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
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
Cross-Project Defect Prediction(CPDP)is a method that utilizes historical data from other source projects to train predictive models for defect prediction in the target project.However,existing CPDP methods only consi...Cross-Project Defect Prediction(CPDP)is a method that utilizes historical data from other source projects to train predictive models for defect prediction in the target project.However,existing CPDP methods only consider linear correlations between features(indicators)of the source and target projects.These models are not capable of evaluating non-linear correlations between features when they exist,for example,when there are differences in data distributions between the source and target projects.As a result,the performance of such CPDP models is compromised.In this paper,this paper proposes a novel CPDP method based on Synthetic Minority Oversampling Technique(SMOTE)and Deep Canonical Correlation Analysis(DCCA),referred to as S-DCCA.Canonical Correlation Analysis(CCA)is employed to address the issue of non-linear correlations between features of the source and target projects.S-DCCA extends CCA by incorporating the MlpNet model for feature extraction from the dataset.The redundant features are then eliminated by maximizing the correlated feature subset using the CCA loss function.Finally,cross-project defect prediction is achieved through the application of the SMOTE data sampling technique.Area Under Curve(AUC)and F1 scores(F1)are used as evaluation metrics.This paper conducted experiments on 27 projects from four public datasets to validate the proposed method.The results demonstrate that,on average,our method outperforms all baseline approaches by at least 1.2%in AUC and 5.5%in F1 score.This indicates that the proposed method exhibits favorable performance characteristics.展开更多
Tactile perception plays a vital role for the human body and is also highly desired for smart prosthesis and advanced robots.Compared to active sensing devices,passive piezoelectric and triboelectric tactile sensors c...Tactile perception plays a vital role for the human body and is also highly desired for smart prosthesis and advanced robots.Compared to active sensing devices,passive piezoelectric and triboelectric tactile sensors consume less power,but lack the capability to resolve static stimuli.Here,we address this issue by utilizing the unique polarization chemistry of conjugated polymers for the first time and propose a new type of bioinspired,passive,and bio-friendly tactile sensors for resolving both static and dynamic stimuli.Specifically,to emulate the polarization process of natural sensory cells,conjugated polymers(including poly(3,4-ethylenedioxythiophen e):poly(styrenesulfonate),polyaniline,or polypyrrole)are controllably polarized into two opposite states to create artificial potential differences.The controllable and reversible polarization process of the conjugated polymers is fully in situ characterized.Then,a micro-structured ionic electrolyte is employed to imitate the natural ion channels and to encode external touch stimulations into the variation in potential difference outputs.Compared with the currently existing tactile sensing devices,the developed tactile sensors feature distinct characteristics including fully organic composition,high sensitivity(up to 773 mV N^(−1)),ultralow power consumption(nW),as well as superior bio-friendliness.As demonstrations,both single point tactile perception(surface texture perception and material property perception)and two-dimensional tactile recognitions(shape or profile perception)with high accuracy are successfully realized using self-defined machine learning algorithms.This tactile sensing concept innovation based on the polarization chemistry of conjugated polymers opens up a new path to create robotic tactile sensors and prosthetic electronic skins.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
Cross-project software defect prediction(CPDP)aims to enhance defect prediction in target projects with limited or no historical data by leveraging information from related source projects.The existing CPDP approaches...Cross-project software defect prediction(CPDP)aims to enhance defect prediction in target projects with limited or no historical data by leveraging information from related source projects.The existing CPDP approaches rely on static metrics or dynamic syntactic features,which have shown limited effectiveness in CPDP due to their inability to capture higher-level system properties,such as complex design patterns,relationships between multiple functions,and dependencies in different software projects,that are important for CPDP.This paper introduces a novel approach,a graph-based feature learning model for CPDP(GB-CPDP),that utilizes NetworkX to extract features and learn representations of program entities from control flow graphs(CFGs)and data dependency graphs(DDGs).These graphs capture the structural and data dependencies within the source code.The proposed approach employs Node2Vec to transform CFGs and DDGs into numerical vectors and leverages Long Short-Term Memory(LSTM)networks to learn predictive models.The process involves graph construction,feature learning through graph embedding and LSTM,and defect prediction.Experimental evaluation using nine open-source Java projects from the PROMISE dataset demonstrates that GB-CPDP outperforms state-of-the-art CPDP methods in terms of F1-measure and Area Under the Curve(AUC).The results showcase the effectiveness of GB-CPDP in improving the performance of cross-project defect prediction.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
