大数据技术在静态软件缺陷检测系统设计中的应用

Application of Big Data Technology in Design of Static Software Defect Detection System

静态软件缺陷检测系统主要用来检测静态软件的缺陷问题,传统的静态软件缺陷检测系统的检测时间周期很长,不能确保整体检测的效果,为有效解决传统检测系统的问题,本文提出采用大数据技术设计新型的静态软件缺陷检测系统的建议,通过系统软硬件的设计、数据无源输入节点的划分,使数据信息能够从接线端子到主控系统形成连接,构建数据采集的模块、数据传导芯片,利用数据调配的方式、训练的方式和指令传达方式等有效进行静态软件的缺陷检测,与此同时,本文还采用实验分析的方式进行缺陷检测系统应用效果的分析,认定所设计系统与传统系统相比检测信号的有效率高,可在短时间内完成检测工作,检测的性能很强,具有一定推广价值。

Static software defect detection system is mainly used to detect the defect of static software.The traditional static software defect detection system has a long detection period and cannot ensure the overall detection effect.In order to effectively solve the problem of the traditional detection system,this paper puts forward a proposal to design a new static software defect detection system using large data technology.Through the design of system software and hardware,the partition of data passive input nodes,make the data information form a connection from the connection terminal to the main control system,build the module of data collection,data conduction chip,and use the methods of data allocation,training and instruction transmission to effectively detect the defects of static software.At the same time,this paper also uses experimental analysis to analyze the application effect of the defect detection system,and confirms that the efficiency of the designed system is higher than that of the traditional system.The detection can be completed in a short time,and the performance of detection is very strong,which has a certain promotion value.