Machine learning approach for quality assessment and prediction in large software organizations
Rakesh Rana, Miroslaw Staron · 2015
The importance of software in everyday products and services has been on constant rise and so is the complexity of software. In face of this rising complexity and our dependence on software - measuring, maintaining and increasing software quality is of critical importance. Software metrics provide a quantitative means to measure and thus control various attributes of software systems. In the paradigm of machine learning, software quality prediction can be cast as a classification or concept learning problem. In this paper we provide a general framework for applying machine learning approaches for assessment and prediction of software quality in large software organizations. Using ISO 15939 measurement information model we show how different software metrics can be used to build software quality model which can be used for quality assessment and prediction that satisfies the information need of these organizations with respect to quality. We also document how machine learning approaches can be effectively used for such evaluation.