ASSESSMENT AND ANALYSIS OF SOFTWARE RELIABILITY USING MACHINE LEARNING TECHNIQUES

Abstract

Software reliability models access the reliability by fault prediction. Reliability is a real world phenomenon with many associated real time problems and to obtain solutions to problems quickly, accurately and acceptably a large no. of soft computing techniques has been developed. We attempt to address the software failure problems by modeling software failure data using the machine learning techniques such as support vector machine (SVM) regression and generalized additive models. The study of software reliability can be categorized into three parts: modeling, measurement, improvement. Programming unwavering quality demonstrating has developed to a point that important outcomes can be acquired by applying appropriate models to the issue; there is no single model all inclusive to every one of the circumstances. We propose different machine learning methods for the evaluation of programming unwavering quality, for example, artificial neural networks, support vector machine calculation approached. We at that point break down the outcomes from machine getting the hang of demonstrating, and contrast them with that of some summed up direct displaying procedures that are proportional to programming dependability models.