Multi-Objective Optimization by CBR GA-Optimizer for Module-Order Modeling

In the case when resources allocated for software quality improvement are limited or unknown, an estimation of the relative rank-order of modules based on a quality factor such as number of faults is of practical importance to the software quality assurance team. This is because improvements can be targeted toward a set of most faulty modules according to resource availability. A module-order model (MOM) can be used to determine the relative rank-order of modules. A MOM usually ranks the modules according to the predicted number of faults obtained from an underlying quantitative prediction technique, such as multiple linear regression and case-based reasoning. In this paper we propose a computational intelligence-based method for targeting the performance behavior of MOM(s). The method maximizes the number of faults accounted for by the given percentage of modules enhanced. A new modeling tool called CBR GA-optimizer is developed through a synergy of genetic algorithms (GA) and case-based reasoning (CBR). The tool automatically finds the best CBR fault prediction models according to a project-specific objective function.