Validating Object-Oriented Design Metrics on a Commercial Java Application
by
Daniela Glasberg, Khaled El Emam, Walcelio
Melo, Nazim Madhavji
National Research Council Canada, Institute for Information Technology,
TR No. 44146. Sep. 2000.
Accepted for publication in the Journal of Software and Systems. 2001.
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
Paper in PDF (332 Kb)
Last updated on Mon Aug 8th, 2000 by Walcelio
L. Melo