Hybrid Software Reliability Prediction Model Using Feature Selection and Support Vector Classifier

Abstract

The primary purpose of the software industry is to provide high-quality software. Software system failure is caused by faulty software components. The goal of reliable software is to reduce the amount of software programme failures. Software defect prediction is a crucial aspect of developing high-quality software. One can predict software failures by implement essential prediction metrics and previous fault information. A good software fault prediction model makes testing easier while also improving the quality and consistency of software. For defect prediction systems based on diverse parameters, several methodologies have been proposed. However, none of the models meet the criteria for software reliability defect prediction. So in this article we proposed a hybrid software reliability model using feature selection and support vector classifier. In terms of software reliability defect prediction, the provided methodology is acceptable for different software metrics with experimental approvals utilizing a standard dataset. In the methodology, the NASA Metrics Data Program datasets are used for real-time verification and validation.