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Neural Network Parameter Optimization Based on Genetic Algorithm for Software Defect Prediction

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Software fault prediction approaches are much more efficient and effective to detect software faults compared to software reviews. Machine learning classification algorithms have been applied for software defect prediction. Neural network has strong fault tolerance and strong ability of nonlinear dynamic processing of software defect data. However, practicability of neural network is affected due to the difficulty of selecting appropriate parameters of network architecture. Software fault prediction datasets are often highly imbalanced class distribution. Class imbalance will reduce classifier performance. A combination of genetic algorithm and bagging technique is proposed for improving the performance of the software defect prediction. Genetic algorithm is applied to deal with the parameter optimization of neural network. Bagging technique is employed to deal with the class imbalance problem. The proposed method is evaluated using the datasets from NASA metric data repository. Results have indicated that the proposed method makes an improvement in neural network prediction performance.

Document Type: Research Article

Publication date: October 1, 2014

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  • ADVANCED SCIENCE LETTERS is an international peer-reviewed journal with a very wide-ranging coverage, consolidates research activities in all areas of (1) Physical Sciences, (2) Biological Sciences, (3) Mathematical Sciences, (4) Engineering, (5) Computer and Information Sciences, and (6) Geosciences to publish original short communications, full research papers and timely brief (mini) reviews with authors photo and biography encompassing the basic and applied research and current developments in educational aspects of these scientific areas.
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