During the prediction of software defect distribution, the data redundancy caused by the multi-dimensional measurement will lead to the decrease of prediction accuracy. In order to solve this problem, this paper propo...During the prediction of software defect distribution, the data redundancy caused by the multi-dimensional measurement will lead to the decrease of prediction accuracy. In order to solve this problem, this paper proposed a novel software defect prediction model based on neighborhood preserving embedded support vector machine(NPESVM) algorithm. The model uses SVM as the basic classifier of software defect distribution prediction model, and the NPE algorithm is combined to keep the local geometric structure of the data unchanged in the process of dimensionality reduction. The problem of precision reduction of SVM caused by data loss after attribute reduction is avoided. Compared with single SVM and LLE-SVM prediction algorithm, the prediction model in this paper improves the F-measure in aspect of software defect distribution prediction by 3%~4%.展开更多
In view of the increasingly rapid development of global economic integration and combined with the existing modes of training international software engineering talents in China,this paper deeply analyzes and obtains ...In view of the increasingly rapid development of global economic integration and combined with the existing modes of training international software engineering talents in China,this paper deeply analyzes and obtains the existing problems in the current teaching process,and proposes various teaching reform measures under the guidance of CDIO higher engineering education thought.Through many years of teaching practice experience,we can find that our reform has achieved remarkable results.展开更多
Powered by advanced information industry and intelligent technology,more and more complex systems are exhibiting characteristics of the cyber-physical-social systems(CPSS).And human factors have become crucial in the ...Powered by advanced information industry and intelligent technology,more and more complex systems are exhibiting characteristics of the cyber-physical-social systems(CPSS).And human factors have become crucial in the operations of complex social systems.Traditional mechanical analysis and social simulations alone are powerless for analyzing complex social systems.Against this backdrop,computational experiments have emerged as a new method for quantitative analysis of complex social systems by combining social simulation(e.g.,ABM),complexity science,and domain knowledge.However,in the process of applying computational experiments,the construction of experiment system not only considers a large number of artificial society models,but also involves a large amount of data and knowledge.As a result,how to integrate various data,model and knowledge to achieve a running experiment system has become a key challenge.This paper proposes an integrated design framework of computational experiment system,which is composed of four parts:generation of digital subject,generation of digital object,design of operation engine,and construction of experiment system.Finally,this paper outlines a typical case study of coal mine emergency management to verify the validity of the proposed framework.展开更多
Despite the advances in automated vulnerability detection approaches,security vulnerabilities caused by design flaws in software systems are continuously appearing in real-world systems.Such security design flaws can ...Despite the advances in automated vulnerability detection approaches,security vulnerabilities caused by design flaws in software systems are continuously appearing in real-world systems.Such security design flaws can bring unrestricted and misimplemented behaviors of a system and can lead to fatal vulnerabilities such as remote code execution or sensitive data leakage.Therefore,it is an essential task to discover unrestricted and misimplemented behaviors of a system.However,it is a daunting task for security experts to discover such vulnerabilities in advance because it is timeconsuming and error-prone to analyze the whole code in detail.Also,most of the existing vulnerability detection approaches still focus on detecting memory corruption bugs because these bugs are the dominant root cause of software vulnerabilities.This paper proposes SMINER,a novel approach that discovers vulnerabilities caused by unrestricted and misimplemented behaviors.SMINER first collects unit test cases for the target system from the official repository.Next,preprocess the collected code fragments.SMINER uses pre-processed data to show the security policies that can occur on the target system and creates a test case for security policy testing.To demonstrate the effectiveness of SMINER,this paper evaluates SMINER against Robot Operating System(ROS),a real-world system used for intelligent robots in Amazon and controlling satellites in National Aeronautics and Space Administration(NASA).From the evaluation,we discovered two real-world vulnerabilities in ROS.展开更多
