A comparative study of fault density prediction in aspect-oriented systems using MLP, RBF, KNN, RT, DENFIS and SVR models

Abstract

This paper investigates and empirically evaluates and compares six popular computational intelligence models in the context of fault density prediction in aspect-oriented systems. These models are multi-layer perceptron (MLP), radial basis function (RBF), k-nearest neighbor (KNN), regression tree (RT), dynamic evolving neuro-fuzzy inference system (DENFIS), and support vector regression (SVR). The models were trained and tested, using leave-one-out procedure, on a dataset that consists of twelve aspect-level metrics (explanatory variables) that measure different structural properties of an aspect. It was observed that the DENFIS, SVR, and RT models were more accurate in predicting fault density compared to the MLP, RBF, and KNN models. The MLP model was the worst model, and all the other models were significantly better than it.