From the perspective of the development of world-class universities,internationalization is an essential strategic choice and external feature,and also an inevitable choice to improve the discourse power and competiti...From the perspective of the development of world-class universities,internationalization is an essential strategic choice and external feature,and also an inevitable choice to improve the discourse power and competitiveness of international higher education.In line with the national“double first-class”international development strategy of higher education,based on the cultivation of students’overall quality,the improvement of teachers’professional ability,and the development of school’s improvement of quality and efficiency,we School of Software,Northwestern Polytechnical University,explore new ideas and new measures for the cultivation of international software engineering talents,build a set of international teaching resources construction system,to form a reference standard and scheme for the cultivation of international software engineering talents.At present,we have achieved excellent results.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
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
Large language models(LLMs)show great potential in educational scenarios but face challenges like hallucination,knowledge gaps,and reasoning discontinuities.This study proposes a dynamic knowledge enhancement framewor...Large language models(LLMs)show great potential in educational scenarios but face challenges like hallucination,knowledge gaps,and reasoning discontinuities.This study proposes a dynamic knowledge enhancement framework.By integrating local knowledge graphs and stepwise prompting mechanisms,it improves LLMs’accuracy and interpretability in solving professional domain problems.The framework has two core modules:an LLM-driven knowledge graph construction system for incremental updates and a unified reasoning engine for generating enhanced prompts.Experiments on 680 educational questions show that the method boosts accuracy by 4.5%and 4.3%for multi-step reasoning and knowledge-dependent questions respectively,and increases reasoning step completeness from 68.2%to 83.7%.It also reduces hallucination problems.Key contributions include the followings:①validation of an effective framework synergizing knowledge graphs with retrieval mechanisms to enhance LLM reliability;②a stepwise prompting strategy enforcing explicit reasoning chain generation,addressing pedagogical requirements for process interpretability;③a lightweight deployment solution for educational systems such as adaptive learning platforms.展开更多
The advancement of Internet of Things(IoT)technology is driving industries toward intelligent digital transformation,highlighting the crucial role of software engineering.Despite this,the integration of software engin...The advancement of Internet of Things(IoT)technology is driving industries toward intelligent digital transformation,highlighting the crucial role of software engineering.Despite this,the integration of software engineering into IoT engineering education remains underexplored.To address this gap,the School of Software at North University of China,in collaboration with QST Innovation Technology Group Co.,Ltd.(QST),has developed an innovative educational mechanism.This initiative focuses on the software engineering IoT track and optimizes the teaching process through the outcome-based education(OBE)concept.It incorporates military-industrial characteristics,introduces advanced information and technology curricula,and enhances laboratory infrastructure.The goal is to cultivate innovative talents with unique capabilities,thereby fostering the comprehensive development and application of IoT technology.展开更多
Spectrum-based fault localization (SBFL) generates a ranked list of suspicious elements by using the program execution spectrum, but the excessive number of elements ranked in parallel results in low localization accu...Spectrum-based fault localization (SBFL) generates a ranked list of suspicious elements by using the program execution spectrum, but the excessive number of elements ranked in parallel results in low localization accuracy. Most researchers consider intra-class dependencies to improve localization accuracy. However, some studies show that inter-class method call type faults account for more than 20%, which means such methods still have certain limitations. To solve the above problems, this paper proposes a two-phase software fault localization based on relational graph convolutional neural networks (Two-RGCNFL). Firstly, in Phase 1, the method call dependence graph (MCDG) of the program is constructed, the intra-class and inter-class dependencies in MCDG are extracted by using the relational graph convolutional neural network, and the classifier is used to identify the faulty methods. Then, the GraphSMOTE algorithm is improved to alleviate the impact of class imbalance on classification accuracy. Aiming at the problem of parallel ranking of element suspicious values in traditional SBFL technology, in Phase 2, Doc2Vec is used to learn static features, while spectrum information serves as dynamic features. A RankNet model based on siamese multi-layer perceptron is constructed to score and rank statements in the faulty method. This work conducts experiments on 5 real projects of Defects4J benchmark. Experimental results show that, compared with the traditional SBFL technique and two baseline methods, our approach improves the Top-1 accuracy by 262.86%, 29.59% and 53.01%, respectively, which verifies the effectiveness of Two-RGCNFL. Furthermore, this work verifies the importance of inter-class dependencies through ablation experiments.展开更多
The advent of large language models(LLMs)has made knowledge acquisition and content creation increasingly easier and cheaper,which in turn redefines learning and urges transformation in software engineering education....The advent of large language models(LLMs)has made knowledge acquisition and content creation increasingly easier and cheaper,which in turn redefines learning and urges transformation in software engineering education.To do so,there is a need to understand the impact of LLMs on software engineering education.In this paper,we conducted a preliminary case study on three software requirements engineering classes where students are allowed to use LLMs to assist in their projects.Based on the students’experience,performance,and feedback from a survey conducted at the end of the courses,we characterized the challenges and benefits of applying LLMs in software engineering education.This research contributes to the ongoing discourse on the integration of LLMs in education,emphasizing both their prominent potential and the need for balanced,mindful usage.展开更多