For a more accurate and comprehensive assessment of the trustworthiness of component-based soft- ware system, the fuzzy analytic hierarchy process is introduced to establish the analysis model. Combine qualitative and...For a more accurate and comprehensive assessment of the trustworthiness of component-based soft- ware system, the fuzzy analytic hierarchy process is introduced to establish the analysis model. Combine qualitative and quantitative analyses, the impacts to overall trustworthiness by the different types of components are distinguished. Considering the coupling relationship between components, dividing the system into several layers from target layer to scheme layer, evaluating the scheme advantages disadvantages by group decision-making, the trustworthiness of a typical J2EE structured component-based software is assessed. The trustworthiness asses model of the software components provides an effective methods of operation.展开更多
The concept of“New Engineering”has put forward new challenges to the talents cultivation of universities.Due to some problems of the traditional Software Engineering curriculum,e.g.separated design at undergraduate-...The concept of“New Engineering”has put forward new challenges to the talents cultivation of universities.Due to some problems of the traditional Software Engineering curriculum,e.g.separated design at undergraduate-level and graduate-level courses,poor curriculum structure,lacking of industry characteristics.This paper proposes an integrated undergraduate-graduate education curriculum for Software Engineering Major,which is based on Software Engineering specialty knowledge system(C-SWEBOK)and focuses on the current national strategic demands.Additionally,the curriculum combines with the University’s transportation characteristics,and fuses the discipline of Software Engineering and Intelligent Transportation.The multi-level curriculum designed in this paper is with reasonable structure,complete system,progressive content,and salient feature,which provides the strong support for cultivating high-qualified software talents in line with national strategies and industry needs.展开更多
In this paper,a case study is carried out in comparison of pipes-and-filters architecture and batch sequential architecture.Concepts on a data flow system and the two mentioned architectures are presented.A Java templ...In this paper,a case study is carried out in comparison of pipes-and-filters architecture and batch sequential architecture.Concepts on a data flow system and the two mentioned architectures are presented.A Java template class design in implementing the "pipes" and "filters" in the pipes-and-filters architecture is given at the design level.Finally,this paper uses a concrete example to show how to use Java to implement the pipesand-filters architecture.Using varied amount of data from text files,performance and memory usage of the two architectures are illustrated.展开更多
With the rapid evolution of technology and the increasing complexity of software systems,there is a growing demand for effective educational approaches that empower learners to acquire and apply software engineering s...With the rapid evolution of technology and the increasing complexity of software systems,there is a growing demand for effective educational approaches that empower learners to acquire and apply software engineering skills in practical contexts.This paper presents an intelligent and interactive learning(Meta-SEE)framework for software engineering education that combines the immersive capabilities of the metaverse with the cognitive processes of metacognition,to create an interactive and engaging learning environment.In the Meta-SEE framework,learners are immersed in a virtual world where they can collaboratively engage with concepts and practices of software engineering.Through the integration of metacognitive strategies,learners are empowered to monitor,regulate,and adapt their learning processes.By incorporating metacognition within the metaverse,learners gain a deeper understanding of their own thinking processes and become self-directed learners.In addition,MetaSEE has the potential to revolutionize software engineering education by offering a dynamic,immersive,and personalized learning experience.It allows learners to engage in realistic software development scenarios,explore complex systems,and collaborate with peers and instructors in virtual spaces.展开更多
