CWE knowledge base
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Exploration and Practice in Building a Diversified Faculty Team for Specialized Software Talent Cultivation 认领 引用
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作者 Fangshi Wang Weiwei Xing +1 位作者 Wei Lu Shunli Zhang 《计算机教育》 2026年第3期67-73,共7页
Faculty development serves as a critical foundation for ensuring the quality of higher education.To meet the needs of cultivating specialized software talents and promoting teaching reform,it is particularly crucial t... Faculty development serves as a critical foundation for ensuring the quality of higher education.To meet the needs of cultivating specialized software talents and promoting teaching reform,it is particularly crucial to build a faculty team with knowledge in industry application fields and experience in domestic software development.This paper first analyzes the new requirements for the faculty imposed by the cultivation of specialized software talents and the existing problems in the current faculty.Then,in response to these issues,it introduces the reforms and explorations carried out by the School of Software Engineering at Beijing Jiaotong University in the construction of the faculty for cultivating specialized software talents.The aim is to build a high-caliber and diversified faculty that boasts strong political qualities,interdisciplinary integration,complementary advantages between full-time and part-time faculty,and in-depth integration of industry and education. 展开更多
关键词 Specialized software talents Diversified faculty team Interdisciplinary integration Integration of industry and education
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PCER:A dynamic feedback model for optimizing graduate student training in software engineering 认领 引用
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作者 Bangchao Wang Yuyang Dai +2 位作者 Hongyan Wan Xinrong Hu Xiong Wei 《计算机教育》 2026年第6期330-342,共13页
Under the national innovation-driven development strategy and the emerging need for new engineering education,the cultivation of high-quality software engineering talent faces increasingly stringent requirements.To ad... Under the national innovation-driven development strategy and the emerging need for new engineering education,the cultivation of high-quality software engineering talent faces increasingly stringent requirements.To address critical challenges in the current graduate training model at regional universities,including fragmented undergraduate-to-graduate transition,ambiguous competency development pathways,and structural deficiencies in teaching resources,this paper proposes a novel PCER(Plan-Carry Out-Evaluate-Refine)dynamic feedback model for software engineering graduate education.The PCER framework establishes phased training objectives,develops an adaptive multi-level resource repository,and implements a multidimensional evaluation mechanism.Empirical results demonstrate that the model significantly enhances students'academic innovation capabilities and engineering practice competencies,providing a replicable reform path for the cultivation of software engineering talents.The PCER approach offers a transferable solution for engineering education reform,particularly for institutions facing similar resource constraints and quality improvement challenges. 展开更多
关键词 Software engineering Training model Graduate education Teaching resources
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Developing Innovation Capacity in Graduate Software Engineering Practice Through Newquality Productive Forces 认领 引用
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作者 Ting Cai Tianyuan Yin +2 位作者 Yuxin Wu Shan Lin Zhiwei Ye 《计算机教育》 2026年第3期220-229,共10页
The rapid development of new-quality productive forces(NQPF)has intensified the demand for high-level innovative talent.As a representative of NQPF,generative artificial intelligence(GenAI)offers powerful tools to res... The rapid development of new-quality productive forces(NQPF)has intensified the demand for high-level innovative talent.As a representative of NQPF,generative artificial intelligence(GenAI)offers powerful tools to reshape talent cultivation but also presents significant challenges,including skill hollowing,ethical risks,and a growing disconnect between education and industry needs.Currently,graduate-level software engineering education struggles with outdated curricula and insufficient alignment with practical demands.In this paper,we propose a dual-core collaborative framework driven by“GenAI technology”and“industry demand”.Under this framework,we design a four-dimensional capability development path to enhance graduate students’innovation in software engineering practice.This path focuses on①scientific research innovation,②engineering problem-solving,③cross-domain collaborative evolution,and④ethical risk governance.The proposed approach promotes a shift from traditional knowledge transfer to human-machine collaborative innovation,aligning talent cultivation with the demands of the NQPF. 展开更多
关键词 New-quality productive forces GenAI Graduate student Software engineering Innovation ability
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Educational practice in software engineering empowered by generative AI and virtual humans 认领 引用
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作者 Yingying Zhang Runze Liu +5 位作者 Hao Sheng Xudong Li Ying Li Haogang Zhu Da Yang Xuefei Huang 《计算机教育》 2026年第6期213-220,共8页
The rapid advancement of information technology catalyzes the digital transformation of education,urging the education sector to adapt to technological changes actively.Focusing on the dual-engine drive of large model... The rapid advancement of information technology catalyzes the digital transformation of education,urging the education sector to adapt to technological changes actively.Focusing on the dual-engine drive of large models and digital human technologies,we innovate across multiple dimensions to reconstruct the software engineering curriculum system.We build multimodal teaching frameworks and personalized learning paths to break through traditional limitations,establish a digital and intelligent course resource supermarket for precise service supply,integrate smart classrooms with blended learning to reshape teaching spaces,and create an all-process digital and intelligent evaluation system to revolutionize assessment and feedback mechanisms.By deeply integrating intelligent technologies into the entire education process,we enhance software engineering course quality and offer new ideas and practical pathways for educational reform. 展开更多
关键词 Large language models Digital human technology Digital education Software engineering
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Research on the Construction of a software engineering course knowledge graph empowered by the Xhang AI teaching assistant 认领 引用
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作者 Xuefei Huang Yiming Gai +3 位作者 Ying Li Hao Sheng Da Yang Yingying Zhang 《计算机教育》 2026年第6期221-228,共8页
In view of the needs and challenges of digital transformation in the field of education under the background ofªnew engineeringº,this paper analyzes how to improve the teaching efficiency of software enginee... In view of the needs and challenges of digital transformation in the field of education under the background ofªnew engineeringº,this paper analyzes how to improve the teaching efficiency of software engineering course programming and realize personalized guidance through the large language models,and proposes to use the multi-channel feedback optimization technology of Xhang AI assistant to extract and construct the multimodal knowledge graph of the adaptive course,and realize the personalized fine-tuning of the model combined with the private data collaborative sharing platform,so as to enhance the accuracy and applicability of the knowledge graph of software engineering course.The automatic construction and updating of curriculum knowledge graph based onªXhang AI assistantºcan cover a wider range of educational courses and fields,and promote the development of intelligent and digital education. 展开更多
关键词 Xhang AI assistant Large language model Multimodal knowledge graph Digital strategy Software engineering
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