In recent years,ROS(Robot Operating System)packages have become increasingly popular as a type of software artifact that can be effectively reused in robotic software development.Indeed,finding suitable ROS packages t...In recent years,ROS(Robot Operating System)packages have become increasingly popular as a type of software artifact that can be effectively reused in robotic software development.Indeed,finding suitable ROS packages that closely match the software’s functional requirements from the vast number of available packages is a nontrivial task using current search methods.The traditional search methods for ROS packages often involve inputting keywords related to robotic tasks into general-purpose search engines(e.g.,Google)or code hosting platforms(e.g.,GitHub)to obtain approximate results of all potentially suitable ROS packages.However,the accuracy of these search methods remains relatively low because the task-related keywords may not precisely match the functionalities offered by the ROS packages.To improve the search accuracy of ROS packages,this paper presents a novel semantic-based search approach that relies on the semanticlevel ROS Package Knowledge Graph(RPKG)to automatically retrieve the most suitable ROS packages.Firstly,to construct the RPKG,we employ multi-dimensional feature extraction techniques to extract semantic concepts,including code file name,category,hardware device,characteristics,and function,from the dataset of ROS package text descriptions.The semantic features extracted from this process result in a substantial number of entities(32,294)and relationships(54,698).Subsequently,we create a robot domain-specific small corpus and further fine-tune a pre-trained language model,BERT-ROS,to generate embeddings that effectively represent the semantics of the extracted features.These embeddings play a crucial role in facilitating semantic-level understanding and comparisons during the ROS package search process within the RPKG.Secondly,we introduce a novel semantic matching-based search algorithm that incorporates the weighted similarities of multiple features from user search queries,which searches out more accurate ROS packages than the traditional keyword search method.To validate the enhanced accuracy of ROS package searching,we conduct comparative case studies between our semantic-based search approach and four baseline search approaches:ROS Index,GitHub,Google,and ChatGPT.The experiment results demonstrate that our approach achieves higher accuracy in terms of ROS package searching,outperforming the other approaches by at least 21%from 5 levels,including top1,top5,top10,top15,and top20.展开更多
The daily regional reanalysis product of the China Ocean Reanalysis(CORA)product was released in website in 2018.Using in situ observational data including Argo profiling floats,drifters,and cruise data,the performanc...The daily regional reanalysis product of the China Ocean Reanalysis(CORA)product was released in website in 2018.Using in situ observational data including Argo profiling floats,drifters,and cruise data,the performance of CORA in the South China Sea in terms of temperature,salinity,current and mixed layer depths is evaluated based on timescale(seasonal and interannual)and spatial distribution characteristics.The CORA temperature,salinity,and mixed layer depth show certain seasonal and interannual variations.In 50-400 m depth in the SCS,the CORA temperature is colder in winter and warmer in summer and autumn.In 0-150 m in the SCS,the CORA salinity is higher in most time of the year.However,in the second half of the year,the salinity is slightly weaker in 100-150 m depth.In most years,the CORA mixed layer depths tend to be shallower,and in season,shallower in winter and deeper in summer.In spatial distribution,the closer the area is to the coast,the greater the CORA errors would be.The CORA temperature is colder in the western side and warmer in the eastern side,resulting in a weaker SCS western boundary current(SCSwbc).In most areas,the CORA mixed layer depths are shallower.In the area close to the coast,the CORA mixed layer depths change rapidly,and the deviations in the mixed layer depths are larger.In the central SCS,the CORA mixed layer depths change slowly,and the deviations in the mixed layer depths are also small.展开更多
The resolution of ocean reanalysis datasets is generally low because of the limited resolution of their associated numerical models.Low-resolution ocean reanalysis datasets are therefore usually interpolated to provid...The resolution of ocean reanalysis datasets is generally low because of the limited resolution of their associated numerical models.Low-resolution ocean reanalysis datasets are therefore usually interpolated to provide an initial or boundary field for higher-resolution regional ocean models.However,traditional interpolation methods(nearest neighbor interpolation,bilinear interpolation,and bicubic interpolation)lack physical constraints and can generate significant errors at land-sea boundaries and around islands.In this paper,a machine learning method is used to design an interpolation algorithm based on Gaussian process regression.The method uses a multiscale kernel function to process two-dimensional space meteorological ocean processes and introduces multiscale physical feature information(sea surface wind stress,sea surface heat flux,and ocean current velocity).This greatly improves the spatial resolution of ocean features and the interpolation accuracy.The eff ectiveness of the algorithm was validated through interpolation experiments relating to sea surface temperature(SST).The root mean square error(RMSE)of the interpolation algorithm was 38.9%,43.7%,and 62.4%lower than that of bilinear interpolation,bicubic interpolation,and nearest neighbor interpolation,respectively.The interpolation accuracy was also significantly better in off shore area and around islands.The algorithm has an acceptable runtime cost and good temporal and spatial generalizability.展开更多
