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Software is becoming an indigenous part of human life with the rapiddevelopment of software engineering, demands the software to be most reliable.The reliability check can be done by efficient software testing methods usinghistorical software prediction data for development of a quality softwaresystem. Machine Learning plays a vital role in optimizing the prediction ofdefect-prone modules in real life software for its effectiveness. The softwaredefect prediction data has class imbalance problem with a low ratio ofdefective class to non-defective class, urges an efficient machine learningclassification technique which otherwise degrades the performance of theclassification. To alleviate this problem, this paper introduces a novel hybridinstance-based classification by combining distribution base balance basedinstance 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 theexperimental analysis. The experimental results in terms of Accuracy,F-measure, AUC, Recall, Precision, and Balance show the effectiveness of theproposed approach. Finally, Statistical significance tests are carried out tounderstand the suitability of the proposed model.

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