Research on Static Software Defect Prediction Algorithm Based on Big Data Technology

Wang Yao
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引用次数: 3

Abstract

The static page processing software is easily disturbed by code defects, which causes the static page processing software to be paralyzed, thus making the accuracy of the static page processing poor. In order to improve the automatic prediction capability of the static page processing software, a code defect prediction technology for the static page processing software based on big data fusion and defect feature location technology algorithm is proposed, and the syntax running state characteristics of the operation and maintenance control management layer and software source code of the static page processing software are analyzed and tested. Using polymorphic software to drive the control program to carry out fault feature monitoring and information fusion of the page static processing software, carrying out polymorphic factor fusion and state feature analysis on the large data of defect fault feature distribution in the pseudo code of the software control program, combining Boehm model and ISO/IEC 9126 model to realize fault feature point location and defect active prediction of the page static processing software, According to the logicality of functions, codes and state variables of the page static processing software, the method of software running program continuity and similarity feature detection is adopted to realize the self-adaptive defect prediction and positioning of the page static processing software, and the global convergence control in defect prediction of the page static processing software is carried out by combining the big data fusion and defect feature positioning algorithm. The simulation results show that the prediction accuracy of page static processing software defects using this method is higher, the localization of defect codes is better, and the reliable operation capability of page static processing software is improved.
基于大数据技术的静态软件缺陷预测算法研究
静态页面处理软件容易受到代码缺陷的干扰,导致静态页面处理软件瘫痪,从而使得静态页面处理的准确性较差。为了提高静态页面处理软件的自动预测能力,提出了一种基于大数据融合和缺陷特征定位技术算法的静态页面处理软件代码缺陷预测技术,并对运维控制管理层的语法运行状态特征和静态页面处理软件的软件源代码进行了分析和测试。利用多态软件驱动控制程序对页面静态处理软件进行故障特征监测和信息融合,对软件控制程序伪代码中缺陷故障特征分布的大数据进行多态因素融合和状态特征分析,结合Boehm模型和ISO/IEC 9126模型实现页面静态处理软件的故障特征点定位和缺陷主动预测;根据页面静态处理软件的功能、代码和状态变量的逻辑性,采用软件运行程序连续性和相似性特征检测的方法,实现页面静态处理软件的自适应缺陷预测和定位,并结合大数据融合和缺陷特征定位算法,对页面静态处理软件缺陷预测进行全局收敛控制。仿真结果表明,该方法对页面静态处理软件缺陷的预测精度较高,缺陷代码的定位效果较好,提高了页面静态处理软件的可靠运行能力。
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