With the rapid development of software engineering,traditional teaching methods are confronted with the challenges of short knowledge update cycles and the rapid emergence of new technologies.By analyzing the current ...With the rapid development of software engineering,traditional teaching methods are confronted with the challenges of short knowledge update cycles and the rapid emergence of new technologies.By analyzing the current situation of the mismatch between educational practices and industrial change,this study proposes an innovative teaching model—“Micro-practices”.This model integrates new knowledge and new technologies into the teaching process quickly and flexibly through practical teaching projects with“short class time,small capacity,and cloud environment”to meet the different educational needs of students,teachers,and enterprises.The aim is to train innovative software engineering talents who can meet the challenges of the future.展开更多
In recent years,service computing has been widely integrated into software development.Web service development,especially under the RESTful schema,needs to guide students in transferring from object-oriented to resour...In recent years,service computing has been widely integrated into software development.Web service development,especially under the RESTful schema,needs to guide students in transferring from object-oriented to resource-oriented architectural thinking and cultivating students’literacy in design thinking,design patterns,and development methods.This paper introduces the foundations of service thinking with a brief review of service sciences,the core features of service thinking,and how to train service thinking of students.It also introduces a case study in Shandong University in the construction of a service computing curriculum system,especially how to highlight the cultivation of service thinking in the design of service software system.展开更多
Aiming at the problems such as low throughput and unbalanced load of data center network caused by traditional multipath routing strategy,a dynamic load balancing strategy for flow classification oriented to Fat-Tree ...Aiming at the problems such as low throughput and unbalanced load of data center network caused by traditional multipath routing strategy,a dynamic load balancing strategy for flow classification oriented to Fat-Tree topology based on the software defined network(SDN)architecture is proposed,named DLB-FC.Multi-index evaluation methods such as link state information and network traffic characteristics are considered.DLB-FC mechanism can dynamically adjust the flow classification threshold to differentiate between large and small flows.The scheme selects different forwarding paths to meet the transmission performance requirements of different flow characteristics.On this basis,an SDN simulation platform is built for performance testing.The simulation results show that DLB-FC algorithm can dynamically distinguish large flows from small flows and achieve load balancing effectively.Compared with equal-cost multi-path(ECMP),global first fit(GFF)and minmum total delay load routing(MTDLR)algorithms,DLB-FC scheme improves the network throughput and link utilization of the data center network effectively.The transmission delay is also reduced with better load balance.展开更多
With the rapid advancement of information technology,the quality assurance and evaluation of software engineering education have become pivotal concerns for higher education institutions.In this paper,we focus on a co...With the rapid advancement of information technology,the quality assurance and evaluation of software engineering education have become pivotal concerns for higher education institutions.In this paper,we focus on a comparative study of software engineering education in China and Europe,aiming to explore the theoretical frameworks and practical pathways employed in both regions.Initially,we introduce and contrast the engineering education accreditation systems of China and Europe,including the Chinese engineering education accreditation framework and the European EUR-ACE(European Accreditation of Engineering Programmes)standards,highlighting their core principles and evaluation methodologies.Subsequently,we provide case studies of several universities in China and Europe,such as Sun Yat-sen University,Tsinghua University,Technical University of Munich,and Imperial College London.Finally,we offer recommendations to foster mutual learning and collaboration between Chinese and European institutions,aiming to enhance the overall quality of software engineering education globally.This work provides valuable insights for educational administrators,faculty members,and policymakers,contributing to the ongoing improvement and innovative development of software engineering education in China and Europe.展开更多
An in-built N^(+)pocket electrically doped tunnel field-effect transistor(ED-TFET)-based biosensor has been reported for the first time.The proposed device begins with a PN junction structure with a control gate(CG)an...An in-built N^(+)pocket electrically doped tunnel field-effect transistor(ED-TFET)-based biosensor has been reported for the first time.The proposed device begins with a PN junction structure with a control gate(CG)and two polarity gates(PG1 and PG2).Utilizing the polarity bias concept,a narrow N^(+)pocket is formed between the source and channel without the need for additional doping steps,achieved through biasing PG1 and PG2 at-1.2 V and 1.2 V,respectively.This method not only addresses issues related to doping control but also eliminates constraints associated with thermal budgets and simplifies the fabrication process compared to traditional TFETs.To facilitate biomolecule sensing within the device,a nanogap cavity is formed in the gate dielectric by selectively etching a section of the polarity gate dielectric layer toward the source side.The investigation into the presence of neutral and charged molecules within the cavities has been conducted by examining variations in the electrical properties of the proposed biosensor.Key characteristics assessed include drain current,energy band,and electric field distribution.The performance of the biosensor is measured using various metrics such as drain current(I_(DS)),subthreshold swing(SS),threshold voltage(V_(TH)),drain current ratio(I_(ON)/I_(OFF)).The proposed in-built N^(+)pocket ED-TFET-based biosensor reaches a peak sensitivity of 1.08×10~(13)for a neutral biomolecule in a completely filled nanogap with a dielectric constant of 12.Additionally,the effects of cavity geometry and different fill factors(FFs)on sensitivity are studied.展开更多
