Abstract:
This paper presents the application of neural networks
in software quality estimation using object-oriented
metrics. Quality estimation includes estimating reliability
as well as maintainability of a software. Reliability is
typically measured as the number of defects.
Maintenance effort can be measured as the number of
lines changed per class. In this paper, two kinds of
investigation are performed. The first on predicting the
number of defects in a class and the second on predicting
the number of lines change per class. Two neural
network models are used, they are Ward neural network
and General Regression neural network (GRNN). Objectoriented design metrics concerning inheritance related
measures, complexity measures, cohesion measures,
coupling measures and memory allocation measures are
used as the independent variables. GRNN network model
is found to predict more accurately than Ward network
model.
