DBBRBF- Convalesce optimization for software defect prediction problem using hybrid distribution base balance instance selection and radial basis Function classifier

TL;DR

This paper proposes a novel hybrid classification method combining distribution-based instance selection and RBF neural networks to improve software defect prediction accuracy on imbalanced datasets.

Contribution

It introduces a new hybrid approach (DBBRBF) that effectively handles class imbalance in software defect prediction using a combination of instance selection and neural network classification.

Findings

The proposed DBBRBF method outperforms existing techniques in accuracy and other metrics.

Experimental results on NASA, Promise, and Softlab datasets demonstrate its effectiveness.

Statistical tests confirm the significance of the improvements.

Abstract

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.