Abstract:
Nowadays it is difficult for us to imagine a life
without devices that is controlled by software. Software
quality prediction is the important process of software
development processes. It is a process of utilizing
software metrics such as code-level measurements and
defect data to estimate software quality modules. A
more useful and efficient mechanism is k Nearest
Neighbor method to classify class of target data based
on k nearest training dataset. By applying the concept of
k-NN, we propose a new mechanism called Class Base
Weighted k-NN with Biner Algorithm (CBW k-NN) to
find the range of training dataset where the target data
has the maximum likelihood of occurrence by Biner and
classify class of target data based on this range. The
main purpose of this paper is to know the effective
classification method for software defect datasets that
exploit information from the NASA MDP (PC1, CM1,
JM1) datasets.
