In response to the problem that students'feedback on software engineering courses does not meet expectations,this paper focuses on analyzing the problems of the existing fundamentals of software engineering course...In response to the problem that students'feedback on software engineering courses does not meet expectations,this paper focuses on analyzing the problems of the existing fundamentals of software engineering courses and the corresponding experimental and practical training courses.Based on the concept of outcome-based education,combined with previous course teaching practices,student course feedback,and new experimental and practical training course design methods provided by the online teaching platform,this paper elaborates on how to modify the corresponding course syllabus and training program,explains the achievements of the curriculum reform,and discusses the software engineering course reform pattern based on the common problems of students.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
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
In view of the needs and challenges of digital transformation in the field of education under the background ofªnew engineeringº,this paper analyzes how to improve the teaching efficiency of software enginee...In view of the needs and challenges of digital transformation in the field of education under the background ofªnew engineeringº,this paper analyzes how to improve the teaching efficiency of software engineering course programming and realize personalized guidance through the large language models,and proposes to use the multi-channel feedback optimization technology of Xhang AI assistant to extract and construct the multimodal knowledge graph of the adaptive course,and realize the personalized fine-tuning of the model combined with the private data collaborative sharing platform,so as to enhance the accuracy and applicability of the knowledge graph of software engineering course.The automatic construction and updating of curriculum knowledge graph based onªXhang AI assistantºcan cover a wider range of educational courses and fields,and promote the development of intelligent and digital education.展开更多
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
Under the national innovation-driven development strategy and the emerging need for new engineering education,the cultivation of high-quality software engineering talent faces increasingly stringent requirements.To ad...Under the national innovation-driven development strategy and the emerging need for new engineering education,the cultivation of high-quality software engineering talent faces increasingly stringent requirements.To address critical challenges in the current graduate training model at regional universities,including fragmented undergraduate-to-graduate transition,ambiguous competency development pathways,and structural deficiencies in teaching resources,this paper proposes a novel PCER(Plan-Carry Out-Evaluate-Refine)dynamic feedback model for software engineering graduate education.The PCER framework establishes phased training objectives,develops an adaptive multi-level resource repository,and implements a multidimensional evaluation mechanism.Empirical results demonstrate that the model significantly enhances students'academic innovation capabilities and engineering practice competencies,providing a replicable reform path for the cultivation of software engineering talents.The PCER approach offers a transferable solution for engineering education reform,particularly for institutions facing similar resource constraints and quality improvement challenges.展开更多
The rapid advancement of information technology catalyzes the digital transformation of education,urging the education sector to adapt to technological changes actively.Focusing on the dual-engine drive of large model...The rapid advancement of information technology catalyzes the digital transformation of education,urging the education sector to adapt to technological changes actively.Focusing on the dual-engine drive of large models and digital human technologies,we innovate across multiple dimensions to reconstruct the software engineering curriculum system.We build multimodal teaching frameworks and personalized learning paths to break through traditional limitations,establish a digital and intelligent course resource supermarket for precise service supply,integrate smart classrooms with blended learning to reshape teaching spaces,and create an all-process digital and intelligent evaluation system to revolutionize assessment and feedback mechanisms.By deeply integrating intelligent technologies into the entire education process,we enhance software engineering course quality and offer new ideas and practical pathways for educational reform.展开更多
