It has been proposed by El Emam et al. (ibid. vol.27 (7), 2001) that size should be taken into account as a confounding variable when validating object-oriented metrics. We take issue with this perspective since the ability to measure size does not temporally precede the ability to measure many of the object-oriented metrics that have been proposed. Hence, the condition that a confounding variable must occur causally prior to another explanatory variable is not met. In addition, when specifying multivariate models of defects that incorporate object-oriented metrics, entering size as an explanatory variable may result in misspecified models that lack internal consistency. Examples are given where this misspecification occurs.

Models for projecting software defects from analyses of Ada designs are described. The research is motivated by the need for technology to analyze designs for their likely effect on software quality. The models predict defect density based on product and process characteristics. Product characteristics are extracted from a static analysis of Ada subsystems, focusing on context coupling, visibility, and the import-export of declarations. Process characteristics provide for effects of reuse level and extent of changes. Multivariate regression analyses were conducted with empirical data from industry/government-developed projects: 16 Ada subsystems totaling 149000 source lines of code. The resulting models explain 63-74% of the variation in defect density of the subsystems. Context coupling emerged as a consistently significant variable in the models.>