Identification of error prone classes for fault prediction using object oriented metrics

Abstract

Various studies have found that software metrics can predict class error proneness. However their study is focused on the relationship between class error proneness and software metrics during the development phase of software projects not in system's post-release evolution. This study is focused on the three releases of Javassist- open source java based software. This paper describes how we calculated the object-oriented metrics to illustrate error-proneness detection. Using Findbugs we collected errors in the post-release system and applied logistic regression to find that some metrics can predict the class error proneness in post release evolution of system. We also calculated model's accuracy by applying one model on other version's data. © 2011 Springer-Verlag.