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Ravendra Ratan Singh, Deepak Tiwari

aults in software systems continue to be a major problem. High quality of software is ensured by Software reliability and Software quality assurance. A software fault is a defect that causes software failure in an executable product. A variety of software fault predictions techniques have been proposed, but none has proven to be consistently accurate. The objective in the construction of models of software error prediction is to use measures that may be obtained relatively early in the software development life cycle to provide reasonable initial estimates of quality of an evolving software system. In the present work an Adaptive Neuro Fuzzy Inference System (ANFIS) Approach has been reviewed for the development of an efficient predictive model using Subtractive Clustering Algorithm. The datasets are taken from NASA Metrics Data Program (MDP) data repository. Through the work presented, it was shown that models developed in this paper using ANFIS technique could be used to effectively address these issues. Low Root Mean Square Error (RMSE) has been obtained, both for training and testing datasets.