The Linux kernel adopts a large number of security checks to prevent security-sensitive operations from being executed under unsafe conditions.If a security-sensitive operation is unchecked,a missing-check issue arise...The Linux kernel adopts a large number of security checks to prevent security-sensitive operations from being executed under unsafe conditions.If a security-sensitive operation is unchecked,a missing-check issue arises.Missing check is a class of severe bugs in software programs especially in operating system kernels,which may cause a variety of security issues,such as out-of-bound accesses,permission bypasses,and privilege escalations.Due to the lack of security specifications,how to automatically identify security-sensitive operations and their required security checks in the Linux kernel becomes a challenge for missing-check analysis.In this paper,we present an accurate missing-check analysis method for Linux kernel,which can automatically infer possible security-sensitive operations.Particularly,we first automatically identify all possible security check functions of Linux.Then according to their callsites,a two-direction analysis method is leveraged to identify possible security-sensitive operations.A missing-check bug is reported when the security-sensitive operation is not protected by its corresponding security check.We have implemented our method as a tool,named AMCheX,on top of the LLVM(Low Level Virtual Machine)framework and evaluated it on the Linux kernel.AMCheX reported 12 new missing-check bugs which can cause security issues.Five of them have been confirmed by Linux maintainers.展开更多
Pull-based development has become an important paradigm for distributed software development.In this model,each developer independently works on a copied repository(i.e.,a fork)from the central repository.It is essent...Pull-based development has become an important paradigm for distributed software development.In this model,each developer independently works on a copied repository(i.e.,a fork)from the central repository.It is essential for developers to maintain awareness of the state of other forks to improve collaboration efficiency.In this paper,we propose a method to automatically generate a summary of a fork.We first use the random forest method to generate the label of a fork,i.e.,feature implementation or a bug fix.Based on the information of the fork-related commits,we then use the TextRank algorithm to generate detailed activity information of the fork.Finally,we apply a set of rules to integrate all related information to construct a complete fork summary.To validate the effectiveness of our method,we conduct 30 groups of manual experiment and 77 groups of case studies on Github.We propose Fea_(avg)to evaluate the performance of Fea_(avg)the generated fork summary,considering the content accuracy,content integrity,sentence fluency,and label extraction accuracy.The results show that the average of of the fork summary generated by this method is 0.672.More than 63%of project maintainers and the contributors believe that the fork summary can improve development efficiency.展开更多
Program synthesis is an exciting topic that desires to generate programs satisfying user intent automatically. But in most cases, only small programs for simple or domain-specific tasks can be synthesized. The major o...Program synthesis is an exciting topic that desires to generate programs satisfying user intent automatically. But in most cases, only small programs for simple or domain-specific tasks can be synthesized. The major obstacle of synthesis lies in the huge search space. A common practice in addressing this problem is using a domain-specific language, while many approaches still wish to synthesize programs in general programming languages. With the rapid growth of reusable libraries, component-based synthesis provides a promising way, such as synthesizing Java programs which are only composed of APIs (application programming interfaces). However, the efficiency of searching for proper solutions for complex tasks is still a challenge. Given an unfamiliar programming task, programmers would search for API usage knowledge from various coding resources to reduce the search space. Considering this, we propose a novel approach named ProSy to synthesize API-based programs in Java. The key novelty is to retrieve related knowledge from Javadoc and Stack Overflow and then construct a probabilistic reachability graph. It assigns higher probabilities to APIs that are more likely to be used in implementing the given task. In the synthesis process, the program sketch with a higher probability will be considered first;thus, the number of explored reachable paths would be decreased. Some extension and optimization strategies are further studied in the paper. We implement our approach and conduct several experiments on it. We compare ProSy with SyPet and other state-of-the-art API-based synthesis approaches. The experimental results show that ProSy reduces the synthesis time of SyPet by up to 80%.展开更多
College students majoring in computer science and software engineering need to master skills for high-quality programming.However,rich research has shown that both the teaching and learning of high-quality programming...College students majoring in computer science and software engineering need to master skills for high-quality programming.However,rich research has shown that both the teaching and learning of high-quality programming are challenging and deficient in most college education systems.Recently,the continuous inspection paradigm has been widely used by developers on social coding sites(e.g.,GitHub)as an important method to ensure the internal quality of massive code contributions.This paper presents a case where continuous inspection is introduced into the classroom setting to improve students’programming quality.In the study,we first designed a specific continuous inspection process for students’collaborative projects and built an execution environment for the process.We then conducted a controlled experiment with 48 students from the same course during two school years to evaluate how the process affects their programming quality.Our results show that continuous inspection can help students in identifying their bad coding habits,mastering a set of good coding rules and significantly reducing the density of code quality issues introduced in the code.Furthermore,we describe the lessons learned during the study and propose ideas to replicate and improve the process and its execution platform.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.62172426)the National Key R&D Program of China(2023YFB4503802)the National University of Defense Technology Young Scientist Self-Innovation Science Fund Project(ZK2023-36).