The rapid development of brain-like neural networks and secure data transmission technologies has placed greater demands on highly complex neural network systems and highly secure encryption methods.To this end,the pa...The rapid development of brain-like neural networks and secure data transmission technologies has placed greater demands on highly complex neural network systems and highly secure encryption methods.To this end,the paper proposes a novel high-dimensional memristor synapse-coupled hyperchaotic neural network by using the designed memristor as the synapse to connect an inertial neuron(IN)and a Hopfield neural network(HNN).By using numerical tools including bifurcation plots,phase plots,and basins of attraction,it is found that the dynamics of this system are closely related to the memristor coupling strength,self-connection synaptic weights,and inter-connection synaptic weights,and it can exhibit excellent hyperchaotic behaviors and coexisting multi-stable patterns.Through PSIM circuit simulations,the complex dynamics of the coupled IN-HNN system are verified.Furthermore,a DNA-encoded encryption algorithm is given,which utilizes generated hyperchaotic sequences to achieve encoding,operation,and decoding of DNA.The results show that this algorithm possesses strong robustness against statistical attacks,differential attacks,and noise interference,and can effectively resist known/selected plaintext attacks.This work will provide new ideas for the modeling of large-scale brainlike neural networks and high-security image encryption.展开更多
Recently,Internet ofThings(IoT)has been increasingly integrated into the automotive sector,enabling the development of diverse applications such as the Internet of Vehicles(IoV)and intelligent connected vehicles.Lever...Recently,Internet ofThings(IoT)has been increasingly integrated into the automotive sector,enabling the development of diverse applications such as the Internet of Vehicles(IoV)and intelligent connected vehicles.Leveraging IoVtechnologies,operational data fromcore vehicle components can be collected and analyzed to construct fault diagnosis models,thereby enhancing vehicle safety.However,automakers often struggle to acquire sufficient fault data to support effective model training.To address this challenge,a robust and efficient federated learning method(REFL)is constructed for machinery fault diagnosis in collaborative IoV,which can organize multiple companies to collaboratively develop a comprehensive fault diagnosis model while keeping their data locally.In the REFL,the gradient-based adversary algorithm is first introduced to the fault diagnosis field to enhance the deep learning model robustness.Moreover,the adaptive gradient processing process is designed to improve the model training speed and ensure the model accuracy under unbalance data scenarios.The proposed REFL is evaluated on non-independent and identically distributed(non-IID)real-world machinery fault dataset.Experiment results demonstrate that the REFL can achieve better performance than traditional learning methods and are promising for real industrial fault diagnosis.展开更多
At the end of 2021 to create a new model of characteristic software talent training for independent and controllable key software fields,the Ministry of Education and the Ministry of Industry and Information Technolog...At the end of 2021 to create a new model of characteristic software talent training for independent and controllable key software fields,the Ministry of Education and the Ministry of Industry and Information Technology jointly approved the establishment of the first batch of 33 Characteristic Pilot Schools of software.As a member of characteristic software schools,the HIT School of Software has been approved and will focus on the construction of 2 characteristic directions,which are large-scale industrial software and industrial professional application software.In order to achieve the goal,it is urgent to develop a comprehensive management platform to control the entire process of talent training,so that we can standardize,modelling,and digitized the entire process of characteristic software talent training.By relating all aspects of student training with and implementing the ability-index mechanisms,we will continuously collect big-data of the entire process of student growth,and generate multidimensional student ability portraits for evaluating the effect of talent training,and adjust as well as optimizing the growth path for students themselves during their studying.Employers will be able to identify talents accurately and provide effective reference for colleges to adjust training plans.This paper will analyze the needs of the platform,provide demand analysis of the platform,extract the correlation model between training,conclude the relations between ability-index activities and ability indicators,and give a reasonable overall system design scheme.展开更多
In order to respond to the new engineering construction of the Ministry of Education,and explore the innovative talent training model of collaborative education and multidisciplinary integration,this paper relies on t...In order to respond to the new engineering construction of the Ministry of Education,and explore the innovative talent training model of collaborative education and multidisciplinary integration,this paper relies on the software engineering teaching team of the School of Software Engineering,Beijing University of Posts and Telecommunications,through the implementation of the collaborative education project of the Ministry of Education,and proposes the multi-course collaborative practice teaching system,through the reasonable cross-fusion of the practical links of the 5 software engineering courses in the college,realizes the multi-course collaborative education and reasonable cross-fusion of courses,shares practical project resources,introduces new enterprise technologies,and guides students’innovation and entrepreneurship provide a meaningful reference for the collaborative arrangement of teaching content and cross-disciplinary integration in the current university education system.展开更多
