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
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
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
Over the past decade, open-source software use has grown. Today, many companies including Google, Microsoft, Meta, RedHat, MongoDB, and Apache are major participants of open-source contributions. With the increased us...Over the past decade, open-source software use has grown. Today, many companies including Google, Microsoft, Meta, RedHat, MongoDB, and Apache are major participants of open-source contributions. With the increased use of open-source software or integration of open-source software into custom-developed software, the quality of this software component increases in importance. This study examined a sample of open-source applications from GitHub. Static software analytics were conducted, and each application was classified for its risk level. In the analyzed applications, it was found that 90% of the applications were classified as low risk or moderate low risk indicating a high level of quality for open-source applications.展开更多
With the rapid development of artificial intelligence,the intelligence level of software is increasingly improving.Intelligent software,which is widely applied in crucial fields such as autonomous driving,intelligent ...With the rapid development of artificial intelligence,the intelligence level of software is increasingly improving.Intelligent software,which is widely applied in crucial fields such as autonomous driving,intelligent customer service,and medical diagnosis,is constructed based on complex technologies like machine learning and deep learning.Its uncertain behavior and data dependence pose unprecedented challenges to software testing.However,existing software testing courses mainly focus on conventional contents and are unable to meet the requirements of intelligent software testing.Therefore,this work deeply analyzed the relevant technologies of intelligent software testing,including reliability evaluation indicator system,neuron coverage,and test case generation.It also systematically designed an intelligent software testing course,covering teaching objectives,teaching content,teaching methods,and a teaching case.Verified by the practical teaching in four classes,this course has achieved remarkable results,providing practical experience for the reform of software testing courses.展开更多
In the context of large language model(LLM)reshaping software engineering education,this paper presents OSSerCopilot,a LLM-based tutoring system designed to address the critical challenge faced by newcomers(especially...In the context of large language model(LLM)reshaping software engineering education,this paper presents OSSerCopilot,a LLM-based tutoring system designed to address the critical challenge faced by newcomers(especially student contributors)in open source software(OSS)communities.Leveraging natural language processing,code semantic understanding,and learner profiling,the system functions as an intelligent tutor to scaffold three core competency domains:contribution guideline interpretation,project architecture comprehension,and personalized task matching.By transforming traditional onboarding barriers-such as complex contribution documentation and opaque project structures-into interactive learning journeys,OSSerCopilot enables newcomers to complete their first OSS contribution more easily and confidently.This paper highlights how LLM technologies can redefine software engineering education by bridging the gap between theoretical knowledge and practical OSS participation,offering implications for curriculum design,competency assessment,and sustainable OSS ecosystem cultivation.A demonstration video of the system is available at https://figshare.com/articles/media/OSSerCopilot_Introduction_mp4/29510276.展开更多
With the advent of the AI era,how can students effectively utilize generative AI large models to assist in course learning?At the same time,how can teachers utilize generative AI tools and the teaching concept of OBE ...With the advent of the AI era,how can students effectively utilize generative AI large models to assist in course learning?At the same time,how can teachers utilize generative AI tools and the teaching concept of OBE to stimulate students’innovative consciousness and teamwork ability,enabling students to identify some problems in a certain industry or field and creatively propose feasible solutions,and truly achieve the cultivation of new models in software engineering course teaching with the assistance of generative AI tools?This paper presents research and practice on a new model for cultivating software engineering courses that integrates generative AI and OBE,introduces the specific process of teaching reform and practice,and finally explains the achievements of teaching reform.展开更多
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
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
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
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
