Classification and Prediction of Software Incidents Using Machine Learning Techniques

Sikandar Ali,Samad Baseer,Sumaira Johar,Muhammad Adeel,Muhammad Zeeshan,Azeem Irshad,Muhammad Arif

doi:10.1155/2021/9609823

Abstract

An incident, in the perception of information technology, is an event that is not part of a normal process and disrupts operational procedure. This research work particularly focuses on software failure incidents. In any operational environment, software failure can put the quality and performance of services at risk. Many efforts are made to overcome this incident of software failure and to restore normal service as soon as possible. The main contribution of this study is software failure incidents classification and prediction using machine learning. In this study, an active learning approach is used to selectively label those data which is considered to be more informative to build models. Firstly, the sample with the highest randomness (entropy) is selected for labeling. Secondly, to classify the labeled observation into either failure or no failure classes, a binary classifier is used that predicts the target class label as failure or not. For classification, Support Vector Machine is used as a main classifier to classify the data. We derived our prediction models from the failure log files collected from the ECLIPSE software repository.

Highlights

In any particular system, failure befalls when the provided service no longer obeys the specified specifications [1]
We suggested a model for predicting software failure incidents using active learning and the Support Vector Machine (SVM) in this study. e dataset was subjected to active learning, which reduced the size of the dataset and picked a sample from it to serve as the training set for the SVM classifier. e sample was chosen because it had occurrences that were both unique and relevant in terms of training the classifier. e clustering technique is used to do active learning in this study
To get the most informative set, different techniques were performed on the clusters. e final sample of the instances obtained was labeled manually. e labeled training set was used as the input to the SVM classifier

Summary

Introduction

Failure befalls when the provided service no longer obeys the specified specifications [1]. Specifications are the agreed description of the system’s functional behavior to provide expected service [1]. Is definition applies to both software and hardware failures. Failures are of different types; i.e., not all the failures are fatal and some of them are even harmless and do not affect the functionality of the system. Other failures are so fatal that they crash the whole system and make the system unavailable for specified services. Failures are the incapability of the software to perform the required action or in other words the deviation from required performance [2]

Objectives

Methods

Results

Discussion

Conclusion