In recent years,it is the general trend to adopt the standards of international engineering education certification to construct curriculum.“Software Process and Tools”is one of the core courses of Harbin Institute ...In recent years,it is the general trend to adopt the standards of international engineering education certification to construct curriculum.“Software Process and Tools”is one of the core courses of Harbin Institute of Technology’s software engineering undergraduate training program.Focusing on the construction work and practical exploration of the course in the process of reforming the software engineering professional curriculum system,and how to achieve the standards of engineering education certification,This paper makes a review and summary.This paper focuses on the status and the role of the course in the whole curriculum system,as well as project-driven teaching content design and practical teaching methods.And summarizes the experience and results of 3 rounds of teaching practice.展开更多
To address the problems of insufficient number of personalized exercises and cases and teachers’lack of grasp of students’weak knowledge points in the current software testing online courses,we study the strategy of...To address the problems of insufficient number of personalized exercises and cases and teachers’lack of grasp of students’weak knowledge points in the current software testing online courses,we study the strategy of establishing and updating intelligent exercise sets and case libraries and analyze the answers and dig out the weak points of knowledge through group intelligence reasoning and interactive machine learning methods.This will help teachers to make uniform and targeted explanations,reduce manual judgment,and achieve intelligent teaching quality reform,and implement the educational concepts of“keeping up with the times”and“teaching according to students’abilities”.展开更多
The purpose of software defect prediction is to identify defect-prone code modules to assist software quality assurance teams with the appropriate allocation of resources and labor.In previous software defect predicti...The purpose of software defect prediction is to identify defect-prone code modules to assist software quality assurance teams with the appropriate allocation of resources and labor.In previous software defect prediction studies,transfer learning was effective in solving the problem of inconsistent project data distribution.However,target projects often lack sufficient data,which affects the performance of the transfer learning model.In addition,the presence of uncorrelated features between projects can decrease the prediction accuracy of the transfer learning model.To address these problems,this article propose a software defect prediction method based on stable learning(SDP-SL)that combines code visualization techniques and residual networks.This method first transforms code files into code images using code visualization techniques and then constructs a defect prediction model based on these code images.During the model training process,target project data are not required as prior knowledge.Following the principles of stable learning,this paper dynamically adjusted the weights of source project samples to eliminate dependencies between features,thereby capturing the“invariance mechanism”within the data.This approach explores the genuine relationship between code defect features and labels,thereby enhancing defect prediction performance.To evaluate the performance of SDP-SL,this article conducted comparative experiments on 10 open-source projects in the PROMISE dataset.The experimental results demonstrated that in terms of the F-measure,the proposed SDP-SL method outperformed other within-project defect prediction methods by 2.11%-44.03%.In cross-project defect prediction,the SDP-SL method provided an improvement of 5.89%-25.46% in prediction performance compared to other cross-project defect prediction methods.Therefore,SDP-SL can effectively enhance within-and cross-project defect predictions.展开更多
This paper focuses on the problems,opportunities,and challenges faced by software engineering education in the new era.We have studied the core ideas of the new model and reform,the specific measures implemented,and t...This paper focuses on the problems,opportunities,and challenges faced by software engineering education in the new era.We have studied the core ideas of the new model and reform,the specific measures implemented,and the challenges and solutions faced.The new model and reform must focus on cultivating practical abilities,introducing interdisciplinary knowledge,and strengthening innovation awareness and entrepreneurial spirit.The process of reform and innovation is carried out from the aspects of teaching methods,teaching means,and course performance evaluation in the teaching practice of software engineering courses.We adopt a method of“question guiding,simple and easy to understand,flexible and diverse,and emphasizing practical results”,optimizing the curriculum design,providing diverse learning opportunities,and establishing a platform for the industry-university-research cooperation.Our teaching philosophy is to adhere to the viewpoint of innovative teaching ideas,optimizing teaching methods and teaching means,and comprehensively improving the teaching quality and level of software engineering education.展开更多
文摘From the perspective of the development of world-class universities,internationalization is an essential strategic choice and external feature,and also an inevitable choice to improve the discourse power and competitiveness of international higher education.In line with the national“double first-class”international development strategy of higher education,based on the cultivation of students’overall quality,the improvement of teachers’professional ability,and the development of school’s improvement of quality and efficiency,we School of Software,Northwestern Polytechnical University,explore new ideas and new measures for the cultivation of international software engineering talents,build a set of international teaching resources construction system,to form a reference standard and scheme for the cultivation of international software engineering talents.At present,we have achieved excellent results.
