Application of locally weighted regression for predicting faults using software entropy metrics

Abstract

There are numerous approaches for predicting faults in the software engineering research field. Software entropy metrics introduced by Hassan (Predicting faults using the complexity of code changes, 78–88, 2009) [1] are also popularly used for fault prediction. In previous studies, statistical linear regression (SLR) and support vector regression (SVR) for predicting faults using software entropy metrics have been validated. However, other machine learning approaches have not yet been explored. This study explores the applicability of locally weighted regression (LWR) approach for predicting faults using the software entropy metrics and compares it with SVR. It is noticed that the LWR performs better than SVR in most of the cases.