PREDICTIVE MODELING TECHNIQUES OF SOFTWARE QUALITY FROM SOFTWARE MEASURES

The objective in the construction of models of software quality is to use measures that may be obtained relatively early in the software development life cycle to provide reasonable initial estimates of the quality of an evolving software system. Measures of software quality and software complexity to be used in this modeling process exhibit systematic departures of the normality assumptions of regression modeling. This paper introduces two new estimation procedures and compares their performance in the modeling of software quality from software complexity in terms of the predictive quality and the quality of fit with the more traditional least squares and least absolute value estimation techniques. The two new estimation techniques did produce regression models with better quality of fit and predictive quality when applied to data obtained from two actual software development projects.