摘要
为了提高编程语言的并行调试和纠错分析能力,提出基于Python的函数式并行编程语言特征提取方法。建立并行编程语言的特征序列分布模型,采用连续概率密度泛函分析方法构建并行编程语言特征分布函数式,通过Python进行函数式并行编程语言的语义分割,提取函数式并行编程语言的语义关联特征量,根据语义关联性进行函数式并行编程语言特征优化提取。仿真结果表明,采用该方法进行函数式并行编程语言特征提取的准确性较高,语义分辨能力较强,提高了函数式并行编程语言的并行调试和纠错分析能力。
In order to improve the ability of parallel debugging and error correction analysis of programming language,a python-based feature extraction method of functional parallel programming language is proposed.The characteristics of parallel programming language sequence distribution model is established,the continuous probability density functional analysis method was used to construct parallel distribution in functional programming language characteristics,through the Python functional semantic segmentation of parallel programming language,extraction and functional semantic correlation characteristic of parallel programming language,according to the semantic relevance for functional parallel programming language features to optimize the extraction.The simulation results show that the method has high accuracy in feature extraction and strong semantic resolution,which improves the ability of parallel debugging and error correction analysis of functional parallel programming languages.
作者
陶婧
TAO Jing(Wuhu Institute of Technology,Wuhu 241000,China)
出处
《长春师范大学学报》
2020年第4期48-52,共5页
Journal of Changchun Normal University
基金
2015年度安徽省高校自然科学重点研究项目“基于移动通讯平台的特种设备检验检测信息管理系统研究”(KJ2015A411)
芜湖职业技术学院人文重点项目“大数据背景下ERP数据新价值分析与研究”(wzyrwzd201904)。
