Validating Object-Oriented Design Metrics on a Commercial Java Application

Validating Object-Oriented Design Metrics on a Commercial Java Application (2000)

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by Daniela Glasberg , Khaled El Emam , Walcélio Melo , Nazim Madhavji

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Datum	Value	Source
TITLE	Validating Object-Oriented Design Metrics on a Commercial Java Application	SVM HeaderParse 0.1
AUTHOR NAME	Daniela Glasberg	user correction
AUTHOR NAME	Khaled El Emam	user correction
AUTHOR NAME	Walcélio Melo	user correction
AUTHOR NAME	Nazim Madhavji	user correction
AUTHOR AFFIL		user correction
ABSTRACT	Many of the object-oriented metrics that have been developed by the research community are believed to measure some aspect of complexity. As such, they can serve as leading indicators of problematic classes, for example, those classes that are most fault-prone. If faulty classes can be detected early in the development project's life cycle, mitigating actions can be taken, such as focused inspections. Prediction models using design metrics can be used to identify faulty classes early on. In this paper, 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 cognitive mechanisms which we apply in this study to object-oriented metrics are based on contemporary models of human memory. They are: familiarity, interference, and fan effects. 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 Ancestor based Import Coupling metrics were not associated with fault-proneness after controlling for the confounding effect of DIT. 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.	user correction - Legacy Corrections
YEAR	2000	user correction
CITATIONS	118 found	ParsCit 1.0