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.
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 in part by the China-Singapore International Joint Research Institute(CSIJRI)under Grant No.206-A023001the Undergraduate Teaching Reform Project of Shandong University under Grant Nos.2023Y235 and 2025Y99.
文摘Large language models(LLMs)show great potential in educational scenarios but face challenges like hallucination,knowledge gaps,and reasoning discontinuities.This study proposes a dynamic knowledge enhancement framework.By integrating local knowledge graphs and stepwise prompting mechanisms,it improves LLMs’accuracy and interpretability in solving professional domain problems.The framework has two core modules:an LLM-driven knowledge graph construction system for incremental updates and a unified reasoning engine for generating enhanced prompts.Experiments on 680 educational questions show that the method boosts accuracy by 4.5%and 4.3%for multi-step reasoning and knowledge-dependent questions respectively,and increases reasoning step completeness from 68.2%to 83.7%.It also reduces hallucination problems.Key contributions include the followings:①validation of an effective framework synergizing knowledge graphs with retrieval mechanisms to enhance LLM reliability;②a stepwise prompting strategy enforcing explicit reasoning chain generation,addressing pedagogical requirements for process interpretability;③a lightweight deployment solution for educational systems such as adaptive learning platforms.
基金supported in part by the Universityindustry Collaborative Education Program of the Ministry of Education under Grant No.202102383004。
文摘The advancement of Internet of Things(IoT)technology is driving industries toward intelligent digital transformation,highlighting the crucial role of software engineering.Despite this,the integration of software engineering into IoT engineering education remains underexplored.To address this gap,the School of Software at North University of China,in collaboration with QST Innovation Technology Group Co.,Ltd.(QST),has developed an innovative educational mechanism.This initiative focuses on the software engineering IoT track and optimizes the teaching process through the outcome-based education(OBE)concept.It incorporates military-industrial characteristics,introduces advanced information and technology curricula,and enhances laboratory infrastructure.The goal is to cultivate innovative talents with unique capabilities,thereby fostering the comprehensive development and application of IoT technology.
基金funded by the Youth Fund of the National Natural Science Foundation of China(Grant No.42261070).
文摘Spectrum-based fault localization (SBFL) generates a ranked list of suspicious elements by using the program execution spectrum, but the excessive number of elements ranked in parallel results in low localization accuracy. Most researchers consider intra-class dependencies to improve localization accuracy. However, some studies show that inter-class method call type faults account for more than 20%, which means such methods still have certain limitations. To solve the above problems, this paper proposes a two-phase software fault localization based on relational graph convolutional neural networks (Two-RGCNFL). Firstly, in Phase 1, the method call dependence graph (MCDG) of the program is constructed, the intra-class and inter-class dependencies in MCDG are extracted by using the relational graph convolutional neural network, and the classifier is used to identify the faulty methods. Then, the GraphSMOTE algorithm is improved to alleviate the impact of class imbalance on classification accuracy. Aiming at the problem of parallel ranking of element suspicious values in traditional SBFL technology, in Phase 2, Doc2Vec is used to learn static features, while spectrum information serves as dynamic features. A RankNet model based on siamese multi-layer perceptron is constructed to score and rank statements in the faulty method. This work conducts experiments on 5 real projects of Defects4J benchmark. Experimental results show that, compared with the traditional SBFL technique and two baseline methods, our approach improves the Top-1 accuracy by 262.86%, 29.59% and 53.01%, respectively, which verifies the effectiveness of Two-RGCNFL. Furthermore, this work verifies the importance of inter-class dependencies through ablation experiments.
基金supported in part by the Teaching Reform Project of Chongqing University of Posts and Telecommunications,China under Grant No.XJG23234Chongqing Municipal Higher Education Teaching Reform Research Project under Grant No.203399the Doctoral Direct Train Project of Chongqing Science and Technology Bureau under Grant No.CSTB2022BSXM-JSX0007。
文摘The advent of large language models(LLMs)has made knowledge acquisition and content creation increasingly easier and cheaper,which in turn redefines learning and urges transformation in software engineering education.To do so,there is a need to understand the impact of LLMs on software engineering education.In this paper,we conducted a preliminary case study on three software requirements engineering classes where students are allowed to use LLMs to assist in their projects.Based on the students’experience,performance,and feedback from a survey conducted at the end of the courses,we characterized the challenges and benefits of applying LLMs in software engineering education.This research contributes to the ongoing discourse on the integration of LLMs in education,emphasizing both their prominent potential and the need for balanced,mindful usage.
