A survey on machine learning techniques used for software quality prediction

Abstract

In the present software development scenario, software quality prediction has become significantly important for successful implementation of the software in real world application and enhances the longevity of its functionality. Moreover, early identification of anticipated fault prone software modules in the process of development of software is crucial in saving efforts involved in this process. Machine learning techniques are considered to be the most appropriate techniques for software quality prediction and a large spectrum of research work has been conducted in this direction by several authors. In this paper, we conduct an extensive survey on various machine learning techniques like fuzzy logic, neural network, and Bayesian model, etc. used for software quality prediction along with an analytical justification for each of the proposed solutions.