The rapid integration of AI programming assistants(AIPAs)into education raises critical questions about their impact on student learning.This study conducts an exploratory three-year longitudinal quasi-experiment in a...The rapid integration of AI programming assistants(AIPAs)into education raises critical questions about their impact on student learning.This study conducts an exploratory three-year longitudinal quasi-experiment in an introductory software engineering course,comparing a cohort with unrestricted AI access(n=21)against two control cohorts without AI(n=24).Results show a significant performance decline in the AI-assisted group,with a mean final examination score(M=58.8)over 20 points lower than the control groups'stable baseline(M=79.2),a statistically significant difference(p<0.001).This suggests that unguided AI use may encourageªcognitive offloadingº,bypassing critical thinking and leading to superficial learning.The performance gap widens with diverse,high-order examination tasks,where AI-dependent students struggle in closedbook settings.The study concludes that the educational potential of AI requires careful integration with pedagogical guardrails,which serves as a cognitive scaffold rather than an answer machine,pending further validation.展开更多
Amid the in-depth advancement of the Belt and Road Initiative and the rapid development of the global digital economy,cross-border cooperation in software engineering education between China and Europe has emerged as ...Amid the in-depth advancement of the Belt and Road Initiative and the rapid development of the global digital economy,cross-border cooperation in software engineering education between China and Europe has emerged as a pivotal strategy for enhancing regional digital economic competitiveness.This paper aims to systematically analyze the promotion mechanisms of such cooperation and its impact on regional digital economic development.It will also explore key management issues,including intellectual property protection and the mutual recognition of standards within cross-border educational collaborations.Furthermore,the paper will propose strategies for enhancing international industrial competitiveness through educational partnerships.The research seeks to provide both theoretical and practical insights to deepen China-Europe cooperation in software engineering education and unlock the full potential of the digital economy.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 http://gffzz6169854dd1504498sonvwv5bwfxx96okp.ffgz.tsg.suse.edu.cn/articles/media/OSSerCopilot_Introduction_mp4/29510276.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
As a core field of information technology,the quality of software engineering education directly impacts the development of the future software industry.Current pedagogy,however,faces critical challenges including rap...As a core field of information technology,the quality of software engineering education directly impacts the development of the future software industry.Current pedagogy,however,faces critical challenges including rapid knowledge obsolescence,inadequate practical skill development,limited personalization,and complex assessment.This paper systematically explores AI's transformative potential in this domain,proposing an application framework that addresses content innovation,skill cultivation,and assessment optimization.We critically analyze implementation opportunities while addressing technical constraints,pedagogical adaptations,and ethical considerations.The contribution of this paper lies in providing a macroscopic and forwardlooking theoretical analysis framework,which offers references for in-depth research and practice of AI in the field of software engineering education.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 rise of artificial intelligence(AI)has advanced automated code generation.At the same time,it has raised new concerns about academic integrity in software engineering education.Previous studies have explored AI-ge...The rise of artificial intelligence(AI)has advanced automated code generation.At the same time,it has raised new concerns about academic integrity in software engineering education.Previous studies have explored AI-generated code detection using pre-trained code models.However,systematic evaluations across different types of models are still limited.This study presents a two-part empirical investigation.We benchmark four representative pre-trained models,including two language models(BERT and Ro BERTa)and two code models(Code BERT and Uni Xcoder),on AI-generated code detection tasks using Java and Python datasets.Results show that Uni Xcoder achieves the best overall detection performance.Pre-trained language models also demonstrate robustness under certain post-processing conditions.From an overall perspective,robust detection of AI-generated code remains an unresolved challenge in software engineering education.We further apply the best-performing detector to real-world student programming assignments collected from 2021 to 2024.Trend analysis reveals patterns that align with the increasing adoption of generative AI tools in student submissions.Our findings highlight both the strengths and limitations of current detectors.They also demonstrate the potential of such tools for monitoring generative AI usage in software engineering education.Our code and data are released at http://gffzz188fe103f8f1460asonvwv5bwfxx96okp.ffgz.tsg.suse.edu.cn/gaohangcodes/AIGCode Detect Benchmark.展开更多