Promoting the integration of industry and education and deepening school-enterprise cooperation in talent cultivation and collaborative innovation are long-term goals of higher education.This paper systematically anal...Promoting the integration of industry and education and deepening school-enterprise cooperation in talent cultivation and collaborative innovation are long-term goals of higher education.This paper systematically analyzes the multiple perspectives,practical challenges,and implementation paths of in-depth school-enterprise cooperation.Based on the typical case of school-enterprise cooperation at the School of Information and Software Engineering,University of Electronic Science and Technology of China(UESTC),this paper explores the innovative practices of in-depth school-enterprise cooperation in talent cultivation,scientific research,and faculty construction.It also explores a multi-party collaborative mechanism from the perspectives of universities,enterprises,students,and the government.By policy guidance,resource integration,and benefit sharing,this mechanism achieves in-depth integration of industry and education,providing references and examples for further development of school-enterprise cooperation in the new era.展开更多
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
Traditional grade-centered evaluation models are inadequate for high-quality software engineering talents in the digital and AI era.This study develops an academic development monitoring system to address shortcomings...Traditional grade-centered evaluation models are inadequate for high-quality software engineering talents in the digital and AI era.This study develops an academic development monitoring system to address shortcomings in dynamics,interdisciplinary integration,and industry adaptability.It builds a multi-dimensional dynamic model covering seven core dimensions with quantitative scoring,non-linear weighting,and DivClust grouping.An intelligent platform with real-time monitoring,early warning,and personalized recommendations integrates AI like multi-modal fusion and large-model diagnosis.The“monitoring-warning-improvement”loop helps optimize training programs,support personalized planning,and bridge talent-industry gaps,enabling digital transformation in software engineering education evaluation.展开更多
In recent years,the widespread applications of open-source software(OSS)have brought great convenience for software developers.However,it is always facing unavoidable security risks,such as open-source code defects an...In recent years,the widespread applications of open-source software(OSS)have brought great convenience for software developers.However,it is always facing unavoidable security risks,such as open-source code defects and security vulnerabilities.To find out the OSS risks in time,we carry out an empirical study to identify the indicators for evaluating the OSS.To achieve a comprehensive understanding of the OSS assessment,we collect 56 papers from prestigious academic venues(such as IEEE Xplore,ACM Digital Library,DBLP,and Google Scholar)in the past 21 years.During the process of the investigation,we first identify the main concerns for selecting OSS and distill five types of commonly used indicators to assess OSS.We then conduct a comparative analysis to discuss how these indicators are used in each surveyed study and their differences.Moreover,we further undertake a correlation analysis between these indicators and uncover 13 confirmed conclusions and four cases with controversy occurring in these studies.Finally,we discuss several possible applications of these conclusions,which are insightful for the research on OSS and software supply chain.展开更多
Although recent studies have examined collaboration within open-source software projects,the focus has primarily been on their motivations and governance.This study explores the complex dynamics of trust and involveme...Although recent studies have examined collaboration within open-source software projects,the focus has primarily been on their motivations and governance.This study explores the complex dynamics of trust and involvement among Cameroonian software developers in open-source projects.In the context of a rapidly evolving software development landscape,these projects have emerged as a transformative force,redefining global collaboration standards.The qualitative methodological approach involved a survey of 22 participants in open-source software projects,including Cameroonian software developers,project governance actors,and open-source community members.Analyses revealed that the trust given to African software developers,including their effective integration into projects and consideration of their specificities and contributions,has a positive impact on their involvement in and ability to appropriate information technologies.By exploring the interaction between cultural,social,and technological factors,this study enhances our understanding of trust mechanisms within open-source communities,especially those involving remote developers.展开更多
An open-source software for field-based plant phenotyping,Precision Plots Analyzer(PREPs),was developed using Window.NET.The software runs on 64-bit Windows computers.This software allows the extraction of phenotypic ...An open-source software for field-based plant phenotyping,Precision Plots Analyzer(PREPs),was developed using Window.NET.The software runs on 64-bit Windows computers.This software allows the extraction of phenotypic traits on a per-microplot basis from orthomosaic and digital surface model(DSM)images generated by Structure-from-Motion/Multi-View-Stereo(SfM-MVS)tools.Moreover,there is no need to acquire skills in geographical information system(GIS)or programming languages for image analysis.Three use cases illustrated the software's functionality.The first involved monitoring the growth of sugar beet varieties in an experimental field using an unmanned aerial vehicle(UAV),where differences among varieties were detected through estimates of crop height,coverage,and volume index.Second,mixed varieties of potato crops were estimated using a UAV and varietal differences were observed from the estimated phenotypic traits.A strong correlation was observed between the manually measured crop height and UAV-estimated crop height.Finally,using a multicamera array attached to a tractor,the height,coverage,and volume index of the 3 potato varieties were precisely estimated.PREPs software is poised to be a useful tool that allows anyone without prior knowledge of programming to extract crop traits for phenotyping.展开更多