基金funded by Universityindustry Collaborative Education Program(No.220605181024725)the Undergraduate Education and Teaching Reform Research Project of Northwestern Polytechnical University(No.22GZ13083)。
文摘With the rapid development of software engineering,traditional teaching methods are confronted with the challenges of short knowledge update cycles and the rapid emergence of new technologies.By analyzing the current situation of the mismatch between educational practices and industrial change,this study proposes an innovative teaching model—“Micro-practices”.This model integrates new knowledge and new technologies into the teaching process quickly and flexibly through practical teaching projects with“short class time,small capacity,and cloud environment”to meet the different educational needs of students,teachers,and enterprises.The aim is to train innovative software engineering talents who can meet the challenges of the future.
基金the support provided by the“New 20 Regulations for Universities”funding program of Jinan(202228089)the TaiS han Industrial Experts Programme(tscx202312128)。
文摘In recent years,service computing has been widely integrated into software development.Web service development,especially under the RESTful schema,needs to guide students in transferring from object-oriented to resource-oriented architectural thinking and cultivating students’literacy in design thinking,design patterns,and development methods.This paper introduces the foundations of service thinking with a brief review of service sciences,the core features of service thinking,and how to train service thinking of students.It also introduces a case study in Shandong University in the construction of a service computing curriculum system,especially how to highlight the cultivation of service thinking in the design of service software system.
基金Supported by the National Natural Science Foundation of China(No.61672270)Jiangsu Provionce Teaching Reform Project for Cloud Computing Technology and Application Talent Training(No.201802130049).
文摘Aiming at the problems such as low throughput and unbalanced load of data center network caused by traditional multipath routing strategy,a dynamic load balancing strategy for flow classification oriented to Fat-Tree topology based on the software defined network(SDN)architecture is proposed,named DLB-FC.Multi-index evaluation methods such as link state information and network traffic characteristics are considered.DLB-FC mechanism can dynamically adjust the flow classification threshold to differentiate between large and small flows.The scheme selects different forwarding paths to meet the transmission performance requirements of different flow characteristics.On this basis,an SDN simulation platform is built for performance testing.The simulation results show that DLB-FC algorithm can dynamically distinguish large flows from small flows and achieve load balancing effectively.Compared with equal-cost multi-path(ECMP),global first fit(GFF)and minmum total delay load routing(MTDLR)algorithms,DLB-FC scheme improves the network throughput and link utilization of the data center network effectively.The transmission delay is also reduced with better load balance.
基金supported by the Guangdong Higher Education Association’s“14th Five Year Plan”2024 Higher Education Research Project(24GYB03)the Natural Science Foundation of Guangdong Province(2024A1515010255)。
文摘With the rapid advancement of information technology,the quality assurance and evaluation of software engineering education have become pivotal concerns for higher education institutions.In this paper,we focus on a comparative study of software engineering education in China and Europe,aiming to explore the theoretical frameworks and practical pathways employed in both regions.Initially,we introduce and contrast the engineering education accreditation systems of China and Europe,including the Chinese engineering education accreditation framework and the European EUR-ACE(European Accreditation of Engineering Programmes)standards,highlighting their core principles and evaluation methodologies.Subsequently,we provide case studies of several universities in China and Europe,such as Sun Yat-sen University,Tsinghua University,Technical University of Munich,and Imperial College London.Finally,we offer recommendations to foster mutual learning and collaboration between Chinese and European institutions,aiming to enhance the overall quality of software engineering education globally.This work provides valuable insights for educational administrators,faculty members,and policymakers,contributing to the ongoing improvement and innovative development of software engineering education in China and Europe.
基金Project supported by the Ministry of Education’s Supply and Demand Matching Employment and Education Project(Grant No.2024110776329)。
文摘An in-built N^(+)pocket electrically doped tunnel field-effect transistor(ED-TFET)-based biosensor has been reported for the first time.The proposed device begins with a PN junction structure with a control gate(CG)and two polarity gates(PG1 and PG2).Utilizing the polarity bias concept,a narrow N^(+)pocket is formed between the source and channel without the need for additional doping steps,achieved through biasing PG1 and PG2 at-1.2 V and 1.2 V,respectively.This method not only addresses issues related to doping control but also eliminates constraints associated with thermal budgets and simplifies the fabrication process compared to traditional TFETs.To facilitate biomolecule sensing within the device,a nanogap cavity is formed in the gate dielectric by selectively etching a section of the polarity gate dielectric layer toward the source side.The investigation into the presence of neutral and charged molecules within the cavities has been conducted by examining variations in the electrical properties of the proposed biosensor.Key characteristics assessed include drain current,energy band,and electric field distribution.The performance of the biosensor is measured using various metrics such as drain current(I_(DS)),subthreshold swing(SS),threshold voltage(V_(TH)),drain current ratio(I_(ON)/I_(OFF)).The proposed in-built N^(+)pocket ED-TFET-based biosensor reaches a peak sensitivity of 1.08×10~(13)for a neutral biomolecule in a completely filled nanogap with a dielectric constant of 12.Additionally,the effects of cavity geometry and different fill factors(FFs)on sensitivity are studied.
