Adequate and Precise Evaluation of Quality Models in Software Engineering Studies

Many statistical techniques have been proposed and introduced to predict fault-proneness of program modules in software engineering. Choosing the "best" candidate among many available models involves performance assessment and detailed comparison. But these comparisons are not simple due to varying performance measures and the related verification and validation cost implications. Therefore, a methodology for precise definition and evaluation of the predictive models is still needed. We believe the procedure we outline here, if followed, has a potential to enhance the statistical validity of future experiments.