Software security poses substantial risks to our society because software has become part of our life. Numerous techniques have been proposed to resolve or mitigate the impact of software security issues. Among them, ...Software security poses substantial risks to our society because software has become part of our life. Numerous techniques have been proposed to resolve or mitigate the impact of software security issues. Among them, software testing and analysis are two of the critical methods, which significantly benefit from the advancements in deep learning technologies. Due to the successful use of deep learning in software security, recently,researchers have explored the potential of using large language models(LLMs) in this area. In this paper, we systematically review the results focusing on LLMs in software security. We analyze the topics of fuzzing, unit test, program repair, bug reproduction, data-driven bug detection, and bug triage. We deconstruct these techniques into several stages and analyze how LLMs can be used in the stages. We also discuss the future directions of using LLMs in software security, including the future directions for the existing use of LLMs and extensions from conventional deep learning research.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 Thought on Socialism with Chinese Characteristics for a New Era 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
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
文摘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.
基金supported by UniversitiKebangsaan Malaysia,under Dana Impak Perdana 2.0.(Ref:DIP–2022–020).
文摘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.
文摘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.
文摘Over the past decade, open-source software use has grown. Today, many companies including Google, Microsoft, Meta, RedHat, MongoDB, and Apache are major participants of open-source contributions. With the increased use of open-source software or integration of open-source software into custom-developed software, the quality of this software component increases in importance. This study examined a sample of open-source applications from GitHub. Static software analytics were conducted, and each application was classified for its risk level. In the analyzed applications, it was found that 90% of the applications were classified as low risk or moderate low risk indicating a high level of quality for open-source applications.
基金Computer Basic Education Teaching Research Project of Association of Fundamental Computing Education in Chinese Universities(Nos.2025-AFCEC-527 and 2024-AFCEC-088)Research on the Reform of Public Course Teaching at Nantong College of Science and Technology(No.2024JGG015).
文摘With the rapid development of artificial intelligence,the intelligence level of software is increasingly improving.Intelligent software,which is widely applied in crucial fields such as autonomous driving,intelligent customer service,and medical diagnosis,is constructed based on complex technologies like machine learning and deep learning.Its uncertain behavior and data dependence pose unprecedented challenges to software testing.However,existing software testing courses mainly focus on conventional contents and are unable to meet the requirements of intelligent software testing.Therefore,this work deeply analyzed the relevant technologies of intelligent software testing,including reliability evaluation indicator system,neuron coverage,and test case generation.It also systematically designed an intelligent software testing course,covering teaching objectives,teaching content,teaching methods,and a teaching case.Verified by the practical teaching in four classes,this course has achieved remarkable results,providing practical experience for the reform of software testing courses.
基金supported by the National Natural Science Foundation of China (62202022, 92582204, and 62572030)the Fundamental Research Funds for the Central Universitiesthe exploratory elective projects of the State Key Laboratory of Complex and Critical Software Environments
文摘In the context of large language model(LLM)reshaping software engineering education,this paper presents OSSerCopilot,a LLM-based tutoring system designed to address the critical challenge faced by newcomers(especially student contributors)in open source software(OSS)communities.Leveraging natural language processing,code semantic understanding,and learner profiling,the system functions as an intelligent tutor to scaffold three core competency domains:contribution guideline interpretation,project architecture comprehension,and personalized task matching.By transforming traditional onboarding barriers-such as complex contribution documentation and opaque project structures-into interactive learning journeys,OSSerCopilot enables newcomers to complete their first OSS contribution more easily and confidently.This paper highlights how LLM technologies can redefine software engineering education by bridging the gap between theoretical knowledge and practical OSS participation,offering implications for curriculum design,competency assessment,and sustainable OSS ecosystem cultivation.A demonstration video of the system is available at https://figshare.com/articles/media/OSSerCopilot_Introduction_mp4/29510276.