Against the background of the deep integration of Emerging Engineering Education construction and digital economy,to address the challenges of vague competency standards and fragmented training paths in software engin...Against the background of the deep integration of Emerging Engineering Education construction and digital economy,to address the challenges of vague competency standards and fragmented training paths in software engineering talent cultivation,this study first constructs a quantifiable post competency model using the Onion Model as the framework,combined with the Delphi method and coefficient of variation method.Furthermore,based on this model,a three-level progressive competency map of“Basic Competency-Professional Competency-Innovative Application Competency”is designed and refined into observable training indicators throughout the four-year undergraduate program.The research results lay a foundation for the precise industry-education connection and the subsequent construction of smart courses.展开更多
Against the backdrop of the digital economy and the construction of emerging engineering disciplines,this paper proposes and constructs a training model for excellent scientific and technological innovation talents,br...Against the backdrop of the digital economy and the construction of emerging engineering disciplines,this paper proposes and constructs a training model for excellent scientific and technological innovation talents,bridging undergraduate and graduate studies in software engineering,characterized by“integrated objectives,project-driven learning,and the integration of science and education.”This model aims to cultivate outstanding scientific and technological innovation talents as its overall goal,achieving seamless integration of undergraduate and graduate ability cultivation through the construction of a hierarchical and progressive objective system.It establishes a comprehensive five-level progressive project training system to bridge the gap between undergraduate practice and graduate research,and sets up a deeply integrated mechanism for the collaboration of science,education,and industry to promote the transformation of scientific research and industrial resources into teaching.Additionally,it improves the support system from three aspects:institutional frameworks,evaluation,and incentives.The study also outlines a five-stage practical path:“top-level design-resource integration-pilot operation-comprehensive promotion-dynamic optimization.”This model effectively breaks down the barriers between undergraduate and graduate training,strengthens students’scientific research innovation and engineering practice abilities,and provides a replicable practical paradigm for the training of scientific and technological innovation talents bridging undergraduate and graduate studies in engineering disciplines.展开更多
This paper presents a case study of the collaborative integration between the School of Information and Software Engineering at the University of Electronic Science and Technology of China(UESTC)and SI-TECH,highlighti...This paper presents a case study of the collaborative integration between the School of Information and Software Engineering at the University of Electronic Science and Technology of China(UESTC)and SI-TECH,highlighting the complementary advantages of both the University and the enterprise.By jointly establishing research institutes and engaging in diversified collaborative initiatives,the University and the enterprise have embarked on a pathway of School-enterprise Integration.Through a virtuous cycle of cooperation and continuous advancement,they have explored a comprehensive talent cultivation model in“5G”software engineering innovation practices based on this integration.Furthermore,this endeavor aims to facilitate the transformation of technological achievements and provides valuable insights for fostering innovative talents in the field of electronic information through enhanced integration between the University and the enterprise.展开更多
Quantum software development utilizes quantum phenomena such as superposition and entanglement to address problems that are challenging for classical systems.However,it must also adhere to critical quantum constraints...Quantum software development utilizes quantum phenomena such as superposition and entanglement to address problems that are challenging for classical systems.However,it must also adhere to critical quantum constraints,notably the no-cloning theorem,which prohibits the exact duplication of unknown quantum states and has profound implications for cryptography,secure communication,and error correction.While existing quantum circuit representations implicitly honor such constraints,they lack formal mechanisms for early-stage verification in software design.Addressing this constraint at the design phase is essential to ensure the correctness and reliability of quantum software.This paper presents a formal metamodeling framework using UML-style notation and and Object Constraint Language(OCL)to systematically capture and enforce the no-cloning theorem within quantum software models.The proposed metamodel formalizes key quantum concepts—such as entanglement and teleportation—and encodes enforceable invariants that reflect core quantum mechanical laws.The framework’s effectiveness is validated by analyzing two critical edge cases—conditional copying with CNOT gates and quantum teleportation—through instance model evaluations.These cases demonstrate that the metamodel can capture nuanced scenarios that are often mistaken as violations of the no-cloning theorem but are proven compliant under formal analysis.Thus,these