As a highly advanced conversational AI chatbot trained on extensive datasets,ChatGPT has garnered significant attention across various domains,including academia,industry,and education.In the field of education,existi...As a highly advanced conversational AI chatbot trained on extensive datasets,ChatGPT has garnered significant attention across various domains,including academia,industry,and education.In the field of education,existing studies primarily focus on 2 areas:Assessing the potential utility of ChatGPT in education by examining its capabilities and limitations;exploring the educational scenarios that could benefit from the integration of ChatGPT.In contrast to these studies,we conduct a user survey targeting undergraduate students specializing in Software Engineering,aiming to gain insights into their perceptions,challenges,and expectations regarding the utilization of ChatGPT.Based on the results of the survey,we provide valuable guidance on the effective incorporation of ChatGPT in the realm of software engineering education.展开更多
"Semester Training"has been adopted as an important part of the personnel training in software engineering majors since it was first put forward.The ultimate goal of semester training is to improve the profe..."Semester Training"has been adopted as an important part of the personnel training in software engineering majors since it was first put forward.The ultimate goal of semester training is to improve the professional quality of students in an all-round way,then eventually achieve the goal of satisfactory employment for both students and enterprises.However,in order to achieve the above purpose,the design of traditional training project still has the following problems:the topic selection of traditional training is designed by teachers in college,which lacks the training of engineering ability aiming at practical problems;the content and technology of traditional project training are out of date,ignoring the urgent demand of software industry development for advanced technology application;the traditional project training inspects the mastery of knowledge in each semester Degree,ignores the incremental of a progressive training system.In view of the above problems,this study proposes an Application-Oriented Software Engineering Semester Training System.Practice has proved that the construction of the training system can effectively improve the quality of teaching,so as to further improve the comprehensive quality of students.展开更多
Compatibility is the precondition to ensure the correct interaction among components in composition process, how to make the mismatch components coordinate correctly is a vital problem in component composition. This p...Compatibility is the precondition to ensure the correct interaction among components in composition process, how to make the mismatch components coordinate correctly is a vital problem in component composition. This paper first modeled component behavior by LTS and expressed action mapping as synchronous vector then defined the sequential relationship among synchronous vectors as adaptation contract. Thirdly we analyzed the different mismatch situations and corresponding adaptation strategies. At last designed adaptation algorithm to produce adaptor specification automatically and ensured the mismatch components can correct interaction under the mediation of adaptor and verified the validity of proposed method through an application system.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 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NationalNatural Science Foundation of China,Grant/AwardNumber:61867004National Natural Science Foundation of China Youth Fund,Grant/Award Number:41801288.
文摘Cross-Project Defect Prediction(CPDP)is a method that utilizes historical data from other source projects to train predictive models for defect prediction in the target project.However,existing CPDP methods only consider linear correlations between features(indicators)of the source and target projects.These models are not capable of evaluating non-linear correlations between features when they exist,for example,when there are differences in data distributions between the source and target projects.As a result,the performance of such CPDP models is compromised.In this paper,this paper proposes a novel CPDP method based on Synthetic Minority Oversampling Technique(SMOTE)and Deep Canonical Correlation Analysis(DCCA),referred to as S-DCCA.Canonical Correlation Analysis(CCA)is employed to address the issue of non-linear correlations between features of the source and target projects.S-DCCA extends CCA by incorporating the MlpNet model for feature extraction from the dataset.The redundant features are then eliminated by maximizing the correlated feature subset using the CCA loss function.Finally,cross-project defect prediction is achieved through the application of the SMOTE data sampling technique.Area Under Curve(AUC)and F1 scores(F1)are used as evaluation metrics.This paper conducted experiments on 27 projects from four public datasets to validate the proposed method.The results demonstrate that,on average,our method outperforms all baseline approaches by at least 1.2%in AUC and 5.5%in F1 score.This indicates that the proposed method exhibits favorable performance characteristics.