基金Project supported by the Training Plan of Young Backbone Teachers in Universities of Henan Province(Grant No.2023GGJS142)the Key Scientific Research of Colleges and Universities in Henan Province,China(Grant No.25A120009)+1 种基金Changzhou Leading Innovative Talent Introduction and Cultivation Project(Grant No.CQ20240102)Changzhou Applied Basic Research Program(Grant No.CJ20253065)。
文摘The rapid development of brain-like neural networks and secure data transmission technologies has placed greater demands on highly complex neural network systems and highly secure encryption methods.To this end,the paper proposes a novel high-dimensional memristor synapse-coupled hyperchaotic neural network by using the designed memristor as the synapse to connect an inertial neuron(IN)and a Hopfield neural network(HNN).By using numerical tools including bifurcation plots,phase plots,and basins of attraction,it is found that the dynamics of this system are closely related to the memristor coupling strength,self-connection synaptic weights,and inter-connection synaptic weights,and it can exhibit excellent hyperchaotic behaviors and coexisting multi-stable patterns.Through PSIM circuit simulations,the complex dynamics of the coupled IN-HNN system are verified.Furthermore,a DNA-encoded encryption algorithm is given,which utilizes generated hyperchaotic sequences to achieve encoding,operation,and decoding of DNA.The results show that this algorithm possesses strong robustness against statistical attacks,differential attacks,and noise interference,and can effectively resist known/selected plaintext attacks.This work will provide new ideas for the modeling of large-scale brainlike neural networks and high-security image encryption.
基金supported in part by National key R&D projects(2024YFB4207203)National Natural Science Foundation of China(52401376)+3 种基金the Zhejiang Provincial Natural Science Foundation of China under Grant(No.LTGG24F030004)Hangzhou Key Scientific Research Plan Project(2024SZD1A24)“Pioneer”and“Leading Goose”R&DProgramof Zhejiang(2024C03254,2023C03154)Jiangxi Provincial Gan-Po Elite Support Program(Major Academic and Technical Leaders Cultivation Project,20243BCE51180).
文摘Recently,Internet ofThings(IoT)has been increasingly integrated into the automotive sector,enabling the development of diverse applications such as the Internet of Vehicles(IoV)and intelligent connected vehicles.Leveraging IoVtechnologies,operational data fromcore vehicle components can be collected and analyzed to construct fault diagnosis models,thereby enhancing vehicle safety.However,automakers often struggle to acquire sufficient fault data to support effective model training.To address this challenge,a robust and efficient federated learning method(REFL)is constructed for machinery fault diagnosis in collaborative IoV,which can organize multiple companies to collaboratively develop a comprehensive fault diagnosis model while keeping their data locally.In the REFL,the gradient-based adversary algorithm is first introduced to the fault diagnosis field to enhance the deep learning model robustness.Moreover,the adaptive gradient processing process is designed to improve the model training speed and ensure the model accuracy under unbalance data scenarios.The proposed REFL is evaluated on non-independent and identically distributed(non-IID)real-world machinery fault dataset.Experiment results demonstrate that the REFL can achieve better performance than traditional learning methods and are promising for real industrial fault diagnosis.
基金supported by the National Key Research and Development Program of China(Grant No.2020AAA0108803).
文摘At the end of 2021 to create a new model of characteristic software talent training for independent and controllable key software fields,the Ministry of Education and the Ministry of Industry and Information Technology jointly approved the establishment of the first batch of 33 Characteristic Pilot Schools of software.As a member of characteristic software schools,the HIT School of Software has been approved and will focus on the construction of 2 characteristic directions,which are large-scale industrial software and industrial professional application software.In order to achieve the goal,it is urgent to develop a comprehensive management platform to control the entire process of talent training,so that we can standardize,modelling,and digitized the entire process of characteristic software talent training.By relating all aspects of student training with and implementing the ability-index mechanisms,we will continuously collect big-data of the entire process of student growth,and generate multidimensional student ability portraits for evaluating the effect of talent training,and adjust as well as optimizing the growth path for students themselves during their studying.Employers will be able to identify talents accurately and provide effective reference for colleges to adjust training plans.This paper will analyze the needs of the platform,provide demand analysis of the platform,extract the correlation model between training,conclude the relations between ability-index activities and ability indicators,and give a reasonable overall system design scheme.
