| In this thesis, we present a cognitive theory of object-oriented metrics and an empirical study which has as objectives to formally test this theory while validating the metrics and to build a post-release fault proneness prediction model. The four cognitive mechanisms which we apply in this study to object-oriented metrics are based on contemporary models of human memory. They are: familiarity, interference, fan effects and the span of working memory. Our empirical study was performed with data from a commercial Java application. We found that Depth of Inheritance Tree (DIT) is a good measure of familiarity and, as predicted, has a quadratic relationship with fault-proneness. Our hypotheses were confirmed for Import Coupling to other classes, Export Coupling and Number of Children metrics. The prediction model constructed had a good accuracy. Finally, we formulated a cost savings model and applied it to our predictive model. This demonstrated a 42% reduction in post-release costs if the prediction model is used to identify the classes that should be inspected. (Abstract shortened by UMI.)... |