serve as constructive validations that demonstrate the metamodel’s expressiveness and correctness in representing operations that may appear to challenge the no-cloning theorem but,upon rigorous analysis,are shown to comply with it.The approach supports early detection of conceptual design errors,promoting correctness prior to implementation.The framework’s extensibility is also demonstrated by modeling projective measurement,further reinforcing its applicability to broader quantum software engineering tasks.By integrating the rigor of metamodeling with fundamental quantum mechanical principles,this work provides a structured,model-driven approach that enables traditional software engineers to address quantum computing challenges.It offers practical insights into embedding quantum correctness at the modeling level and advances the development of reliable,error-resilient quantum software systems.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
The agility of Internet of Things(IoT)software engineering is benchmarked based on its systematic insights for wide application support infrastructure developments.Such developments are focused on reducing the interfa...The agility of Internet of Things(IoT)software engineering is benchmarked based on its systematic insights for wide application support infrastructure developments.Such developments are focused on reducing the interfacing complexity with heterogeneous devices through applications.To handle the interfacing complexity problem,this article introduces a Semantic Interfacing Obscuration Model(SIOM)for IoT software-engineered platforms.The interfacing obscuration between heterogeneous devices and application interfaces from the testing to real-time validations is accounted for in this model.Based on the level of obscuration between the infrastructure hardware to the end-user software,the modifications through device replacement,capacity amendments,or interface bug fixes are performed.These modifications are based on the level of semantic obscurations observed during the application service intervals.The obscuration level is determined using knowledge learning as a progression from hardware to software semantics.The results reported were computed using specific metrics obtained from these experimental evaluations:an 8.94%reduction in interfacing complexity and a 15.04%improvement in integration progression.The knowledge of obscurationsmaps themodifications appropriately to reinstate the agility testing of the hardware/software integrations.This modification-based semantics is verified using semantics error,modification time,and complexity.展开更多
基金supported by the National Natural Science Foundation of China(Nos.62302029,62577005 and 62302029)the Fundamental Research Funds for the Central Universities and the Universitylevel Teaching Reform Project“Construction of Core Courses for Postgraduates”of Beihang University。
摘要In response to the problem that students'feedback on software engineering courses does not meet expectations,this paper focuses on analyzing the problems of the existing fundamentals of software engineering courses and the corresponding experimental and practical training courses.Based on the concept of outcome-based education,combined with previous course teaching practices,student course feedback,and new experimental and practical training course design methods provided by the online teaching platform,this paper elaborates on how to modify the corresponding course syllabus and training program,explains the achievements of the curriculum reform,and discusses the software engineering course reform pattern based on the common problems of students.
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.
摘要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.
摘要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 by the National Higher Education Computer Education Research Association Teaching and Education Research Project(No.CERACU2025R06)the Strategic Research Project on the Training Reform of Excellent Engineers(No.ZD-20250102)+2 种基金the Teaching Reform Project of the International Innovation Institute of Beihang University in Hangzhou(No.JG202505)the Research Startup Fund of the International Innovation Institute of Beihang University in Hangzhou(No.2024KQ086)the Zhejiang Provincial Graduate Education Association Research Project(No.2025-020)。
摘要In view of the needs and challenges of digital transformation in the field of education under the background ofªnew engineeringº,this paper analyzes how to improve the teaching efficiency of software engineering course programming and realize personalized guidance through the large language models,and proposes to use the multi-channel feedback optimization technology of Xhang AI assistant to extract and construct the multimodal knowledge graph of the adaptive course,and realize the personalized fine-tuning of the model combined with the private data collaborative sharing platform,so as to enhance the accuracy and applicability of the knowledge graph of software engineering course.The automatic construction and updating of curriculum knowledge graph based onªXhang AI assistantºcan cover a wider range of educational courses and fields,and promote the development of intelligent and digital education.
摘要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.