基金supported in part by Educational Reform Projects of BUPT.
文摘In order to respond to the new engineering construction of the Ministry of Education,and explore the innovative talent training model of collaborative education and multidisciplinary integration,this paper relies on the software engineering teaching team of the School of Software Engineering,Beijing University of Posts and Telecommunications,through the implementation of the collaborative education project of the Ministry of Education,and proposes the multi-course collaborative practice teaching system,through the reasonable cross-fusion of the practical links of the 5 software engineering courses in the college,realizes the multi-course collaborative education and reasonable cross-fusion of courses,shares practical project resources,introduces new enterprise technologies,and guides students’innovation and entrepreneurship provide a meaningful reference for the collaborative arrangement of teaching content and cross-disciplinary integration in the current university education system.
文摘In recent years,it is the general trend to adopt the standards of international engineering education certification to construct curriculum.“Software Process and Tools”is one of the core courses of Harbin Institute of Technology’s software engineering undergraduate training program.Focusing on the construction work and practical exploration of the course in the process of reforming the software engineering professional curriculum system,and how to achieve the standards of engineering education certification,This paper makes a review and summary.This paper focuses on the status and the role of the course in the whole curriculum system,as well as project-driven teaching content design and practical teaching methods.And summarizes the experience and results of 3 rounds of teaching practice.
文摘To address the problems of insufficient number of personalized exercises and cases and teachers’lack of grasp of students’weak knowledge points in the current software testing online courses,we study the strategy of establishing and updating intelligent exercise sets and case libraries and analyze the answers and dig out the weak points of knowledge through group intelligence reasoning and interactive machine learning methods.This will help teachers to make uniform and targeted explanations,reduce manual judgment,and achieve intelligent teaching quality reform,and implement the educational concepts of“keeping up with the times”and“teaching according to students’abilities”.
基金supported by the NationalNatural Science Foundation of China(Grant No.61867004)the Youth Fund of the National Natural Science Foundation of China(Grant No.41801288).
文摘The purpose of software defect prediction is to identify defect-prone code modules to assist software quality assurance teams with the appropriate allocation of resources and labor.In previous software defect prediction studies,transfer learning was effective in solving the problem of inconsistent project data distribution.However,target projects often lack sufficient data,which affects the performance of the transfer learning model.In addition,the presence of uncorrelated features between projects can decrease the prediction accuracy of the transfer learning model.To address these problems,this article propose a software defect prediction method based on stable learning(SDP-SL)that combines code visualization techniques and residual networks.This method first transforms code files into code images using code visualization techniques and then constructs a defect prediction model based on these code images.During the model training process,target project data are not required as prior knowledge.Following the principles of stable learning,this paper dynamically adjusted the weights of source project samples to eliminate dependencies between features,thereby capturing the“invariance mechanism”within the data.This approach explores the genuine relationship between code defect features and labels,thereby enhancing defect prediction performance.To evaluate the performance of SDP-SL,this article conducted comparative experiments on 10 open-source projects in the PROMISE dataset.The experimental results demonstrated that in terms of the F-measure,the proposed SDP-SL method outperformed other within-project defect prediction methods by 2.11%-44.03%.In cross-project defect prediction,the SDP-SL method provided an improvement of 5.89%-25.46% in prediction performance compared to other cross-project defect prediction methods.Therefore,SDP-SL can effectively enhance within-and cross-project defect predictions.
基金supported in part by the postgraduate demonstration course of Guangdong Province Department of Education Programmed Trading(No.2023SFKC_022)the Computer Architecture First Class Course Project,South China Normal University-Baidu Pineapple Talent Training Practice Basethe 2023 Project of Computer Education Research Association of Chinese Universities(No.CERACU2023R02)。
文摘This paper focuses on the problems,opportunities,and challenges faced by software engineering education in the new era.We have studied the core ideas of the new model and reform,the specific measures implemented,and the challenges and solutions faced.The new model and reform must focus on cultivating practical abilities,introducing interdisciplinary knowledge,and strengthening innovation awareness and entrepreneurial spirit.The process of reform and innovation is carried out from the aspects of teaching methods,teaching means,and course performance evaluation in the teaching practice of software engineering courses.We adopt a method of“question guiding,simple and easy to understand,flexible and diverse,and emphasizing practical results”,optimizing the curriculum design,providing diverse learning opportunities,and establishing a platform for the industry-university-research cooperation.Our teaching philosophy is to adhere to the viewpoint of innovative teaching ideas,optimizing teaching methods and teaching means,and comprehensively improving the teaching quality and level of software engineering education.
