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There is lot of work done in prediction of the fault proneness of the software systems. But, it is the severity of the faults that is more important than number of faults existing in the developed system as the major faults matters most for a developer and those major faults needs immediate attention. As, Neural networks, which have been already applied in software engineering applications to build reliability growth models predict the gross change or reusability metrics. Neural networks are non-linear sophisticated modeling techniques that are able to model complex functions. Neural network techniques are used when exact nature of input and outputs is not known. A key feature is that they learn the relationship between input and output through training. In this paper, five Neural Network Based techniques are explored and comparative analysis is performed for the modeling of severity of faults present in function based software systems. The NASA's public domain defect dataset is used for the modeling. The comparison of different algorithms is made on the basis of Mean Absolute Error, Root Mean Square Error and Accuracy Values. It is concluded that out of the five neural network based techniques Resilient Backpropagation algorithm based Neural Network is the best for modeling of the software components into different level of severity of the faults. Hence, the proposed algorithm can be used to identify modules that have major faults and require immediate attention.