基金financially supported by the Sichuan Science and Technology Program(2022YFS0025 and 2024YFFK0133)supported by the“Fundamental Research Funds for the Central Universities of China.”。
文摘Tactile perception plays a vital role for the human body and is also highly desired for smart prosthesis and advanced robots.Compared to active sensing devices,passive piezoelectric and triboelectric tactile sensors consume less power,but lack the capability to resolve static stimuli.Here,we address this issue by utilizing the unique polarization chemistry of conjugated polymers for the first time and propose a new type of bioinspired,passive,and bio-friendly tactile sensors for resolving both static and dynamic stimuli.Specifically,to emulate the polarization process of natural sensory cells,conjugated polymers(including poly(3,4-ethylenedioxythiophen e):poly(styrenesulfonate),polyaniline,or polypyrrole)are controllably polarized into two opposite states to create artificial potential differences.The controllable and reversible polarization process of the conjugated polymers is fully in situ characterized.Then,a micro-structured ionic electrolyte is employed to imitate the natural ion channels and to encode external touch stimulations into the variation in potential difference outputs.Compared with the currently existing tactile sensing devices,the developed tactile sensors feature distinct characteristics including fully organic composition,high sensitivity(up to 773 mV N^(−1)),ultralow power consumption(nW),as well as superior bio-friendliness.As demonstrations,both single point tactile perception(surface texture perception and material property perception)and two-dimensional tactile recognitions(shape or profile perception)with high accuracy are successfully realized using self-defined machine learning algorithms.This tactile sensing concept innovation based on the polarization chemistry of conjugated polymers opens up a new path to create robotic tactile sensors and prosthetic electronic skins.
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.
基金supported by Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.RS-2022-00155885).
文摘Cross-project software defect prediction(CPDP)aims to enhance defect prediction in target projects with limited or no historical data by leveraging information from related source projects.The existing CPDP approaches rely on static metrics or dynamic syntactic features,which have shown limited effectiveness in CPDP due to their inability to capture higher-level system properties,such as complex design patterns,relationships between multiple functions,and dependencies in different software projects,that are important for CPDP.This paper introduces a novel approach,a graph-based feature learning model for CPDP(GB-CPDP),that utilizes NetworkX to extract features and learn representations of program entities from control flow graphs(CFGs)and data dependency graphs(DDGs).These graphs capture the structural and data dependencies within the source code.The proposed approach employs Node2Vec to transform CFGs and DDGs into numerical vectors and leverages Long Short-Term Memory(LSTM)networks to learn predictive models.The process involves graph construction,feature learning through graph embedding and LSTM,and defect prediction.Experimental evaluation using nine open-source Java projects from the PROMISE dataset demonstrates that GB-CPDP outperforms state-of-the-art CPDP methods in terms of F1-measure and Area Under the Curve(AUC).The results showcase the effectiveness of GB-CPDP in improving the performance of cross-project defect prediction.
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 National Natural Science Foundation of China(Grant No.U1636115)the PAPD fund+1 种基金the CICAEET fundthe Open Foundation of Guizhou Provincial Key Laboratory of Public Big Data(2017BDKFJJ017)
文摘During the prediction of software defect distribution, the data redundancy caused by the multi-dimensional measurement will lead to the decrease of prediction accuracy. In order to solve this problem, this paper proposed a novel software defect prediction model based on neighborhood preserving embedded support vector machine(NPESVM) algorithm. The model uses SVM as the basic classifier of software defect distribution prediction model, and the NPE algorithm is combined to keep the local geometric structure of the data unchanged in the process of dimensionality reduction. The problem of precision reduction of SVM caused by data loss after attribute reduction is avoided. Compared with single SVM and LLE-SVM prediction algorithm, the prediction model in this paper improves the F-measure in aspect of software defect distribution prediction by 3%~4%.
文摘In view of the increasingly rapid development of global economic integration and combined with the existing modes of training international software engineering talents in China,this paper deeply analyzes and obtains the existing problems in the current teaching process,and proposes various teaching reform measures under the guidance of CDIO higher engineering education thought.Through many years of teaching practice experience,we can find that our reform has achieved remarkable results.