文摘In recent years,ROS(Robot Operating System)packages have become increasingly popular as a type of software artifact that can be effectively reused in robotic software development.Indeed,finding suitable ROS packages that closely match the software’s functional requirements from the vast number of available packages is a nontrivial task using current search methods.The traditional search methods for ROS packages often involve inputting keywords related to robotic tasks into general-purpose search engines(e.g.,Google)or code hosting platforms(e.g.,GitHub)to obtain approximate results of all potentially suitable ROS packages.However,the accuracy of these search methods remains relatively low because the task-related keywords may not precisely match the functionalities offered by the ROS packages.To improve the search accuracy of ROS packages,this paper presents a novel semantic-based search approach that relies on the semanticlevel ROS Package Knowledge Graph(RPKG)to automatically retrieve the most suitable ROS packages.Firstly,to construct the RPKG,we employ multi-dimensional feature extraction techniques to extract semantic concepts,including code file name,category,hardware device,characteristics,and function,from the dataset of ROS package text descriptions.The semantic features extracted from this process result in a substantial number of entities(32,294)and relationships(54,698).Subsequently,we create a robot domain-specific small corpus and further fine-tune a pre-trained language model,BERT-ROS,to generate embeddings that effectively represent the semantics of the extracted features.These embeddings play a crucial role in facilitating semantic-level understanding and comparisons during the ROS package search process within the RPKG.Secondly,we introduce a novel semantic matching-based search algorithm that incorporates the weighted similarities of multiple features from user search queries,which searches out more accurate ROS packages than the traditional keyword search method.To validate the enhanced accuracy of ROS package searching,we conduct comparative case studies between our semantic-based search approach and four baseline search approaches:ROS Index,GitHub,Google,and ChatGPT.The experiment results demonstrate that our approach achieves higher accuracy in terms of ROS package searching,outperforming the other approaches by at least 21%from 5 levels,including top1,top5,top10,top15,and top20.
基金Supported by the National Key R&D Program of China(No.2018YFC1406202)the National Natural Science Foundation of China(No.41976188)。
文摘The daily regional reanalysis product of the China Ocean Reanalysis(CORA)product was released in website in 2018.Using in situ observational data including Argo profiling floats,drifters,and cruise data,the performance of CORA in the South China Sea in terms of temperature,salinity,current and mixed layer depths is evaluated based on timescale(seasonal and interannual)and spatial distribution characteristics.The CORA temperature,salinity,and mixed layer depth show certain seasonal and interannual variations.In 50-400 m depth in the SCS,the CORA temperature is colder in winter and warmer in summer and autumn.In 0-150 m in the SCS,the CORA salinity is higher in most time of the year.However,in the second half of the year,the salinity is slightly weaker in 100-150 m depth.In most years,the CORA mixed layer depths tend to be shallower,and in season,shallower in winter and deeper in summer.In spatial distribution,the closer the area is to the coast,the greater the CORA errors would be.The CORA temperature is colder in the western side and warmer in the eastern side,resulting in a weaker SCS western boundary current(SCSwbc).In most areas,the CORA mixed layer depths are shallower.In the area close to the coast,the CORA mixed layer depths change rapidly,and the deviations in the mixed layer depths are larger.In the central SCS,the CORA mixed layer depths change slowly,and the deviations in the mixed layer depths are also small.
基金Supported by the National Natural Science Foundation of China(Nos.41675097,41375113)。
文摘The resolution of ocean reanalysis datasets is generally low because of the limited resolution of their associated numerical models.Low-resolution ocean reanalysis datasets are therefore usually interpolated to provide an initial or boundary field for higher-resolution regional ocean models.However,traditional interpolation methods(nearest neighbor interpolation,bilinear interpolation,and bicubic interpolation)lack physical constraints and can generate significant errors at land-sea boundaries and around islands.In this paper,a machine learning method is used to design an interpolation algorithm based on Gaussian process regression.The method uses a multiscale kernel function to process two-dimensional space meteorological ocean processes and introduces multiscale physical feature information(sea surface wind stress,sea surface heat flux,and ocean current velocity).This greatly improves the spatial resolution of ocean features and the interpolation accuracy.The eff ectiveness of the algorithm was validated through interpolation experiments relating to sea surface temperature(SST).The root mean square error(RMSE)of the interpolation algorithm was 38.9%,43.7%,and 62.4%lower than that of bilinear interpolation,bicubic interpolation,and nearest neighbor interpolation,respectively.The interpolation accuracy was also significantly better in off shore area and around islands.The algorithm has an acceptable runtime cost and good temporal and spatial generalizability.
