Features Enhancement based Testing Effort Dependent Learning SRGM

Abstract

There is urgent requirement to upgrade software reliability by removing the faults or errors which accrued during the development of software codes. The academic institutions and industry have largely responded to this requirement by enhancing the development techniques in the name of software engineering and their by employing regular testing for finding faults in programmers of software during the development. The many errors are superior candidates or being robotically fixed is the simple however trendy bugs which can be modeled by suitable altered operators. New programs are composed by altered the original code by comprised more of a bias near statements that arise in pessimistic execution paths. For that purpose in proposed method contains fault localization information to indicate the position of fault. In experimental as well as regression based equations represent the soft computing techniques results is better compare to the other techniques. Evaluation of soft-computing techniques represented that accuracy of the ANN model is superior to the other models. Data bases for performing the training and testing stages were collected, these soft computing techniques had low computational errors than the empirical equations. Finally says that soft computing models are better compare to the regression models. Hence, finding faults and correcting a serious software problem would be better instead of recalling thousands of products, especially in automotive sector. SRGM success mainly reliable by gathering the accurate failure information. The functions of the software reliability growth model were predicted in terms of such information gathered only. SRGM techniques in the literature and it gives a reasonable capability of value for actual software failure data. Therefore, this model, in future, can be applied to operate a wide range of software and its applications.
Keywords: SRGM, FDP, FCP