Analysis of CK Metrics to Predict Software Fault-Proneness using Bayesian Inference

Abstract

The fault prediction model grants assistance during the software development by providing recourse to the present faults with the Bayesian Interference. All faults prediction techniques get a help in this study with the designing of Logistic regression model and Bayesian inference altogether. It is also told as fact that Bayesian inference graph can be represented for probabilistic approach for the faults both presented and identified for the upcoming release. For Probabilistic reliability analysis, Bayesian inference is intended to be evaluated for risk related data. These findings suggest that there is a relationship between faulty classes and object-oriented metrics. This study demonstrates as the performance evaluation technique for any piece of software. We examine the open source Eclipse system, which has a strong industrial usage. The focus of the study is to design Bayesian Inference graph and predict faults for next piece of software.