Dynamic program slicing is an effective technique for narrowing the errors to the relevant parts of a program when debugging. Given a slicing criterion, the dynamic slice contains only those statements that actually a...Dynamic program slicing is an effective technique for narrowing the errors to the relevant parts of a program when debugging. Given a slicing criterion, the dynamic slice contains only those statements that actually affect the variables in the slicing criterion. This paper proposes a dynamic slicing method based on static dependence analysis. It uses the program dependence graph and other static information to reduce the information needed to be traced during program execution. Thus, the efficiency is dramatically improved while the precision is not depressed. The slicing criterion is modified to fit for debugging. It consists of file name and the line number at which the statement is.展开更多
The lack of existing solutions makes it really hard to understand formal specification languages since the application domain for representations is useful for the purpose of carrying out certain software engineering ...The lack of existing solutions makes it really hard to understand formal specification languages since the application domain for representations is useful for the purpose of carrying out certain software engineering operations such as slicing and the computation of program metrics.A Z specification dependence graph is presented in this letter. It draws on the strengths of a range of earlier works and adapts them, if necessary, to the Z language.展开更多
Developers integrate web Application Programming Interfaces(APIs)into edge applications,enabling data expansion to the edge computing area for comprehensive coverage of devices in that region.To develop edge applicati...Developers integrate web Application Programming Interfaces(APIs)into edge applications,enabling data expansion to the edge computing area for comprehensive coverage of devices in that region.To develop edge applications,developers search API categories to select APIs that meet specific functionalities.Therefore,the accurate classification of APIs becomes critically important.However,existing approaches,as evident on platforms like programableweb.com,face significant challenges.Firstly,sparsity in API data reduces classification accuracy in works focusing on single-dimensional API information.Secondly,the multidimensional and heterogeneous structure of web APIs adds complexity to data mining tasks,requiring sophisticated techniques for effective integration and analysis of diverse data aspects.Lastly,the long-tailed distribution of API data introduces biases,compromising the fairness of classification efforts.Addressing these challenges,we propose MDGCN-Lt,an API classification approach offering flexibility in using multi-dimensional heterogeneous data.It tackles data sparsity through deep graph convolutional networks,exploring high-order feature interactions among API nodes.MDGCN-Lt employs a loss function with logit adjustment,enhancing efficiency in handling long-tail data scenarios.Empirical results affirm our approach’s superiority over existing methods.展开更多
文摘Dynamic program slicing is an effective technique for narrowing the errors to the relevant parts of a program when debugging. Given a slicing criterion, the dynamic slice contains only those statements that actually affect the variables in the slicing criterion. This paper proposes a dynamic slicing method based on static dependence analysis. It uses the program dependence graph and other static information to reduce the information needed to be traced during program execution. Thus, the efficiency is dramatically improved while the precision is not depressed. The slicing criterion is modified to fit for debugging. It consists of file name and the line number at which the statement is.
文摘The lack of existing solutions makes it really hard to understand formal specification languages since the application domain for representations is useful for the purpose of carrying out certain software engineering operations such as slicing and the computation of program metrics.A Z specification dependence graph is presented in this letter. It draws on the strengths of a range of earlier works and adapts them, if necessary, to the Z language.
基金partially supported by the National Natural Science Foundation of China(No.92267104)the project of Key Science Foundation of Yunnan Province China(No.202101AS070007)the Project of Dou Wanchun Expert Workstation of Yunnan Province,China(No.202205AF1500).
文摘Developers integrate web Application Programming Interfaces(APIs)into edge applications,enabling data expansion to the edge computing area for comprehensive coverage of devices in that region.To develop edge applications,developers search API categories to select APIs that meet specific functionalities.Therefore,the accurate classification of APIs becomes critically important.However,existing approaches,as evident on platforms like programableweb.com,face significant challenges.Firstly,sparsity in API data reduces classification accuracy in works focusing on single-dimensional API information.Secondly,the multidimensional and heterogeneous structure of web APIs adds complexity to data mining tasks,requiring sophisticated techniques for effective integration and analysis of diverse data aspects.Lastly,the long-tailed distribution of API data introduces biases,compromising the fairness of classification efforts.Addressing these challenges,we propose MDGCN-Lt,an API classification approach offering flexibility in using multi-dimensional heterogeneous data.It tackles data sparsity through deep graph convolutional networks,exploring high-order feature interactions among API nodes.MDGCN-Lt employs a loss function with logit adjustment,enhancing efficiency in handling long-tail data scenarios.Empirical results affirm our approach’s superiority over existing methods.
