Predictive metric based on discriminant statistical analysis

Abstract

The purpose of this paper is to put forward a methodology based on discriminant statistical analysis, which, by evaluating a series of structural parameters of a program, is able to predict its risk level, namely how prone it is to containing faults. The metric was constructed in an experimental context in which the high number of available observations (almost 350,000 lines of code) allowed us to divide the body of data into two parts, one for the effective creation of the model, the other as an objective, statistical means by which the proposed methodology could be evaluated. The conclusions we have reached allow us to assert that the basic assumption holds true, and that this particular type of analysis can be used in a fixed environment, during the release and testing of software as a predictive metric for the early identification of dangerous programs.