  Software fault prediction model are employed to optimize testing resource
allocation by identifying fault-prone classes before testing phases. Several
researchers' have validated the use of different classification techniques to
develop predictive models for fault prediction. The performance of the
statistical models are proven to be influenced by the training and testing
dataset. Ensemble method learning algorithms have been widely used because it
combines the capabilities of its constituent models towards a dataset to come
up with a potentially higher performance as compared to individual models
(improves generalizability). In the study presented in this paper, three
different ensemble methods have been applied to develop a model for predicting
fault proneness. The efficacy and usefulness of a fault prediction model also
depends on the source code metrics which are considered as the input for the
model.
  In this paper, we propose a framework to validate the source code metrics and
select the right set of metrics with the objective to improve the performance
of the fault prediction model. The fault prediction models are then validated
using a cost evaluation framework. We conduct a series of experiments on 45
open source project dataset. Key conclusions from our experiments are: (1)
Majority Voting Ensemble (MVE) methods outperformed other methods; (2) selected
set of source code metrics using the suggested source code metrics using
validation framework as the input achieves better results compared to all other
metrics; (3) fault prediction method is effective for software projects with a
percentage of faulty classes lower than the threshold value (low - 54.82%,
medium - 41.04%, high - 28.10%)
