Prediction of Software Anomalies using Time Series Analysis – A recent study

Modern society relies on the fault-free operation of complex computing systems. So, requests on software reliability and availability have increased greatly due to the current applications. Software applications executing continuously for a long period of time show a degraded performance and/or an increased occurrence rate of hang/crash failure. Computer system outages are more often due to software faults than hardware faults. Predicting software anomalies (like software aging) caused by resource exhaustion is not an easy task. It is difficult to know a priori the parameters involved with the software aging. Thus the capabilities of Machine Learning (ML) algorithms and the statistical methods can be analysed to predict the system crash due to software aging caused by resource exhaustion. In this paper one of the statistical methods, time series analysis has been studied and a survey of the recent work in prediction of software anomalies using the same has been conducted.