基金the Provincial Teaching Reform Research Project of Higher Education Institutions in Hubei Province(No.2024346)the National Natural Science Foundation of China(No.62102291)+3 种基金the Graduate-level Quality Course“Advanced Software Engineering”of Wuhan Textile University(2024)the Practical Platform Construction Project for Algorithm Design Courses(No.231002405072709)the IndustryUniversity Collaborative Education Program of the Ministry of Education(No.220606008213849)the University-level Teaching Research Project of Wuhan Textile University titled“A Study on the Teaching Model of Software Engineering Courses Integrating Research Resources into Classroom Content”。
摘要Under the national innovation-driven development strategy and the emerging need for new engineering education,the cultivation of high-quality software engineering talent faces increasingly stringent requirements.To address critical challenges in the current graduate training model at regional universities,including fragmented undergraduate-to-graduate transition,ambiguous competency development pathways,and structural deficiencies in teaching resources,this paper proposes a novel PCER(Plan-Carry Out-Evaluate-Refine)dynamic feedback model for software engineering graduate education.The PCER framework establishes phased training objectives,develops an adaptive multi-level resource repository,and implements a multidimensional evaluation mechanism.Empirical results demonstrate that the model significantly enhances students'academic innovation capabilities and engineering practice competencies,providing a replicable reform path for the cultivation of software engineering talents.The PCER approach offers a transferable solution for engineering education reform,particularly for institutions facing similar resource constraints and quality improvement challenges.
基金supported by General Research Project of the Zhejiang Provincial Association for Graduate Education(No.2025-020)General University-level Teaching Reform Project of Beihang University:Teaching Reform Practice of a Generative AI-driven Dynamic Source Code Error Correction System in the Course Fundamentals of University Computer Science(No.JG202502)+1 种基金Educational Teaching Research Project of the Computer Education Research Association of Chinese Universities(No.CERACU2025R04)General School-level Teaching Reform Project of Beihang University:Exploration of Teaching Reform in the Course Introduction to Artificial Intelligence Empowered by Knowledge Graphs(No.JG202520)。
摘要The rapid advancement of information technology catalyzes the digital transformation of education,urging the education sector to adapt to technological changes actively.Focusing on the dual-engine drive of large models and digital human technologies,we innovate across multiple dimensions to reconstruct the software engineering curriculum system.We build multimodal teaching frameworks and personalized learning paths to break through traditional limitations,establish a digital and intelligent course resource supermarket for precise service supply,integrate smart classrooms with blended learning to reshape teaching spaces,and create an all-process digital and intelligent evaluation system to revolutionize assessment and feedback mechanisms.By deeply integrating intelligent technologies into the entire education process,we enhance software engineering course quality and offer new ideas and practical pathways for educational reform.
摘要The rapid integration of AI programming assistants(AIPAs)into education raises critical questions about their impact on student learning.This study conducts an exploratory three-year longitudinal quasi-experiment in an introductory software engineering course,comparing a cohort with unrestricted AI access(n=21)against two control cohorts without AI(n=24).Results show a significant performance decline in the AI-assisted group,with a mean final examination score(M=58.8)over 20 points lower than the control groups'stable baseline(M=79.2),a statistically significant difference(p<0.001).This suggests that unguided AI use may encourageªcognitive offloadingº,bypassing critical thinking and leading to superficial learning.The performance gap widens with diverse,high-order examination tasks,where AI-dependent students struggle in closedbook settings.The study concludes that the educational potential of AI requires careful integration with pedagogical guardrails,which serves as a cognitive scaffold rather than an answer machine,pending further validation.
摘要Amid the in-depth advancement of the Belt and Road Initiative and the rapid development of the global digital economy,cross-border cooperation in software engineering education between China and Europe has emerged as a pivotal strategy for enhancing regional digital economic competitiveness.This paper aims to systematically analyze the promotion mechanisms of such cooperation and its impact on regional digital economic development.It will also explore key management issues,including intellectual property protection and the mutual recognition of standards within cross-border educational collaborations.Furthermore,the paper will propose strategies for enhancing international industrial competitiveness through educational partnerships.The research seeks to provide both theoretical and practical insights to deepen China-Europe cooperation in software engineering education and unlock the full potential of the digital economy.
