Control Flow Graphs(CFGs)are essential for understanding the execution and data flow within software,serving as foundational structures in program analysis.Traditional CFG construction methods,such as bytecode analysi...Control Flow Graphs(CFGs)are essential for understanding the execution and data flow within software,serving as foundational structures in program analysis.Traditional CFG construction methods,such as bytecode analysis and Abstract Syntax Trees(ASTs),often face challenges due to the complex syntax of programming languages like Java and Python.This paper introduces a novel approach that leverages Large Language Models(LLMs)to generate CFGs through a methodical Chain of Thought(CoT)process.By employing CoT,the proposed approach systematically interprets code semantics directly from natural language,enhancing the adaptability across various programming languages and simplifying the CFG construction process.By implementing a modular AI chain strategy that adheres to the single responsibility principle,our approach breaks down CFG generation into distinct,manageable steps handled by separate AI and non-AI units,which can significantly improve the precision and coverage of CFG nodes and edges.The experiments with 245 Java and 281 Python code snippets from Stack Overflow demonstrate that our method achieves efficient performance on different programming languages and exhibits strong robustness.展开更多
We presented a quantity judgment task that involved comparing two sequentially presented sets of items to preschoolers and chimpanzees using nearly identical procedures that excluded verbal instructions to children. T...We presented a quantity judgment task that involved comparing two sequentially presented sets of items to preschoolers and chimpanzees using nearly identical procedures that excluded verbal instructions to children. Trial difficulty in this task reflected the ratio difference between sets of discrete items where larger ratios （e.g., 0.80 as from comparing 4 to 5） were more difficult than smaller ones （e.g., 0.50 as from comparing 4 to 8）. Children also completed verbal-based tasks probing the relationskip between counting proficiency and performance on the quantity judgment task of sequentially presented identical sized items. Both species＇ performance was best when ratios between comparison sets were small regardless of set size in all types of tasks. Generally, chimpanzees and older children performed better than younger children except at larger ratios. Children＇s counting proficiency was not related to success in choosing the larger of two quantities of identical-sized items. These results indicate that chimpanzees and children share an approximate number sense that is reflected through analog magnitude estimation when comparing quantities [Current Zoology 57 （4）： 419-428, 2011].展开更多
The construction of“X What(Y)+All+Z”generally appears in speaking and listening TCSL textbooks and some reading materials.In most cases,it is not listed in those textbooks as a key expression and more likely to be s...The construction of“X What(Y)+All+Z”generally appears in speaking and listening TCSL textbooks and some reading materials.In most cases,it is not listed in those textbooks as a key expression and more likely to be shown together with other similar constructions.However,foreign students tend to make mistakes when using it.This paper designs a teaching plan of the construction by using the“constructionchunk”teaching method,and puts forward some suggestions for teaching from the aspects of teaching materials,teachers and students.展开更多
Using intermediate and advanced Chinese students as the research subjects,HSK corpus and questionnaire surveys were used to explore the error types in the construction of"X什么(Y)+都+Z."The types of errors c...Using intermediate and advanced Chinese students as the research subjects,HSK corpus and questionnaire surveys were used to explore the error types in the construction of"X什么(Y)+都+Z."The types of errors can be divided into component errors,relationship errors between components,and collocation errors between components and sentences.The main reasons for the errors are the particularity of the construction,the immature development of construction teaching,the negative transfer of students5 mother tongue,as well as the generalization and avoidance of the target language.展开更多
基金Supported by the National Natural Science Foundation of China(62462036,62262031)Jiangxi Provincial Natural Science Foundation(20242BAB26017,20232BAB202010)+1 种基金Distinguished Youth Fund Project of the Natural Science Foundation of Jiangxi Province(20242BAB23011)the Jiangxi Province Graduate Innovation Found Project(YJS2023032)。
文摘Control Flow Graphs(CFGs)are essential for understanding the execution and data flow within software,serving as foundational structures in program analysis.Traditional CFG construction methods,such as bytecode analysis and Abstract Syntax Trees(ASTs),often face challenges due to the complex syntax of programming languages like Java and Python.This paper introduces a novel approach that leverages Large Language Models(LLMs)to generate CFGs through a methodical Chain of Thought(CoT)process.By employing CoT,the proposed approach systematically interprets code semantics directly from natural language,enhancing the adaptability across various programming languages and simplifying the CFG construction process.By implementing a modular AI chain strategy that adheres to the single responsibility principle,our approach breaks down CFG generation into distinct,manageable steps handled by separate AI and non-AI units,which can significantly improve the precision and coverage of CFG nodes and edges.The experiments with 245 Java and 281 Python code snippets from Stack Overflow demonstrate that our method achieves efficient performance on different programming languages and exhibits strong robustness.
基金We wish to acknowledge the staff at the Berry College Child Development Center and Georgia State University Language Research Center for their support in data collection. Research with chimpanzees was supported by grants from the National Institutes of Health （HD 38051 and HD 060563） and the National Science Foundation （BCS 0924811）. The authors thank Mary Beran and John Kelley for their assistance in conducting experimental sessions with chimpanzees, and the staff at the Berry College Child Development Center for data collection with preschoolers. We thank Carla Moldavan for helpful comments pertaining to early childhood mathematical curricula.
文摘We presented a quantity judgment task that involved comparing two sequentially presented sets of items to preschoolers and chimpanzees using nearly identical procedures that excluded verbal instructions to children. Trial difficulty in this task reflected the ratio difference between sets of discrete items where larger ratios （e.g., 0.80 as from comparing 4 to 5） were more difficult than smaller ones （e.g., 0.50 as from comparing 4 to 8）. Children also completed verbal-based tasks probing the relationskip between counting proficiency and performance on the quantity judgment task of sequentially presented identical sized items. Both species＇ performance was best when ratios between comparison sets were small regardless of set size in all types of tasks. Generally, chimpanzees and older children performed better than younger children except at larger ratios. Children＇s counting proficiency was not related to success in choosing the larger of two quantities of identical-sized items. These results indicate that chimpanzees and children share an approximate number sense that is reflected through analog magnitude estimation when comparing quantities [Current Zoology 57 （4）： 419-428, 2011].
文摘The construction of“X What(Y)+All+Z”generally appears in speaking and listening TCSL textbooks and some reading materials.In most cases,it is not listed in those textbooks as a key expression and more likely to be shown together with other similar constructions.However,foreign students tend to make mistakes when using it.This paper designs a teaching plan of the construction by using the“constructionchunk”teaching method,and puts forward some suggestions for teaching from the aspects of teaching materials,teachers and students.
文摘Using intermediate and advanced Chinese students as the research subjects,HSK corpus and questionnaire surveys were used to explore the error types in the construction of"X什么(Y)+都+Z."The types of errors can be divided into component errors,relationship errors between components,and collocation errors between components and sentences.The main reasons for the errors are the particularity of the construction,the immature development of construction teaching,the negative transfer of students5 mother tongue,as well as the generalization and avoidance of the target language.
