Neural network based approach for time to crash prediction to cope with software aging

作     者：Moona Yakhchi Javier Alonso Mahdi Fazeli Amir Akhavan Bitaraf Ahmad Patooghy 

作者机构：School of Computer Science Institute for Research in Fundamental Sciences (IPM) Institute of Advanced Studies on Cybersecurity University of Leon Department of Computer Engineering Iran University of Technology 

出 版 物：《Journal of Systems Engineering and Electronics》 (系统工程与电子技术（英文版）)

年 卷 期：2015年第26卷第2期

页      面：407-414页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 081202[工学-计算机软件与理论] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：software reliability software rejuvenation machine learning 

摘      要：Recent studies have shown that software is one of the main reasons for computer systems unavailability. A growing ac- cumulation of software errors with time causes a phenomenon called software aging. This phenomenon can result in system per- formance degradation and eventually system hang/crash. To cope with software aging, software rejuvenation has been proposed. Software rejuvenation is a proactive technique which leads to re- moving the accumulated software errors by stopping the system, cleaning up its internal state, and resuming its normal operation. One of the main challenges of software rejuvenation is accurately predicting the time to crash due to aging factors such as me- mory leaks. In this paper, different machine learning techniques are compared to accurately predict the software time to crash un- der different aging scenarios. Finally, by comparing the accuracy of different techniques, it can be concluded that the multilayer per- ceptron neural network has the highest prediction accuracy among all techniques studied.