Software Defect Prediction Using Radial Basis and Probabilistic Neural Networks

Abstract

Defects in modules of software systems is a major problem in software development. There are a variety of data mining techniques used to predict software defects such as regression, association rules, clustering, and classification. This paper is concerned with classification based software defect prediction. This paper investigates the effectiveness of using a radial basis function neural network and a probabilistic neural network on prediction accuracy and defect prediction. The conclusions to be drawn from this work is that the neural networks used in here provide an acceptable level of accuracy but a poor defect prediction ability. Probabilistic neural networks perform consistently better with respect to the two performance measures used across all datasets. It may be advisable to use a range of software defect prediction models to complement each other rather than relying on a single technique.