基金supported by the Shanghai Municipal Education Research Project“Exploring the Practical Application of Generative Artificial Intelligence in Cultivating Innovative Thinking and Capabilities of Interdisciplinary Application Technology Talents‘Practice Path’”(C2025299)the university-level postgraduate course project“Software Process Management”(PX-2025251502)of Shanghai Sanda Universitythe key course project at the university level of Shanghai Sanda University,“Introduction to Software Engineering”(PX-5241216).
文摘With the advent of the AI era,how can students effectively utilize generative AI large models to assist in course learning?At the same time,how can teachers utilize generative AI tools and the teaching concept of OBE to stimulate students’innovative consciousness and teamwork ability,enabling students to identify some problems in a certain industry or field and creatively propose feasible solutions,and truly achieve the cultivation of new models in software engineering course teaching with the assistance of generative AI tools?This paper presents research and practice on a new model for cultivating software engineering courses that integrates generative AI and OBE,introduces the specific process of teaching reform and practice,and finally explains the achievements of teaching reform.
基金supported in part by the Graduate Education Reform Research Project of Hubei University of Technology under Grant 2024YB003the Hubei University of Arts and Science,Teaching Research Project,under Grant JY2025018.
文摘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.
基金supported in part by the Northeastern University’s 2024 Undergraduate Education and Teaching Reform Research Project:Innovation and Practice of Professional Course Teaching Paradigms in the Context of Digital Education.
文摘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.
基金Project supported by the Project of the Anhui Provincial Natural Science Foundation(Grant No.2308085MA19)Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDA0410401)+2 种基金the National Natural Science Foundation of China(Grant No.52202120)the National Key Research and Development Program of China(Grant No.2023YFA1609800)USTC Research Funds of the Double First-Class Initiative(Grant No.YD2310002013)。
文摘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.
文摘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.
文摘Promoting the integration of industry and education and deepening school-enterprise cooperation in talent cultivation and collaborative innovation are long-term goals of higher education.This paper systematically analyzes the multiple perspectives,practical challenges,and implementation paths of in-depth school-enterprise cooperation.Based on the typical case of school-enterprise cooperation at the School of Information and Software Engineering,University of Electronic Science and Technology of China(UESTC),this paper explores the innovative practices of in-depth school-enterprise cooperation in talent cultivation,scientific research,and faculty construction.It also explores a multi-party collaborative mechanism from the perspectives of universities,enterprises,students,and the government.By policy guidance,resource integration,and benefit sharing,this mechanism achieves in-depth integration of industry and education,providing references and examples for further development of school-enterprise cooperation in the new era.
文摘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.
基金supported by the Research Funding Project for Graduate Education and Teaching Reform of Beijing University of Posts and Telecommunications(No.2024Y036)the Postgraduate Education and Teaching Reform Research Fund Project of Beijing University of Posts and Telecommunications(No.2024Z007)the Postgraduate Education and Teaching Reform Project of Beijing University of Posts and Telecommunications(2025).
文摘Traditional grade-centered evaluation models are inadequate for high-quality software engineering talents in the digital and AI era.This study develops an academic development monitoring system to address shortcomings in dynamics,interdisciplinary integration,and industry adaptability.It builds a multi-dimensional dynamic model covering seven core dimensions with quantitative scoring,non-linear weighting,and DivClust grouping.An intelligent platform with real-time monitoring,early warning,and personalized recommendations integrates AI like multi-modal fusion and large-model diagnosis.The“monitoring-warning-improvement”loop helps optimize training programs,support personalized planning,and bridge talent-industry gaps,enabling digital transformation in software engineering education evaluation.
