IDENTIFICATION OF DEFECTS IN A SOFTWARE USING MACHINE LEARNING TECHNIQUES

Abstract

The end users who are using the software and its products is vastly increased when compared to the earlier days. As we are seeing that the technology has evolved a lot and it has delivered an extraordinary technology named artificial intelligence. Identifying defects in a software in the current time can be held with Software Development Life Cycle (SDLC) and it stays a fundamental and crucial task. In the present days, a few instances of flawed and non-defective modules are used to construct prediction models which utilize machine learning techniques. To address the software modules, software metrics were used as input to these machine learning algorithms. In order to detect the defects in a software, few powerful machine learning techniques are implemented and in existing system the algorithm named Random Forest (RF) gives an adequate accuracy. But we need to identify the defects in a software using machine learning methods with better model which must give some improved accuracy when compared with RF. So here in this study we are using extra trees classifier and hybrid model to identify the defects in a software.