  Software is becoming an indigenous part of human life with the rapid
development of software engineering, demands the software to be most reliable.
The reliability check can be done by efficient software testing methods using
historical software prediction data for development of a quality software
system. Machine Learning plays a vital role in optimizing the prediction of
defect-prone modules in real life software for its effectiveness. The software
defect prediction data has class imbalance problem with a low ratio of
defective class to non-defective class, urges an efficient machine learning
classification technique which otherwise degrades the performance of the
classification. To alleviate this problem, this paper introduces a novel hybrid
instance-based classification by combining distribution base balance based
instance selection and radial basis function neural network classifier model
(DBBRBF) to obtain the best prediction in comparison to the existing research.
Class imbalanced data sets of NASA, Promise and Softlab were used for the
experimental analysis. The experimental results in terms of Accuracy,
F-measure, AUC, Recall, Precision, and Balance show the effectiveness of the
proposed approach. Finally, Statistical significance tests are carried out to
understand the suitability of the proposed model.
