  Change-prone classes or modules are defined as software components in the
source code which are likely to change in the future. Change-proneness
prediction is useful to the maintenance team as they can optimize and focus
their testing resources on the modules which have a higher likelihood of
change. Change-proneness prediction model can be built by using source code
metrics as predictors or features within a machine learning classification
framework. In this paper, twenty one source code metrics are computed to
develop a statistical model for predicting change-proneness modules. Since the
performance of the change-proneness model depends on the source code metrics,
they are used as independent variables or predictors for the change-proneness
model. Eleven different feature selection techniques (including the usage of
all the $21$ proposed source code metrics described in the paper) are used to
remove irrelevant features and select the best set of features. The
effectiveness of the set of source code metrics are evaluated using eighteen
different classiffication techniques and three ensemble techniques.
Experimental results demonstrate that the model based on selected set of source
code metrics after applying feature selection techniques achieves better
results as compared to the model using all source code metrics as predictors.
Our experimental results reveal that the predictive model developed using
LSSVM-RBF yields better result as compared to other classification techniques
