Taxonomy of machine learning algorithms in software fault prediction using object oriented metrics

Abstract

Prediction of Fault proneness of a software component is the compelling field of investigations in software testing arena. Software coupling plays a vital role in assessing the software quality through fault prediction and complexity measures. Various fault prediction models, have used the object oriented metrics for the predicting and localizing the faults. Many of these metrics have direct influence on the quality of software. More over prior knowledge of the fault proneness of a component may significantly reduce the testing effort and time. The measures of object oriented features like inheritance, polymorphism and encapsulation etc may be used to estimate fault proneness. Many researchers have investigated the usage of object oriented metrics in the software fault prediction. In this study we present taxonomy of usage these metrics in the fault prediction. We also present the analysis of machine learning techniques in fault prediction.