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
摘要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 http://gffzz6169854dd1504498sonvwv5bwfxx96okp.ffgz.tsg.suse.edu.cn/articles/media/OSSerCopilot_Introduction_mp4/29510276.
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).
摘要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.
基金supported by the Research Project on Teaching Reform of Higher Education in Jiangsu Province(Grant No.2025ZNT-22)。
摘要As a core field of information technology,the quality of software engineering education directly impacts the development of the future software industry.Current pedagogy,however,faces critical challenges including rapid knowledge obsolescence,inadequate practical skill development,limited personalization,and complex assessment.This paper systematically explores AI's transformative potential in this domain,proposing an application framework that addresses content innovation,skill cultivation,and assessment optimization.We critically analyze implementation opportunities while addressing technical constraints,pedagogical adaptations,and ethical considerations.The contribution of this paper lies in providing a macroscopic and forwardlooking theoretical analysis framework,which offers references for in-depth research and practice of AI in the field of software engineering education.
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).
摘要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 by the National Natural Science Foundation of China(No.62406151)。
摘要The rise of artificial intelligence(AI)has advanced automated code generation.At the same time,it has raised new concerns about academic integrity in software engineering education.Previous studies have explored AI-generated code detection using pre-trained code models.However,systematic evaluations across different types of models are still limited.This study presents a two-part empirical investigation.We benchmark four representative pre-trained models,including two language models(BERT and Ro BERTa)and two code models(Code BERT and Uni Xcoder),on AI-generated code detection tasks using Java and Python datasets.Results show that Uni Xcoder achieves the best overall detection performance.Pre-trained language models also demonstrate robustness under certain post-processing conditions.From an overall perspective,robust detection of AI-generated code remains an unresolved challenge in software engineering education.We further apply the best-performing detector to real-world student programming assignments collected from 2021 to 2024.Trend analysis reveals patterns that align with the increasing adoption of generative AI tools in student submissions.Our findings highlight both the strengths and limitations of current detectors.They also demonstrate the potential of such tools for monitoring generative AI usage in software engineering education.Our code and data are released at http://gffzz188fe103f8f1460asonvwv5bwfxx96okp.ffgz.tsg.suse.edu.cn/gaohangcodes/AIGCode Detect Benchmark.
基金2025 Project of the Network Course Construction Working Committee of China Education Technology Association:Research on the Construction of Computer Major Competency Map and Smart Course Construction Based on Post Competency(No.KYKFYB25008)。
摘要Against the background of the deep integration of Emerging Engineering Education construction and digital economy,to address the challenges of vague competency standards and fragmented training paths in software engineering talent cultivation,this study first constructs a quantifiable post competency model using the Onion Model as the framework,combined with the Delphi method and coefficient of variation method.Furthermore,based on this model,a three-level progressive competency map of“Basic Competency-Professional Competency-Innovative Application Competency”is designed and refined into observable training indicators throughout the four-year undergraduate program.The research results lay a foundation for the precise industry-education connection and the subsequent construction of smart courses.
基金Research Project on Undergraduate Teaching Reform in General Higher Education Institutions of Liaoning Province,titled“Research and Practice of a Software Engineering Excellence Innovation and Entrepreneurship Talent Cultivation Model Connecting Undergraduate and Graduate Studies”(2025YBXM030)。
摘要Against the backdrop of the digital economy and the construction of emerging engineering disciplines,this paper proposes and constructs a training model for excellent scientific and technological innovation talents,bridging undergraduate and graduate studies in software engineering,characterized by“integrated objectives,project-driven learning,and the integration of science and education.”This model aims to cultivate outstanding scientific and technological innovation talents as its overall goal,achieving seamless integration of undergraduate and graduate ability cultivation through the construction of a hierarchical and progressive objective system.It establishes a comprehensive five-level progressive project training system to bridge the gap between undergraduate practice and graduate research,and sets up a deeply integrated mechanism for the collaboration of science,education,and industry to promote the transformation of scientific research and industrial resources into teaching.Additionally,it improves the support system from three aspects:institutional frameworks,evaluation,and incentives.The study also outlines a five-stage practical path:“top-level design-resource integration-pilot operation-comprehensive promotion-dynamic optimization.”This model effectively breaks down the barriers between undergraduate and graduate training,strengthens students’scientific research innovation and engineering practice abilities,and provides a replicable practical paradigm for the training of scientific and technological innovation talents bridging undergraduate and graduate studies in engineering disciplines.