文摘In recent years,the widespread applications of open-source software(OSS)have brought great convenience for software developers.However,it is always facing unavoidable security risks,such as open-source code defects and security vulnerabilities.To find out the OSS risks in time,we carry out an empirical study to identify the indicators for evaluating the OSS.To achieve a comprehensive understanding of the OSS assessment,we collect 56 papers from prestigious academic venues(such as IEEE Xplore,ACM Digital Library,DBLP,and Google Scholar)in the past 21 years.During the process of the investigation,we first identify the main concerns for selecting OSS and distill five types of commonly used indicators to assess OSS.We then conduct a comparative analysis to discuss how these indicators are used in each surveyed study and their differences.Moreover,we further undertake a correlation analysis between these indicators and uncover 13 confirmed conclusions and four cases with controversy occurring in these studies.Finally,we discuss several possible applications of these conclusions,which are insightful for the research on OSS and software supply chain.
文摘Although recent studies have examined collaboration within open-source software projects,the focus has primarily been on their motivations and governance.This study explores the complex dynamics of trust and involvement among Cameroonian software developers in open-source projects.In the context of a rapidly evolving software development landscape,these projects have emerged as a transformative force,redefining global collaboration standards.The qualitative methodological approach involved a survey of 22 participants in open-source software projects,including Cameroonian software developers,project governance actors,and open-source community members.Analyses revealed that the trust given to African software developers,including their effective integration into projects and consideration of their specificities and contributions,has a positive impact on their involvement in and ability to appropriate information technologies.By exploring the interaction between cultural,social,and technological factors,this study enhances our understanding of trust mechanisms within open-source communities,especially those involving remote developers.
基金partially supported by CREST(JPMJCR1512)AIP Acceleration Research(JPMJCR21U3)of JST.
文摘An open-source software for field-based plant phenotyping,Precision Plots Analyzer(PREPs),was developed using Window.NET.The software runs on 64-bit Windows computers.This software allows the extraction of phenotypic traits on a per-microplot basis from orthomosaic and digital surface model(DSM)images generated by Structure-from-Motion/Multi-View-Stereo(SfM-MVS)tools.Moreover,there is no need to acquire skills in geographical information system(GIS)or programming languages for image analysis.Three use cases illustrated the software's functionality.The first involved monitoring the growth of sugar beet varieties in an experimental field using an unmanned aerial vehicle(UAV),where differences among varieties were detected through estimates of crop height,coverage,and volume index.Second,mixed varieties of potato crops were estimated using a UAV and varietal differences were observed from the estimated phenotypic traits.A strong correlation was observed between the manually measured crop height and UAV-estimated crop height.Finally,using a multicamera array attached to a tractor,the height,coverage,and volume index of the 3 potato varieties were precisely estimated.PREPs software is poised to be a useful tool that allows anyone without prior knowledge of programming to extract crop traits for phenotyping.
文摘Software security poses substantial risks to our society because software has become part of our life. Numerous techniques have been proposed to resolve or mitigate the impact of software security issues. Among them, software testing and analysis are two of the critical methods, which significantly benefit from the advancements in deep learning technologies. Due to the successful use of deep learning in software security, recently,researchers have explored the potential of using large language models(LLMs) in this area. In this paper, we systematically review the results focusing on LLMs in software security. We analyze the topics of fuzzing, unit test, program repair, bug reproduction, data-driven bug detection, and bug triage. We deconstruct these techniques into several stages and analyze how LLMs can be used in the stages. We also discuss the future directions of using LLMs in software security, including the future directions for the existing use of LLMs and extensions from conventional deep learning research.
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 Thought on Socialism with Chinese Characteristics for a New Era 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.
