Design Of Software Fault Prediction Model Using BR Technique

During the previous years, the demand for producing the quality of software has been quickly increased. In this paper, Bayesian Regularization (BR) technique has been used for finding the software faults before the testing process. This technique helps us to reduce the cost of software testing which reduces the cost of the software project. The basic purpose of BR technique is to minimizes a combination of squared errors and weights, and then determine the correct combination so as to produce an efficient network. BR Technique algorithm based neural network tool is used for finding the results on the given public dataset. The accuracy of BR algorithm based neural network has been compared with Levenberg-Marquardt(LM) algorithm and Back Propagation (BPA) algorithm for finding the software defects. Our results signify that the software fault prediction model using BR technique provide better accuracy than Levenberg-Marquardt (LM) algorithm and Back Propagation (BPA) algorithm. (C) 2015 The Authors. Published by Elsevier B.V.