摘要This paper presents a case study of the collaborative integration between the School of Information and Software Engineering at the University of Electronic Science and Technology of China(UESTC)and SI-TECH,highlighting the complementary advantages of both the University and the enterprise.By jointly establishing research institutes and engaging in diversified collaborative initiatives,the University and the enterprise have embarked on a pathway of School-enterprise Integration.Through a virtuous cycle of cooperation and continuous advancement,they have explored a comprehensive talent cultivation model in“5G”software engineering innovation practices based on this integration.Furthermore,this endeavor aims to facilitate the transformation of technological achievements and provides valuable insights for fostering innovative talents in the field of electronic information through enhanced integration between the University and the enterprise.
摘要Quantum software development utilizes quantum phenomena such as superposition and entanglement to address problems that are challenging for classical systems.However,it must also adhere to critical quantum constraints,notably the no-cloning theorem,which prohibits the exact duplication of unknown quantum states and has profound implications for cryptography,secure communication,and error correction.While existing quantum circuit representations implicitly honor such constraints,they lack formal mechanisms for early-stage verification in software design.Addressing this constraint at the design phase is essential to ensure the correctness and reliability of quantum software.This paper presents a formal metamodeling framework using UML-style notation and and Object Constraint Language(OCL)to systematically capture and enforce the no-cloning theorem within quantum software models.The proposed metamodel formalizes key quantum concepts—such as entanglement and teleportation—and encodes enforceable invariants that reflect core quantum mechanical laws.The framework’s effectiveness is validated by analyzing two critical edge cases—conditional copying with CNOT gates and quantum teleportation—through instance model evaluations.These cases demonstrate that the metamodel can capture nuanced scenarios that are often mistaken as violations of the no-cloning theorem but are proven compliant under formal analysis.Thus,these serve as constructive validations that demonstrate the metamodel’s expressiveness and correctness in representing operations that may appear to challenge the no-cloning theorem but,upon rigorous analysis,are shown to comply with it.The approach supports early detection of conceptual design errors,promoting correctness prior to implementation.The framework’s extensibility is also demonstrated by modeling projective measurement,further reinforcing its applicability to broader quantum software engineering tasks.By integrating the rigor of metamodeling with fundamental quantum mechanical principles,this work provides a structured,model-driven approach that enables traditional software engineers to address quantum computing challenges.It offers practical insights into embedding quantum correctness at the modeling level and advances the development of reliable,error-resilient quantum software systems.
摘要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.
摘要The agility of Internet of Things(IoT)software engineering is benchmarked based on its systematic insights for wide application support infrastructure developments.Such developments are focused on reducing the interfacing complexity with heterogeneous devices through applications.To handle the interfacing complexity problem,this article introduces a Semantic Interfacing Obscuration Model(SIOM)for IoT software-engineered platforms.The interfacing obscuration between heterogeneous devices and application interfaces from the testing to real-time validations is accounted for in this model.Based on the level of obscuration between the infrastructure hardware to the end-user software,the modifications through device replacement,capacity amendments,or interface bug fixes are performed.These modifications are based on the level of semantic obscurations observed during the application service intervals.The obscuration level is determined using knowledge learning as a progression from hardware to software semantics.The results reported were computed using specific metrics obtained from these experimental evaluations:an 8.94%reduction in interfacing complexity and a 15.04%improvement in integration progression.The knowledge of obscurationsmaps themodifications appropriately to reinstate the agility testing of the hardware/software integrations.This modification-based semantics is verified using semantics error,modification time,and complexity.