A constructive RBF neural network for estimating the probability of defects in software modules

Much of the current research in software defect prediction focuses on building classifiers to predict only whether a software module is fault-prone or not. Using these techniques, the effort to test the software is directed at modules that are labelled as fault-prone by the classifier. This paper introduces a novel algorithm based on constructive RBF neural networks aimed at predicting the probability of errors in fault-prone modules; it is called RBF-DDA with Probabilistic Outputs and is an extension of RBF-DDA neural networks. The advantage of our method is that we can inform the test team of the probability of defect in a module, instead of indicating only if the module is fault-prone or not. Experiments carried out with static code measures from well-known software defect datasets from NASA show the effectiveness of the proposed method. We also compared the performance of the proposed method in software defect prediction with kNN and two of its variants, the S-POC-NN and R-POC-NN. The experimental results showed that the proposed method outperforms both S-POC-NN and R-POCNN and that it is equivalent to kNN in terms of performance with the advantage of producing less complex classifiers.