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The Scientific World Journal
Volume 2016, Article ID 2401496, 20 pages
Research Article

Cost-Sensitive Radial Basis Function Neural Network Classifier for Software Defect Prediction

1Department of Computer Science and Engineering, Coimbatore Institute of Technology, Coimbatore, Tamil Nadu 641 014, India
2Department of Computer Science and Engineering, PSG College of Technology, Coimbatore, Tamil Nadu 641 004, India

Received 18 October 2015; Accepted 10 November 2015

Academic Editor: Juan Manuel Gorriz Saez

Copyright © 2016 P. Kumudha and R. Venkatesan. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Effective prediction of software modules, those that are prone to defects, will enable software developers to achieve efficient allocation of resources and to concentrate on quality assurance activities. The process of software development life cycle basically includes design, analysis, implementation, testing, and release phases. Generally, software testing is a critical task in the software development process wherein it is to save time and budget by detecting defects at the earliest and deliver a product without defects to the customers. This testing phase should be carefully operated in an effective manner to release a defect-free (bug-free) software product to the customers. In order to improve the software testing process, fault prediction methods identify the software parts that are more noted to be defect-prone. This paper proposes a prediction approach based on conventional radial basis function neural network (RBFNN) and the novel adaptive dimensional biogeography based optimization (ADBBO) model. The developed ADBBO based RBFNN model is tested with five publicly available datasets from the NASA data program repository. The computed results prove the effectiveness of the proposed ADBBO-RBFNN classifier approach with respect to the considered metrics in comparison with that of the early predictors available in the literature for the same datasets.