A feature selection model for prediction of software defects

Abstract

Software is a collection of computer programs written in a programming language. Software contains various modules which make it a complex entity and it can increase the defect probability at the time of development of the modules. In turn, cost and time to develop the software can be increased. Sometimes, these defects can lead to failure of entire software. It will lead to untimely delivery of the software to the customer. This untimely delivery can responsible for withdrawal or cancellation of project in future. Hence, in this research work, some machine learning algorithms are applied to ensure timely delivery and prediction of defects. Further, several feature selection techniques are also adopted to determine relevant features for defect prediction.