Research on System Software Fault Data Analysis and Diagnosis Improvement Based on ISOA-SVM

Abstract

With rapid development of information technology, the complexity and scale of system software continue to expand, and software failure has become a key factor affecting system stability and reliability. Aiming at this problem, this study puts forward an improved system software fault data analysis and diagnosis method based on ISOA-SVM (Support Vector Machine with Improved Seagull Optimization Algorithm). An efficient fault diagnosis model is constructed by deeply analyzing the characteristics of software fault data and combining the advantages of ISOA-SVM in dealing with high-dimensional and nonlinear data. Experimental results show that compared with the traditional SVM method, ISOA-SVM improves fault diagnosis accuracy by 15.3% and shortens the fault detection time by 20.7%. In addition, this study also explores the influence of different parameter configurations on model performance. Further, it improves diagnostic efficiency and accuracy of model by optimizing the parameter combination. The results show that fault diagnosis method based on ISOA-SVM has obvious advantages in improving the stability and reliability of system software and provides strong support for the fault diagnosis of large-scale and complex system software in the future.