A Study on Software Fault Detection Methods

Exploring Various Techniques and Algorithms for Software Fault Detection

Abstract

Quality assurance activities such as testing, verification, and validation, defect tolerance, and fault prediction is some of the software engineering interests. When every organization doesn't have enough resources and money to check the whole program, a project manager may use other fault detection algorithms to classify systems components that are more vulnerable to faults. Throughout the field of information engineering, there are so many prediction methods including monitoring, health, and cost prediction. In this paper, the prediction of soft-faulting has been studied using several machines, for example, decision-making bodies, decision tables, random forest, neural networks, Naïve Bayes, and distinctive classifications of artificial immune systems, as most of them do not have a stable model.