基金supported by the National Nature Science Foundation of China under Grant Nos.61802415,62032019 and 62032024.PDF(PC)23。
文摘The Linux kernel adopts a large number of security checks to prevent security-sensitive operations from being executed under unsafe conditions.If a security-sensitive operation is unchecked,a missing-check issue arises.Missing check is a class of severe bugs in software programs especially in operating system kernels,which may cause a variety of security issues,such as out-of-bound accesses,permission bypasses,and privilege escalations.Due to the lack of security specifications,how to automatically identify security-sensitive operations and their required security checks in the Linux kernel becomes a challenge for missing-check analysis.In this paper,we present an accurate missing-check analysis method for Linux kernel,which can automatically infer possible security-sensitive operations.Particularly,we first automatically identify all possible security check functions of Linux.Then according to their callsites,a two-direction analysis method is leveraged to identify possible security-sensitive operations.A missing-check bug is reported when the security-sensitive operation is not protected by its corresponding security check.We have implemented our method as a tool,named AMCheX,on top of the LLVM(Low Level Virtual Machine)framework and evaluated it on the Linux kernel.AMCheX reported 12 new missing-check bugs which can cause security issues.Five of them have been confirmed by Linux maintainers.
基金This work was supported by the National Key Research and Development Program of China(2018YFB1004202).
文摘Pull-based development has become an important paradigm for distributed software development.In this model,each developer independently works on a copied repository(i.e.,a fork)from the central repository.It is essential for developers to maintain awareness of the state of other forks to improve collaboration efficiency.In this paper,we propose a method to automatically generate a summary of a fork.We first use the random forest method to generate the label of a fork,i.e.,feature implementation or a bug fix.Based on the information of the fork-related commits,we then use the TextRank algorithm to generate detailed activity information of the fork.Finally,we apply a set of rules to integrate all related information to construct a complete fork summary.To validate the effectiveness of our method,we conduct 30 groups of manual experiment and 77 groups of case studies on Github.We propose Fea_(avg)to evaluate the performance of Fea_(avg)the generated fork summary,considering the content accuracy,content integrity,sentence fluency,and label extraction accuracy.The results show that the average of of the fork summary generated by this method is 0.672.More than 63%of project maintainers and the contributors believe that the fork summary can improve development efficiency.
基金This paper was supported by the National Natural Science Foundation of China under Grant No.61690203the National Key Research and Development Program of China under Grant No.2018YFB0204301.
文摘Program synthesis is an exciting topic that desires to generate programs satisfying user intent automatically. But in most cases, only small programs for simple or domain-specific tasks can be synthesized. The major obstacle of synthesis lies in the huge search space. A common practice in addressing this problem is using a domain-specific language, while many approaches still wish to synthesize programs in general programming languages. With the rapid growth of reusable libraries, component-based synthesis provides a promising way, such as synthesizing Java programs which are only composed of APIs (application programming interfaces). However, the efficiency of searching for proper solutions for complex tasks is still a challenge. Given an unfamiliar programming task, programmers would search for API usage knowledge from various coding resources to reduce the search space. Considering this, we propose a novel approach named ProSy to synthesize API-based programs in Java. The key novelty is to retrieve related knowledge from Javadoc and Stack Overflow and then construct a probabilistic reachability graph. It assigns higher probabilities to APIs that are more likely to be used in implementing the given task. In the synthesis process, the program sketch with a higher probability will be considered first;thus, the number of explored reachable paths would be decreased. Some extension and optimization strategies are further studied in the paper. We implement our approach and conduct several experiments on it. We compare ProSy with SyPet and other state-of-the-art API-based synthesis approaches. The experimental results show that ProSy reduces the synthesis time of SyPet by up to 80%.
基金We gratefully acknowledge the financial support from National Key R&D Program of China(2018 YFB1004202)the National Natural Science Foundation of China(Grant Nos.61472430,61502512,61532004 and 61379051).We also want to thank our students on their active participation in our study.
文摘College students majoring in computer science and software engineering need to master skills for high-quality programming.However,rich research has shown that both the teaching and learning of high-quality programming are challenging and deficient in most college education systems.Recently,the continuous inspection paradigm has been widely used by developers on social coding sites(e.g.,GitHub)as an important method to ensure the internal quality of massive code contributions.This paper presents a case where continuous inspection is introduced into the classroom setting to improve students’programming quality.In the study,we first designed a specific continuous inspection process for students’collaborative projects and built an execution environment for the process.We then conducted a controlled experiment with 48 students from the same course during two school years to evaluate how the process affects their programming quality.Our results show that continuous inspection can help students in identifying their bad coding habits,mastering a set of good coding rules and significantly reducing the density of code quality issues introduced in the code.Furthermore,we describe the lessons learned during the study and propose ideas to replicate and improve the process and its execution platform.