基金supported in part by the National Key Research and Development Program of China(2021YFF0900800)the National Natural Science Foundation of China(61972276,62206116,62032016)+3 种基金Open Research Fund of The State Key Laboratory for Management and Control of Complex Systems(20210101)New Liberal Arts Reform and Practice Project of National Ministry of Education(2021170002)Tianjin University Talent InnovationReward Program for Literature&Science Graduate Student(C1-2022-010)。
文摘Powered by advanced information industry and intelligent technology,more and more complex systems are exhibiting characteristics of the cyber-physical-social systems(CPSS).And human factors have become crucial in the operations of complex social systems.Traditional mechanical analysis and social simulations alone are powerless for analyzing complex social systems.Against this backdrop,computational experiments have emerged as a new method for quantitative analysis of complex social systems by combining social simulation(e.g.,ABM),complexity science,and domain knowledge.However,in the process of applying computational experiments,the construction of experiment system not only considers a large number of artificial society models,but also involves a large amount of data and knowledge.As a result,how to integrate various data,model and knowledge to achieve a running experiment system has become a key challenge.This paper proposes an integrated design framework of computational experiment system,which is composed of four parts:generation of digital subject,generation of digital object,design of operation engine,and construction of experiment system.Finally,this paper outlines a typical case study of coal mine emergency management to verify the validity of the proposed framework.
基金This work was supported in part by the National Research Foundation of Korea(NRF)funded by the Ministry of Science and ICT(MSIT)Future Planning under Grant NRF-2020R1A2C2014336 and Grant NRF-2021R1A4A1029650.
文摘Despite the advances in automated vulnerability detection approaches,security vulnerabilities caused by design flaws in software systems are continuously appearing in real-world systems.Such security design flaws can bring unrestricted and misimplemented behaviors of a system and can lead to fatal vulnerabilities such as remote code execution or sensitive data leakage.Therefore,it is an essential task to discover unrestricted and misimplemented behaviors of a system.However,it is a daunting task for security experts to discover such vulnerabilities in advance because it is timeconsuming and error-prone to analyze the whole code in detail.Also,most of the existing vulnerability detection approaches still focus on detecting memory corruption bugs because these bugs are the dominant root cause of software vulnerabilities.This paper proposes SMINER,a novel approach that discovers vulnerabilities caused by unrestricted and misimplemented behaviors.SMINER first collects unit test cases for the target system from the official repository.Next,preprocess the collected code fragments.SMINER uses pre-processed data to show the security policies that can occur on the target system and creates a test case for security policy testing.To demonstrate the effectiveness of SMINER,this paper evaluates SMINER against Robot Operating System(ROS),a real-world system used for intelligent robots in Amazon and controlling satellites in National Aeronautics and Space Administration(NASA).From the evaluation,we discovered two real-world vulnerabilities in ROS.
基金Sponsored by the National High Technology Research and Development Program of China ("863"Program) (2009AA01Z433)
文摘For a more accurate and comprehensive assessment of the trustworthiness of component-based soft- ware system, the fuzzy analytic hierarchy process is introduced to establish the analysis model. Combine qualitative and quantitative analyses, the impacts to overall trustworthiness by the different types of components are distinguished. Considering the coupling relationship between components, dividing the system into several layers from target layer to scheme layer, evaluating the scheme advantages disadvantages by group decision-making, the trustworthiness of a typical J2EE structured component-based software is assessed. The trustworthiness asses model of the software components provides an effective methods of operation.
文摘The concept of“New Engineering”has put forward new challenges to the talents cultivation of universities.Due to some problems of the traditional Software Engineering curriculum,e.g.separated design at undergraduate-level and graduate-level courses,poor curriculum structure,lacking of industry characteristics.This paper proposes an integrated undergraduate-graduate education curriculum for Software Engineering Major,which is based on Software Engineering specialty knowledge system(C-SWEBOK)and focuses on the current national strategic demands.Additionally,the curriculum combines with the University’s transportation characteristics,and fuses the discipline of Software Engineering and Intelligent Transportation.The multi-level curriculum designed in this paper is with reasonable structure,complete system,progressive content,and salient feature,which provides the strong support for cultivating high-qualified software talents in line with national strategies and industry needs.
