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

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.