Symbolic regression, an application domain of genetic programming (GP), aims to find a function whose output has some desired property, like matching target values of a particular data set. While typical regression involves finding the coefficients of a pre-defined function,symbolic regression finds a general function, with coefficients,fitting the given set of data points. The conceptsof symbolic regression using genetic programming can be used to evolve a model for fault countpredictions.Such a model has the advantages that the evolution is not dependent on a particular structure of the model and is also independent of any assumptions, which are common in traditional time-domain parametric software reliability growth models. This research aims at applying experiments targeting fault predictionsusing genetic programming and comparing the results with traditional approaches to compare efficiency gains