文摘In this paper,a case study is carried out in comparison of pipes-and-filters architecture and batch sequential architecture.Concepts on a data flow system and the two mentioned architectures are presented.A Java template class design in implementing the "pipes" and "filters" in the pipes-and-filters architecture is given at the design level.Finally,this paper uses a concrete example to show how to use Java to implement the pipesand-filters architecture.Using varied amount of data from text files,performance and memory usage of the two architectures are illustrated.
基金partially funded by the 2023 Teaching Quality Engineering Construction Project of Sun Yat-sen University(No.76250-12230036)the 2023 Project of Computer Education Research Association of Chinese Universities(No.CERACU2023R02)。
文摘With the rapid evolution of technology and the increasing complexity of software systems,there is a growing demand for effective educational approaches that empower learners to acquire and apply software engineering skills in practical contexts.This paper presents an intelligent and interactive learning(Meta-SEE)framework for software engineering education that combines the immersive capabilities of the metaverse with the cognitive processes of metacognition,to create an interactive and engaging learning environment.In the Meta-SEE framework,learners are immersed in a virtual world where they can collaboratively engage with concepts and practices of software engineering.Through the integration of metacognitive strategies,learners are empowered to monitor,regulate,and adapt their learning processes.By incorporating metacognition within the metaverse,learners gain a deeper understanding of their own thinking processes and become self-directed learners.In addition,MetaSEE has the potential to revolutionize software engineering education by offering a dynamic,immersive,and personalized learning experience.It allows learners to engage in realistic software development scenarios,explore complex systems,and collaborate with peers and instructors in virtual spaces.
基金supported in part by the Guangdong Basic and Applied Basic Research Foundation(Grant No.2023A1515012292)the 2023 Teaching Quality Engineering Construction Project of Sun Yat-sen University(Grant No.76250-12230036)the 2023 Project of Computer Education Research Association ofChinese Universities(Grant No.CERACU2023R02)。
文摘As a highly advanced conversational AI chatbot trained on extensive datasets,ChatGPT has garnered significant attention across various domains,including academia,industry,and education.In the field of education,existing studies primarily focus on 2 areas:Assessing the potential utility of ChatGPT in education by examining its capabilities and limitations;exploring the educational scenarios that could benefit from the integration of ChatGPT.In contrast to these studies,we conduct a user survey targeting undergraduate students specializing in Software Engineering,aiming to gain insights into their perceptions,challenges,and expectations regarding the utilization of ChatGPT.Based on the results of the survey,we provide valuable guidance on the effective incorporation of ChatGPT in the realm of software engineering education.
基金supported by the Fundamental Research Funds for the Central Universities under Grant 2020RC011.
文摘"Semester Training"has been adopted as an important part of the personnel training in software engineering majors since it was first put forward.The ultimate goal of semester training is to improve the professional quality of students in an all-round way,then eventually achieve the goal of satisfactory employment for both students and enterprises.However,in order to achieve the above purpose,the design of traditional training project still has the following problems:the topic selection of traditional training is designed by teachers in college,which lacks the training of engineering ability aiming at practical problems;the content and technology of traditional project training are out of date,ignoring the urgent demand of software industry development for advanced technology application;the traditional project training inspects the mastery of knowledge in each semester Degree,ignores the incremental of a progressive training system.In view of the above problems,this study proposes an Application-Oriented Software Engineering Semester Training System.Practice has proved that the construction of the training system can effectively improve the quality of teaching,so as to further improve the comprehensive quality of students.
文摘Compatibility is the precondition to ensure the correct interaction among components in composition process, how to make the mismatch components coordinate correctly is a vital problem in component composition. This paper first modeled component behavior by LTS and expressed action mapping as synchronous vector then defined the sequential relationship among synchronous vectors as adaptation contract. Thirdly we analyzed the different mismatch situations and corresponding adaptation strategies. At last designed adaptation algorithm to produce adaptor specification automatically and ensured the mismatch components can correct interaction under the mediation of adaptor and verified the validity of proposed method through an application system.
